code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import 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 __magic_name__ ( ... | 298 | from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimensio... | 666 | 0 |
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_simplify,
require_pytesseract... | 636 | 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 (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
ViltForMaskedLM,
ViltForQu... | 636 | 1 |
SCREAMING_SNAKE_CASE : Union[str, Any] = tuple[float, float, float]
SCREAMING_SNAKE_CASE : Dict = tuple[float, float, float]
def __A ( _A , _A ):
"""simple docstring"""
__a = end_pointa[0] - end_pointa[0]
__a = end_pointa[1] - end_pointa[... | 197 | from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Any = """T5Config"""
class A_ ( a... | 197 | 1 |
'''simple docstring'''
def __UpperCAmelCase ( lowerCamelCase_ : str ) -> int:
"""simple docstring"""
return "".join(chr(ord(_SCREAMING_SNAKE_CASE ) - 32 ) if 'a' <= char <= 'z' else char for char in word )
if __name__ == "__main__":
from doctes... | 707 |
class lowerCAmelCase_ ( lowerCamelCase_ ):
pass
class lowerCAmelCase_ ( lowerCamelCase_ ):
pass
class lowerCAmelCase_ :
def __init__( self ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = [
[],
... | 685 | 0 |
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_cast
from datasets.utils.p... | 428 |
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class A_ ( __a , ... | 428 | 1 |
from math import factorial
def __UpperCamelCase ( _A : int , _A : int , _A : float ) ->float:
"""simple docstring"""
if successes > trials:
raise ValueError("""successes must be lower or equal to trials""" )
if trials <... | 75 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__A : Tuple = {'configuration_reformer': ['REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ReformerConfig']}
try:
... | 75 | 1 |
"""simple docstring"""
def a ( __UpperCAmelCase : int = 1_0_0_0 ) -> int:
__magic_name__: int = 2**power
__magic_name__: Any = 0
while n:
__magic_name__, __magic_name__: int = r + n % 1_0, n // 1_... | 96 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common imp... | 397 | 0 |
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 FlaxAutoModel
from... | 23 |
import math
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> list[int]:
UpperCAmelCase_ = []
UpperCAmelCase_ = 2
UpperCAmelCase_ = int(math.sqrt(__SCREAMING_SNAKE_CASE ) ) # Size of every segment
UpperCAmelCase_ = [True] * (end + 1)
UpperCAmelCase_ = ... | 23 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transforme... | 604 | import argparse
from collections import defaultdict
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase )-> List[Any]:
"""simple docstring"""
lowercase = f'{file}_{class_name}_{test_name... | 604 | 1 |
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
"RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
}
class _a ... | 707 |
"""simple docstring"""
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
UpperCamelCase = {
"""susnato/ernie-m-base_pytorch""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json""",
"""susnato/ernie-m-l... | 120 | 0 |
"""simple docstring"""
from torch import nn
def a_ ( __a ):
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueErr... | 571 |
"""simple docstring"""
def a_ ( __a ):
assert (
isinstance(__a , __a ) and number_of_steps > 0
), f'''number_of_steps needs to be positive integer, your input {number_of_steps}'''
if number_of_steps == 1:
return 1
A__ , A__ ... | 571 | 1 |
'''simple docstring'''
class lowerCAmelCase_:
'''simple docstring'''
def __init__( self ,__UpperCAmelCase ) -> List[str]:
lowerCAmelCase__ : Optional[Any] = val
lowerCAmelCase__ : Optional[int] = None
lowerCAmelCase__ : ... | 709 |
'''simple docstring'''
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCAmelCase =... | 160 | 0 |
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( ) -> i... | 108 |
import os
import sys
__a: Union[str, Any] = os.path.join(os.path.dirname(__file__), '''src''')
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassi... | 108 | 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_doc... | 103 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...tes... | 103 | 1 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase ) -> int:
"""simple docstring"""
__UpperCAmelCase : list[list[int]] = [[0 for _ in range(UpperCamelCase )] for _ in range(m + 1 )]
for i in range(m + 1 ):
__UpperCAmelCase... | 77 |
"""simple docstring"""
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_mode... | 77 | 1 |
from collections.abc import Sequence
def _snake_case (_snake_case : Sequence[int] | None = None) -> int:
if nums is None or not nums:
raise ValueError('Input sequence should not be empty')
_lowercase =nums[0]
for i in range(1 , len(_snake_case)):
... | 720 |
def _snake_case (_snake_case : str , _snake_case : str) -> float:
def get_matched_characters(_snake_case : str , _snake_case : str) -> str:
_lowercase =[]
_lowercase =min(len(_stra) , len(_stra)) // 2
... | 557 | 0 |
import os
from math import logaa
def __a ( __UpperCAmelCase = "base_exp.txt" ):
a__ = 0
a__ = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(__UpperCAmelCase ) , __UpperCAmelCase ) ) ):
a__ , a__ = list(m... | 194 |
from __future__ import annotations
import requests
def __a ( __UpperCAmelCase ):
a__ = f"https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"
return requests.get(__UpperCAmelCase ).json()
def __a ( __UpperCAmelCase = 10 ):
a__ ... | 194 | 1 |
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def _UpperCamelCase ( UpperCamelCase_ : List[Any] ) -> List[Any]:
"""simple docstring"""
if not is_accelerate_available():
... | 710 |
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
from transformers impor... | 365 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : Optional[int] = logging.get_logger(__name__)
__lowerCamelCase : List[str] = {
"google/vivit-b-16x2-kinetics400": (
"https://huggingface.co/google/vivit-b-16x2... | 404 |
'''simple docstring'''
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def UpperCAmelCase_ ( A , A , A = 1 / sqrt(2 ) ):
'''simple docstring'''
_a : List[Any] = tau * frequency / samplerate
_a : Tuple = sin(A )
... | 120 | 0 |
# coding=utf-8
# Copyright 2023 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 applica... | 650 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applic... | 650 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ = {
'configuration_luke': ['LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LukeConfig'],
'tokenization_luke': ['LukeTokenizer']... | 560 |
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
fr... | 492 | 0 |
"""simple docstring"""
lowercase__ = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def __magic_name__ ( ):
__a : Dict = input("""Enter message: """ )
__a : Union[str, Any] = input("""Enter key [alphanumeric]: """ )
__a : Optional[An... | 718 |
"""simple docstring"""
import os
def __magic_name__ ( _lowerCamelCase : Dict ):
__a : List[str] = len(grid[0] )
__a : int = len(_lowerCamelCase )
__a : Tuple = 0
__a : List[Any] = ... | 63 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelin... | 534 | import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__versi... | 534 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ : str = logging.get_logger(__name__)
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = 'timm_backbone'
def __in... | 660 |
"""simple docstring"""
import math
def a__ ( lowerCAmelCase : 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, 1, all even numbers, all mul... | 660 | 1 |
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
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
snake_ca... | 335 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
snake_case : str = logging.get_logger(__name__)
snake_case : List[str] = {
'''SenseTime/deformable-detr''': '''https://huggingface.co/sensetime/deformable-detr/re... | 335 | 1 |
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_v... | 704 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( snake_case_ = 100 ):
_lowercase = set()
_lowercase = 0
_lowercase = n + 1 # maximum limit
for a in range(2 , snake_case_ ):
for b in range(2 , snake_case_ ):
_lowercase = a**b # calculat... | 572 | 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
#... | 578 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase__ : int = {
'''configuration_poolformer''': [
'''POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
... | 578 | 1 |
'''simple docstring'''
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,
)
... | 721 |
from __future__ import annotations
def lowerCAmelCase_ ( __UpperCAmelCase: str , __UpperCAmelCase: str ) -> bool:
UpperCamelCase__ : List[str] = get_failure_array(__UpperCAmelCase )
# 2) Step through text searching for pattern
UpperCam... | 369 | 0 |
"""simple docstring"""
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse('3.8'):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
SCREA... | 34 |
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
impor... | 176 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'''unc-nlp/lxmert-base-uncased''': '''https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json''',
}
... | 701 |
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import Aut... | 569 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, t... | 400 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=snake_case__ )
class __UpperCAmelCase ( snake_case__ ):
"""simple docstring"""
_snake_case :... | 505 | 0 |
'''simple docstring'''
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_mod... | 701 |
'''simple docstring'''
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import loggi... | 220 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a =logging.get_logger(__name__)
a ={
"""shi-labs/dinat-mini-in1k-224""": """https://huggingface.co/shi-labs/di... | 652 |
import sys
lowerCAmelCase_ = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'6689664895044524452316173185... | 217 | 0 |
'''simple docstring'''
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...tes... | 718 |
import math
SCREAMING_SNAKE_CASE : List[str] = 10
SCREAMING_SNAKE_CASE : Dict = 7
SCREAMING_SNAKE_CASE : int = BALLS_PER_COLOUR * NUM_COLOURS
def UpperCamelCase ( _a = 2_0 ) -> str:
'''simple docstring'''
... | 441 | 0 |
'''simple docstring'''
import math
def __lowercase ( __lowercase = 100 ) -> int:
'''simple docstring'''
_A = sum(i * i for i in range(1 , n + 1 ) )
_A = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of_sum - sum... | 330 |
'''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_docstrings, add_star... | 330 | 1 |
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimen... | 387 | import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common impor... | 387 | 1 |
from __future__ import annotations
def __snake_case ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> int | float:
if len(lowerCAmelCase_ ) == 0:
raise ValueError('''find_max() arg is an empty sequence''' )
if (
left >= len(lowerCAmelCase_ )
... | 100 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, r... | 461 | 0 |
"""simple docstring"""
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_c... | 710 |
"""simple docstring"""
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base impo... | 518 | 0 |
"""simple docstring"""
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils impor... | 586 | """simple docstring"""
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrateg... | 586 | 1 |
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
_UpperCAmelCase = 'https://www.indeed.co.in/jobs?q=mobile+app+development&l='
def lowerCAmelCase_ ( UpperCamelCase_ = "mumbai" ) -> Generator[tuple[str, str], None, ... | 717 |
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 _UpperCamelCase ( lowerCAmelCase_ , unittest.TestCase ... | 371 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__lowerCamelCase : Dict = {
... | 653 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProce... | 653 | 1 |
"""simple docstring"""
# Function to print upper half of diamond (pyramid)
def _lowerCAmelCase ( lowerCamelCase__ : Optional[int] ) -> Optional[int]:
for i in range(0, lowerCamelCase__ ):
for _ in range(0, n - i - 1 ): # printing spaces
print(" ... | 295 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
lowercase_ : Union[str, Any] = logging.get_logger(__name__)
class UpperCamelCase ( __SCREAMING_SNAKE_CASE ):
def __init__( self ... | 295 | 1 |
import os
from collections.abc import Iterator
def __lowerCAmelCase ( A = "." ):
for dir_path, dir_names, filenames in os.walk(_SCREAMING_SNAKE_CASE ):
UpperCAmelCase_ = [d for d in dir_names if d != "scripts" and d[0] not in "._"]
for filename in filenames:
if filename == "... | 162 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compose,
Normalize,
... | 235 | 0 |
import functools
def snake_case__ ( UpperCAmelCase : str , UpperCAmelCase : str ):
lowerCAmelCase__ :Any = len(UpperCAmelCase )
lowerCAmelCase__ :List[Any] = len(UpperCAmelCase )
@functools.cache
... | 712 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils im... | 111 | 0 |
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_... | 545 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
snake_case : Dict ... | 545 | 1 |
"""simple docstring"""
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHEC... | 718 |
"""simple docstring"""
import math
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __SCREAMING_SNAKE_CASE ( self : Any , __a : list[list[float]] , __a : list[int] ) -> int:
_UpperCamelCase : List[Any] = 0.0
... | 51 | 0 |
from ...processing_utils import ProcessorMixin
class snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = ["""image_processor""", """feature_extractor"""]
_SCREAMING_SNAKE_CASE = """TvltImageProcessor"""
_SCREAMING_SNAKE_C... | 478 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a_ :List[Any] = {"configuration_unispeech": ["UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP", "UniSpeechConfig"]}
try:
if not is_torc... | 478 | 1 |
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from transformers import DPRC... | 53 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
def snake_case ( lowerCamelCase ... | 53 | 1 |
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
UpperCAmelCase__ : Dict = logging.get_logger(__name__)
class UpperCamelCase_ ( __UpperCamelCase ):
'''simple docstring'''
def __init__( self , ... | 410 |
import math
def _lowercase ( __SCREAMING_SNAKE_CASE ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All pr... | 410 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Union[str, Any] ... | 629 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : str = {
"""huggingface/informer-tourism-monthly""": (
... | 629 | 1 |
'''simple docstring'''
def _UpperCamelCase ( __UpperCamelCase ) -> tuple[int, int]:
try:
lowerCamelCase_ = float(__UpperCamelCase )
except ValueError:
raise ValueError('Please enter a valid number' )
lowerCamelCase_ = decimal - int(__UpperCamelCas... | 42 |
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, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
import ... | 70 | 0 |
"""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
lowerCAmelCase__ =logging.get_logger(... | 690 |
"""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
if is_torc... | 690 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ... | 69 |
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/confi... | 625 | 0 |
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
_a : List[str] = object()
# For specifying empty leaf dict `{}`
_a : int = object()
def UpperCamelC... | 571 |
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
_a : List[str] = datasets.logging.get_logger(__name__)
_a : Optional[Any] = '\\n@inproceedings{bleurt,\n title={BLEURT: Learning Robust Metrics for Text Generation... | 571 | 1 |
'''simple docstring'''
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def lowercase_ ( __A : Tuple ) -> Any:
"""simple docstring"""
lowercase : int =[]
l... | 94 |
'''simple docstring'''
import re
def _a (__SCREAMING_SNAKE_CASE ):
"""simple docstring"""
return [char.split() for char in re.split(r'''[^ a-z A-Z 0-9 \s]''' , str_ )]
def _a (__SCREAMING_SNAKE_CASE ):
"""simple docstring"""
_UpperCamelCase =split_input(str_ )
... | 404 | 0 |
lowercase_ = {
"""A""": """.-""", """B""": """-...""", """C""": """-.-.""", """D""": """-..""", """E""": """.""", """F""": """..-.""", """G""": """--.""",
"""H""": """....""", """I""": """..""", """J""": """.---""", """K""": """-.-""", """L""": """.-..""", """M""": """--""", """N""": """-... | 45 |
from string import ascii_uppercase
lowercase_ = {str(ord(c) - 55): c for c in ascii_uppercase}
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> str:
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise TypeError('int()... | 45 | 1 |
from math import ceil, sqrt
def lowerCAmelCase_ ( __a = 1000000 ) -> int:
"""simple docstring"""
lowerCamelCase__: Any =0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
lowerCamelCase__: Optional[int] =... | 59 |
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class __A :
'''simple docstring'''
lowerCAmelCase : int
lowerCAmelCase : TreeNode | None = ... | 560 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ = {
'configuration_bigbird_pegasus': [
'BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BigBirdPegasusConfig',
'BigBi... | 709 |
'''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, ... | 471 | 0 |
from math import sqrt
def lowercase ( SCREAMING_SNAKE_CASE ) -> bool:
'''simple docstring'''
assert isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) and (
number >= 0
), "'number' must been an int and positive"
SCREAMING_SNAKE_CASE_ = True
... | 205 |
import qiskit
def lowercase ( SCREAMING_SNAKE_CASE = 2 ) -> qiskit.result.counts.Counts:
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = qubits
# Using Aer's simulator
SCREAMING_SNAKE_CASE_ = qiskit.Aer.get_backend('aer_simulator' )
# Creating a Quantum... | 205 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
snake_case__ : Optional[Any] = {'configuration_encoder_decoder': ['EncoderDecoderConfig']}
try:... | 706 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
snake_case__ : List[Any] = '2.13.1'
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse('3.7'):
... | 592 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : Tuple = {
"""configuration_time_series_transformer""": [
"""TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""TimeSeriesTransformerCon... | 429 |
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
__lowercase : List[Any] =WebClient(token=os.environ["""CI_SLACK_BOT_TOKEN"""])
def a__ ( ... | 54 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"sail/poolforme... | 712 | 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_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/p... | 286 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from... | 501 |
'''simple docstring'''
import json
import os
import unittest
from typing import Tuple
from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVeca... | 501 | 1 |
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def ... | 409 |
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
__a = logging.get_logger(__name__)
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
def __init__( self : List[str] , *SCREAMING_... | 409 | 1 |
'''simple docstring'''
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
lowerCAmelCase__ = '''\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},... | 41 |
'''simple docstring'''
import argparse
import os
import re
lowerCAmelCase__ = '''src/diffusers'''
# Pattern that looks at the indentation in a line.
lowerCAmelCase__ = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
lowerCAmelCase__ = re.compi... | 41 | 1 |
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require... | 565 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
loggi... | 565 | 1 |
'''simple docstring'''
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def _A ( lowercase__ , lowercase__ , lowercase__ ):
lowercase__ = AutoConfig.from_pretrained(lowercase__ )
lowercase__ = F... | 325 |
'''simple docstring'''
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDi... | 325 | 1 |
'''simple docstring'''
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_ : ... | 710 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
UpperCAmelCase_ : int = ''
UpperCAmelCase_ : Union[str, Any] = ''
UpperCAmelCase_ : Any = ''
UpperCAmelCase_ : int = 1 # (0 is vertical, 1... | 540 | 0 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def UpperCamelCase ( _UpperCAmelCase : str ) -> Dict:
'''simple docstring'''
_lowercase : Optional[int] = [
"encoder.version",
... | 461 |
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class __lowercase :
_A = field(
default="codeparrot/codeparrot" , metadata={"help": "Model name or path of model to be trained."} )
_A = field(
default="./" , metadata={"help": "Save dir whe... | 461 | 1 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class UpperCamelCase ( lowercase__ ):
'''simple docstring'''
def UpperCamelCase ( self , UpperCamelCase_ ):
with open(UpperC... | 441 |
from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
SCREAMING_SNAKE_CASE : List[str] = HfArgumentParser(InitializationArguments)
SCREAMING_SNAKE_CASE : List[Any] = par... | 441 | 1 |
"""simple docstring"""
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, requir... | 227 |
"""simple docstring"""
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def UpperCAmelCase ( snake_case : BertModel , snake_case : str , snake_case : str ):
_lowerCAmelCa... | 227 | 1 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import Generati... | 573 |
"""simple docstring"""
import unittest
from knapsack import greedy_knapsack as kp
class lowerCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def A__ ( self ) -> Dict:
__lowerCAmelCase = [10, 20, 30, 40, 50, 60]... | 573 | 1 |
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vi... | 463 |
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
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 (
A... | 463 | 1 |
'''simple docstring'''
def _a( UpperCamelCase__ : int, UpperCamelCase__ : int ):
'''simple docstring'''
return int((input_a, input_a).count(0 ) != 0 )
def _a( ):
'''simple docstring'''
ass... | 665 |
'''simple docstring'''
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCL... | 665 | 1 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test... | 112 |
'''simple docstring'''
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
_UpperCamelCase : List[str] = re.compile(R'\b(a|an|the)\b', re.UNICODE)
_UpperCamelCase : Optional[int] = None
def __UpperCAmelCase ( ... | 541 | 0 |
def snake_case_ (__A : int ) -> str:
if isinstance(__A , __A ):
raise TypeError("""'float' object cannot be interpreted as an integer""" )
if isinstance(__A , __A ):
raise TypeError("""'str' object cannot be interpreted as an integer""" )
if num == 0:... | 218 |
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class SCREAMING_SNAKE_CASE ( a_ ):
"""simple doc... | 218 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__UpperCAmelCase = {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
imp... | 65 | '''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
__a: Optional[int] = logging.get_logger(__name__)
__a: Any = {
"""t5-small""": """https://huggingface.co/t5-small/re... | 152 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
"""microsoft/markuplm-base""": """https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json""",
"""microsoft/markup... | 715 |
'''simple docstring'''
import heapq
import sys
import numpy as np
snake_case_ = tuple[int, int]
class a__ :
def __init__(self : int ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = []
SCREAMING_SNAKE_CASE : Tuple = set()
... | 355 | 0 |
from typing import Any
class __lowerCamelCase :
def __init__( self: int,A_: Any ):
'''simple docstring'''
__UpperCamelCase = data
__UpperCamelCase = None
def __repr__( self: Any ):
... | 1 |
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: Tuple = logging.get_logger(__name__)
__a: ... | 108 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case : Optional[int] = {
'configuration_time_series_transformer': [
'TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP'... | 712 | '''simple docstring'''
from timeit import timeit
__snake_case : List[Any] = {
'MALAYALAM': True,
'String': False,
'rotor': True,
'level': True,
'A': True,
'BB': True,
'ABC': False,
'amanaplanacanalpanama': True, # "a man a plan a canal panama"
}
# Ensure our test d... | 174 | 0 |
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
__A = 4
__A = 3
class __lowerCAmelCase ( Upper... | 469 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/config.json',
'google/fnet-large': 'https://huggingfa... | 486 | 0 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class UpperCamelCase__ :
'''simple docstring'''
pass | 436 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ ... | 436 | 1 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def snake_case () -> int:
'''simple docstring'''
_snake_case : Optional[Any] = 9
_snake_case : Optional[int] = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
... | 670 | from manim import *
class lowercase_ ( __snake_case ):
def UpperCamelCase ( self ):
_snake_case : Tuple = Rectangle(height=0.5 , width=0.5 )
_snake_case : List[str] = Rectangle(height=0.46 , width=0.46 ).se... | 670 | 1 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelFo... | 553 |
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
f... | 553 | 1 |
"""simple docstring"""
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def lowercase__ ( lowerCamelCase, lowerCamelCase, lowerC... | 621 |
"""simple docstring"""
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
lowerCAmelCase__ = get_logger(__name__)
lowerCAmelCase__ = r'\n Args:\n input_ids (`jnp.ndarray` of ... | 621 | 1 |
"""simple docstring"""
def lowercase__( __SCREAMING_SNAKE_CASE : list , __SCREAMING_SNAKE_CASE : int = 0 ):
lowercase_ : List[str] = length or len(__SCREAMING_SNAKE_CASE )
lowercase_ : Union[str, Any] = False
for i in range(length - 1 ... | 477 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE =logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE ={
"google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.json",
}
class ... | 477 | 1 |
"""simple docstring"""
from __future__ import annotations
import requests
A = set(
'approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked conte... | 449 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, ... | 25 | 0 |
import argparse
from collections import defaultdict
import yaml
snake_case : Union[str, Any] = 'docs/source/en/_toctree.yml'
def snake_case__ ( __lowercase ) -> int:
"""simple docstring"""
A__ : str = defaultdict(__lowercase )
for ... | 706 |
from collections import Counter
from timeit import timeit
def snake_case__ ( __lowercase = "" , ) -> bool:
"""simple docstring"""
return sum(c % 2 for c in Counter(input_str.replace(" " , "" ).lower() ).values() ) < 2
def snake_case__ ( ... | 182 | 0 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_ ):
'''simple docstring'''
lowercase : Dict = ["image_processor", "tokenizer"]
lowercase : Optional[Any] = "Au... | 305 |
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase : Any = logging.get_logger(__name__)
_lowerCamelCase : Tuple = {
'''nielsr/canine-s''': 2048,
}
# Unicode defines... | 184 | 0 |
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def A_ ( A__ ) -> Dict:
a__ : Any = [
'encoder.version',
'decoder.version',
'model.encoder.version',
'model.decoder... | 392 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : Optional[Any] = logging.get_logger(__name__)
lowercase : Optional[int] = {
"""sayakpaul/vit-msn-base""": """https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json""",
# S... | 392 | 1 |
import cmath
import math
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: float , lowerCAmelCase_: float , lowerCAmelCase_: float , lowerCAmelCase_: float ):
snake_case_ : List[Any] = math.radians(__UpperCamelCase )
snake_case_ : Li... | 666 |
'''simple docstring'''
def lowerCamelCase_ ( __UpperCamelCase : int ) -> bool:
"""simple docstring"""
if num < 0:
return False
_A = num
_A = 0
while num > 0:
_A = rev_num * 1_0 + (num % 1_0)
... | 292 | 0 |
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class A_ ( __lowercase , unittest.TestCase ... | 186 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class A_ ( pl.LightningModule ):
'''simple docstring'''
def __init__( self , _A) -> List[str]:... | 186 | 1 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if ... | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
a__ = {
"""configuration_blip""": [
"""BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Blip... | 477 | 0 |
import string
def _lowercase ( a_ : str ) -> Optional[int]:
'''simple docstring'''
for key in range(len(string.ascii_uppercase ) ):
__magic_name__ = ''
for symbol in message:
if symbol in string.ascii_uppercase:
... | 709 |
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTest... | 184 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
UpperCAmelCase = logging.get_logger(__name__)
def __UpperCamelCase ( lowercase__ : Dict ):
'''simple docstring'''
if isinstance(__lowe... | 119 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_t... | 81 | 0 |
'''simple docstring'''
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
__lowerCAmelCase : ... | 712 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.ut... | 284 | 0 |
"""simple docstring"""
from math import factorial, pi
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : int = 3_0 ):
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE__ , (int, float) ):
raise V... | 480 |
"""simple docstring"""
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : list[int] , SCREAMING_SNAKE_CASE__ : list[int] , SCREAMING_SNAKE_CASE__ : list[int] , SCREAMING_SNAKE_CASE__ : list[list[str]] , SCREAMING_SNAKE_CASE... | 480 | 1 |
"""simple docstring"""
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def lowerCamelCase_ ( __lowerCAmelCase ) -> List[Any]:
'''simple docstring'''
for param in module.parameters():
lowerCamelCase__ =False
def ... | 132 | """simple docstring"""
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_... | 132 | 1 |
__UpperCamelCase: str = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
__UpperCamelCase: Tuple = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def SCREAMING_SNAKE_CASE__ ( _lowercase : dict[int, list[int]] , _lowercase : int , _lowercase : list[bool] ) -> ... | 266 |
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_model... | 266 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 719 |
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,
cached_file,
get_... | 440 | 0 |
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