code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from functools import lru_cache
def a_ ( lowerCAmelCase_ : int ):
__lowerCAmelCase = 2
__lowerCAmelCase = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.add(lowerCAmelCase_ )
if n > 1:
... | 53 |
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_verbosity_info()
_sn... | 53 | 1 |
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tenso... | 715 | import pprint
import requests
UpperCamelCase = 'https://zenquotes.io/api'
def lowerCamelCase_ ( ) -> list:
return requests.get(API_ENDPOINT_URL + "/today" ).json()
def lowerCamelCase_ ( ) -> list:
return requests.get(API_ENDPOINT_URL + "/r... | 387 | 0 |
def A__ ( snake_case_ : int = 2_000_000 ):
SCREAMING_SNAKE_CASE__: Optional[Any]= [0 for i in range(n + 1 )]
SCREAMING_SNAKE_CASE__: Any= 1
SCREAMING_SNAKE_CASE__: Union[str, Any]= 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i ... | 64 |
import string
from math import logaa
def A__ ( _a : str , _a : str ):
'''simple docstring'''
snake_case__ : List[Any] =document.translate(
str.maketrans("""""" , """""" , string.punctuation ) ).replace("""\n""" , """""" )
sna... | 385 | 0 |
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class snake_case... | 709 |
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 ConfigT... | 262 | 0 |
import inspect
import unittest
from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...t... | 225 |
import random
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
__a = num - 1
__a = 0
while s % 2 == 0:
__a = s // 2
t += 1
for _ in range(5 ):
__a = random.randrange(2 , num - 1 )
... | 225 | 1 |
'''simple docstring'''
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
__lowercase : Optional[Any] = logging.... | 708 |
'''simple docstring'''
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_comm... | 357 | 0 |
from __future__ import annotations
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : int =0.0_0
__magic_name__ : Tuple =0
for resistor in resistors:
if resistor <= 0:
__magic_name__ : Optional[int] ... | 21 |
'''simple docstring'''
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def _lowerCAmelCase (_lowercase ):
"""simple docstring"""
return x + 2
class lowerCamelCase__ (... | 331 | 0 |
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowercase__ ( _UpperCamelCase , _UpperCamelCase , ... | 410 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer... | 410 | 1 |
lowercase_ = """Input must be a string of 8 numbers plus letter"""
lowercase_ = """TRWAGMYFPDXBNJZSQVHLCKE"""
def a__ ( snake_case ):
"""simple docstring"""
if not isinstance(snake_case , snake_case ):
__SCREAMING_SNAKE_CASE : List[Any] = F'''E... | 74 |
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
lowercase_ = logging.get_logger(__name__)
class __UpperCamelCase ( l... | 74 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__lowercase :Optional[int] = {
"configuration_rag": ["RagConfig"],
"retrieval_rag": ["RagRetriever"],
"tokenization_rag": ["RagTokenizer"],
}
try:
... | 26 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a ( lowercase__ ):
"""simple docstring"""
snake_case_ = ["image_processor", "tokenizer"]
snake_case_ = "CLIPImageProces... | 26 | 1 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
... | 11 |
'''simple docstring'''
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
lowerCAmelCase :Dict = datasets.utils.logging.get_logger(__name__)
class _lowerCamelCase ( folder_based_builder.FolderBasedBuil... | 561 | 0 |
"""simple docstring"""
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_commo... | 711 |
"""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 OptionalDep... | 87 | 0 |
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_collator... | 84 |
def __lowerCamelCase ( _lowercase ) -> str:
return "".join(chr(ord(_lowercase ) - 32 ) if 'a' <= char <= 'z' else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 282 | 0 |
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE :str ) -> list:
if n_term == "":
return []
__lowerCAmelCase : list = []
for temp in range(int(SCREAMING_SNAKE_CASE ) ):
series.append(F'''1/{temp + 1}''' if series else """1""" )
return series
if __name__ == "__main... | 718 |
from math import asin, atan, cos, radians, sin, sqrt, tan
_UpperCAmelCase = 6_378_137.0
_UpperCAmelCase = 6_356_752.314_245
_UpperCAmelCase = 637_8137
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE :float , SCREAMING_SNAKE_CASE :float , SCREAM... | 240 | 0 |
"""simple docstring"""
def A__ ( A__ = 5000_0000 ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = set()
_UpperCAmelCase = int((limit - 24) ** (1 / 2) )
_UpperCAmelCase = set(range(3 , prime_square_limit + 1 , 2 ) ... | 426 | import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class UpperCAmelCase ( __A ... | 558 | 0 |
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
a_ :Optional[int] = logging.getLogger(__name_... | 709 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
a_ :Dict =... | 243 | 0 |
'''simple docstring'''
from scipy.stats import spearmanr
import datasets
UpperCAmelCase_ : int = '\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no corre... | 365 |
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
A : Any = 'scheduler_config.json'
class __A( a ):
snake_case_... | 219 | 0 |
'''simple docstring'''
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def lowercase__ ( __lowercase : Optional[int] ) -> str:
... | 711 |
'''simple docstring'''
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def lowercase__ ( ) -> Optional[int]:
"""simple docstring"""
... | 434 | 0 |
print((lambda quine: quine % quine)('print((lambda quine: quine %% quine)(%r))'))
| 101 |
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
_snake_case : str = logging.get_logge... | 693 | 0 |
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, Par... | 15 | import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@req... | 15 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase ... | 26 |
from __future__ import annotations
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> list[int]:
lowercase : int = [True] * limit
lowercase : Tuple = False
lowercase : List[Any] = False
lowercase : Union[str, Any] = True
... | 336 | 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... | 460 |
'''simple docstring'''
from torch import nn
def _snake_case ( A_ : Dict ):
"""simple docstring"""
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
... | 460 | 1 |
import numpy as np
def UpperCamelCase ( lowercase_ , lowercase_ , lowercase_ = 1E-12 , lowercase_ = 1_00 , ) -> tuple[float, np.ndarray]:
'''simple docstring'''
assert np.shape(lowercase_ )[0] == np.shape(lowercase_ )[1]
# Ensure proper dimensionality.
ass... | 12 |
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes... | 12 | 1 |
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Acc... | 708 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__UpperCAmelCase = {
"""configuration_mobilevit""": ["""MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Mobil... | 218 | 0 |
"""simple docstring"""
from __future__ import annotations
from math import pi
def lowercase (_snake_case ,_snake_case ,_snake_case ) -> Dict:
'''simple docstring'''
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )... | 505 |
'''simple docstring'''
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class __UpperCamelCase... | 476 | 0 |
"""simple docstring"""
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase = logging.get_logger(__name__)
lowercase = {
"""snap-research/efficientformer-l1-300""": (
"""https://huggingface.co/snap-research/efficientforme... | 150 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE_ ( metaclass=_lowercase):
'''simple docstring'''
__magic_name__ : List[str] = ['''torch''']
def __init__( self , *lowerCamelCase__ , **... | 150 | 1 |
'''simple docstring'''
__a = 65_521
def __UpperCAmelCase ( a_: str ):
_UpperCAmelCase : Dict = 1
_UpperCAmelCase : Union[str, Any] = 0
for plain_chr in plain_text:
_UpperCAmelCase : List[Any] = (a + ord(a_ )) % MOD_ADLER
_Up... | 494 | '''simple docstring'''
import warnings
from .generation import TFGenerationMixin
class A__ ( UpperCamelCase ):
"""simple docstring"""
warnings.warn(
'''Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will '''
'''be rem... | 494 | 1 |
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> float:
"""simple docstring"""
if digit_amount > 0:
return round(number - int(__A ) , __A )
return number - int(__A )
if __name__ == "__main__":
print(decimal_isolate(1.5... | 708 |
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
A__ = {
'''<''': operator.lt,
'''<=''': operator.le,
'''==''': operator.eq,
'''!=''': operator.ne,
'''>=''': operator.ge,
'''>''': operator.gt,
}... | 219 | 0 |
from __future__ import annotations
from decimal import Decimal
from numpy import array
def __magic_name__ ( SCREAMING_SNAKE_CASE ) -> list[list[float]]:
_lowercase : List[str] = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since ... | 66 |
from __future__ import annotations
import math
def __magic_name__ ( SCREAMING_SNAKE_CASE ) -> list[int]:
if num <= 0:
_lowercase : List[str] = F"""{num}: Invalid input, please enter a positive integer."""
raise ValueError(SCREAMING_SNAKE... | 66 | 1 |
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class lowercase ( UpperCAmelCase_ , unittest.TestCase ... | 235 |
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
def __init__( self : Optional[Any] , a_ : List[str]="" , a_ : str="train" ):
"""simple ... | 235 | 1 |
'''simple docstring'''
import json
import sys
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> Optional[Any]:
with open(__UpperCamelCase ,encoding='utf-8' ) as f:
lowerCamelCase_ = json.load(__UpperCamelCase )
lowerCamelCase_ = ['<deta... | 42 |
'''simple docstring'''
import pickle
import numpy as np
from matplotlib import pyplot as plt
class UpperCAmelCase :
def __init__( self :Dict , lowercase_ :Union[str, Any] , lowercase_ :Tuple , lowercase_ :List[Any] , lowercase_ :List[str] , lowercase_ :Dict , lowercase_ :Tu... | 440 | 0 |
'''simple docstring'''
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v ... | 654 | '''simple docstring'''
def lowerCAmelCase ( UpperCamelCase__ : Tuple ):
"""simple docstring"""
# if the collection is empty, returns empty
if collection == []:
return []
# get some information about the collection
__UpperCAmelCase = len(UpperCamelCase__ )
__... | 654 | 1 |
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property
from ...... | 401 |
"""simple docstring"""
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
lowerCAmelCase : List[str] = object()
# For specifying empty leaf dict `{}`
lowerCAmelCase : ... | 543 | 0 |
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
__lowerCamelCase : List[Any] = parse(importlib.metadata.version("""torch"""))
def SCREAMING_SNAKE_CASE ( snake_case_ : Union[... | 25 |
def SCREAMING_SNAKE_CASE ( snake_case_ : list ):
if len(snake_case_ ) <= 1:
return lst
snake_case__ : List[Any] = 1
while i < len(snake_case_ ):
if lst[i - 1] <= lst[i]:
i += 1
else:
snake_case__, snake_case__ : Tuple = lst[i], lst[i... | 25 | 1 |
"""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/LI... | 49 |
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cache... | 180 | 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,
)
a : Dict = {
'configuration_electra': ['ELECTRA_PRETR... | 593 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a : Optional[Any] = {
'configuration_autoformer': [
'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
... | 593 | 1 |
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbart.... | 234 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE =logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE ={
"""huggingface/informer-tourism-monthly""": (
"""https://huggingface.co... | 234 | 1 |
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_available():
import onnxruntime as ort
@ni... | 707 |
from __future__ import annotations
def UpperCamelCase_ ( a_ ) ->None:
create_state_space_tree(a_ , [] , 0 , [0 for i in range(len(a_ ) )] )
def UpperCamelCase_ ( a_ , a_ , a_ , a_ , ) ->None:
if index == len(a_ ):... | 689 | 0 |
"""simple docstring"""
# Copyright 2022 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... | 96 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
__lowerCamelCase ... | 96 | 1 |
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.test_utils import require_mul... | 721 |
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class UpperCamelCase__ :
_SCREAMING_SNAKE_CASE : Optional[str] = field(
default="codeparrot/codeparrot" ,metadata={"help": "Model name or path of model to be trained."} )
_SCREAMING_SNAKE_CASE :... | 326 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.u... | 434 | """simple docstring"""
class _UpperCAmelCase :
"""simple docstring"""
def __init__( self , _lowercase ) -> Dict:
# we need a list not a string, so do something to change the type
_lowerCamelCase : List[str] = arr.split(''',''' )
... | 434 | 1 |
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
__a = get_tests_dir('fixtures/test_sentencepiece_bpe.model')
... | 719 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | 300 | 0 |
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerat... | 144 |
from __future__ import annotations
def lowerCAmelCase__( lowercase : list[int] , lowercase : int ) -> int:
if len(lowercase ) < k or k < 0:
raise ValueError("Invalid Input" )
__snake_case : Tuple = sum(array[:k] )
for i in rang... | 243 | 0 |
'''simple docstring'''
from itertools import permutations
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> bool:
"""simple docstring"""
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
_S... | 718 |
'''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class _a (_lowerCamelC... | 0 | 0 |
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokenizer,
JumanppTokeniz... | 579 |
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case = logging.get_logger(__name__)
snake_case = {
"""microsoft/xprophetnet-large-wiki100-cased""": (
"""https://huggingface.co/microsoft/xprophe... | 62 | 0 |
def _lowerCAmelCase ( __lowerCamelCase : int , __lowerCamelCase : int ):
"""simple docstring"""
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 447 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
)
from t... | 447 | 1 |
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
SCREAMING_SNAKE_CASE_ = 0b101_100_111_110_110_010_010_000_011_110_111_011_000_110_01... | 300 |
from __future__ import annotations
SCREAMING_SNAKE_CASE_ = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
SCREAMING_SNAKE_CASE_ = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: list[float] ) -> list[float]... | 300 | 1 |
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 (
AlbertTokenizer,
AutoToken... | 590 |
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def SCREAMING_SNAKE_CASE_ ... | 590 | 1 |
def A__ ( lowercase: Optional[Any] ) -> tuple[int, int]:
try:
A : int =float(lowerCamelCase_ )
except ValueError:
raise ValueError('Please enter a valid number' )
A : Optional[int] =decimal - int(lowerCamelCase_ ... | 305 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
SCREAMING_SNAKE_CASE : str = {
"configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}
try:
if not is_torch_available()... | 89 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowerCAmelCase = {
'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}
try:
if not... | 348 |
"""simple docstring"""
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap... | 348 | 1 |
'''simple docstring'''
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ... | 72 |
from __future__ import annotations
def _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ) ->Optional[int]:
# Checks if the entire collection has been sorted
if len(lowerCAmelCase_ ) <= 1 or n <= 1:
return
insert_next(lowerCAmelCase_ , n - 1 )
rec_insertion_sort(lowerC... | 377 | 0 |
'''simple docstring'''
def _lowerCAmelCase ( _lowerCAmelCase = 10_00 )-> int:
__UpperCAmelCase , __UpperCAmelCase = 1, 1
__UpperCAmelCase = []
for i in range(1 , n + 1 ):
__UpperCAmelCase = prev_numerator + 2 * prev_denominator
__UpperCAmelCase =... | 617 |
'''simple docstring'''
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def _lowerCAmelCase ( _lowerCAmelCase = 3 )-> qiskit.result.counts.Counts:
if isinstance(_lowerCAmelCase , _lowerCAmelCase ... | 617 | 1 |
def SCREAMING_SNAKE_CASE__ ( snake_case_ = 1_0_0 ) -> str:
"""simple docstring"""
a = set()
a = 0
a = n + 1 # maximum limit
for a in range(2, lowerCamelCase__ ):
for b in range(2, lowerCamelCase__ ):
a = ... | 387 |
import random
from typing import Any
def __lowerCamelCase ( lowerCamelCase__ : list ):
'''simple docstring'''
for _ in range(len(lowerCamelCase__ ) ):
lowerCamelCase = random.randint(0 , len(lowerCamelCase__ ) - 1 )
lowerCamelCa... | 457 | 0 |
"""simple docstring"""
def lowerCAmelCase (__UpperCamelCase : int ):
"""simple docstring"""
__UpperCamelCase =0
while num > 0:
digit_sum += num % 1_0
num //= 1_0
return digit_sum
def lowerCAmelCase (__UpperCamelCase ... | 296 | """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
__lowercase = logging.get_logger(__name__)
__lowercase ... | 296 | 1 |
'''simple docstring'''
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
... | 24 |
'''simple docstring'''
def _UpperCamelCase (_lowerCamelCase : int )-> int:
'''simple docstring'''
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
__snake_case = 1
__snake_case = 1
while repunit:
__snake_case ... | 24 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
... | 372 |
__UpperCamelCase : List[str] = 256
# Modulus to hash a string
__UpperCamelCase : int = 1000003
def a_ ( _A , _A ) -> bool:
"""simple docstring"""
snake_case__ = len(_A )
snake_case__ = len(_A )
if ... | 372 | 1 |
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def UpperCAmelCase_ ( snake_case__ , snake_case__ ) -> Dict:
"""simple docstring"""
lowerCAmelCase__ = ... | 193 |
from typing import Dict
from .base import GenericTensor, Pipeline
class __snake_case ( SCREAMING_SNAKE_CASE ):
def SCREAMING_SNAKE_CASE_ ( self ,a_=None ,a_=None ,a_=None ,**a_ ):
"""simple docstring"""
if tokenize_kwargs is None:
lowerCAmelCase_... | 193 | 1 |
"""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 transformers... | 707 | """simple docstring"""
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
fro... | 121 | 0 |
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
SCREAMING_SNAKE_CASE: Tuple = parse(importlib.metadata.version('''torch'''))
def _a ( lowerCAmelCase , lowerCAmelCase... | 360 |
"""simple docstring"""
import os
import sys
UpperCamelCase : Optional[int] = os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
... | 690 | 0 |
'''simple docstring'''
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def lowercase_ ( lowercase__ ) ->t... | 710 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import... | 273 | 0 |
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__snake_case : List[Any] = get_tests_dir("""fixtures/... | 540 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.util... | 540 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import TypedDict
class SCREAMING_SNAKE_CASE__ ( __snake_case ):
_A = 42
_A = 42
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : str ) -> ... | 719 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class SCREAMING_SNAKE_CASE... | 68 | 0 |
'''simple docstring'''
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_v... | 578 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,... | 578 | 1 |
from string import ascii_uppercase
lowerCamelCase__ = {str(ord(c) - 55): c for c in ascii_uppercase}
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> str:
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
raise TypeError('int(... | 69 |
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class A__ ( __magic_name__ ):
lowercase = (DDPMParallelScheduler,)
def _lowerCamelCase ( self : str , **a : Optional[i... | 69 | 1 |
"""simple docstring"""
def lowerCamelCase_ ( UpperCAmelCase_ = 10_00 ) ->int:
"""simple docstring"""
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution()) | 522 |
'''simple docstring'''
def lowerCamelCase ( __lowerCamelCase : str ) ->bool:
return credit_card_number.startswith(("""34""", """35""", """37""", """4""", """5""", """6""") )
def lowerCamelCase ( __lowerCamelCase : str ) ->bool:
_SCREAMING_SNAKE_CASE ... | 314 | 0 |
import math
def lowercase__( A , A ):
if (
not isinstance(A , (int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError('power_factor must be a valid float value between -1 and 1.' )
return apparent_power * powe... | 705 |
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
lowerCamelCase : str = 2_9_9_7_9_2_4_5_8
# Symbols
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase : Union[str, Any] = symbols('ct x y z')
def lowercase... | 303 | 0 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: Tuple , UpperCamelCase__: Optional[Any] , UpperCamelCase__: List[Any] ):
SCREAMING_SNAKE_CASE__ = Auto... | 6 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms imp... | 71 | 0 |
UpperCAmelCase_ = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
UpperCAmelCase_ = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def lowerCamelCase__ ( UpperCamelCase__ : dict[int, list[int]] , UpperCamelCase__ : int , Uppe... | 541 |
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
UpperCAmelCase_ = logging.getLogger(_... | 541 | 1 |
from random import shuffle
import tensorflow as tf
from numpy import array
def UpperCamelCase ( _A : Dict , _A : str )-> Tuple:
"""simple docstring"""
A__ = int(UpperCAmelCase__ )
assert noofclusters < len(UpperCAmelCase__ )
# Find out ... | 491 | '''simple docstring'''
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import... | 209 | 0 |
'''simple docstring'''
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
... | 709 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
a_ = logging.get... | 193 | 0 |
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
_lowerCamelCase : str = logging.get_logger(__na... | 184 |
"""simple docstring"""
import logging
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_S... | 391 | 0 |
'''simple docstring'''
def __lowerCAmelCase ( snake_case__ ):
try:
__UpperCamelCase : List[str] = float(snake_case__ )
except ValueError:
raise ValueError("Please enter a valid number" )
__UpperCamelCase : Tuple = decimal -... | 399 |
'''simple docstring'''
from collections import deque
from .hash_table import HashTable
class A ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
def __init__(self , *_UpperCAmelCase , **_UpperCAmelCase ) -> List[str]:
su... | 399 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_... | 665 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ ... | 665 | 1 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ = 50 ):
UpperCAmelCase : Optional[int] = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(row_length - block_length ):
... | 701 |
'''simple docstring'''
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = 1 / sqrt(2 ) ):
UpperCAmelCase : int = tau * frequency / samplerate
UpperCAmelCase : ... | 695 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ : List[Any] = logging.get_logger(__name__)
UpperCAmelCase__ : List[str] = {
'''SCUT-DLVCLab/lilt-roberta-en-base''': (
'''https://huggingface.co/SCUT-DLVCLab/lilt-r... | 410 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
... | 410 | 1 |
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def lowerCAmelCase__ ( lowerCamelCase_ : Dict ,lowerCamelCase_ : Any ,lowerCamelCase_ : List[str]):
'''simple... | 90 |
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_series import ... | 90 | 1 |
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 = {"v... | 684 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.uti... | 684 | 1 |
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''sim... | 717 |
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ..... | 437 | 0 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_comm... | 81 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase : List[str] = {
"configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
"tokenization_bio... | 405 | 0 |
"""simple docstring"""
__lowerCAmelCase : Union[str, Any] = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def __lowerCAmelCase ( __UpperCamelCase : ... | 21 |
"""simple docstring"""
# Copyright (c) 2021-, NVIDIA CORPORATION. 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/... | 21 | 1 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
... | 301 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase )-> bool:
UpperCamelCase = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def lowercase__ ( __UpperCamelCase = 5000 )-> int:
UpperCamelCase ... | 301 | 1 |
from ..utils import DummyObject, requires_backends
class _SCREAMING_SNAKE_CASE ( metaclass=_a ):
'''simple docstring'''
lowercase_ = ["""flax"""]
def __init__(self : Optional[int] , *UpperCAmelCase_ : Tuple , **UpperCAmelCase_ : Dict) ->Dict:
... | 720 |
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
__A = logging.get_logger(__name__)
__A = ... | 437 | 0 |
'''simple docstring'''
import math
import sys
def _lowerCAmelCase ( lowercase : int ) ->Optional[Any]:
"""simple docstring"""
if number != int(lowerCamelCase_ ):
raise ValueError('''the value of input must be a natural number''' )
if num... | 161 |
"""simple docstring"""
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
A = importlib.util.find_spec('s3fs') is not None
if _has_safs:... | 449 | 0 |
"""simple docstring"""
def _A ( __lowercase ):
"""simple docstring"""
if len(__lowercase ) <= 1:
return lst
lowerCamelCase__ = 1
while i < len(__lowercase ):
if lst[i - 1] <= lst[i]:
i += 1
... | 258 |
"""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 _A ( __lowercase ):
... | 258 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCAmelCase = {'''configuration_encoder_decoder''': ['''EncoderDecoderConfig''']}
try:
if not is_torch_available():
rais... | 84 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def __a ( __lowerCAmelCase ) -> int:
for param in module.parameters():
SCREAMING_SNAKE_CASE : List[Any] = False
def __a ( ) -> List[str]:
SCREAMIN... | 352 | 0 |
'''simple docstring'''
from numpy import exp, pi, sqrt
def UpperCamelCase_ ( A__ , A__ = 0.0 , A__ = 1.0 ):
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 702 |
'''simple docstring'''
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy... | 511 | 0 |
'''simple docstring'''
def lowerCamelCase_ ( __UpperCamelCase : Dict , __UpperCamelCase : Optional[Any] ) -> int:
"""simple docstring"""
return 1 if input_a == input_a else 0
def lowerCamelCase_ ( ) -> None:
"""simpl... | 292 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __a ( unitte... | 228 | 0 |
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
i... | 716 |
def lowerCamelCase__ ( _lowerCamelCase = 1000 ) ->int:
_UpperCAmelCase =2**power
_UpperCAmelCase =str(_lowerCamelCase )
_UpperCAmelCase =list(_lowerCamelCase )
_UpperCAmelCase =0
for i in list_num:
sum_of_num += int(_lowerCamelCase )
return sum_o... | 592 | 0 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_... | 71 |
'''simple docstring'''
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class _snake_case (nn.Module):
def __init__( self ,_snake_case = 16 ,_snake_case = 88 ,_snake_case = None ,_snake_case = 1 ,_snake... | 71 | 1 |
"""simple docstring"""
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = set()
# edges = list of graph's edges
lowerCAmelCase__ = get_edges(lowerCamelCase__ )
# While there are still elements in edges list, take an arbitra... | 674 | """simple docstring"""
def _UpperCAmelCase ( lowerCamelCase__ = 50 ):
"""simple docstring"""
lowerCAmelCase__ = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(ro... | 674 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase_ : Optional[int] = {
'''configurati... | 414 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ : Optional[Any] = logging.get_logger(__name__)
lowerCAmelCase_ : Any = {
'''uclanlp/visualbert-vqa''': '''https://huggingface.co/uclanlp/visualbert-vqa/r... | 414 | 1 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be check... | 717 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClas... | 391 | 0 |
def lowerCamelCase__ ( snake_case_ : List[Any] ) -> Dict:
if not all(char in '''01''' for char in bin_string ):
raise ValueError('''Non-binary value was passed to the function''' )
if not bin_string:
raise ValueError('''Empty string was passed t... | 592 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class lowerCamelCase_ ( lowerCamelCase ... | 0 | 0 |
from ..utils import DummyObject, requires_backends
class _a ( metaclass=UpperCAmelCase__ ):
"""simple docstring"""
A_ = ["""flax""", """transformers"""]
def __init__( self , *_UpperCAmelCase , **_UpperCAmelCase ) -> List[Any]:
... | 618 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTo... | 618 | 1 |
'''simple docstring'''
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
... | 199 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.... | 199 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_a : str = {
"""configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxConfig"""],
"""tokenization... | 714 |
"""simple docstring"""
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transfo... | 87 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase = logging.get_logger(__name__)
__lowercase = {
"""facebook/s2t-wav2vec2-large-en-de""": (
"""https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"""
... | 203 |
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
IMAGENET_STANDAR... | 268 | 0 |
from __future__ import annotations
from cmath import sqrt
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> tuple[complex, complex]:
if a == 0:
raise ValueError('''Coefficient \'a\' must not be zero.''' ... | 208 |
from jiwer import compute_measures
import datasets
lowerCamelCase__ : Optional[int] = """\
@inproceedings{inproceedings,
author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
year = {2004},
month = {01},
pages = {},
title = {From WER and RIL to MER and WIL... | 208 | 1 |
import pprint
import requests
__a : List[Any] = """https://zenquotes.io/api"""
def UpperCAmelCase ( ):
"""simple docstring"""
return requests.get(API_ENDPOINT_URL + '''/today''' ).json()
def UpperCAmelCase ( ):
"""simple docstring"""
... | 534 | from argparse import ArgumentParser
from .env import EnvironmentCommand
def UpperCAmelCase ( ):
"""simple docstring"""
__lowercase = ArgumentParser('''Diffusers CLI tool''' , usage='''diffusers-cli <command> [<args>]''' )
__lowercase = parser.a... | 534 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase : Dict = {
"configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE... | 293 |
"""simple docstring"""
def A ( snake_case :int ) -> bool:
return str(snake_case ) == str(snake_case )[::-1]
def A ( snake_case :int ) -> int:
return int(snake_case ) + int(str(snake_case )[::-1] )
def A ( snake_case :int = 1_0_0_0_0 ) -> int:
... | 293 | 1 |
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