code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFXLMRobertaModel
@r... | 19 |
import requests
_A = "https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="
def lowerCamelCase__ ( __lowerCAmelCase : str ):
"""simple docstring"""
lowerCAmelCase_ = requests.get(_NEWS_API + bbc_news_api_key ).json()
# each article in the list is a dict
... | 231 | 0 |
def lowerCAmelCase_ ( snake_case_ ):
return sum(i for i in range(1,number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print("Program to check whether a number is a Perfect number or not...")
_snake_case = int(input("Enter number: ").strip())
print(f""... | 350 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_snake... | 343 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : Optional[Any] = logging.get_logger(__name__)
__lowercase : Tuple = {
'microsoft/markuplm-base': 'https://huggingface.co/microsoft/markuplm-base/resolve/main/config.jso... | 27 |
'''simple docstring'''
import os
def lowerCAmelCase_ ( ):
'''simple docstring'''
A : List[Any] = os.path.join(os.path.dirname(snake_case__ ) , '''num.txt''' )
with open(snake_case__ ) as file_hand:
return str(sum(... | 3 | 0 |
"""simple docstring"""
A : Optional[int] = {
"Pillow": "Pillow<10.0.0",
"accelerate": "accelerate>=0.20.3",
"av": "av==9.2.0",
"beautifulsoup4": "beautifulsoup4",
"black": "black~=23.1",
"codecarbon": "codecarbon==1.2.0",
"cookiecutter": "cookiecutter==1.7.3",
"dat... | 354 |
"""simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase = "cpu" , _UpperCamelCase = None ):
'''simple docstring'''
__lowerCAmelCase = torch.load(_UpperCamelCase , map_location... | 259 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"uclanlp/visualbert-vqa": "https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json",
"uclanlp/visualbert-vqa-pre": "htt... | 26 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
... | 26 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
'caidas/swin2sr-classicalsr-x2-64': (
'https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json'
),
}... | 364 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
_a = logging.get_logger(__name__)
_a =... | 144 | 0 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'vocab_file': 'vocab.json',
'tokenizer_config_file': 'tokenizer_confi... | 11 |
"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokeniz... | 316 | 0 |
"""simple docstring"""
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from tra... | 358 |
"""simple docstring"""
import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Traine... | 326 | 0 |
from __future__ import annotations
def lowerCamelCase_ ( _a : Optional[Any] ): # This function is recursive
'''simple docstring'''
UpperCAmelCase_ : Union[str, Any] = len(SCREAMING_SNAKE_CASE__ )
# If the array contains only one element, we return it (it's the stop ... | 345 |
from __future__ import annotations
from math import pi, sqrt
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
if inductance <= 0:
raise ValueError('''Inductance cannot be 0 or negative''' )
elif capacitance <= 0:
r... | 8 | 0 |
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class _lowerCAmelCase ( __lowercase ):
__SCREAMING_SNAKE_CASE : List[str] = "MCTCTFeatureExtractor"
__SCREAMING_SNAKE_CASE : int = ... | 352 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_availabl... | 135 | 0 |
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_availab... | 50 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> bool:
lowerCamelCase__ : List[str] = len(_UpperCAmelCase )
lowerCamelCase__ : str = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of zer... | 50 | 1 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def lower... | 353 | from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, r... | 105 | 0 |
'''simple docstring'''
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_avai... | 158 |
'''simple docstring'''
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def __a(SCREAMING_SNAKE_CASE_ ... | 158 | 1 |
'''simple docstring'''
import itertools
import math
def lowercase__ ( __UpperCamelCase )-> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, ... | 351 |
'''simple docstring'''
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class a_ ( unittest.TestCase ):
def A__ ( self ) -> ... | 183 | 0 |
'''simple docstring'''
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
UpperCamelCase__ = argparse.ArgumentParser('''Stable Diffusion script with intel optimization''', add_help=False)
par... | 181 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
... | 217 | 0 |
"""simple docstring"""
def _snake_case ( ) -> int:
'''simple docstring'''
return 1
def _snake_case ( lowercase__ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
... | 369 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCAmelCase = {
'configuration_squeezebert': [
'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Squ... | 1 | 0 |
import math
import qiskit
def lowerCamelCase_ ( UpperCamelCase__ : int = 1 , UpperCamelCase__ : int = 1 , UpperCamelCase__ : int = 1 ) -> qiskit.result.counts.Counts:
"""simple docstring"""
if (
isinsta... | 90 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transfo... | 90 | 1 |
"""simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->str:
a__: int = len(_SCREAMING_SNAKE_CASE )
a__: int = len(_SCREAMING_SNAKE_CASE )
a__: int = (
first_str_length if first_... | 203 | """simple docstring"""
import unittest
from knapsack import knapsack as k
class __snake_case ( unittest.TestCase ):
def lowerCamelCase_ ( self) -> Dict:
'''simple docstring'''
a__: List[Any] = 0
a__: Dict ... | 203 | 1 |
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class SCREAMING_SNAKE_CASE__ ( ... | 193 |
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_com... | 193 | 1 |
"""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,... | 355 |
"""simple docstring"""
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def _lowerCamelCase(__UpperCamelCase , __UpperCamelCase=1 ) -> Tuple:
if n_shave_prefix_segments >= 0:
return ".".join(path.split(""".""" )[n_shave... | 341 | 0 |
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_dimension_format,
)
from ... | 176 |
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 lowerCAmelCase__( lowercase : Dict ) -> str: # picklable for multip... | 326 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, 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_t... | 349 |
'''simple docstring'''
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from ... | 349 | 1 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from fla... | 60 | """simple docstring"""
# 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/LICENS... | 221 | 0 |
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__lowerc... | 105 | import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForConditionalGeneration as ProphetN... | 105 | 1 |
"""simple docstring"""
def __lowerCAmelCase ( lowercase : int ) -> bool:
"""simple docstring"""
if p < 2:
raise ValueError("p should not be less than 2!" )
elif p == 2:
return True
snake_case : Dict = 4
snake_case : str ... | 203 |
"""simple docstring"""
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
... | 203 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, ... | 356 |
# Algorithm for the pigeonhole sorting
def lowerCamelCase__ ( snake_case_ : int ) -> Optional[int]:
__snake_case = min(snake_case_ ) # min() finds the minimum value
__snake_case = max(snake_case_ ) # max() finds the maximum value
... | 238 | 0 |
import unittest
import numpy as np
def __lowerCAmelCase ( a__ , a__ , a__ , a__ = None , ) -> np.ndarray:
__a = np.shape(a__ )
__a = np.shape(a__ )
__a = np.shape(a__ )
if shape_a[0] != shape_b[0]:
__a ... | 6 |
from math import pi
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
return 2 * pi * radius * (angle / 3_60)
if __name__ == "__main__":
print(arc_length(90, 10))
| 327 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowerCamelCase = ... | 241 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
"""RWKV/rwkv-4-169m-pile""": """https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/conf... | 241 | 1 |
from datetime import datetime as dt
import os
from github import Github
__lowerCamelCase : Optional[Any] = [
'good first issue',
'good second issue',
'good difficult issue',
'feature request',
'new model',
'wip',
]
def A_ ( ) -> Union[str, Any]:
UpperCa... | 52 | '''simple docstring'''
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class __A ( UpperCame... | 1 | 0 |
lowerCamelCase : Tuple =[0, 2, 4, 6, 8]
lowerCamelCase : str =[1, 3, 5, 7, 9]
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> int:
if remaining_length == 0:
if digi... | 196 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=A__ )
class __a ( A__ ):
_lowerCAmelCase : str = field(default='''language-modeling''' , metadata={'''in... | 196 | 1 |
from __future__ import annotations
import math
def UpperCAmelCase ( a_ ) -> int:
"""simple docstring"""
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 mult... | 15 |
"""simple docstring"""
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from tran... | 108 | 0 |
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import Config... | 216 |
from __future__ import annotations
import math
def snake_case__ ( SCREAMING_SNAKE_CASE_ : int ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, ... | 216 | 1 |
"""simple docstring"""
from __future__ import annotations
from math import pi
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> dict[str, float]:
'''simple docstring'''
if (inductance, frequency, reactance).... | 136 |
"""simple docstring"""
from math import pi, sqrt, tan
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> float:
'''simple docstring'''
if side_length < 0:
raise ValueError("""surface_area_cube() only accepts non-negative values""" )
return 6 * side_length... | 136 | 1 |
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
... | 177 |
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Dict=2_8123 ) -> str:
UpperCAmelCase_ = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i + 1 , limit // i + 1 ):
sum... | 177 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTeste... | 349 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
a__ : int = {
'configuration_layoutlmv3': [
... | 349 | 1 |
'''simple docstring'''
from __future__ import annotations
def __lowerCAmelCase (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
if days_between_payments <= 0:
raise ValueError("days_between_payments must be > 0" )
if daily_interest_rate < 0:
raise ValueError("dai... | 322 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
... | 322 | 1 |
"""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'''))... | 105 |
"""simple docstring"""
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testin... | 105 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
def __lowerCAmelCase ( lowercase : Callable[[int | float], int | float] , lowercase : int | float , lowercase : int | float , lowercase : int = 100 , ... | 112 |
"""simple docstring"""
import math
import sys
def __lowerCAmelCase ( lowercase : int ) -> int:
"""simple docstring"""
if number != int(lowercase ):
raise ValueError("the value of input must be a natural number" )
if number < 0:
raise ValueErro... | 112 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCamelCase : Any = {
"configuration_git": ["GIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "GitConfig", "GitVisionConfig"],
"processing_git": ["GitProcessor"],
}
try:
if... | 219 |
"""simple docstring"""
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAP... | 238 | 0 |
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
_SCREAMING_SNAKE_CASE = 3
def lowercase( UpperCamelCase_ ) -> int:
'''simple docstring'''
print("""Generating primitive root of p""" )
while True:
UpperCamelC... | 365 | from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import stable_softmax
if is_torc... | 165 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase : Dict = {
'''configuration_blip''': [
'''BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'... | 282 |
'''simple docstring'''
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from... | 164 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
__UpperCamelCase : Any = logging.get_logger(__name__)
__UpperCa... | 350 |
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 diffusers.utils.tes... | 51 | 0 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determi... | 82 |
"""simple docstring"""
def A ( snake_case :int ) -> int:
__UpperCamelCase = [1]
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase = 0, 0, 0
__UpperCamelCase = ugly_nums[ia] * 2
__UpperCamelCase = ugly_nums[ia] * 3
__UpperCamelCase ... | 316 | 0 |
'''simple docstring'''
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('''dataset_size''', [None, 4_0_0 * 2**2_0, 6_0_0 * 2**2_0])
@pytest.mark.parametrize('''input_in_memory_max_size''', ['''default''', 0, 1_0_0 * 2**2_0, 9_0_0 *... | 9 |
'''simple docstring'''
# Imports
import numpy as np
class a__ :
"""simple docstring"""
def __init__(self , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase=None ):
self.set_matricies... | 9 | 1 |
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class __snake_case :
def __init__( self : List[str] , A_ : Union[str, Any]):
if isinstanc... | 103 |
'''simple docstring'''
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def A_ ( snake_case ):
return 1 / (1 + np.exp(-z ))
... | 139 | 0 |
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 tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM
@req... | 362 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ... | 42 | 0 |
'''simple docstring'''
def _lowerCAmelCase ( __snake_case : int = 1_00 ) -> int:
__A : List[Any] = n * (n + 1) * (2 * n + 1) / 6
__A : Optional[Any] = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if ... | 190 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipe... | 190 | 1 |
'''simple docstring'''
def snake_case_ (UpperCamelCase : int , UpperCamelCase : int ):
'''simple docstring'''
return abs(UpperCamelCase ) if a == 0 else greatest_common_divisor(b % a , UpperCamelCase )
def snake_case_ (UpperCamelCase : int ... | 179 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor... | 179 | 1 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 287 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"""BridgeTower/bridgetower-base""": """https://huggingface.co/BridgeTower/bridgetower-base/blob/main/con... | 287 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMSchedule... | 294 |
'''simple docstring'''
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 i... | 294 | 1 |
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 import cached_property
from ...test_token... | 9 |
'''simple docstring'''
import string
def lowerCAmelCase_ ( _lowerCamelCase: str ):
__SCREAMING_SNAKE_CASE : Dict = """"""
for i in sequence:
__SCREAMING_SNAKE_CASE : Any = ord(_lowerCamelCase )
if 65 <= extract <= 90:
output += chr(1_55 - e... | 112 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
... | 371 | """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_att... | 321 | 0 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from ... | 164 |
"""simple docstring"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def A ( snake_case__ ):
... | 165 | 0 |
'''simple docstring'''
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from tra... | 367 |
'''simple docstring'''
import operator
def __lowercase ( __lowercase , __lowercase = False , __lowercase = None ) -> list:
'''simple docstring'''
_A = operator.lt if reverse else operator.gt
_A = solution or []
if not arr:... | 174 | 0 |
import logging
from transformers.configuration_utils import PretrainedConfig
UpperCamelCase = logging.getLogger(__name__)
class snake_case_ ( __A ):
__A : List[Any] = "masked_bert"
def __init__( self : Optional[int] , lowercase_... | 87 |
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __snake_case ( a , unittest.TestCase ):
UpperCAmelCase__ : Any = PhobertTo... | 51 | 0 |
'''simple docstring'''
class _lowerCAmelCase :
'''simple docstring'''
def __init__(self , UpperCAmelCase ) -> List[Any]:
_snake_case = val
_snake_case = None
_snake_case = None
def lowercase (se... | 270 |
'''simple docstring'''
import qiskit
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
_snake_case = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
_snake_case = qiskit.QuantumCircui... | 270 | 1 |
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('''dataset_size''' , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize('''input_in_memory_max_size''' , ['''default''', 0, 100 * 2**20, 900 * 2**20] )
def ... | 9 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.te... | 9 | 1 |
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
f... | 35 | import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineO... | 35 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
snake_case_ = {
"""configuration_bloom""": ["""BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BloomConfig""", """Bloom... | 78 |
'''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, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.ut... | 91 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase : List[str] = {
"""configuration_time_series_transformer""": [
"""TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP"""... | 91 | 1 |
"""simple docstring"""
from typing import List
import numpy as np
def lowercase_ ( __UpperCAmelCase ) -> int:
lowerCAmelCase__ : List[Any] = {key: len(__UpperCAmelCase ) for key, value in gen_kwargs.items() if isinstance(__UpperCAmelCase , __UpperCAmelCas... | 242 |
"""simple docstring"""
from itertools import product
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase ) -> list[int]:
lowerCAmelCase__ : Union[str, Any] = sides_number
lowerCAmelCase__ : Optional[int] = max_face_number * dice_number
... | 242 | 1 |
'''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,
WavaVecaCT... | 369 |
'''simple docstring'''
from timeit import timeit
def lowercase__ ( __UpperCamelCase )-> int:
if number < 0:
raise ValueError("""the value of input must not be negative""" )
UpperCamelCase = 0
while number:
number &= number... | 183 | 0 |
"""simple docstring"""
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSe... | 294 |
"""simple docstring"""
from collections.abc import Iterable
from typing import Generic, TypeVar
_snake_case = TypeVar('_T')
class UpperCamelCase ( Generic[_T] ):
def __init__( self : Optional[int] , UpperCAmelCase__ : Iter... | 294 | 1 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transf... | 268 | """simple docstring"""
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEm... | 268 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
"""transfo-xl-wt103""": """https://huggingface.co/transfo-xl-wt103/resolve/main/config.json""",
}
... | 78 |
"""simple docstring"""
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class lowercase ( __UpperCA... | 167 | 0 |
import faiss # 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 requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here... | 365 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_seri... | 93 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils... | 27 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def __UpperCamelCase ( _A = 3 ):
if isinstance(_A , _A ):
raise TypeError('''number of qubits must be a integer.''' )
if number_of_qubits <= 0:
... | 278 | 0 |
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_common import ModelTesterMixin, ids_... | 171 |
def UpperCAmelCase_ (_lowerCAmelCase : list ):
if len(_lowerCAmelCase ) <= 1:
return lst
__UpperCamelCase : Dict = 1
while i < len(_lowerCAmelCase ):
if lst[i - 1] <= lst[i]:
i += 1
else:
__UpperCamelCase , __UpperCamelCa... | 171 | 1 |
from pathlib import Path
import numpy as np
from PIL import Image
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
snake_case_, snake_case_, snake_case_ = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.2989 * r + 0.5870 * g + 0.1140 * b
def __SCREAMING_SNAKE... | 8 | """simple docstring"""
import os
from typing import Dict, List, Tuple, TypeVar, Union
lowercase__ = TypeVar('T')
lowercase__ = Union[List[T], Tuple[T, ...]]
lowercase__ = Union[T, List[T], Dict[str, T]]
lowercase__ = Union[str, bytes, os.PathLike]
| 290 | 0 |
'''simple docstring'''
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
'''kwargs, expected''' , [
({'''num_shards''': 0, '''max_num_jobs''': 1}, []),
({'''num_shards''': 10, '''max_num... | 364 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase = {
"configuration_clap": [
"CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
"ClapAudioConfig",
"ClapConfig",
"ClapT... | 98 | 0 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def __snake_case( _lowerCAmelCase ) -... | 35 |
'''simple docstring'''
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def __snake_case( _lowerCAmelCase ) -> List[Any]:
... | 35 | 1 |
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 huggingface_hub.utils as hf_... | 140 |
from scipy.stats import spearmanr
import datasets
__lowerCamelCase : List[str] = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive... | 140 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
UpperCAmelCase_ : Optional[int] = {
"""google/tapas-base-finetuned-sqa""": (
"""https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"""
),
"""google/tapas-base-finetuned-wtq"""... | 91 |
"""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_co... | 91 | 1 |
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def __snake_case ( *__UpperCamelCase : Union[str, Any] ,__UpperCamelCase : Optional[Union[Dict, Any]] = None ,__UpperCamelCase : Optional[Any]=True ,__Up... | 329 |
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_co... | 329 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
snake_case_ : List[str] = logging... | 125 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ : Any = logging.get_logger(__name__)
snake_case_ : Dict = {
"weiweishi/roc-bert-base-zh": "https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.jso... | 125 | 1 |
"""simple docstring"""
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def a__ ( lowerCAmelCase ) -> Dict:
if not is_accelerate_available():
return method
UpperCAmelCase__ : List[Any] ... | 361 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_A = ... | 166 | 0 |
"""simple docstring"""
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.tes... | 241 |
"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
lowercase__ = datasets.utils.logging.get_logger(__name__)
@dataclass
class __lowerC... | 241 | 1 |
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@re... | 359 |
from __future__ import annotations
from decimal import Decimal
from numpy import array
def A__ ( SCREAMING_SNAKE_CASE__) -> list[list[float]]:
__snake_case: Any = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation only wo... | 293 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
snake_case_ = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
... | 78 |
'''simple docstring'''
from __future__ import annotations
import math
class a_ :
def __init__( self , _SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
UpperCamelCase = size
# approximate the ov... | 321 | 0 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffu... | 284 | from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
__SCREAMING_SNAKE_CASE : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
__SCREAMING_SNAKE_CASE : list[int] = [ord(letter) for le... | 284 | 1 |
def _SCREAMING_SNAKE_CASE ( a , a ) -> str:
__A : list[list[str]] = [[] for _ in range(a )]
__A : Dict = key - 1
if key <= 0:
raise ValueError('Height of grid can\'t be 0 or negative' )
if key == 1 or len(a ) <= key:
... | 280 |
import math
def _SCREAMING_SNAKE_CASE ( a ) -> list[int]:
__A : List[str] = []
__A : Any = 2
__A : Union[str, Any] = int(math.sqrt(a ) ) # Size of every segment
__A : Any = [True] * (end + 1)
__A : Lis... | 280 | 1 |
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : Any = logging.get_logger(__name__)
_lowercase : Optional[int] = {
"facebook/encodec_24khz"... | 370 | '''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : int = 1000000 ) -> int:
lowercase_ : List[Any] = limit + 1
lowercase_ : Optional[Any] = [0] * limit
for first_term in range(1 , UpperCAmelCase__ ):
for n in r... | 21 | 0 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class UpperCAmelCase_ ( unittest.TestCase ):
... | 235 | """simple docstring"""
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
lowerCAmelCase__ : Optional[Any] ... | 98 | 0 |
def lowerCamelCase__ ( a ) -> list:
if len(a ) <= 1:
return lst
_A: int = 1
while i < len(a ):
if lst[i - 1] <= lst[i]:
i += 1
else:
_A , _A: Optional[int] = lst[i], lst[i - 1]
i -= 1
if i == 0:
... | 301 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def lowerCamelCase__ ... | 301 | 1 |
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, Traini... | 140 | from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"""huggingface/time-series-transformer-tourism-monthly""": (
"""https://huggingface.co/huggin... | 140 | 1 |
__UpperCAmelCase = '0.18.2'
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
... | 364 |
def lowercase__ ( __snake_case : str , __snake_case : int , __snake_case : List[str] ):
'''simple docstring'''
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(__snake_case , n - 1 , __... | 145 | 0 |
'''simple docstring'''
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = 10**-10 ) -> float:
snake_case__ : List[str] = ... | 35 |
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 torch.utils.data import ... | 310 | 0 |
'''simple docstring'''
class UpperCAmelCase__ :
"""simple docstring"""
def __init__( self : Optional[int] ):
'''simple docstring'''
_a : Any = ''
_a : List[str] = ''
_a : str = []
def __lowercase ( se... | 5 |
'''simple docstring'''
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def UpperCAmelCase_ (__a : Optional[Any] ):
"""simple docstring"""
_a : int = FileLock(str(tmpdir / 'foo.lock' ) )
_a : List[Any] = ... | 5 | 1 |
'''simple docstring'''
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
warnings.wa... | 181 |
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
"""google/umt5-small""": """https://huggi... | 166 | 0 |
"""simple docstring"""
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
if exponent == 1:
return base
if exponent % 2 == 0:
lowerCamelCase__ : Any = _modexpt(_lowerCamelCase , exponent // 2 , _lowerCame... | 316 |
"""simple docstring"""
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config impo... | 316 | 1 |
"""simple docstring"""
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
UpperCAmelCase = """src/diffusers"""
# Matches is_xxx_available()
UpperCAmelCase = re.compile(r"""is\_([a... | 256 | """simple docstring"""
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class UpperCAmelCase_... | 256 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''huggingface/informer-tourism-monthly''': (
'''https... | 133 |
"""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, ... | 133 | 1 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository ... | 314 |
from functools import reduce
_SCREAMING_SNAKE_CASE : Any = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
... | 314 | 1 |
"""simple docstring"""
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
UpperCamelCase_ =TypeVar("""KEY""")
UpperCamelCase_ =TypeVar("""VAL""")
@dataclass(frozen=_lowerCAmelCase , slots=_... | 371 |
"""simple docstring"""
from __future__ import annotations
from random import choice
def a_ ( _lowercase ):
return choice(_lowercase )
def a_ ( _lowercase , _lowercase ):
_UpperCamelCase : Optional[int] = ... | 128 | 0 |
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
lowercase__ : Union[str, Any] = "<<<<<<< This should probably be modified because it mentions: "
lowe... | 187 |
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers im... | 21 | 0 |
'''simple docstring'''
import os
import sys
import unittest
__lowercase : Any = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create... | 294 |
'''simple docstring'''
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .t... | 294 | 1 |
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
_A = object()
# For specifying empty leaf dict `{}`
_A = object()
def lowerCamelCase__ ( a__ : List[Any] , ... | 122 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
snake_case : str = {
'''configuration_rag''': ['''RagConfig'''],
'''retrieval_rag''': ['''RagRetriever'''],
'''tokenization_rag''': ['''RagToke... | 240 | 0 |
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_flax_available():
im... | 360 |
from pathlib import Path
import numpy as np
from PIL import Image
def A_ ( _lowerCAmelCase ) -> np.ndarray:
UpperCamelCase , UpperCamelCase , UpperCamelCase : Optional[Any] = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.2_989 * r + 0.5_870 * g + 0.1_140 * ... | 140 | 0 |
'''simple docstring'''
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines... | 80 |
'''simple docstring'''
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.d... | 166 | 0 |
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import Pa... | 368 |
import logging
from transformers.configuration_utils import PretrainedConfig
UpperCamelCase__ = logging.getLogger(__name__)
class __SCREAMING_SNAKE_CASE ( _a ):
snake_case : Optional[Any] = """masked_bert"""
def __init__( self , __lowerCAmelCase=305... | 87 | 0 |
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