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 |
|---|---|---|---|---|
'''simple docstring'''
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class lowerCamelCase ( lowercase__ ):
'''simple docstring'''
lowerCAmelCase_ : str = 'EncodecFeatureEx... | 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 transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class lowerCamelCase ( l... | 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 = 10 , __SCREAMING_SNAKE_CASE = 22 ) -> int:
UpperCAmelCase_ = range(1 , __SCREAMING_SNAKE_CASE )
UpperCAmelCase_ = range(1 , __SCREAMING_SNAKE_CASE )
return sum(
1 for power in powers for base in bases if len(... | 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 |
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... | 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 statistics import mean, stdev
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = 3 ) -> list:
UpperCAmelCase_ = min(__SCREAMING_SNAKE_CASE )
UpperCAmelCase_ = max(__SCREAMING_SNAKE_CASE )
# normalize data
return [round((x - x_min) / (x_max - x_mi... | 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 json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
SCREAMING_SNAKE_CASE ... | 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 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... | 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 CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE... | 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 ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...uti... | 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 argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_attention_paths,
ren... | 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 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(... | 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 __future__ import annotations
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> dict[str, float]:
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if resistance < 0:
... | 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 unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import To... | 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 math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def snake_case__ ( __SCREAMING_SNAKE_CASE = 3 ) -> qiskit.result.counts.Counts:
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
raise Typ... | 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 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... | 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 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... | 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 requests
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> None:
UpperCAmelCase_ = {"Content-Type": "application/json"}
UpperCAmelCase_ = requests.post(__SCREAMING_SNAKE_CASE , json={"text": message_body} , ... | 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 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... | 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 inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class lowerCamelCase ( unittest.TestCase ):
'''simple d... | 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 inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
def A__ ( self ):
UpperCAmelCase_ = inspec... | 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 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... | 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 |
# 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... | 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'''
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> int:
if index == number_of_items:
return 0
UpperCAmelCase_ = 0
UpperCAmelCase_ = ... | 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 bisect import bisect
from itertools import accumulate
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> Optional[Any]:
UpperCAmelCase_ = sorted(zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNA... | 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 typing import List
from .keymap import KEYMAP, get_character
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> Any:
def decorator(__SCREAMING_SNAKE_CASE ):
UpperCAmelCase_ = getattr(__SCREAMING_SNAKE_CASE , "handle_key" , [] )
handle += [key]
... | 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 tensorflow as tf
from ...tf_utils import shape_list
class lowerCamelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase=1 , lowerCAmelCase=Fal... | 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 numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class lowerCamelCase ( lowercase__ ):
'''simple docstring'''
def __init__( self , lowerCAmelCase , 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 |
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> int:
UpperCAmelCase_ , UpperCAmelCase_ = len(__SCREAMING_SNAKE_CASE ), len(grid[0] )
if (
min(__SCREAMING_SNAKE_CASE , __SCREAMING_SN... | 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 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 ... | 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 inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils imp... | 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 |
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 % 10
sum_... | 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 os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class lowerCamelCase ( datasets.BeamBasedBuilder ):
'''simple docstring'''
def A__ ... | 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 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaag... | 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 |
import re
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> list:
return [char.split() for char in re.split(R"[^ a-z A-Z 0-9 \s]" , str_ )]
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> str:
UpperCAmelCase_ = split_input(str_ )
return "".join(
["".join([char... | 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 inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class lowerCamelCase ( unittest.TestCase ):
'''si... | 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 __future__ import annotations
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> list[int]:
UpperCAmelCase_ = 2
UpperCAmelCase_ = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(__SCREAMING_SNAKE_CASE )
if n > 1:... | 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 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE = {
"configuration_megatron_bert": ["MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegatronBertConfig"],
}
try:
if not is_torch_available():
raise... | 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 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... | 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 ) -> bool:
UpperCAmelCase_ = 0
for ch in input_str:
UpperCAmelCase_ = ord(__SCREAMING_SNAKE_CASE )
UpperCAmelCase_ = pow(2 , __SCREAMING_SNAKE_CASE )
# If we already turned on bit for curre... | 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 gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import re... | 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 , __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... | 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 gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableU... | 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 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_... | 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 parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ... | 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_sentencepiece_available,
is_speech_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE = {
"configuration_trocr": ["TROCR_PRETRAINED_CONFIG_AR... | 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 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
@requ... | 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 typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
SCREAMING_SNAKE_CASE = {
"configuration_efficientformer": [
"EFFICIENTFORMER_PRETRAINED_CO... | 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 ) -> str:
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
UpperCAmelCase_ = str(bin(__SCREAMING_SNAKE_CASE ) )[2:] # remove the leading "0b"
UpperCAmelCase_ = str(bin(__SCR... | 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 typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE = {
"configuration_pegasus_x": ["PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP", "PegasusXConfig"],
}
try:
if not is_torch_available():
raise OptionalDepend... | 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 secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def snake_case__ ( __SCREAMING_SNAKE_CASE = 8 ) -> str:
UpperCAmelCase_ = ascii_letters + digits + punctuation
return "".join(secrets.choice(__SCREAMING_SNAK... | 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 pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def snake_case__ ( ) -> List[Any]:
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with pytest.ra... | 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 logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoic... | 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 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_ =... | 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 logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobe... | 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 |
import logging
from transformers.configuration_utils import PretrainedConfig
SCREAMING_SNAKE_CASE = logging.getLogger(__name__)
class lowerCamelCase ( lowercase__ ):
'''simple docstring'''
lowerCAmelCase_ : List[str] = 'masked_bert'
def __i... | 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 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
... | 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'''
# 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... | 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 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... | 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 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def snake_case__ ( ) -> int:
UpperCAmelCase_ = HfArgumentParser(__SCREAMING_SNAKE_CASE )
UpperCAmelCase_ = parser.parse_args_into_dataclasses()[0]
UpperCAmelCase_ = TensorFlow... | 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 |
"""simple docstring"""
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from tra... | 24 |
"""simple docstring"""
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
lowercase = logging.get_logger(__name__)
low... | 24 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inp... | 24 |
"""simple docstring"""
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
lowercase = logging.getLogger(__... | 24 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowercase = {
'''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConf... | 24 |
"""simple docstring"""
from itertools import permutations
def UpperCAmelCase ( A : tuple ):
'''simple docstring'''
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
... | 24 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...t... | 24 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase = {
'''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxCo... | 24 | 1 |
"""simple docstring"""
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def UpperCAmelCase ( A : str , A : complex , A : str = "x" , A : float = 10**-10 , A : int = 1 , ):
'''simple d... | 24 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig''',
... | 24 | 1 |
"""simple docstring"""
import math
import os
import sys
def UpperCAmelCase ( A : str ):
'''simple docstring'''
_UpperCAmelCase = ''
try:
with open(A , 'rb' ) as binary_file:
_UpperCAmelCase = binary_file.read()
f... | 24 |
"""simple docstring"""
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
from ...utils.backbone_utils import BackboneConfigMixin, get_al... | 24 | 1 |
"""simple docstring"""
def UpperCAmelCase ( A : int ):
'''simple docstring'''
_UpperCAmelCase = abs(A )
_UpperCAmelCase = 0
while n > 0:
res += n % 10
n //= 10
return res
def UpperCAmelCase ( A : int ):
... | 24 |
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class lowercase__ ( nn.Module ):
'''simple docstring'''
def __init__( self , snake_case = 1... | 24 | 1 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase ( A : int , A : int ):
'''simple docstring'''
if partitions <= 0:
raise ValueError('partitions must be a positive number!' )
if partitions > number_of_bytes:
... | 24 |
"""simple docstring"""
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
... | 24 | 1 |
"""simple docstring"""
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
lowercase = '''<<<<<<< This should probably be modified because it mention... | 24 |
"""simple docstring"""
from __future__ import annotations
from cmath import sqrt
def UpperCAmelCase ( A : int , A : int , A : int ):
'''simple docstring'''
if a == 0:
raise ValueError('Coefficient \'a\' must not be zero.' )
... | 24 | 1 |
"""simple docstring"""
lowercase = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
... | 24 |
"""simple docstring"""
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@req... | 24 | 1 |
"""simple docstring"""
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def UpperCAmelCase ( A : int , A : int , A : Any ):
... | 24 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.... | 24 | 1 |
"""simple docstring"""
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDa... | 24 |
"""simple docstring"""
def UpperCAmelCase ( A : int ):
'''simple docstring'''
_UpperCAmelCase = abs(A )
_UpperCAmelCase = 0
while n > 0:
res += n % 10
n //= 10
return res
def UpperCAmelCase ( A : int ):
... | 24 | 1 |
"""simple docstring"""
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_util... | 24 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase ( A : int , A : int ):
'''simple docstring'''
_UpperCAmelCase = []
create_all_state(1 , A , A , [] , A )
return result
... | 24 | 1 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowercase = logging.get_logger(__name__)
... | 24 |
"""simple docstring"""
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCa... | 24 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class lowercase__ :
'''simple docstring'''
_UpperCAmelCas... | 24 |
"""simple docstring"""
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDa... | 24 | 1 |
"""simple docstring"""
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('''.''')
def UpperCAmelCase ( A : int ):
'''simple docstring'''
... | 24 |
"""simple docstring"""
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
fr... | 24 | 1 |
"""simple docstring"""
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling... | 24 |
"""simple docstring"""
def UpperCAmelCase ( A : int ):
'''simple docstring'''
_UpperCAmelCase = [[0 for _ in range(A )] for _ in range(m + 1 )]
for i in range(m + 1 ):
_UpperCAmelCase = 1
for n in range(m + 1 ):
fo... | 24 | 1 |
"""simple docstring"""
def UpperCAmelCase ( A : float , A : float ):
'''simple docstring'''
if mass < 0:
raise ValueError('The mass of a body cannot be negative' )
return 0.5 * mass * abs(A ) * abs(A )
if __name__ == "__main__":
... | 24 |
"""simple docstring"""
import os
lowercase = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 1_00, '''D''': 5_00, '''M''': 10_00}
def UpperCAmelCase ( A : str ):
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase ... | 24 | 1 |
"""simple docstring"""
class lowercase__ :
'''simple docstring'''
def __init__( self ) -> Dict:
_UpperCAmelCase = {}
def lowerCamelCase_ ( self ) -> None:
print(self.vertex )
... | 24 |
"""simple docstring"""
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,
... | 24 | 1 |
"""simple docstring"""
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def UpperCAmelCase ( A : float , A : float , A : bool = False ):
'''simple docstring'''
... | 24 |
"""simple docstring"""
import os
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = os.path.join(os.path.dirname(A ) , 'num.txt' )
with open(A ) as file_hand:
return str(sum(int(A ) for line in file_hand ) ... | 24 | 1 |
"""simple docstring"""
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(ch... | 24 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase = {
'''configuration_roberta... | 24 | 1 |
"""simple docstring"""
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...uti... | 24 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
lowercase = logging.get_logger(__name__)
class lowercase__ ( A ):
'''simple docstring'''
def __init__( ... | 24 | 1 |
"""simple docstring"""
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCa... | 24 |
"""simple docstring"""
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
lowercase = logging.get_logger(__name__)
low... | 24 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from di... | 24 |
"""simple docstring"""
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
lowercase = logging.getLogger(__... | 24 | 1 |
"""simple docstring"""
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def UpperCAmelCase ( *A : Any , A : Optional[Union[Dict, Any]] = None , A : str=True , A : Any=2 ):
... | 24 |
"""simple docstring"""
from itertools import permutations
def UpperCAmelCase ( A : tuple ):
'''simple docstring'''
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
... | 24 | 1 |
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip i... | 24 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase = {
'''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxCo... | 24 | 1 |
"""simple docstring"""
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffuser... | 24 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig''',
... | 24 | 1 |
"""simple docstring"""
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@req... | 24 |
"""simple docstring"""
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
from ...utils.backbone_utils import BackboneConfigMixin, get_al... | 24 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionMo... | 24 |
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class lowercase__ ( nn.Module ):
'''simple docstring'''
def __init__( self , snake_case = 1... | 24 | 1 |
"""simple docstring"""
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def ... | 24 |
"""simple docstring"""
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
... | 24 | 1 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_t... | 24 |
"""simple docstring"""
from __future__ import annotations
from cmath import sqrt
def UpperCAmelCase ( A : int , A : int , A : int ):
'''simple docstring'''
if a == 0:
raise ValueError('Coefficient \'a\' must not be zero.' )
... | 24 | 1 |
"""simple docstring"""
def UpperCAmelCase ( A : int ):
'''simple docstring'''
_UpperCAmelCase = [[0 for _ in range(A )] for _ in range(m + 1 )]
for i in range(m + 1 ):
_UpperCAmelCase = 1
for n in range(m + 1 ):
fo... | 24 |
"""simple docstring"""
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@req... | 24 | 1 |
"""simple docstring"""
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
from ...utils.backbone_utils import BackboneConfigMixin, get_al... | 24 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.... | 24 | 1 |
"""simple docstring"""
import pprint
import requests
lowercase = '''https://zenquotes.io/api'''
def UpperCAmelCase ( ):
'''simple docstring'''
return requests.get(API_ENDPOINT_URL + '/today' ).json()
def UpperCAmelCase ( ):
'''sim... | 24 |
"""simple docstring"""
def UpperCAmelCase ( A : int ):
'''simple docstring'''
_UpperCAmelCase = abs(A )
_UpperCAmelCase = 0
while n > 0:
res += n % 10
n //= 10
return res
def UpperCAmelCase ( A : int ):
... | 24 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inp... | 24 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase ( A : int , A : int ):
'''simple docstring'''
_UpperCAmelCase = []
create_all_state(1 , A , A , [] , A )
return result
... | 24 | 1 |
"""simple docstring"""
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,
... | 24 |
"""simple docstring"""
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCa... | 24 | 1 |
"""simple docstring"""
from functools import reduce
lowercase = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715... | 24 |
"""simple docstring"""
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDa... | 24 | 1 |
"""simple docstring"""
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
fr... | 24 |
"""simple docstring"""
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
fr... | 24 | 1 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVeca... | 24 |
"""simple docstring"""
def UpperCAmelCase ( A : int ):
'''simple docstring'''
_UpperCAmelCase = [[0 for _ in range(A )] for _ in range(m + 1 )]
for i in range(m + 1 ):
_UpperCAmelCase = 1
for n in range(m + 1 ):
fo... | 24 | 1 |
"""simple docstring"""
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import Tokeni... | 24 |
"""simple docstring"""
import os
lowercase = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 1_00, '''D''': 5_00, '''M''': 10_00}
def UpperCAmelCase ( A : str ):
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase ... | 24 | 1 |
"""simple docstring"""
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,... | 24 |
"""simple docstring"""
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,
... | 24 | 1 |
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