code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(... | 94 |
'''simple docstring'''
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class UpperCAmelCase_ ( _... | 94 | 1 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import... | 94 |
'''simple docstring'''
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_av... | 94 | 1 |
'''simple docstring'''
import collections
import importlib.util
import os
import re
from pathlib import Path
SCREAMING_SNAKE_CASE = 'src/transformers'
# Matches is_xxx_available()
SCREAMING_SNAKE_CASE = re.compile(r'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {x... | 94 |
'''simple docstring'''
SCREAMING_SNAKE_CASE = frozenset(
[
'prompt',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
SCREAMING_SNAKE_CASE =... | 94 | 1 |
'''simple docstring'''
from collections.abc import Sequence
def lowercase_ ( __A : Sequence[int] | None = None ) -> int:
"""simple docstring"""
if nums is None or not nums:
raise ValueError('''Input sequence should not be empty''' )
lowercase : List[s... | 94 |
'''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class... | 94 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE = {
'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
'tokenization_tapas': ['Ta... | 94 |
'''simple docstring'''
def lowercase_ ( __A : int ) -> int:
"""simple docstring"""
if n == 1 or not isinstance(__A , __A ):
return 0
elif n == 2:
return 1
else:
lowercase : Tuple =[0, 1]
for i in range(2 , n + 1 ):
seq... | 94 | 1 |
'''simple docstring'''
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
SCREAMING_SNAKE_CASE = {
'tiny.en': 'https://openaipublic.azureedge.n... | 94 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def lowercase_ ( __A : dict , __A : str , __A : set , __A : set , __A : dict , __A : dict , __A : ... | 94 | 1 |
'''simple docstring'''
def lowercase_ ( __A : int = 6_0_0_8_5_1_4_7_5_1_4_3 ) -> int:
"""simple docstring"""
try:
lowercase : str =int(__A )
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''' )
... | 94 |
'''simple docstring'''
from math import isqrt
def lowercase_ ( __A : int ) -> list[int]:
"""simple docstring"""
lowercase : Dict =[True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i... | 94 | 1 |
'''simple docstring'''
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 tran... | 94 |
'''simple docstring'''
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 tran... | 94 | 1 |
'''simple docstring'''
# Copyright 2021 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/LICENS... | 94 |
'''simple docstring'''
import argparse
from collections import defaultdict
def lowercase_ ( __A : Optional[Any] , __A : Optional[int] , __A : str , __A : Tuple , __A : int ) -> Dict:
"""simple docstring"""
... | 94 | 1 |
'''simple docstring'''
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
SCREAMING_SNAKE_CASE ... | 94 |
'''simple docstring'''
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationCo... | 94 | 1 |
'''simple docstring'''
from sklearn.metrics import fa_score
import datasets
SCREAMING_SNAKE_CASE = '\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n'
SCREAMING_SNAKE_CASE = ... | 94 |
'''simple docstring'''
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():
f... | 94 | 1 |
'''simple docstring'''
import torch
from torch import nn
class UpperCAmelCase_ ( nn.Module ):
"""simple docstring"""
def __init__( self : List[Any] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : str ... | 94 |
'''simple docstring'''
# 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/LICENS... | 94 | 1 |
'''simple docstring'''
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 tr... | 94 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
... | 94 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
SCREAMING_SNAKE_CASE = {
'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
'tokenizat... | 94 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils i... | 94 | 1 |
'''simple docstring'''
# 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/LICENS... | 94 |
'''simple docstring'''
from __future__ import annotations
def lowercase_ ( __A : list[list[int]] ) -> int:
"""simple docstring"""
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in ... | 94 | 1 |
'''simple docstring'''
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def lowercase_ ( ... | 94 |
'''simple docstring'''
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
SCREAMING_SNAKE_CASE = {
'E': 12.70,
'T': 9.06,
'A': 8.17,
'O': 7.51,
'I': 6.97,
'N': 6.75,
'S': 6.33,
'H': 6.09,
'R': 5.99,
'D': 4.25,
'L': 4.03,
... | 94 | 1 |
'''simple docstring'''
import argparse
import os
from . import (
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
DISTILBERT_PRETRAINED_CONFIG_ARCH... | 94 |
'''simple docstring'''
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def lowercase_ ( __A : str ) -> Union[str, Any]:
"""simple docstring"""
return x + 2
class UpperCAmelCase_ ... | 94 | 1 |
'''simple docstring'''
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class UpperCAmelCase_ ( __A ):
"""simple docstring"""
def A__ ( self : Optional[int] ) -> List[str]:
'''simple docstr... | 94 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCAmelCase_ ( metaclass=__A ):
"""simple docstring"""
UpperCamelCase_ = ['''flax''']
def __init__( self : Dict , *UpperCAmelCase : List[Any] , ... | 94 | 1 |
'''simple docstring'''
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class UpperCAmelCase_ ... | 94 |
'''simple docstring'''
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class UpperCAmelCase_ ( __... | 94 | 1 |
'''simple docstring'''
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
SCREAMING_SNAKE_CASE = '\\n@misc{chen2021evaluating,\n t... | 94 |
'''simple docstring'''
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class UpperCAmelCase_ ( __A ):
"""simple docstring"""
def A__ ( self : Optional[int] ) -> List[str]:
'''simple docstr... | 94 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE = {
'configuration_pix2struct': [
'PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Pix2StructConf... | 94 |
'''simple docstring'''
def lowercase_ ( __A : int , __A : int ) -> int:
"""simple docstring"""
return 1 if input_a == input_a else 0
def lowercase_ ( ) -> None:
"""simple docstring"""
assert xnor_gate(0 , 0 )... | 94 | 1 |
'''simple docstring'''
import math
def lowercase_ ( ) -> None:
"""simple docstring"""
lowercase : Union[str, Any] =input('''Enter message: ''' )
lowercase : List[Any] =int(input(F'Enter key [2-{len(__A ) - 1}]: ' ) )
lowercase : ... | 94 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_c... | 94 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
'ksst... | 94 |
'''simple docstring'''
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class UpperCAmelCase_ ( _... | 94 | 1 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils impor... | 94 |
'''simple docstring'''
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_av... | 94 | 1 |
'''simple docstring'''
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
... | 94 |
'''simple docstring'''
SCREAMING_SNAKE_CASE = frozenset(
[
'prompt',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
SCREAMING_SNAKE_CASE =... | 94 | 1 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def lowercase_ ( __A : dict , __A : str , __A : set , __A : set , __A : dict , __A : dict , __A : ... | 94 |
'''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class... | 94 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE = {'configuration_encoder_decoder': ['EncoderDecoderConfig']}
try:
... | 94 |
'''simple docstring'''
def lowercase_ ( __A : int ) -> int:
"""simple docstring"""
if n == 1 or not isinstance(__A , __A ):
return 0
elif n == 2:
return 1
else:
lowercase : Tuple =[0, 1]
for i in range(2 , n + 1 ):
seq... | 94 | 1 |
'''simple docstring'''
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenizatio... | 94 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def lowercase_ ( __A : dict , __A : str , __A : set , __A : set , __A : dict , __A : dict , __A : ... | 94 | 1 |
'''simple docstring'''
import numpy
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self : Union[str, Any] , UpperCAmelCase : numpy.ndarray , UpperCAmelCase : numpy.ndarray ) -> None:
'''simple docstring'''
lowercase : An... | 94 |
'''simple docstring'''
from math import isqrt
def lowercase_ ( __A : int ) -> list[int]:
"""simple docstring"""
lowercase : Dict =[True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i... | 94 | 1 |
'''simple docstring'''
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
SCREAMING_SNAKE_CASE = {
'E': 12.70,
'T': 9.06,
'A': 8.17,
'O': 7.51,
'I': 6.97,
'N': 6.75,
'S': 6.33,
'H': 6.09,
'R': 5.99,
'D': 4.25,
'L': 4.03,
... | 94 |
'''simple docstring'''
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 tran... | 94 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE = {
'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}
try:
if not is_torch_available():
... | 94 |
'''simple docstring'''
import argparse
from collections import defaultdict
def lowercase_ ( __A : Optional[Any] , __A : Optional[int] , __A : str , __A : Tuple , __A : int ) -> Dict:
"""simple docstring"""
... | 94 | 1 |
'''simple docstring'''
def lowercase_ ( __A : int ) -> int:
"""simple docstring"""
if n == 1 or not isinstance(__A , __A ):
return 0
elif n == 2:
return 1
else:
lowercase : Tuple =[0, 1]
for i in range(2 , n + 1 ):
seq... | 94 |
'''simple docstring'''
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationCo... | 94 | 1 |
'''simple docstring'''
from statistics import mean
import numpy as np
def lowercase_ ( __A : list , __A : list , __A : list , __A : int ) -> list:
"""simple docstring"""
lowercase : Dict =0
# Number of pro... | 94 |
'''simple docstring'''
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():
f... | 94 | 1 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokeniza... | 94 |
'''simple docstring'''
# 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/LICENS... | 94 | 1 |
'''simple docstring'''
from __future__ import annotations
def lowercase_ ( __A : float , __A : float , __A : float ) -> float:
"""simple docstring"""
if days_between_payments <= 0:
raise ValueError('''days_between_payments must be >... | 94 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
... | 94 | 1 |
'''simple docstring'''
def lowercase_ ( __A : List[str] ) -> Union[str, Any]:
"""simple docstring"""
lowercase : str =[0] * len(__A )
lowercase : Union[str, Any] =[]
lowercase : str =[]
lowercase : List[Any] =0
for... | 94 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils i... | 94 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def lowercase_ ( __A : Tuple , __A : Union[str, Any] , __A ... | 94 |
'''simple docstring'''
from __future__ import annotations
def lowercase_ ( __A : list[list[int]] ) -> int:
"""simple docstring"""
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in ... | 94 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
'edbeeching/decision-transformer-gym-hopper-medium': (
'https://huggingface.co/edbeeching/de... | 94 |
'''simple docstring'''
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
SCREAMING_SNAKE_CASE = {
'E': 12.70,
'T': 9.06,
'A': 8.17,
'O': 7.51,
'I': 6.97,
'N': 6.75,
'S': 6.33,
'H': 6.09,
'R': 5.99,
'D': 4.25,
'L': 4.03,
... | 94 | 1 |
'''simple docstring'''
from __future__ import annotations
def lowercase_ ( __A : list[int] ) -> list[int]: # This function is recursive
"""simple docstring"""
lowercase : str =len(__A )
# If the array contains only one element, we return it (it's t... | 94 |
'''simple docstring'''
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def lowercase_ ( __A : str ) -> Union[str, Any]:
"""simple docstring"""
return x + 2
class UpperCAmelCase_ ... | 94 | 1 |
'''simple docstring'''
import copy
import re
class UpperCAmelCase_ :
"""simple docstring"""
UpperCamelCase_ = '''hp'''
UpperCamelCase_ = {}
UpperCamelCase_ = None
@classmethod
def A__ ( cls ... | 94 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCAmelCase_ ( metaclass=__A ):
"""simple docstring"""
UpperCamelCase_ = ['''flax''']
def __init__( self : Dict , *UpperCAmelCase : List[Any] , ... | 94 | 1 |
'''simple docstring'''
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_doc... | 94 |
'''simple docstring'''
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class UpperCAmelCase_ ( __... | 94 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import... | 94 |
'''simple docstring'''
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class UpperCAmelCase_ ( __A ):
"""simple docstring"""
def A__ ( self : Optional[int] ) -> List[str]:
'''simple docstr... | 94 | 1 |
'''simple docstring'''
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines... | 94 |
'''simple docstring'''
def lowercase_ ( __A : int , __A : int ) -> int:
"""simple docstring"""
return 1 if input_a == input_a else 0
def lowercase_ ( ) -> None:
"""simple docstring"""
assert xnor_gate(0 , 0 )... | 94 | 1 |
'''simple docstring'''
import argparse
from collections import defaultdict
def lowercase_ ( __A : Optional[Any] , __A : Optional[int] , __A : str , __A : Tuple , __A : int ) -> Dict:
"""simple docstring"""
... | 94 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_c... | 94 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_c... | 94 |
'''simple docstring'''
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class UpperCAmelCase_ ( _... | 94 | 1 |
'''simple docstring'''
from __future__ import annotations
def lowercase_ ( __A : list[int] ) -> list[int]:
"""simple docstring"""
if len(__A ) == 0:
return array
lowercase , lowercase : List[str] =min(__A ), max(__A )
# Compute the v... | 94 |
'''simple docstring'''
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_av... | 94 | 1 |
'''simple docstring'''
# 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/LICENS... | 94 |
'''simple docstring'''
SCREAMING_SNAKE_CASE = frozenset(
[
'prompt',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
SCREAMING_SNAKE_CASE =... | 94 | 1 |
'''simple docstring'''
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
SCREAMING_SNAKE_CASE ... | 94 |
'''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class... | 94 | 1 |
'''simple docstring'''
from __future__ import annotations
def lowercase_ ( __A : list[list[int]] ) -> int:
"""simple docstring"""
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in ... | 94 |
'''simple docstring'''
def lowercase_ ( __A : int ) -> int:
"""simple docstring"""
if n == 1 or not isinstance(__A , __A ):
return 0
elif n == 2:
return 1
else:
lowercase : Tuple =[0, 1]
for i in range(2 , n + 1 ):
seq... | 94 | 1 |
'''simple docstring'''
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 accel... | 94 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def lowercase_ ( __A : dict , __A : str , __A : set , __A : set , __A : dict , __A : dict , __A : ... | 94 | 1 |
'''simple docstring'''
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def lowercase_ ( __A : Dict , __A : str=1 ) -> Any:
"""simple docstring"""
if n_shave_prefix_segments >= 0:
ret... | 94 |
'''simple docstring'''
from math import isqrt
def lowercase_ ( __A : int ) -> list[int]:
"""simple docstring"""
lowercase : Dict =[True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i... | 94 | 1 |
'''simple docstring'''
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def lowercase_ ( __A : str ) -> Union[str, Any]:
"""simple docstring"""
return x + 2
class UpperCAmelCase_ ... | 94 |
'''simple docstring'''
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 tran... | 94 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE = {
'configuration_blip_2': [
'BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Blip2Config',
'Blip2QFormerConfig',... | 94 |
'''simple docstring'''
import argparse
from collections import defaultdict
def lowercase_ ( __A : Optional[Any] , __A : Optional[int] , __A : str , __A : Tuple , __A : int ) -> Dict:
"""simple docstring"""
... | 94 | 1 |
'''simple docstring'''
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
SCREAMING_SNAKE_CASE = '\\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n... | 94 |
'''simple docstring'''
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationCo... | 94 | 1 |
'''simple docstring'''
def lowercase_ ( __A : str ) -> list[int]:
"""simple docstring"""
lowercase : List[str] =[0 for i in range(len(__A ) )]
# initialize interval's left pointer and right pointer
lowercase , lowercase : Any =0, 0
... | 94 |
'''simple docstring'''
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():
f... | 94 | 1 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
SCREAMING_SNAKE_CASE = True
except (ImportError, ModuleNotFoundError):
SCREAMING_SNAKE_CASE = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('p... | 94 |
'''simple docstring'''
# 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/LICENS... | 94 | 1 |
'''simple docstring'''
def lowercase_ ( __A : Any ) -> Optional[int]:
"""simple docstring"""
lowercase : Tuple =0
lowercase : List[str] =len(__A )
for i in range(n - 1 ):
for j in range(i + 1 , __A ):
if arr[i] > ar... | 94 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
... | 94 | 1 |
'''simple docstring'''
from typing import List
from .keymap import KEYMAP, get_character
def lowercase_ ( __A : str ) -> List[Any]:
"""simple docstring"""
def decorator(__A : Tuple ):
lowercase : List[Any] =getattr(__A , '''handle_... | 94 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils i... | 94 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}
cl... | 94 |
'''simple docstring'''
from __future__ import annotations
def lowercase_ ( __A : list[list[int]] ) -> int:
"""simple docstring"""
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in ... | 94 | 1 |
'''simple docstring'''
from math import isqrt
def lowercase_ ( __A : int ) -> list[int]:
"""simple docstring"""
lowercase : Dict =[True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i... | 94 |
'''simple docstring'''
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
SCREAMING_SNAKE_CASE = {
'E': 12.70,
'T': 9.06,
'A': 8.17,
'O': 7.51,
'I': 6.97,
'N': 6.75,
'S': 6.33,
'H': 6.09,
'R': 5.99,
'D': 4.25,
'L': 4.03,
... | 94 | 1 |
'''simple docstring'''
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
SCREAMING_SNAKE_CASE = collections.namedtup... | 94 |
'''simple docstring'''
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def lowercase_ ( __A : str ) -> Union[str, Any]:
"""simple docstring"""
return x + 2
class UpperCAmelCase_ ... | 94 | 1 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
SCREAMING_SNAKE_CASE = True
except (ImportError, ModuleNotFoundError):
SCREAMING_SNAKE_CASE = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('p... | 94 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCAmelCase_ ( metaclass=__A ):
"""simple docstring"""
UpperCamelCase_ = ['''flax''']
def __init__( self : Dict , *UpperCAmelCase : List[Any] , ... | 94 | 1 |
'''simple docstring'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
SCREAMING_SNAKE_CASE = TypeVar('KEY')
SCREAMING_SNAKE_CASE = TypeVar('VAL')
@dataclass(frozen=__A , slots=__A )
class UpperCAme... | 94 |
'''simple docstring'''
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class UpperCAmelCase_ ( __... | 94 | 1 |
'''simple docstring'''
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def lowercase_ ( __A : int ) -> int:
"""simple docstring"""
lowercase : int =[
'''decoder.v... | 94 |
'''simple docstring'''
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class UpperCAmelCase_ ( __A ):
"""simple docstring"""
def A__ ( self : Optional[int] ) -> List[str]:
'''simple docstr... | 94 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
'microsoft/swinv2-tiny-patch4-window8-256': (
'https://huggingface.co/microsoft/swinv2-tiny-... | 94 |
'''simple docstring'''
def lowercase_ ( __A : int , __A : int ) -> int:
"""simple docstring"""
return 1 if input_a == input_a else 0
def lowercase_ ( ) -> None:
"""simple docstring"""
assert xnor_gate(0 , 0 )... | 94 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAK... | 94 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_c... | 94 | 1 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class UpperCAmelCase_ ( __A ):
"""simple docstring"""
def __init__( self : Any , UpperCAmelCase : List[str] ... | 94 |
'''simple docstring'''
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class UpperCAmelCase_ ( _... | 94 | 1 |
'''simple docstring'''
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
SCREAMING_SNAKE_CASE = HUGGINGFACE_HUB_CACHE
SCREAMING_SNAKE_CASE = 'config.json'
SCREAMING_SNAKE_CASE = 'diffusion_pytorch_model.bin'
SCREAMING_SNAKE_CASE = ... | 94 |
'''simple docstring'''
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_av... | 94 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class UpperCAmelCase_ :
"""simple docstring"""
UpperCamelCase_ = 42
... | 94 |
'''simple docstring'''
SCREAMING_SNAKE_CASE = frozenset(
[
'prompt',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
SCREAMING_SNAKE_CASE =... | 94 | 1 |
'''simple docstring'''
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():
f... | 94 |
'''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class... | 94 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
SCREAMING_SNAKE_CASE = {'vocab_file': 'vocab.txt', 'tokenizer_file': '... | 94 |
'''simple docstring'''
def lowercase_ ( __A : int ) -> int:
"""simple docstring"""
if n == 1 or not isinstance(__A , __A ):
return 0
elif n == 2:
return 1
else:
lowercase : Tuple =[0, 1]
for i in range(2 , n + 1 ):
seq... | 94 | 1 |
'''simple docstring'''
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
SCREAMING_SNAKE_CASE = logging... | 94 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def lowercase_ ( __A : dict , __A : str , __A : set , __A : set , __A : dict , __A : dict , __A : ... | 94 | 1 |
'''simple docstring'''
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationCo... | 94 |
'''simple docstring'''
from math import isqrt
def lowercase_ ( __A : int ) -> list[int]:
"""simple docstring"""
lowercase : Dict =[True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i... | 94 | 1 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common im... | 94 |
'''simple docstring'''
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 tran... | 94 | 1 |
'''simple docstring'''
def lowercase_ ( __A : int = 1_0_0_0 ) -> int:
"""simple docstring"""
lowercase : Any =2**power
lowercase : Dict =str(__A )
lowercase : Optional[int] =list(__A )
lowercase : List[str] =0
... | 94 |
'''simple docstring'''
import argparse
from collections import defaultdict
def lowercase_ ( __A : Optional[Any] , __A : Optional[int] , __A : str , __A : Tuple , __A : int ) -> Dict:
"""simple docstring"""
... | 94 | 1 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscrete... | 94 |
'''simple docstring'''
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationCo... | 94 | 1 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except Optional... | 94 |
'''simple docstring'''
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():
f... | 94 | 1 |
'''simple docstring'''
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
SCREAMING_SNAKE_CASE = m... | 94 |
'''simple docstring'''
# 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/LICENS... | 94 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
impor... | 94 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
... | 94 | 1 |
'''simple docstring'''
SCREAMING_SNAKE_CASE = '0.21.0'
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loade... | 94 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils i... | 94 | 1 |
'''simple docstring'''
def lowercase_ ( __A : int ) -> bool:
"""simple docstring"""
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 94 |
'''simple docstring'''
from __future__ import annotations
def lowercase_ ( __A : list[list[int]] ) -> int:
"""simple docstring"""
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in ... | 94 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import MobileViTConfig
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 im... | 94 |
'''simple docstring'''
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
SCREAMING_SNAKE_CASE = {
'E': 12.70,
'T': 9.06,
'A': 8.17,
'O': 7.51,
'I': 6.97,
'N': 6.75,
'S': 6.33,
'H': 6.09,
'R': 5.99,
'D': 4.25,
'L': 4.03,
... | 94 | 1 |
'''simple docstring'''
import re
import string
import numpy as np
import datasets
SCREAMING_SNAKE_CASE = '\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'
SCREAMING_SNAKE_CASE = ... | 94 |
'''simple docstring'''
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def lowercase_ ( __A : str ) -> Union[str, Any]:
"""simple docstring"""
return x + 2
class UpperCAmelCase_ ... | 94 | 1 |
'''simple docstring'''
def lowercase_ ( __A : int , __A : list ) -> Tuple:
"""simple docstring"""
_enforce_args(__A , __A )
if n == 0:
return 0
lowercase : Optional[int] =float('''-inf''' )
for i in range(1 , n... | 94 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCAmelCase_ ( metaclass=__A ):
"""simple docstring"""
UpperCamelCase_ = ['''flax''']
def __init__( self : Dict , *UpperCAmelCase : List[Any] , ... | 94 | 1 |
'''simple docstring'''
def lowercase_ ( __A : int ) -> int:
"""simple docstring"""
lowercase : Dict =1
for i in range(1 , num + 1 ):
fact *= i
return fact
def lowercase_ ( __A : int ) -> int:
"""simple... | 94 |
'''simple docstring'''
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class UpperCAmelCase_ ( __... | 94 | 1 |
'''simple docstring'''
from __future__ import annotations
SCREAMING_SNAKE_CASE = list[tuple[int, int]]
SCREAMING_SNAKE_CASE = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[... | 94 |
'''simple docstring'''
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class UpperCAmelCase_ ( __A ):
"""simple docstring"""
def A__ ( self : Optional[int] ) -> List[str]:
'''simple docstr... | 94 | 1 |
'''simple docstring'''
SCREAMING_SNAKE_CASE = {
'meter': 'm',
'kilometer': 'km',
'megametre': 'Mm',
'gigametre': 'Gm',
'terametre': 'Tm',
'petametre': 'Pm',
'exametre': 'Em',
'zettametre': 'Zm',
'yottametre': 'Ym',
}
# Exponent of the factor(meter)
SCREAMING_SNAK... | 94 |
'''simple docstring'''
def lowercase_ ( __A : int , __A : int ) -> int:
"""simple docstring"""
return 1 if input_a == input_a else 0
def lowercase_ ( ) -> None:
"""simple docstring"""
assert xnor_gate(0 , 0 )... | 94 | 1 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
SCREAMING_SNAKE_CASE = ''
SCREAMING_SNAKE_CASE = ''
SCREAMING_SNAKE_CASE = ''
SCREAMING_SNAKE_CASE = 1 # (0 is vertical, 1 is horizontal)
def low... | 94 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_c... | 94 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
SCREAMING_SNAKE_CASE = {'processing_layoutxlm': ['La... | 94 |
'''simple docstring'''
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class UpperCAmelCase_ ( _... | 94 | 1 |
'''simple docstring'''
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_backb... | 94 |
'''simple docstring'''
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_av... | 94 | 1 |
'''simple docstring'''
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # no... | 94 |
'''simple docstring'''
SCREAMING_SNAKE_CASE = frozenset(
[
'prompt',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
SCREAMING_SNAKE_CASE =... | 94 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCAmelCase_ ( metaclass=__A ):
"""simple docstring"""
UpperCamelCase_ = ['''onnx''']
def __init__( self : Optional[int] , *UpperCAmelCase : Option... | 94 |
'''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class... | 94 | 1 |
'''simple docstring'''
import math
import tensorflow as tf
from packaging import version
def lowercase_ ( __A : str ) -> List[Any]:
"""simple docstring"""
lowercase : int =tf.convert_to_tensor(__A )
lowercase : Dict =0.5 * (1.0 + tf.math... | 94 |
'''simple docstring'''
def lowercase_ ( __A : int ) -> int:
"""simple docstring"""
if n == 1 or not isinstance(__A , __A ):
return 0
elif n == 2:
return 1
else:
lowercase : Tuple =[0, 1]
for i in range(2 , n + 1 ):
seq... | 94 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCAmelCase_ ( metaclass=__A ):
"""simple docstring"""
UpperCamelCase_ = ['''transformers''', '''torch''', '''note_seq''']
def __init__( self : List[str] ,... | 94 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def lowercase_ ( __A : dict , __A : str , __A : set , __A : set , __A : dict , __A : dict , __A : ... | 94 | 1 |
'''simple docstring'''
from string import ascii_uppercase
SCREAMING_SNAKE_CASE = {char: i for i, char in enumerate(ascii_uppercase)}
SCREAMING_SNAKE_CASE = dict(enumerate(ascii_uppercase))
def lowercase_ ( __A : str , __A : str ) -> str... | 94 |
'''simple docstring'''
from math import isqrt
def lowercase_ ( __A : int ) -> list[int]:
"""simple docstring"""
lowercase : Dict =[True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i... | 94 | 1 |
'''simple docstring'''
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils ... | 94 |
'''simple docstring'''
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 tran... | 94 | 1 |
'''simple docstring'''
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase_ ( __A ):
"""simple docstring"""
UpperCamelCase_ = (DDPMScheduler,)
def A__ ( self : Tup... | 94 |
'''simple docstring'''
import argparse
from collections import defaultdict
def lowercase_ ( __A : Optional[Any] , __A : Optional[int] , __A : str , __A : Tuple , __A : int ) -> Dict:
"""simple docstring"""
... | 94 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def lowercase_ ( __A : str ) -> None:
"""simple docstring"""
lowercase , lowercase : Any =analyze_text(__A )
l... | 94 |
'''simple docstring'''
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationCo... | 94 | 1 |
'''simple docstring'''
def lowercase_ ( __A : int = 3 , __A : int = 7 , __A : int = 1_0_0_0_0_0_0 ) -> int:
"""simple docstring"""
lowercase : Union[str, Any] =0
lowercase : List[Any] =1
for current_denominator ... | 94 |
'''simple docstring'''
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():
f... | 94 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class UpperCAmelCase_ ( __A ):
... | 94 |
'''simple docstring'''
# 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/LICENS... | 94 | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.