code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=1024 )-> Union[str,... | 321 |
import string
def UpperCamelCase (lowercase_: str ) -> None:
for key in range(len(string.ascii_uppercase ) ):
A__ : Dict = """"""
for symbol in message:
if symbol in string.ascii_uppercase:
A__ : Dict = string.ascii_uppercase.find(lowercase... | 192 | 0 |
def A_ ( ) -> int:
return [
a * b * (1000 - a - b)
for a in range(1 , 999 )
for b in range(A__ , 999 )
if (a * a + b * b == (1000 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(F"""{solution() = }""")
| 365 |
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_mvp import MvpTokenizer
lowerca... | 225 | 0 |
'''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import Tens... | 79 |
'''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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import... | 79 | 1 |
"""simple docstring"""
from __future__ import annotations
from fractions import Fraction
def snake_case (A_ :int , A_ :int ):
'''simple docstring'''
return (
num != den and num % 1_0 == den // 1_0 and (num // 1_0) / (den % 1_0) == num / den
)
def snak... | 186 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def snake_case (A_ :Dict ):
'''simple docstri... | 186 | 1 |
'''simple docstring'''
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
_SCREAMING_SNAKE_CASE : List[Any] = logging.get_logger(__name__)
_SCREAMING_SNAKE_CA... | 85 |
'''simple docstring'''
from statistics import mean, stdev
def UpperCamelCase_( snake_case : list , snake_case : int = 3 ):
'''simple docstring'''
snake_case_ = min(snake_case )
snake_case_ = max(snake_case )
... | 85 | 1 |
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
a :List[Any] = Mapping[str, np.ndarray]
a :str = Mapping[str, Any] # Is a nested dict.
a :Optional[Any] = 0.01
@data... | 351 |
"""simple docstring"""
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class __a (tf.keras.optimizers.schedules.LearningRateSchedule):
... | 56 | 0 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> bool:
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(_lowerCAmelCase ) )
def __snake_case( _lowerCAme... | 35 |
'''simple docstring'''
import numpy as np
from transformers import Pipeline
def __snake_case( _lowerCAmelCase ) -> Optional[int]:
snake_case__ : Optional[Any] = np.max(_lowerCAmelCase , axis=-1 , keepdims=_lowerCAmelCase )
snake_case__ : List[str]... | 35 | 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 snake_case ( _UpperCamelCase):
... | 163 |
"""simple docstring"""
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutpu... | 163 | 1 |
def _a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase__ = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 110 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDE... | 225 | 0 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
"""microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base... | 5 |
'''simple docstring'''
from functools import lru_cache
@lru_cache
def UpperCAmelCase_ (__a : int ):
"""simple docstring"""
if num < 0:
raise ValueError('Number should not be negative.' )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__"... | 5 | 1 |
from pathlib import Path
import fire
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
A_ : Tuple = Path(SCREAMING_SNAKE_CASE )
A_ : Union[str, Any] = Path(SCREAMING_SNAKE_CASE )
dest_dir.mkdir(exist_... | 186 |
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ):
A_ : List[Any] = current_set.copy()
for row_index, row in enumerate(SCREAMING_SNAKE_CASE ):
A_ : List[str] = row[0]
for column_index, column in enumerate(SCREAMING_SNAKE_CASE ):
if magnitude ... | 186 | 1 |
'''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 _lowercase ( __A ):
'''simple docstring'''
... | 370 |
'''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class UpperCAmelCase__ ( UpperCAmelCase_):
_... | 243 | 0 |
UpperCAmelCase_ = {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
UpperCAmelCase_ = ['a', 'b', 'c', 'd', 'e']
def lowerCAmelCase_ ( __UpperCAmelCase: str , __UpperCAmelCase: Dict , __UpperCAmelCase: List[str] ) -> Any:
UpperCamel... | 201 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_ax... | 56 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( snake_case_ : int = 1_00_00_00 ) -> int:
'''simple docstring'''
UpperCAmelCase_ = limit + 1
UpperCAmelCase_ = [0] * limit
for first_term in range(1 , snake_case_ ):
for n in range(snake_cas... | 106 | '''simple docstring'''
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XL... | 106 | 1 |
'''simple docstring'''
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class _snake_case (... | 163 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A =logging.get_logger(__name__)
__A ={
'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
# See all PEGASUS models at https://huggingfa... | 163 | 1 |
"""simple docstring"""
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
_UpperCamelCase : Tuple = parse(importlib.metadata.version('torch'))
def snake_case (A_ :Union[str, Version] ,... | 371 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
_UpperCamelCase : Optional[Any] = 'examples/'
_UpperCamelCase : Any = {
'examples': (re.compile(r'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
'i... | 186 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
'''microsoft/wavlm-base''': '''https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json''',
... | 5 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from t... | 5 | 1 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
ne... | 369 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ : List[str] ={
'''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}
try:
if not is_torch_available():... | 118 | 0 |
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_sc... | 14 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
... | 243 | 0 |
'''simple docstring'''
a_ = [
[0, 1_6, 1_3, 0, 0, 0],
[0, 0, 1_0, 1_2, 0, 0],
[0, 4, 0, 0, 1_4, 0],
[0, 0, 9, 0, 0, 2_0],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def _a( UpperCamelCase__ : Dict, UpperCamelCase__ : Optional[int], UpperCamelCase__ ... | 361 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determini... | 222 | 0 |
"""simple docstring"""
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
__UpperCamelCase : List[str] = False
__UpperCamelCase : List[Any] = True
__UpperCamelCase : ... | 106 |
"""simple docstring"""
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
__UpperCamelCase : Tuple = '''%20'''.join(argv[1:]) if len(argv) > 1 els... | 106 | 1 |
'''simple docstring'''
import math
def _a ( _lowercase : float , _lowercase : float ):
'''simple docstring'''
if initial_intensity < 0:
raise ValueError('''The value of intensity cannot be negative''' )
# handling... | 356 |
'''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, ... | 240 | 0 |
"""simple docstring"""
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_do... | 105 |
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxForce... | 186 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowerCamelCase = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
lowerCame... | 48 |
'''simple docstring'''
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
lowerCamelCase = logging.get_lo... | 48 | 1 |
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
lowerCamelCase__ : List[Any] = ... | 225 | import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
A : Tuple = datasets.utils.logging.get_logger(__nam... | 118 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'microsof... | 359 |
'''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_ava... | 322 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_con... | 72 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase : Union[str, Any] = {
"configuration_instructblip": [
"INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"InstructBlipConfig",
"InstructBlipQFor... | 222 | 0 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_toke... | 236 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
snake_case_ : Any = {
'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
'tokenization_mvp'... | 236 | 1 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 232 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def __lowercase ( ):
a__ = ArgumentParser('Diffusers CLI tool' , usage='diffusers-cli <command> [<args>]' )
a__ = parser.add_subparsers(help='diffusers-cli command helpers' ... | 240 | 0 |
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def __lowerCamelCase ( lowerCamelCase__ : int ... | 351 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase : Union[str, Any] = {
"configuration_mobilebert": [
"MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_M... | 66 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
SCREAMING_SNAKE_CASE__ : Tuple = logging.get_logg... | 48 |
from __future__ import annotations
import requests
def A ( _SCREAMING_SNAKE_CASE ) -> dict:
lowerCamelCase : Tuple = f'''https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'''
return requests.get(_SCREAMING_SNAKE_CASE ).json()
def... | 48 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
"edbeeching/decision-transformer-gym-hopper-medium": (
"https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/ma... | 353 | import unittest
import numpy as np
from transformers import RoFormerConfig, 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():
import jax.numpy as jnp
from transf... | 63 | 0 |
"""simple docstring"""
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerT... | 45 |
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def _a ( SCREAMING_SNAKE_CASE : Optional[int] ) -> Union[str, Any]:
"""simple docstring... | 322 | 0 |
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin... | 352 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import requ... | 9 | 0 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
re... | 236 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : List[Any] = {"voca... | 236 | 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... | 365 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, loa... | 267 | 0 |
import string
import numpy
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Any:
return b if a == 0 else greatest_common_divisor(b % a , _lowercase )
class A__ :
lowercase = string.ascii_uppercase + string.d... | 212 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def A_ ( _lowercase ):
'''simple docstring'''
snake_case_ :Union[str, Any] = os.path.join(args.tf_model_dir, """parameters.j... | 66 | 0 |
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class a_ ( __lowercase ):
'''simple docstring'''
__a: List[str] = ''''''
__a: ... | 371 |
def lowerCamelCase ( a_ , a_ ) -> List[Any]:
lowerCAmelCase_ = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def lowerCamelCase ( a_ , a... | 14 | 0 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self , lowerCamelCase__ ) -> Any:
'''simple docstring'''
_... | 90 |
'''simple docstring'''
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 __SCREAMING_SNAKE... | 63 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__a = {
'configuration_bridgetower': [
'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BridgeTowerConfig',
'Bridge... | 17 | '''simple docstring'''
from importlib import import_module
from .logging import get_logger
__a = get_logger(__name__)
class A__ :
"""simple docstring"""
def __init__( self : List[str] , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : O... | 17 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowerCamelCase : List[str] = {
'config... | 2 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _lowercase ( A__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_... | 9 | 0 |
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
__a = {
'''<''': operator.lt,
'''<=''': operator.le,
'''==''': operator.eq,
'''!=''': operator.ne,
'''>=''': operator.ge,
'''>''': operator.gt,
... | 368 |
# Algorithm for the pigeonhole sorting
def __lowercase ( _UpperCamelCase ) ->List[Any]:
"""simple docstring"""
lowercase : List[Any] = min(_UpperCamelCase ) # min() finds the minimum value
lowercase : Union[str, Any] = max(_... | 173 | 0 |
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, ra... | 317 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCAmelCase : List[str] = logging.get_logger(__name__)
class lowerCAmelCase__ ... | 267 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a :Union[str, Any] = logging.get_logger(__name__)
a :Optional[int] = {
'''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json'''... | 362 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a :List[str] = logging.get_logger(__name__)
a :Union[str, Any] = {
"MIT/ast-finetuned-audioset-10-10-0.4593": (
"https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve... | 56 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
__SCREAMING_SNAKE_CASE :Optional[int] = TypeVar('''T''')
class A_ ( Generic[T] ):
def __init__( self : List[Any] , snake_case_ ... | 22 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : List[str] = {"""configuration_vit_msn""": ["""VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMSNConfig"""]}
try:
if not is_torch_available():
raise Opti... | 14 | 0 |
'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class __UpperCAmelCase :
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Tuple:
if dst_width < 0 or dst_heig... | 365 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case : List[str] = logging.get_logger(__name__)
__snake_case : Union[str, Any] = {
'alibaba-damo/mgp-str-base': 'https://huggingface.co/alibaba-damo/mgp-str-base... | 18 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_a = {
'configuration_bridgetower': [
'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BridgeTowerConfig',
'Bri... | 17 |
"""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_ca... | 17 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer
__UpperCamelCase : List[Any] = log... | 362 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
class a ( a__ ):
def __init__( self , *_snake_case , ... | 309 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {'vocab_file': 'spm_c... | 17 |
"""simple docstring"""
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def __magic_name__ ( *lowercase ):
if not isinstance(lowercase , lowercase ):
SCREAMING_SNAKE_CASE_: Optional[Any]... | 173 | 0 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def SCREAMING_SNAKE_CASE ( ) -> Tuple:
UpperCAmelCase_ = {
'''repo_name''': ['''test_repo1''', '''test_repo2''', '''test... | 356 |
import numpy as np
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : np.array ) -> np.array:
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 177 | 0 |
"""simple docstring"""
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
_UpperCamelCase : Optional[int] = TypeVar("KT")
_UpperCamelCase : Tuple = TypeVar("VT")
class UpperCAmelCase_ ( Generic[KT, VT]):
def __init__( ... | 77 |
'''simple docstring'''
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_availabl... | 56 | 0 |
from math import sqrt
def lowercase__ ( __snake_case : int = 1_000_000 ):
'''simple docstring'''
UpperCAmelCase_ : int = 0
UpperCAmelCase_ : int = 0
UpperCAmelCase_ : int
while num_cuboids <= limit:
... | 145 |
def lowercase__ ( __snake_case : str , __snake_case : int , __snake_case : List[str] ):
'''simple docstring'''
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(__snake_case , n - 1 , __... | 145 | 1 |
from __future__ import annotations
__snake_case = list[tuple[int, int]]
__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],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, ... | 348 | from collections.abc import Sequence
from queue import Queue
class a__ :
def __init__( self : int,_A : List[Any],_A : Optional[Any],_A : Optional[int],_A : int=None,_A : List[str]=None ):
"""simple docstring"""
... | 18 | 0 |
import gc
import inspect
import unittest
import torch
from parameterized import parameterized
from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import Mode... | 359 |
'''simple docstring'''
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
if not (isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )):
raise ValueError("""longest_comm... | 270 | 0 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class UpperCamelCase__ :
'''simple docstring'''
pass
| 48 |
'''simple docstring'''
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase_ = logging.get_lo... | 309 | 0 |
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"""vocab_file""": """vocab.json""",
"""merges_file""": """me... | 350 |
def lowerCamelCase__ ( A__ : int ):
'''simple docstring'''
__lowerCamelCase = [[0 for _ in range(A__ )] for _ in range(m + 1 )]
for i in range(m + 1 ):
__lowerCamelCase = 1
for n in range(m + 1 ):
for k... | 29 | 0 |
'''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
... | 3 | """simple docstring"""
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , __UpperCAmelCase , __U... | 177 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase = {'''configuration_xglm''': ['''XGLM... | 355 |
from math import isqrt
def lowerCamelCase_ ( _a ):
"""simple docstring"""
lowerCAmelCase__ : Dict = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , _a ,... | 211 | 0 |
'''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_cas... | 145 | '''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'kssteven/ibert-roberta-base': 'https:/... | 145 | 1 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def UpperCAmelCase ( a_ = "AAPL" ) -> str:
"""simple docstring"""
A_ : List[str] = F"https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"
A_ : Union[st... | 164 |
'''simple docstring'''
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def UpperCAmelCase ( a_ , a_ , a_ , a_ , a_ ) -> ... | 164 | 1 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : List[Any] =logging.get_logger(__name__)
class a_ ( __lowercase ):
__A = """encoder-decoder"""
__A = ... | 223 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
SCREAMING_SNAKE_CASE__ : List[Any] = TypeVar("T")
def __magic_name__ ( __lowerCAmelCase : int ) -> int:
return (position - 1) // 2
def __magic_name__ ( __lowerCAmelCase : ... | 270 | 0 |
from math import factorial
lowerCAmelCase = {str(d): factorial(d) for d in range(1_0)}
def _lowerCamelCase( lowercase__ ) -> List[str]:
'''simple docstring'''
return sum(DIGIT_FACTORIAL[d] for d in str(A_ ) )
def _lowerCamelCase( ) -> Tuple:
'... | 358 |
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
lowerCAmelCase = logging.... | 304 | 0 |
"""simple docstring"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def... | 44 |
def lowercase__ ( __snake_case : list ):
'''simple docstring'''
for i in range(len(__snake_case ) - 1 , 0 , -1 ):
UpperCAmelCase_ : Dict = False
for j in range(__snake_case , 0 ,... | 29 | 0 |
"""simple docstring"""
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class __UpperCamelCase ( _A ):
def __lt__(self : Union[str, Any] , __SCREAMING_SNAKE_CASE : Any):
... | 353 |
"""simple docstring"""
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
__A : Union[str, Any] = get_logger(__name__)
class __UpperCamelCase ( enum.Enum ):
SCREAMING_SNAKE_CASE = "all_c... | 57 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : str = {
'configuration_blip_2': [
'BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Blip2Config',
'Blip2QFormerConfig',
'Blip2VisionConfig',
],
... | 154 |
'''simple docstring'''
import random
def lowerCAmelCase (__A):
"""simple docstring"""
_a = num - 1
_a = 0
while s % 2 == 0:
_a = s // 2
t += 1
for _ in range(5):
_a = random.randrange(2 , num - 1)
_a = ... | 211 | 0 |
import math
def lowerCAmelCase_ ( __A, __A ) -> int:
'''simple docstring'''
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(__A )
else:
if x == ... | 354 | import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
Up... | 143 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class A :
def __init__( self , lowerCamelCase__ ) -> str:
'''simple docstring'''
lowercase__ = data
lowercase__ ... | 164 |
'''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 (
AudioLDMPipeline,
... | 164 | 1 |
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMSchedul... | 357 |
'''simple docstring'''
import string
def _A ( A__ ):
"""simple docstring"""
for key in range(len(string.ascii_uppercase ) ):
__lowercase = ''''''
for symbol in message:
if symbol in string.ascii_uppercase:
__lowercase = string.ascii_uppercase.find(A__... | 52 | 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 a__ ( UpperCAmelCase__ , unittest.... | 67 |
'''simple docstring'''
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixi... | 304 | 0 |
from __future__ import annotations
from typing import Any
def a ( SCREAMING_SNAKE_CASE_ : list ):
"""simple docstring"""
if not postfix_notation:
return 0
UpperCamelCase : Optional[Any] = {'''+''', '''-'''... | 315 |
def a ( SCREAMING_SNAKE_CASE_ : list[list[float]] ):
"""simple docstring"""
UpperCamelCase : list[list[float]] = []
for data in source_data:
for i, el in enumerate(SCREAMING_SNAKE_CASE_ ):
i... | 315 | 1 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_availabl... | 199 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] =["""image_processor""", """t... | 57 | 0 |
"""simple docstring"""
class a_ :
'''simple docstring'''
def __init__(self, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Tuple = len(lowerCamelCase_ )
lowerCamelCase__ : Any = [0] * len_array
... | 316 |
"""simple docstring"""
import cva
import numpy as np
class a_ :
'''simple docstring'''
def __init__(self, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
if k in (0.04, 0.06):
lowerCamelCase__ : Tuple = k
lower... | 316 | 1 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class _... | 303 | from abc import ABC, abstractmethod
from argparse import ArgumentParser
class __snake_case ( _lowerCamelCase ):
@staticmethod
@abstractmethod
def __a ( __UpperCamelCase ) -> Dict:
'''simple docstring'''
raise NotImplementedEr... | 143 | 0 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoToken... | 176 |
from collections import defaultdict
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ) -> bool:
snake_case : List[str] = first_str.lower().strip()
snake_case : List[str] = second_str.lower().strip()
# Remove whitespace
snake_case : Any... | 176 | 1 |
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
__snake_case :Optional[int] ... | 49 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase : Any = {
"""configuration_electra""": ["""ELECTRA_PRETRAINED_CONF... | 52 | 0 |
"""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/LICENSE-... | 202 |
"""simple docstring"""
def __snake_case ( SCREAMING_SNAKE_CASE__ : List[str] ) -> str:
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase : Dict = [], []
while len(SCREAMING_SNAKE_CASE__ ) > 1:
_UpperCAmelCase , _Up... | 202 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'''kssteven/ibert-roberta-base''': '''https://huggingface... | 315 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
a = logging.get_logger(__name__)
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Any , *... | 315 | 1 |
from collections.abc import Callable
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: Any , __A: Callable | None = None ) -> None:
# Stores actual heap items.
_A = []
# Stores indexes of... | 75 |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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 app... | 75 | 1 |
"""simple docstring"""
def UpperCamelCase ( UpperCAmelCase = 1_000 ) ->int:
"""simple docstring"""
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution()) | 243 |
"""simple docstring"""
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
a = logging.getLogger(__name__)
a = 50 # max width of layer name... | 315 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import logging
logging.set... | 355 |
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
SCREAMING_SNAKE_CASE_:Any = """src/diffusers"""
# Matches is_xxx_available()
SCREAMING_SNAKE_CASE_:Optional[Any] = re.com... | 115 | 0 |
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
__snake_case = {
"""facebook/maskformer-swin-base-ade""": (
"""https://hugg... | 176 |
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_tokenization_common import TokenizerTesterM... | 176 | 1 |
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class _lowerCAmelCase( snake_case_ ):
"""simple docstring"""
a : Tuple ='''M-CLIP'''
def __init__( self , _lowerCamelCase=1_0_2... | 356 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class _lowerCAmelCase( unittest.... | 292 | 0 |
"""simple docstring"""
import heapq
import sys
import numpy as np
_A : Dict = tuple[int, int]
class a__ :
def __init__( self ):
lowercase : str = []
lowercase : Optional[Any] = set()
def __magic_n... | 202 |
"""simple docstring"""
from __future__ import annotations
from functools import lru_cache
from math import ceil
_A : Optional[Any] = 1_00
_A : Optional[int] = set(range(3, NUM_PRIMES, 2))
primes.add(2)
_A : int
for prime in range(3, ceil(NUM_PRIMES**... | 202 | 1 |
def lowerCamelCase_ ( _UpperCamelCase ) -> str:
"""simple docstring"""
return "".join([hex(_UpperCamelCase )[2:].zfill(2 ).upper() for byte in list(_UpperCamelCase )] )
def lowerCamelCase_ ( _UpperCamelCase ) -> bytes:
"""simple doc... | 354 |
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, PartialState... | 279 | 0 |
'''simple docstring'''
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __UpperCamelCase ( lowerCamelCase__ ):
lowercase : Optional[Any] =(IPNDMScheduler,)
lowercase : Optional[int] ... | 75 |
'''simple docstring'''
from __future__ import annotations
def a_ ( __snake_case : str , __snake_case : list[str] | None = None , __snake_case : dict[str, float] | None = None , __snake_case : bool = False , ) -> tup... | 75 | 1 |
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=__magic_name__ )
class __lowerCAmelCase ( __magic_name__ ):
"""simple docst... | 365 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : Optional[Any] = logging.get_logger(__name__)
lowercase__ : List[Any] = {
'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
... | 287 | 0 |
"""simple docstring"""
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 = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7:... | 135 |
"""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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
Im... | 115 | 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
SCREAMING_SNAKE_CASE : List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : str ... | 84 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
"The `inpainting.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionInpaintPipeline` instead."
)
| 84 | 1 |
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_validate_point(SCREAMING_SNAKE_CASE )
_validate_point(SCREAMING_SNAKE_CASE )
if len(SCREAMING_SNAKE_CASE ) != len(SCREAMING_SNAKE_CASE ):
raise ValueError('''Both points must be in the same n... | 43 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case : Optional[int] = logging.get_logger(__name__)
_snake_case : Optional[int] = {
'google/vivit-b-16x2-kinetics400': (
'https://huggingface.co/google/vi... | 292 | 0 |
"""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 : List[Any] = {'''vocab_file''': '''vocab.txt''... | 363 |
"""simple docstring"""
import argparse
from collections import defaultdict
def lowerCAmelCase_( lowercase_ : str , lowercase_ : Dict , lowercase_ : Tuple , lowercase_ : str , lowercase_ : str ) -> Optional[int]:
_low... | 73 | 0 |
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 ... | 19 |
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
infer_... | 279 | 0 |
"""simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def lowercase ( ) -> Dict:
__a = HfArgumentParser(lowerCAmelCase__ )
__a = parser.parse_args_into_dataclasses()[0]
__a = ... | 11 |
"""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,
Euler... | 11 | 1 |
from math import factorial
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Dict:
'''simple docstring'''
if successes > trials:
raise ValueError("""successes must be lower or equal to trials""" )
... | 296 |
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 numpy as np
import tensorflow as tf
from transformers import TFCamembertMode... | 287 | 0 |
'''simple docstring'''
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...tes... | 164 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
UpperCamelCase__ : Any = {'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}
try:
if not is_t... | 164 | 1 |
"""simple docstring"""
def _snake_case ( lowercase__ : int = 1_0_0_0 ) -> int:
'''simple docstring'''
lowerCAmelCase_ , lowerCAmelCase_ :Optional[int] = 1, 1
lowerCAmelCase_ :Dict = 2
while True:
lowerCAmelCase_ :s... | 84 |
"""simple docstring"""
def _snake_case ( lowercase__ : list[int] ) -> list[list[int]]:
'''simple docstring'''
lowerCAmelCase_ :Optional[Any] = []
if len(lowercase__ ) == 1:
return [nums.copy()]
for _ in range(len(lowercase__ ) ):
... | 84 | 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,
convert_to_rgb,
get_resize_output_image_size,
normaliz... | 354 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
_U... | 53 | 0 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
UpperCamelCase = pytest.mark.integration
@pytest.mark.parametrize("""path""" ... | 306 |
from __future__ import annotations
import time
a =list[tuple[int, int]]
a =[
[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],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, ... | 73 | 0 |
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 import SequenceFeatureExtractio... | 290 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import H... | 290 | 1 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def _UpperCAmelCase ():
_A : int = HfArgumentParser(UpperCamelCase__ )
_A : Tuple = parser.parse_args_into_dataclasses()[0]
_A : Optional[Any... | 11 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_... | 11 | 1 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def __A (_SCREAMING_SNAKE_CASE ) ->Optional[Any]:
"""simple docstring"""
lowerCAmelCase__ :Dict = [
'encoder.ver... | 361 |
"""simple docstring"""
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processor... | 254 | 0 |
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