code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import numpy as np
class A :
def __init__( self: Tuple ) -> Any:
'''simple docstring'''
UpperCAmelCase_ =(0, 0)
UpperCAmelCase_ =None
UpperCAmelCase_ =0
UpperCAmelCase_ =0... | 54 |
"""simple docstring"""
def _lowercase ( __snake_case ,__snake_case ) -> int:
return int((input_a, input_a).count(1 ) != 0 )
def _lowercase ( ) -> None:
assert or_gate(0 ,0 ) == 0
assert or_gate(0 ,1 ) == 1
assert or... | 293 | 0 |
'''simple docstring'''
def __lowerCAmelCase (SCREAMING_SNAKE_CASE = 10 , SCREAMING_SNAKE_CASE = 1000 , SCREAMING_SNAKE_CASE = True )-> int:
"""simple docstring"""
assert (
isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
... | 703 |
# Copyright 2021 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 a... | 531 | 0 |
import os
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> List[Any]:
_A = len(grid[0] )
_A = len(_snake_case )
_A = 0
_A = 0
_A = 0
# Check vertically, horizontally, diagonally at the same time (only wor... | 2 | '''simple docstring'''
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def ... | 251 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from... | 419 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor... | 419 | 1 |
'''simple docstring'''
from statistics import mean, stdev
def __UpperCAmelCase ( _UpperCAmelCase : List[str] , _UpperCAmelCase : Union[str, Any] = 3 ) -> Union[str, Any]:
__snake_case = min(__lowercase )
__snake_case = max(__lowercase )
# normaliz... | 69 |
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... | 23 | 0 |
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''snap-research/efficientformer-l1-300''': (
'''https://huggingface.co/snap-research/efficientformer-l1... | 703 |
import unittest
import numpy as np
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ , lowercase_ = None , ) -> np.ndarray:
'''simple docstring'''
snake_case_ = np.shape(lowercase_ )
snake_case_ = np.shape(lowercase_ )
... | 161 | 0 |
"""simple docstring"""
import sys
from collections import defaultdict
class snake_case_ :
def __init__( self ):
a_ : int = []
def snake_case_ ( self , a_ ):
return self.node_position[vertex]
def snake_case_ ( self , a_ ... | 237 |
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE__ ) -> bytes:
# Make sure the supplied data is a bytes-like object
if not isinstance(SCREAMING_SNAKE_CASE__, ... | 237 | 1 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSe... | 709 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
__lowercase = logging.get_logger(__name__)
class _snake_case ( lowerCAmelCase_ ):
"""simple docstring"""
def __init__( self : Union[str... | 305 | 0 |
'''simple docstring'''
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def _UpperCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
for param in module.parameters():
__magic_name__ : Tuple =... | 436 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : Union[str, Any] = {
"micros... | 436 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils... | 22 |
"""simple docstring"""
import unittest
from transformers import XLMConfig, 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_modeling_common import... | 22 | 1 |
'''simple docstring'''
import unittest
import numpy as np
def __UpperCamelCase ( a : np.ndarray , a : np.ndarray , a : np.ndarray , a : np.ndarray | None = None , ) ->np.ndarray:
snake_case = np.shape(a )
snake_c... | 342 |
'''simple docstring'''
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
parser.add... | 342 | 1 |
"""simple docstring"""
import numpy as np
def lowercase ( __UpperCamelCase ) -> np.array:
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 718 |
"""simple docstring"""
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVi... | 190 | 0 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def _UpperCAmelCase ( a : str ):
if "model" in orig_key:
snake_case__ = orig_key.replace("""model.""" , """""" )
if "norm1" in orig_key:
snake_case__ ... | 654 |
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self : List[Any] , UpperCamelCase__ : int):
'''simple docstring'''
snake_case__ = size
snake_case__ = [0] * size
snake_case__ ... | 654 | 1 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class snake_case ( UpperCAmelCase ):
def __init__( self : Optional[Any] , A : str , A : List[st... | 118 |
"""simple docstring"""
import os
def snake_case ():
'''simple docstring'''
a : str = os.path.join(os.path.dirname(A_ ) , 'num.txt' )
with open(A_ ) as file_hand:
return str(sum(int(A_ ) for line in file_hand ) )[:1_0]
if __name__ == "__main__":
... | 118 | 1 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import Bat... | 434 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
SCREAMING_SNAKE_CASE__ : Dict ={
'configuration_perceiver': ['PERCEIVER_PRE... | 434 | 1 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that... | 334 |
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
require_torch... | 334 | 1 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def _a ( UpperCamelCase_ : Union[str, Any] ) -> Dict:
"""simple docstring"""
for param in module.parameters():
lowerCAmelCase__ = False
def _a ( ) -> Optional[Any]:... | 339 |
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_diffusion.pipeline_stable_diffus... | 339 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
fro... | 721 |
'''simple docstring'''
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils ... | 464 | 0 |
def lowercase__ ( __snake_case : Tuple ):
'''simple docstring'''
UpperCAmelCase_ : Tuple = 0
UpperCAmelCase_ : List[str] = len(__snake_case )
for i in range(n - 1 ):
for j in range(i + 1 , __snake_case ):
... | 406 |
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
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = R'\n Args:\n input_ids (... | 406 | 1 |
import argparse
import struct
import unittest
class _UpperCamelCase :
def __init__( self :List[str] , lowerCamelCase :bytes ) -> Dict:
UpperCAmelCase__ = data
# Initialize hash values
UpperCAmelCase__ = [
0x6a09e667,
... | 712 |
import torch
from transformers import AutoModel
class _UpperCamelCase ( torch.nn.Module ):
def __init__( self :str , lowerCamelCase :Tuple="sayef/fsner-bert-base-uncased" ) -> int:
super(lowerCamelCase , self ).__init__()
UpperCAmelCase__ = ... | 364 | 0 |
"""simple docstring"""
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__lowerCAmelCase ... | 58 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : float , __UpperCamelCase : float ):
'''simple docstring'''
if density <= 0:
raise ValueError("""Impossible fluid density""" )
if bulk_modulus <= 0... | 58 | 1 |
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_ca... | 458 |
def _UpperCAmelCase ( UpperCAmelCase : int = 4_000_000 ):
"""simple docstring"""
__lowerCamelCase : Union[str, Any] = [0, 1]
__lowerCamelCase : Dict = 0
while fib[i] <= n:
fib.append(fib[i]... | 458 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
resc... | 90 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase = logging.get_logger(__na... | 104 | 0 |
from timeit import timeit
def _A ( _UpperCamelCase ):
if number < 0:
raise ValueError('''the value of input must not be negative''' )
_UpperCAmelCase : Any = 0
while number:
number &= number - 1
result += 1
return result
def _A ( _UpperCame... | 720 |
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase__ : Optional[int] = get_tests_dir('fixtures... | 416 | 0 |
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to... | 606 |
'''simple docstring'''
from collections import defaultdict
from math import gcd
def A_ ( __SCREAMING_SNAKE_CASE : int = 1_50_00_00 ) -> int:
__SCREAMING_SNAKE_CASE : defaultdict = defaultdict(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE : ... | 158 | 0 |
from __future__ import annotations
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , ):
'''simple docstring'''
_lowerCAmelCase : List[str] = len(_lowerCamelC... | 658 |
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
_snake_case = "\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n title = {BLEU: a Method for Automatic Evalua... | 658 | 1 |
'''simple docstring'''
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_at... | 495 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __UpperCAmelCase ( _lowerCamelCase , unittest... | 495 | 1 |
"""simple docstring"""
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
_snake_case : int = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), ... | 721 |
"""simple docstring"""
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
_snake_case : List[str] = Lock()
def A__ ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCa... | 524 | 0 |
"""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,
ViTImage... | 560 |
"""simple docstring"""
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class __A :
'''simple docstring'''
def UpperCAmelCase ( self : Any ,_snake_ca... | 560 | 1 |
def __lowercase ( _A , _A ) -> str:
SCREAMING_SNAKE_CASE : int = len(_A )
SCREAMING_SNAKE_CASE : int = len(_A )
SCREAMING_SNAKE_CASE : int = (
first_str_length if first_str_length > second_str_length else sec... | 446 |
def __lowercase ( _A , _A ) -> int:
assert x is not None
assert y is not None
SCREAMING_SNAKE_CASE : int = len(_A )
SCREAMING_SNAKE_CASE : Union[str, Any] = len(_A )
# declaring the array for storing the dp values
... | 446 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ : Tuple = logging.get_logger(__name__)
UpperCamelCase_ : List[Any] = {
"""facebook/timesformer""": """https://huggingface.co/facebook/timesformer... | 331 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuratio... | 331 | 1 |
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self : List[str] ):
'''simple docstring'''
... | 707 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : Optional[Any] = {
'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}
try:
if not is_torch_available():... | 458 | 0 |
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.utils import cached_pro... | 12 | '''simple docstring'''
import os
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
UpperCamelCase_ = '''.'''
if __name__ == "__main__":
UpperCamelCase_ = os.path.join(REPO_PATH, '''utils/documentation_t... | 209 | 0 |
from timeit import timeit
__lowerCAmelCase : Tuple = {
"MALAYALAM": True,
"String": False,
"rotor": True,
"level": True,
"A": True,
"BB": True,
"ABC": False,
"amanaplanacanalpanama": True, # "a man a plan a canal panama"
}
# Ensure our test data is valid
a... | 284 |
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 __lowerCAmelCase ( lowerCAmelCase_ , unittest.TestCase ... | 284 | 1 |
"""simple docstring"""
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common impor... | 88 |
"""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
__snake_case = logging.get_logger(__name__)
__snake_case ... | 200 | 0 |
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 ...t... | 604 |
import heapq
import sys
import numpy as np
_lowerCAmelCase : str = tuple[int, int]
class __snake_case :
def __init__( self ):
"""simple docstring"""
lowerCAmelCase__ = []
lowerCAmelCase__ = set()
def SCREAMING_SNAKE_CASE_ ( ... | 604 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__A = {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}
try:
if not... | 59 |
'''simple docstring'''
from math import sqrt
def A ( UpperCamelCase_ : int ) -> int:
'''simple docstring'''
lowerCAmelCase__ = 0
for i in range(1 , int(sqrt(UpperCamelCase_ ) + 1 ) ):
if n % i == 0 and i != sqrt(UpperCamelCase_ ):
... | 48 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_):
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(lowerCamelCase_))
def SCREAMING_SNAKE_CASE (... | 715 |
"""simple docstring"""
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a : List[Any] = logging.get_logger(__name__)
__a : Tuple = {
'... | 200 | 0 |
from __future__ import annotations
def a_ ( _A ) -> int:
"""simple docstring"""
snake_case__ = len(_A ) // 2
# choose the middle 3 elements
snake_case__ = lst[m - 1 : m + 2]
# if middle element is peak
if three[1] > three[0] and ... | 328 |
# 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
#
# U... | 328 | 1 |
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available()... | 718 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
UpperCAmelCase_ : int = get_logger(__name__)
class lowercase__ ( enum.Enum ):
__UpperCamelCase = """all_checks"""
__UpperCamelCase ... | 440 | 0 |
'''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 _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Dict[str, torch.Tensor] ):
'''simple docstring'''
UpperCAmelCase_... | 603 |
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
_a : Tuple = [
# tf -> hf
("""/""", """."""),
("""layer_""", """layers."""),
("""kernel""", """w... | 145 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_UpperCAmelCase = {
'configuration_layoutlmv3': [
'LAYOUTLMV3_PRETRAINED_CO... | 240 |
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, RegNetYaagf, RegNetYaagf, RegNetY... | 240 | 1 |
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from unilm.wavlm.WavLM impo... | 271 |
def lowerCamelCase_ ( UpperCAmelCase_ : int = 10**12 ):
lowercase : List[str] = 1
lowercase : Optional[Any] = 0
lowercase : Tuple = 1
lowercase : Optional[int] = 1
while numerator ... | 583 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from typing import Any
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self ):
'''simple docstring'''
_lowerCAmelCase : ... | 630 |
'''simple docstring'''
import pytest
import datasets
# Import fixture modules as plugins
lowerCAmelCase : List[str] = ["""tests.fixtures.files""", """tests.fixtures.hub""", """tests.fixtures.fsspec"""]
def lowercase (_A , _A ):
... | 630 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_ch... | 48 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def SCREAMING_SNAKE_CASE ( snake_case__ , snake_case__ = "cpu" , snake_case__ = None ) -> None:
__UpperCAmelCase =torch.load(snake_case__ , map_location=snake_case__ )
for k, v in tqdm(state_... | 132 | 0 |
__lowerCamelCase : str = [
9_99,
8_00,
7_99,
6_00,
5_99,
5_00,
4_00,
3_99,
3_77,
3_55,
3_33,
3_11,
2_88,
2_66,
2_44,
2_22,
2_00,
1_99,
1_77,
1_55,
1_33,
1_11,
88,
66,
44,
22,
0,
]
__lowerCamelCa... | 501 |
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
__lowerCamelCase : List[Any] = logging.get_logger(__name__)
de... | 501 | 1 |
import unittest
import numpy as np
from datasets import load_dataset
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 ... | 60 |
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.modeli... | 121 | 0 |
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
_lowercase : List[str] =logging.get_logger(__name__)
class lowerCAmelCase_ ( A_ ):
'''simple docstring'''
def __init__( self , *lowerCamelCase , ... | 412 |
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Ac... | 412 | 1 |
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class SCREAMING_SNAKE_CASE ( yaml.SafeLoader ):
"""simple docstring"""
def __A ( self: Dict , __A: Any ) -> int:
_A = [self.const... | 484 |
import os
import unicodedata
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 SPIECE_UNDERLINE, logging
__A = logging.get_logger(__name__)
__A = {... | 484 | 1 |
'''simple docstring'''
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentPa... | 702 |
'''simple docstring'''
def A__ ( A_ ) -> int:
assert (
isinstance(A_ , A_ ) and number_of_steps > 0
), F"""number_of_steps needs to be positive integer, your input {number_of_steps}"""
if number_of_steps == 1:
return 1
_lowercase , _lowercase = ... | 602 | 0 |
"""simple docstring"""
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def a__ ( lowe... | 82 |
'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from ... | 143 | 0 |
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def __UpperCAmelCase ( lowercase = "AAPL" ):
"""simple docstring"""
_UpperCAmelCase = f'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
_UpperCAmelCase = BeautifulSoup(requests.get(lowe... | 275 | """simple docstring"""
import unittest
from knapsack import knapsack as k
class a ( unittest.TestCase ):
def lowerCAmelCase_ ( self : List[Any] ):
_UpperCAmelCase = 0
_UpperCAmelCase = [0]
_UpperCAmelCase = [0]
_UpperCAmelCa... | 275 | 1 |
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def _A ( _lowercase ) -> Tuple:
"""simple docstring"""
monkeypatch.setattr('datasets.utils.deprecation_utils._emitted_deprecation_warnings' , set() )
@pytest.... | 1 |
'''simple docstring'''
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionM... | 51 | 0 |
"""simple docstring"""
class lowerCamelCase__ :
"""simple docstring"""
def __init__( self : Optional[int] ):
'''simple docstring'''
__UpperCAmelCase : Optional[int] = """"""
__UpperCAmelCase : Any = ... | 299 |
"""simple docstring"""
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():
from PIL import Imag... | 299 | 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_re... | 20 | '''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConf... | 494 | 0 |
from __future__ import annotations
def __lowerCamelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
if len(lowerCamelCase__ ) < k or k < 0:
raise ValueError("Invalid Input" )
lowercase__ : Dict = sum(array[:k] )
for i in range(len... | 81 |
# 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 appli... | 81 | 1 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def lowerCamelCase__ ():
_SCREAMING_SNAKE_CASE : List[Any] = ArgumentParser(
description=(
"... | 249 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
lowerCAmelCase__ = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( lowercase ):
"""simple docstring"""
def __init_... | 645 | 0 |
"""simple docstring"""
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():
... | 720 |
"""simple docstring"""
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_fu... | 317 | 0 |
from numpy import exp, pi, sqrt
def A ( snake_case__ : List[str] , snake_case__ : float = 0.0 , snake_case__ : float = 1.0 ) -> Optional[Any]:
'''simple docstring'''
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
... | 313 |
from __future__ import annotations
def A(__a: list[int] , __a: list[int] , __a: int ):
lowerCAmelCase_ = list(range(len(__a ) ) )
lowerCAmelCase_ = [v / w for v, w in zip(__a , __a )]
index.sort(key=lambda __a : ratio[i] , ... | 122 | 0 |
'''simple docstring'''
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 ... | 368 |
'''simple docstring'''
import cva
import numpy as np
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self , lowerCamelCase , lowerCamelCase ):
if k in (0.04, 0.06):
_snake_case = k
_snake_case = window_... | 368 | 1 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def snake_case__ ( SCREAMING_SNAKE_CASE_ : str ):
'''simple docstring'''
return "".join(sorted(SCREAMING_SNAKE_CASE_ ) )
def snake_case__ ( SCREAMING... | 164 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
snake_case_ = datasets.utils.logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ (folder_based_builder.FolderBasedBuilderConfig ):
__lowerCamelC... | 164 | 1 |
"""simple docstring"""
import os
a :List[str] = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1_000}
def _lowercase ( __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ : Any = 0
SCREAMING_SNAKE_CASE__ : Dict = 0
while in... | 12 |
"""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,
AutoModelForSequence... | 12 | 1 |
'''simple docstring'''
from math import pi
def __lowercase (_lowercase, _lowercase ) -> float:
"""simple docstring"""
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(90, 10))
| 150 |
'''simple docstring'''
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def __lowercase (_lowercase, _lowercase, _lowercase, _lowercase, _lowercase = None, _lowercase = None, _lowercase... | 150 | 1 |
from functools import lru_cache
def lowercase ( a ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ :List[str] = 2
SCREAMING_SNAKE_CASE_ :Dict = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.add(a )
if n > 1:
... | 140 |
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_diffusion.pipeline_stable_diffusion_... | 140 | 1 |
from __future__ import annotations
import math
from collections.abc import Callable
def lowercase__ ( A_: Callable[[int | float], int | float] , A_: int | float , A_: int | float , A_: int = 100 , ) -> float:
"""simple docstring"""
... | 68 |
'''simple docstring'''
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMR... | 372 | 0 |
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : Any = logging.get_logger(__name__)
__lowerCAmelCase : Dict = {
"facebook/data2vec-base-960h": "https://huggingface.co/facebook/data2vec-audio-base-960h/resolve... | 284 |
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class __lowerCAmelCase ( datasets.BuilderConfig ):
"""simple docstring"""
... | 284 | 1 |
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)
#
# order specific notes:
# - tqdm must be checked before tokenizers
__... | 629 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
__lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
class A__ ( __snake_case ):
def __init__( self , *A_ , **A_ ):
'''simple docstring'''
... | 629 | 1 |
"""simple docstring"""
def UpperCamelCase ( UpperCAmelCase ) ->int:
"""simple docstring"""
if not isinstance(UpperCAmelCase , UpperCAmelCase ):
a_ = F'''Input value of [number={number}] must be an integer'''
raise TypeError(UpperCAmelCase )
if numbe... | 714 |
"""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... | 210 | 0 |
'''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 _snake_case ( A ) -> Dict[str, torch.Tensor]:
lowerCAmelCase__ = []
lowerCAme... | 90 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
from .... | 298 | 0 |
'''simple docstring'''
def lowerCAmelCase( a__ : Tuple=2_8123 ):
'''simple docstring'''
lowerCamelCase__ = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i +... | 426 |
'''simple docstring'''
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if... | 426 | 1 |
"""simple docstring"""
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
__A = 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_copies # noqa: E402
... | 93 |
'''simple docstring'''
import heapq
import sys
import numpy as np
UpperCAmelCase__ :Optional[Any] = tuple[int, int]
class SCREAMING_SNAKE_CASE :
def __init__( self : int ):
"""simple docstring"""
__lowerCamelCase :... | 150 | 0 |
'''simple docstring'''
lowercase_ = [
'''DownloadConfig''',
'''DownloadManager''',
'''DownloadMode''',
'''StreamingDownloadManager''',
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownlo... | 58 | '''simple docstring'''
import unittest
from knapsack import knapsack as k
class SCREAMING_SNAKE_CASE ( unittest.TestCase):
"""simple docstring"""
def UpperCamelCase__ ( self ) -> Optional[Any]:
_lowerCAmelCase =0
_lowerCAmelCase =[0]
... | 58 | 1 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils imp... | 657 |
"""simple docstring"""
def __A ( a_ : int = 10 , a_ : int = 10_00 , a_ : bool = True )-> int:
'''simple docstring'''
assert (
isinstance(a_ , a_ )
and isinstance(a_ , a_ )
and isinstance(a_ , a_ )
), "Invalid type of value(s) specified to funct... | 698 | 0 |
import copy
import re
class snake_case__ :
__A = '''hp'''
__A = {}
__A = None
@classmethod
def UpperCamelCase ( cls : Union[str, Any] , lowerCAmelCase_ : List[str] , lowerCAm... | 712 |
from __future__ import annotations
import math
def _lowerCAmelCase ( __magic_name__ :int ):
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 m... | 407 | 0 |
import random
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> tuple:
"""simple docstring"""
snake_case_ , snake_case_ , snake_case_ : Optional[Any] = [], [], []
for element in data:
if element < pivot:
... | 60 |
"""simple docstring"""
import argparse
import os
import re
lowerCamelCase_ = "src/transformers/models/auto"
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
lowerCamelCase_ = re.compile(r... | 498 | 0 |
'''simple docstring'''
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class lowercase ( __lowerCamelCase ):
def __UpperCAmelCase ( self : Optional[Any]) -> Optional[int]:
return [
{"col_... | 461 | '''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 a... | 461 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
snake_case__ : Tuple = {
"""configuration_longt5""": ["""LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LongT5Config""", """LongT5OnnxConfig"""],
}
try:
if not ... | 402 |
from math import sqrt
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format o... | 402 | 1 |
import collections
import inspect
import unittest
from transformers import FocalNetConfig
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... | 32 |
def UpperCAmelCase__ ( UpperCAmelCase__ :Any ):
'''simple docstring'''
if not head:
return True
# split the list to two parts
a , a = head.next, head
while fast and fast.next:
a = fast.next.next
a = slow.next
a = slow.next
a = N... | 32 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils impo... | 159 |
from ..utils import DummyObject, requires_backends
class __lowercase ( metaclass=_SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCAmelCase_ : Optional[Any] = ['''speech''']
def __init__( self , *__UpperCAmelCase ... | 542 | 0 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__a : Dict = logging.get_logger(__name__)
__a : List[Any] = {
'''speechbrain/m-ctc-t-large''': '''https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json''',
# See al... | 298 |
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr... | 298 | 1 |
"""simple docstring"""
import unittest
from transformers import LiltConfig, 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_modeling... | 224 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )-> int:
"""simple docstring"""
if exponent == 1:
return base
if exponent % 2 == 0:
UpperCamelCase_ = _modexpt(SCREAMING_SNAKE_CASE_ , ... | 628 | 0 |
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
a__ = TypeVar("""KEY""")
a__ = TypeVar("""VAL""")
@dataclass(frozen=SCREAMING_SNAKE_CASE_ ,slots=SCREAMING_SNAKE_CASE_ )
class snake_case ( Generic[K... | 198 |
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... | 198 | 1 |
"""simple docstring"""
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
lowercase__ = input("""Enter image url: """).strip()
print(F"Downloading image from {url} ...")
lowercase__ = BeautifulSoup(requests.get(url).cont... | 630 |
"""simple docstring"""
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class lowerCAmelCase__ ( tf.keras.l... | 630 | 1 |
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def A ( _lowercase ):
return x + 2
class lowercase__ ( unittest.TestCase):
def __A ( self : Tuple ):
'''... | 703 | import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class lowercase__ ( UpperCamelCase_):
def __init__( self : str , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Optio... | 34 | 0 |
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common impo... | 15 |
import numpy
# List of input, output pairs
A_ : Any = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
A_ : List[Any] = (((515, 22, 13), 555), ((61, 35, 49), 150))
A_ : ... | 57 | 0 |
def lowerCamelCase__ ( snake_case_ : str ) -> int:
assert column_title.isupper()
__snake_case = 0
__snake_case = len(snake_case_ ) - 1
__snake_case = 0
while index >= 0:
__snake_case = (ord(column_title[index] ... | 388 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switchi... | 388 | 1 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase (_SCREAMING_SNAKE_CASE : list[float] ):
__a : List[str] = 0.0_0
__a : int = 0
for resistor in resistors:
if resistor <= 0:
__a : Dict ... | 476 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common im... | 146 | 0 |
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
lowerCamelCase : str = logging.getLogger(__name__)
class __lowercase (UpperCAmelCase__ ):
"""simple docstrin... | 710 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
lowerCamelCase : Dict = {
'MIT/ast-finetuned-audioset-10-10-0.4593': (
'https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolv... | 684 | 0 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class _a ( UpperCamelCase__ ):
def lowerCamelCase_ ( self: List[Any] , UpperCamelCase_: float ) -> float:
... | 43 |
'''simple docstring'''
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class lowerCAmelCase_ ( lowerCamelCase_ ):
'''simple docstring'''
def __lt__( self : List[Any] , _Upper... | 603 | 0 |
'''simple docstring'''
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_... | 711 |
'''simple docstring'''
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
UpperCamelCase__ : Optional[Any] = logging.get_logger(__name__)
UpperCamelC... | 385 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_camembert import ... | 192 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_lowercase: Optional[Any] = {
'''configuration_rag''': ['''RagConfig'''],
'''retrieval_rag''': ['''RagRetriever'''],
'''tokenization_rag''': ['''RagTokenizer'''... | 192 | 1 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
... | 711 |
'''simple docstring'''
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Tuple ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = [1]
for i in range(2 , SCREAMING... | 68 | 0 |
'''simple docstring'''
from math import sqrt
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 0
for i in range(1 , int(sqrt(SCREAMING_SNAKE_CASE_ ) + 1 ) ):
if n % i == 0 and i != sqrt(SCREAMING_SNAKE_CASE_ ... | 591 |
'''simple docstring'''
import unittest
from transformers import DebertaVaConfig, 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 ModelTes... | 591 | 1 |
"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Co... | 704 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATIO... | 239 | 0 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import Se... | 39 |
import os
import jsonlines
import numpy as np
from tqdm import tqdm
_UpperCamelCase = 2048
_UpperCamelCase = 4096
_UpperCamelCase = 42
_UpperCamelCase = os.environ.pop('''PROCESS_TRAIN''', '''false''')
_UpperCamelCase = {'''null''': 0, '''short'''... | 146 | 0 |
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def _snake_case( ) -> List[str]... | 586 |
def _snake_case( SCREAMING_SNAKE_CASE__ : int = 10 , SCREAMING_SNAKE_CASE__ : int = 22 ) -> int:
'''simple docstring'''
A__ = range(1 , SCREAMING_SNAKE_CASE__ )
A__ = range(1 , SCREAMING_SNAKE_CASE__ )
... | 586 | 1 |
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=() , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CA... | 167 | import requests
__lowercase = '''''' # <-- Put your OpenWeatherMap appid here!
__lowercase = '''https://api.openweathermap.org/data/2.5/'''
def lowerCamelCase ( SCREAMING_SNAKE_CASE = "Chicago" , SCREAMING_SNAKE_CASE = APPID ):
'''simple docstring'''
return reques... | 167 | 1 |
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = 0
__lowerCAmelCase = len(snake_case_ ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_co... | 719 |
"""simple docstring"""
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
A : Dict = logging.get_logger(__name__) # pylint: disable=invalid-name
class _UpperCame... | 282 | 0 |
"""simple docstring"""
from collections import defaultdict
from math import gcd
def lowercase ( UpperCamelCase : int = 1500000 ):
"""simple docstring"""
A__ : defaultdict =defaultdict(UpperCamelCase )
A__ : Any =2
while 2 * euclid_m * (euclid_m + ... | 656 | """simple docstring"""
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class __lowerCAmelCase ( unittest.TestCase):
'''simple docstring'''
def ... | 656 | 1 |
def __snake_case ( UpperCamelCase , UpperCamelCase ) -> float:
"""simple docstring"""
if density <= 0:
raise ValueError('''Impossible fluid density''' )
if bulk_modulus <= 0:
raise ValueError('''Impossible bulk modulus''' )
return (bulk_modulus / density) ** 0.5
... | 713 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_fl... | 158 | 0 |
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