code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from __future__ import annotations
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
lowercase = sorted(numsa + numsa )
lowercase , lowercase = divmod(len(lowerCAmelCase__ ) , 2 )
if mod == 1:
return al... | 101 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get... | 42 | 0 |
"""simple docstring"""
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE : List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : List[str] = {"""vocab_file""": """v... | 102 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( __A ) -> bool:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 42 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__ : Any = logging.get_logger(__name__)
A__ : List[str] = {
'''facebook/data2vec-text-base''': '''https://... | 103 |
'''simple docstring'''
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
lowercase : Optional[Any] = argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
p... | 42 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase__ = {
'''configuration_vision_encoder_decoder''': ['''VisionEncoderDecoderConf... | 104 |
'''simple docstring'''
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __UpperCAmelCase ( _lowerCamelCase ):
def lowerCamelCase ( self , lowerCAmelCase_ ):
""... | 42 | 0 |
"""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_DEF... | 105 |
'''simple docstring'''
import tensorflow as tf
from ...tf_utils import shape_list
class __UpperCAmelCase ( tf.keras.layers.Layer ):
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_=1 , ... | 42 | 0 |
"""simple docstring"""
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
__UpperCamelCase : Optional[Any] = '''scheduler_conf... | 106 |
'''simple docstring'''
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
lowercase : Dict = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to wors... | 42 | 0 |
def __magic_name__ ( A : list ):
'''simple docstring'''
def merge(A : list, A : list ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yield from left
yield from right
... | 107 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase : Any = {
"configuration_chinese_clip": [
"CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"ChineseCLI... | 42 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
def a__ ( SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : float ):
'''simple docstring'''
lowerCAmelCase : Opti... | 108 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( __A ) -> str:
_snake_case = 1
_snake_case = 2
while i * i <= n:
_snake_case = 0
while n % i == 0:
n //= i
multiplicity += 1
n_divisors *= multiplicity + 1
i += 1
if n > 1:
n_divisors *= 2
return n_div... | 42 | 0 |
"""simple docstring"""
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def _snake_case ( UpperCamelCase : int , UpperCamelCase : int , UpperCamelCase : int , UpperCamelCase : int , UpperCamelCase : int... | 109 |
'''simple docstring'''
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class __UpperCAmelCase ( _lowerCamelCase ):
@require_torch
def lowerCamelCase ... | 42 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = {'vocab_f... | 110 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@... | 42 | 0 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__UpperCamelCase : List[str] = "src/transformers"
# Th... | 182 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase : List[str] = {
"configuration_pix2struct": [
"PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Pix2Stru... | 42 | 0 |
'''simple docstring'''
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class lowercase__ ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
lowercase__ = [(""... | 83 |
'''simple docstring'''
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
lowercase : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
lowercase : list[int] = [ord(letter)... | 42 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from... | 347 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( __A = 1_000_000 ) -> int:
_snake_case = limit + 1
_snake_case = [0] * limit
for first_term in range(1 , __A ):
for n in range(__A , __A , __A ):
_snake_case = first_term + n / first_term
if commo... | 42 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCamelCase = {
"configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
"tokenization_xlm": ["XLMTokenizer"],
}
t... | 87 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowercase : Tuple = {
"configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
"tokenization... | 42 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a__ = {"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLNetC... | 317 |
'''simple docstring'''
from collections import defaultdict
from math import gcd
def SCREAMING_SNAKE_CASE__ ( __A = 1_500_000 ) -> int:
_snake_case = defaultdict(__A )
_snake_case = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for euclid_n in range((euclid_m % 2) + 1 ... | 42 | 0 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
lowerCAmelCase_ = logging.get_logger(__name__)
def snake_case( __magic_name__ , __magic_name_... | 308 |
'''simple docstring'''
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
lowercase : Optional[Any] = False
class __... | 42 | 0 |
"""simple docstring"""
def a__ ( snake_case__ ) -> int:
if not isinstance(__A , __A ):
lowerCamelCase = F'Input value of [number={number}] must be an integer'
raise TypeError(__A )
if number < 1:
lowerCamelCase = F'Input value of ... | 291 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( __A = 100 ) -> int:
_snake_case = n * (n + 1) * (2 * n + 1) / 6
_snake_case = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 42 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a : List[str] = {
"configuration_pix2struct": [
"PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Pix2StructConfig",
... | 311 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVeca... | 42 | 0 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(_lowerCamel... | 108 |
'''simple docstring'''
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : int = logging.get_logger(__name__)
lowercase : Union[str, Any] = {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/... | 42 | 0 |
"""simple docstring"""
a = range(2, 20 + 1)
a = [10**k for k in range(ks[-1] + 1)]
a = {}
def _snake_case ( _snake_case : Dict , _snake_case : List[str] , _snake_case : Dict , _snake_case : int ) -> Any:
... | 315 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transfor... | 42 | 0 |
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimension,
I... | 310 |
'''simple docstring'''
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 42 | 0 |
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_... | 182 |
'''simple docstring'''
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
lowercase : List[str] = logging.get_logger("transformers.models.speecht5")
def SCREAMING_SNAKE_CASE__ ( ... | 42 | 0 |
'''simple docstring'''
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
snake_case_ : Tuple = input('Enter image url: ').strip()
print(F"""Downloading image from {url} ...""")
snake_case_ : Optional[int] = BeautifulSoup(request... | 83 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get... | 42 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
__SCREAMING_S... | 347 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( __A ) -> bool:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 42 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCamelCase = {
"configuration_layoutlmv2": ["LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Layout... | 87 |
'''simple docstring'''
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
lowercase : Optional[Any] = argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
p... | 42 | 0 |
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class snake_case :
'''simple docstring'''
def __init__( self : Optional[Any] , lowerCAmelCase : List[str]) -> int:
"""simple docstring"""
... | 317 |
'''simple docstring'''
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __UpperCAmelCase ( _lowerCamelCase ):
def lowerCamelCase ( self , lowerCAmelCase_ ):
""... | 42 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _A ( _lowerCamelCase ):
_UpperCamelCase : int = ['''image_processor''', '''tokenizer''']
_UpperCamelCase : int = '''CLIPImageProcessor'''
_UpperCa... | 308 |
'''simple docstring'''
import tensorflow as tf
from ...tf_utils import shape_list
class __UpperCAmelCase ( tf.keras.layers.Layer ):
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_=1 , ... | 42 | 0 |
"""simple docstring"""
def a__ ( snake_case__ , snake_case__ , snake_case__ ) -> list:
lowerCamelCase = len(__A )
lowerCamelCase = [[0] * n for i in range(__A )]
for i in range(__A ):
lowerCamelCase = y_points[i]
for... | 291 |
'''simple docstring'''
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
lowercase : Dict = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to wors... | 42 | 0 |
'''simple docstring'''
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 OptionalDependenc... | 311 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase : Any = {
"configuration_chinese_clip": [
"CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"ChineseCLI... | 42 | 0 |
"""simple docstring"""
def a__ ( SCREAMING_SNAKE_CASE : str = 1_0_0 ):
'''simple docstring'''
lowerCAmelCase : int = n * (n + 1) * (2 * n + 1) / 6
lowerCAmelCase : Any = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squar... | 108 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( __A ) -> str:
_snake_case = 1
_snake_case = 2
while i * i <= n:
_snake_case = 0
while n % i == 0:
n //= i
multiplicity += 1
n_divisors *= multiplicity + 1
i += 1
if n > 1:
n_divisors *= 2
return n_div... | 42 | 0 |
"""simple docstring"""
import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname
import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary
from transformers import FSMTConfig, FSMTForConditionalGenerat... | 315 |
'''simple docstring'''
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class __UpperCAmelCase ( _lowerCamelCase ):
@require_torch
def lowerCamelCase ... | 42 | 0 |
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_device
from... | 310 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@... | 42 | 0 |
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS... | 182 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase : List[str] = {
"configuration_pix2struct": [
"PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Pix2Stru... | 42 | 0 |
'''simple docstring'''
def A__ ( UpperCAmelCase_ = 1_0_0_0 ):
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 83 |
'''simple docstring'''
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
lowercase : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
lowercase : list[int] = [ord(letter)... | 42 | 0 |
"""simple docstring"""
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 ... | 347 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( __A = 1_000_000 ) -> int:
_snake_case = limit + 1
_snake_case = [0] * limit
for first_term in range(1 , __A ):
for n in range(__A , __A , __A ):
_snake_case = first_term + n / first_term
if commo... | 42 | 0 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_... | 87 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowercase : Tuple = {
"configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
"tokenization... | 42 | 0 |
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize("""repo_id""" , ["""canonical_dataset_name""", """org-name/dataset-name"""] )
@pytest.mark.parametrize("""path""" , ["""filename.csv""", """filename with blanks.csv"""] )
@pytest.... | 317 |
'''simple docstring'''
from collections import defaultdict
from math import gcd
def SCREAMING_SNAKE_CASE__ ( __A = 1_500_000 ) -> int:
_snake_case = defaultdict(__A )
_snake_case = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for euclid_n in range((euclid_m % 2) + 1 ... | 42 | 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/xmod-base": "https://hugg... | 308 |
'''simple docstring'''
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
lowercase : Optional[Any] = False
class __... | 42 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase : Tuple = {
"configuration_ctrl": ["CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CTRLConfig"],
"tokenization_... | 291 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( __A = 100 ) -> int:
_snake_case = n * (n + 1) * (2 * n + 1) / 6
_snake_case = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 42 | 0 |
'''simple docstring'''
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def lowercase ( __magic_name__ ): # picklable for mu... | 311 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVeca... | 42 | 0 |
"""simple docstring"""
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
lowerCAmelCase__ = logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE__ :
"""simple... | 108 |
'''simple docstring'''
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : int = logging.get_logger(__name__)
lowercase : Union[str, Any] = {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/... | 42 | 0 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( _snake_case : Dict ) -> int:
'''simple docstring'''
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing t... | 315 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transfor... | 42 | 0 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDe... | 310 |
'''simple docstring'''
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 42 | 0 |
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class lowercase__ ( _lowerCamelCase):
UpperCamelCase_ ... | 182 |
'''simple docstring'''
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
lowercase : List[str] = logging.get_logger("transformers.models.speecht5")
def SCREAMING_SNAKE_CASE__ ( ... | 42 | 0 |
'''simple docstring'''
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def A__ ( UpperCAmelCase_ = 3 ):
if isinstance(__A , __A ):
raise TypeError('number of qubits must be a integer.' ... | 83 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get... | 42 | 0 |
"""simple docstring"""
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
__SCREAMING_SNAKE_CASE : Dict = 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 #... | 347 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( __A ) -> bool:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 42 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available()... | 87 |
'''simple docstring'''
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
lowercase : Optional[Any] = argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
p... | 42 | 0 |
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, re... | 317 |
'''simple docstring'''
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __UpperCAmelCase ( _lowerCamelCase ):
def lowerCamelCase ( self , lowerCAmelCase_ ):
""... | 42 | 0 |
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 ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_ver... | 308 |
'''simple docstring'''
import tensorflow as tf
from ...tf_utils import shape_list
class __UpperCAmelCase ( tf.keras.layers.Layer ):
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_=1 , ... | 42 | 0 |
"""simple docstring"""
from copy import deepcopy
class __magic_name__ :
'''simple docstring'''
def __init__( self , _a = None , _a = None ):
"""simple docstring"""
if arr is None and size is not None:
lowerCamelCase = siz... | 291 |
'''simple docstring'''
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
lowercase : Dict = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to wors... | 42 | 0 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate... | 311 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase : Any = {
"configuration_chinese_clip": [
"CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"ChineseCLI... | 42 | 0 |
"""simple docstring"""
import os
lowerCAmelCase__ = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1_000}
def a__ ( SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
lowerCAmelCase : Optional[Any] = 0
lowerCAmelCase : ... | 108 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( __A ) -> str:
_snake_case = 1
_snake_case = 2
while i * i <= n:
_snake_case = 0
while n % i == 0:
n //= i
multiplicity += 1
n_divisors *= multiplicity + 1
i += 1
if n > 1:
n_divisors *= 2
return n_div... | 42 | 0 |
"""simple docstring"""
from math import pow
def _snake_case ( _snake_case : Dict , _snake_case : Tuple , _snake_case : Tuple , _snake_case : Optional[int] , _snake_case : Optional[int] , ) -> tuple[int, int]:
... | 315 |
'''simple docstring'''
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class __UpperCAmelCase ( _lowerCamelCase ):
@require_torch
def lowerCamelCase ... | 42 | 0 |
import re
def _A ( _lowercase ) -> str:
"""simple docstring"""
if len(re.findall('[ATCG]' , __A ) ) != len(__A ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans('ATCG' , 'TAGC' ) ... | 310 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@... | 42 | 0 |
import datasets
from .evaluate import evaluate
__UpperCamelCase : List[str] = "\\n@inproceedings{Rajpurkar2016SQuAD10,\n title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n booktitle={EMN... | 182 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase : List[str] = {
"configuration_pix2struct": [
"PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Pix2Stru... | 42 | 0 |
'''simple docstring'''
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transform... | 83 |
'''simple docstring'''
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
lowercase : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
lowercase : list[int] = [ord(letter)... | 42 | 0 |
"""simple docstring"""
import os
def _a ( _SCREAMING_SNAKE_CASE = "input.txt" ) -> int:
with open(os.path.join(os.path.dirname(__A ) , __A ) ) as input_file:
snake_case_ = [
[int(__A ) for element in line.split(""",""" )]
for l... | 347 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( __A = 1_000_000 ) -> int:
_snake_case = limit + 1
_snake_case = [0] * limit
for first_term in range(1 , __A ):
for n in range(__A , __A , __A ):
_snake_case = first_term + n / first_term
if commo... | 42 | 0 |
import tensorflow as tf
from ...tf_utils import shape_list
class snake_case_ ( tf.keras.layers.Layer ):
def __init__( self : Dict , lowercase_ : Optional[Any] , lowercase_ : Union[str, Any] , lowercase_ : List[Any] , lowercase... | 87 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowercase : Tuple = {
"configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
"tokenization... | 42 | 0 |
def lowercase ( SCREAMING_SNAKE_CASE__ : str ) -> str:
_snake_case : int = 1
_snake_case : Dict = 2
while i * i <= n:
_snake_case : Union[str, Any] = 0
while n % i == 0:
n //= i
multiplicity += 1
n_divisors *= multiplicity + 1
i +... | 317 |
'''simple docstring'''
from collections import defaultdict
from math import gcd
def SCREAMING_SNAKE_CASE__ ( __A = 1_500_000 ) -> int:
_snake_case = defaultdict(__A )
_snake_case = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for euclid_n in range((euclid_m % 2) + 1 ... | 42 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase... | 308 |
'''simple docstring'''
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
lowercase : Optional[Any] = False
class __... | 42 | 0 |
"""simple docstring"""
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
lowerCAmelCase : Union[str, Any] = logging.get_logger(__name__)
class ... | 291 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( __A = 100 ) -> int:
_snake_case = n * (n + 1) * (2 * n + 1) / 6
_snake_case = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 42 | 0 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@r... | 311 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVeca... | 42 | 0 |
"""simple docstring"""
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer... | 108 |
'''simple docstring'''
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : int = logging.get_logger(__name__)
lowercase : Union[str, Any] = {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/... | 42 | 0 |
"""simple docstring"""
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class lowercase_ ( _lowerCamelCase ):
'''simple docstring'''
@require_torch
def lowerCA... | 315 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transfor... | 42 | 0 |
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torc... | 310 |
'''simple docstring'''
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 42 | 0 |
from __future__ import annotations
def A ( _lowercase ):
SCREAMING_SNAKE_CASE : List[Any] = len(__A )
# We need to create solution object to save path.
SCREAMING_SNAKE_CASE : Dict = [[0 for _ in range(__A )] for _ in range(__A ... | 182 |
'''simple docstring'''
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
lowercase : List[str] = logging.get_logger("transformers.models.speecht5")
def SCREAMING_SNAKE_CASE__ ( ... | 42 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils... | 83 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get... | 42 | 0 |
"""simple docstring"""
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class __A (unittest.TestCase):
'''simple docstrin... | 347 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( __A ) -> bool:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 42 | 0 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
Uppe... | 87 |
'''simple docstring'''
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
lowercase : Optional[Any] = argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
p... | 42 | 0 |
def lowercase ( SCREAMING_SNAKE_CASE__ : int ) -> List[Any]:
_snake_case : Union[str, Any] = [0] * len(__A )
_snake_case : str = []
_snake_case : Optional[Any] = [1] * len(__A )
for values in graph.values():
for i in values:
indegree[i] +=... | 317 |
'''simple docstring'''
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __UpperCAmelCase ( _lowerCamelCase ):
def lowerCamelCase ( self , lowerCAmelCase_ ):
""... | 42 | 0 |
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
S... | 308 |
'''simple docstring'''
import tensorflow as tf
from ...tf_utils import shape_list
class __UpperCAmelCase ( tf.keras.layers.Layer ):
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_=1 , ... | 42 | 0 |
"""simple docstring"""
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 # noq... | 291 |
'''simple docstring'''
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
lowercase : Dict = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to wors... | 42 | 0 |
'''simple docstring'''
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertToken... | 311 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase : Any = {
"configuration_chinese_clip": [
"CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"ChineseCLI... | 42 | 0 |
"""simple docstring"""
from __future__ import annotations
def a__ ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : Optional[Any] ):
'''simple docstring'''
lowerCAmelCase : int = list(range(len(__... | 108 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( __A ) -> str:
_snake_case = 1
_snake_case = 2
while i * i <= n:
_snake_case = 0
while n % i == 0:
n //= i
multiplicity += 1
n_divisors *= multiplicity + 1
i += 1
if n > 1:
n_divisors *= 2
return n_div... | 42 | 0 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import requi... | 315 |
'''simple docstring'''
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class __UpperCAmelCase ( _lowerCamelCase ):
@require_torch
def lowerCamelCase ... | 42 | 0 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
__snake_case = datasets.logging.get_logger(__name__)
__snake_case = "\\n@InProceedings{moosavi2019minimum,\n auth... | 310 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@... | 42 | 0 |
from __future__ import annotations
def A ( _lowercase , _lowercase , _lowercase ):
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError('''One and only one argument must be 0''' )
if resistance < 0:
raise ValueError('''Resistance cannot ... | 182 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase : List[str] = {
"configuration_pix2struct": [
"PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Pix2Stru... | 42 | 0 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeat... | 83 |
'''simple docstring'''
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
lowercase : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
lowercase : list[int] = [ord(letter)... | 42 | 0 |
"""simple docstring"""
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_avail... | 347 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( __A = 1_000_000 ) -> int:
_snake_case = limit + 1
_snake_case = [0] * limit
for first_term in range(1 , __A ):
for n in range(__A , __A , __A ):
_snake_case = first_term + n / first_term
if commo... | 42 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase = {
"configuration_blip_2": [
"BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Blip2Config",
"Blip2QFormerConfig",
"Blip2VisionConfig... | 87 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowercase : Tuple = {
"configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
"tokenization... | 42 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
a__ = {
"configuration_blip": [
"BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BlipConfig",
... | 317 |
'''simple docstring'''
from collections import defaultdict
from math import gcd
def SCREAMING_SNAKE_CASE__ ( __A = 1_500_000 ) -> int:
_snake_case = defaultdict(__A )
_snake_case = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for euclid_n in range((euclid_m % 2) + 1 ... | 42 | 0 |
from __future__ import annotations
from collections.abc import MutableSequence
class _A :
def __init__( self : List[str] , _A : str , _A : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
if len(l... | 308 |
'''simple docstring'''
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
lowercase : Optional[Any] = False
class __... | 42 | 0 |
"""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 __magic_name__ ( unittest.TestCase ):
'''simple docstring'''
... | 291 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( __A = 100 ) -> int:
_snake_case = n * (n + 1) * (2 * n + 1) / 6
_snake_case = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 42 | 0 |
'''simple docstring'''
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
a : Any = 5_00_00
a : str = 50_00
a : List[Any] = os.path.split(__file__)
a : List[str] = os.path.join(RESULTS_BASEPATH, "results",... | 311 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVeca... | 42 | 0 |
"""simple docstring"""
def a__ ( SCREAMING_SNAKE_CASE : List[Any] ):
'''simple docstring'''
lowerCAmelCase : Tuple = []
lowerCAmelCase : Tuple = []
lowerCAmelCase : Any = {
"^": 3,
"*": 2,
"/":... | 108 |
'''simple docstring'''
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : int = logging.get_logger(__name__)
lowercase : Union[str, Any] = {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/... | 42 | 0 |
"""simple docstring"""
import math
def _snake_case ( _snake_case : Optional[int] ) -> list:
'''simple docstring'''
_A = [True] * n
_A = False
_A = False
_A = True
for i in range(3 , int(... | 315 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transfor... | 42 | 0 |
__snake_case = "Input must be a string of 8 numbers plus letter"
__snake_case = "TRWAGMYFPDXBNJZSQVHLCKE"
def _A ( _lowercase ) -> bool:
"""simple docstring"""
if not isinstance(__A , __A ):
__UpperCamelCase = ... | 310 |
'''simple docstring'''
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 42 | 0 |
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
__UpperCamelCase : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
__UpperCamelCase : list[int] = [ord(letter) for l... | 182 |
'''simple docstring'''
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
lowercase : List[str] = logging.get_logger("transformers.models.speecht5")
def SCREAMING_SNAKE_CASE__ ( ... | 42 | 0 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def A__ ( UpperCAmelCase_ ):
# vision encoder
if "img_encoder.pos_embed" in name:
_UpperCamelCase : Union[... | 83 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get... | 42 | 0 |
"""simple docstring"""
from __future__ import annotations
__SCREAMING_SNAKE_CASE : Union[str, Any] = 10
def _a ( _SCREAMING_SNAKE_CASE ) -> list[int]:
snake_case_ = 1
snake_case_ = max(__A )
while placement <= max_digit:
# declar... | 347 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( __A ) -> bool:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 42 | 0 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class snake_case_ (... | 87 |
'''simple docstring'''
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
lowercase : Optional[Any] = argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
p... | 42 | 0 |
from __future__ import annotations
def lowercase ( SCREAMING_SNAKE_CASE__ : int ) -> list[int]: # This function is recursive
_snake_case : List[Any] = len(__A )
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
if ar... | 317 |
'''simple docstring'''
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __UpperCAmelCase ( _lowerCamelCase ):
def lowerCamelCase ( self , lowerCAmelCase_ ):
""... | 42 | 0 |
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...ut... | 308 |
'''simple docstring'''
import tensorflow as tf
from ...tf_utils import shape_list
class __UpperCAmelCase ( tf.keras.layers.Layer ):
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_=1 , ... | 42 | 0 |
"""simple docstring"""
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
lowerCAmelCase : Dict ... | 291 |
'''simple docstring'''
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
lowercase : Dict = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to wors... | 42 | 0 |
'''simple docstring'''
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
a : Union[str, Any] = logging.getLogger(__name__)
if __name__ ==... | 311 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase : Any = {
"configuration_chinese_clip": [
"CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"ChineseCLI... | 42 | 0 |
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
... | 108 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( __A ) -> str:
_snake_case = 1
_snake_case = 2
while i * i <= n:
_snake_case = 0
while n % i == 0:
n //= i
multiplicity += 1
n_divisors *= multiplicity + 1
i += 1
if n > 1:
n_divisors *= 2
return n_div... | 42 | 0 |
"""simple docstring"""
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...te... | 315 |
'''simple docstring'''
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class __UpperCAmelCase ( _lowerCamelCase ):
@require_torch
def lowerCamelCase ... | 42 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
__snake_case = logging.get_logger(__name__)
__snake... | 310 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@... | 42 | 0 |
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transforme... | 182 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase : List[str] = {
"configuration_pix2struct": [
"PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Pix2Stru... | 42 | 0 |
'''simple docstring'''
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
snake_case_ : List[str] ... | 83 |
'''simple docstring'''
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
lowercase : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
lowercase : list[int] = [ord(letter)... | 42 | 0 |
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