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 pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def UpperCAmelCase_ ( __lowercase : int , ... | 22 |
'''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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET... | 341 | 0 |
"""simple docstring"""
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
cach... | 203 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE ) ->bool:
return credit_card_number.startswith(('34', '35', '37', '4', '5', '6') )
def __a ( _SCREAMING_SNAKE_CASE ) ->bool:
a__: Any = credit_card_number
a__: Tuple ... | 203 | 1 |
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
lowercase__ :Optional[Any] = parse(importlib.metadata.version("torch"))
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase... | 101 |
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,_SCREA... | 48 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
'''tanreinama/GPTSAN-2.8B-spout_is_uniform''': (
'''https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json'''
),
}
class l... | 261 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
_A = logging.get_logger(__name__)
_A = {'''vocab_fil... | 261 | 1 |
"""simple docstring"""
from heapq import heappop, heappush
import numpy as np
def snake_case_ ( A_ : np.ndarray, A_ : tuple[int, int], A_ : tuple[int, int], A_ : bool, ):
'''simple docstring'''
_lowerCamelCase , _lowerCamelCase ... | 72 |
"""simple docstring"""
import math
def snake_case_ ( A_ : int ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, ... | 72 | 1 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def SCREAMING_SNAKE_CASE_ (UpperCamelCase ) -> str:
lowerCamelCase__ : str =... | 352 |
'''simple docstring'''
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentPar... | 129 | 0 |
class snake_case__ :
"""simple docstring"""
def __init__( self : Optional[Any] , __lowerCamelCase : int ) -> List[str]:
# we need a list not a string, so do something to change the type
a = arr.split("," )
def _... | 107 |
"""simple docstring"""
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
UpperCam... | 243 | 0 |
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_c... | 355 | import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
_UpperCAmelCase = logging.get_logger(__name__)
_Uppe... | 192 | 0 |
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, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils i... | 232 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
lowercase : Optional[Any] = TypeVar('T')
class lowerCamelCase__ ( Generic[T]):
'''simple docstring'''
_A = 42 # Cache store ... | 232 | 1 |
import fire
from utils import calculate_rouge, save_json
def lowerCamelCase_ ( lowerCAmelCase: List[Any] , lowerCAmelCase: int , lowerCAmelCase: Any=None , **lowerCAmelCase: List[Any] )-> Any:
_snake_case : int = [x.strip() for x in open(l... | 358 |
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATAS... | 260 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import DistilBertConfig, 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 j... | 83 |
"""simple docstring"""
UpperCamelCase_ = {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
UpperCamelCase_ = ['a', 'b', 'c', 'd', 'e']
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) ->Optional[int]:
"""simple docstring"""
... | 243 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE :Optional[Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE :Optional[int] = {
'RWKV/rwkv-4-169m-pile': 'https://huggingface.co/RWKV/rwkv-4-1... | 359 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : int , __lowercase : Optional[int] , __lowercase : List[Any] , __lowercase : int ) -> Tuple:
'''simple docstring'''
_UpperCAmelCase = [False] * l... | 156 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
def lowerCAmelCase_ ( snake_case__ , snake_case__ , snake_case__ ):
'''simple docstring'''
A : Optional[int] = namedtuple('''result''' ,... | 3 |
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
cl... | 240 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load... | 245 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ :Union[str, Any] = logging.get_logger(__name__)
A_ :Tuple = {
'''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/config.json''',
'''x... | 245 | 1 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class a_ (unittest.TestCase ):
def ... | 309 |
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn
... | 280 | 0 |
"""simple docstring"""
import math
import unittest
def __lowerCamelCase ( a_ : int ) -> bool:
assert isinstance(a_ , a_ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
... | 239 |
"""simple docstring"""
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class _SCREAMING_SNAKE_CASE( A ):
... | 239 | 1 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipeline... | 112 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCamelCase = {
'''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}
try:
if n... | 254 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ : Union[str, Any] = {
"""config... | 318 |
"""simple docstring"""
from itertools import permutations
def _A (__a ) -> bool:
"""simple docstring"""
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
SCREAMING_SNAK... | 318 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
'andreasmadsen/efficient_mlm_... | 65 |
def lowerCAmelCase__ ( lowerCamelCase_ : str):
'''simple docstring'''
lowerCAmelCase__ : Any = [0] * len(lowerCamelCase_)
for i in range(1 ,len(lowerCamelCase_)):
# use last results for better performance - dynamic programming
lowerCAmelCase__ ... | 129 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
a_ : List[Any] = logging.get_logger(__name__)
class __UpperCamelCase ( lowerCamelCase__ ):
def __init__( self, *lowerCAmelCase, **... | 6 |
'''simple docstring'''
from itertools import product
def a_ ( __snake_case : int , __snake_case : int ) -> list[int]:
"""simple docstring"""
lowerCamelCase_ =sides_number
lowerCamelCase_ =max_face_number * dice_... | 6 | 1 |
"""simple docstring"""
import argparse
import os
import re
__a = 'src/diffusers'
# Pattern that looks at the indentation in a line.
__a = re.compile(r"^(\s*)\S")
# Pattern that matches `"key":" and puts `key` in group 0.
__a = re.compile(r"^\s*\"([^\"]+)\":")
# Pattern that matches `_impo... | 66 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, requir... | 192 | 0 |
"""simple docstring"""
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import data... | 215 |
"""simple docstring"""
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
... | 215 | 1 |
"""simple docstring"""
from __future__ import annotations
def _A ( UpperCamelCase_ : list[int]) -> int:
'''simple docstring'''
if not nums:
return 0
__lowercase = nums[0]
__lowercase = 0
for num in nums[1:]:
__lowercase ,__lowercase ... | 17 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : Union[str, Any] = logging.get_logger(__name__)
__A : Dict ... | 260 | 0 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler... | 353 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepie... | 25 | 0 |
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStructTextConfig,
... | 50 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCAmelCase : str = {
"configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"],
}
try:
if not is_torch_available():
raise... | 156 | 0 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase , lowerCamelCase_ ):
def UpperCamelCase ( self ):
A__ = load_tool('''text-classification'... | 366 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__: List[Any] = logging.get_logger(__name__)
a__: Optional[Any] = {
'microsoft/unispeech-large-1500h-cv': (
'https://huggingface.co/microsoft/unispeec... | 39 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {
'configuration_time_series_transformer': [
'TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Time... | 84 |
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
... | 245 | 0 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before toke... | 361 |
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 ...test_image_processing_common import ImageProcessingSavi... | 223 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_device... | 89 | 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... | 343 | 0 |
import inspect
import unittest
import numpy as np
from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available
from ...test_configuration_common import ConfigTester
... | 315 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def a ( SCREAMING_SNAKE_CASE_ : str ):
"""simple docstring"""
return "".join(sorted(SCREAMING_SNAKE_CASE_ ) )
def a ( SCREAMIN... | 315 | 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,
)
__lowercase : int = {
'''configuration_blenderbot''': [
... | 318 |
'''simple docstring'''
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_r... | 318 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_t... | 362 |
import math
def __UpperCamelCase ( _A : int = 100 ) ->int:
"""simple docstring"""
lowerCamelCase_ =sum(i * i for i in range(1 , n + 1 ) )
lowerCamelCase_ =int(math.pow(sum(range(1 , n + 1 ) ) , 2 ... | 49 | 0 |
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
A : Tuple = logging.get_logger(__name__)
class __A( a ):
def __init__( self , *_snake_case , **_snake_case ) -> None:
'''simple docstring'''
... | 6 |
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
A : List[str] = logging.get_logger(__name__)
A : Optional[int] = {
'facebook/levit-128S': '... | 6 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"facebook/xmod-base":... | 26 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase__ ... | 26 | 1 |
'''simple docstring'''
import argparse
import json
import subprocess
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> Optional[int]:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = []
_UpperCAmelCase : Optional[Any] ... | 215 |
'''simple docstring'''
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from tr... | 215 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowercase = {
"""configuration_rag""": ["""RagConfig"""],
"""retrieval_rag""": ["""RagRetriever"""],
"""tokenization_rag""": ["""RagTokenizer"""],
... | 370 | from __future__ import annotations
class __lowercase :
'''simple docstring'''
def __init__( self : List[Any] , _a : int ):
UpperCamelCase__ = data
UpperCamelCase__ = None
UpperCamelCase__ = ... | 35 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCamelCase : int = {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}
try:
if not is_vision_available()... | 47 |
"""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, ... | 25 | 0 |
def a__ ( lowerCAmelCase , lowerCAmelCase ) -> str:
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
UpperCAmelCase__ : Optional[Any] = str(bin(lowerCAmelCase ) )[2:] # remove the leading "0b"
UpperCAmelCase__ : str... | 358 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from ... | 166 | 0 |
'''simple docstring'''
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''': P... | 37 |
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 ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set... | 39 | 0 |
'''simple docstring'''
from __future__ import annotations
from random import choice
def __lowerCamelCase ( __lowerCAmelCase : Optional[int] ) -> str:
return choice(__lowerCAmelCase )
def __lowerCamelCase ( __lowerCAmelCase :... | 3 |
'''simple docstring'''
def __lowerCamelCase ( __lowerCAmelCase : Dict ) -> Optional[Any]:
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
... | 3 | 1 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( A_ ):
lowerCAmelCase__ : Union[str, Any] = len(A_ )
for i in range(length - 1 ):
lowerCAmelCase__ : Tuple = i
for k in range(i + 1 , A_ ):
if collection[k] < collection[least]:
lowerCAmelCase__ : Optional[i... | 106 |
"""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_comm... | 255 | 0 |
def UpperCamelCase_( lowerCamelCase_ ) -> Optional[Any]:
# if the collection is empty, returns empty
if collection == []:
return []
# get some information about the collection
_lowercase : str = len(lowerCamelCase_ )
_lowercase : Optional[int] = ... | 84 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
SCREAMING_SNAKE_CASE : Optional[int] = {
"configuration_altclip": [
"ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"AltCLIPConfig",
"AltC... | 84 | 1 |
"""simple docstring"""
import inspect
import unittest
import numpy as np
from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available
from ...test_configuration_common import ... | 315 |
"""simple docstring"""
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDepe... | 315 | 1 |
from __future__ import annotations
snake_case__ : List[str] = list[tuple[int, int]]
snake_case__ : str = [
[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... | 250 |
def _a ( lowerCamelCase: str ) -> bool:
'''simple docstring'''
__A = [int(lowerCamelCase ) for i in ip_va_address.split('''.''' ) if i.isdigit()]
return len(lowerCamelCase ) == 4 and all(0 <= int(lowerCamelCase ) <= 2_5... | 250 | 1 |
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A : int = logging.get_logger(__name__)
A : Union[str, Any] = '''▁'''
A : List[str] = {'''vocab_file''... | 6 |
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def __snake_case ( _UpperCAmelCase = "isbn/0140328726" ):
__a = olid.strip().strip('''/''' ) # Remove leading/trailing whitespace & slashes
if new_olid.count('''/''' )... | 49 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class a ( __lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase :List[str] = ['''image_processor''', '''tokenizer''']
lowerCamelC... | 81 | import colorsys
from PIL import Image # type: ignore
def snake_case ( snake_case__ :float , snake_case__ :float , snake_case__ :int) -> float:
_A = x
_A = y
for step in range(snake_case__): # noqa: B007
... | 81 | 1 |
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
FEATU... | 26 |
from math import asin, atan, cos, radians, sin, sqrt, tan
_snake_case = 6_3_7_8_1_3_7.0
_snake_case = 6_3_5_6_7_5_2.3_1_4_2_4_5
_snake_case = 6378137
def lowerCAmelCase_ ( snake_case_,snake_case_,snake_case_,snake_case_ ):
_A : Any ... | 26 | 1 |
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TE... | 39 |
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 SCREAMING_SNAKE_CASE__ ( tf.keras.optimizers.sched... | 39 | 1 |
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
A : Optional[int] = logging.get_logger(__name__)
class _lowercase ( _a):
"""simple docstring"""
def __init__( self : ... | 184 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaV... | 35 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenizati... | 352 | """simple docstring"""
import unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow
if is_flax_available():
import jax
from transformers.models.auto.modeling_flax_auto import FlaxAu... | 203 | 0 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx... | 273 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase = {
"""configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
"""feature_extraction_mctct""": ["""MCT... | 166 | 0 |
'''simple docstring'''
def _lowerCAmelCase ( _UpperCamelCase : int = 60_08_51_47_51_43 ) -> int:
"""simple docstring"""
try:
_SCREAMING_SNAKE_CASE =int(_UpperCamelCase )
except (TypeError, ValueError):
raise TypeError('Parameter n must be in... | 114 |
'''simple docstring'''
from math import factorial
def _lowerCAmelCase ( _UpperCamelCase : int , _UpperCamelCase : int , _UpperCamelCase : float ) -> float:
"""simple docstring"""
if successes > trials:
raise ValueError('successes mu... | 114 | 1 |
'''simple docstring'''
from __future__ import annotations
from random import choice
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
return choice(snake_case__ )
def lowerCAmelCase_ ( snake_case__ , snake_case__ ... | 3 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resiz... | 3 | 1 |
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
__A = logging.get_logger(__name__)
class __lowerCAmelCase ( UpperCamelCase_ ):
"""simple docstring"""
def __init__( self , *lowerCamelCase__ , **lowerCam... | 362 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"asapp/sew-d-tiny-100k": "https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json",
# See all SEW-D models... | 348 | 0 |
"""simple docstring"""
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :Dict = (PNDMScheduler,)
UpperCAmelCase_ :Tuple = (("n... | 84 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCAmelCase = {
'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
'token... | 84 | 1 |
from __future__ import annotations
_UpperCamelCase = []
def UpperCamelCase_( snake_case__: list[list[int]] , snake_case__: int , snake_case__: int ) -> bool:
for i in range(len(snake_case__ ) ):
if board[row][i] == 1:
return False
for i in range(len(... | 335 |
class lowercase : # Public class to implement a graph
'''simple docstring'''
def __init__(self , __a , __a , __a ) -> None:
"""simple docstring"""
UpperCAmelCase__ = row
UpperCAmelCase__ = col
UpperCAmelCase__ = graph
... | 335 | 1 |
'''simple docstring'''
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.ut... | 250 |
'''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,
normalize,
rescale,
resi... | 250 | 1 |
import re
import tempfile
from pathlib import Path
import pytest
import yaml
from datasets.utils.readme import ReadMe
# @pytest.fixture
# def example_yaml_structure():
lowercase : Optional[int] = yaml.safe_load(
"""\
name: \"\"
allow_empty: false
allow_empty_text: true
subsections:
- nam... | 225 |
def A_ ( A__ ) -> List[str]: # noqa: E741
a__ : Dict = len(A__ )
a__ : str = 0
a__ : Any = [0] * n
a__ : int = [False] * n
a__ : Optional[Any] = [False] * n
def dfs(A__ , A__ , A_... | 225 | 1 |
"""simple docstring"""
def _A ( lowercase , lowercase ):
"""simple docstring"""
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(lowercase , int(b / 2 ) ) * actual_power(lowercase , int(b / 2 ) )
e... | 81 |
"""simple docstring"""
lowerCamelCase_ : Any = [
"""DownloadConfig""",
"""DownloadManager""",
"""DownloadMode""",
"""StreamingDownloadManager""",
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .st... | 81 | 1 |
"""simple docstring"""
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'... | 367 |
"""simple docstring"""
def lowerCamelCase (a_ :int) -> None:
lowercase :Tuple = generate_pascal_triangle(a_)
for row_idx in range(a_):
# Print left spaces
for _ in range(num_rows - row_idx - 1):
print(end=''' ''')
... | 172 | 0 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_a = logging.get_logger(__name__)
_a = {
'''post_extract_proj''': '''feature_projection.projectio... | 39 |
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
_a = logging.get_logger(__name__)
def __A ( ... | 39 | 1 |
"""simple docstring"""
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils ... | 362 |
"""simple docstring"""
from math import factorial
lowerCamelCase_ = {str(d): factorial(d) for d in range(10)}
def snake_case ( A__ ):
return sum(DIGIT_FACTORIAL[d] for d in str(A__ ) )
def snake_case ( ):
UpperCAmelCase_ : int = 7 * fac... | 253 | 0 |
'''simple docstring'''
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from ... | 34 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
"""facebook/xmod-base""": """ht... | 203 | 0 |
"""simple docstring"""
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available()... | 365 |
"""simple docstring"""
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from tra... | 320 | 0 |
from collections.abc import Iterable
from typing import Any
class a :
"""simple docstring"""
def __init__( self : Dict , __lowercase : int | None = None ) -> Dict:
__UpperCAmelCase : Union[str, Any] = value
... | 114 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Optional[int] = logging.get_logger(__name__)
a : List[Any] = {
"facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/config.json",
# See all XGLM mode... | 114 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import t... | 360 |
from maths.prime_factors import prime_factors
def _A ( lowerCAmelCase_ : int ):
"""simple docstring"""
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
lowerCAmelCase__ = F'Input value of [number={number}] must be an int... | 221 | 0 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase = 200 ) -> int:
'''simple docstring'''
snake_case_ = [1, 2, 5, 10, 20, 50, 100, 200]
snake_case_ = [0] * (pence + 1)
snake_case_ = 1 # base case: 1 way to make 0 pence
for coin in c... | 56 | from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def lowerCAmelCase_ ( __lowerCAmelCase )-> Optional[Any]:
'''simple docstring'''
def is_in_circle(__lowerCAmelCase , __lowerCAmelCase ) -> bool:
... | 348 | 0 |
"""simple docstring"""
from pathlib import Path
import numpy as np
from PIL import Image
def _A (__a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b
de... | 366 |
"""simple docstring"""
UpperCAmelCase_ : List[Any] = 9.8_0_6_6_5
def _A (__a , __a , __a = g ) -> float:
"""simple docstring"""
if fluid_density <= 0:
raise ValueError('''Impossible fluid density''' )
if volume < 0:
ra... | 318 | 0 |
"""simple docstring"""
from __future__ import annotations
SCREAMING_SNAKE_CASE_ : Optional[Any] = []
def _snake_case ( UpperCAmelCase_ : list[list[int]] , UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
for i in range(len(Up... | 335 |
"""simple docstring"""
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class a ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase ( self: str ):
"""simple doc... | 335 | 1 |
"""simple docstring"""
from __future__ import annotations
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> list:
SCREAMING_SNAKE_CASE__ : List[str] = []
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CA... | 56 |
"""simple docstring"""
from __future__ import annotations
from PIL import Image
# Define glider example
a :str = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0... | 56 | 1 |
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
__lowerCAmelCase : Dict =logging.get_logger(__name__)
def _UpperCamelCase ( lowercase__ , lowercase__ ):
__SCREAMING_SNAKE_CASE : ... | 9 |
'''simple docstring'''
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorT... | 251 | 0 |
from __future__ import annotations
class _snake_case :
def __init__( self , a = 0) -> Optional[Any]:
SCREAMING_SNAKE_CASE = key
def SCREAMING_SNAKE_CASE__ ( self , a , a) -> list[str]:
assert isinstance(a , a) and i... | 363 |
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_availa... | 327 | 0 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase_ ( A_ ):
lowercase__ = ["""image_processor""", """tokenizer"""]
lowercase__ = """CLIPImageProces... | 247 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_a : str= {
"configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
"tokenization_xlm":... | 172 | 0 |
"""simple docstring"""
# 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
#
... | 359 |
"""simple docstring"""
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu,... | 182 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_b... | 312 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
__a :List[str] = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
_... | 312 | 1 |
'''simple docstring'''
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
__lowercase... | 367 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE__( _UpperCamelCase : str ) -> bool:
'''simple docstring'''
return credit_card_number.startswith(("34", "35", "37", "4", "5", "6") )
def SCREAMING_SNAKE_CASE__( _UpperCamelCase : str ) ... | 31 | 0 |
'''simple docstring'''
import requests
UpperCamelCase__ : Dict = 'YOUR API KEY'
def UpperCAmelCase ( a_ , a_ = giphy_api_key ) -> Tuple:
"""simple docstring"""
A_ : Any = """+""".join(query.split() )
A_ : ... | 344 |
"""simple docstring"""
import warnings
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 c... | 320 | 0 |
"""simple docstring"""
# 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
#
# U... | 126 | """simple docstring"""
import socket
def SCREAMING_SNAKE_CASE ( ) -> str:
_lowerCAmelCase : Optional[int] = socket.socket(socket.AF_INET ,socket.SOCK_STREAM )
_lowerCAmelCase : Optional[int] = socket.gethostname()
_lowerCAmelCase : Tuple ... | 126 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
UpperCAmelCase__ = {"tokenization_herbert": ["HerbertTokenizer"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalD... | 339 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
UpperCAmelCase__ = {
"tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32acce... | 339 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase : str = logging.get_logger(__name__)
__UpperCAmelCase : Tuple = {
"google/vivit-b-16x2-kinetics400": (
"https://huggingface.co/google/vivit-b-16x2-kinetics400/reso... | 367 |
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...t... | 293 | 0 |
class A :
'''simple docstring'''
def __init__(self : Any , _UpperCAmelCase : str = "" , _UpperCAmelCase : bool = False ) -> None:
"""simple docstring"""
lowercase__ = {}
# A node will ... | 305 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseS... | 305 | 1 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
snake_case_ : List[Any] = logging.get_logger(__name__)
snake_case_ : Optional[int] = {
"vocab_file": "vocab.json",
"... | 350 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
snake_case_ : Dict = "\\n@inproceedings{snover-etal-2006-study,\n title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",\n author = \"Snover, Matthew and\n Dorr, ... | 7 | 0 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common imp... | 56 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simp... | 56 | 1 |
"""simple docstring"""
import os
from datetime import datetime as dt
from github import Github
_A = [
"""good first issue""",
"""feature request""",
"""wip""",
]
def a__ ( ) -> List[str]:
UpperCAmelCase__ : Any = Github(os.environ["""GITHUB_TOKEN"""] ... | 350 |
"""simple docstring"""
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = (DDIMParallelScheduler,)
SCREAMING_SNAKE_CASE = ... | 166 | 0 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from ... | 333 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class A_ ( _a ):
'''simple docstring'''
a__ = (IPNDMScheduler,)
a__ = (("num_inference_steps", 50),)
def lower... | 333 | 1 |
'''simple docstring'''
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.au... | 368 |
'''simple docstring'''
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBe... | 174 | 0 |
class __A :
"""simple docstring"""
def __init__( self , lowerCamelCase__ ):
"""simple docstring"""
__UpperCamelCase : str =set_counts
__UpperCamelCase : Optional[Any] =max(lowerCam... | 71 | import itertools
import string
from collections.abc import Generator, Iterable
def A ( _lowercase , _lowercase ):
SCREAMING_SNAKE_CASE : Union[str, Any] = iter(_lowercase )
while True:
SCREAMING_SNAKE_CASE : Optional[Any] = tup... | 182 | 0 |
'''simple docstring'''
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weight... | 311 |
'''simple docstring'''
from __future__ import annotations
from random import random
class A :
def __init__( self , SCREAMING_SNAKE_CASE = None ) -> Tuple:
"""simple docstring"""
A : Optional[Any] = ... | 311 | 1 |
"""simple docstring"""
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from trans... | 291 | '''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : List[Any] = {
"""facebook/en... | 31 | 0 |
"""simple docstring"""
import requests
def lowerCamelCase (a_ :str , a_ :str) -> None:
lowercase :str = {'''Content-Type''': '''application/json'''}
lowercase :Any = requests.post(a_ , json={'''text''': message_body} , headers=a_)
... | 172 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCAmelCase = logging.get_logger(__name__)
# TODO: upload to AWS
UpperCAmelCase = {
'''yjernite/retribert-base-uncased''': (
'''https://huggingface.co/yjernite/retribert-... | 172 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase = {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig""... | 126 |
"""simple docstring"""
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase = get_test... | 126 | 1 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : List[Any] = logging.get_logger(__name__)
a__ : Tuple = {
'''Salesforce/blip-vqa-base''': '''https... | 195 |
"""simple docstring"""
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = [0 for i in range(r + 1 )]
# nc0 = 1
__SCREAMING_SNAKE_CASE = 1
for i in range(1 , n + 1... | 195 | 1 |
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
UpperCAmelCase__ : str = logging.getLogger()
def lowerCamelCase__ ( ... | 121 |
"""simple docstring"""
from __future__ import annotations
__A = tuple[int, int, int]
__A = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
__A = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
# -------------------------- default selection -... | 293 | 0 |
'''simple docstring'''
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class lowerCAmelCase__ ( UpperCAmelCase__ ):
def __init__... | 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"""
def __lowerCamelCase ( a_ : float , a_ : float , a_ : float , a_ : float , a_ : float , ) -> float:
__SCREAMING_SNAKE_CASE :Dict = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for ... | 191 |
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 _snake_case( SCREAMING_SNAKE... | 7 | 0 |
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 (
BitConfig,
ViTHybridCon... | 362 | import argparse
from collections import defaultdict
import yaml
lowerCAmelCase__ = 'docs/source/en/_toctree.yml'
def __lowerCamelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = defaultdict(lowerCAmelCase__ )
for doc in model_doc:
counts[doc["loca... | 119 | 0 |
def _snake_case ( lowerCAmelCase : list ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = len(lowerCAmelCase )
for i in range(1 , lowerCAmelCase ):
SCREAMING_SNAKE_CASE_ : int = collection[i]
SCREAMING_SNAKE_CASE_ : Any = 0
SCRE... | 18 |
'''simple docstring'''
import numpy
# List of input, output pairs
lowerCamelCase = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
lowerCamelCase = (((515, 22, 13), 555), ((61, 35, 49), 150))
lowerCamelCase = [2, ... | 166 | 0 |
"""simple docstring"""
import random
def _lowerCamelCase(__UpperCamelCase ) -> bool:
_lowerCAmelCase =num - 1
_lowerCAmelCase =0
while s % 2 == 0:
_lowerCAmelCase =s // 2
t += 1
for _ in range(5 ):
_lowerCAmelCase =random.randrange(2 , num - 1 )... | 341 |
"""simple docstring"""
from __future__ import annotations
def _lowerCamelCase(__UpperCamelCase ) -> bool:
_lowerCAmelCase =str(__UpperCamelCase )
return n == n[::-1]
def _lowerCamelCase(__UpperCamelCase = 1000000 ) -> str:
_lowerCAmelCase =0
for i in range(1 ... | 341 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCamelCase__: Optional[Any] = logging.get_logger(__name__)
UpperCamelC... | 23 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase : Optional[Any] = {
"""config... | 174 | 0 |
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"""kwargs, expected""" , [
({"""num_shards""": 0, """max_num_jobs""": 1}, []),
({"""num_shards""": 1_0, """max_num_jobs""": 1}, [range(1_... | 20 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase_ = {
"configuration_blenderbot_small": [
"BLENDERBOT_SMALL_PRETRAINED_CONFIG_... | 20 | 1 |
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