code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import requests
lowerCamelCase_ = 'YOUR API KEY'
def SCREAMING_SNAKE_CASE_ ( __A : str , __A : str = giphy_api_key ) -> list:
_SCREAMING_SNAKE_CASE = "+".join(query.split() )
_SCREAMING_SNAKE_CASE = f"""https://api.giphy.... | 418 |
'''simple docstring'''
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchE... | 418 | 1 |
'''simple docstring'''
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_te... | 718 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
a : Tuple = False
class UpperCamelCase_ ( unit... | 672 | 0 |
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tenso... | 62 |
"""simple docstring"""
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def _lowerCAmelCase ( UpperCamelCase_ ):
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_S... | 155 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase ={
"configuration_speech_to_text": ["SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_M... | 716 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase ={
"configuration_clipseg": [
"CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP",
"CLIPSegConfig",
"CLIPSegTextConfig",
"CLIPSegVisionConfig",
],
... | 462 | 0 |
"""simple docstring"""
import sys
import turtle
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelC... | 644 | """simple docstring"""
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
__lowerCAmelCase : Dict = TypeVar("KT")
__lowerCAmelCase : Optional[Any] = TypeVar("VT")
class a_ ( Generic[KT, VT] ):
def __init__( s... | 644 | 1 |
"""simple docstring"""
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# o... | 85 |
"""simple docstring"""
import argparse
import os
from . import (
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
DISTI... | 85 | 1 |
"""simple docstring"""
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@requ... | 580 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
'''tanreinama/GPTSAN-2.8B-spout_is_uniform''': (
'''https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_i... | 580 | 1 |
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils import OnnxRuntimeMod... | 709 |
def _A (UpperCamelCase : List[Any] , UpperCamelCase : Dict ) ->List[str]:
'''simple docstring'''
lowerCamelCase__ : int = [1]
for i in range(2 , UpperCamelCase ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k... | 96 | 0 |
"""simple docstring"""
from __future__ import annotations
__UpperCamelCase : List[str] = '''#'''
class a :
def __init__( self ):
"""simple docstring"""
lowerCAmelCase = {}
def UpperCamelCase__ ( self , _snake_case ... | 4 |
'''simple docstring'''
from __future__ import annotations
from scipy.special import comb # type: ignore
class _SCREAMING_SNAKE_CASE :
def __init__( self : Optional[Any] , a__ : list[tuple[float, float]] ):
__magic_name__ = list_of_points
... | 432 | 0 |
import warnings
from .generation import TFGenerationMixin
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ):
# warning at import time
warnings.warn(
'Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will '
'be... | 708 |
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.tra... | 388 | 0 |
from __future__ import annotations
import math
def lowerCAmelCase_ ( __UpperCAmelCase: Tuple ) -> 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... | 253 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils impo... | 591 | 0 |
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... | 701 |
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
__A = re.compile(R"^(?P<major>\d+)" R"\.(?P<minor>\d+)" R"\.(?P<patch>\d+)$")
@total_ordering
@dataclass
class _SCREAMING_SNAKE_CASE... | 437 | 0 |
from math import ceil, sqrt
def lowerCAmelCase_ ( __a = 1000000 ) -> int:
"""simple docstring"""
lowerCamelCase__: Any =0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
lowerCamelCase__: Optional[int] =... | 59 |
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class UpperCamelCase__ ( lowercase_ ):
... | 379 | 0 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia ... | 330 | '''simple docstring'''
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_util... | 330 | 1 |
'''simple docstring'''
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_fl... | 44 |
import argparse
import os
import re
import packaging.version
SCREAMING_SNAKE_CASE__ = "examples/"
SCREAMING_SNAKE_CASE__ = {
"examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
"init": (re.compile(R"^__version__\s+=\s+\... | 631 | 0 |
'''simple docstring'''
def lowercase ( lowerCAmelCase : int = 100_0000):
"""simple docstring"""
_A : Any = 1
_A : str = 1
_A : Dict = {1: 1}
for inputa in range(2 , lowerCAmelCase):
_A : Any = 0
... | 417 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2... | 417 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
"vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
# See all GLPN models at https://huggingface.co/mod... | 109 |
"""simple docstring"""
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def a__ ( snake_case__ ) -> Dict[str, torch.Tensor]:
lowerCamelCase = []
lowerCamelCase = [... | 543 | 0 |
"""simple docstring"""
from sklearn.metrics import matthews_corrcoef
import datasets
__A = """
Compute the Matthews correlation coefficient (MCC)
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into acco... | 173 | """simple docstring"""
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class a ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
A_ : Dict = [(... | 173 | 1 |
'''simple docstring'''
from statistics import mean, stdev
def _lowerCAmelCase ( lowerCamelCase_ : list , lowerCamelCase_ : int = 3 ):
__lowercase = min(lowerCamelCase_ )
__lowercase = max(lowerCamelCase_ )
# normalize dat... | 502 |
'''simple docstring'''
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 GenerationTesterMi... | 502 | 1 |
'''simple docstring'''
from math import log
from scipy.constants import Boltzmann, physical_constants
a__ = 300 # TEMPERATURE (unit = K)
def snake_case__ ( a , a , a , ) -> float:
'''simple docstring'''
if donor_conc <= 0:
raise ValueErr... | 566 |
'''simple docstring'''
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... | 566 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
lowerCAmelCase: List[str] = 'https://www.indeed.co.in/jobs?q=mobile+app+development&l='
def lowerCamelCase__ ( _A = "mumbai" ):
a : List[str] ... | 526 |
'''simple docstring'''
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
lowerCAmelCase: Optional[int] = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
lowerCAmelCase: O... | 526 | 1 |
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... | 73 |
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowerCAmelCase__ ( ... | 73 | 1 |
"""simple docstring"""
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class __lowercase ( _UpperCAmelCase):
"""simple docstring"""
_A : Optional[int] = ["""image_processor""", """tokenizer"""]
... | 480 |
"""simple docstring"""
from math import factorial
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : int = 2_0 ):
"""simple docstring"""
snake_case_ : int = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...... | 480 | 1 |
"""simple docstring"""
from collections import Counter
from timeit import timeit
def lowercase ( __snake_case : List[Any] = "" , ):
return sum(c % 2 for c in Counter(input_str.replace(''' ''' , '''''' ).lower() ).values() ) < 2
def lowercase ... | 710 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForOb... | 141 | 0 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def SCREAMING_SNAKE_CASE__ ( snake_case__ :BertModel , snake_case__ :str , snake_case__ :str ) -> int:
_lowercase = ('dense.weight', ... | 67 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case = {
"""configuration_nllb_moe""": [
"""NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""NllbMoeConfig""",
]
}
try:
if not ... | 67 | 1 |
'''simple docstring'''
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
UpperCamelCase__: List[Any] = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
" (KHTML, like Geck... | 528 |
'''simple docstring'''
import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
... | 528 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
__A : Tuple = {'configuration_vit': ['VIT_PRETRAINED_CONFIG_ARCH... | 394 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_config... | 394 | 1 |
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def __snake_case ( _UpperCamelC... | 346 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required ... | 346 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class UpperCAmelCase_ ( metaclass=snake_case ):
UpperCamelCase =["torch", "transformers", "onnx"]
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ) -> Union... | 76 |
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceC... | 453 | 0 |
"""simple docstring"""
from __future__ import annotations
from functools import lru_cache
from math import ceil
_SCREAMING_SNAKE_CASE : str = 100
_SCREAMING_SNAKE_CASE : Any = set(range(3, NUM_PRIMES, 2))
primes.add(2)
_SCREAMING_SNAKE_CAS... | 137 |
"""simple docstring"""
import math
def lowerCamelCase__ ( _lowerCamelCase : int ) -> bool:
assert isinstance(_lowerCamelCase , _lowerCamelCase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2... | 137 | 1 |
"""simple docstring"""
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_c... | 182 |
"""simple docstring"""
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_... | 182 | 1 |
"""simple docstring"""
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import pa... | 274 |
"""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 ... | 274 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import Model... | 320 | '''simple docstring'''
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 tra... | 614 | 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 OptionalDependencyN... | 114 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase : Union[str, Any] = {'''configuration_xglm''':... | 114 | 1 |
'''simple docstring'''
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
snake_case_ = log... | 507 |
from math import factorial
def __lowercase ( __lowerCAmelCase : int , __lowerCAmelCase : int , __lowerCAmelCase : float ):
if successes > trials:
raise ValueError('successes must be lower or equal to trials' )
if trials < 0 or su... | 335 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case : List[str] = {
'''configuration_blenderbot''': [
'''BLENDERBOT_PRETRA... | 657 |
def __lowercase ( __lowerCAmelCase : int ):
a__ = generate_pascal_triangle(__lowerCAmelCase )
for row_idx in range(__lowerCAmelCase ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 ):
print(end=' ... | 657 | 1 |
"""simple docstring"""
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def __magic_name__ ( ) -> str:
'''simple docstring'''
raise RuntimeError('''CUDA out of m... | 535 |
"""simple docstring"""
import warnings
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAm... | 535 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_re... | 231 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
_snake_case = {'''configuration_beit''': ['''BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BeitConfig''', '''BeitOnnx... | 231 | 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 app... | 60 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def lowerCamelCase_ ( _UpperCamelCase ) -> Union[str, Any]:
"""simple docstring"""
return getitem, k
def lowerCamelCase_ ( _UpperCamelCase ,... | 60 | 1 |
from itertools import permutations
def UpperCamelCase (lowercase_: tuple ) -> bool:
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
A__ : Tuple = [7, 11, 13, 17]
for i, test in enumerate(lowercase_ ... | 64 |
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 Config... | 64 | 1 |
from __future__ import annotations
def _lowerCamelCase ( lowerCamelCase_: list[int] , lowerCamelCase_: int , lowerCamelCase_: int , lowerCamelCase_: int ):
'''simple docstring'''
if (direction == 1 and array[indexa] > array[indexa]) o... | 256 |
'''simple docstring'''
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,
transp... | 158 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import requir... | 708 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_uti... | 523 | 0 |
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from ... | 699 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"vocab_file": "vocab.json",
"tokenizer_config_file": "to... | 699 | 1 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def __snake_case ( ):
__a = ArgumentParser(
description=(
'''PyTorch TPU distributed training launch '''
... | 719 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
__snake_case :Union[str, Any] = {'''configuration_vit''': ['''VIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''V... | 60 | 0 |
import operator as op
def __a ( SCREAMING_SNAKE_CASE ) -> str:
'''simple docstring'''
__UpperCAmelCase = []
__UpperCAmelCase = lambda SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : int(x / y ) # noqa: E731 integer division opera... | 303 |
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 OptionalDependencyNo... | 303 | 1 |
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, Path... | 712 |
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...t... | 235 | 0 |
'''simple docstring'''
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _A ( __l... | 26 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
... | 76 | 0 |
from typing import Any
def UpperCamelCase ( _A : Optional[Any] , _A : Optional[Any] , _A : Dict , _A : Optional[int] , _A : Union[str, Any] , )-> list:
"""simple docstring"""
_validation(
__snake_cas... | 712 |
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():
fr... | 232 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''facebook/data2vec-text-bas... | 39 |
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
fro... | 39 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A_ = logging.get_logger(__name__)
A_ = {
"shi-labs/nat-min... | 465 |
'''simple docstring'''
def A_ ( snake_case = 100 ):
SCREAMING_SNAKE_CASE:Dict = 0
SCREAMING_SNAKE_CASE:Optional[int] = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
if __name__ =... | 465 | 1 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available(... | 13 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : list[int] ,_UpperCAmelCase : int ) -> bool:
_a : Optional[int] =len(_UpperCAmelCase )
_a : Tuple =[[False] * (required_sum + 1) for _ in r... | 694 | 0 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import AN... | 212 |
# 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 r... | 212 | 1 |
"""simple docstring"""
class _UpperCAmelCase:
def __init__( self , __a , __a , __a) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = None
_UpperCamelCase = None
_UpperCamelCa... | 19 |
"""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
__lowerCAmelCase : ... | 58 | 0 |
"""simple docstring"""
def lowercase_ ( _lowercase : int , _lowercase : List[str] , _lowercase : Dict , _lowercase : int , _lowercase : Union[str, Any] ):
'''simple docstring'''
if index ... | 710 |
"""simple docstring"""
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_configura... | 292 | 0 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPMSol... | 192 | import unittest
from transformers import SqueezeBertConfig, 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 ModelTesterMixin, ids_tensor, ran... | 192 | 1 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import patch_envir... | 717 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase : List[Any] = {
"configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}
try:
if not is_torch_available():
raise Option... | 453 | 0 |
'''simple docstring'''
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class a__ :
def __init__( self : List[str] ):
"""simple docstring"""
__lowerCamelCase = ''''''
__lowerCamelCase ... | 546 | '''simple docstring'''
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
__Upp... | 546 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowercase : Union[str, Any] ={"configuration_opt": ["OPT_PRETRAINE... | 574 |
'''simple docstring'''
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import hugg... | 574 | 1 |
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
lowerCAmelCase__ ="\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbo... | 482 |
"""simple docstring"""
import torch
def _a ( ) -> List[Any]:
if torch.cuda.is_available():
__SCREAMING_SNAKE_CASE = torch.cuda.device_count()
else:
__SCREAMING_SNAKE_CASE = 0
print(f"""Successfully ran on {num_gpus} GPUs""" )... | 482 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
class _snake_case :
"""simple docstring"""
def __init__( self : int , UpperCamelCase_ : list[str] ):
lowerCAmelCase_ : list[dict] =[]
self.adlist.append(
... | 305 |
'''simple docstring'''
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def SCREAMING_SNAKE_CASE__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ... | 305 | 1 |
'''simple docstring'''
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
a : List[Any] = "\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n and Saurav K... | 679 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
fro... | 130 | 0 |
# 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... | 721 | _SCREAMING_SNAKE_CASE = {
'A': '.-', 'B': '-...', 'C': '-.-.', 'D': '-..', 'E': '.', 'F': '..-.', 'G': '--.',
'H': '....', 'I': '..', 'J': '.---', 'K': '-.-', 'L': '.-..', 'M': '--', 'N': '-.',
'O': '---', 'P': '.--.', 'Q': '--.-', 'R': '.-.', 'S': '...', 'T': '-', 'U': '..-',
'V': '...-'... | 83 | 0 |
from collections import Counter
from timeit import timeit
def SCREAMING_SNAKE_CASE__ ( snake_case_ = "", ) -> bool:
"""simple docstring"""
return sum(c % 2 for c in Counter(input_str.replace(''' ''', '''''' ).lower() ).values() ) < 2
def SCREAMING_SNAKE_CASE__ ... | 387 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class a_ :
def __init__( self , _SCREAMING_SNAKE_CASE = 6 ) -> None:
"""simple docstring"""
UpperCamelCase = None
UpperCamel... | 301 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
... | 718 |
import datasets
from .evaluate import evaluate
snake_case__ = '''\
@inproceedings{Rajpurkar2016SQuAD10,
title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
booktitle={EMNLP},
year={2016}
}
'''
snake... | 373 | 0 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class _lowerCamelCase :
__a = 42
__a = None
__a = None
def A__ ( snake_case_ : TreeNode | None ):
# Validation
def is_valid_tree(snake_case_ : TreeNode | None ) -> ... | 64 | # 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 .scheduling_utils_flax import (
Co... | 64 | 1 |
'''simple docstring'''
import functools
def lowerCamelCase__ ( A : list[int] , A : list[int] ):
'''simple docstring'''
if not isinstance(A , A ) or not all(isinstance(A , A ) for day in days ):
raise ValueError('''The parameter days shou... | 50 |
'''simple docstring'''
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def lowerCamelCase__ ( A : Optional[Any] , A : Tuple=1 ):
'''simple docstring'''
if n_shave_prefix_segments >= 0:
r... | 50 | 1 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE... | 341 |
"""simple docstring"""
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... | 341 | 1 |
"""simple docstring"""
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def UpperCAmelCase ( snake_case : str ):
_lowerCAmelCase:str = args.pruning_method
_lowerCAmelCase:Any ... | 439 |
"""simple docstring"""
def UpperCAmelCase ( snake_case : str , snake_case : str ):
assert x is not None
assert y is not None
_lowerCAmelCase:Optional[Any] = len(snake_case )
_lowerCAmelCase:Tuple = len(snake_case )
# declar... | 439 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowercase = {
'''configuration_electra''': ['''ELECTRA_PRETRAINED_CONF... | 91 | '''simple docstring'''
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def lowerCAmelCase ( UpperCamelCase__ : int ):
"""simple docstring"""
# A local function to see if a dot lands in the circle.
de... | 262 | 0 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
... | 206 |
def __lowerCAmelCase ( __magic_name__ , __magic_name__ , __magic_name__ ):
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(__magic_name__ , n - 1 , __magic_name__ ) * a) % mod
else:
_lowercase: int = binary_exponentiation(__magic_n... | 206 | 1 |
def _lowerCamelCase( __snake_case = 10**9 ) -> int:
__snake_case = 1
__snake_case = 2
__snake_case = 0
__snake_case = 0
__snake_case = 0
while perimeter <= max_perimeter:
perimeters_sum += perimeter
prev_value += 2 * value
value += prev... | 524 | #
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-tes... | 524 | 1 |
from math import isqrt
def SCREAMING_SNAKE_CASE_ ( __A : int ) -> bool:
"""simple docstring"""
return all(number % divisor != 0 for divisor in range(2 , isqrt(__A ) + 1 ) )
def SCREAMING_SNAKE_CASE_ ( __A : ... | 443 |
def SCREAMING_SNAKE_CASE_ ( __A : str ) -> list:
"""simple docstring"""
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(__A ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__('d... | 443 | 1 |
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers import Gradi... | 117 |
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class __magic_name__ (snake_case_ ):
'''simple docstring'''
__lowercase : List[str] = ['image_processor', 'tokenizer']
__lowercase :... | 33 | 0 |
import uuid
from typing import Any, Dict, List, Optional, Union
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
if is_torch_available():
import torch
__lowercase = ... | 713 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowercase :str = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependen... | 26 | 0 |
"""simple docstring"""
from __future__ import annotations
from typing import Generic, TypeVar
lowercase_ = TypeVar("T")
class UpperCAmelCase_ (Generic[T] ):
"""simple docstring"""
def __init__( self : Dict , a_ : T )-> None:
"""simple docstr... | 470 |
"""simple docstring"""
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_g... | 470 | 1 |
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def lowerCamelCase( a__ = "isbn/0140328726"):
'''simple docstring'''
_SCREAMING_SNAKE_CASE =olid.strip().strip('''/''') # Remove leading/trailing whitespace & slashes
if new_o... | 707 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def lowerCamelCase( a__):
for param in module.parameters():
_SCREAMING_SNAKE_CASE =False
def lowerCamelCase( ):
_SCREAMING_SNAKE_CASE ='''cuda''' if torch.cuda.is_available() else '''cpu'... | 191 | 0 |
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_flax, require_tf, require_torch
fr... | 339 |
'''simple docstring'''
def _snake_case ( A_ : str , A_ : str ):
"""simple docstring"""
if not (isinstance(A_ , A_ ) and isinstance(A_ , A_ )):
raise ValueError("""longest_common_substring() takes two strings for inputs""" )
a_ : Optional[int... | 577 | 0 |
'''simple docstring'''
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision impor... | 211 |
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
Au... | 211 | 1 |
'''simple docstring'''
import os
import sys
UpperCamelCase__ = os.path.join(os.path.dirname(__file__), '''src''')
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
... | 75 |
from __future__ import annotations
def a__ ( A_, A_ ):
'''simple docstring'''
if b == 0:
return (1, 0)
((__magic_name__) , (__magic_name__)) = extended_euclid(A_, a % b )
__magic_name__ = a // b
return (y, x - k * y)
def a__ ( ... | 529 | 0 |
'''simple docstring'''
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
a : Any = importlib.util.find_spec('''s3fs''') is not None
if _ha... | 719 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : Union[str, Any] = logging.get_logger(__name__)
a : List[Any] = {
... | 680 | 0 |
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 onnxruntime as ort... | 37 | # This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
Au... | 64 | 0 |
_UpperCAmelCase = {
"""A""": """.-""", """B""": """-...""", """C""": """-.-.""", """D""": """-..""", """E""": """.""", """F""": """..-.""", """G""": """--.""",
"""H""": """....""", """I""": """..""", """J""": """.---""", """K""": """-.-""", """L""": """.-..""", """M""": """--""", """N""": """-.""",
... | 70 | import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class UpperCAmelCase ( tf.keras.optimizers.schedules.LearningRateSchedule ):
... | 70 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_A : Any = {'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}
try:
if n... | 315 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def _a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> List[str]:
"""simple docstring"""
lowerCamelCase__ : Tuple = 0
if start < end:
lowerCamelCase__ : ... | 315 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A = {
'configuration_bloom': ['BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BloomConfig', 'BloomOnnxConfig'],
}
try:
if not is_tokenizers_available():
... | 234 | A = 8.3_14_45_98
def lowerCamelCase ( UpperCamelCase : float , UpperCamelCase : float ) -> 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... | 234 | 1 |
import argparse
import os
from pathlib import Path
import fairseq
import torch
from packaging import version
from torch import nn
from transformers import (
BartConfig,
BartForConditionalGeneration,
BartForSequenceClassification,
BartModel,
BartTokenizer,
)
from transformer... | 556 |
from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def __lowerCamelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
if not arr:
return None, N... | 496 | 0 |
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def __lowercase ( ... | 703 |
"""simple docstring"""
import unittest
from transformers import BertGenerationConfig, 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
from ...test_mo... | 497 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ : Dict = {
"""configuration_clipseg""": [
"""CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""CLIPSegConfig""",
"""CLIPSegTextCon... | 13 |
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.t... | 265 | 0 |
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, l... | 709 |
from math import pow
def UpperCAmelCase__ ( _A , _A , _A , _A , _A , ):
"""simple docstring"""
if current_sum == needed_sum:
# If the sum of the powers is equal to needed_sum, then we have a solution.
solutions_count += 1
r... | 143 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"""xlm-mlm-en-2048""": """https://huggingf... | 29 |
"""simple docstring"""
import os
from datetime import datetime as dt
from github import Github
A_ = [
"""good first issue""",
"""feature request""",
"""wip""",
]
def lowercase ( ):
lowerCamelCase_ = Github(os.environ['''GITHUB_TOKEN'''] )
lowerCamelCase_ =... | 29 | 1 |
'''simple docstring'''
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
i... | 644 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __a (unittest.Te... | 644 | 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_STANDARD_MEAN,
... | 461 |
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_forma... | 461 | 1 |
import unittest
from diffusers.models.unet_ad_blocks import * # noqa F403
from diffusers.utils import torch_device
from .test_unet_blocks_common import UNetBlockTesterMixin
class snake_case_ (lowerCamelCase_ , unittest.TestCase ):
UpperCAmelCase__ : str = DownBlockaD # n... | 714 |
from math import pi
def __lowercase ( __lowerCAmelCase : int , __lowerCAmelCase : int ):
return 2 * pi * radius * (angle / 3_6_0)
if __name__ == "__main__":
print(arc_length(90, 10))
| 657 | 0 |
def _a ( lowercase__ : Tuple , lowercase__ : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = [1]
for i in range(2 , lowercase__ ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k ou... | 85 |
"""simple docstring"""
def snake_case__ ( _lowerCamelCase ) ->str:
"""simple docstring"""
if isinstance(_lowerCamelCase, _lowerCamelCase ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(_lowerCamelCase, _lowerCamelCase... | 575 | 0 |
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
__snake_case : Optional[int] = logging.get_logger(__name__)
__snake_case : str = R"""
Args:
in... | 365 |
from __future__ import annotations
__snake_case : Any = {
"""A""": ["""B""", """C""", """E"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F""", """G"""],
"""D""": ["""B"""],
"""E""": ["""A""", """B""", """D"""],
"""F""": ["""C"""],
"""G""": ["""C""... | 365 | 1 |
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
lowercase_: int = '\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n author=... | 648 |
def lowerCamelCase_ ( UpperCamelCase__ : str , UpperCamelCase__ : int ) -> list:
"""simple docstring"""
__lowerCamelCase = word.split()
def justify(UpperCamelCase__ : list , UpperCamelCase__ : int , UpperCamelCase__... | 469 | 0 |
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_ = logging.get_logger(__name__)
UpperCAmelCase_ = '▁'
Uppe... | 716 |
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
... | 369 | 0 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, 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 Co... | 240 | from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
lowercase = HfArgumentParser(InitializationArguments)
lowercase = parser.parse_args()
# Load codeparrot tokenizer trained for Python... | 240 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__magic_name__ = {
'configuration_conditional_detr': [
'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
'ConditionalDetrConfig',
... | 709 |
import re
def __snake_case ( _UpperCAmelCase ):
"""simple docstring"""
if len(re.findall('[ATCG]' , _UpperCAmelCase ) ) != len(_UpperCAmelCase ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans(... | 314 | 0 |
import qiskit
def __lowercase ( __lowerCAmelCase : int , __lowerCAmelCase : int ):
a__ = qiskit.Aer.get_backend('aer_simulator' )
# Create a Quantum Circuit acting on the q register
a__ = qiskit.QuantumCircuit(__lowerCAmelCase , __lowerCA... | 335 |
import argparse
import datetime
def __lowercase ( __lowerCAmelCase : str ):
a__ = {
'0': 'Sunday',
'1': 'Monday',
'2': 'Tuesday',
'3': 'Wednesday',
'4': 'Thursday',
'5': 'Friday',
'6': 'Saturday',
}... | 335 | 1 |
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
UpperCAmelCase__ = logging.getLogger(__name__)
UpperCAmelCase__ = 50 # max width of layer nam... | 639 |
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoModelForMaskedLM,
... | 639 | 1 |
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