code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, Dist... | 348 |
from math import factorial, pi
def UpperCAmelCase__ ( __magic_name__ : float , __magic_name__ : int = 30 ):
'''simple docstring'''
if not isinstance(__magic_name__ , (int, float) ):
raise ValueError('''maclaurin_sin() requires either an int or float for theta''' )
... | 348 | 1 |
def A ( SCREAMING_SNAKE_CASE = 600851475143 ):
"""simple docstring"""
try:
UpperCAmelCase__ :int = int(SCREAMING_SNAKE_CASE )
except (TypeError, ValueError):
raise TypeError('Parameter n must be int or castable to int.' )
if n <= 0:
raise ValueError... | 433 |
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
__snake_case : Dict = {
'gwf-440... | 433 | 1 |
"""simple docstring"""
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
__UpperCamelCase = '''src/diffusers'''
# Matches is_xxx_available()
__UpperCamelCase = re.com... | 247 |
"""simple docstring"""
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_token... | 247 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, 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():
... | 700 |
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
"""simple docstring"""
A : Dict = [0 for i in range(r + 1 )]
# nc0 = 1
A : Dict = 1
for i in range(1 , n + 1 ):
# to compute current row from previous row.
... | 520 | 0 |
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Imag... | 511 |
from __future__ import annotations
import os
from typing import Any
import requests
lowerCAmelCase : str = 'https://api.github.com'
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
lowerCAmelCase : Optional[Any] = BASE_URL + '/user'
# https://github.co... | 511 | 1 |
def _SCREAMING_SNAKE_CASE ( a ) -> int:
__A : List[str] = []
__A : Tuple = []
__A : Union[str, Any] = {
'^': 3,
'*': 2,
'/': 2,
'%': 2,
'+': 1,
'-': 1,
} # Priori... | 77 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
UpperCAmelCase : Dict = ''''''
UpperCAmelCase : Union[str, Any] = ''''''
UpperCAmelCase : Optional[int] = ''''''
UpperCAmelCase : Union[str, Any] = 1 # (0 is vert... | 77 | 1 |
import random
from typing import Any
def __lowercase ( a__ ) -> Dict:
for _ in range(len(SCREAMING_SNAKE_CASE_ ) ):
__SCREAMING_SNAKE_CASE = random.randint(0 , len(SCREAMING_SNAKE_CASE_ ) - 1 )
__SCREAMING_SNAKE_CASE = ... | 148 |
'''simple docstring'''
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_te... | 18 | 0 |
"""simple docstring"""
def lowercase__(A = 1_000 ) ->int:
"""simple docstring"""
lowercase__ : Union[str, Any]= 2**power
lowercase__ : Dict= str(A )
lowercase__ : List[str]= list(A )
lowercase__ : Un... | 85 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class ... | 85 | 1 |
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils i... | 225 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase__ = {
"""configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
"""tokenization_mvp""": ["""... | 225 | 1 |
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def A(__a: Any , __a: str , __a: List[Any]=1024 , __a: Optional[int]=1024 , __a: List[str]=False , **__a: Optional[Any] ... | 226 |
def A(__a: int ):
lowerCAmelCase_ = abs(__a )
lowerCAmelCase_ = 0
while n > 0:
res += n % 10
n //= 10
return res
def A(__a: int ):
lowerCAmelCase_ = abs(__a )
return n if n < 10 else n % 10 + sum_of_digits(n // 10 )
def A(__a: int ... | 226 | 1 |
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class A_ ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
... | 303 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
Audio... | 71 | 0 |
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
warnings.warn(
'The converted tokeni... | 704 | import collections
import os
import re
from pathlib import Path
__lowerCAmelCase : Tuple = 'src/transformers'
# Matches is_xxx_available()
__lowerCAmelCase : Union[str, Any] = re.compile(R'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
__lowerCAmelCase ... | 164 | 0 |
def _snake_case ( __snake_case , __snake_case ):
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_terms=10))
| 10 |
'''simple docstring'''
import warnings
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
UpperCAmelCase_ = logging.get_logger(__n... | 539 | 0 |
"""simple docstring"""
from __future__ import annotations
UpperCAmelCase : Dict = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
UpperCAmelCase : Optional[Any] = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def __a ( _lowercase ):
... | 706 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase : str = {
"configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP",... | 121 | 0 |
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, re... | 556 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenize... | 556 | 1 |
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class _A ( UpperCAmelCase_):
SCREAMING_SNAKE_CASE : int = (DDPMScheduler,)
def UpperCAmelCase ( self , **_SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE_ : Dict ... | 708 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def A_ ( a , a , a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = TaConfig.from... | 353 | 0 |
"""simple docstring"""
import numpy as np
a_ : Optional[int] = [
['''a''', '''b''', '''c''', '''d''', '''e'''],
['''f''', '''g''', '''h''', '''i''', '''k'''],
['''l''', '''m''', '''n''', '''o''', '''p'''],
['''q''', '''r''', '''s''', '''t''', '''u'''],
['''v''', '''w''', '''x'... | 594 |
"""simple docstring"""
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class __lowercase( unittest.TestCase ):
'''simple docstring'''
def snake_case_ ( self ):
__lowerCamelCase : int = ... | 594 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import log... | 274 |
"""simple docstring"""
import operator as op
def UpperCAmelCase__ ( A__ ) -> Dict:
"""simple docstring"""
lowerCamelCase__ = []
lowerCamelCase__ = lambda A__ , A__ : int(x / y ) # noqa: E731 integer division operation
lowerCamelCase__ = {
"^... | 274 | 1 |
from datetime import datetime as dt
import os
from github import Github
__lowercase : Optional[int] =[
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""feature request""",
"""new model""",
"""wip""",
]
def a__ ( ... | 54 | """simple docstring"""
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
SCREAMING_SNAKE_CASE__:int = logging.get_logger(__name__)
class snake_case__ ( snake_case_ ):
def __init__( self , *lowerCamelCase... | 528 | 0 |
'''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... | 119 |
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table... | 119 | 1 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _A( snake_case__ ):
"""simple docstring"""
UpperCamelCase : Union[str, Any] = ['''image_processor''', '''tokenizer''']
UpperCamelCase : ... | 239 |
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class _A( yaml.SafeLoader ):
"""simple docstring"""
def UpperCAmelCase_ ( self , _A ):
__A : Optional[int] = [self.constructed_objects[key_node]... | 239 | 1 |
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
"""simple docstring"""
while a != 0:
snake_case__ , snake_case__ : str = b % a, a
return b
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAme... | 219 |
# 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 requi... | 219 | 1 |
"""simple docstring"""
from datetime import datetime
import requests
def A__ ( __lowerCamelCase ):
"""simple docstring"""
_lowerCAmelCase = 'https://downloadgram.net/wp-json/wppress/video-downloader/video?url='
_lowerCAmelCase = requests.get(base_url +... | 589 |
"""simple docstring"""
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : Any = logging.get_logger(__name__)
a__ : Tuple = {
"""facebook/encodec_24khz""": """https... | 589 | 1 |
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
a_ = "http://www.mocksite.com/file1.txt"
a_ = "\"te... | 375 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
)
def... | 375 | 1 |
import heapq as hq
import math
from collections.abc import Iterator
class lowerCAmelCase_ :
def __init__( self , _lowerCAmelCase ):
_lowercase : List[str] = str(id_ )
_lowercase : Tuple = None
_lowercase : Op... | 66 |
"""simple docstring"""
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concate... | 499 | 0 |
"""simple docstring"""
import numpy as np
from transformers import Pipeline
def __A (_SCREAMING_SNAKE_CASE ) ->List[Any]:
"""simple docstring"""
lowerCAmelCase__ :Union[str, Any] = np.max(_SCREAMING_SNAKE_CASE , axis=-1 , keepdims=_SCREAMING_SNAKE_C... | 560 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, Blipa... | 560 | 1 |
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...mod... | 213 | """simple docstring"""
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils... | 213 | 1 |
'''simple docstring'''
import tensorflow as tf
from ...tf_utils import shape_list
class SCREAMING_SNAKE_CASE( tf.keras.layers.Layer ):
def __init__( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase... | 163 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common imp... | 163 | 1 |
"""simple docstring"""
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 ImagePro... | 549 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
_lowerCamelCase ={
"""linear""": PIL.Image.Resampling.BILINEAR,
"""bilinear""": PIL.Image.Resampling.BILI... | 681 | 0 |
'''simple docstring'''
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class lowe... | 706 |
'''simple docstring'''
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
a__ : Any = parse(importlib.metadata.version('torch'))
def __snake_case ( SCREAMING_SNAKE_CASE_ : Union[str, Version] , SCREAMI... | 570 | 0 |
from typing import Dict, Optional
import numpy as np
import datasets
_lowerCamelCase : Optional[int] = '''
IoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union
between the predicted segmentation and the ground truth. For ... | 663 |
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
warnings.warn(
'''The ... | 663 | 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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel... | 144 |
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoModelForMaskedL... | 144 | 1 |
"""simple docstring"""
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
fro... | 153 |
"""simple docstring"""
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class _lowercase ( tf.keras.layers.Layer ):
def ... | 490 | 0 |
def UpperCAmelCase_ ( _UpperCAmelCase ):
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print("""Program to check whether a number is a Perfect number or not...""")
lowercase : List[Any] = int(in... | 584 | import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelera... | 584 | 1 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applic... | 35 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ..... | 123 | 0 |
"""simple docstring"""
import warnings
from typing import List
from unittest.mock import Mock
import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset
from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType
class _UpperCAmelCas... | 700 |
"""simple docstring"""
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization imp... | 524 | 0 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requir... | 256 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase : Optional[Any] = {
'configuration_mobilevit': ['MOBILEVIT_PRETRAINED_CONFIG_AR... | 121 | 0 |
'''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 UpperCAmelCase__( ... | 717 | import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
f... | 679 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPanorama... | 33 |
from copy import deepcopy
class __magic_name__ :
'''simple docstring'''
def __init__( self:int , _a:list[int] | None = None , _a:int | None = None ):
if arr is None and size is not None:
snake_case__ = size
snake_case__ = ... | 33 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__snake_case : Dict = {
'configuration_vision_encoder_decoder'... | 615 |
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtraction... | 615 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transformers import AutoTokenizer, TFAutoM... | 625 |
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class snake_case_ ( a ):
'''simple docstring'''
__UpperCamelCase = 'EncodecFeatureExtractor'
__UpperCamelCase ... | 625 | 1 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _snake_case ( lowerCAmel... | 708 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _snake_case ( lowerCAmel... | 305 | 0 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGeneratio... | 2 |
import argparse
import json
import subprocess
def UpperCamelCase ( _A, _A ):
"""simple docstring"""
__magic_name__ : Union[str, Any] = []
__magic_name__ : Optional[int] = (
f'curl -H "Accept: application/vnd.github+json" -H "Autho... | 324 | 0 |
print((lambda quine: quine % quine)("""print((lambda quine: quine %% quine)(%r))"""))
| 700 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def UpperCAmelCase ( _snake_case ):
lowerCAmelCase = args.pruning_method
lowerCAmelCase = args.threshold
lo... | 33 | 0 |
'''simple docstring'''
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,
)
if is_sentencepiece_av... | 459 |
import numpy as np
def a_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
lowerCAmelCase__ = int(np.ceil((x_end - xa) / h ) )
lowerCAmelCase__ = np.zeros((n + 1,) )
lowerCAmelCase__ = ... | 615 | 0 |
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _lowerCAmelCase ( __a , unittest.TestCase ):
_lowercase =PhobertTokenizer
_lowercase =Fal... | 279 |
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import... | 279 | 1 |
def snake_case ( lowerCamelCase ):
'''simple docstring'''
for i in range(len(lowerCamelCase ) - 1 , 0 , -1 ):
__lowercase = False
for j in range(lowerCamelCase , 0 , -1 ):
if unsorted[j] < unsorted[j - 1]:
__lowercase , __lo... | 80 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : int = 4000000 ) -> int:
_a : Optional[Any] =[]
_a , _a : Union[str, Any] =0, 1
while b <= n:
if b % 2 == 0:
... | 694 | 0 |
'''simple docstring'''
import numpy as np
def __lowercase ( __SCREAMING_SNAKE_CASE ) -> np.ndarray:
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
def __lowercase ( __SCREAMING_SNAKE_CASE ) -> np.ndarray:
"""simple docstri... | 702 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {... | 201 | 0 |
"""simple docstring"""
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipel... | 621 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
fro... | 621 | 1 |
"""simple docstring"""
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
f... | 91 |
"""simple docstring"""
from math import sqrt
def __snake_case ( UpperCamelCase__ ) -> int:
"""simple docstring"""
A = 0
for i in range(1 , int(sqrt(UpperCamelCase__ ) + 1 ) ):
if n % i == 0 and i != sqrt(UpperCamelCase__ ):
total += i + n // i
elif... | 91 | 1 |
from collections import deque
from .hash_table import HashTable
class UpperCAmelCase_ ( __lowerCamelCase ):
def __init__( self , *_lowerCAmelCase , **_lowerCAmelCase ):
super().__init__(*_lowerCAmelCase , **_lowerCAmelCase )
... | 79 |
'''simple docstring'''
from __future__ import annotations
def a_ ( __snake_case : int ) -> list[int]:
"""simple docstring"""
lowerCamelCase_ =[True] * limit
lowerCamelCase_ =False
lowerCamelCase_ =False
lowerCamelCase_ =True
for i i... | 676 | 0 |
'''simple docstring'''
def __UpperCAmelCase ( a_: str = 1_000_000 ):
_UpperCAmelCase : Optional[int] = limit + 1
_UpperCAmelCase : List[Any] = [0] * limit
for first_term in range(1, SCREAMING_SNAKE_CASE_ ):
for n in range(SCREAMING_S... | 715 | '''simple docstring'''
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class A__ :
"""simple docstring"""
def __init__( self ... | 257 | 0 |
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def snake_case__ ( lowercase ):
lowerCAmelCase_: List[str] = SwinConfig()
... | 613 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
A_ : List[Any] =argparse.ArgumentParser("""Stable Diffusion script with intel optimization""", add_help=False)
parser.add_argument("""-... | 483 | 0 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case : str = logging.get_logger(__name__)
__snake_case : Dict = {
"microsoft/unispeech-large-1500h-cv": (
"https://hugging... | 718 |
'''simple docstring'''
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet imp... | 691 | 0 |
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
UpperCAmelCase... | 533 |
'''simple docstring'''
from __future__ import annotations
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
if len(SCREAMING_SNAKE_CASE__ ) <= 1 or n <= 1:
return
insert_next(SCREAMING_SNAKE_CASE__ , n - 1 )
rec_in... | 533 | 1 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImagePr... | 708 |
'''simple docstring'''
import warnings
from functools import wraps
from typing import Callable
def A_ ( snake_case ):
@wraps(snake_case )
def _inner_fn(*snake_case , **snake_case ):
warnings.warn(
(F'''\'{fn.__name__}\' is experimental and might be subject to bre... | 465 | 0 |
'''simple docstring'''
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def __lowercase () -> str:
"""simple docstring"""
import os as original_os
from os import path as original_path
from os import rename as original_rename
fr... | 150 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ :List[str] = logging.get_logger(__name__)
UpperCAmelCase__ :Union[str, Any] = {
"""BAAI/AltCLIP""": """htt... | 150 | 1 |
import numpy as np
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : List[Any] ):
SCREAMING_SNAKE_CASE = (0, 0)
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = 0
SCREAMIN... | 715 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__A : Any = {
'configuration_longt5': ['LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LongT5Config', 'LongT5OnnxConfig'],
}
try:
if not is_torch_available():
... | 698 | 0 |
from __future__ import annotations
import math
_lowerCAmelCase = "2020.9.26"
_lowerCAmelCase = "xcodz-dot, cclaus, dhruvmanila"
def _snake_case ( __snake_case , __snake_case , __snake_case , __snake_case , __snake_case ):
if not all(isinstance(__snake_case , (flo... | 10 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self : List[Any] , UpperCamelCase__ : Collection[float] | None = None ):
if... | 699 | 0 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=_A )
class _lowercase ( _A ):
# `task` is not a ClassVar since we want it to be part of the `asdi... | 448 |
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def A__ ( _a : int , _a : Any , _a : Union[str, Any] , _a : ... | 448 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTok... | 606 |
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
i... | 606 | 1 |
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from .... | 713 | 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 ..... | 225 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
SCREAMING_SNAKE_CASE_: Tuple ={
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EncodecConfig',
],
'fe... | 78 | import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _lowercase ( snake_case_ ):
lowercase ... | 417 | 0 |
"""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_tokenizati... | 439 |
"""simple docstring"""
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_... | 439 | 1 |
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def A__ ( __A : Optional[int] ) ->Optional[Any]:
if "cls_token" in name:
__A =name.replace('''cls_token''' , ''... | 184 |
"""simple docstring"""
from typing import Dict
from .base import GenericTensor, Pipeline
class SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
def lowerCamelCase(self , lowerCAmelCase_=None , lowerCAmelCase_=None , lowerCAmelCase_=None , **lowerCAmelCase_ ... | 180 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_lowercase : Tuple ={
"configuration_longt5": ["LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP", "LongT5Config", "LongT5OnnxConfig"],
}
try:
... | 708 |
'''simple docstring'''
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
_lowercase : List[Any] =TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow... | 574 | 0 |
"""simple docstring"""
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 ...... | 624 |
"""simple docstring"""
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
Stab... | 624 | 1 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=__lowercase )
class a ( __lowercase ):
# `task` is not a ClassVar since we want it to be part of the `asdict` output for J... | 146 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def lowerCAmelCase ( UpperCamelCase__ : int = 3 ) -> qiskit.result.counts.Counts:
"""simple docstring"""
if isinstance(UpperCamelCase... | 146 | 1 |
'''simple docstring'''
import numpy as np
class _UpperCAmelCase :
def __init__( self ):
'''simple docstring'''
__lowerCAmelCase = (0, 0)
__lowerCAmelCase = None
__lowerCAmelCase = 0
__lowerCAmelCase = 0
__lowe... | 689 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def _lowerCAmelCase ( lowercase ) -> Optional[Any]:
# vision encoder
if "img_encoder.pos_embed" in name:
__lowerCAm... | 689 | 1 |
from collections import deque
def __lowerCAmelCase (SCREAMING_SNAKE_CASE )-> Dict:
"""simple docstring"""
snake_case_ = len(SCREAMING_SNAKE_CASE )
snake_case_ = deque()
snake_case_ = [False for _ in range(SCREAMING_SNAKE_CASE )]
snake_case_ = ... | 531 |
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineT... | 531 | 1 |
"""simple docstring"""
def UpperCAmelCase ( a__ , a__ ):
'''simple docstring'''
lowerCAmelCase :Tuple = len(a__ )
print('The following activities are selected:' )
# The first activity is always selected
lowerCAmelCase :Dict = ... | 553 |
"""simple docstring"""
import qiskit
def UpperCAmelCase ( a__ , a__ ):
'''simple docstring'''
lowerCAmelCase :List[Any] = qiskit.Aer.get_backend('aer_simulator' )
# Create a Quantum Circuit acting on the q register
lowerCAmelCase :Option... | 553 | 1 |
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, lo... | 563 |
def _lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
A_ = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def _lowerCamelCase ... | 563 | 1 |
def SCREAMING_SNAKE_CASE__ ( ):
return [list(range(1_000 - i , -1_000 - i , -1 ) ) for i in range(1_000 )]
_lowerCamelCase = generate_large_matrix()
_lowerCamelCase = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[3, 2], ... | 6 |
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_byte... | 6 | 1 |
'''simple docstring'''
from __future__ import annotations
def a__ ( _SCREAMING_SNAKE_CASE : Tuple ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase_ : Optional[int] = 0.00
UpperCAmelCase_ : List[Any] = 0
for resistor in... | 717 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase = {
"""configuration_swinv2""": ["""SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Swinv2Config"""],
}
try:
if not is_torch_ava... | 323 | 0 |
'''simple docstring'''
from pathlib import Path
import fire
from tqdm import tqdm
def lowerCamelCase_ ( __UpperCamelCase : Optional[int]="ro" , __UpperCamelCase : Dict="en" , __UpperCamelCase : Union[str, Any]="wmt16" , __UpperCamelCase : ... | 292 |
'''simple docstring'''
def lowerCamelCase_ ( __UpperCamelCase : int ) -> list:
"""simple docstring"""
_A = int(__UpperCamelCase )
if n_element < 1:
_A = ValueError('a should be a positive number' )
raise my_error
... | 292 | 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
from ..auto import CONFIG_MAPPING
UpperCamelCase__ =logging.get_logger(__name__)
UpperCamelCase__ ... | 381 |
from PIL import Image
def lowerCamelCase__ (__lowerCamelCase ):
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : List[str] = image.size
_SCREAMING_SNAKE_CASE : Tuple = 0
_SCREAMING_SNAKE_CASE : Dict = image.load()
f... | 381 | 1 |
'''simple docstring'''
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class lowercase_ (lowerCamelCase__ )... | 41 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
_UpperCamelCase = {"""configuration_vit""": ["""VIT_PRETRAI... | 111 | 0 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase_ ( _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CA... | 702 | import baseaa
def lowerCAmelCase_ ( _SCREAMING_SNAKE_CASE : str ):
return baseaa.baaencode(string.encode("""utf-8""" ) )
def lowerCAmelCase_ ( _SCREAMING_SNAKE_CASE : bytes ):
return baseaa.baadecode(_SCREAMING_SNAKE_CASE ).decode("""utf-8"... | 138 | 0 |
'''simple docstring'''
import unittest
from transformers import MPNetConfig, 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_att... | 69 |
"""simple docstring"""
from __future__ import annotations
def __UpperCamelCase ( snake_case__ , snake_case__ = None , snake_case__ = None ):
if start is None:
A_ : Dict = 0
if end is None:
A_ : Dict = len(snake_case__ ) - 1
if start >= end:
ret... | 180 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
a : int = {
"""configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP... | 719 |
"""simple docstring"""
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __Upper... | 85 | 0 |
class __lowercase :
def __init__( self ) ->int:
'''simple docstring'''
__lowerCAmelCase : List[str] = 0
__lowerCAmelCase : Optional[int] = 0
__lowerCAmelCase : Union[str, Any] = {}
def UpperCamelCase__ ( self , A_ ) ->Tuple:
... | 492 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCamelCase = {"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "X... | 492 | 1 |
"""simple docstring"""
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisio... | 158 |
"""simple docstring"""
from __future__ import annotations
__lowerCAmelCase : Union[str, Any] = list[tuple[int, int]]
__lowerCAmelCase : Optional[int] = [
[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, ... | 158 | 1 |
"""simple docstring"""
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class __lowerCAmelCase ( _lowercase ):
"""simple docstring"""
def __init__( self : List[Any... | 115 |
"""simple docstring"""
def A_ (__a ):
'''simple docstring'''
A_ = len(__a )
while cur > 1:
# Find the maximum number in arr
A_ = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
A_ = arr[mi::-1] + ar... | 115 | 1 |
from string import ascii_uppercase
UpperCAmelCase_ = {char: i for i, char in enumerate(ascii_uppercase)}
UpperCAmelCase_ = dict(enumerate(ascii_uppercase))
def UpperCAmelCase__ ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str )->str:
_lowerCAmel... | 706 |
from __future__ import annotations
def UpperCAmelCase__ ( _SCREAMING_SNAKE_CASE : list )->list:
if len(_SCREAMING_SNAKE_CASE ) == 0:
return []
_lowerCAmelCase , _lowerCAmelCase = min(_SCREAMING_SNAKE_CASE ), max(_SCREAMING_SNAKE_CASE )
_lowerCAmelCase ... | 664 | 0 |
"""simple docstring"""
from string import ascii_uppercase
_snake_case = {str(ord(c) - 55): c for c in ascii_uppercase}
def snake_case ( _a: int , _a: int )-> str:
'''simple docstring'''
if isinstance(_a , _a ):
raise TypeError('int() can\'... | 510 |
"""simple docstring"""
import os
from datetime import datetime as dt
from github import Github
_snake_case = [
"good first issue",
"good second issue",
"good difficult issue",
"enhancement",
"new pipeline/model",
"new scheduler",
"wip",
]
def sna... | 510 | 1 |
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("""dataset_size""" , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize("""input_in_memory_max_size""" , ["""default""", 0, 100 * 2**20, 900 * 2**20] )
def ... | 321 |
def lowerCamelCase ( UpperCAmelCase_ : int = 10 , UpperCAmelCase_ : int = 1000 , UpperCAmelCase_ : bool = True )-> int:
"""simple docstring"""
assert (
isinstance(UpperCAmelCase_ , UpperCAmelCase_ )
and isinstance(Up... | 321 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokeniza... | 65 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def __UpperCAmelCase ( __UpperCamelCase ):
# encod... | 76 | 0 |
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : int ,lowerCAmelCase_ : int ) -> int:
"""simple docstring"""
while b:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Optional[int] =b, a % b
return a
def ... | 153 |
from __future__ import annotations
__SCREAMING_SNAKE_CASE = '#'
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self ):
SCREAMING_SNAKE_CASE_ : dict ={}
def __lowerCamelCase ( self , __UpperCAmelCase ):
... | 153 | 1 |
'''simple docstring'''
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def _UpperCame... | 42 |
'''simple docstring'''
def a__ ( lowercase : Dict, lowercase : Optional[Any] ) -> int:
"""simple docstring"""
_UpperCamelCase = 0
_UpperCamelCase = len(lowercase ) - 1
while left <= right:
# avoid divided by 0 durin... | 98 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available,... | 117 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _lowerCAmelCase ( unittest.T... | 117 | 1 |
def UpperCamelCase ( _A : int = 600851475143 )-> Tuple:
"""simple docstring"""
try:
A__ = int(_A )
except (TypeError, ValueError):
raise TypeError("Parameter n must be int or castable to int." )
if n <= 0:
raise V... | 491 |
"""simple docstring"""
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.... | 179 | 0 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
_lowerCamelCase =(7_20, 12_80) # Height, Width
_lowerCamelCase =(0.4, 0.6) # if height or width lower than this scale, drop it.
_lowerCamelCase =1 / 1_00
_lowerCamelCase ... | 710 |
from ..utils import DummyObject, requires_backends
class a_ ( metaclass=lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = ['torch', 'scipy']
def __init__( self : Any ,*snake_case : Any ,**snake_case : str ):
requires... | 252 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""google/mobilenet_v2_1.4_224""": "... | 74 |
"""simple docstring"""
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__A = "src/transformers"
# This is to make sure the transformers m... | 346 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class SCREAMING_SNAKE_CASE__ ( snake_case_ ... | 716 |
'''simple docstring'''
lowerCAmelCase : Optional[Any] = '\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install ... | 432 | 0 |
'''simple docstring'''
import socket
def _UpperCamelCase ( ) -> Optional[int]:
'''simple docstring'''
snake_case : Optional[Any] = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
snake_case : Dict = socket.gethostname()
... | 638 |
'''simple docstring'''
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCo... | 638 | 1 |
"""simple docstring"""
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ : Any = logging.get_logger(__name__)
A__ : Optional[Any] = {
'snap-research/efficientformer-l1-300': (
'https://huggingface.co/snap-researc... | 707 |
"""simple docstring"""
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils ... | 272 | 0 |
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
__A : Union[str, Any] = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, ... | 27 |
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,
)
snake_case_ : Tuple = {
"configuration_albert": ["ALBERT_PRE... | 488 | 0 |
'''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 impo... | 703 |
'''simple docstring'''
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forwa... | 271 | 0 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"""files""" , [
["""full:README.md""", """dataset_infos.json"""],
["""empty:README.md""", """dataset_infos.json... | 354 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_avai... | 354 | 1 |
"""simple docstring"""
import pprint
import requests
SCREAMING_SNAKE_CASE_ = '''https://zenquotes.io/api'''
def A__ ( ) -> list:
'''simple docstring'''
return requests.get(API_ENDPOINT_URL + "/today" ).json()
def A__ ( ) -> list:
'''simple docstring''... | 579 |
"""simple docstring"""
def A__ ( A__ ) -> str:
'''simple docstring'''
_UpperCAmelCase = ""
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 A__ ( A__ ) -> dict[str, str]:
... | 579 | 1 |
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