code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE = {
'configuration_trajectory_transformer': [
'TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'TrajectoryTransformerConfig'... | 81 | from math import isqrt
def snake_case ( snake_case__ :int) -> list[int]:
_A = [True] * max_number
for i in range(2 , isqrt(max_number - 1) + 1):
if is_prime[i]:
for j in range(i**2 , snake_case__ ... | 81 | 1 |
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
_SCREAMING_SNAKE_CASE = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow('', '|', '|... | 81 | import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerF... | 81 | 1 |
import numpy as np
def snake_case ( snake_case__ :np.array) -> np.array:
return (2 / (1 + np.exp(-2 * vector))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 81 | def snake_case ( snake_case__ :str , snake_case__ :str) -> list:
_A = len(snake_case__)
_A = []
for i in range(len(snake_case__) - pat_len + 1):
_A = True
for j in range(snake_case__):
... | 81 | 1 |
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
_SCREAMING_SNAKE_CASE = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.195... | 81 | import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
class a ( __lowerCAmelCase ):
"""simple docstring"""
def __init__( self , *lowerCAmelC... | 81 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CAS... | 81 | from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler... | 81 | 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
_SCREAMING_SNAKE_CASE = logging.get_logger(__... | 81 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE = {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
raise OptionalDep... | 81 | 1 |
def snake_case ( snake_case__ :str) -> bool:
return credit_card_number.startswith(("""34""", """35""", """37""", """4""", """5""", """6"""))
def snake_case ( snake_case__ :str) -> bool:
_A = credit_card_number
_A = 0
... | 81 | from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from fastapi.routing impor... | 81 | 1 |
def snake_case ( snake_case__ :float , snake_case__ :float) -> float:
if mass < 0:
raise ValueError("""The mass of a body cannot be negative""")
return 0.5 * mass * abs(snake_case__) * abs(snake_case__)
if __name__ == "__main__":
import doctest... | 81 | import colorsys
from PIL import Image # type: ignore
def snake_case ( snake_case__ :float , snake_case__ :float , snake_case__ :int) -> float:
_A = x
_A = y
for step in range(snake_case__): # noqa: B007
... | 81 | 1 |
from 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 = {
'distilbert-b... | 81 | def snake_case ( snake_case__ :int , snake_case__ :int) -> str:
return "\n".join(
F'''{number} * {i} = {number * i}''' for i in range(1 , number_of_terms + 1))
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
... | 81 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_SCREAMING_SNAKE_CASE = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
... | 81 | import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE = [
'word_embeddi... | 81 | 1 |
def snake_case ( snake_case__ :str) -> list:
if n_term == "":
return []
_A = []
for temp in range(int(snake_case__)):
series.append(F'''1/{temp + 1}''' if series else """1""")
return series
if __name__ == "__main__":
... | 81 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE = {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetCo... | 81 | 1 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_... | 81 | # DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor... | 81 | 1 |
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
_SCREAMING_SNAKE_CASE = logging.getLogger(__name__)
def snake_case ( ) -> Tuple:
_A = argparse.ArgumentParser(
... | 81 | from collections.abc import Sequence
def snake_case ( snake_case__ :Sequence[float] , snake_case__ :bool = False) -> float:
if not arr:
return 0
_A = 0 if allow_empty_subarrays else float("""-inf""")
_A = 0.... | 81 | 1 |
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
if is_vision_available():
... | 81 | def snake_case ( ) -> Any:
for n in range(1 , 1_000_000):
yield n * (n + 1) // 2
def snake_case ( snake_case__ :Dict) -> Optional[Any]:
_A = 1
_A = 2
while i * i <= n:
_A ... | 81 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_SCREAMING_SNAKE_CASE = {
'configuration_data2vec_audio': ['DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Data2VecAudioConfig'],
'configuration_data... | 81 | import requests
from bsa import BeautifulSoup
def snake_case ( snake_case__ :str = "AAPL") -> str:
_A = F'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
_A = BeautifulSoup(requests.get(snake_case__).text , """html.parser"""... | 81 | 1 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class a ( __lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase :int = (UnCLIPScheduler,)
def UpperCAmelCase ( self , **lo... | 81 | import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class a ( __lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase :int = (UnCLIPScheduler,)
def UpperCAmelCase ( self , **lo... | 81 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE = {
'configuration_x_clip': [
'XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XCLIPConfig',
'XCLIPTextConfig',
'XCLIPVisio... | 81 | import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def snake_case ( ) -> List[Any]:
_A = ArgumentParser(
description=(
"""PyTorch TPU... | 81 | 1 |
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .be... | 81 | import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
_SCREAMING_SNAKE_CASE = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow('', '|', '|... | 81 | 1 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler... | 81 | import cva
import numpy as np
class a :
"""simple docstring"""
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_ ) -> Any:
if k in (0.04, 0.06):
_A = k
_A = window_size
else:
... | 81 | 1 |
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_modeling_flax_common import F... | 81 | def snake_case ( snake_case__ :str) -> int:
_A = hex_num.strip()
if not hex_num:
raise ValueError("""No value was passed to the function""")
_A = hex_num[0] == """-"""
if is_negative:
_A = hex_num[1:]... | 81 | 1 |
import os
import pytest
from attr import dataclass
_SCREAMING_SNAKE_CASE = 'us-east-1' # defaults region
@dataclass
class a :
"""simple docstring"""
lowerCamelCase :str
lowerCamelCase :int = '''arn:aws:iam::558105141721:role/s... | 81 | # 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... | 81 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE = {
'configuration_speech_to_text': ['SPEECH_TO_... | 81 | import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
_SCREAMING_SNAKE_CASE = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.195... | 81 | 1 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class a ( unittest.TestCase , __lowerCAmelCase ):
"""simple docstring"""
def UpperCAmelCase ( self ) -> int:
_A = load_t... | 81 | from math import isqrt
def snake_case ( snake_case__ :int) -> list[int]:
_A = [True] * max_number
for i in range(2 , isqrt(max_number - 1) + 1):
if is_prime[i]:
for j in range(i**2 , snake_case__ ... | 81 | 1 |
from math import log
from scipy.constants import Boltzmann, physical_constants
_SCREAMING_SNAKE_CASE = 300 # TEMPERATURE (unit = K)
def snake_case ( snake_case__ :float , snake_case__ :float , snake_case__ :float , ) -> flo... | 81 | import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerF... | 81 | 1 |
def snake_case ( snake_case__ :int) -> int:
if not isinstance(snake_case__ , snake_case__) or number < 0:
raise ValueError("""Input must be a non-negative integer""")
_A = 0
while number:
# This way we arrive at next set b... | 81 | def snake_case ( snake_case__ :str , snake_case__ :str) -> list:
_A = len(snake_case__)
_A = []
for i in range(len(snake_case__) - pat_len + 1):
_A = True
for j in range(snake_case__):
... | 81 | 1 |
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.... | 81 | import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
class a ( __lowerCAmelCase ):
"""simple docstring"""
def __init__( self , *lowerCAmelC... | 81 | 1 |
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 81 | from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler... | 81 | 1 |
def snake_case ( snake_case__ :str , snake_case__ :str) -> list:
_A = len(snake_case__)
_A = []
for i in range(len(snake_case__) - pat_len + 1):
_A = True
for j in range(snake_case__):
... | 81 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE = {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
raise OptionalDep... | 81 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE = {'configuration_encoder_decoder': ['EncoderDecoderConfig']}
try:
if not is_tor... | 81 | from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from fastapi.routing impor... | 81 | 1 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
... | 81 | import colorsys
from PIL import Image # type: ignore
def snake_case ( snake_case__ :float , snake_case__ :float , snake_case__ :int) -> float:
_A = x
_A = y
for step in range(snake_case__): # noqa: B007
... | 81 | 1 |
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DA... | 81 | def snake_case ( snake_case__ :int , snake_case__ :int) -> str:
return "\n".join(
F'''{number} * {i} = {number * i}''' for i in range(1 , number_of_terms + 1))
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
... | 81 | 1 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class a :
"""simple docstring"""
lowerCamelCase :int
lowerCamelCase :Node | None ... | 81 | import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE = [
'word_embeddi... | 81 | 1 |
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class a ( __lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase :int = (DDIMParallelScheduler,)
lowerCamelCase :Any = (('''e... | 81 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE = {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetCo... | 81 | 1 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class a :
"""simple docstring"""
pass
| 81 | # DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor... | 81 | 1 |
def snake_case ( snake_case__ :int) -> int:
_A = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def snake_case ( snake_case__ :int = 100) -> int:
_A = 1
... | 81 | from collections.abc import Sequence
def snake_case ( snake_case__ :Sequence[float] , snake_case__ :bool = False) -> float:
if not arr:
return 0
_A = 0 if allow_empty_subarrays else float("""-inf""")
_A = 0.... | 81 | 1 |
_SCREAMING_SNAKE_CASE = '\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/tra... | 81 | def snake_case ( ) -> Any:
for n in range(1 , 1_000_000):
yield n * (n + 1) // 2
def snake_case ( snake_case__ :Dict) -> Optional[Any]:
_A = 1
_A = 2
while i * i <= n:
_A ... | 81 | 1 |
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversa... | 81 | import requests
from bsa import BeautifulSoup
def snake_case ( snake_case__ :str = "AAPL") -> str:
_A = F'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
_A = BeautifulSoup(requests.get(snake_case__).text , """html.parser"""... | 81 | 1 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = [
['attention... | 81 | import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class a ( __lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase :int = (UnCLIPScheduler,)
def UpperCAmelCase ( self , **lo... | 81 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_... | 81 | import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def snake_case ( ) -> List[Any]:
_A = ArgumentParser(
description=(
"""PyTorch TPU... | 81 | 1 |
_SCREAMING_SNAKE_CASE = [
'DownloadConfig',
'DownloadManager',
'DownloadMode',
'StreamingDownloadManager',
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager... | 81 | import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
_SCREAMING_SNAKE_CASE = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow('', '|', '|... | 81 | 1 |
# 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... | 81 | import cva
import numpy as np
class a :
"""simple docstring"""
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_ ) -> Any:
if k in (0.04, 0.06):
_A = k
_A = window_size
else:
... | 81 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE = {}
try:
if not is_sentencepiece_available():
raise O... | 81 | def snake_case ( snake_case__ :str) -> int:
_A = hex_num.strip()
if not hex_num:
raise ValueError("""No value was passed to the function""")
_A = hex_num[0] == """-"""
if is_negative:
_A = hex_num[1:]... | 81 | 1 |
def snake_case ( snake_case__ :str , snake_case__ :str = " ") -> list:
_A = []
_A = 0
for index, char in enumerate(snake_case__):
if char == separator:
split_words.append(string[last_index:index])
... | 81 | # 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... | 81 | 1 |
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
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)... | 81 | import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
_SCREAMING_SNAKE_CASE = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.195... | 81 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_SCREAMING_SNAKE_CASE = {
'configuration_owlvit':... | 81 | from math import isqrt
def snake_case ( snake_case__ :int) -> list[int]:
_A = [True] * max_number
for i in range(2 , isqrt(max_number - 1) + 1):
if is_prime[i]:
for j in range(i**2 , snake_case__ ... | 81 | 1 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class a ( __lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase :Dict = ['''image_processor''', '''tokenizer''']
lowerCamelCase :Dict = ... | 81 | import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerF... | 81 | 1 |
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def snake_case ( snake_case__ :str , snake_case__ :str , **snake_case__ :int) -> Any:
_A = AutoConfig.from_pretrained(snake_case__ , **sna... | 81 | def snake_case ( snake_case__ :str , snake_case__ :str) -> list:
_A = len(snake_case__)
_A = []
for i in range(len(snake_case__) - pat_len + 1):
_A = True
for j in range(snake_case__):
... | 81 | 1 |
def snake_case ( ) -> int:
return 1
def snake_case ( snake_case__ :int) -> int:
return 0 if x < 0 else two_pence(x - 2) + one_pence()
def snake_case ( snake_case__ :int) -> int:
return 0 if x < 0 else five_pence(x -... | 81 | import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
class a ( __lowerCAmelCase ):
"""simple docstring"""
def __init__( self , *lowerCAmelC... | 81 | 1 |
from __future__ import annotations
def snake_case ( snake_case__ :list[float] , snake_case__ :list[float]) -> float:
_A = sorted(numsa + numsa)
_A , _A = divmod(len(snake_case__) , 2)
if mod == 1:
... | 81 | from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler... | 81 | 1 |
def snake_case ( snake_case__ :int , snake_case__ :int) -> str:
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(snake_case__ , int(b / 2)) * actual_power(snake_case__ , int(b / 2))
else:
... | 81 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE = {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
raise OptionalDep... | 81 | 1 |
def snake_case ( snake_case__ :int) -> int:
if not isinstance(snake_case__ , snake_case__):
_A = F'''Input value of [number={number}] must be an integer'''
raise TypeError(snake_case__)
if number < 1:
_A ... | 81 | from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from fastapi.routing impor... | 81 | 1 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class a ( __lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase :List[str] = ['''image_processor''', '''tokenizer''']
lowerCamelC... | 81 | import colorsys
from PIL import Image # type: ignore
def snake_case ( snake_case__ :float , snake_case__ :float , snake_case__ :int) -> float:
_A = x
_A = y
for step in range(snake_case__): # noqa: B007
... | 81 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}
class ... | 81 | def snake_case ( snake_case__ :int , snake_case__ :int) -> str:
return "\n".join(
F'''{number} * {i} = {number * i}''' for i in range(1 , number_of_terms + 1))
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
... | 81 | 1 |
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging... | 81 | import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE = [
'word_embeddi... | 81 | 1 |
from ..utils import DummyObject, requires_backends
class a ( metaclass=__lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase :Any = ['''onnx''']
def __init__( self , *lowerCAmelCase_ , **lowerCAmelCase_ ) -> Dict:
requi... | 81 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE = {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetCo... | 81 | 1 |
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 ( snake_case__ :int) ... | 81 | # DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor... | 81 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
'google/realm-cc-news-pretrained-embedder': (
'https://huggingface.co/google/realm-cc-news-pretrained-e... | 81 | from collections.abc import Sequence
def snake_case ( snake_case__ :Sequence[float] , snake_case__ :bool = False) -> float:
if not arr:
return 0
_A = 0 if allow_empty_subarrays else float("""-inf""")
_A = 0.... | 81 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoConfig,
... | 81 | def snake_case ( ) -> Any:
for n in range(1 , 1_000_000):
yield n * (n + 1) // 2
def snake_case ( snake_case__ :Dict) -> Optional[Any]:
_A = 1
_A = 2
while i * i <= n:
_A ... | 81 | 1 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conv... | 350 | import requests
from bsa import BeautifulSoup
def snake_case ( snake_case__ :str = "AAPL") -> str:
_A = F'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
_A = BeautifulSoup(requests.get(snake_case__).text , """html.parser"""... | 81 | 0 |
"""simple docstring"""
import numpy as np
_SCREAMING_SNAKE_CASE = [
['a', 'b', 'c', 'd', 'e'],
['f', 'g', 'h', 'i', 'k'],
['l', 'm', 'n', 'o', 'p'],
['q', 'r', 's', 't', 'u'],
['v', 'w', 'x', 'y', 'z'],
]
class a :
"""simple docstri... | 351 | import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class a ( __lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase :int = (UnCLIPScheduler,)
def UpperCAmelCase ( self , **lo... | 81 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE = {
'configuration_nllb_moe': [
'NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP',
'NllbMoeConfig',
]
}
try:
if not is_torch_av... | 352 | import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def snake_case ( ) -> List[Any]:
_A = ArgumentParser(
description=(
"""PyTorch TPU... | 81 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_SCREAMING_SNAKE_CASE = {
'configuration_squeezebert': [
'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'SqueezeBertConfig',
... | 353 | import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
_SCREAMING_SNAKE_CASE = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow('', '|', '|... | 81 | 0 |
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import Accelerator... | 354 | import cva
import numpy as np
class a :
"""simple docstring"""
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_ ) -> Any:
if k in (0.04, 0.06):
_A = k
_A = window_size
else:
... | 81 | 0 |
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class a ... | 355 | def snake_case ( snake_case__ :str) -> int:
_A = hex_num.strip()
if not hex_num:
raise ValueError("""No value was passed to the function""")
_A = hex_num[0] == """-"""
if is_negative:
_A = hex_num[1:]... | 81 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_eff... | 356 | # 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... | 81 | 0 |
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
from sagemaker.h... | 357 | import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
_SCREAMING_SNAKE_CASE = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.195... | 81 | 0 |
"""simple docstring"""
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 snake_case ( snake_case__ :List[Any]) ... | 358 | from math import isqrt
def snake_case ( snake_case__ :int) -> list[int]:
_A = [True] * max_number
for i in range(2 , isqrt(max_number - 1) + 1):
if is_prime[i]:
for j in range(i**2 , snake_case__ ... | 81 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_SCREAMING_SNAKE_CASE = {
"""configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
"""conf... | 359 | import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerF... | 81 | 0 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
requir... | 360 | def snake_case ( snake_case__ :str , snake_case__ :str) -> list:
_A = len(snake_case__)
_A = []
for i in range(len(snake_case__) - pat_len + 1):
_A = True
for j in range(snake_case__):
... | 81 | 0 |
from string import ascii_uppercase
_SCREAMING_SNAKE_CASE = {char: i for i, char in enumerate(ascii_uppercase)}
_SCREAMING_SNAKE_CASE = dict(enumerate(ascii_uppercase))
def snake_case ( snake_case__ :str , snake_case__ :str) -> str:
... | 361 | import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
class a ( __lowerCAmelCase ):
"""simple docstring"""
def __init__( self , *lowerCAmelC... | 81 | 0 |
"""simple docstring"""
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
imp... | 362 | from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler... | 81 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_SCREAMING_SNAKE_CASE = {
'configuration_vivit': ['VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VivitConfig'],
}
try:... | 363 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE = {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
raise OptionalDep... | 81 | 0 |
def snake_case ( snake_case__ :Any) -> int:
assert isinstance(a_ , a_), F'''The input value of [n={number}] is not an integer'''
if number == 1:
return 2
elif number < 1:
_A = F'''The input value of [n={number}] has to be ... | 364 | from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from fastapi.routing impor... | 81 | 0 |
import inspect
import unittest
from transformers import BitConfig
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_backbone_common import BackboneTesterMixin
from ...tes... | 365 | import colorsys
from PIL import Image # type: ignore
def snake_case ( snake_case__ :float , snake_case__ :float , snake_case__ :int) -> float:
_A = x
_A = y
for step in range(snake_case__): # noqa: B007
... | 81 | 0 |
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
_SCREAMING_SNAKE_CASE = 'src/diffusers'
# Matches is_xxx_available()
_SCREAMING_SNAKE_CASE = re.c... | 366 | def snake_case ( snake_case__ :int , snake_case__ :int) -> str:
return "\n".join(
F'''{number} * {i} = {number * i}''' for i in range(1 , number_of_terms + 1))
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
... | 81 | 0 |
def snake_case ( snake_case__ :Tuple) -> Tuple:
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: [6, 8],
... | 367 | import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE = [
'word_embeddi... | 81 | 0 |
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.util... | 368 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE = {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetCo... | 81 | 0 |
from __future__ import annotations
from typing import TypedDict
class a ( a__ ):
"""simple docstring"""
lowerCamelCase :str
lowerCamelCase :int
def snake_case ( snake_case__ :Union[str, Any]) -> Optional[Any]:
if not isinstance(a__ , ... | 369 | # DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor... | 81 | 0 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
_SCREAMING_SNAKE_CASE = l... | 370 | from collections.abc import Sequence
def snake_case ( snake_case__ :Sequence[float] , snake_case__ :bool = False) -> float:
if not arr:
return 0
_A = 0 if allow_empty_subarrays else float("""-inf""")
_A = 0.... | 81 | 0 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_... | 371 | def snake_case ( ) -> Any:
for n in range(1 , 1_000_000):
yield n * (n + 1) // 2
def snake_case ( snake_case__ :Dict) -> Optional[Any]:
_A = 1
_A = 2
while i * i <= n:
_A ... | 81 | 0 |
import string
from math import logaa
def snake_case ( snake_case__ :str , snake_case__ :str) -> Optional[int]:
_A = document.translate(
str.maketrans("""""" , """""" , string.punctuation)).replace("""\n""" , "... | 350 | import requests
from bsa import BeautifulSoup
def snake_case ( snake_case__ :str = "AAPL") -> str:
_A = F'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
_A = BeautifulSoup(requests.get(snake_case__).text , """html.parser"""... | 81 | 0 |
"""simple docstring"""
import random
from .binary_exp_mod import bin_exp_mod
def snake_case ( snake_case__ :str , snake_case__ :Any=1_000) -> Any:
if n < 2:
return False
if n % 2 == 0:
return n == 2
# t... | 351 | import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class a ( __lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase :int = (UnCLIPScheduler,)
def UpperCAmelCase ( self , **lo... | 81 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
"""tanreinama/GPTSAN-2.8B-spout_is_uniform""": (
"""https://huggingface.co/tanreinama/GPTSAN-2.8B-spout... | 352 | import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def snake_case ( ) -> List[Any]:
_A = ArgumentParser(
description=(
"""PyTorch TPU... | 81 | 0 |
import os
import sys
import unittest
_SCREAMING_SNAKE_CASE = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test... | 353 | import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
_SCREAMING_SNAKE_CASE = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow('', '|', '|... | 81 | 0 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_comm... | 354 | import cva
import numpy as np
class a :
"""simple docstring"""
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_ ) -> Any:
if k in (0.04, 0.06):
_A = k
_A = window_size
else:
... | 81 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCRE... | 355 | def snake_case ( snake_case__ :str) -> int:
_A = hex_num.strip()
if not hex_num:
raise ValueError("""No value was passed to the function""")
_A = hex_num[0] == """-"""
if is_negative:
_A = hex_num[1:]... | 81 | 0 |
"""simple docstring"""
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_SCREAMING_SNAKE_CASE = get_tests_dir('... | 356 | # 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... | 81 | 0 |
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from .... | 357 | import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
_SCREAMING_SNAKE_CASE = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.195... | 81 | 0 |
"""simple docstring"""
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 log... | 358 | from math import isqrt
def snake_case ( snake_case__ :int) -> list[int]:
_A = [True] * max_number
for i in range(2 , isqrt(max_number - 1) + 1):
if is_prime[i]:
for j in range(i**2 , snake_case__ ... | 81 | 0 |
class a :
"""simple docstring"""
def __init__( self , lowerCAmelCase_ ) -> Dict:
_A = arr.split(""",""" )
def UpperCAmelCase ( self ) -> str:
_A = [int(self.array[0] )] * len(self.array ... | 359 | import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerF... | 81 | 0 |
from PIL import Image
def snake_case ( snake_case__ :Image) -> Image:
_A , _A = image.size
_A = 0
_A = image.load()
for i in range(lowerCAmelCase__):
for j in range(lowerCAmelCase__):
... | 360 | def snake_case ( snake_case__ :str , snake_case__ :str) -> list:
_A = len(snake_case__)
_A = []
for i in range(len(snake_case__) - pat_len + 1):
_A = True
for j in range(snake_case__):
... | 81 | 0 |
import numpy
class a :
"""simple docstring"""
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_ ) -> None:
_A = input_array
# Random initial weights are assigned where first argument is the
# ... | 361 | import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
class a ( __lowerCAmelCase ):
"""simple docstring"""
def __init__( self , *lowerCAmelC... | 81 | 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,
)
_SCREAMING_SNAKE_CASE = {
'configuration_... | 362 | from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler... | 81 | 0 |
import collections
import os
import re
from pathlib import Path
_SCREAMING_SNAKE_CASE = 'src/transformers'
# Matches is_xxx_available()
_SCREAMING_SNAKE_CASE = re.compile(R'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
_SCREAMING_SNAKE_CASE ... | 363 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE = {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
raise OptionalDep... | 81 | 0 |
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
_SCREAMING_SNAKE_CAS... | 364 | from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from fastapi.routing impor... | 81 | 0 |
from datetime import datetime as dt
import os
from github import Github
_snake_case = [
"good first issue",
"good second issue",
"good difficult issue",
"feature request",
"new model",
"wip",
]
def snake_case ( ) -> Dict:
_A ... | 365 | import colorsys
from PIL import Image # type: ignore
def snake_case ( snake_case__ :float , snake_case__ :float , snake_case__ :int) -> float:
_A = x
_A = y
for step in range(snake_case__): # noqa: B007
... | 81 | 0 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class a ( unittest.TestCase , lowerCamelCase_ ):
"""simple docstring"""
def UpperCAmelCase ( self ) -> Any:
_A = load_to... | 366 | def snake_case ( snake_case__ :int , snake_case__ :int) -> str:
return "\n".join(
F'''{number} * {i} = {number * i}''' for i in range(1 , number_of_terms + 1))
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
... | 81 | 0 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common im... | 367 | import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE = [
'word_embeddi... | 81 | 0 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
cla... | 368 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE = {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetCo... | 81 | 0 |
import math
import sys
def snake_case ( snake_case__ :int) -> int:
if number != int(_snake_case):
raise ValueError("""the value of input must be a natural number""")
if number < 0:
raise ValueError("""the value of input must not be a negative number"... | 369 | # DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor... | 81 | 0 |
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