code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> list:
UpperCAmelCase_ = False
while is_sorted is False: # Until all the indices are traversed keep looping
UpperCAmelCase_ = True
for i in range(0 , len(__SCREAMING_SNAKE_CASE ) - 1 , 2 ): # iterating over all even in... | 23 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> np.array:
UpperCAmelCase_ = f'''{sampling_rate}'''
UpperCAmelCase_ = "1"
UpperCAmelCase... | 23 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
... | 23 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class lowerCamelCase ( lowercase__, lowercase__ ):
'''simple docstring'''
@register_to_... | 23 | 1 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = "x" , __SCREAMING_SNAKE_CASE = 10**-10 , __SCREAMING_SNAKE_CASE = 1 , ) -> complex:
Upp... | 23 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import ... | 23 | 1 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... | 23 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_availa... | 23 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING... | 23 |
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> int:
UpperCAmelCase_ = [1]
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = 0, 0, 0
UpperCAmelCase_ = ugly_nums[ia] * 2
UpperCAmelCase_ = ugly_nums[ia] * 3
UpperCAmelCase_ = ugly_nums[i... | 23 | 1 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
from ..... | 23 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test... | 23 | 1 |
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import Pr... | 23 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
SCREAMING_SNAKE_CASE = {"tokenization_herbert": ["HerbertTokenizer"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDepe... | 23 | 1 |
import numpy as np
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> np.ndarray:
return 1 / (1 + np.exp(-vector ))
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> np.ndarray:
return vector * sigmoid(__SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
import doctest
doctest... | 23 |
import math
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> list[int]:
UpperCAmelCase_ = []
UpperCAmelCase_ = 2
UpperCAmelCase_ = int(math.sqrt(__SCREAMING_SNAKE_CASE ) ) # Size of every segment
UpperCAmelCase_ = [True] * (end + 1)
UpperCAmelCase_ = ... | 23 | 1 |
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> bool:
UpperCAmelCase_ = len(__SCREAMING_SNAKE_CASE )
UpperCAmelCase_ = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of zero(0) can be formed by not taking any ele... | 23 |
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor impor... | 23 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
"unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json",
}
class low... | 23 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
SCREAMING_SNAKE_CASE = {
"configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}
try:
if not is_torch_available():
... | 23 | 1 |
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta import TaTokenizer
else:
SC... | 23 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
"xlm-roberta-base": "https://h... | 23 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE ... | 23 |
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> str:
UpperCAmelCase_ = int(__SCREAMING_SNAKE_CASE )
if decimal in (0, 1): # Exit cases for the recursion
return str(__SCREAMING_SNAKE_CASE )
UpperCAmelCase_ , UpperCAmelCase_ = divmod(__SCREAMING_SNAKE_CASE , 2 )
re... | 23 | 1 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def snake_case__ ( ) -> int:
UpperCAmelCase_ = HfArgumentParser(__SCREAMING_SNAKE_CASE )
UpperCAmelCase_ = parser.parse_args_into_dataclasses()[0]
UpperCAmelCase_ = TensorFlowB... | 23 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@requi... | 23 | 1 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class lowerCamelCase ( lowercase__, lowercase__ ):
'''simple docstring'''
@register_to_... | 23 |
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
UpperCAmelCase_ = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAM... | 23 | 1 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_... | 23 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
fr... | 23 | 1 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_availa... | 23 |
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> int:
UpperCAmelCase_ = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> int:
UpperCAmelCase_ = 0
while number > 0:
UpperCAmelCase_ = number % ... | 23 | 1 |
import math
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> list[int]:
UpperCAmelCase_ = []
UpperCAmelCase_ = 2
UpperCAmelCase_ = int(math.sqrt(__SCREAMING_SNAKE_CASE ) ) # Size of every segment
UpperCAmelCase_ = [True] * (end + 1)
UpperCAmelCase_ = ... | 23 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE = {"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MA... | 23 | 1 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class lowerCamelCase ( lowercase__, lowercase__ ):
'''simple docstring'''
@register_to_config
def __init__( self , *,
l... | 23 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) ... | 23 | 1 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutputW... | 23 |
import heapq as hq
import math
from collections.abc import Iterator
class lowerCamelCase :
'''simple docstring'''
def __init__( self , lowerCAmelCase ):
UpperCAmelCase_ = str(id_ )
UpperCAmelCase_ = None
UpperCAmelCase_ = Non... | 23 | 1 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> tuple:
return (da... | 23 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE = {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}
try... | 23 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE = {"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MA... | 23 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING... | 23 | 1 |
import doctest
from collections import deque
import numpy as np
class lowerCamelCase :
'''simple docstring'''
def __init__( self ):
UpperCAmelCase_ = [2, 1, 2, -1]
UpperCAmelCase_ = [1, 2, 3, 4]
def A__ ( self ):
UpperCAm... | 23 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING... | 23 | 1 |
import sys
import turtle
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> tuple[float, float]:
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __S... | 23 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> np.array:
UpperCAmelCase_ = f'''{sampling_rate}'''
UpperCAmelCase_ = "1"
UpperCAmelCase... | 23 | 1 |
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMIN... | 23 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class lowerCamelCase ( lowercase__, lowercase__ ):
'''simple docstring'''
@register_to_... | 23 | 1 |
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(lowercase__ ), 'Tatoeba directory ... | 23 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import ... | 23 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING... | 23 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_availa... | 23 | 1 |
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
class lowerCamelCase ( lowercase__ ):
'''simple docstring'''
def __init__( self , *lowerCAmel... | 23 |
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> int:
UpperCAmelCase_ = [1]
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = 0, 0, 0
UpperCAmelCase_ = ugly_nums[ia] * 2
UpperCAmelCase_ = ugly_nums[ia] * 3
UpperCAmelCase_ = ugly_nums[i... | 23 | 1 |
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> int:
UpperCAmelCa... | 23 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test... | 23 | 1 |
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
SCREAMING_SNAKE_CASE ... | 23 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
SCREAMING_SNAKE_CASE = {"tokenization_herbert": ["HerbertTokenizer"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDepe... | 23 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
fr... | 23 |
import math
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> list[int]:
UpperCAmelCase_ = []
UpperCAmelCase_ = 2
UpperCAmelCase_ = int(math.sqrt(__SCREAMING_SNAKE_CASE ) ) # Size of every segment
UpperCAmelCase_ = [True] * (end + 1)
UpperCAmelCase_ = ... | 23 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_availabl... | 23 |
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor impor... | 23 | 1 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, D... | 23 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
SCREAMING_SNAKE_CASE = {
"configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}
try:
if not is_torch_available():
... | 23 | 1 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that ... | 23 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
"xlm-roberta-base": "https://h... | 23 | 1 |
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional i... | 23 |
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> str:
UpperCAmelCase_ = int(__SCREAMING_SNAKE_CASE )
if decimal in (0, 1): # Exit cases for the recursion
return str(__SCREAMING_SNAKE_CASE )
UpperCAmelCase_ , UpperCAmelCase_ = divmod(__SCREAMING_SNAKE_CASE , 2 )
re... | 23 | 1 |
import os
from typing import Dict, List, Tuple, TypeVar, Union
SCREAMING_SNAKE_CASE = TypeVar("T")
SCREAMING_SNAKE_CASE = Union[List[T], Tuple[T, ...]]
SCREAMING_SNAKE_CASE = Union[T, List[T], Dict[str, T]]
SCREAMING_SNAKE_CASE = Union[str, bytes, os.PathLike]
... | 23 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@requi... | 23 | 1 |
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> np.ndarray:
UpperCAmelCase_ ... | 23 |
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
UpperCAmelCase_ = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAM... | 23 | 1 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import ... | 23 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
fr... | 23 | 1 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class lowerCamelCase ( ctypes.Structure ):
'''simple docstring'''
lowerCAmelCase_ : Dict = [('size', ctypes.c_int),... | 23 |
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> int:
UpperCAmelCase_ = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> int:
UpperCAmelCase_ = 0
while number > 0:
UpperCAmelCase_ = number % ... | 23 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
"xlm-roberta-base": "https://h... | 23 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE = {"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MA... | 23 | 1 |
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available... | 700 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) ... | 23 | 0 |
'''simple docstring'''
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> Optional[Any]:
UpperCAmelCase_ = []
UpperCAmelCase_ = set({"(", "[", "{"} )
UpperCAmelCase_ = set({")", "]", "}"} )
UpperCAmelCase_ = {"{": "}", "[": "]", "(": ")"}
for i in range(len(__A ... | 701 |
import heapq as hq
import math
from collections.abc import Iterator
class lowerCamelCase :
'''simple docstring'''
def __init__( self , lowerCAmelCase ):
UpperCAmelCase_ = str(id_ )
UpperCAmelCase_ = None
UpperCAmelCase_ = Non... | 23 | 0 |
from math import factorial
def snake_case__ ( __SCREAMING_SNAKE_CASE = 20 ) -> int:
UpperCAmelCase_ = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
UpperCAmelCase_ = n // 2
return int(factorial(_SCREAMING_SNAKE_CASE ) / (factorial... | 702 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE = {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}
try... | 23 | 0 |
import numpy as np
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> int:
UpperCAmelCase_ = int(np.ceil((x_end - xa) / h ) )
UpperCAmelCase_ = np.zeros((n + 1... | 703 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING... | 23 | 0 |
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
SCREAMING_SNAKE_CASE = logging.getLogger()
def snake_case__ ( __SCREAMING_S... | 704 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING... | 23 | 0 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> Dict:
return (dat... | 705 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> np.array:
UpperCAmelCase_ = f'''{sampling_rate}'''
UpperCAmelCase_ = "1"
UpperCAmelCase... | 23 | 0 |
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
def snake_... | 706 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class lowerCamelCase ( lowercase__, lowercase__ ):
'''simple docstring'''
@register_to_... | 23 | 0 |
'''simple docstring'''
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
"RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
... | 707 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import ... | 23 | 0 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=__A )
class lowerCamelCase ( __A ):
'''simple docstring'''
lowerCAmelCase_ : Dict = field(defaul... | 708 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_availa... | 23 | 0 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> Optional[int]:
for param in module.parameters():
UpperCAmelCase_ = False
def snake_case__ ( ) -> Optional[int]:
Upper... | 709 |
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> int:
UpperCAmelCase_ = [1]
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = 0, 0, 0
UpperCAmelCase_ = ugly_nums[ia] * 2
UpperCAmelCase_ = ugly_nums[ia] * 3
UpperCAmelCase_ = ugly_nums[i... | 23 | 0 |
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
def snake_case__ ( __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CAS... | 710 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test... | 23 | 0 |
from ..utils import DummyObject, requires_backends
class lowerCamelCase ( metaclass=lowercase__ ):
'''simple docstring'''
lowerCAmelCase_ : str = ['torch', 'transformers', 'onnx']
def __init__( self , *lowerCAmelCase , **lowerCAmelCase ):
... | 711 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
SCREAMING_SNAKE_CASE = {"tokenization_herbert": ["HerbertTokenizer"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDepe... | 23 | 0 |
SCREAMING_SNAKE_CASE = {}
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> int:
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possible
if late == 3 or absent == 2:
return 0
... | 712 |
import math
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> list[int]:
UpperCAmelCase_ = []
UpperCAmelCase_ = 2
UpperCAmelCase_ = int(math.sqrt(__SCREAMING_SNAKE_CASE ) ) # Size of every segment
UpperCAmelCase_ = [True] * (end + 1)
UpperCAmelCase_ = ... | 23 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE = {
"configuration_convbert": ["CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvBertConfig",... | 713 |
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor impor... | 23 | 0 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase ( lowercase__ ):
'''simple docstring'''
lowerCAmelCase_ : int = (UnCLIPScheduler,)
def A__ ( self , **lowerCAmelCase )... | 714 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
SCREAMING_SNAKE_CASE = {
"configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}
try:
if not is_torch_available():
... | 23 | 0 |
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
SCREAMING_SNAKE_CASE = ... | 715 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
"xlm-roberta-base": "https://h... | 23 | 0 |
SCREAMING_SNAKE_CASE = 8.31_44_62 # Unit - J mol-1 K-1
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("Invalid inputs. Enter positive valu... | 716 |
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> str:
UpperCAmelCase_ = int(__SCREAMING_SNAKE_CASE )
if decimal in (0, 1): # Exit cases for the recursion
return str(__SCREAMING_SNAKE_CASE )
UpperCAmelCase_ , UpperCAmelCase_ = divmod(__SCREAMING_SNAKE_CASE , 2 )
re... | 23 | 0 |
from __future__ import annotations
import math
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> Tuple:
if depth < 0:
raise ValueError("Depth cannot be less than 0" )
if... | 717 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@requi... | 23 | 0 |
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
def snake_case__ ( __SCREAMING_SNA... | 718 |
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
UpperCAmelCase_ = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAM... | 23 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.ut... | 719 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
fr... | 23 | 0 |
from PIL import Image
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> Image:
UpperCAmelCase_ = (259 * (level + 255)) / (255 * (259 - level))
def contrast(__SCREAMING_SNAKE_CASE ) -> int:
return int(128 + factor * (c - 128) )
return img.point(__... | 720 |
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> int:
UpperCAmelCase_ = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> int:
UpperCAmelCase_ = 0
while number > 0:
UpperCAmelCase_ = number % ... | 23 | 0 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=False ) -> Optional[int]:
UpperCAmelCase_ = OmegaConf.load(__UpperCamelCase )
if display:
prin... | 721 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE = {"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MA... | 23 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
"facebook/dpr-ctx_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/... | 700 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) ... | 23 | 0 |
'''simple docstring'''
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> Optional[in... | 701 |
import heapq as hq
import math
from collections.abc import Iterator
class lowerCamelCase :
'''simple docstring'''
def __init__( self , lowerCAmelCase ):
UpperCAmelCase_ = str(id_ )
UpperCAmelCase_ = None
UpperCAmelCase_ = Non... | 23 | 0 |
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
SCREAMING_SNAKE_CASE = parse(importlib.metadata.version("torch"))
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , _... | 702 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE = {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}
try... | 23 | 0 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def snake_case__ ( ) -> List[str]:
UpperCAmelCase_ = [randint(-1000 , 1000 ) for i in range(10 )]
UpperCAmelCase_ = randint(-5000 , 5000 )
re... | 703 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING... | 23 | 0 |
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
SCREAMING_SNAKE_CASE = {
"sample_size": 32,
"in_channels": 3,
"out_channels": 3,
"layers_per_block": 2,
"num_class_embeds": 1000,
... | 704 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING... | 23 | 0 |
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> List[str]:
if not isinstance(snake_case__ , snake_case__ ):
raise ValueError("iterations must be defined as integers" )
if not isinstance(snake_case__ , snake_case__ ) or not number >= 1:
raise ValueErro... | 705 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> np.array:
UpperCAmelCase_ = f'''{sampling_rate}'''
UpperCAmelCase_ = "1"
UpperCAmelCase... | 23 | 0 |
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, FlaxM... | 706 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class lowerCamelCase ( lowercase__, lowercase__ ):
'''simple docstring'''
@register_to_... | 23 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool
if TYPE_CHECKING:
from PIL import Image
class lowerCamelCase ( _UpperCAmelCase ):
'''simple docstring'''... | 707 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import ... | 23 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE = {'''configuration_mbart''': ['''MBART... | 708 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_availa... | 23 | 0 |
import copy
import re
class lowerCamelCase :
'''simple docstring'''
lowerCAmelCase_ : Tuple = 'hp'
lowerCAmelCase_ : int = {}
lowerCAmelCase_ : Tuple = None
@classmethod
def A__ (... | 709 |
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> int:
UpperCAmelCase_ = [1]
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = 0, 0, 0
UpperCAmelCase_ = ugly_nums[ia] * 2
UpperCAmelCase_ = ugly_nums[ia] * 3
UpperCAmelCase_ = ugly_nums[i... | 23 | 0 |
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> int:
UpperCAmelCase_ = len(_snake_case )
UpperCAmelCase_ = [[0] * n for i in range(_snake_case )]
for i in range(_snake_case ):
UpperCAmelCase_ = y_points[i]
fo... | 710 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test... | 23 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.se... | 711 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
SCREAMING_SNAKE_CASE = {"tokenization_herbert": ["HerbertTokenizer"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDepe... | 23 | 0 |
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE = logging.getLogger(__name__)
@dataclass
... | 712 |
import math
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> list[int]:
UpperCAmelCase_ = []
UpperCAmelCase_ = 2
UpperCAmelCase_ = int(math.sqrt(__SCREAMING_SNAKE_CASE ) ) # Size of every segment
UpperCAmelCase_ = [True] * (end + 1)
UpperCAmelCase_ = ... | 23 | 0 |
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
"nielsr/canine-s": 2048,
}
# Unicode defines 1,114,112 total “codepo... | 713 |
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor impor... | 23 | 0 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_avai... | 714 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
SCREAMING_SNAKE_CASE = {
"configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}
try:
if not is_torch_available():
... | 23 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
... | 715 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
"xlm-roberta-base": "https://h... | 23 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer
S... | 716 |
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> str:
UpperCAmelCase_ = int(__SCREAMING_SNAKE_CASE )
if decimal in (0, 1): # Exit cases for the recursion
return str(__SCREAMING_SNAKE_CASE )
UpperCAmelCase_ , UpperCAmelCase_ = divmod(__SCREAMING_SNAKE_CASE , 2 )
re... | 23 | 0 |
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> Tuple:
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(__lowerCAmelCase , __lowerCAmelCase ) ) )
de... | 717 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@requi... | 23 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
SCREAMING_SNAKE_CASE = {
"configuration_m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config", "M2M100OnnxConfig"],
"tokenization_m2m_10... | 718 |
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
UpperCAmelCase_ = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAM... | 23 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class lowerCamelCase :
'''simple docstring'''
l... | 719 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
fr... | 23 | 0 |
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class lowerCamelCase ( datasets.BuilderConfig ):
'''simple docstring'''
lowerCAmelCase_ ... | 720 |
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> int:
UpperCAmelCase_ = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> int:
UpperCAmelCase_ = 0
while number > 0:
UpperCAmelCase_ = number % ... | 23 | 0 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, D... | 721 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE = {"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MA... | 23 | 0 |
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
class lowerCamelCase ( lowercase__ ):
'''simple docstring'''
def __init__( self , *lowerCAmelCase , ... | 700 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) ... | 23 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE = {
"configuration_roberta": ["ROBERTA_... | 701 |
import heapq as hq
import math
from collections.abc import Iterator
class lowerCamelCase :
'''simple docstring'''
def __init__( self , lowerCAmelCase ):
UpperCAmelCase_ = str(id_ )
UpperCAmelCase_ = None
UpperCAmelCase_ = Non... | 23 | 0 |
import os
from distutils.util import strtobool
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> Any:
for e in env_keys:
UpperCAmelCase_ = int(os.environ.get(lowerCAmelCase_ , -1 ) )
if val >= 0:
return val
return default
def sna... | 702 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE = {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}
try... | 23 | 0 |
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
UpperCAmelCase_ = len(SCREAMING_SNAKE_CASE__ )
for i in range(SCREAMING_SNAKE_CASE__ ):
for j in range(i + 1 , SCREAMING_SNAKE_CASE__ ):
if numbers[j] < numbers[i]:
UpperCAmelCase_ = numbers[j], num... | 703 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING... | 23 | 0 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFXLMRobertaModel
... | 704 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING... | 23 | 0 |
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> List[str]:
UpperCAmelCase_ = 0
# if input_string is "aba" than new_input_string become "a|b|a"
UpperCAmelCase_ = ""
UpperCAmelCase_ = ""
# append each character + "|" in new_string for range(0, length-1)
for i in input_st... | 705 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> np.array:
UpperCAmelCase_ = f'''{sampling_rate}'''
UpperCAmelCase_ = "1"
UpperCAmelCase... | 23 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
"Intel/dpt-large": "https://huggingface.co/Intel/dpt-large/resolve/main/config.json"... | 706 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class lowerCamelCase ( lowercase__, lowercase__ ):
'''simple docstring'''
@register_to_... | 23 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE = {
"configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
"tokenization_... | 707 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import ... | 23 | 0 |
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configu... | 708 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_availa... | 23 | 0 |
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> List[str]:
if len(snake_case_ ) != len(snake_case_ ):
raise ValueError("The length of profit and weight must be same." )
if max_weight <= 0:
raise ValueError("max_weight must gr... | 709 |
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> int:
UpperCAmelCase_ = [1]
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = 0, 0, 0
UpperCAmelCase_ = ugly_nums[ia] * 2
UpperCAmelCase_ = ugly_nums[ia] * 3
UpperCAmelCase_ = ugly_nums[i... | 23 | 0 |
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> bool:
UpperCAmelCase_ = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
UpperCAmelCase_ = set()
return any(
node not in visited and depth_first_search(__UpperCamelCase , __UpperCamelCa... | 710 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test... | 23 | 0 |
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclass
class lowerCamelCase ... | 711 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
SCREAMING_SNAKE_CASE = {"tokenization_herbert": ["HerbertTokenizer"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDepe... | 23 | 0 |
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
SCREAMING_SNAKE_CASE = "\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n and Saurav Kadavath\n and ... | 712 |
import math
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> list[int]:
UpperCAmelCase_ = []
UpperCAmelCase_ = 2
UpperCAmelCase_ = int(math.sqrt(__SCREAMING_SNAKE_CASE ) ) # Size of every segment
UpperCAmelCase_ = [True] * (end + 1)
UpperCAmelCase_ = ... | 23 | 0 |
import pprint
import requests
SCREAMING_SNAKE_CASE = "https://zenquotes.io/api"
def snake_case__ ( ) -> Any:
return requests.get(API_ENDPOINT_URL + "/today" ).json()
def snake_case__ ( ) -> int:
return requests.get(API_ENDPOINT_URL + "/random" ).json()
if __name__ == "__... | 713 |
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor impor... | 23 | 0 |
import argparse
from collections import defaultdict
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> Optional[int]:
UpperCAmelCase_ = f'''{file}_{class_name}_{test_nam... | 714 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
SCREAMING_SNAKE_CASE = {
"configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}
try:
if not is_torch_available():
... | 23 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
if TYPE_CHECKING:
... | 715 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
"xlm-roberta-base": "https://h... | 23 | 0 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import ... | 716 |
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> str:
UpperCAmelCase_ = int(__SCREAMING_SNAKE_CASE )
if decimal in (0, 1): # Exit cases for the recursion
return str(__SCREAMING_SNAKE_CASE )
UpperCAmelCase_ , UpperCAmelCase_ = divmod(__SCREAMING_SNAKE_CASE , 2 )
re... | 23 | 0 |
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