code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def UpperCamelCase ( snake_case__ : Tuple ) -> Dict: # picklable for ... | 40 |
from math import asin, atan, cos, radians, sin, sqrt, tan
lowerCAmelCase : Union[str, Any] = 637_8137.0
lowerCAmelCase : int = 635_6752.31_4245
lowerCAmelCase : Union[str, Any] = 6378137
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _Up... | 671 | 0 |
'''simple docstring'''
# 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
#
... | 41 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
# Initialise PyTorch... | 671 | 0 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import Batch... | 42 |
import math
def A_ ( _UpperCAmelCase ):
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
... | 671 | 0 |
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vis... | 43 |
import re
def A_ ( _UpperCAmelCase ):
return [char.split() for char in re.split(R"[^ a-z A-Z 0-9 \s]" , str_ )]
def A_ ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: int = split_input(str_ )
return "".join(
... | 671 | 0 |
'''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, TensorFlowBenchmarkArg... | 44 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
lowerCAmelCase : Union[str, Any] = logging.get_logger(__name__)
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
... | 671 | 0 |
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def A ( ... | 45 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class __lowercase ( unittest.TestCase ):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ... | 671 | 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,
)
_lowerCAmelCase : List[Any] = {
'''configuration_electra'... | 46 |
from itertools import count
def A_ ( _UpperCAmelCase = 50 ):
SCREAMING_SNAKE_CASE_: Union[str, Any] = [1] * min_block_length
for n in count(_UpperCAmelCase ):
fill_count_functions.append(1 )
for block_length in range(_UpperCAmelC... | 671 | 0 |
def UpperCAmelCase__ ( lowerCamelCase_ : str ):
if n_term == "":
return []
__a : list = []
for temp in range(int(lowerCamelCase_ ) ):
series.append(f'''1/{temp + 1}''' if series else '1' )
return series
if __name__ =... | 47 |
def A_ ( _UpperCAmelCase ):
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError("only integers accepted as input" )
else:
SCREAMING_SNAKE_CASE_: List[Any] = str(abs(_UpperCAmelCase ) )
... | 671 | 0 |
'''simple docstring'''
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def A ( UpperCamelCase_ : Tuple , UpperCamelCase_ : Optional[int]=None ) -> List[str]:
'''simpl... | 48 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class __lowercase :
"""simple docstring"""
def __init__( self : List[str] , lowerCAmelCase__ : Any):
SCREAMING_SNAKE_CASE_: Any = data
SCREAMING_SN... | 671 | 0 |
"""simple docstring"""
def lowercase__ ( snake_case_ :List[str]=28_123 ):
__UpperCAmelCase = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i + 1 , limit // i + 1 ):
sum_divs[k * i] += k + i
__... | 49 |
from collections import defaultdict
from math import ceil, sqrt
def A_ ( _UpperCAmelCase = 1_00_00_00 , _UpperCAmelCase = 10 ):
SCREAMING_SNAKE_CASE_: defaultdict = defaultdict(_UpperCAmelCase )
for outer_width in range(3 , (t_limit // 4)... | 671 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
UpperCamelCase : Optional[int] = {
'alibaba-damo/mgp-str-base': 'https://huggingface.co/alibaba-damo/mgp-st... | 50 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase : str = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""tokenization_xlm""": [""... | 671 | 0 |
'''simple docstring'''
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
a__ : Optional[Any] = pd.read_csv('sample_data.csv', ... | 51 |
lowerCAmelCase : List[str] = {
"""A""": ["""B""", """C""", """E"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F""", """G"""],
"""D""": ["""B"""],
"""E""": ["""A""", """B""", """D"""],
"""F""": ["""C"""],
"""G""": ["""C"""],
}
def A_ ( _Upp... | 671 | 0 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
def __A ( a_ :list[Any]) -> None:
create_state_space_tree(a_ , [] , 0)
def __A ( a_ :list[Any] , a_ :list[Any] , a_ :int) -> None:
... | 52 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( self : List[Any] , lowerCAmelCase__ ... | 671 | 0 |
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
@dataclass
class _UpperCA... | 53 |
from __future__ import annotations
from math import ceil, floor, sqrt
def A_ ( _UpperCAmelCase = 2_00_00_00 ):
SCREAMING_SNAKE_CASE_: list[int] = [0]
SCREAMING_SNAKE_CASE_: int
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ... | 671 | 0 |
import heapq
def a__ ( lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =[]
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
... | 54 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase : Optional[int] = {
"""configuration_longformer""": [
"""LONGFORMER_PRETRAINED_CONFIG_ARC... | 671 | 0 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
SCREAMING_SNAKE_CASE :int = namedtuple('covid_data', 'cases deaths recovered')
def UpperCAmelCase ( a_ = "https://www.worldometers.info/coronavirus/" ) -> covid_data:
"""simple... | 55 |
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
lowerCAmelCase : Optional[int] = [
# (stable-diffusion, HF Diffusers)
("""time_embed.0.weight""", """time_embedding.li... | 671 | 0 |
'''simple docstring'''
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def _a (lowercase__ : int , lowercase__ : int , lowercase__ : float = 1 / sqrt(2 ) ) -> IIRFilter:
"""simple docstring"""
__snake_case ... | 56 |
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : int = logging.get_logger(__name__)
lowerCAmelCase : Dict = {
"""microsoft/xprophetnet-large-wiki100-cased""": (
"""https://huggingf... | 671 | 0 |
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
... | 57 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampli... | 671 | 0 |
"""simple docstring"""
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample... | 58 |
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
lowerCAmelCase : Optional[int] = logging.get_logger(__... | 671 | 0 |
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
__A = TypeVar("T")
class _SCREAMING_SNAKE_CASE ( Generic[T] ):
'''simple docstring'''
def __init__(self : Optional[Any] , UpperCAmelCase_ ... | 59 |
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
... | 671 | 0 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
)
from... | 60 |
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
lowerCAmelCase : List[Any] = WebClient(token=os.environ["""CI_SLACK_BOT_TOKEN"""])
def A_ ( _UpperCAmelCase ... | 671 | 0 |
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noqa: F401
... | 61 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, Dis... | 671 | 0 |
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokenizer,
JumanppToke... | 62 |
from math import asin, atan, cos, radians, sin, sqrt, tan
lowerCAmelCase : Union[str, Any] = 637_8137.0
lowerCAmelCase : int = 635_6752.31_4245
lowerCAmelCase : Union[str, Any] = 6378137
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _Up... | 671 | 0 |
def lowerCamelCase__ ( __lowerCamelCase : int , __lowerCamelCase : float , __lowerCamelCase : float ):
return round(float(moles / volume ) * nfactor )
def lowerCamelCase__ ( __lowerCamelCase : float , __lowerC... | 63 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
# Initialise PyTorch... | 671 | 0 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
lowercase_ : List[Any] = argparse.ArgumentParser()
parser.add_argument('--dump_path', default=None, type=s... | 64 |
import math
def A_ ( _UpperCAmelCase ):
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
... | 671 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCAmelCase = {}
try:
if not is_sentencepiece_availab... | 65 |
import re
def A_ ( _UpperCAmelCase ):
return [char.split() for char in re.split(R"[^ a-z A-Z 0-9 \s]" , str_ )]
def A_ ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: int = split_input(str_ )
return "".join(
... | 671 | 0 |
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, require_pyazr, require_zstandard
... | 66 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
lowerCAmelCase : Union[str, Any] = logging.get_logger(__name__)
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
... | 671 | 0 |
def SCREAMING_SNAKE_CASE__ ( snake_case__ :str ) -> Union[str, Any]:
_lowercase = len(snake_case__ )
_lowercase = sum(snake_case__ )
_lowercase = [[False for x in range(s + 1 )] for y in range(n + 1 )]
for i in range(1 , n + 1 ):
... | 67 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class __lowercase ( unittest.TestCase ):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ... | 671 | 0 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def lowercase__ ( ) -> Union[str, Any]:
"""simple docstring"""
with offline(... | 68 |
from itertools import count
def A_ ( _UpperCAmelCase = 50 ):
SCREAMING_SNAKE_CASE_: Union[str, Any] = [1] * min_block_length
for n in count(_UpperCAmelCase ):
fill_count_functions.append(1 )
for block_length in range(_UpperCAmelC... | 671 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTMSNConfig
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_co... | 69 |
def A_ ( _UpperCAmelCase ):
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError("only integers accepted as input" )
else:
SCREAMING_SNAKE_CASE_: List[Any] = str(abs(_UpperCAmelCase ) )
... | 671 | 0 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
lowerCamelC... | 70 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class __lowercase :
"""simple docstring"""
def __init__( self : List[str] , lowerCAmelCase__ : Any):
SCREAMING_SNAKE_CASE_: Any = data
SCREAMING_SN... | 671 | 0 |
'''simple docstring'''
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
i... | 71 |
from collections import defaultdict
from math import ceil, sqrt
def A_ ( _UpperCAmelCase = 1_00_00_00 , _UpperCAmelCase = 10 ):
SCREAMING_SNAKE_CASE_: defaultdict = defaultdict(_UpperCAmelCase )
for outer_width in range(3 , (t_limit // 4)... | 671 | 0 |
'''simple docstring'''
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
_UpperCAmelCase : List[Any] = datasets.load_iris()
_UpperCAmelCase : Dict = np.array(data['''data'''])
_UpperCAmelCase : Union[str, Any] = np.array... | 72 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase : str = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""tokenization_xlm""": [""... | 671 | 0 |
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
a_ ... | 73 |
lowerCAmelCase : List[str] = {
"""A""": ["""B""", """C""", """E"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F""", """G"""],
"""D""": ["""B"""],
"""E""": ["""A""", """B""", """D"""],
"""F""": ["""C"""],
"""G""": ["""C"""],
}
def A_ ( _Upp... | 671 | 0 |
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVPro... | 74 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( self : List[Any] , lowerCAmelCase__ ... | 671 | 0 |
'''simple docstring'''
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from... | 75 |
from __future__ import annotations
from math import ceil, floor, sqrt
def A_ ( _UpperCAmelCase = 2_00_00_00 ):
SCREAMING_SNAKE_CASE_: list[int] = [0]
SCREAMING_SNAKE_CASE_: int
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ... | 671 | 0 |
"""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, T... | 76 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase : Optional[int] = {
"""configuration_longformer""": [
"""LONGFORMER_PRETRAINED_CONFIG_ARC... | 671 | 0 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> str:
"""simple docstring"""
__UpperCAmelCase : Any = ""
for word_or_phrase in separated:
if not isinstance(UpperCamelCase , UpperCamelCase... | 77 |
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
lowerCAmelCase : Optional[int] = [
# (stable-diffusion, HF Diffusers)
("""time_embed.0.weight""", """time_embedding.li... | 671 | 0 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, r... | 78 |
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : int = logging.get_logger(__name__)
lowerCAmelCase : Dict = {
"""microsoft/xprophetnet-large-wiki100-cased""": (
"""https://huggingf... | 671 | 0 |
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 im... | 79 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampli... | 671 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__UpperCamelCase : Union[str, Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvai... | 80 |
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
lowerCAmelCase : Optional[int] = logging.get_logger(__... | 671 | 0 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
_snake_case : str ... | 81 |
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
... | 671 | 0 |
"""simple docstring"""
def a__ ( lowerCAmelCase__ ):
if len(lowerCAmelCase__ ) <= 1:
return lst
UpperCAmelCase_ = 1
while i < len(lowerCAmelCase__ ):
if lst[i - 1] <= lst[i]:
i += 1
else:
... | 82 |
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
lowerCAmelCase : List[Any] = WebClient(token=os.environ["""CI_SLACK_BOT_TOKEN"""])
def A_ ( _UpperCAmelCase ... | 671 | 0 |
"""simple docstring"""
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def snake_case_ ( A_ : ... | 83 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, Dis... | 671 | 0 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
UpperCAmelCase = logging.get_logger(__name__)
@dat... | 84 |
from math import asin, atan, cos, radians, sin, sqrt, tan
lowerCAmelCase : Union[str, Any] = 637_8137.0
lowerCAmelCase : int = 635_6752.31_4245
lowerCAmelCase : Union[str, Any] = 6378137
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _Up... | 671 | 0 |
import qiskit
def _a ( lowercase__ : int = 2 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = qubits
# Using Aer's simulator
SCREAMING_SNAKE_CASE__ : Optional[Any] = qiskit.Aer.get_backend('aer_simulator' )
# Cr... | 85 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
# Initialise PyTorch... | 671 | 0 |
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 __snake_case ( __UpperCamelCase : Tuple ,__UpperCamelCase : Dict ,__UpperC... | 86 |
import math
def A_ ( _UpperCAmelCase ):
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
... | 671 | 0 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : Any = {
"""configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
"""feature_extraction_mctct""": ["""MCTCTFeatureExt... | 87 |
import re
def A_ ( _UpperCAmelCase ):
return [char.split() for char in re.split(R"[^ a-z A-Z 0-9 \s]" , str_ )]
def A_ ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: int = split_input(str_ )
return "".join(
... | 671 | 0 |
"""simple docstring"""
def _snake_case ( __snake_case : str , __snake_case : str ):
"""simple docstring"""
_lowerCamelCase : str = len(__snake_case )
_lowerCamelCase : Union[str, Any] = len(__snake_case )
_lowerCamelC... | 88 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
lowerCAmelCase : Union[str, Any] = logging.get_logger(__name__)
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
... | 671 | 0 |
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...tes... | 89 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class __lowercase ( unittest.TestCase ):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ... | 671 | 0 |
'''simple docstring'''
from __future__ import annotations
def _snake_case ( A , A ) -> float:
lowerCAmelCase__ = sorted(numsa + numsa )
lowerCAmelCase__ , lowerCAmelCase__ = divmod(len(A ) , 2 )
if mod == 1:
... | 90 |
from itertools import count
def A_ ( _UpperCAmelCase = 50 ):
SCREAMING_SNAKE_CASE_: Union[str, Any] = [1] * min_block_length
for n in count(_UpperCAmelCase ):
fill_count_functions.append(1 )
for block_length in range(_UpperCAmelC... | 671 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_lowercase = {
'''configuration_groupvit''': [
'''GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''GroupViTConfig''',
'''Gr... | 91 |
def A_ ( _UpperCAmelCase ):
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError("only integers accepted as input" )
else:
SCREAMING_SNAKE_CASE_: List[Any] = str(abs(_UpperCAmelCase ) )
... | 671 | 0 |
'''simple docstring'''
# Imports
import numpy as np
class __SCREAMING_SNAKE_CASE :
def __init__( self : Union[str, Any] , UpperCAmelCase__ : Optional[Any]=None , UpperCAmelCase__ : Union[str, Any]=None , UpperCAmelCase__ : str=None , UpperCAme... | 92 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class __lowercase :
"""simple docstring"""
def __init__( self : List[str] , lowerCAmelCase__ : Any):
SCREAMING_SNAKE_CASE_: Any = data
SCREAMING_SN... | 671 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixi... | 93 |
from collections import defaultdict
from math import ceil, sqrt
def A_ ( _UpperCAmelCase = 1_00_00_00 , _UpperCAmelCase = 10 ):
SCREAMING_SNAKE_CASE_: defaultdict = defaultdict(_UpperCAmelCase )
for outer_width in range(3 , (t_limit // 4)... | 671 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
'dist... | 94 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase : str = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""tokenization_xlm""": [""... | 671 | 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_common impor... | 95 |
lowerCAmelCase : List[str] = {
"""A""": ["""B""", """C""", """E"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F""", """G"""],
"""D""": ["""B"""],
"""E""": ["""A""", """B""", """D"""],
"""F""": ["""C"""],
"""G""": ["""C"""],
}
def A_ ( _Upp... | 671 | 0 |
"""simple docstring"""
def a ( __UpperCAmelCase : str ) -> bool:
if not all(x.isalpha() for x in string ):
raise ValueError("""String must only contain alphabetic characters.""" )
__magic_name__: Optional[Any] = sorted(string... | 96 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( self : List[Any] , lowerCAmelCase__ ... | 671 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__a = {'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}
try:
if not is... | 97 |
from __future__ import annotations
from math import ceil, floor, sqrt
def A_ ( _UpperCAmelCase = 2_00_00_00 ):
SCREAMING_SNAKE_CASE_: list[int] = [0]
SCREAMING_SNAKE_CASE_: int
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ... | 671 | 0 |
'''simple docstring'''
from datetime import datetime as dt
import os
from github import Github
lowercase__ : Optional[int] = [
'good first issue',
'good second issue',
'good difficult issue',
'feature request',
'new model',
'wip',
]
def a__ ( ) -... | 98 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase : Optional[int] = {
"""configuration_longformer""": [
"""LONGFORMER_PRETRAINED_CONFIG_ARC... | 671 | 0 |
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
SCREAMING_SNAKE_CASE = logging.getLogger(__name__)
SCREAMING_SNAKE_CASE ... | 99 |
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
lowerCAmelCase : Optional[int] = [
# (stable-diffusion, HF Diffusers)
("""time_embed.0.weight""", """time_embedding.li... | 671 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_A : Any = {"""configuration_xglm""": ["""XGLM_PRETRAINED_C... | 100 |
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : int = logging.get_logger(__name__)
lowerCAmelCase : Dict = {
"""microsoft/xprophetnet-large-wiki100-cased""": (
"""https://huggingf... | 671 | 0 |
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def a__ ( A__ ):
if "cls_token" in name:
SCREAMING_SNAKE_CASE_ : int = name.replace('cls_token', 'vit... | 101 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampli... | 671 | 0 |
"""simple docstring"""
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random... | 102 |
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
lowerCAmelCase : Optional[int] = logging.get_logger(__... | 671 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case = logging.get_logger(__name__)
snake_case = {
'''google/switch-base-8''': '''https://huggingface.co/google/switch-base-8/blob/main/config.json''',
}
... | 103 |
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
... | 671 | 0 |
"""simple docstring"""
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,... | 104 |
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
lowerCAmelCase : List[Any] = WebClient(token=os.environ["""CI_SLACK_BOT_TOKEN"""])
def A_ ( _UpperCAmelCase ... | 671 | 0 |
def __UpperCAmelCase ( lowerCamelCase_ : str ) -> bool:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = 0
for ch in input_str:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ord(lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : Tup... | 105 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, Dis... | 671 | 0 |
import logging
from transformers import PretrainedConfig
__snake_case :int =logging.getLogger(__name__)
__snake_case :Tuple ={
'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json',
}
class... | 106 |
from math import asin, atan, cos, radians, sin, sqrt, tan
lowerCAmelCase : Union[str, Any] = 637_8137.0
lowerCAmelCase : int = 635_6752.31_4245
lowerCAmelCase : Union[str, Any] = 6378137
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _Up... | 671 | 0 |
'''simple docstring'''
import warnings
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... | 107 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
# Initialise PyTorch... | 671 | 0 |
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import P... | 108 |
import math
def A_ ( _UpperCAmelCase ):
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
... | 671 | 0 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(_snake_ca... | 109 |
import re
def A_ ( _UpperCAmelCase ):
return [char.split() for char in re.split(R"[^ a-z A-Z 0-9 \s]" , str_ )]
def A_ ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: int = split_input(str_ )
return "".join(
... | 671 | 0 |
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...t... | 668 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
lowerCAmelCase : Union[str, Any] = logging.get_logger(__name__)
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
... | 671 | 0 |
import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Trainer,
TrainerCallback,... | 121 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class __lowercase ( unittest.TestCase ):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ... | 671 | 0 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class _lowerCamelCase( UpperCAmelCase_ ):
def UpperCamelCase ( self, lowerCamelCase) -> Union[str, Any]:
"""simple docstring"""
r... | 89 |
from itertools import count
def A_ ( _UpperCAmelCase = 50 ):
SCREAMING_SNAKE_CASE_: Union[str, Any] = [1] * min_block_length
for n in count(_UpperCAmelCase ):
fill_count_functions.append(1 )
for block_length in range(_UpperCAmelC... | 671 | 0 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class lowerCAmelCase__ :
def __init__( self , a ) -> Dict:
'''simple docstring'''
_UpperCamelCase = data
_UpperCamelCase = ... | 612 |
def A_ ( _UpperCAmelCase ):
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError("only integers accepted as input" )
else:
SCREAMING_SNAKE_CASE_: List[Any] = str(abs(_UpperCAmelCase ) )
... | 671 | 0 |
"""simple docstring"""
lowercase__ = frozenset(
[
"""prompt""",
"""height""",
"""width""",
"""guidance_scale""",
"""negative_prompt""",
"""prompt_embeds""",
"""negative_prompt_embeds""",
"""cross_attention_kwargs""",
]
)
lowercase_... | 610 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class __lowercase :
"""simple docstring"""
def __init__( self : List[str] , lowerCAmelCase__ : Any):
SCREAMING_SNAKE_CASE_: Any = data
SCREAMING_SN... | 671 | 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_c... | 519 |
from collections import defaultdict
from math import ceil, sqrt
def A_ ( _UpperCAmelCase = 1_00_00_00 , _UpperCAmelCase = 10 ):
SCREAMING_SNAKE_CASE_: defaultdict = defaultdict(_UpperCAmelCase )
for outer_width in range(3 , (t_limit // 4)... | 671 | 0 |
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : Any , _lowerCAmelCase : str = "" , _lowerCAmelCase : bool = False ):
# Mapping from the first character of the prefix of the node
SCREAMING_SNAKE_CASE_ = ... | 31 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase : str = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""tokenization_xlm""": [""... | 671 | 0 |
"""simple docstring"""
def snake_case__ ( _lowerCamelCase ) ->Any:
"""simple docstring"""
if isinstance(_UpperCAmelCase, _UpperCAmelCase ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(_UpperCAmelCase, _UpperCAmelCase... | 575 |
lowerCAmelCase : List[str] = {
"""A""": ["""B""", """C""", """E"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F""", """G"""],
"""D""": ["""B"""],
"""E""": ["""A""", """B""", """D"""],
"""F""": ["""C"""],
"""G""": ["""C"""],
}
def A_ ( _Upp... | 671 | 0 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
_a... | 168 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( self : List[Any] , lowerCAmelCase__ ... | 671 | 0 |
import warnings
warnings.warn(
'memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: '
'`from accelerate import find_executable_batch_size` to avoid this warning.',
FutureWarning,
)
| 417 |
from __future__ import annotations
from math import ceil, floor, sqrt
def A_ ( _UpperCAmelCase = 2_00_00_00 ):
SCREAMING_SNAKE_CASE_: list[int] = [0]
SCREAMING_SNAKE_CASE_: int
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ... | 671 | 0 |
"""simple docstring"""
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisio... | 646 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase : Optional[int] = {
"""configuration_longformer""": [
"""LONGFORMER_PRETRAINED_CONFIG_ARC... | 671 | 0 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required ... | 668 |
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
lowerCAmelCase : Optional[int] = [
# (stable-diffusion, HF Diffusers)
("""time_embed.0.weight""", """time_embedding.li... | 671 | 0 |
import argparse
_lowerCamelCase : Optional[Any] = """docs/source/_static/js/custom.js"""
def _lowerCAmelCase ( __magic_name__ :str ):
with open(_UpperCAmelCase , encoding='''utf-8''' , newline='''\n''' ) as f:
UpperCAmelCa... | 121 |
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : int = logging.get_logger(__name__)
lowerCAmelCase : Dict = {
"""microsoft/xprophetnet-large-wiki100-cased""": (
"""https://huggingf... | 671 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Dict = {
"""microsoft/unispeech-sat-base-100h-libri-ft""": (
"""https://huggingfac... | 89 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampli... | 671 | 0 |
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class lowerCAmelCase__ ( UpperCAmelCase_ ):
UpperCamelCase_ : Optiona... | 612 |
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
lowerCAmelCase : Optional[int] = logging.get_logger(__... | 671 | 0 |
"""simple docstring"""
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,... | 610 |
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
... | 671 | 0 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def _UpperCAmelCase ( UpperCAmelCase : List[str] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Tuple ):
"""simple docstring"""
... | 519 |
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
lowerCAmelCase : List[Any] = WebClient(token=os.environ["""CI_SLACK_BOT_TOKEN"""])
def A_ ( _UpperCAmelCase ... | 671 | 0 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, ... | 31 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, Dis... | 671 | 0 |
"""simple docstring"""
class lowerCAmelCase__ :
"""simple docstring"""
def __init__( self : Tuple , lowercase__ : list ):
__lowercase : Union[str, Any] = set_counts
__lowercase : Optional[Any] = max(lowerCAmelC... | 575 |
from math import asin, atan, cos, radians, sin, sqrt, tan
lowerCAmelCase : Union[str, Any] = 637_8137.0
lowerCAmelCase : int = 635_6752.31_4245
lowerCAmelCase : Union[str, Any] = 6378137
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _Up... | 671 | 0 |
'''simple docstring'''
def _lowercase ( lowerCamelCase__ ) -> Optional[int]:
"""simple docstring"""
__UpperCAmelCase : Optional[int] = 1
__UpperCAmelCase : Dict = 2
while i * i <= n:
__UpperCAmelC... | 168 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
# Initialise PyTorch... | 671 | 0 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _lowercase ( UpperCAmelCase_ ):
lowercase = ['''... | 417 |
import math
def A_ ( _UpperCAmelCase ):
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
... | 671 | 0 |
"""simple docstring"""
import collections
import os
import re
from pathlib import Path
__A = """src/transformers"""
# Matches is_xxx_available()
__A = re.compile(R'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_struct = {xxx}
__A = re.compile(R'''^_import_structure\s+=\s+\{([^\}]+)\}'... | 646 |
import re
def A_ ( _UpperCAmelCase ):
return [char.split() for char in re.split(R"[^ a-z A-Z 0-9 \s]" , str_ )]
def A_ ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: int = split_input(str_ )
return "".join(
... | 671 | 0 |
from __future__ import annotations
from math import ceil, floor, sqrt
def lowerCAmelCase_ (lowercase__ : Tuple = 2_00_00_00 ) -> Any:
'''simple docstring'''
lowerCAmelCase__ = [0]
lowerCAmelCase__ = 42
for idx in range(1 , ceil(... | 668 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
lowerCAmelCase : Union[str, Any] = logging.get_logger(__name__)
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
... | 671 | 0 |
from ..utils import DummyObject, requires_backends
class snake_case__ ( metaclass=UpperCAmelCase_ ):
'''simple docstring'''
__A = ['''onnx''']
def __init__( self : List[str] , *lowerCAmelCase_ : Optional[i... | 121 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class __lowercase ( unittest.TestCase ):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ... | 671 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUn... | 89 |
from itertools import count
def A_ ( _UpperCAmelCase = 50 ):
SCREAMING_SNAKE_CASE_: Union[str, Any] = [1] * min_block_length
for n in count(_UpperCAmelCase ):
fill_count_functions.append(1 )
for block_length in range(_UpperCAmelC... | 671 | 0 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM
@r... | 612 |
def A_ ( _UpperCAmelCase ):
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError("only integers accepted as input" )
else:
SCREAMING_SNAKE_CASE_: List[Any] = str(abs(_UpperCAmelCase ) )
... | 671 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import RegNetConfig
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 im... | 610 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class __lowercase :
"""simple docstring"""
def __init__( self : List[str] , lowerCAmelCase__ : Any):
SCREAMING_SNAKE_CASE_: Any = data
SCREAMING_SN... | 671 | 0 |
def _UpperCAmelCase ( UpperCAmelCase : int , UpperCAmelCase : List[str] ):
"""simple docstring"""
__lowerCamelCase : int = len(_UpperCAmelCase )
__lowerCamelCase : List[str] = [[False] * (required_sum + 1) for _ in rang... | 519 |
from collections import defaultdict
from math import ceil, sqrt
def A_ ( _UpperCAmelCase = 1_00_00_00 , _UpperCAmelCase = 10 ):
SCREAMING_SNAKE_CASE_: defaultdict = defaultdict(_UpperCAmelCase )
for outer_width in range(3 , (t_limit // 4)... | 671 | 0 |
from ..utils import DummyObject, requires_backends
class lowerCamelCase_ ( metaclass=UpperCAmelCase_ ):
'''simple docstring'''
lowercase_ = ['''flax''']
def __init__( self : Union[str, Any] , *_lowerCAmelCase : int , **_lower... | 31 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase : str = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""tokenization_xlm""": [""... | 671 | 0 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import ... | 575 |
lowerCAmelCase : List[str] = {
"""A""": ["""B""", """C""", """E"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F""", """G"""],
"""D""": ["""B"""],
"""E""": ["""A""", """B""", """D"""],
"""F""": ["""C"""],
"""G""": ["""C"""],
}
def A_ ( _Upp... | 671 | 0 |
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