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 |
|---|---|---|---|---|
import inspect
import unittest
from transformers import MobileNetVaConfig
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_configuration_common import ConfigTester
from ... | 715 | 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_aligned_output_features_output_indices
... | 15 | 0 |
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
lowerCAmelCase : Any ={
"sample_size": 32,
"in_channels": 3,
"out_channels": 3,
"layers_per_block": 2,
"num_class_embed... | 716 | import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __snake_case ( __lowerCAmelCase ):
'''simple docstring'''
_snake_case = (EulerDiscreteScheduler,)
... | 15 | 0 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor... | 717 | import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase : Dict =logging.get_logger(__name__)
lowerCAmelCase : Dict ={"vocab_file": "vocab.json"}
lowerCAmelCase : List[str] ... | 15 | 0 |
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __snake_case ( SCR... | 718 | import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class __snake_case ( unittest.TestCase ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE (... | 15 | 0 |
from collections.abc import Iterable
from typing import Generic, TypeVar
lowerCAmelCase : int =TypeVar("_T")
class __snake_case ( Generic[_T] ):
'''simple docstring'''
def __init__( self : List[Any] , _UpperCamelCase : List[Any] = None... | 719 | import math
def A__ ( __A ):
'''simple docstring'''
assert isinstance(__A , __A ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or not numbe... | 15 | 0 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : Dict =logging.get_logger(__name__)
lowerCAmelCase : Tuple ={
"""google/pix2struct-textcaps-base"""... | 720 | 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... | 15 | 0 |
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
... | 721 | import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformer... | 15 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : Dict =logging.get_logger(__name__)
lowerCAmelCase : Dict ={
"facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/config.json",
# See all XGLM models at https://hu... | 700 | import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def A__ ( ):
'''simple docstring'''
_lowerCamelCase : Optional[int] = ArgumentParser(
... | 15 | 0 |
# Copyright 2023 The HuggingFace Inc. 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... | 701 | def A__ ( __A , __A ):
'''simple docstring'''
_enforce_args(__A , __A )
if n == 0:
return 0
_lowerCamelCase : Tuple = float("""-inf""" )
for i in range(1 , n + 1 ):
_lowerCamelCase : Any = max(
__A ,... | 15 | 0 |
'''simple docstring'''
def A__ ( ):
'''simple docstring'''
return 1
def A__ ( __A ):
'''simple docstring'''
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def A__ ( __A ):
... | 702 | from __future__ import annotations
class __snake_case :
'''simple docstring'''
def __init__( self : Tuple , _UpperCamelCase : int = 0) ->str:
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = ke... | 15 | 0 |
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_ima... | 703 | from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class __snake_case ( __lowerCAmelCase ):
'''simple docstring'''
... | 15 | 0 |
from torch import nn
class __snake_case ( nn.Module ):
'''simple docstring'''
def __init__( self : Union[str, Any] , _UpperCamelCase : List[str] , _UpperCamelCase : str) ->Union[str, Any]:
"""simple docstring"""
... | 704 | lowerCAmelCase : Tuple =0 # The first color of the flag.
lowerCAmelCase : Union[str, Any] =1 # The second color of the flag.
lowerCAmelCase : Any =2 # The third color of the flag.
lowerCAmelCase : List[str] =(red, white, blue)
def A__ ( __A... | 15 | 0 |
from __future__ import annotations
def A__ ( __A ):
'''simple docstring'''
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in range(1 , len(_lowerCamelCase ) ):
mat... | 705 | from __future__ import annotations
lowerCAmelCase : int =[]
def A__ ( __A , __A , __A ):
'''simple docstring'''
for i in range(len(__A ) ):
if board[row][i] == 1:
return False
for i in range(len(__A ) ):
if board[i]... | 15 | 0 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def A__ ( __A ):
'''simple docstring'''
def wrapper(*__A , **__A ):
_lowerCamelCase : int ... | 706 | import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
lowerCAmelCase : int ... | 15 | 0 |
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
... | 707 | def A__ ( __A ):
'''simple docstring'''
_lowerCamelCase : Tuple = 0
for ch in input_str:
_lowerCamelCase : Optional[Any] = ord(__A )
_lowerCamelCase : List[str] = pow(2 , __A )
# If we already turned o... | 15 | 0 |
'''simple docstring'''
import math
def A__ ( __A ):
'''simple docstring'''
_lowerCamelCase : Tuple = []
_lowerCamelCase : Optional[Any] = 2
_lowerCamelCase : Optional[int] = int(math.sqrt(lowerCAmelCas... | 708 | import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@req... | 15 | 0 |
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_att... | 709 | 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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
... | 15 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : List[str] =logging.get_logger(__name__)
lowerCAmelCase : Union[str, Any] ={
"microsoft/swinv2-tiny-patch4-window8-256": (
"https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-... | 710 | from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __snake_case ( __lowerCAmelCase ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ... | 15 | 0 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipel... | 711 | import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
lowerCAmelCase : Tuple =version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def A__ ( ... | 15 | 0 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
i... | 712 | from math import log
from scipy.constants import Boltzmann, physical_constants
lowerCAmelCase : List[Any] =300 # TEMPERATURE (unit = K)
def A__ ( __A , __A , __A , ):
'''simple docstring'''
if donor_conc <= 0:
raise ValueError("""Dono... | 15 | 0 |
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def A__ ( ... | 713 | import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def A__ ( __A ):
'''simple docstring'''
_lowerCamelCase : Tuple = {}
... | 15 | 0 |
import json
from typing import TYPE_CHECKING, 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_blenderbot impor... | 714 | import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import A... | 15 | 0 |
def A__ ( __A = 10 , __A = 22 ):
'''simple docstring'''
_lowerCamelCase : List[str] = range(1 , _UpperCamelCase )
_lowerCamelCase : List[str] = range(1 , _UpperCamelCase )
return sum(
1 for power in powers for base in bases if l... | 715 | 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_aligned_output_features_output_indices
... | 15 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
lowerCAmelCase : Tuple ={"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenize... | 716 | import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __snake_case ( __lowerCAmelCase ):
'''simple docstring'''
_snake_case = (EulerDiscreteScheduler,)
... | 15 | 0 |
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...ima... | 717 | import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase : Dict =logging.get_logger(__name__)
lowerCAmelCase : Dict ={"vocab_file": "vocab.json"}
lowerCAmelCase : List[str] ... | 15 | 0 |
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_inputs
if is_torch_available():
impor... | 718 | import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class __snake_case ( unittest.TestCase ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE (... | 15 | 0 |
import pytest
import datasets
# Import fixture modules as plugins
lowerCAmelCase : Any =["""tests.fixtures.files""", """tests.fixtures.hub""", """tests.fixtures.fsspec"""]
def A__ ( __A , __A ):
'''simple docstring'''
for item in items... | 719 | import math
def A__ ( __A ):
'''simple docstring'''
assert isinstance(__A , __A ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or not numbe... | 15 | 0 |
'''simple docstring'''
from __future__ import annotations
def A__ ( __A , __A ):
'''simple docstring'''
if len(lowerCamelCase__ ) == 0:
return False
_lowerCamelCase : Tuple = len(lowerCamelCase__ ) // 2
if a_list[midpoint]... | 720 | 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... | 15 | 0 |
from collections import defaultdict
def A__ ( __A ):
'''simple docstring'''
_lowerCamelCase : Tuple = 1
_lowerCamelCase : str = True
for v in tree[start]:
if v not in visited:
ret += dfs(__A )
if ret %... | 721 | import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformer... | 15 | 0 |
import argparse
import struct
import unittest
class __snake_case :
'''simple docstring'''
def __init__( self : List[Any] , _UpperCamelCase : bytes) ->None:
"""simple docstring"""
_lowerCamelCase : str = ... | 700 | import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def A__ ( ):
'''simple docstring'''
_lowerCamelCase : Optional[int] = ArgumentParser(
... | 15 | 0 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
i... | 701 | def A__ ( __A , __A ):
'''simple docstring'''
_enforce_args(__A , __A )
if n == 0:
return 0
_lowerCamelCase : Tuple = float("""-inf""" )
for i in range(1 , n + 1 ):
_lowerCamelCase : Any = max(
__A ,... | 15 | 0 |
'''simple docstring'''
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torch... | 702 | from __future__ import annotations
class __snake_case :
'''simple docstring'''
def __init__( self : Tuple , _UpperCamelCase : int = 0) ->str:
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = ke... | 15 | 0 |
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMaskedLM,
DistilBert... | 703 | from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class __snake_case ( __lowerCAmelCase ):
'''simple docstring'''
... | 15 | 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 __snake_case ( __low... | 704 | lowerCAmelCase : Tuple =0 # The first color of the flag.
lowerCAmelCase : Union[str, Any] =1 # The second color of the flag.
lowerCAmelCase : Any =2 # The third color of the flag.
lowerCAmelCase : List[str] =(red, white, blue)
def A__ ( __A... | 15 | 0 |
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tests
from os.path import j... | 705 | from __future__ import annotations
lowerCAmelCase : int =[]
def A__ ( __A , __A , __A ):
'''simple docstring'''
for i in range(len(__A ) ):
if board[row][i] == 1:
return False
for i in range(len(__A ) ):
if board[i]... | 15 | 0 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
... | 706 | import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
lowerCAmelCase : int ... | 15 | 0 |
from __future__ import annotations
class __snake_case :
'''simple docstring'''
def __init__( self : Tuple , _UpperCamelCase : int = 0) ->str:
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = ke... | 707 | def A__ ( __A ):
'''simple docstring'''
_lowerCamelCase : Tuple = 0
for ch in input_str:
_lowerCamelCase : Optional[Any] = ord(__A )
_lowerCamelCase : List[str] = pow(2 , __A )
# If we already turned o... | 15 | 0 |
'''simple docstring'''
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
lowerCAmelCase : Tuple ="\\n@inproceedings{popovic-2015-chrf,\n title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",\n author = \"Popovi... | 708 | import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@req... | 15 | 0 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase : str ={
"configuration_trajectory_transformer": [
"TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"TrajectoryTransformerConfig",
... | 709 | 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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
... | 15 | 0 |
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, Traini... | 710 | from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __snake_case ( __lowerCAmelCase ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ... | 15 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase : List[Any] ={
"configuration_clap": [
"CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
"ClapAudioConfig",
"ClapConfig",
"ClapTextConfig",
],
... | 711 | import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
lowerCAmelCase : Tuple =version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def A__ ( ... | 15 | 0 |
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
lowerCAmelCase : Union[str, Any] =logging.getLogger(__name__)
if is_torch_tpu_... | 712 | from math import log
from scipy.constants import Boltzmann, physical_constants
lowerCAmelCase : List[Any] =300 # TEMPERATURE (unit = K)
def A__ ( __A , __A , __A , ):
'''simple docstring'''
if donor_conc <= 0:
raise ValueError("""Dono... | 15 | 0 |
lowerCAmelCase : Tuple =0 # The first color of the flag.
lowerCAmelCase : Union[str, Any] =1 # The second color of the flag.
lowerCAmelCase : Any =2 # The third color of the flag.
lowerCAmelCase : List[str] =(red, white, blue)
def ... | 713 | import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def A__ ( __A ):
'''simple docstring'''
_lowerCamelCase : Tuple = {}
... | 15 | 0 |
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def A__ ( __A ): # picklable for multiprocessing
'''s... | 714 | import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import A... | 15 | 0 |
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def... | 715 | 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_aligned_output_features_output_indices
... | 15 | 0 |
lowerCAmelCase : Optional[int] =[0, 2, 4, 6, 8]
lowerCAmelCase : Tuple =[1, 3, 5, 7, 9]
def A__ ( __A , __A , __A , __A ):
'''simple docstring'''
if remaining_length == 0:
if digits[0] == 0 or digits[-1] == 0:
return ... | 716 | import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __snake_case ( __lowerCAmelCase ):
'''simple docstring'''
_snake_case = (EulerDiscreteScheduler,)
... | 15 | 0 |
import qiskit
def A__ ( __A = 2 ):
'''simple docstring'''
_lowerCamelCase : str = qubits
# Using Aer's simulator
_lowerCamelCase : Optional[Any] = qiskit.Aer.get_backend("""aer_simulator""" )
# Creating a Quantum Circui... | 717 | import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase : Dict =logging.get_logger(__name__)
lowerCAmelCase : Dict ={"vocab_file": "vocab.json"}
lowerCAmelCase : List[str] ... | 15 | 0 |
from math import log
from scipy.constants import Boltzmann, physical_constants
lowerCAmelCase : List[Any] =300 # TEMPERATURE (unit = K)
def A__ ( __A , __A , __A , ):
'''simple docstring'''
if donor_conc <= 0:
raise ValueError("""Dono... | 718 | import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class __snake_case ( unittest.TestCase ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE (... | 15 | 0 |
def A__ ( __A , __A ):
'''simple docstring'''
_lowerCamelCase : Union[str, Any] = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
_lowerCamelCase : str = n - k
# Calculate C(n,k)
for i in rang... | 719 | import math
def A__ ( __A ):
'''simple docstring'''
assert isinstance(__A , __A ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or not numbe... | 15 | 0 |
'''simple docstring'''
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
lowerCAmelC... | 720 | 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... | 15 | 0 |
lowerCAmelCase : Dict =[sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(100000)]
def A__ ( __A ):
'''simple docstring'''
_lowerCamelCase : List[Any] = 0
while number:
# Increased Speed Slightly by checking every 5 digit... | 721 | import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformer... | 15 | 0 |
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class __snake_case ( unittest.TestCase ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE (... | 700 | import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def A__ ( ):
'''simple docstring'''
_lowerCamelCase : Optional[int] = ArgumentParser(
... | 15 | 0 |
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 701 | def A__ ( __A , __A ):
'''simple docstring'''
_enforce_args(__A , __A )
if n == 0:
return 0
_lowerCamelCase : Tuple = float("""-inf""" )
for i in range(1 , n + 1 ):
_lowerCamelCase : Any = max(
__A ,... | 15 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ..... | 702 | from __future__ import annotations
class __snake_case :
'''simple docstring'''
def __init__( self : Tuple , _UpperCamelCase : int = 0) ->str:
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = ke... | 15 | 0 |
def A__ ( __A ):
'''simple docstring'''
def merge(__A , __A ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yield from left
yield from right
return list(_merge() )
... | 703 | from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class __snake_case ( __lowerCAmelCase ):
'''simple docstring'''
... | 15 | 0 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Datase... | 704 | lowerCAmelCase : Tuple =0 # The first color of the flag.
lowerCAmelCase : Union[str, Any] =1 # The second color of the flag.
lowerCAmelCase : Any =2 # The third color of the flag.
lowerCAmelCase : List[str] =(red, white, blue)
def A__ ( __A... | 15 | 0 |
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase : List[Any] =get_tests_dir("fixtures/test_sentencepiece_with_... | 705 | from __future__ import annotations
lowerCAmelCase : int =[]
def A__ ( __A , __A , __A ):
'''simple docstring'''
for i in range(len(__A ) ):
if board[row][i] == 1:
return False
for i in range(len(__A ) ):
if board[i]... | 15 | 0 |
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 706 | import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
lowerCAmelCase : int ... | 15 | 0 |
from math import factorial
def A__ ( __A , __A ):
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
raise ValueError("""Please enter positive integers for n a... | 707 | def A__ ( __A ):
'''simple docstring'''
_lowerCamelCase : Tuple = 0
for ch in input_str:
_lowerCamelCase : Optional[Any] = ord(__A )
_lowerCamelCase : List[str] = pow(2 , __A )
# If we already turned o... | 15 | 0 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
lowerCAmelCase : int ="\\n@inproceedings{Rajpurkar2016SQuAD10,\n title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},... | 708 | import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@req... | 15 | 0 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
lowerCAmelCase : str ="\nimport os\n"
lowerCAmelCase : int ="\ndef foo():\n import os\n return False\n"
lowerCAmelCase : Optional[int] ="\ndef foo():\n def bar():\n if T... | 709 | 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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
... | 15 | 0 |
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.utils.testing_utils import enable_full_det... | 710 | from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __snake_case ( __lowerCAmelCase ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ... | 15 | 0 |
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProc... | 711 | import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
lowerCAmelCase : Tuple =version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def A__ ( ... | 15 | 0 |
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def A__ ( __A = 8 ):
'''simple docstring'''
_lowerCamelCase : str = ascii_letters + digits + punctuation
return "".jo... | 712 | from math import log
from scipy.constants import Boltzmann, physical_constants
lowerCAmelCase : List[Any] =300 # TEMPERATURE (unit = K)
def A__ ( __A , __A , __A , ):
'''simple docstring'''
if donor_conc <= 0:
raise ValueError("""Dono... | 15 | 0 |
from __future__ import annotations
def A__ ( __A ): # This function is recursive
'''simple docstring'''
_lowerCamelCase : Tuple = len(__A )
# If the array contains only one element, we return it (it's the stop condition of
# recu... | 713 | import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def A__ ( __A ):
'''simple docstring'''
_lowerCamelCase : Tuple = {}
... | 15 | 0 |
from __future__ import annotations
def A__ ( __A , __A ):
'''simple docstring'''
_lowerCamelCase : Dict = sorted(numsa + numsa )
_lowerCamelCase : Optional[int] = divmod(len(__A ) , 2 )
if mod == 1:
return all_numbe... | 714 | import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import A... | 15 | 0 |
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class __snake_case :
'''simple docstring'''
_snake_case = 42
_snake_case = 42
cla... | 715 | 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_aligned_output_features_output_indices
... | 15 | 0 |
class __snake_case :
'''simple docstring'''
def __init__( self : Optional[int] , _UpperCamelCase : str = "" , _UpperCamelCase : bool = False) ->None:
"""simple docstring"""
_lowerCamelCase : dict[str, RadixNode] ... | 716 | import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __snake_case ( __lowerCAmelCase ):
'''simple docstring'''
_snake_case = (EulerDiscreteScheduler,)
... | 15 | 0 |
def A__ ( __A , __A ):
'''simple docstring'''
return int((input_a, input_a).count(1 ) != 0 )
def A__ ( ):
'''simple docstring'''
assert or_gate(0 , 0 ) == 0
assert or_gate(0 , 1 ) == 1
assert or_gate(1 , 0 ) == 1
... | 717 | import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase : Dict =logging.get_logger(__name__)
lowerCAmelCase : Dict ={"vocab_file": "vocab.json"}
lowerCAmelCase : List[str] ... | 15 | 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 Accelera... | 718 | import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class __snake_case ( unittest.TestCase ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE (... | 15 | 0 |
def A__ ( __A ):
'''simple docstring'''
assert isinstance(__A , __A ), F"""The input value of [n={number}] is not an integer"""
if number == 1:
return 2
elif number < 1:
_lowerCamelCase : Optional[int] = F"""The input value of [n={number... | 719 | import math
def A__ ( __A ):
'''simple docstring'''
assert isinstance(__A , __A ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or not numbe... | 15 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
lowerCAmelCase : str ={
"google/tapas-base-finetuned-sqa": (
"https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"
),
"google/tapas-base-finetuned-wtq": (
"... | 720 | 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... | 15 | 0 |
import inspect
import unittest
from transformers import MobileNetVaConfig
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_configuration_common import ConfigTester
from ... | 721 | import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformer... | 15 | 0 |
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_table,
query_... | 700 | import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def A__ ( ):
'''simple docstring'''
_lowerCamelCase : Optional[int] = ArgumentParser(
... | 15 | 0 |
class __snake_case :
'''simple docstring'''
def __init__( self : int) ->Tuple:
"""simple docstring"""
_lowerCamelCase : Tuple = {}
def _SCREAMING_SNAKE_CASE ( self : str) ->None:
... | 701 | def A__ ( __A , __A ):
'''simple docstring'''
_enforce_args(__A , __A )
if n == 0:
return 0
_lowerCamelCase : Tuple = float("""-inf""" )
for i in range(1 , n + 1 ):
_lowerCamelCase : Any = max(
__A ,... | 15 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase : str ={
"configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
"tokeni... | 702 | from __future__ import annotations
class __snake_case :
'''simple docstring'''
def __init__( self : Tuple , _UpperCamelCase : int = 0) ->str:
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = ke... | 15 | 0 |
def A__ ( __A , __A ):
'''simple docstring'''
_lowerCamelCase : int = word.split()
def justify(__A , __A , __A ) -> str:
_lowerCamelCase : List[str] = max_width - width
_lowerCamelCase : Any = len(__A )
if len(_... | 703 | from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class __snake_case ( __lowerCAmelCase ):
'''simple docstring'''
... | 15 | 0 |
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def A__ ( __A , __A , __A = False ):
'''simple docstring'''
if radian_mode:
return [magnitude * cos(__A ), magnitude * sin(__A... | 704 | lowerCAmelCase : Tuple =0 # The first color of the flag.
lowerCAmelCase : Union[str, Any] =1 # The second color of the flag.
lowerCAmelCase : Any =2 # The third color of the flag.
lowerCAmelCase : List[str] =(red, white, blue)
def A__ ( __A... | 15 | 0 |
from ..utils import DummyObject, requires_backends
class __snake_case ( metaclass=__lowerCAmelCase ):
'''simple docstring'''
_snake_case = ['onnx']
def __init__( self : Optional[Any] , *_UpperCamelCase : Tuple , **_UpperCam... | 705 | from __future__ import annotations
lowerCAmelCase : int =[]
def A__ ( __A , __A , __A ):
'''simple docstring'''
for i in range(len(__A ) ):
if board[row][i] == 1:
return False
for i in range(len(__A ) ):
if board[i]... | 15 | 0 |
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import ... | 706 | import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
lowerCAmelCase : int ... | 15 | 0 |
import collections
import inspect
import unittest
from transformers import FocalNetConfig
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_backbone_common import Backbon... | 707 | def A__ ( __A ):
'''simple docstring'''
_lowerCamelCase : Tuple = 0
for ch in input_str:
_lowerCamelCase : Optional[Any] = ord(__A )
_lowerCamelCase : List[str] = pow(2 , __A )
# If we already turned o... | 15 | 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 A__ ( __A ):
'''s... | 708 | import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@req... | 15 | 0 |
def A__ ( __A ):
'''simple docstring'''
_lowerCamelCase : Tuple = 0
for ch in input_str:
_lowerCamelCase : Optional[Any] = ord(__A )
_lowerCamelCase : List[str] = pow(2 , __A )
# If we already turned o... | 709 | 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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
... | 15 | 0 |
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tenso... | 710 | from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __snake_case ( __lowerCAmelCase ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ... | 15 | 0 |
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def A__ ( __A ):
'''simple docstring'''
_lowerCamelCase : List[Any] = prime_factors(__A )
if is_square_free(__A ):
return -1 if len(__A ) % 2 else 1
return 0
i... | 711 | import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
lowerCAmelCase : Tuple =version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def A__ ( ... | 15 | 0 |
from __future__ import annotations
import math
def A__ ( __A , __A , __A , __A , __A ):
'''simple docstring'''
if depth < 0:
raise ValueError("""Depth cannot be less than 0""" )
if len(__A ) == 0:
raise ValueError("""Scores cannot b... | 712 | from math import log
from scipy.constants import Boltzmann, physical_constants
lowerCAmelCase : List[Any] =300 # TEMPERATURE (unit = K)
def A__ ( __A , __A , __A , ):
'''simple docstring'''
if donor_conc <= 0:
raise ValueError("""Dono... | 15 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokeni... | 713 | import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def A__ ( __A ):
'''simple docstring'''
_lowerCamelCase : Tuple = {}
... | 15 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformer... | 714 | import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import A... | 15 | 0 |
def A__ ( __A , __A ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
_lowerCamelCase : str = str(bin(__A ) )[2:] # remove the leading "0b"
_lowerCamelCase : Dict = st... | 715 | 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_aligned_output_features_output_indices
... | 15 | 0 |
import os
import string
import sys
lowerCAmelCase : Optional[int] =1 << 8
lowerCAmelCase : int ={
"tab": ord("\t"),
"newline": ord("\r"),
"esc": 27,
"up": 65 + ARROW_KEY_FLAG,
"down": 66 + ARROW_KEY_FLAG,
"right": 67 + ARROW_KEY_FLAG,
"left": 68 + ARR... | 716 | import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __snake_case ( __lowerCAmelCase ):
'''simple docstring'''
_snake_case = (EulerDiscreteScheduler,)
... | 15 | 0 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __snake_case ( __lowerCAmelCase ):
... | 717 | import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase : Dict =logging.get_logger(__name__)
lowerCAmelCase : Dict ={"vocab_file": "vocab.json"}
lowerCAmelCase : List[str] ... | 15 | 0 |
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from transformers import... | 718 | import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class __snake_case ( unittest.TestCase ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE (... | 15 | 0 |
from collections.abc import Callable
import numpy as np
def A__ ( __A , __A , __A , __A , __A ):
'''simple docstring'''
_lowerCamelCase : Union[str, Any] = int(np.ceil((x_end - xa) / step_size ) )
_lowerCamelCase : int = n... | 719 | import math
def A__ ( __A ):
'''simple docstring'''
assert isinstance(__A , __A ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or not numbe... | 15 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch,... | 720 | 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... | 15 | 0 |
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def A__ ( __A ):
'''simple docstring'''
_lowerCamelCase : Dict = []
_lowerCamelCase : List... | 721 | import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformer... | 15 | 0 |
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def A__ ( __A , __A=7 ):
'''simple docstring'''
_lowerCamelCase : List[Any] = None
if token is not None:
_lowerCamelC... | 700 | import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def A__ ( ):
'''simple docstring'''
_lowerCamelCase : Optional[int] = ArgumentParser(
... | 15 | 0 |
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import... | 701 | def A__ ( __A , __A ):
'''simple docstring'''
_enforce_args(__A , __A )
if n == 0:
return 0
_lowerCamelCase : Tuple = float("""-inf""" )
for i in range(1 , n + 1 ):
_lowerCamelCase : Any = max(
__A ,... | 15 | 0 |
'''simple docstring'''
from __future__ import annotations
def A__ ( __A , __A ):
'''simple docstring'''
_lowerCamelCase : Union[str, Any] = 0
_lowerCamelCase : Optional[int] = len(__A ) - 1
while i < j:
i... | 702 | from __future__ import annotations
class __snake_case :
'''simple docstring'''
def __init__( self : Tuple , _UpperCamelCase : int = 0) ->str:
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = ke... | 15 | 0 |
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
lowerCAmelCase : Optional[int] =TypeVar("T")
class __snake_case ( Generic[T] ):
'''simple docstring'''
def __init__( self : Tuple , _UpperCamelCase ... | 703 | from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class __snake_case ( __lowerCAmelCase ):
'''simple docstring'''
... | 15 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.ut... | 704 | lowerCAmelCase : Tuple =0 # The first color of the flag.
lowerCAmelCase : Union[str, Any] =1 # The second color of the flag.
lowerCAmelCase : Any =2 # The third color of the flag.
lowerCAmelCase : List[str] =(red, white, blue)
def A__ ( __A... | 15 | 0 |
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