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 |
|---|---|---|---|---|
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_available():
import ... | 73 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbed... | 59 | 0 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _UpperCAmelCase ( _UpperCamelCase ):
... | 207 |
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import UNCONDI... | 207 | 1 |
import random
from .binary_exp_mod import bin_exp_mod
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=1000 ) -> List[str]:
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
lowerCamelCase :... | 48 |
from __future__ import annotations
import requests
def A ( _SCREAMING_SNAKE_CASE ) -> dict:
lowerCamelCase : Tuple = f'''https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'''
return requests.get(_SCREAMING_SNAKE_CASE ).json()
def... | 48 | 1 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | 210 |
def UpperCAmelCase_ ( __UpperCAmelCase : str ) -> str:
SCREAMING_SNAKE_CASE_ = ''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def UpperCAmelCase_ ( __... | 210 | 1 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : str = {
"""microsoft/unispeech-large-1500h-cv""": (... | 102 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"""microsoft/git-base""": """https://huggingface.co/microsoft/gi... | 323 | 0 |
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_param... | 194 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_available(check_device=Fa... | 194 | 1 |
import math
from numpy import inf
from scipy.integrate import quad
def _a ( SCREAMING_SNAKE_CASE_ : float ):
if num <= 0:
raise ValueError("math domain error" )
return quad(SCREAMING_SNAKE_CASE_ , 0 , SCREAMING_SNAKE_CASE_ , arg... | 92 | """simple docstring"""
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_... | 221 | 0 |
"""simple docstring"""
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.util... | 363 |
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class UpperCAmelCase_ ( nn.Module ):
def __init__( self : Optional[int] , snake_case_ : int = 16 , snake_case_ ... | 230 | 0 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
a__ : List[str] = 10
def UpperCAmelCase_( a__ , a__ , a__ , a__ ):
"""simple docs... | 313 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxT... | 313 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def UpperCAmelCase ( UpperCamelCase__ , UpperCamelC... | 154 | """simple docstring"""
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ... | 154 | 1 |
import argparse
import os
import re
import packaging.version
_A : Optional[int] = 'examples/'
_A : str = {
'examples': (re.compile(r'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
'init': (re.compile(r'^__version__\s+=\s+"([^"]+)"\s*$', re.MULT... | 142 |
def _a ( UpperCAmelCase , UpperCAmelCase ) -> str:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
lowerCamelCase__ : List[str] = str(bin(UpperCAmelCase ) )[2:] # remove ... | 142 | 1 |
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def __init__( self : int , __magic_name__ : Union[str, Any] , __magic_name__ : List[str] ) ... | 369 | import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
""... | 305 | 0 |
'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduli... | 185 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase_ : List[Any] ) -> str:
__lowerCamelCase : Tuple = 0
__lowerCamelCase : Optional[int] = len(UpperCAmelCase_ )
for i in range(n - 1 ):
for j in range(i + 1 , UpperCA... | 185 | 1 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : float , __lowercase : float ) -> float:
'''simple docstring'''
if mass < 0:
raise ValueError("The mass of a body cannot be negative" )
return 0.5 * mass * abs(__lowercase ) * abs(__l... | 354 |
'''simple docstring'''
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from .... | 156 | 0 |
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def _a ( SCREAMING_SNAKE_CASE , SCREAMI... | 110 |
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if exponent == 1:
return base
if exponent % 2 == 0:
lowercase__ = _modexpt(SCREAMING_SNAKE_CASE , exponent // 2 , SCREAMI... | 110 | 1 |
"""simple docstring"""
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 Backbo... | 371 |
"""simple docstring"""
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import Base... | 58 | 0 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetC... | 188 |
"""simple docstring"""
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y ... | 191 | 0 |
"""simple docstring"""
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
fr... | 370 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
res... | 326 | 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 by... | 225 |
def snake_case_ ( snake_case , snake_case ) -> bool:
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 196 | 0 |
'''simple docstring'''
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope="""session""" ... | 365 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
a__ : List[Any] = ... | 243 | 0 |
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.robert... | 303 |
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
lowercase_ = numpy.array([0, 0])
lowercase_ = numpy.array([0.5, 0.866_0254])
lowercase_ = numpy.array([1, 0])
lowercase_ ... | 303 | 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 = {
"""facebook/xmod-base""": """htt... | 165 | def lowercase( UpperCamelCase_ = 1000000 ) -> int:
'''simple docstring'''
UpperCamelCase = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , UpperCamelCase_ ):
phi[j] -= phi[j] // ... | 165 | 1 |
"""simple docstring"""
def __lowercase ( snake_case_ : int ,snake_case_ : int ) ->int:
'''simple docstring'''
return int(input_a == input_a == 0 )
def __lowercase ( ) ->None:
'''simple docstring'''
print('''Truth Table of NOR Gate:... | 179 |
"""simple docstring"""
def __lowercase ( snake_case_ : int ,snake_case_ : list ) ->Any:
'''simple docstring'''
_enforce_args(snake_case_ ,snake_case_ )
if n == 0:
return 0
__A : int = float('''-inf''' )
for i in range(1 ... | 179 | 1 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def A_ ( _lowerCAmelCase : Optional[int] ... | 350 |
"""simple docstring"""
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configurati... | 153 | 0 |
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils ... | 313 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MC... | 313 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get... | 70 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : Any = {
'''configuration_clap''': [
'''CLAP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ClapAudioConfig''',
'''ClapConfig''',
'''ClapTe... | 70 | 1 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutputW... | 330 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class __lowerCAmelCase ( lo... | 330 | 1 |
'''simple docstring'''
import os
import sys
import unittest
lowerCamelCase : Optional[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files... | 360 |
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class A__ ( A__ ):
A__ = 'MCTCTFeatureExtractor'
A__ = 'AutoTokenizer'
def __init__( self : Optional[Any] , _a : Optional[int] ... | 114 | 0 |
"""simple docstring"""
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCamelCase__... | 86 |
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
a_ = [
# tf -> hf
("""/""", """."""),
("""layer_""", """layers."""),
("""kernel""", """weight"""),
... | 330 | 0 |
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
lowerCamelCase_ = {'''UserAgent''': UserAgent().random}
def __magic_name__ ( __a : Union[str, Any] ):
'''simple docstring'''
Uppe... | 178 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
def __magic_name__ ( __a : Optional[int] ):
'''simple docstring'''
... | 178 | 1 |
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def A ( a_ ,a_ ) -> Union[str, Any]:
__UpperCamelCase : List[Any] =... | 71 |
from torch import nn
class __A ( nn.Module ):
"""simple docstring"""
def __init__( self , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
super().__init__()
__UpperCamelCase : ... | 71 | 1 |
"""simple docstring"""
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaag... | 112 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __lowerCAmelCase ... | 112 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_A = {'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}
try:
if not is_torch_... | 62 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
lowercase = {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}
try:
if not is_tokenizers_av... | 178 | 0 |
"""simple docstring"""
import re
def UpperCAmelCase ( UpperCamelCase__ ):
"""simple docstring"""
A__ = re.compile(r'^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$' )
if match := re.search(UpperCamelCase__ , UpperCamelCase__ ):
... | 154 | """simple docstring"""
def UpperCAmelCase ( UpperCamelCase__ = 100 ):
"""simple docstring"""
A__ = (n * (n + 1) // 2) ** 2
A__ = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main_... | 154 | 1 |
"""simple docstring"""
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...model... | 64 | import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
__lowercase = (720, 1280) # Height, Width
__lowercase = (0.4, 0.6) # if height or width lower than this scale, drop it.
__lowercase = 1 / 100
__lowercase ... | 43 | 0 |
"""simple docstring"""
__A = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
__A = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->list[int]:
"""simple docstrin... | 254 |
"""simple docstring"""
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mod... | 254 | 1 |
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class __lowerCAmelCase ( UpperCamelCase__):
def... | 95 |
'''simple docstring'''
import math
from numpy import inf
from scipy.integrate import quad
def __a(SCREAMING_SNAKE_CASE_ : float ):
'''simple docstring'''
if num <= 0:
raise ValueError("math domain error" )
return quad(SCREAMING_SNAKE_CASE_ , 0 , SCREA... | 158 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''naver-clova-ix/donut-base''': '''https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json''',
... | 175 |
"""simple docstring"""
def snake_case_ ( A_ : float ):
'''simple docstring'''
if edge <= 0 or not isinstance(A_, A_ ):
raise ValueError('''Length must be a positive.''' )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
def ... | 175 | 1 |
'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, Trainin... | 70 |
'''simple docstring'''
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffuser... | 70 | 1 |
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test... | 350 |
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,
PILImageResampl... | 109 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy,... | 312 |
"""simple docstring"""
import warnings
from ..trainer import Trainer
from ..utils import logging
lowercase_ = logging.get_logger(__name__)
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self , _a=None ... | 45 | 0 |
import torch
from torch import nn
class SCREAMING_SNAKE_CASE__ (nn.Module ):
def __init__( self , a , a , a , a , a=1 , a=False):
super().__init__()
lowercase__ : Optional[int] = n_token
lowe... | 355 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionInpaintPipeline` instead.'''
)
| 216 | 0 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProce... | 16 | import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs("""hub/hopper-medium-v2/unet/hor32""", exist_ok=True)
os.makedirs("""hub/hopper-medium-v2/unet/hor128""", exist_ok=True)
os.makedirs("""hub/hopper-medium-v2/value_function""", exist_ok=True)
def UpperCamelCase ... | 140 | 0 |
def A (__A : int ) -> str:
"""simple docstring"""
if isinstance(__A , __A ):
raise TypeError('''\'float\' object cannot be interpreted as an integer''' )
if isinstance(__A , __A ):
raise TypeError('''\'str... | 7 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class __snake_case ( a ):
UpperCAmelCa... | 7 | 1 |
'''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ..... | 112 |
'''simple docstring'''
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
UpperCamelCase__ : List[Any] = {
'''E''': 1_2.7_0,
'''T''': 9.0_6,
'''A''': 8.1_7,
'''O''': 7.5_1,
'''I''': 6.9_7,
'''N''': 6.7_5,
'''S''': 6.3_3,
'''H''': 6.0_9,
... | 112 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class __lowerCAmelCase :
"""simple docstring"""
_snake_case : int
_snake_cas... | 287 |
'''simple docstring'''
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 ... | 287 | 1 |
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
_snake_case = logging.get_logger(__name__)
_snake_cas... | 283 |
"""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 logging
... | 247 | 0 |
from string import ascii_lowercase, ascii_uppercase
def a__ ( UpperCAmelCase : str ) -> str:
if not sentence:
return ""
UpperCAmelCase : Tuple = dict(zip(UpperCAmelCase , UpperCAmelCase ) )
return lower_to_upper.get(sentence[0] , sentence[0]... | 99 |
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common imp... | 99 | 1 |
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
snake_case : List[Any] = datasets.utils.logging.get_logger(__name__)
@dataclass
class _snake_ca... | 94 |
from collections import defaultdict
from math import gcd
def _a ( UpperCamelCase_ : int = 1_500_000 ) -> int:
"""simple docstring"""
lowerCAmelCase__ = defaultdict(UpperCamelCase_ )
lowerCAmelCase__ = 2
while 2 * euclid_m ... | 340 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
DiffusionPipel... | 213 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(F... | 213 | 1 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import Backbon... | 175 | from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def __lowercase ( ):
UpperCamelCase_ : Optional[Any] = HfArgumentParser(lowerCamelCase )
UpperCamelCase_ : Tuple = parser.parse_args_into_dataclasses()[0]
UpperCamelCase_ : ... | 175 | 1 |
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
lowercase_ = TypeVar("""KEY""")
lowercase_ = TypeVar("""VAL""")
@dataclass(frozen=UpperCAmelCase , slots=UpperCAmelCase )
class SCREAMING_SNA... | 269 |
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
lowercase_ = {
# 1536-bit
5: {
"""prime""": int(
... | 269 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_t... | 136 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> list:
'''simple docstring'''
lowercase_ = len(__lowerCAmelCase )
lowercase_ = [[0] * n for i in range(__lowerCAme... | 136 | 1 |
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
lowerCamelCase_ = datasets.utils.logging.get_logger(__name__)
@dataclass
class ... | 354 |
import baseaa
def lowerCamelCase ( a_ ) -> bytes:
return baseaa.baaencode(string.encode('utf-8' ) )
def lowerCamelCase ( a_ ) -> str:
return baseaa.baadecode(a_ ).decode('utf-8' )
if __name__ == "__main__":
lowerCamelC... | 14 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A = {
"""configuration_x_clip""": [
"""XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""XCLIPConfig""",
"""XCLIPTextConfig""",
"""XCLIP... | 171 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
... | 171 | 1 |
import comet # From: unbabel-comet
import torch
import datasets
a_ : Union[str, Any] = datasets.logging.get_logger(__name__)
a_ : Optional[int] = '\\n@inproceedings{rei-EtAl:2020:WMT,\n author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},\n ... | 327 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertMode... | 327 | 1 |
def _snake_case( SCREAMING_SNAKE_CASE__ : int ) -> str:
'''simple docstring'''
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
raise TypeError('\'float\' object cannot be interpreted as an integer' )
if isinstan... | 7 |
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common... | 7 | 1 |
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
IMAGENET_STANDAR... | 366 |
from __future__ import annotations
def __UpperCAmelCase ( __a : list ) -> float:
"""simple docstring"""
if not nums:
raise ValueError('''List is empty''' )
return sum(__a ) / len(__a )
if __name__ == "__main__":
import do... | 15 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def snake_case_ ( A_ : Tuple, A_ : int, A_ : Dict ):
... | 72 |
'''simple docstring'''
import math
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> float:
if initial_intensity < 0:
raise ValueError("""The value of intensity cannot be negative""" )
# handling of negative values of initia... | 321 | 0 |
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
... | 44 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class a ( __lowerCamelCase , unittest.TestCase ):
__lowerCAmelCase : Dict ... | 44 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
logging.set... | 216 |
import requests
from bsa import BeautifulSoup
def _a ( SCREAMING_SNAKE_CASE : str = "AAPL" ):
"""simple docstring"""
UpperCamelCase__ : Tuple = F"https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"
UpperCamelCase__ : int = BeautifulSoup(requests.get(SCR... | 146 | 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 = ... | 357 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase ) -> int:
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise ValueError("""multiplicative_persistence() only accepts integral values""" )
if num < 0:
raise ValueError("""multiplicati... | 43 | 0 |
"""simple docstring"""
__UpperCamelCase = 9.80665
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = g ) -> float:
if fluid_density <= 0:
raise ValueError('Impossible fluid density' )
if volume < 0:
raise V... | 69 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase = {
'''configuratio... | 69 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase: str = logging.get_logger(__name__)
... | 368 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase: Opt... | 336 | 0 |
def _A ( SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
raise ValueError("multiplicative_persistence() only accepts integral values" )
if num < 0:
raise Valu... | 95 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : Optional[Any] = logging.get_logger(__name__)
UpperCAmelCase : Optional[int] = {
"""google/canine-s""": """https://huggingface.co/google/canine-s/resolve/main/config.json"""... | 95 | 1 |
import heapq as hq
import math
from collections.abc import Iterator
class __magic_name__ :
"""simple docstring"""
def __init__( self :int , snake_case :Optional[Any] ):
'''simple docstring'''
A_ : Dict = str(id_ )
A_... | 70 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | 70 | 1 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class UpperCAmelCase_ ... | 4 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Any = logging.get_logger(__name__)
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase__ = '''encoder-decoder'''
... | 14 | 0 |
'''simple docstring'''
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
_lowercase : int = logging.get_logger(__name__)
def lowerCamelCase ( UpperCAmelCase__ : Optio... | 366 | '''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_av... | 21 | 0 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
'google/pix2struct-textcaps-base': (
'https://huggingface.co/google/pix2struct-tex... | 250 |
'''simple docstring'''
_snake_case = 8.3_1_4_4_5_9_8
def _A ( snake_case , snake_case ) -> float:
if temperature < 0:
raise Exception("Temperature cannot be less than 0 K" )
if molar_mass <= 0:
raise Exception("Molar mass cannot be less than or equal to 0 kg/mol" ... | 250 | 1 |
lowercase_ = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
def __lowerCAmelCase ( ):
lowercase__ = input("Enter message: " )
lowercase__ = input("Enter key [alphanumeric]: " )
lowercase__ = input("Encrypt/Decrypt [e/d]: " )
if mode.lower().startswith("e" ... | 362 |
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
lowercase_ = 2_9979_2458
# Symbols
lowercase_ , lowercase_ , lowercase_ , lowercase_ = symbols("""ct x y z""")
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ ... | 224 | 0 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Union[str, Any] = logging.get_logger(__name__)
_UpperCAmelCase : Any = {
'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}
class... | 50 |
SCREAMING_SNAKE_CASE :Any = 256
# Modulus to hash a string
SCREAMING_SNAKE_CASE :Union[str, Any] = 100_0003
def UpperCAmelCase ( a_ , a_ ) -> bool:
"""simple docstring"""
__A = len(a_ )
__A = len(a_ )
if p_len > t_len:
... | 15 | 0 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def __lowerCamelCase ( lowerCAmelCase__ ):
for param in module.parameters():
lowerCAmelCase__ = False
def __lowerCamelCase ( ):
lowerCAmelCase__ = 'cuda' if torch... | 119 | import warnings
from typing import Any, Dict, 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 ...uti... | 119 | 1 |
'''simple docstring'''
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
... | 80 |
'''simple docstring'''
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
... | 80 | 1 |
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
lowerCamelCase_ : Optional[Any] = logging.get_logger(__name__)
def A__ ( lowerCamelCase ) -> List[int]:
if isinstance(lowerCamelCase , np.ndarray ):
re... | 223 |
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_common import ConfigT... | 223 | 1 |
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTe... | 277 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
a_ :Any = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui Wu and Mi... | 277 | 1 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
__magic_name__: str = collections.namedtuple("_Datasets", ["train"... | 138 |
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, Stab... | 138 | 1 |
import gc
import inspect
import unittest
import torch
from parameterized import parameterized
from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import Mo... | 62 |
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...tes... | 336 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common impor... | 95 | """simple docstring"""
def __UpperCAmelCase ( UpperCAmelCase_ : int , UpperCAmelCase_ : int ) -> int:
'''simple docstring'''
while a != 0:
__snake_case , __snake_case : Union[str, Any] = b % a, a
return b
def __UpperC... | 95 | 1 |
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,... | 0 |
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_device
fr... | 147 | 0 |
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import req... | 167 |
import string
def __UpperCamelCase ( _A ):
for key in range(len(string.ascii_uppercase ) ):
lowerCAmelCase_ = ''''''
for symbol in message:
if symbol in string.ascii_uppercase:
lowerCAmelCase_ = string.ascii_up... | 167 | 1 |
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
SCREAMING_SNAKE_CASE :Dict = '\\n@misc{chen2021evaluating,\n title={Evaluating Larg... | 15 |
from __future__ import annotations
from math import ceil, floor, sqrt
def UpperCamelCase_( lowerCamelCase_ = 200_0000 ) -> int:
_lowercase : list[int] = [0]
_lowercase : int
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ):
triangle... | 21 | 0 |
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto import TF_MODEL... | 371 |
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def lowerCAmelCase__ ( a__: NDArray[floataa] , a__: NDArray[floataa] , a__: list[int] , a__: int , ) -> list[float]:
'''simple docstring'''
_Up... | 185 | 0 |
"""simple docstring"""
def lowercase ( __snake_case : str , __snake_case : bool = False ):
if not isinstance(__lowerCamelCase , __lowerCamelCase ):
lowercase_ : Any = F'''Expected string as input, found {type(__lowerCamelCase )}'''
raise Val... | 33 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_d... | 114 | 0 |
"""simple docstring"""
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : List[Any] ) -> Tuple:
def wrapper(*_lowerCamelCase : int ... | 367 | """simple docstring"""
import math
def SCREAMING_SNAKE_CASE ( ) -> None:
_lowerCAmelCase : Any = input("""Enter message: """ )
_lowerCAmelCase : List[Any] = int(input(f"Enter key [2-{len(_lowerCamelCase ) - 1}]: " ) )
_lowerCAmelCase ... | 126 | 0 |
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
from transformers import (
... | 62 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A = {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDepend... | 62 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase__ = {
"configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try... | 280 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
if len(UpperCAmelCase_ ) == 0:
raise ValueError('find_max() arg is an empty sequence' )
if (
left >= len(UpperCAmelCase_ )
or ... | 280 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_c... | 27 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowerCamelCase : int = 1_00 ):
lowercase_ :Tuple = n * (n + 1) * (2 * n + 1) / 6
lowercase_ :List[str] = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
p... | 223 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ = {
"""configuration_convbert""": ["""CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MA... | 354 | """simple docstring"""
def __UpperCAmelCase ( lowercase ):
"""simple docstring"""
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 30 | 0 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
A__ = []
create_all_state(1 , UpperCAmelCase_ , UpperCAmelCase_ , [] , UpperCAmelCase... | 335 |
"""simple docstring"""
import math
class a :
"""simple docstring"""
def __init__( self: List[Any] , UpperCamelCase: List[str]=0 ): # a graph with Node 0,1,...,N-1
"""simple docstring"""
A__ = n
A__... | 335 | 1 |
from __future__ import annotations
from typing import TypedDict
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
_UpperCAmelCase : str
_UpperCAmelCase : int
def A_ ( _UpperCAmelCase ):
if not isinstance(_UpperCAmelCase ... | 127 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_to... | 127 | 1 |
from ..utils import DummyObject, requires_backends
class A ( metaclass=__UpperCAmelCase ):
__snake_case = ['torch', 'transformers', 'onnx']
def __init__( self, *UpperCamelCase__, **UpperCamelCase__ ):
"""simple docstring"""
requires_backends(self, ['''torch... | 278 |
def __UpperCamelCase ( _A ):
if not numbers:
return 0
if not isinstance(_A , (list, tuple) ) or not all(
isinstance(_A , _A ) for number in numbers ):
raise ValueError('''numbers must be an iterable of integers''' )
lowerCAmelCase_ = low... | 278 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_a = {
'configuration_bert': ... | 356 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
'caidas/swin2sr-classicalsr-x2-64': (
'https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json'
),
}... | 144 | 0 |
'''simple docstring'''
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusio... | 265 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import Iterab... | 230 | 0 |
import torch
def A__ ( ) -> str:
if torch.cuda.is_available():
__snake_case: int = torch.cuda.device_count()
else:
__snake_case: Optional[Any] = 0
print(F'''Successfully ran on {num_gpus} GPUs''')
if __name__ == "__main__":
main()
| 354 |
from __future__ import annotations
import numpy as np
def A__ ( SCREAMING_SNAKE_CASE__) -> List[str]:
return np.maximum(0 , SCREAMING_SNAKE_CASE__)
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 293 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}
class __lowerCamelCase ( snake_case_ ... | 241 |
'''simple docstring'''
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
lowercase_ = """Usage of script: script_name <size_of_canvas:int>"""
lowercase_ = [0] * 100 + [1] * 10
random.shuffle(choice)
def lowerCamelCase ... | 58 | 0 |
"""simple docstring"""
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
... | 354 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
@register_to_config
def __init__( sel... | 110 | 0 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDependencyNotAvailable()
except O... | 225 |
from math import sqrt
def UpperCAmelCase_ ( __UpperCAmelCase : int ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiple... | 225 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline... | 41 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case : Union[str, Any] = logging.get_logger(__name__)
snake_case : List[Any] = {
"junnyu/roformer_chin... | 41 | 1 |
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor, r... | 82 |
"""simple docstring"""
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
fro... | 242 | 0 |
from ..utils import DummyObject, requires_backends
class lowercase_ ( metaclass=_UpperCAmelCase ):
_lowerCamelCase = ['flax', 'transformers']
def __init__( self , *lowercase_ , **lowercase_ ):
requires_backends(self , ["flax", "transform... | 355 | import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class lowercase_ ( __snake_case ):
_lowerCamelCase = 'M-CLIP'
def __init__( self , lowercase_=1_024 , lowercase_=768 , **lowercase_ ):
_snake_case ... | 284 | 0 |
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