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 dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import... | 35 | """simple docstring"""
from collections.abc import Sequence
from queue import Queue
class UpperCAmelCase_ :
def __init__( self : Optional[Any] , __UpperCamelCase : List[Any] , __UpperCamelCase : List[str] , __UpperCamelCase : Tuple , __UpperCamelCase : Optional[int]=... | 420 | 0 |
'''simple docstring'''
def UpperCamelCase ( lowercase_ : float , lowercase_ : int ) -> float:
'''simple docstring'''
if digit_amount > 0:
return round(number - int(lowercase_ ) , lowercase_ )
return number - int(lowercase_ )
if __name__ == "__main__":
pri... | 145 |
'''simple docstring'''
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 UpperCamelCase ( lowercase_ : List[str] , lowercase_ : Optional[Any] , ... | 145 | 1 |
"""simple docstring"""
from itertools import product
def _snake_case ( _snake_case : Dict , _snake_case : Dict ) -> list[int]:
'''simple docstring'''
_A = sides_number
_A = max_face_number * dice_number
_A = [0] * (max_total + ... | 7 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
lowerCamelCase_ : int = argparse.ArgumentParser()
parser.add_argument("""--dump_path""", default=None, type... | 548 | 0 |
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def _SCREAMING_SNAKE_CASE ( __lowercase : Optional[int] ) -> List[str]:
"""simple docstring"""
return x + 2
class __lowercase ... | 199 |
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def _SCREAMING_SNAKE_CASE ( __lowercase : str ) -> str:
"""simple docstring"""
if "cls_token" in name:
__A ... | 199 | 1 |
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoModelForMaskedLM,
... | 514 |
'''simple docstring'''
import argparse
import os
import re
__snake_case : Dict = 'src/diffusers'
# Pattern that looks at the indentation in a line.
__snake_case : Optional[Any] = re.compile(r'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
__snake_case : ... | 215 | 0 |
from __future__ import annotations
import math
def lowerCAmelCase ( _lowerCAmelCase : int ):
"""simple docstring"""
if num <= 0:
UpperCAmelCase__ = F'''{num}: Invalid input, please enter a positive integer.'''
raise ValueError(_lowerCAmelCase )
UpperCAmelCase__ ... | 364 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowerCAmelCase : Optional[int] = {
"configuration_maskformer": ["MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "MaskFormerConfig"],
"configuration_maskformer... | 364 | 1 |
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE )
class SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_C... | 435 | '''simple docstring'''
def __A ( UpperCAmelCase ) -> str:
'''simple docstring'''
if isinstance(UpperCAmelCase ,UpperCAmelCase ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(UpperCAmelCase ,U... | 435 | 1 |
def __UpperCAmelCase ( __A ) -> int:
'''simple docstring'''
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
UpperCAmelCase__ = 1
UpperCAmelCase__ = 1
while repunit:
UpperCAmelCase__ ... | 277 |
from __future__ import annotations
class lowercase__ :
def __init__( self : int , _lowercase : list[list[int]] ):
"""simple docstring"""
UpperCAmelCase__ = TypeError(
"Matrices must be formed ... | 277 | 1 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import ... | 223 |
"""simple docstring"""
import math
class lowerCAmelCase_ :
"""simple docstring"""
def __init__(self , SCREAMING_SNAKE_CASE__=0 ) -> str: # a graph with Node 0,1,...,N-1
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : ... | 223 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCamelCase_ = {'''configuration_swin''': ['''SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SwinConfig''', '''SwinOnnxConfig''']}
try:
if not is_torch_available():
... | 86 |
def __magic_name__ ( __a : str , __a : str ):
'''simple docstring'''
UpperCamelCase__ = len(__a )
UpperCamelCase__ = len(__a )
UpperCamelCase__ = [[False for _ in range(m + 1 )] for _ in range(n + 1 )]
UpperCamelCase__ = True
for i in... | 86 | 1 |
lowerCamelCase =8.31_4462 # Unit - J mol-1 K-1
def SCREAMING_SNAKE_CASE_ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError('''Invalid inputs. Enter positive value.''' )
return moles * kelvin * UNIVERSAL_... | 285 |
"""simple docstring"""
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,
CharacterToke... | 155 | 0 |
import sys
def __UpperCAmelCase ( snake_case_ : List[str] ):
'''simple docstring'''
UpperCAmelCase: Optional[Any] = len(snake_case_ )
UpperCAmelCase: Optional[int] = [[0 for x in range(snake_case_ )] for x in range(snake_case_ )]
... | 704 |
from __future__ import annotations
import numpy as np
def __UpperCAmelCase ( snake_case_ : list[float] ):
'''simple docstring'''
return np.maximum(0 , snake_case_ )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 166 | 0 |
import pprint
import requests
_A = "https://zenquotes.io/api"
def lowerCamelCase__ ( ):
"""simple docstring"""
return requests.get(API_ENDPOINT_URL + "/today" ).json()
def lowerCamelCase__ ( ):
"""simple docstring"""
return requests.get(API_ENDPO... | 290 |
from math import loga
def lowerCamelCase__ ( __lowerCAmelCase : int ):
"""simple docstring"""
if a < 0:
raise ValueError("Input value must be a positive integer" )
elif isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise TypeError("Inp... | 290 | 1 |
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, ImagePipel... | 705 |
def a ( A__ : str , A__ : int ) -> list[str]:
"""simple docstring"""
return [sentence[i : i + ngram_size] for i in range(len(A__ ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
testmod()
| 380 | 0 |
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class UpperCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : Union[str, Any] ):
... | 322 |
def UpperCamelCase__ ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ) -> float:
'''simple docstring'''
if principal <= 0:
raise Exception('''Principal borrowed must be > 0''' )
if rate_per_annum < 0:
raise Exception('''R... | 322 | 1 |
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
... | 713 |
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,
)
UpperCAmelCase__ : Optional[Any] = {
... | 676 | 0 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .mod... | 27 |
from collections.abc import Callable
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> float:
"""simple docstring"""
_A = a
_A = b
if function(_SCREAMING_S... | 27 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_a... | 370 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_toke... | 370 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ : Dict = logging.get_logger(__name__)
UpperCamelCase__ : Tuple = {
"""google/pegasus-large""": """https://huggingface.co/google/pegasus-large/resolve/main/config.... | 387 |
import os
def lowerCAmelCase_ ( ):
with open(os.path.dirname(snake_case_ ) + """/p022_names.txt""" ) as file:
_A : Optional[Any] = str(file.readlines()[0] )
_A : Dict = names.replace("""\"""","""""" ).split(""",""" )
names.sort()
_A ... | 307 | 0 |
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def lowercase_( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
'''simple docstrin... | 716 |
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {'''voca... | 231 | 0 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def A__ ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase = 1_0**-1_0 ):
"""simple docstring"""
_lowerCAmelCase = ... | 589 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {
'configuration_table_transformer': [
'TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'TableTransformerConfig',
'Ta... | 406 | 0 |
'''simple docstring'''
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import... | 245 |
'''simple docstring'''
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def UpperCamelCase ( a ) -> Optional[int]:
'''simple docstring'''
return ConvertCommand(
args.model_type , args.tf_checkpoint , arg... | 245 | 1 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 73 |
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTest... | 439 | 0 |
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, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import ... | 703 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( snake_case : int )-> int:
return 1 if digit in (0, 1) else (digit * factorial(digit - 1 ))
def SCREAMING_SNAKE_CASE_ ( snake_case : int )-> bool:
_lowerCamelCase = 0
_lowerCamelCase = n... | 222 | 0 |
import colorsys
from PIL import Image # type: ignore
def __snake_case ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> float:
SCREAMING_SNAKE_CASE__ = x
SCREAMING_SNAKE_CASE__ = y
for step in range(lowerCAmelCase_ ): # noqa: B0... | 100 |
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def __snake_case ( lowerCAmelCase_ ) -> Optional[Any]:
SCREAMING_SN... | 100 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...toke... | 702 |
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from ... | 552 | 0 |
import argparse
import os
import re
_a = "src/diffusers"
# Pattern that looks at the indentation in a line.
_a = re.compile(r"^(\s*)\S")
# Pattern that matches `"key":" and puts `key` in group 0.
_a = re.compile(r"^\s*\"([^\"]+)\":")
# Pattern that matches `_import_structure["key"]` and puts ... | 481 |
'''simple docstring'''
from statistics import mean
import numpy as np
def a_ ( lowerCamelCase : list , lowerCamelCase : list , lowerCamelCase : list , lowerCamelCase : int ):
lowerCAmelCase = 0
# Number of processe... | 133 | 0 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import... | 716 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE : List[Any] = {
'''configuration_roberta''': ['''ROBERTA_PRETRAI... | 472 | 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, """m... | 38 |
'''simple docstring'''
import argparse
import os
import re
__lowerCAmelCase = "src/diffusers"
# Pattern that looks at the indentation in a line.
__lowerCAmelCase = re.compile(R"^(\s*)\S")
# Pattern that matches `"key":" and puts `key` in group 0.
__lowerCAmelCase = ... | 536 | 0 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia ... | 709 | '''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_t... | 512 | 0 |
'''simple docstring'''
from __future__ import annotations
def __snake_case (__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
"""simple docstring"""
lowerCamelCase_ : Union[str, Any] = len(__Uppe... | 501 |
'''simple docstring'''
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configu... | 501 | 1 |
'''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
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase ... | 715 |
'''simple docstring'''
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
__UpperCAmelCase = [
# tf -> hf
("/", "."),
("layer_", "layers."),
("k... | 692 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class A_ ( metaclass=_a ):
lowerCAmelCase__ = ['transformers', 'torch', 'note_seq']
def __init__( self: Union[str, Any] ,*__lowerCAmelCase: List[str] ,**__lowerCAmelCase: L... | 46 |
'''simple docstring'''
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_c... | 653 | 0 |
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
C... | 688 |
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
SCREAMING_SNAKE_CASE... | 688 | 1 |
from __future__ import annotations
import math
def snake_case( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ) -> int:
'''simple docstring'''
if depth < 0:
raise ... | 217 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_asy... | 217 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import Padd... | 490 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class __lowercase ( metaclass=__magic_name__ ):
_a = ["""onnx"""]
def __init__( self , *UpperCamelCase , **UpperCamelCase ) -> str:
require... | 490 | 1 |
import unittest
import numpy as np
import requests
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_... | 21 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_A = {
"""configuration_roberta_prelayernorm""": [
"""ROBERTA_PRELAYERNORM_PRETRAINED_CONF... | 258 | 0 |
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def _UpperCamelCase ( ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = 9, 14 # noqa: F841
__SCREAMING_SNAKE_CASE : Dict = [
[0, 1, 4],
... | 716 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _lowercase ( A__ ):
'''simple docstring'''
SCRE... | 260 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE ... | 502 |
'''simple docstring'''
import math
from collections.abc import Iterator
from itertools import takewhile
def _lowerCAmelCase ( lowerCamelCase_ : int ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number %... | 502 | 1 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class UpperCamelCase ( ... | 700 | '''simple docstring'''
import numpy
# List of input, output pairs
UpperCamelCase : List[str] = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
UpperCamelCase : Dict = (((515, 22, 13), 555), ((61, 35, 49), 150))
UpperCa... | 610 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_snake_case = {
"configuration_graphormer": ["GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "GraphormerConfig"],
}
try:
if not is_torch_available():
... | 500 |
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
_snake_case = loggi... | 500 | 1 |
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCAmelCase__( __lowercase , unittest.TestCase ):
... | 719 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCAmelCase__ ( __lowercase ):
... | 202 | 0 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_lowercase : List[Any] = logging.get_logger(__name__)
_lowercase : List[str] = {
"speechbrain/m-ctc-t-large": "https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json",
... | 641 |
def _lowerCAmelCase ( UpperCamelCase__: str , UpperCamelCase__: int , UpperCamelCase__: Any=False ) -> str:
"""simple docstring"""
if isinstance(UpperCamelCase__ , UpperCamelCase__ ) and isinstance(UpperCamelCase__ , UpperCamelCase__ ):
A ... | 641 | 1 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class a ( unittest.TestCase ):
def _UpperCAmelCase ( self ):
'''simple docstring'''... | 707 |
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: Any ) -> Optional[int]:
_UpperCAmelCase : Optional[Any] = os.path.join(args.tf_model_dir , "pa... | 467 | 0 |
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
snake_case_ : Union[str, Any] = int(lowerCAmelCase_ )
if decimal in (0, 1): # Exit cases for the recursion
return str(lowerCAmelCase_ )
snake_case_ ,snake_case_ : Tuple = divmod(lowerCAme... | 666 | import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/mai... | 666 | 1 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowercase_ ( lowerCAmelCase__ ):
__UpperCamelCase = ["image_processor", "tokenizer"]
__UpperCamelCase = "ViTImageProcessor"
... | 706 |
def UpperCAmelCase ( UpperCamelCase__ ) -> str:
'''simple docstring'''
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise TypeError("""'float' object cannot be interpreted as an integer""" )
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
... | 334 | 0 |
"""simple docstring"""
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils... | 277 | """simple docstring"""
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class a ( lowerCAmelCase_ ):
_snake_case : Dict = CustomTokenizer
pass
| 277 | 1 |
def lowerCamelCase__ ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def lowerCamelCase__ ( _lowerCamelCase = 100 ):
'''simple docstr... | 704 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
_lowerCAmelCase = list[list[float | int]]
def lowerCamelCase__ ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : int = len... | 16 | 0 |
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backb... | 400 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
_SCREAMING_SNAKE_CASE : Optional[int] = True
except (ImportError, ModuleNotFoundError):
_SCREAMING_SNAKE_CASE : Optional[Any] = False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
... | 400 | 1 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torc... | 717 |
"""simple docstring"""
import math
lowerCAmelCase__ =10
lowerCAmelCase__ =7
lowerCAmelCase__ =BALLS_PER_COLOUR * NUM_COLOURS
def _a ( UpperCAmelCase__ = 20 ) -> str:
__SCREAMING_SNAKE_CASE = math.comb(UpperCAmelCase__ , UpperCAmelCase__ ... | 690 | 0 |
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require... | 62 |
"""simple docstring"""
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sente... | 657 | 0 |
"""simple docstring"""
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
_UpperCamelCase = ... | 363 |
"""simple docstring"""
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-m... | 363 | 1 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase__ ( __magic_name__ : str , __magic_name__ : str ) -> bool:
'''simple docstring'''
snake_case__ : Union[str, Any] = get_failure_array(__magic_name__ )
# 2) Step throu... | 38 |
from __future__ import annotations
def _a ( lowerCamelCase ):
lowerCamelCase : Union[str, Any] = str(lowerCamelCase )
return n == n[::-1]
def _a ( lowerCamelCase = 100_0000 ):
lowerCamelCase : Any = 0
for i in range(1, lowerCame... | 681 | 0 |
from __future__ import annotations
snake_case_ : int = []
def __UpperCAmelCase ( snake_case_ : Optional[Any] , snake_case_ : Union[str, Any] , snake_case_ : int ):
'''simple docstring'''
for i in range(len(__A )... | 715 |
from collections import deque
class __lowerCamelCase :
def __init__( self , __snake_case , __snake_case , __snake_case ) -> None:
"""simple docstring"""
UpperCAmelCase: Tuple = process_name # process name
... | 166 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging
logging.... | 271 | import torch
def lowerCAmelCase_ ( ) -> int:
'''simple docstring'''
if torch.cuda.is_available():
_UpperCamelCase: Any = torch.cuda.device_count()
else:
_UpperCamelCase: Union[str, Any] = 0
print(F"""Successfully ran on {num_gpus} GPUs""" )
if __name__ == "__ma... | 271 | 1 |
UpperCamelCase__ = "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
UpperCamelCase__ = [... | 548 |
from math import ceil, sqrt
def _UpperCamelCase (a__ :int = 100_0000 ):
"""simple docstring"""
UpperCamelCase__ = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
UpperCamelCas... | 548 | 1 |
from __future__ import annotations
def lowerCamelCase ( a_ ) -> bool:
lowerCAmelCase_ = str(a_ )
return len(a_ ) == 9 and set(a_ ) == set('123456789' )
def lowerCamelCase ( ) -> int | None:
for base_num in range(9_999 ... | 318 |
from __future__ import annotations
def lowerCamelCase ( a_ ) -> bool:
lowerCAmelCase_ = str(a_ )
return len(a_ ) == 9 and set(a_ ) == set('123456789' )
def lowerCamelCase ( ) -> int | None:
for base_num in range(9_999 ... | 318 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
snake_case__ = {
'configuration_ernie': ['ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ErnieConfig', 'ErnieOnnxConfig'],
}
try:
if not is_torch_availabl... | 638 | import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
snake_case__ = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbe... | 638 | 1 |
'''simple docstring'''
from __future__ import annotations
class UpperCAmelCase :
"""simple docstring"""
def __init__( self : int , UpperCamelCase__ : list[list[int]] ) -> Tuple:
_UpperCamelCase =TypeError(
'''Matrices must be ... | 404 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def _a (__SCREAMING_SNAKE_CASE ):
"""simple docstring"""
_UpperCamelCase =[
'''encoder.version''',
'''decoder.ver... | 404 | 1 |
'''simple docstring'''
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOC... | 715 |
'''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.testing_utils import (... | 8 | 0 |
"""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 = {
... | 449 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0... | 353 | 0 |
"""simple docstring"""
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, R... | 705 |
"""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/LICENSE-2.0
#
# Un... | 229 | 0 |
"""simple docstring"""
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
SC... | 465 |
"""simple docstring"""
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.a... | 465 | 1 |
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',
'AutoformerConfig',
... | 712 |
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
_a : Union[str, Any] = 1.0_54_57_18_17e-34 # unit of ℏ : J * s
_a : Optional[Any] = 3e8 # unit of c : m * s^-1
def Up... | 571 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 371 |
"""simple docstring"""
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
lowerCAmelCase : Dict = namedtuple(
"""_TestC... | 543 | 0 |
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np
from .im... | 701 |
def lowercase_ ( _A : int , _A : int ):
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
lowerCamelCase__ : List[str] = str(bin(_A ) )[2:] # remove the leading "0b"
... | 5 | 0 |
"""simple docstring"""
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp... | 480 |
"""simple docstring"""
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class __lowercase ( _UpperCAmelCase):
"""simple docst... | 480 | 1 |
"""simple docstring"""
def _lowerCamelCase( a ):
__a = generate_pascal_triangle(a )
for row_idx in range(a ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 ):
print(end=" " )
# Print ro... | 67 | """simple docstring"""
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class snake_case__ :
_snake_case : torch.Tensor # [batch_size x 3]
_snake_case : torch.Tensor # [batch_size x 3]
_snake_case : torch.Tensor # [batch_size... | 67 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_: Tuple ={'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP'... | 78 | '''simple docstring'''
import logging
from transformers import PretrainedConfig
SCREAMING_SNAKE_CASE_: Any =logging.getLogger(__name__)
SCREAMING_SNAKE_CASE_: Any ={
'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/conf... | 78 | 1 |
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike... | 700 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requ... | 423 | 0 |
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteria,
... | 99 |
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import Ar... | 419 | 0 |
'''simple docstring'''
import math
A_ = 10
A_ = 7
A_ = BALLS_PER_COLOUR * NUM_COLOURS
def _UpperCamelCase ( __UpperCamelCase = 20 ) -> str:
lowerCamelCase_ = math.comb(__UpperCamelCase ,__UpperCamelCase )
lowerCamelCase_ = math.comb(NUM_BALLS... | 384 |
'''simple docstring'''
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class Uppe... | 384 | 1 |
"""simple docstring"""
from __future__ import annotations
from PIL import Image
# Define glider example
__lowerCamelCase = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, ... | 490 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, Te... | 490 | 1 |
'''simple docstring'''
def __lowerCamelCase ( _UpperCamelCase : int , _UpperCamelCase : int ):
'''simple docstring'''
return int((input_a, input_a).count(0 ) != 0 )
def __lowerCamelCase ( ):
'''simple docstring'''
assert nand_gate(0 ... | 43 | '''simple docstring'''
lowercase__ : Union[str, Any] = "\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
lowercase__ : str = [{"type": "code", "con... | 43 | 1 |
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import... | 203 |
def _lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
A_ = len(SCREAMING_SNAKE_CASE )
A_ = len(SCREAMING_SNAKE_CASE )
A_ = (
first_str_length if first_str_length > second_str_l... | 203 | 1 |
__SCREAMING_SNAKE_CASE : List[Any] ='''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'''
def UpperCamelCase__ ( lowerCAmelCase__ ):
# Make sure the supplied data is a bytes-like object
if not isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ):
... | 715 |
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONFIG_MAPPING,
FEATUR... | 72 | 0 |
import comet # From: unbabel-comet
import torch
import datasets
snake_case = datasets.logging.get_logger(__name__)
snake_case = """\
@inproceedings{rei-EtAl:2020:WMT,
author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},
title = {Unbabel's Part... | 62 |
"""simple docstring"""
def UpperCAmelCase ( A : list[int] , A : list[int] ):
'''simple docstring'''
if not len(A ) == len(A ) == 3:
raise ValueError('Please enter a valid equation.' )
if equationa[0] == equationa[1] == equationa... | 573 | 0 |
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class a__ ( nn.Module ):
def __init__( self, _UpperCAmelCase = 16, _UpperCAmelCase = 88, _UpperCAmelCase = None, _UpperCAmelCase = 1, ... | 668 | """simple docstring"""
from typing import Any
import numpy as np
def __a ( A ):
'''simple docstring'''
return np.array_equal(A , matrix.conjugate().T )
def __a ( A , A ):
'''simple docstring'''
lowercase__ = v.co... | 668 | 1 |
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 _snake_case ( UpperCAmelC... | 12 |
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class __SCREAMING_SNAKE_CASE( a_... | 328 | 0 |
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs("hub/hopper-medium-v2/unet/hor32", exist_ok=True)
os.makedirs("hub/hopper-medium-v2/unet/hor128", exist_ok=True)
os.makedirs("hub/hopper-medium-v2/value_function", exist_ok=True)
def __lowercase( __snake_case : Opti... | 345 |
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_token... | 345 | 1 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a ( UpperCamelCase__ ):
_lowercase : str = ['''image_processor''', '''tokenizer''']
_lowercase : Any = '''CLIPImagePro... | 43 | """simple docstring"""
from __future__ import annotations
import math
from collections.abc import Callable
def SCREAMING_SNAKE_CASE ( snake_case, snake_case, snake_case, snake_case = 1_00, ):
__snake_case = x_start
__snake_case = fnc(snake_case)
__snak... | 564 | 0 |
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class a ( _lowerCamelCase ):
@staticmethod
@abstractmethod
def A_ ( lowercase_ : ArgumentParser ):
raise NotImplementedError()
@abstractmethod
d... | 593 |
'''simple docstring'''
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
a : Any = '%20'.join(argv[1:]) if len(argv) > 1 else quote(str(input(... | 593 | 1 |
from __future__ import annotations
snake_case = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def lowerCamelCase__ ( lowercase , lowercase , lowercase , lowercase , lowercase , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE... | 62 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case__ : Dict = {
"""configuration_whisper""": ["""WHISPER_PRETRAINED_CONFIG_ARCHI... | 402 | 0 |
'''simple docstring'''
a : Any = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def lowercase ( __magic_name__ ):
'''simple docstring'''
if not isinstance(__magic_name__ , __magic_name__ ):
UpperCAmelCase : Dict ... | 713 |
'''simple docstring'''
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclas... | 609 | 0 |
'''simple docstring'''
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class SCREAMING_SNAKE_CASE (a__ ):
# to overw... | 8 |
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
from ...test_bac... | 570 | 0 |
'''simple docstring'''
from __future__ import annotations
from typing import TypedDict
class __A ( UpperCamelCase__ ):
a__ : str
a__ : int
def lowerCAmelCase_ ( snake_case_ : str ) -> list[str]:
'''simple docstring'''
if not isinstance(snake_cas... | 415 | '''simple docstring'''
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import... | 415 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__A : List[str] = {
"configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
"tokenization_tapas": ["TapasTokenizer... | 27 |
UpperCAmelCase_ : dict[str, float] = {
"joule": 1.0,
"kilojoule": 1_000,
"megajoule": 1_000_000,
"gigajoule": 1_000_000_000,
"wattsecond": 1.0,
"watthour": 3_600,
"kilowatthour": 3_600_000,
"newtonmeter": 1.0,
"calorie_nutr": 4_186.8,
"kilocalorie_nutr": 4_186_800... | 17 | 0 |
"""simple docstring"""
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
_lowerCAmelCase = 2_0_0
# Number of elements selected in every generation of evolution. The selection takes
# place from best to... | 16 |
"""simple docstring"""
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
_lowerCAmelCase = {"""UserAgent""": UserAgent().random}
def lowerCamelCase__ ( _lowerCamelCase ):
'''simple docstr... | 16 | 1 |
"""simple docstring"""
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
SCREAMING_SNAKE_CASE_ = lo... | 237 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
SCREAMING_SNAKE_CASE_ = {"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP"... | 237 | 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
@requ... | 714 |
"""simple docstring"""
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
SCREAMING_SNAKE_CASE = 4
SCREAMING_SNAKE_CASE = 3
class... | 556 | 0 |
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
@require_t... | 583 |
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
snake_case__ = logging.get_logger(__name__)
class UpperCAmelCase ( __lowerCamelCase ):
def __init__( self : Optional[Any] , *lowerCAmelCase : ... | 583 | 1 |
"""simple docstring"""
import numpy as np
def __lowerCamelCase ( lowerCAmelCase__ ):
return 1 / (1 + np.exp(-vector ))
def __lowerCamelCase ( lowerCAmelCase__ ):
return vector * sigmoid(lowerCAmelCase__ )
if __name__ == "__main__":
... | 554 |
"""simple docstring"""
import os
import numpy
import onnx
def __lowerCamelCase ( lowerCAmelCase__ ,lowerCAmelCase__ ):
A__ = a.name
A__ = b.name
A__ = ''
A__ = ''
A__ = a... | 554 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 30 |
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
snake_case__ : Optional[Any] = logging.get_logger(__name__)
class _A ( _lowercase ):
'''simple docstring'''
def __init__( self : Dict , *lowerCam... | 402 | 0 |
"""simple docstring"""
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
fro... | 715 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
fr... | 24 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transfo... | 90 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAn... | 90 | 1 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
... | 500 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : int ):
A__ = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def _snake_case ( UpperCAmelCase_ : int ):
A__ = 0
while number > 0:
... | 500 | 1 |
'''simple docstring'''
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 _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,... | 42 |
'''simple docstring'''
from math import isclose, sqrt
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> tuple[float, float, float]:
lowerCamelCase_ = point_y / 4 / point_x
lowerCamelCase_ = 2 * normal_gradient / (1 + normal_gradi... | 42 | 1 |
import numpy as np
UpperCAmelCase_ : List[Any] = [
["""a""", """b""", """c""", """d""", """e"""],
["""f""", """g""", """h""", """i""", """k"""],
["""l""", """m""", """n""", """o""", """p"""],
["""q""", """r""", """s""", """t""", """u"""],
["""v""", """w""", """x""", """y""",... | 440 |
from collections import namedtuple
UpperCAmelCase_ : Union[str, Any] = namedtuple("""from_to""", """from_ to""")
UpperCAmelCase_ : int = {
"""cubicmeter""": from_to(1, 1),
"""litre""": from_to(0.0_01, 10_00),
"""kilolitre""": from_to(1, 1),
"""gallon""": from_... | 440 | 1 |
'''simple docstring'''
class __UpperCamelCase :
def __init__( self :List[Any] ):
snake_case_ : Union[str, Any] = {}
def a__ ( self :List[Any] ):
print(self.vertex )
for i in self.vertex:
print(_UpperCAm... | 334 |
'''simple docstring'''
def _lowerCAmelCase ( __snake_case : Optional[Any] ) -> Optional[Any]: # noqa: E741
__A : Tuple = len(__snake_case )
__A : Optional[int] = 0
__A : str = [0] * n
__A : int = [Fals... | 8 | 0 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_com... | 716 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
"""google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
"""... | 440 | 0 |
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