code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
def _A ( lowercase , lowercase ):
"""simple docstring"""
print('''\nThe shortest path matrix using Floyd Warshall algorithm\n''' )
for i in range(_A ):
for j in range(_A ):
if dist[i][j] != float('''inf... | 81 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_d... | 307 | 0 |
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditionalGeneration, BartTokenizer... | 367 |
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_available()... | 65 | 0 |
from functools import lru_cache
def lowerCAmelCase_ (lowerCAmelCase__: int ):
"""simple docstring"""
UpperCAmelCase_: List[str] = 2
UpperCAmelCase_: Union[str, Any] = set()
while i * i <= n:
if n % i:
i += 1
... | 147 |
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
a : List[str] ... | 147 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ : Optional[int] = logging.get_logger(__name__)
lowerCAmelCase_ : A... | 248 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
lowerCAmelCase_ : Any = {
'''google/tapas-base-finetuned-sqa''': (
'''https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'''
),
'''google/tapas-base-finetuned-wtq'''... | 248 | 1 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 15 |
"""simple docstring"""
from sklearn.metrics import recall_score
import datasets
lowerCAmelCase__ = '''
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives and F... | 153 | 0 |
"""simple docstring"""
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... | 195 |
"""simple docstring"""
from __future__ import annotations
import requests
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = f"""https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"""
return request... | 195 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ : Optional[Any] = logging.get_logger(__name__)
UpperCAmelCase__ : Dict = {
'MIT/ast-finetuned-audioset-10-10-0.4593': (
'https://... | 25 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase = 2000000 )-> int:
UpperCamelCase = [0 for i in range(n + 1 )]
UpperCamelCase = 1
UpperCamelCase = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if prima... | 321 | 0 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
__UpperCAmelCase =logging.get_logger(__name__)
class a__ ( UpperCAmelCase__ ):
... | 237 | '''simple docstring'''
def __lowerCAmelCase ( UpperCamelCase__ ) -> str:
return "".join(chr(ord(UpperCamelCase__ ) - 32 ) if '''a''' <= char <= '''z''' else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 237 | 1 |
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ):
if not head:
return True
# split the list to two parts
A_ , A_ : List[str] = head.next, head
while fast and fast.next:
A_ : List[Any] = fast.next.next
A_ : Optional[int] = s... | 186 |
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 transformers.generation import (
FlaxForce... | 186 | 1 |
def _a ( SCREAMING_SNAKE_CASE_ : str ):
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 364 |
import enum
import shutil
import sys
UpperCamelCase__ , UpperCamelCase__ = shutil.get_terminal_size()
UpperCamelCase__ = {"""UP""": """A""", """DOWN""": """B""", """RIGHT""": """C""", """LEFT""": """D"""}
class a__ ( enum.Enum ):
_a : Any = 0
_... | 102 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_a : Optional[int]= {"configuration_xlnet"... | 172 | """simple docstring"""
from __future__ import annotations
_a : List[Any]= []
def __UpperCAmelCase ( UpperCAmelCase_ : list[list[int]] , UpperCAmelCase_ : int , UpperCAmelCase_ : int ) -> bool:
'''simple docstring'''
for i in r... | 172 | 1 |
from PIL import Image
def lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase ):
_SCREAMING_SNAKE_CASE : Any = (259 * (level + 255)) / (255 * (259 - level))
def contrast(__lowerCamelCase ) -> int:
return int(128 + factor * (c - 128) )
return img.p... | 362 |
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutp... | 325 | 0 |
def A ( _SCREAMING_SNAKE_CASE ) -> int:
lowerCamelCase : list[list[int]] = [[0 for _ in range(_SCREAMING_SNAKE_CASE )] for _ in range(m + 1 )]
for i in range(m + 1 ):
lowerCamelCase : Optional[int] = 1
for n in ran... | 48 |
def A ( _SCREAMING_SNAKE_CASE = 100_0000 ) -> int:
lowerCamelCase : Tuple = 1
lowerCamelCase : int = 1
lowerCamelCase : Optional[Any] = {1: 1}
for inputa in range(2 ,_SCREAMING_SNAKE_CASE ):
lowerCa... | 48 | 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 NestedDataS... | 196 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase ) -> list[int]:
UpperCamelCase__ : Optional[Any] = 0
UpperCamelCase__ : Any = len(__lowerCAmelCase ) - 1
while i < ... | 196 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase : List[Any] ={
"""configuration_electra""": ["""ELECTRA_PRETRA... | 128 | """simple docstring"""
from __future__ import annotations
from math import pi, sqrt
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , __UpperCAmelCase ) -> tuple:
if inductance <= 0:
raise ValueError('''Inductance cannot be 0 or negative''' )
elif capacitance <= 0:
raise Value... | 177 | 0 |
'''simple docstring'''
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
f... | 356 |
'''simple docstring'''
def a__ ( lowercase : int, lowercase : int ) -> int:
"""simple docstring"""
return x if y == 0 else greatest_common_divisor(lowercase, x % y )
def a__ ( lowercase : int, lowercase : int ) -> int:
"""simple docstrin... | 287 | 0 |
"""simple docstring"""
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"""facebook/encodec_24khz""": """https://huggingface.co/... | 173 |
'''simple docstring'''
from datetime import datetime
import requests
def _A ( _lowerCAmelCase ):
"""simple docstring"""
__lowercase ='https://downloadgram.net/wp-json/wppress/video-downloader/video?url='
__lowercase =requests.get(base_url + url ).json()... | 166 | 0 |
'''simple docstring'''
import collections
import importlib.util
import os
import re
from pathlib import Path
lowercase ='src/transformers'
# Matches is_xxx_available()
lowercase =re.compile(r'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
lowercase =re.compile(r'^... | 358 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax impo... | 242 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
class SCREAMING_SNAKE_CASE:
"""simple docstring"""
def __init__( self : Any , __snake_case : int ) -> None:
UpperCAmelCase : ... | 23 | """simple docstring"""
from __future__ import annotations
def lowerCAmelCase__ ( _UpperCamelCase : list[list[int]] ) -> int:
"""simple docstring"""
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# ... | 150 | 0 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if... | 15 |
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... | 15 | 1 |
'''simple docstring'''
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common impo... | 272 | '''simple docstring'''
from math import sqrt
def snake_case__ ( _A: int = 1000000 ) -> int:
'''simple docstring'''
lowerCAmelCase = 0
lowerCAmelCase = 0
lowerCAmelCase = 42
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides ... | 272 | 1 |
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
from digital_image_pr... | 260 |
def lowerCamelCase_ ( lowerCAmelCase: int )-> bool:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 260 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
lowerCAmelCase__ = logging.get_logger(__name__)
class lowercase_ (lowerCamelCase__ ):
"""simple docstring"""
def __init__( self : List[Any] ... | 104 | 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
@dataclass
# Copied f... | 65 | 0 |
'''simple docstring'''
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class UpperCAmelCase__ :
"""simple docstring"""
def __lowercase ( self : Any ,_a : List[Any] ):
'''simple do... | 5 |
'''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
... | 5 | 1 |
"""simple docstring"""
def __UpperCAmelCase ( lowercase ):
"""simple docstring"""
if p < 2:
raise ValueError("""p should not be less than 2!""" )
elif p == 2:
return True
_UpperCAmelCase = 4
_UpperCAmelCase = (1 << p) - 1
for _ in range(p - 2 )... | 289 | """simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_availa... | 289 | 1 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
_SCREAMING_SNAKE_CASE = (720, 1280) # Height, Width
_SCREAMING_SNAKE_CASE = (0.4, 0.6) # if height or width lower than th... | 3 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def __lowerCamelCase ( __lowerCAmelCase : str = "https://www.worldometers.info/coronavirus" ) -> dict:
snake_case = BeautifulSoup(requests.get(__lowerCAmelCase ).text , ... | 3 | 1 |
def a__ ( A_ ):
'''simple docstring'''
if not all(x.isalpha() for x in string ):
raise ValueError("""String must only contain alphabetic characters.""" )
__magic_name__ = sorted(string.lower() )
return len(A_ ) == len(set(A_ ) ... | 88 |
import re
import string
import numpy as np
import datasets
__lowerCAmelCase : Optional[int] = '\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'
__lowerCAmelCase : Optional... | 88 | 1 |
def UpperCamelCase ( __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : list[list[int]] ):
def update_area_of_max_square(__lowerCamelCase : int , __lowerCamelCase : int ) -> int:
# BASE CASE
if row >= rows or ... | 354 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
... | 10 | 0 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class a__ ( UpperCamelCase__ ):
def __SCREAMING_SNAKE_CASE( self , _A ):
"""simple docstring"""
return 0.0
... | 92 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : Dict = logging.get_logger(__name__)
UpperCAmelCase : Tuple = {
"""caidas/swin2sr-classicalsr-x2-64""": (
"""https://huggingface.co/caidas/swin2sr-classicalsr-x2-64... | 95 | 0 |
"""simple docstring"""
from collections.abc import Sequence
from queue import Queue
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self : Optional[int] , lowercase_ : Optional[Any] , lowercase_ : Optional[int] , ... | 318 |
"""simple docstring"""
from __future__ import annotations
import queue
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self : Tuple , lowercase_ : Optional[int]):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ ... | 318 | 1 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_fla... | 78 |
"""simple docstring"""
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
SCREAMING_SNAKE_CASE : Union[str, Any] = """\
"""
SCREAMING_SNAKE_CASE : Any = """
Pe... | 102 | 0 |
from maths.prime_check import is_prime
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ):
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
A_ : Dict = f'''Input value of [number={number}] must be an integer'''
raise TypeError(SCREAMING_SNAKE_CASE )
if is_prim... | 65 |
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
UpperCamelCase = logging.get_logger(__name__)
class _lowerCamelCase ( UpperCamelCase ):
"""simple docstring"""
def __init__( self , *_SCREAMING_SNAKE_CASE , ... | 65 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowercase__ = {"configuration_vit_mae": ["VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTMAEConfig"]}... | 241 | import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
A : Tuple = "src/transformers"
A : Optional[Any] = "docs/sour... | 118 | 0 |
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def __UpperCamelCase ( *_lowerCAmelCase ) -> List[str]:
"""simple docstring"""
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
A : List[str] ... | 368 |
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_vision
from transformers.utils impo... | 115 | 0 |
'''simple docstring'''
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class lowercase_ ( tf.keras.optimizers.schedules.LearningRate... | 80 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import BitConfig, Bit... | 101 | 0 |
'''simple docstring'''
from math import factorial
lowerCAmelCase_ : Any = {str(d): factorial(d) for d in range(10)}
def _lowerCamelCase ( lowercase : List[str] ) -> Dict:
return sum(DIGIT_FACTORIAL[d] for d in str(lowercase ) )
def _... | 367 |
'''simple docstring'''
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForCo... | 346 | 0 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applic... | 5 |
import datasets
from .evaluate import evaluate
lowerCAmelCase__ = """\
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={arXiv preprint arXiv:2103.06268... | 68 | 0 |
"""simple docstring"""
def __UpperCAmelCase ( UpperCAmelCase_ : str , UpperCAmelCase_ : str ) -> str:
'''simple docstring'''
__snake_case : int = len(UpperCAmelCase_ )
__snake_case : int = len(UpperCAmelCase_ ... | 95 | """simple docstring"""
def __UpperCAmelCase ( UpperCAmelCase_ : int , UpperCAmelCase_ : int ) -> int:
'''simple docstring'''
while a != 0:
__snake_case , __snake_case : Union[str, Any] = b % a, a
return b
def __UpperC... | 95 | 1 |
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICA... | 94 |
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def __lowerCamelCase ( UpperCAmelCase_ : Unio... | 94 | 1 |
from sklearn.metrics import matthews_corrcoef
import datasets
__A = "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and fals... | 350 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_te... | 348 | 0 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
... | 15 |
def UpperCAmelCase ( a_ ) -> Optional[int]:
"""simple docstring"""
__A = [0] * len(a_ )
__A = []
__A = [1] * len(a_ )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(a_ ) ):
... | 15 | 1 |
'''simple docstring'''
from abc import ABC, abstractmethod
from typing import List, Optional
class lowerCAmelCase__ ( a ):
"""simple docstring"""
def __init__( self : int ) -> Dict:
"""simple docstring"""
self.test()
def UpperCAmelCase__ ( self : Dict... | 331 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase : Tuple = {'configuration_reformer': ['REFORMER_PRETRAINED_C... | 331 | 1 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
def _A ( snake_case ) -> str:
_lowercase , _lowercase : Optional[int] = np.shape(lowercase__ )
if rows != columns:
_lowercase : str = (
"\'table\' has to ... | 250 |
'''simple docstring'''
import copy
import random
from transformers import CLIPTokenizer
class lowerCAmelCase ( A ):
def __init__( self : Optional[Any] , *__lowercase : str , **__lowercase : Union[str, Any] ):
"""simple docstrin... | 141 | 0 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def lowerCAmelCase_ (lowerCAmelCase__: ... | 362 |
from __future__ import annotations
def lowerCAmelCase_ (lowerCAmelCase__: list[float] ):
"""simple docstring"""
UpperCAmelCase_: Union[str, Any] = 0.00
UpperCAmelCase_: List[str] = 0
for resistor in resistors:
... | 82 | 0 |
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class lowerCamelCase__ :
def __A (self , UpperCAmelCase ) -> Dict:
raise NotImplementedError()
def __A (self ... | 5 |
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class lowerCamelCase__ ( nn.Module):
def __init__(self , UpperCAmelCase = 1_6 , UpperCAmelCase = 8_8 , UpperCAmelCase = None , UpperCAmelCase ... | 5 | 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 ( snake_case__ , unittest.TestCase):
... | 277 |
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, Par... | 277 | 1 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
lowercase : Optional[int] = (7_20, 12_80) # Height, Width
lowercase : List[str] = (0.4, 0.6) # if height or width lower than ... | 3 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
lowercase : Dict = {
'configuration_speec... | 3 | 1 |
'''simple docstring'''
from torch import nn
class A_ ( nn.Module ):
'''simple docstring'''
def __init__( self : Union[str, Any] , lowercase_ : Optional[Any] , lowercase_ : List[Any] ) -> int:
super().__init__(... | 280 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
... | 280 | 1 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_ava... | 48 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"microsoft/unispeech-large-1500h-cv": (
"https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.... | 10 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart im... | 350 |
'''simple docstring'''
from __future__ import annotations
def _snake_case ( A , A ) -> float:
lowerCAmelCase__ = sorted(numsa + numsa )
lowerCAmelCase__ , lowerCAmelCase__ = divmod(len(A ) , 2 )
if mod == 1:
... | 228 | 0 |
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
lowercase_ = (
'This metric will be removed from the library soon, metric... | 7 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case : Union[str, Any] = logging.get_logger(__name__)
__snake_case : List[str] = ... | 134 | 0 |
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
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, p... | 355 | import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
... | 204 | 0 |
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@... | 65 | def lowerCAmelCase_ ( __A, __A ) -> float:
'''simple docstring'''
def get_matched_characters(__A, __A ) -> str:
UpperCAmelCase__ = []
UpperCAmelCase__ = min(len(_stra ), len(_stra ) ) // 2
... | 65 | 1 |
"""simple docstring"""
import math
import os
import sys
def A_ ( snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = """"""
try:
with open(snake_case_ ,"""rb""" ) as binary_file:
UpperCamelCase ... | 360 |
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class lowerCamelCase ( nn.Module ):
def __init__( self , SCREAMING_SNAKE_CASE_ = 16 , SCREAMING_SNAKE_CASE_ = 88 , SCRE... | 27 | 0 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class UpperCAmelCa... | 237 |
'''simple docstring'''
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
cl... | 237 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
... | 354 |
"""simple docstring"""
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/c... | 241 | 0 |
lowercase__ : Optional[int] = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
lowercase__ : Tuple = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
lowercase__ : str = {
0: "Sunday",
1: "Monday",
2: "Tuesday",
3: "Wednesday",
4: "Thursday",
5: "... | 328 |
from math import factorial
def A_ ( snake_case : int = 100 ) -> int:
'''simple docstring'''
return sum(int(snake_case ) for x in str(factorial(snake_case ) ) )
if __name__ == "__main__":
print(solution(int(input("Enter the Number: "... | 328 | 1 |
UpperCAmelCase_ : Optional[Any] = {'''a''': ['''c''', '''b'''], '''b''': ['''d''', '''e'''], '''c''': [], '''d''': [], '''e''': []}
UpperCAmelCase_ : Tuple = ['''a''', '''b''', '''c''', '''d''', '''e''']
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : Any , ... | 62 |
from math import pi
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : int , __magic_name__ : int ) -> float:
"""simple docstring"""
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(90, 10))
| 62 | 1 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def _A ( ) -> int:
"""simple docstring"""
__UpperCamelCase = 9
__UpperCamelCase = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
... | 310 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def __lowerCamelCase ( __snake_case : str, __snake_case : dict ) -> str:
"""simple docstring"""
A__ : Optional[Any] =BeautifulSoup(requests.get(__snake_case, params=__s... | 134 | 0 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_av... | 336 |
"""simple docstring"""
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
UpperCAmelCase: Optional[Any] = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defin... | 336 | 1 |
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase = [
'''word_embeddings_layernorm.weight'''... | 295 |
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 = '''▁'''
lowerCAmelCase = {'''vocab_file''': '''spiece.model'''}
lowerCA... | 295 | 1 |
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 lowercase ( UpperCamelCase__ ):
... | 355 |
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
_snake_case = logging.getLogger()
def lowerCAmelCase_ ... | 343 | 0 |
def lowerCAmelCase ( lowerCAmelCase_ )-> int:
assert (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and number_of_steps > 0
), f"""number_of_steps needs to be positive integer, your input {number_of_steps}"""
if number_of_steps == 1:
return 1
... | 262 |
import inspect
import unittest
class snake_case__( unittest.TestCase ):
'''simple docstring'''
def lowercase_ ( self ) -> int:
try:
import diffusers # noqa: F401
except ImportError:
assert False
d... | 262 | 1 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
ViTHybridCo... | 47 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
__A =logging.get_logger(__name__)
__A ={
'''post_extract_proj''': '''feature_projection.projection''',
'''encoder.pos_conv.0''... | 47 | 1 |
'''simple docstring'''
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import Tok... | 258 |
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class __UpperCAmelCase ( datasets.BuilderConfig ):
'''simple docstring'''
... | 258 | 1 |
'''simple docstring'''
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def snake_case__ ( lowerCamelCase__ : str , lowerCamelCase__ : Tuple... | 363 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
"""facebook/s2t-wav2vec2-large-en-de""": (
"""https://huggingface.co/facebook/s2t-wav2vec2-large-... | 4 | 0 |
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 A... | 277 |
from math import factorial
def lowercase_ (A : int , A : int , A : float ):
if successes > trials:
raise ValueError('successes must be lower or equal to trials' )
if trials < 0 or successes < 0:
raise ValueError('the function is defined for ... | 277 | 1 |
import warnings
from .generation import TFGenerationMixin
class UpperCAmelCase__ ( A__ ):
"""simple docstring"""
warnings.warn(
"Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will "
"be removed in Transformers v5. Import as `from transf... | 218 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : Optional[Any] = {
'''google/vivit-b-16x2-kinetics400''': (
'''https://huggingface.co/google/viv... | 218 | 1 |
'''simple docstring'''
def lowercase__ ( __lowercase : list[int] ) -> int:
"""simple docstring"""
if not numbers:
return 0
if not isinstance(__lowercase , (list, tuple) ) or not all(
isinstance(__lowercase , __lowercase ... | 53 |
'''simple docstring'''
import warnings
warnings.warn(
"memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: "
"`from accelerate import find_executable_batch_size` to avoid this warning.",
FutureWarning,
)
| 237 | 0 |
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import SwinvaConfig
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 ...tes... | 371 |
'''simple docstring'''
from __future__ import annotations
def _snake_case ( _SCREAMING_SNAKE_CASE : int | str ) -> bool:
"""simple docstring"""
lowerCAmelCase = str(_SCREAMING_SNAKE_CASE )
return n == n[::-1]
def _snake_c... | 187 | 0 |
'''simple docstring'''
import sys
a_ : Tuple = (
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""12540698747158523863050715693290963295227443043... | 75 |
"""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 requ... | 288 | 0 |
import re
import string
import numpy as np
import datasets
__UpperCamelCase : Tuple = "\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n"
__UpperCamelCase : int = "\nArgs:\n pre... | 371 |
def _a ( SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
if principal <= 0:
raise Exception('''Principal borrowed must be > 0''' )
if rate_per_annum < 0:
raise Exception('''Rate of interest m... | 51 | 0 |
"""simple docstring"""
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class __SCREAMING_SNAKE_CASE ( ctypes.Structure ):
'''simple docstring'''
_a = [('size', ctypes.c_int)... | 238 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def _SCREAMING_SNAKE_CASE ( lowercase : Union[dict, list, tuple, torch... | 204 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import... | 370 |
"""simple docstring"""
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
__snake_case = logging.getLogger(__name__)
if __nam... | 112 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase = {'configuration_wavlm': ['WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WavLMConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAva... | 188 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int ):
__a : int = int(number**0.5 )
return number == sq * sq
def lowerCamelCase (_SCRE... | 27 | 0 |
'''simple docstring'''
import functools
def _A ( snake_case , snake_case ) -> int:
_lowercase : List[str] = len(snake_case )
_lowercase : Any = len(snake_case )
@functools.cache
def min_distance(snake_case , s... | 199 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json',
}
class a__ ... | 199 | 1 |
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import... | 30 |
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : list[list[int]]) -> bool:
'''simple docstring'''
__UpperCamelCase : Any = len(_lowerCamelCase)
# We need to create solution object to save path.
__U... | 232 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, ... | 153 |
"""simple docstring"""
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
__snake_case = datasets.load_iris()
__snake_case = np.array(data['''data'''])
__snake_case = np.array(data['''target'''])
__snake_case... | 153 | 1 |
from math import factorial
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : int = 1_00 ):
return sum(map(SCREAMING_SNAKE_CASE__ , str(factorial(SCREAMING_SNAKE_CASE__ ) ) ) )
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip())))
| 62 |
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : int = 10**12 ):
__UpperCamelCase =1
__UpperCamelCase =0
__UpperCamelCase =1
__UpperCamelCase =1
while numerator <= 2 * min_total - 1:
prev_numerator += 2 * numerator
... | 62 | 1 |
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params i... | 351 |
"""simple docstring"""
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
lowerCAmelCase_ : Tuple ... | 248 | 0 |
"""simple docstring"""
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common impor... | 69 |
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
lowerCamelCase_ = ['''small''', '''medium''', '''large''']
lowerCamelCase_ = '''lm_head.decoder.weight'''
lowerCamelCase_ = '''lm_head.weight'''
def __magic_name__ ( __a : str ... | 244 | 0 |
"""simple docstring"""
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_=1024 , lowerCAmelCase_=1... | 361 |
"""simple docstring"""
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
return " ".join(
"".join(word[::-1] ) if len(lowerCAmelCase_ ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.te... | 195 | 0 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
"xlnet-large-cased": "https:/... | 26 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''facebook/dpr-ctx_encoder-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-ctx_encoder-... | 108 | 0 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Da... | 60 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = False )-> str:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase_ = f"Expected string as input, found {type(SCREAMING_SNAKE_CASE_ )}"
... | 60 | 1 |
'''simple docstring'''
import os
def _lowerCAmelCase ( _UpperCamelCase : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =len(grid[0] )
_SCREAMING_SNAKE_CASE =len(_UpperCamelCase )
_SCREAMING_SNAKE... | 47 |
'''simple docstring'''
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : List[Any] = logging.get_logger(__name__)
lowerCamelCase : str = {
"huggingface/time-series-transformer-tourism-monthly":... | 47 | 1 |
'''simple docstring'''
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.con... | 361 |
'''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : list[list[int]] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : set ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ = ... | 61 | 0 |
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_v... | 201 |
'''simple docstring'''
class UpperCAmelCase_ :
def __init__( self : List[str] , UpperCAmelCase__ : list[int] ) -> None:
lowerCAmelCase = len(UpperCAmelCase__ )
lowerCAmelCase = [0] * len_array
if len_a... | 4 | 0 |
"""simple docstring"""
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test... | 359 |
"""simple docstring"""
from decimal import Decimal, getcontext
from math import ceil, factorial
def __lowercase ( snake_case_ : int ) ->str:
'''simple docstring'''
if not isinstance(snake_case_ ,snake_case_ ):
raise TypeError('''Undefined for no... | 291 | 0 |
"""simple docstring"""
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
lowerCamelCase_ : int = loggi... | 81 |
"""simple docstring"""
def _A ( ):
"""simple docstring"""
for n in range(1 , 1_00_00_00 ):
yield n * (n + 1) // 2
def _A ( lowercase ):
"""simple docstring"""
a =1
a =2
while i * i <= n:
... | 81 | 1 |
"""simple docstring"""
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_c... | 161 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
lowercase__ =... | 161 | 1 |
from __future__ import annotations
def __UpperCamelCase ( _A , _A ):
lowerCAmelCase_ = get_failure_array(_A )
# 2) Step through text searching for pattern
lowerCAmelCase_ , lowerCAmelCase_ = 0, 0 # index into text, pattern
while i < len(_A ):
... | 278 |
def __UpperCamelCase ( _A ):
if not numbers:
return 0
if not isinstance(_A , (list, tuple) ) or not all(
isinstance(_A , _A ) for number in numbers ):
raise ValueError('''numbers must be an iterable of integers''' )
lowerCAmelCase_ = low... | 278 | 1 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDep... | 354 |
"""simple docstring"""
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
SCREAMING_SNAKE_CASE_ : List[str] = '\\n@inproceedings{snover-etal-2006-study,\n title = "A Study of Translation Edit Rate with Targeted Human Annotation",\n a... | 69 | 0 |
"""simple docstring"""
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
lowerCamelCase__ = {
# 1536-bit
5: {
"""prime""... | 86 |
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_ ... | 51 | 0 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")
class lowerCamelCase :... | 350 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def lowercase_ ( A__ ) -> str:
"""simple docstring"""
return getitem, k
def lowercase_ ( A__ , A__ ) -> str:
... | 137 | 0 |
"""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
from transformers.utils imp... | 102 |
"""simple docstring"""
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
lowercase__ = {
"""facebook/maskformer-swin-base-ade... | 241 | 0 |
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import ... | 348 |
from ..utils import DummyObject, requires_backends
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ... | 348 | 1 |
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from data... | 199 |
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor,... | 199 | 1 |
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params ... | 205 |
"""simple docstring"""
def UpperCAmelCase ( a_, a_ ):
'''simple docstring'''
while b:
lowerCamelCase , lowerCamelCase : Tuple = b, a % b
return a
def UpperCAmelCase ( a_, a_ ):
'''simple docstring'''
return a if b ... | 205 | 1 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class _a :
_lowercase : float
_lowercase : TreeNode | None = None
_lowercase : TreeNode | None = None
def _a ( SCREAMING_SNAKE_CASE ):
"""simple doc... | 110 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
lowerCamelCase : int ={
'''configuration_audio_spectrogram_transformer''': [
'''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_... | 189 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
A_ : Union[str, Any] = {'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailabl... | 141 |
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class _a (__magic_name__ ):
'''simple docstring'''
UpperCAmelCase__: Optional[Any] = '''MCTCTFeatureExtractor'''
UpperCAmelCase__: Optional[int] = '''... | 141 | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.