code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
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# Copyright 2023 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 applica... | 34 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
lowerCamelCase_ = re.compile(R'''\b(a|an|the)\b''', re.UNICODE)
lowerCamelCase_ = None
def UpperCamelCase( ) -> List[Any]:
'''simple docstring'''
snake_ca... | 34 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, t... | 34 |
from torch import nn
def UpperCamelCase( lowercase_ ) -> Tuple:
'''simple docstring'''
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(f'''Unsupported acti... | 34 | 1 |
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint
lowerCamelCase_ ... | 34 |
from __future__ import annotations
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ ) -> dict[str, float]:
'''simple docstring'''
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if r... | 34 | 1 |
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
from ...test_configuration_common import Conf... | 34 |
def UpperCamelCase( lowercase_ , lowercase_ ) -> str:
'''simple docstring'''
return "\n".join(
f'''{number} * {i} = {number * i}''' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_t... | 34 | 1 |
import unittest
import numpy as np
import requests
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():
... | 34 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCamelCase_ = {
'''configuration_groupvit''': [
'''GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''GroupViTConfig''',
'''GroupViTOnnxConfig''... | 34 | 1 |
from __future__ import annotations
def UpperCamelCase( lowercase_ ) -> list[int]: # This function is recursive
'''simple docstring'''
snake_case_ = len(lowercase_ )
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
... | 34 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def UpperCamelCase( lowercase_ = "" ) -> dict[str, float]:
'''simple docstring'''
snake_case_ = url or """https://www.imdb.com/chart/top/?ref_=nv_mv_250"""
snake_case_ = ... | 34 | 1 |
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 __lowerCamelCase ( ... | 34 |
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
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''facebook/levit-1... | 34 | 1 |
from collections import defaultdict
from math import ceil, sqrt
def UpperCamelCase( lowercase_ = 1000000 , lowercase_ = 10 ) -> int:
'''simple docstring'''
snake_case_ = defaultdict(lowercase_ )
for outer_width in range(3 , (t_limit // 4) + 2 ):
... | 34 |
import logging
from transformers.configuration_utils import PretrainedConfig
lowerCamelCase_ = logging.getLogger(__name__)
class __lowerCamelCase ( __snake_case ):
lowerCamelCase_ : Optional[int] = 'masked_bert'
def __init__( self , lowerCam... | 34 | 1 |
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
from ... | 34 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def UpperCamelCase( ) -> Optional[Any]:
'''simple docstring'''
snake_case_ = {
"""repo_name""": ["""test_repo1""", """test_repo2""", ... | 34 | 1 |
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logging.set_verbosity_info()
l... | 34 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
... | 34 | 1 |
from queue import PriorityQueue
from typing import Any
import numpy as np
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , ) -> float | int:
'''simple docstring... | 34 |
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
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''google/mobilenet... | 34 | 1 |
from numpy import exp, pi, sqrt
def UpperCamelCase( lowercase_ , lowercase_ = 0.0 , lowercase_ = 1.0 ) -> int:
'''simple docstring'''
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest... | 34 |
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning thing... | 34 | 1 |
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __lowerCamelCase ( __snake_case ):
lowerCamelCase_ : Optional[int] = (PNDMScheduler,)
lowerCamelCase_ : Optional[int] = (('num_inferen... | 34 |
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
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ ... | 34 | 1 |
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class __lowerCamelCase ( __snake_case ):
lowerCamelCase_ : Union[str, Any] = CustomTokenizer
pass | 34 |
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.ut... | 34 | 1 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def UpperCamelCase( ) -> Optional[Any]:
'''simple docstring'''
snake_case_ = ArgumentParser(
description=(
... | 34 |
import numpy as np
def UpperCamelCase( lowercase_ ) -> np.array:
'''simple docstring'''
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod() | 34 | 1 |
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
from transformers import (
... | 34 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase_ = {
'''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GraphormerConfig'''],
}
try:
if not is_torch_avail... | 34 | 1 |
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
def UpperCamelCase( lowercase_ , lowerca... | 34 |
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMSchedule... | 34 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDependencyNotAvailable()
... | 34 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
lowerCamelCase_ = get_logger(__name__)
class __lowerCamelCase ( enum.Enum ):
lowerCamelCase_ : Dict = 'all_checks'
lowerCamelCas... | 34 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCamelCase_ = {
'''configuration_groupvit''': [
'''GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''GroupViTConfig''',
'''GroupViTOnnxConfig''... | 34 |
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
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''google/mobilenet... | 34 | 1 |
import logging
from transformers import PretrainedConfig
lowerCamelCase_ = logging.getLogger(__name__)
lowerCamelCase_ = {
'''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}
class ... | 34 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
Efficie... | 34 | 1 |
import requests
from bsa import BeautifulSoup
def UpperCamelCase( lowercase_ = "https://www.worldometers.info/coronavirus" ) -> dict:
'''simple docstring'''
snake_case_ = BeautifulSoup(requests.get(lowercase_ ).text , """html.parser""" )
snake_case_ ... | 34 |
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 UpperCamelCase( lowercase_ ) -> tuple:
'''simple do... | 34 | 1 |
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def UpperCamelCase( lowercase_ , lowercase_ ) -> str:
'''si... | 34 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ ) -> Any:
'''simple docstring'''
snake_case_ = AutoConfig.from_pretrained(lowercase_ )
s... | 34 | 1 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
... | 34 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
lowerCamelCase_ = re.compile(R'''\b(a|an|the)\b''', re.UNICODE)
lowerCamelCase_ = None
def UpperCamelCase( ) -> List[Any]:
'''simple docstring'''
snake_ca... | 34 | 1 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_to... | 34 |
from torch import nn
def UpperCamelCase( lowercase_ ) -> Tuple:
'''simple docstring'''
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(f'''Unsupported acti... | 34 | 1 |
from __future__ import annotations
def UpperCamelCase( lowercase_ , lowercase_ ) -> set[str]:
'''simple docstring'''
snake_case_ , snake_case_ = set(lowercase_ ), [start]
while stack:
snake_case_ = stack.pop()
explored.add(lowercase_... | 34 |
from __future__ import annotations
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ ) -> dict[str, float]:
'''simple docstring'''
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if r... | 34 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_t... | 34 |
def UpperCamelCase( lowercase_ , lowercase_ ) -> str:
'''simple docstring'''
return "\n".join(
f'''{number} * {i} = {number * i}''' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_t... | 34 | 1 |
from __future__ import annotations
def UpperCamelCase( lowercase_ , lowercase_ = None , lowercase_ = None ) -> None:
'''simple docstring'''
if start is None:
snake_case_ = 0
if end is None:
snake_case_ = len(lowercase_ ) - 1
if start... | 34 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCamelCase_ = {
'''configuration_groupvit''': [
'''GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''GroupViTConfig''',
'''GroupViTOnnxConfig''... | 34 | 1 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase_ = {
'''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
'''feature_extraction_mctct''': ['''MCTCTFeatureExtractor'''],
'... | 34 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def UpperCamelCase( lowercase_ = "" ) -> dict[str, float]:
'''simple docstring'''
snake_case_ = url or """https://www.imdb.com/chart/top/?ref_=nv_mv_250"""
snake_case_ = ... | 34 | 1 |
class __lowerCamelCase :
def __init__( self ) -> List[str]:
snake_case_ = """"""
snake_case_ = """"""
snake_case_ = []
def lowerCAmelCase_ ( self , lowerCamelCase , lowerCamelCase ) ->... | 34 |
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
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''facebook/levit-1... | 34 | 1 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
lowerCamelCase_ = lo... | 34 |
import logging
from transformers.configuration_utils import PretrainedConfig
lowerCamelCase_ = logging.getLogger(__name__)
class __lowerCamelCase ( __snake_case ):
lowerCamelCase_ : Optional[int] = 'masked_bert'
def __init__( self , lowerCam... | 34 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase_ = {
'''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GraphormerConfig'''],
}
try:
if not is_torch_avail... | 34 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def UpperCamelCase( ) -> Optional[Any]:
'''simple docstring'''
snake_case_ = {
"""repo_name""": ["""test_repo1""", """test_repo2""", ... | 34 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.d... | 34 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
... | 34 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowerCamelCase_ = {
'''configuration_perceiver''': ['''PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PerceiverCo... | 34 |
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
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''google/mobilenet... | 34 | 1 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class __lowerCamelCase ( __snake_case ):
@staticmethod
@abstractmethod
def lowerCAmelCase_ ( lowerCamelCase ) -> int:
raise NotImplementedError()
@abstractme... | 34 |
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning thing... | 34 | 1 |
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataLoa... | 34 |
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
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ ... | 34 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IM... | 34 |
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.ut... | 34 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''shi-labs/nat-mini-in1k-224''': '''https://... | 34 |
import numpy as np
def UpperCamelCase( lowercase_ ) -> np.array:
'''simple docstring'''
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod() | 34 | 1 |
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class __lowerCamelCase :
def __init__( self , lowerCamelCase , lowerCamelCase , lowerCamelCase ) -> List[str]:
if dst_width < 0 or dst_height < 0:
raise ValueEr... | 34 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase_ = {
'''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GraphormerConfig'''],
}
try:
if not is_torch_avail... | 34 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ = {
'''configuration_funnel''': ['''FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FunnelConfig'''],
... | 34 |
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMSchedule... | 34 | 1 |
import doctest
from collections import deque
import numpy as np
class __lowerCamelCase :
def __init__( self ) -> None:
snake_case_ = [2, 1, 2, -1]
snake_case_ = [1, 2, 3, 4]
def lowerCAmelCase_ ( self ) -> l... | 34 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
lowerCamelCase_ = get_logger(__name__)
class __lowerCamelCase ( enum.Enum ):
lowerCamelCase_ : Dict = 'all_checks'
lowerCamelCas... | 34 | 1 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ ) -> Any:
'''simple docstring'''
snake_case_ = AutoConfig.from_pretrained(lowercase_ )
s... | 34 |
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
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''google/mobilenet... | 34 | 1 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils ... | 34 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
Efficie... | 34 | 1 |
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENE... | 34 |
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 UpperCamelCase( lowercase_ ) -> tuple:
'''simple do... | 34 | 1 |
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
from transf... | 34 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ ) -> Any:
'''simple docstring'''
snake_case_ = AutoConfig.from_pretrained(lowercase_ )
s... | 34 | 1 |
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGH... | 34 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
lowerCamelCase_ = re.compile(R'''\b(a|an|the)\b''', re.UNICODE)
lowerCamelCase_ = None
def UpperCamelCase( ) -> List[Any]:
'''simple docstring'''
snake_ca... | 34 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json''',
# See all GPTNeoX models at htt... | 34 |
from torch import nn
def UpperCamelCase( lowercase_ ) -> Tuple:
'''simple docstring'''
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(f'''Unsupported acti... | 34 | 1 |
def UpperCamelCase( lowercase_ , lowercase_ ) -> float:
'''simple docstring'''
return base * power(lowercase_ , (exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print('''Raise base to the power of exponent using recursion...''')
lowerCamelCase_... | 34 |
from __future__ import annotations
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ ) -> dict[str, float]:
'''simple docstring'''
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if r... | 34 | 1 |
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
lowerCamelCase_ = get_tests_dir('''fixtures/test_sentencepiece_bpe.m... | 34 |
def UpperCamelCase( lowercase_ , lowercase_ ) -> str:
'''simple docstring'''
return "\n".join(
f'''{number} * {i} = {number * i}''' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_t... | 34 | 1 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
lowerCamelCase_ = pd.read_csv('''sample_data.csv''', header=None)
lowerCamelCase_ =... | 34 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCamelCase_ = {
'''configuration_groupvit''': [
'''GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''GroupViTConfig''',
'''GroupViTOnnxConfig''... | 34 | 1 |
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetrie... | 34 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def UpperCamelCase( lowercase_ = "" ) -> dict[str, float]:
'''simple docstring'''
snake_case_ = url or """https://www.imdb.com/chart/top/?ref_=nv_mv_250"""
snake_case_ = ... | 34 | 1 |
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMSchedule... | 34 |
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
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''facebook/levit-1... | 34 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''google/mobilenet... | 34 |
import logging
from transformers.configuration_utils import PretrainedConfig
lowerCamelCase_ = logging.getLogger(__name__)
class __lowerCamelCase ( __snake_case ):
lowerCamelCase_ : Optional[int] = 'masked_bert'
def __init__( self , lowerCam... | 34 | 1 |
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope="""session""" )
def UpperCamelCase( ) -> List[Any]:
... | 34 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def UpperCamelCase( ) -> Optional[Any]:
'''simple docstring'''
snake_case_ = {
"""repo_name""": ["""test_repo1""", """test_repo2""", ... | 34 | 1 |
class __lowerCamelCase :
def __init__( self ) -> List[Any]:
snake_case_ = {}
def lowerCAmelCase_ ( self ) -> None:
print(self.vertex )
for i in self.vertex:
print(lowerCamelCase , """ -> """ ... | 34 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
... | 34 | 1 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json''',
}
class __lowerCamelCase ( ... | 34 |
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
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''google/mobilenet... | 34 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''facebook/convnextv2-tiny-1k-224''': '''htt... | 34 |
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning thing... | 34 | 1 |
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''facebook/encodec_24khz''': '''https://huggingface.co/facebook/encodec_24khz/resol... | 34 |
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
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ ... | 34 | 1 |
# Copyright 2022 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 applica... | 34 |
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.ut... | 34 | 1 |
def UpperCamelCase( lowercase_ ) -> list:
'''simple docstring'''
def merge(lowercase_ , lowercase_ ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yield from left
yield from right
... | 34 |
import numpy as np
def UpperCamelCase( lowercase_ ) -> np.array:
'''simple docstring'''
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod() | 34 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDependencyNotAvailable()
... | 34 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase_ = {
'''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GraphormerConfig'''],
}
try:
if not is_torch_avail... | 34 | 1 |
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
lowerCamelCase_ = logging.get_logger(__name__)
class __lowerCamelCase ( __snake_case ):
def __init__( self , *lowerCamelCase , **lowerCamelCase ... | 34 |
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMSchedule... | 34 | 1 |
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common impo... | 34 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
lowerCamelCase_ = get_logger(__name__)
class __lowerCamelCase ( enum.Enum ):
lowerCamelCase_ : Dict = 'all_checks'
lowerCamelCas... | 34 | 1 |
def UpperCamelCase( lowercase_ , lowercase_ ) -> bool:
'''simple docstring'''
snake_case_ = len(lowercase_ ) + 1
snake_case_ = len(lowercase_ ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string... | 34 |
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
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''google/mobilenet... | 34 | 1 |
def UpperCamelCase( lowercase_ , lowercase_ = False ) -> str:
'''simple docstring'''
if not isinstance(lowercase_ , lowercase_ ):
snake_case_ = f'''Expected string as input, found {type(lowercase_ )}'''
raise ValueError(lowercase_ )
if n... | 34 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
Efficie... | 34 | 1 |
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_c... | 34 |
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 UpperCamelCase( lowercase_ ) -> tuple:
'''simple do... | 34 | 1 |
from jiwer import compute_measures
import datasets
lowerCamelCase_ = '''\
@inproceedings{inproceedings,
author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
year = {2004},
month = {01},
pages = {},
title = {From WER and RIL to MER and WIL: improved evaluation measures for con... | 34 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ ) -> Any:
'''simple docstring'''
snake_case_ = AutoConfig.from_pretrained(lowercase_ )
s... | 34 | 1 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_commo... | 34 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
lowerCamelCase_ = re.compile(R'''\b(a|an|the)\b''', re.UNICODE)
lowerCamelCase_ = None
def UpperCamelCase( ) -> List[Any]:
'''simple docstring'''
snake_ca... | 34 | 1 |
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, ... | 34 |
from torch import nn
def UpperCamelCase( lowercase_ ) -> Tuple:
'''simple docstring'''
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(f'''Unsupported acti... | 34 | 1 |
def UpperCamelCase( lowercase_ ) -> List[str]:
'''simple docstring'''
snake_case_ = []
snake_case_ = set({"""(""", """[""", """{"""} )
snake_case_ = set({""")""", """]""", """}"""} )
snake_case_ = {"""{""": """}""", """[""": """]"... | 34 |
from __future__ import annotations
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ ) -> dict[str, float]:
'''simple docstring'''
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if r... | 34 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''facebook/levit-1... | 34 |
def UpperCamelCase( lowercase_ , lowercase_ ) -> str:
'''simple docstring'''
return "\n".join(
f'''{number} * {i} = {number * i}''' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_t... | 34 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''facebook/data2vec-text-base''': '''https://hugg... | 34 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCamelCase_ = {
'''configuration_groupvit''': [
'''GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''GroupViTConfig''',
'''GroupViTOnnxConfig''... | 34 | 1 |
import qiskit
def UpperCamelCase( lowercase_ , lowercase_ ) -> qiskit.result.counts.Counts:
'''simple docstring'''
snake_case_ = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
snake_case_ = qiskit.... | 34 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def UpperCamelCase( lowercase_ = "" ) -> dict[str, float]:
'''simple docstring'''
snake_case_ = url or """https://www.imdb.com/chart/top/?ref_=nv_mv_250"""
snake_case_ = ... | 34 | 1 |
from __future__ import annotations
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> Tuple: # noqa: E741
'''simple docstring'''
while r - l > 1:
snake_case_ = (l + r) // 2
if v[m] >= key:
snake_case_ = m
... | 34 |
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
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''facebook/levit-1... | 34 | 1 |
def UpperCamelCase( lowercase_ ) -> int:
'''simple docstring'''
if not isinstance(lowercase_ , lowercase_ ):
raise ValueError("""multiplicative_persistence() only accepts integral values""" )
if num < 0:
raise ValueError("""multiplicative_persistence() does... | 34 |
import logging
from transformers.configuration_utils import PretrainedConfig
lowerCamelCase_ = logging.getLogger(__name__)
class __lowerCamelCase ( __snake_case ):
lowerCamelCase_ : Optional[int] = 'masked_bert'
def __init__( self , lowerCam... | 34 | 1 |
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, 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 replicate
f... | 34 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def UpperCamelCase( ) -> Optional[Any]:
'''simple docstring'''
snake_case_ = {
"""repo_name""": ["""test_repo1""", """test_repo2""", ... | 34 | 1 |
def UpperCamelCase( lowercase_ ) -> Dict:
'''simple docstring'''
stooge(lowercase_ , 0 , len(lowercase_ ) - 1 )
return arr
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ ) -> Any:
'''simple docstring'''
... | 34 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
... | 34 | 1 |
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
... | 34 |
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
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''google/mobilenet... | 34 | 1 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_... | 34 |
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning thing... | 34 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ ... | 34 |
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
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ ... | 34 | 1 |
# 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 by ap... | 34 |
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.ut... | 34 | 1 |
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
from ...test_backbone_common ... | 34 |
import numpy as np
def UpperCamelCase( lowercase_ ) -> np.array:
'''simple docstring'''
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod() | 34 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
DiffusionPipeline,
UnCLIPImage... | 34 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase_ = {
'''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GraphormerConfig'''],
}
try:
if not is_torch_avail... | 34 | 1 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
lowerCamelCase_ = '''\
@misc{wu2016googles,
title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
author={Yonghui Wu and M... | 34 |
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMSchedule... | 34 | 1 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __lowerCamelCase ( unittest.TestCase ):
... | 34 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
lowerCamelCase_ = get_logger(__name__)
class __lowerCamelCase ( enum.Enum ):
lowerCamelCase_ : Dict = 'all_checks'
lowerCamelCas... | 34 | 1 |
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
lowerCamelCase_ = input('''Enter image url: ''').strip()
print(f"""Downloading image from {url} ...""")
lowerCamelCase_ = BeautifulSoup(requests.get(url).content, '''html.parser''')
... | 34 |
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
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''google/mobilenet... | 34 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class __lowerCamelCase ( unittest.TestCase ):
de... | 34 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
Efficie... | 34 | 1 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTe... | 34 |
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 UpperCamelCase( lowercase_ ) -> tuple:
'''simple do... | 34 | 1 |
from __future__ import annotations
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ , ) -> tuple:
'''simple docstring'''
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError("""You cannot supply more or less than 2 values""" ... | 34 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ ) -> Any:
'''simple docstring'''
snake_case_ = AutoConfig.from_pretrained(lowercase_ )
s... | 34 | 1 |
def UpperCamelCase( lowercase_ ) -> str:
'''simple docstring'''
snake_case_ = """"""
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def UpperCamelCase( lowercase_ ) -> dict[str,... | 34 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
lowerCamelCase_ = re.compile(R'''\b(a|an|the)\b''', re.UNICODE)
lowerCamelCase_ = None
def UpperCamelCase( ) -> List[Any]:
'''simple docstring'''
snake_ca... | 34 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCamelCase_ = {
'''configuration_pix2struct''': [
'''PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Pix2StructConfig''',
'''Pix2Struct... | 34 |
from torch import nn
def UpperCamelCase( lowercase_ ) -> Tuple:
'''simple docstring'''
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(f'''Unsupported acti... | 34 | 1 |
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common imp... | 34 |
from __future__ import annotations
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ ) -> dict[str, float]:
'''simple docstring'''
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if r... | 34 | 1 |
lowerCamelCase_ = {
'''a''': '''AAAAA''',
'''b''': '''AAAAB''',
'''c''': '''AAABA''',
'''d''': '''AAABB''',
'''e''': '''AABAA''',
'''f''': '''AABAB''',
'''g''': '''AABBA''',
'''h''': '''AABBB''',
'''i''': '''ABAAA''',
'''j''': '''BBBAA''',
'''k''': '''ABAAB''',
... | 34 |
def UpperCamelCase( lowercase_ , lowercase_ ) -> str:
'''simple docstring'''
return "\n".join(
f'''{number} * {i} = {number * i}''' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_t... | 34 | 1 |
import math
def UpperCamelCase( lowercase_ ) -> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return F... | 34 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCamelCase_ = {
'''configuration_groupvit''': [
'''GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''GroupViTConfig''',
'''GroupViTOnnxConfig''... | 34 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ = {
'''configuration_xlm_roberta''': [
... | 34 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def UpperCamelCase( lowercase_ = "" ) -> dict[str, float]:
'''simple docstring'''
snake_case_ = url or """https://www.imdb.com/chart/top/?ref_=nv_mv_250"""
snake_case_ = ... | 34 | 1 |
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configurat... | 34 |
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
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''facebook/levit-1... | 34 | 1 |
from __future__ import annotations
def UpperCamelCase( lowercase_ , lowercase_ ) -> list[str]:
'''simple docstring'''
if nth_term == "":
return [""]
snake_case_ = int(lowercase_ )
snake_case_ = int(lowercase_ )
snake_case_ = []... | 34 |
import logging
from transformers.configuration_utils import PretrainedConfig
lowerCamelCase_ = logging.getLogger(__name__)
class __lowerCamelCase ( __snake_case ):
lowerCamelCase_ : Optional[int] = 'masked_bert'
def __init__( self , lowerCam... | 34 | 1 |
def UpperCamelCase( lowercase_ , lowercase_ ) -> str:
'''simple docstring'''
return "\n".join(
f'''{number} * {i} = {number * i}''' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_t... | 34 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def UpperCamelCase( ) -> Optional[Any]:
'''simple docstring'''
snake_case_ = {
"""repo_name""": ["""test_repo1""", """test_repo2""", ... | 34 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ = {
'''configuration_whisper''': ['''WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP''',... | 34 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
... | 34 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
lowerCamelCase_ = {'''configuration_beit''': ['''BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BeitConfig''', '''BeitOnnxConfig... | 34 |
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
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''google/mobilenet... | 34 | 1 |
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class __lowerCamelCase ( unittest.TestCase ):
def lowerCAmelCase_ ( self ) -> Optional[Any]:
snake_case_ = get_activation("""swish""" ... | 34 |
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning thing... | 34 | 1 |
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