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 UpperCAmelCase_ ( __lowerCamelCase : float ,__lowerCamelCase : float ):
if density <= 0:
raise ValueError("Impossible fluid density" )
if bulk_modulus <= 0:
raise ValueError("Impossible bulk modulus" )
return (bulk_mo... | 223 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : Optional[Any] =logging.get_logger(__name__)
lowerCAmelCase : Tuple ={
'''facebook/vit-mae-base''': '''https://huggingface.co/faceb... | 223 | 1 |
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> L... | 364 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise OptionalDependencyNotA... | 224 | 0 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_A : Optional[Any] = logging.get_logger(__name__)
_A : Union[str, Any] = {
'''SenseTime/deformable-detr''': '... | 229 | '''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.uti... | 229 | 1 |
'''simple docstring'''
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class _lowerCAmelCase ( __UpperCAmelCase ):
__SCREAMING_SNAKE_CASE : Any = (EulerDiscreteScheduler,... | 135 |
'''simple docstring'''
from typing import Dict
from .base import GenericTensor, Pipeline
class _lowerCAmelCase ( __UpperCAmelCase ):
def _a (self , lowercase=None , lowercase=None , lowercase=None , **lowercase ):
if tokenize_kwargs is None:
A_ ... | 135 | 1 |
'''simple docstring'''
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionM... | 223 |
'''simple docstring'''
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : Optional[int] =logging.get_logger(__name__)
lowerCAmelCase : Tuple ={
'''huggingface/autoform... | 223 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCAmelCase_ = {
'configuration_altclip': [
'ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'AltCLIPConfig',
'AltCLI... | 247 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ = {
'configuration... | 247 | 1 |
'''simple docstring'''
import math
def A_ ( snake_case , snake_case ):
if initial_intensity < 0:
raise ValueError("The value of intensity cannot be negative" )
# handling of negative values of initial intensity
if angle < 0 or angle > 360:
rais... | 139 |
'''simple docstring'''
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class _snake_... | 139 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__UpperCAmelCase :str = {'configuration_yolos': ['YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YolosConfig', 'YolosOnnxConfig'... | 356 |
'''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, ... | 240 | 0 |
'''simple docstring'''
def _A ( snake_case ) -> bool:
_lowercase : Dict = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def _A ( snake_case = 50_00 ) -> int:
_lowercase : Dict = [(i * (3 * i - 1)) // 2 for i in range(1 , snake_case ... | 250 |
'''simple docstring'''
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class a__ ( unittest.TestCase ):
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : ... | 250 | 1 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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... | 99 |
def a__ ( UpperCAmelCase : int , UpperCAmelCase : int ) -> str:
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
UpperCAmelCase : List[Any] = str(bin(UpperCAmelCase ) )[2:] # remove the leading "... | 99 | 1 |
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class lowercas... | 30 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDependencyNotAvai... | 30 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMi... | 228 |
'''simple docstring'''
import argparse
import json
import subprocess
def _snake_case ( A , A ) -> Tuple:
lowerCAmelCase__ = []
lowerCAmelCase__ = (
F"""curl -H \"Accept: application/vnd.github+json\" -H \"Authorization: ... | 228 | 1 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class _snake_case ( nn.Module ):
SCREAMING_SNAKE_CASE__ = 42
SCREAMING_SNAKE_CASE__ = jnp.floataa
def SCREAMING_SNAKE_CASE__ ( self ):
a :Optional[Any] = nn.Conv(
... | 94 |
import string
import numpy
def __lowerCamelCase ( UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
"""simple docstring"""
return b if a == 0 else greatest_common_divisor(b % a , UpperCAmelCase_ )
class _snake_case :
SCREAMING_SNAKE_CASE__ = ... | 94 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
_lowercase : List[str] = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
'''simple docstring'''
... | 272 |
"""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_weight... | 272 | 1 |
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokenizer,
Juma... | 8 |
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'''
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
... | 246 |
'''simple docstring'''
def lowercase__( __UpperCamelCase: list[list[float]] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : list[list[float]] = []
for data in source_data:
for i, el in enumerate(__UpperCamelCase ):
... | 246 | 1 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def snake_case_ (_a : List[Any] ):
UpperCAmelCase = [
'''encoder.version''',
'''decoder.version''',
... | 34 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Dict = logging.get_logger(__name__)
lowerCamelCase : Union[str, Any] = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class ... | 2 | 0 |
import inspect
import unittest
from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
... | 356 |
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def _A ( lowerCAmelCase_ : str , lowerCAmelCase_ : str , **lowerCAmelCase_ : str ):
"""simple docstring"""
lowerCAmelCase__ = AutoConfig... | 221 | 0 |
import math
from collections.abc import Callable
def __lowerCamelCase ( __a :Callable[[float], float] , __a :float , __a :float ) -> float:
"""simple docstring"""
A__ = xa
A__ = xa
while True:
if x_n == ... | 274 |
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
A : Dict = Lock()
def __lowerCamelCase ( __a :Dict , __a :List[str] , __a :Optional[int] , __a :Optional[int... | 274 | 1 |
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
__A = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__(self : Union[str, Any] , *UpperCAm... | 362 |
from math import pow
def lowerCAmelCase_ ( __a , __a , __a , __a , __a , ) -> tuple[int, int]:
"""simple docstring"""
if current_sum == needed_sum:
# If the sum of the powers is equal to needed_sum, then we have a solution.
solutions_count += ... | 273 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_co... | 70 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ : Optional[Any] ={
'''configuration_bigbird_pegasus''': [
'''BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
... | 70 | 1 |
"""simple docstring"""
import os
__A : Dict = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1000}
def A_ ( snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : Any = 0
UpperCamel... | 355 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline... | 27 | 0 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, ... | 23 |
from pathlib import Path
import fire
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = Path(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = ... | 296 | 0 |
from __future__ import annotations
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Tuple , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : Dict = order
# a_{0} ... a_{k}
SCREAMING_SNAKE_CASE : Dict = [1.0] + [0.0]... | 370 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
snake_case ... | 319 | 0 |
def UpperCamelCase_( _snake_case : list ):
"""simple docstring"""
if not isinstance(_snake_case , _snake_case ):
raise ValueError('Input series is not valid, valid series - [2, 4, 6]' )
if len(_snake_case ) == 0:
raise ValueError('Input list mu... | 218 |
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... | 218 | 1 |
"""simple docstring"""
def A_ ( snake_case_ : int = 2_0_0 ):
'''simple docstring'''
UpperCamelCase : Any = [1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 2_0_0]
UpperCamelCase : int = [0] * (pence + 1)
UpperCamelCase : Any = ... | 27 |
"""simple docstring"""
import unittest
from transformers import XLMConfig, 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 ... | 27 | 1 |
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
lowerca... | 303 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def a__ ( snake_case , snake_case=False ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Dict = OmegaConf.load(snake_case )
if display:
print(yaml.dump(Om... | 303 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase__ : Any = logging.get_logger(__name__)
UpperCAmelCase... | 301 |
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase__ : int = logging.get_logger(__name__)
UpperCAmelCase__ : Union[str, Any] = {
'vocab_file': 'vocab.j... | 301 | 1 |
"""simple docstring"""
def lowerCAmelCase_ ( snake_case_ : List[Any] ) ->List[Any]:
lowerCamelCase__ : Union[str, Any] =len(snake_case_ )
while cur > 1:
# Find the maximum number in arr
lowerCamelCase__ : str =arr.index(max(arr[0:cur] ) )... | 126 |
"""simple docstring"""
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
'split_dict' , [
SplitDict(),
SplitDict({'train': SplitInfo(name='train' , num_bytes=1_3_3_7 ,... | 126 | 1 |
"""simple docstring"""
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class _lowerCamelCase ( unittest.TestCase ):
def _lowerCAmelCase ( self : Dict ) -> None:
"""simple d... | 212 |
"""simple docstring"""
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def lowercase_ ( __UpperCAmelCase ) -> None:
lowerCAmelCase__ , lowerCAmelCase__ : int = analyze_text(__UpperCAmelCase )
lowerCAm... | 212 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
__A =R'''
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be used to control the model outputs. Read the documentation from [`P... | 19 |
import math
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ = 0 , lowerCamelCase__ = 0 ):
lowerCamelCase_ = end or len(lowerCamelCase__ )
for i in range(lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = i
lowerCamelCase_ = array[i]
... | 19 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase ={
"configuration_swiftformer": [
"SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"SwiftFormerCon... | 351 |
"""simple docstring"""
import pytest
UpperCAmelCase ="__dummy_dataset1__"
UpperCAmelCase ="\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \... | 77 | 0 |
import numpy as np
_lowerCAmelCase : str = [
["a", "b", "c", "d", "e"],
["f", "g", "h", "i", "k"],
["l", "m", "n", "o", "p"],
["q", "r", "s", "t", "u"],
["v", "w", "x", "y", "z"],
]
class __magic_name__ :
def __init__( self ) -> None:
... | 218 |
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_( _snake_case : Dict , _snake_case... | 218 | 1 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __snake_case ( __UpperCamelCase : Dict ):
"""simple docstring"""
return "".join(sorted(lowerCAmelCase_ ) )
def __snake_case ( __UpperCamelCase : List[Any] ):
... | 369 |
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 from ... | 329 | 0 |
import math
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 SchedulerMixin, SchedulerOutput
class _UpperCamelCase ( lowerCamelCase__ ,lowerCamelCase__ ):
"""simple... | 210 |
'''simple docstring'''
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def lowerCamelCase__ ( _A = "laptop" ):
a : Any = f"""https://www.amazon.in/laptop/s?k={product}"""
a : Tuple = {... | 297 | 0 |
"""simple docstring"""
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# 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 : Union[st... | 150 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a : List[str] = {
"""configuration_xlm_roberta_xl""": [
"""XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"... | 150 | 1 |
"""simple docstring"""
from math import isqrt
def _snake_case ( lowercase__ ):
return all(number % divisor != 0 for divisor in range(2 , isqrt(lowercase__ ) + 1 ) )
def _snake_case ( lowercase__ = 10**6 ):
_lowerCamelCase ... | 96 |
"""simple docstring"""
import argparse
from collections import defaultdict
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
UpperCAmelCase_ : int = f"""{file}_{class_name}_{test_name}"""
done_test[_id] += 1
with ope... | 61 | 0 |
'''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int ):
'''simple docstring'''
UpperCAmelCase__ = abs(SCREAMING_SNAKE_CASE__ )
UpperCAmelCase__ = 0
while n > 0:
res += n % 10
n //= 10
return res
def _UpperCamelCase ( SCRE... | 354 |
'''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : bool = False ):
'''simple docstring'''
if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
UpperCAmelCase__ = F'''Expected string as input, ... | 61 | 0 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
lowercase : Tuple = logging.getLogger(__name__)
class __snake_case ( lowerCAmelCase ):
def __init__( self ,snake_case=... | 20 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase : str = {
"""configuration_funnel""": ["""FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FunnelConfi... | 20 | 1 |
'''simple docstring'''
import math
import unittest
from transformers import BioGptConfig, 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_mod... | 351 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
"""microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main/confi... | 48 | 0 |
_lowerCamelCase : Any = "\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"
_lowerCamelCase : ... | 336 |
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from... | 336 | 1 |
def lowerCAmelCase_ ( __lowerCamelCase = 3 , __lowerCamelCase = 7 , __lowerCamelCase = 1_0_0_0_0_0_0 ):
__snake_case : Dict = 0
__snake_case : int = 1
for current_denominator in range(1 , limit + 1 ):
... | 363 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging... | 134 | 0 |
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
_snake_case... | 26 |
'''simple docstring'''
import inspect
import unittest
from transformers import ConvNextConfig
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_backbone_commo... | 28 | 0 |
"""simple docstring"""
import os
import jsonlines
import numpy as np
from tqdm import tqdm
lowerCAmelCase__ = 2048
lowerCAmelCase__ = 4096
lowerCAmelCase__ = 42
lowerCAmelCase__ = os.environ.pop('''PROCESS_TRAIN''', '''false''')
lowerCAmelCase__ = {'''null''': 0, '''sh... | 361 |
"""simple docstring"""
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config i... | 175 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus impo... | 40 |
'''simple docstring'''
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttent... | 58 | 0 |
"""simple docstring"""
def _A ( _a : str ):
"""simple docstring"""
A = [int(_a ) for i in ip_va_address.split(""".""" ) if i.isdigit()]
return len(_a ) == 4 and all(0 <= int(_a ) <= 2_5_4 for octet in octet... | 363 |
"""simple docstring"""
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class lowerCamelCase__ ( SC... | 77 | 0 |
"""simple docstring"""
import os
import string
import sys
_a = 1 << 8
_a = {
'tab': ord('\t'),
'newline': ord('\r'),
'esc': 27,
'up': 65 + ARROW_KEY_FLAG,
'down': 66 + ARROW_KEY_FLAG,
'right': 67 + ARROW_KEY_FLAG,
'left': 68 + ARROW_KEY_FLAG,
'mod_int... | 61 |
"""simple docstring"""
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transfo... | 61 | 1 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..ima... | 369 |
# using dfs for finding eulerian path traversal
def lowerCamelCase_ ( _a , _a , _a , _a=None ):
"""simple docstring"""
lowerCAmelCase__ : Optional[Any] = (path or []) + [u]
for v in graph[u]:
if visited_edge[u][v... | 211 | 0 |
"""simple docstring"""
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
A__ : int = {'UserAgent': UserAgent().random}
def _snake_case ( lowerCamelCase__ : int ) -> str:
lowerCamelCase_ ... | 144 |
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Pa... | 312 | 0 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class SCREAMING_SNAKE_CASE_ ( snake_case_ , snake_case_ ):
@register_to_config
def __init__( self : Optional[Any] , *,
... | 88 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import It... | 88 | 1 |
"""simple docstring"""
import argparse
from collections import defaultdict
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
UpperCAmelCase_ : int = f"""{file}_{class_name}_{test_name}"""
done_test[_id] += 1
with ope... | 61 |
'''simple docstring'''
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_spe... | 75 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
_a : Tuple = {
'configuration_speecht5': [
'SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP',
'SPEE... | 126 | """simple docstring"""
import math
def SCREAMING_SNAKE_CASE ( ) -> None:
_lowerCAmelCase : Any = input("""Enter message: """ )
_lowerCAmelCase : List[Any] = int(input(f"Enter key [2-{len(_lowerCamelCase ) - 1}]: " ) )
_lowerCAmelCase ... | 126 | 1 |
"""simple docstring"""
import os
def __lowercase ( snake_case_ : Optional[Any] ) ->Union[str, Any]:
'''simple docstring'''
__A : Optional[int] = len(grid[0] )
__A : int = len(snake_case_ )
__A : str =... | 179 |
"""simple docstring"""
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, ... | 179 | 1 |
'''simple docstring'''
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as... | 274 | '''simple docstring'''
import os
import time
import numpy as np
import onnxruntime as ort
snake_case__ : Optional[int] = '''1'''
snake_case__ : str = '''0'''
snake_case__ : List[str] = '''1'''
snake_case__ : List[str] = ort.SessionOptions... | 274 | 1 |
'''simple docstring'''
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_comm... | 239 |
"""simple docstring"""
def A ( snake_case :int ) -> list:
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError('The given input must be positive' )
# get the generated string sequence
__UpperCamelCase = gray_code_sequence_string(snake_cas... | 316 | 0 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
fro... | 168 |
"""simple docstring"""
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 a... | 168 | 1 |
'''simple docstring'''
def _A ( _lowerCAmelCase ):
"""simple docstring"""
__lowercase =abs(a__ )
__lowercase =0
while n > 0:
res += n % 10
n //= 10
return res
def _A ( _lowerCAmelCase ):
... | 166 |
from abc import ABC, abstractmethod
from typing import List, Optional
class a_ ( a__ ):
"""simple docstring"""
def __init__( self ) ->List[str]:
# test for the above condition
self.test()
def __lowerCAmelCase ( self ... | 313 | 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 imp... | 368 |
'''simple docstring'''
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
i... | 338 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__a: List[str] = logging.get_logger(__name__)
__a: Union[str, Any] ... | 198 |
"""simple docstring"""
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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
#
... | 241 | 0 |
from collections.abc import Generator
def lowerCAmelCase__ ( ):
snake_case_ : Optional[int] = 0, 1
while True:
snake_case_ : str = b, a + b
yield b
def lowerCAmelCase__ ( _a : int = 10_00 ):
snake_case_ : int = 1
snake... | 361 |
def lowerCAmelCase__ ( _a : float , _a : float ):
if density <= 0:
raise ValueError("Impossible fluid density" )
if bulk_modulus <= 0:
raise ValueError("Impossible bulk modulus" )
return (bulk_modulus / density) ** 0.5
if __name__ == "__main__":
... | 36 | 0 |
import random
from typing import Any
def UpperCamelCase_( lowerCamelCase_ ) -> list[Any]:
for _ in range(len(lowerCamelCase_ ) ):
_lowercase : Optional[int] = random.randint(0 , len(lowerCamelCase_ ) - 1 )
_lowercase : str = random... | 21 |
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
UpperCAmelCase__ ... | 245 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
_lowerCAmelCase = (3, 9, -11, 0, 7, 5, 1, -1)
_lowerCAmelCase = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class lowerCAmelCase_:
'''simple... | 351 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
'''face... | 184 | 0 |
'''simple docstring'''
import pytest
import datasets
# Import fixture modules as plugins
snake_case_ : str = ['tests.fixtures.files', 'tests.fixtures.hub', 'tests.fixtures.fsspec']
def A__ ( UpperCAmelCase_ , UpperCAmelCase_ ):
# Mark tests as "unit" by default if not marked a... | 83 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {"""configuration_mmbt""": ["""MMBTConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
... | 330 | 0 |
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"""kwargs, expected""" , [
({"""num_shards""": 0, """max_num_jobs""": 1}, []),
({"""num_shards""": 10, """max_num_jobs""": 1}, [range(10... | 59 |
import os
def lowerCamelCase_ ( _a : str = "input.txt" ):
'''simple docstring'''
with open(os.path.join(os.path.dirname(_a ) , _a ) ) as input_file:
UpperCAmelCase_ : Dict = [
[int(_a ) for element in line.split(""",""" )]
... | 59 | 1 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def _lowerCamelCase ( lowercase : str = "https://www.worldometers.info/coronavirus" ) -> dict:
_a = BeautifulSoup(requests.get(lowercase ).text , "html.parser" )
_a = soup.... | 63 |
'''simple docstring'''
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
_SCREAMING_SNAKE_CASE : Any = F... | 85 | 0 |
"""simple docstring"""
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available,... | 371 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a :Dict = {
"configuration_rag": ["RagConfig"],
"retrieval_rag": ["RagRetriever"],
"tokenization_rag": ["RagTokenizer"],
}
try:
if no... | 56 | 0 |
from collections import defaultdict
def lowerCAmelCase__( lowercase : int ) -> int:
__snake_case : str = 1
__snake_case : Optional[Any] = True
for v in tree[start]:
if v not in visited:
ret += dfs(__A )
if ret % 2 == 0:
cuts.append(__A )
... | 326 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql... | 32 | 0 |
'''simple docstring'''
class UpperCAmelCase :
'''simple docstring'''
def __init__( self , __lowerCAmelCase ) -> Tuple:
lowercase__ : Tuple = set_counts
lowercase__ : Union[str, Any] = max(_a )
lowercase__ : Optional[... | 355 | '''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
__a: Tuple = True
except (ImportError, ModuleNotFoundError):
__a: List[Any] = False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.download("""punkt""", quiet=True)
def __Up... | 214 | 0 |
'''simple docstring'''
def _A ( snake_case ) -> int:
_lowercase : Optional[Any] = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def _A ( snake_case ) -> int:
_lowercase : List[str] = 0
while number > 0:
_lower... | 250 |
'''simple docstring'''
_snake_case = 8.3_1_4_4_5_9_8
def _A ( snake_case , snake_case ) -> float:
if temperature < 0:
raise Exception("Temperature cannot be less than 0 K" )
if molar_mass <= 0:
raise Exception("Molar mass cannot be less than or equal to 0 kg/mol" ... | 250 | 1 |
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)
__UpperCAmelCase : Union[str, Any] = logging.getL... | 351 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def a ( SCREAMING_SNAKE_CASE_ : str ):
"""simple docstring"""
return "".join(sorted(SCREAMING_SNAKE_CASE_ ) )
def a ( SCREAMIN... | 315 | 0 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def lowerCamelCase_ (UpperCamelCase__ : List[Any] ):
# vision encoder
if "img_encoder.pos_embed" in name:
_UpperCAmelCa... | 263 |
"""simple docstring"""
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor... | 263 | 1 |
class __lowerCAmelCase ( UpperCamelCase__):
pass
class __lowerCAmelCase ( UpperCamelCase__):
pass
class __lowerCAmelCase :
def __init__( self ) -> Tuple:
'''simple docstring'''
... | 148 |
def _A ( SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : float ):
"""simple docstring"""
return price * (1 + tax_rate)
if __name__ == "__main__":
print(F"""{price_plus_tax(100, 0.2_5) = }""")
print(F"""{price_plus_tax(1_2_5.5_0, 0.0... | 148 | 1 |
'''simple docstring'''
import os
from typing import Dict, List, Tuple, TypeVar, Union
lowerCAmelCase_ : Optional[Any] = TypeVar('T')
lowerCAmelCase_ : int = Union[List[T], Tuple[T, ...]]
lowerCAmelCase_ : int = Union[T, List[T], Dict[str, T]]
lowerCAmelCase_ ... | 63 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transf... | 63 | 1 |
from __future__ import annotations
def lowerCamelCase_ ( _a : list[int] ):
'''simple docstring'''
return len(set(_a ) ) == len(_a )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 59 |
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils... | 59 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase__ = {
"""configuration_graphormer""": ["""GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Gra... | 96 |
"""simple docstring"""
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextI... | 96 | 1 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
c... | 47 |
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import con... | 47 | 1 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
a_ = logging.get_logger(__name__)
class _UpperCamelCase ( __A ):
'''simple docstring'''
def __init__( self : List[str] , *a : Any , **... | 76 |
import numpy as np
from PIL import Image
def UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> np.ndarray:
__lowercase : Optional[int] = np.array(__lowerCAmelCase )
if arr.shape[0] != arr.shape[1]:
raise ValueError('''The input ... | 156 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
__UpperCamelCase = logging.get_logger(__name__)
class _A ( __lowercase ):
def __init__( self : Tuple , *__magic_name... | 13 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__UpperCamelCase = {
"configuration_conditional_detr": [
"CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MA... | 13 | 1 |
'''simple docstring'''
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
lowerCAmelCase: Any = np.linspace(start=0, stop=7_5, num=7_5, endpoint=True, retstep=False)
# Create two fuzzy sets by de... | 297 |
'''simple docstring'''
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class a__( nn.Module ):
def __init__( self : Any , __snake_case : int = 16 , __snake_case : int = 88 , __snake_... | 297 | 1 |
import os
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = len(grid[0] )
lowerCamelCase_ = len(lowerCamelCase__ )
lowerCamelCase_ = 0
lowerCamelCase_ = 0
lowerCamelCase_ = 0
# Check vertically, horizontally, diagonally at the sa... | 365 |
from sklearn.metrics import recall_score
import datasets
__A ='''
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 FN is the false negatives.
'''
__A ='''
Arg... | 47 | 0 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''... | 43 |
"""simple docstring"""
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common imp... | 40 | 0 |
'''simple docstring'''
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
lowerCAmelCase_ : str = logging.getLogger(__name__)
class __lowerCAmelCase... | 170 |
'''simple docstring'''
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_... | 170 | 1 |
"""simple docstring"""
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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
#
#... | 243 |
"""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/check_copies.py
UpperCam... | 243 | 1 |
"""simple docstring"""
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel... | 352 | """simple docstring"""
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
_a : Dict= logging.get_logger(__name__)
class UpperCamelCase ( lowercase ):
def __init__(self : List[str] , *_A : Dict ... | 95 | 0 |
'''simple docstring'''
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
a_ : Optional[int] = logging.get_l... | 55 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase : List[str] = {
"configuration_x_clip": [
"XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"XCLIPConfig",
"XCLIPTextConfig",
"XCLIPVisionConfig... | 252 | 0 |
'''simple docstring'''
import random
from .binary_exp_mod import bin_exp_mod
def SCREAMING_SNAKE_CASE__ ( __A , __A=1_000 ) -> str:
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
_snake_case = n - 1
_snake_case ... | 160 |
'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class __UpperCAmelCase :
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
if dst_width < ... | 160 | 1 |
import colorsys
from PIL import Image # type: ignore
def a_ ( lowerCAmelCase_ : int, lowerCAmelCase_ : List[str], lowerCAmelCase_ : Union[str, Any] ):
__lowerCAmelCase = x
__lowerCAmelCase = y
for step in range(UpperCamelCase__ ): # noqa: B007
__lowerCAme... | 284 |
'''simple docstring'''
from math import log
from scipy.constants import Boltzmann, physical_constants
__A =3_00 # TEMPERATURE (unit = K)
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ):
if donor_conc <= ... | 163 | 0 |
def _lowerCAmelCase ( A__: Optional[int] , A__: List[str] ):
'''simple docstring'''
UpperCAmelCase = [0 for i in range(r + 1 )]
# nc0 = 1
UpperCAmelCase = 1
for i in range(1 , n + 1 ):
# to compute current row from p... | 354 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
__magic_name__ = {"configuration_beit": ["BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BeitConfig", "BeitOnnxC... | 152 | 0 |
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 im... | 52 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : list ):
'''simple docstring'''
if len(_SCREAMING_SNAKE_CASE ) <= 1:
return lst
_UpperCAmelCase = 1
while i < len(_SCREAMING_SNAKE_CASE ):
if lst[i - 1] <= lst... | 260 | 0 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
UpperCamelCase = '\\n@inproceedings{snover-etal-2006-study,\n title = "A Study of Translation Edit Rate with Targeted Human Annotation",\n author = "Snover, Matthew and\n Dorr, Bonn... | 221 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
... | 221 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class A__ ( unittest.TestCase ):
"""simple ... | 127 |
def UpperCAmelCase__ (UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_=False ):
"""simple docstring"""
if isinstance(UpperCamelCase_ ,UpperCamelCase_ ) and isinstance(UpperCamelCase_ ,UpperCamelCase_ ):
snake_case = len(set_a.... | 127 | 1 |
"""simple docstring"""
def _lowerCAmelCase ( UpperCAmelCase__ : bytes ) ->str:
return "".join([hex(lowercase__ )[2:].zfill(2 ).upper() for byte in list(lowercase__ )] )
def _lowerCAmelCase ( UpperCAmelCase__ : str ) ->bytes:
if (len(... | 356 |
"""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
from ..auto import CONFIG_MAPPING
A_ = logging.get_logger(__name__)... | 296 | 0 |
'''simple docstring'''
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')
class lowerCAmelCase__ :
def __init__( self : Tuple , lowerCamelCase__ : ... | 234 |
'''simple docstring'''
import argparse
import copy
def snake_case_ ( __SCREAMING_SNAKE_CASE : Optional[int] ):
"""simple docstring"""
lowercase_ : List[Any] = {}
with open(__SCREAMING_SNAKE_CASE ) as f:
... | 93 | 0 |
"""simple docstring"""
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test... | 303 |
"""simple docstring"""
import unittest
from transformers import BertGenerationConfig, 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_modelin... | 303 | 1 |
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 logging
__A = logging.get_logger(__name__)
__A = {... | 90 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl ... | 90 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase__ = {
"""configuration_speech_to_text""": ["""SPEECH_TO_TEXT_PR... | 307 |
from ..utils import DummyObject, requires_backends
class A__ ( metaclass=__magic_name__ ):
lowercase = ['torch', 'transformers', 'onnx']
def __init__( self : Any , *a : Any , **a : Any ):
'''simple doc... | 307 | 1 |
'''simple docstring'''
import unittest
from transformers import DonutProcessor
_lowercase : int = "naver-clova-ix/donut-base"
class lowerCAmelCase__ ( unittest.TestCase ):
def _snake_case ( self ):
"""simple docstrin... | 93 |
"""simple docstring"""
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class _UpperCAmelCase( lowerCamelCase ):
... | 194 | 0 |
'''simple docstring'''
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
snake_case__ : List[str] = None
try:
import msvcrt
except ImportError:
snake_case__ : Optional[Any] = None
try:
import fcntl
except ImportError:
snake_c... | 274 | '''simple docstring'''
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, C... | 274 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
A =logging.get_logger(__name__)
class _a ( __a ):
def __init__( self : List[str] , *lowercase : Optional[Any] , **lowercase : Union[str, Any]... | 34 |
'''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
#
# Unl... | 250 | 0 |
"""simple docstring"""
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegas... | 313 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Generator
def _SCREAMING_SNAKE_CASE () -> Generator[int, None, None]:
'''simple docstring'''
lowercase_ = {}
lowercase_ = 2
while True:
lowercase_ = facto... | 313 | 1 |
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