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 |
|---|---|---|---|---|
import string
import numpy
def _a ( UpperCAmelCase , UpperCAmelCase ) -> str:
"""simple docstring"""
return b if a == 0 else greatest_common_divisor(b % a , SCREAMING_SNAKE_CASE_ )
class __SCREAMING_SNAKE_CASE :
_UpperCAmelCase : Dict = ... | 142 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pip... | 214 | 0 |
"""simple docstring"""
from collections.abc import Sequence
def _lowercase ( __snake_case ,__snake_case = False ) -> float:
if not arr:
return 0
__lowerCAmelCase : Any = 0 if allow_empty_subarrays else float("-inf" )
__lowerCAmel... | 365 |
"""simple docstring"""
def _lowercase ( __snake_case ) -> int:
if not isinstance(__snake_case ,__snake_case ):
raise ValueError("Input must be an integer" )
if input_num <= 0:
raise ValueError("Input must be positive" )
return sum(
... | 58 | 0 |
'''simple docstring'''
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def __snake_case( _lowerCAmelCase ) -> List[Any]:
... | 35 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from... | 35 | 1 |
import logging
from transformers.configuration_utils import PretrainedConfig
_lowerCamelCase : Optional[Any] = logging.getLogger(__name__)
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = """masked_bert"""
def __init__( self : ... | 370 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def a__ ( UpperCAmelCase : int ) -> Dict:
# A local function to see if a dot lands in the circle.
def is_in_circle(UpperCAmelCase : float , Upper... | 99 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 73 |
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
a ="""\
@inproceedings{kakwani2020indicnlpsuite,
title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},
... | 73 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTra... | 354 |
'''simple docstring'''
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
lowerCAmelCase_ : Tuple = WebClient(token=os.environ['CI_SLACK_BOT_TOKEN'])
def _l... | 346 | 0 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class A ( __A , __A ):
'''simple docstring'''
@register_to_config
def __init__(self : Any , *,
... | 305 | import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def lowercase_ ( _lowerCamelCase : str , _lowerCamelCase : List[Any] , _lowerCamelCase ... | 87 | 0 |
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, 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 ..image_utils import load_image
if is_torch_availab... | 84 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.test... | 84 | 1 |
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
lowercase_ = {
... | 7 |
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 ..image_utils impor... | 227 | 0 |
'''simple docstring'''
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
a__ = logging.getLogger(__name__)
class UpperCAmelCase__ :
def __init_... | 365 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
a__ : List[Any] = ... | 243 | 0 |
"""simple docstring"""
from collections import namedtuple
_a = namedtuple('from_to', 'from_ to')
_a = {
'cubicmeter': from_to(1, 1),
'litre': from_to(0.001, 1_000),
'kilolitre': from_to(1, 1),
'gallon': from_to(0.0_0454, 264.172),
'cubicyard': from_to(0.7_6455, 1.... | 61 |
import argparse
import gc
import json
import os
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerat... | 252 | 0 |
"""simple docstring"""
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWaterma... | 175 |
"""simple docstring"""
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers... | 175 | 1 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( lowercase_ ) -> str:
return "".join(chr(ord(lowercase_ ) - 32 ) if "a" <= char <= "z" else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 247 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> int:
if exponent == 1:
return base
if exponent % 2 == 0:
A__ = _modexpt(lowercase_ , exponent // 2 , lowercase_ ) % modulo_value
return (x * x) % modulo_value
else:
... | 247 | 1 |
"""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_tok... | 352 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
A : Dict = {'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}
try:
if not is_vision_available():
raise OptionalDependencyNo... | 146 | 0 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : int ):
if "model" in orig_key:
__UpperCamelCase =orig_key.replace('model.' , '' )
if "norm1" in orig_key:
__UpperCam... | 62 |
"""simple docstring"""
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__) # pylint: disable=invalid-name
class Uppe... | 268 | 0 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
fr... | 131 |
def __snake_case ( _UpperCAmelCase ):
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError('''Input value must be an \'int\' type''' )
__a = 0
while number:
position += 1
number >>= 1
return position
if __name__ ==... | 131 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
__A = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
__A ... | 177 | """simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prep... | 177 | 1 |
"""simple docstring"""
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedC... | 360 |
"""simple docstring"""
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@re... | 291 | 0 |
'''simple docstring'''
import string
def _SCREAMING_SNAKE_CASE (A ) -> str:
"""simple docstring"""
lowercase__ = ''''''
for i in sequence:
lowercase__ = ord(A )
if 65 <= extract <= 90:
ou... | 2 |
'''simple docstring'''
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
lowerCamelCase : Optional[Any] = (
'4S 3H 2C 7S 5H',
'9D 8H 2C 6S 7H',
'2D 6D 9D TH 7D',
'TC 8C 2S JH 6C',
'JH 8S TH AH QH',
'TS KS 5... | 2 | 1 |
UpperCamelCase = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
def _A ( ):
"""simple docstring"""
lowerCAmelCase__ = input("Enter message: " )
lowerCAmelCase__ = input("Enter key [alphanumeric]: " )
lowerCAmelCase__ = inpu... | 221 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProc... | 221 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class SCREAMING_SN... | 23 |
'''simple docstring'''
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings... | 161 | 0 |
import argparse
import os
import re
_SCREAMING_SNAKE_CASE : List[str] = """src/diffusers"""
# Pattern that looks at the indentation in a line.
_SCREAMING_SNAKE_CASE : Optional[int] = re.compile(r"""^(\s*)\S""")
# Pattern that matches `"key":" and puts `key` in group 0.
_SCREAMING_SNAKE_CASE ... | 364 |
"""simple docstring"""
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxruntim... | 157 | 0 |
"""simple docstring"""
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
A__ : Tuple = '\\n @inproceedings{kakwani2020indicnlpsuite,\n title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Mul... | 144 |
"""simple docstring"""
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test... | 144 | 1 |
from math import ceil, sqrt
def lowerCamelCase__ ( A__ : int = 1000000 ):
'''simple docstring'''
__lowerCamelCase = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
__lowerCamelCase ... | 29 |
import os
from math import logaa
def lowerCamelCase__ ( A__ : str = "base_exp.txt" ):
'''simple docstring'''
__lowerCamelCase = 0
__lowerCamelCase = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(A__ ) , A__ ) )... | 29 | 1 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {"configuration_mmbt": ["MMBTConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
e... | 299 |
from ..utils import DummyObject, requires_backends
class UpperCamelCase__ ( metaclass=__SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCAmelCase_ =["torch", "scipy"]
def __init__( self , *_A , **_A ) -> Tuple:
... | 299 | 1 |
'''simple docstring'''
from collections.abc import Callable
class UpperCAmelCase :
def __init__( self :Tuple , lowercase_ :Callable | None = None )-> None:
# Stores actual heap items.
A__ = []
# Stores indexes of each item for supporting updates ... | 123 |
'''simple docstring'''
def UpperCamelCase ( ):
A__ = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
A__ = 6
A__ = 1
A__ = 19_01
A__ = 0
while year < 20_01:
day += 7
if (year % 4 == 0 and year % 1_00 != 0) or (yea... | 123 | 1 |
'''simple docstring'''
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def lowe... | 58 | 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 transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging
logging.set_verbosity_... | 118 | 0 |
'''simple docstring'''
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_tr... | 91 |
'''simple docstring'''
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
fr... | 91 | 1 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=A__ )
class lowerCAmelCase_ ( A__ ):
'''simple docstring'''
... | 301 |
"""simple docstring"""
from __future__ import annotations
def lowercase (_lowerCAmelCase , _lowerCAmelCase ):
__lowerCAmelCase = []
create_all_state(1 , _lowerCAmelCase , _lowerCAmelCase , [] , _lowerCAmelCase )
return result
def lower... | 301 | 1 |
from __future__ import annotations
def A ( _UpperCAmelCase : list[int] ) -> list[int]: # This function is recursive
'''simple docstring'''
_UpperCAmelCase = len(_UpperCAmelCase )
# If the array contains only one element, we return it (it's the stop cond... | 290 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def A ( ) -> tuple[list[int], int]:
'''simple docstring'''
_UpperCAmelCase = [randint(-1_000 , 1_000 ) for i in range(10 )]
_Up... | 290 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a :Optional[int] = logging.get_logger(__name__)
a :int = {
"tiiuae/falcon-40b": "https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json",
"tiiuae/falcon-7b": "https://h... | 132 |
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = 8.31_44_62 # Unit - J mol-1 K-1
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> float:
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("""Invalid in... | 321 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_UpperCamelCase: Union[str, Any] = logging.get_logger(__name__)
_UpperC... | 355 |
"""simple docstring"""
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class a__ ( SCREAMING_SNAKE_CASE__, unittest.TestCase ):
_l... | 53 | 0 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class lowercas... | 101 |
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_=7 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =None
if token is not None:
SCRE... | 334 | 0 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir... | 169 |
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXT... | 169 | 1 |
"""simple docstring"""
import argparse
import os
import re
import tensorflow as tf
import torch
from transformers import BertConfig, BertModel
from transformers.utils import logging
logging.set_verbosity_info()
_UpperCAmelCase = logging.get_logger(__name__)
def __magic_name__ ( ... | 173 |
"""simple docstring"""
# flake8: noqa
# Lint as: python3
_UpperCAmelCase = [
"""VerificationMode""",
"""Version""",
"""disable_progress_bar""",
"""enable_progress_bar""",
"""is_progress_bar_enabled""",
"""experimental""",
]
from .info_utils import VerificationMode
from .l... | 173 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ : List[str] = logging.get_logger(__name__)
A_ : List[str] = ... | 353 |
from queue import PriorityQueue
from typing import Any
import numpy as np
def UpperCamelCase (lowercase_: dict , lowercase_: str , lowercase_: set , lowercase_: set , lowercase_: dict , lowercase_: dict , lowercase_: PriorityQueue , lowercase_: dict , lo... | 141 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import MutableSequence
class lowercase_ :
"""simple docstring"""
def __init__( self : List[Any] ,lowercase__ : int ,lowercase__ : MutableSequence[float] ):
if len(lowercase__ ... | 104 |
'''simple docstring'''
def lowerCAmelCase_ ( _lowerCamelCase: float , _lowerCamelCase: list[float] ):
if discount_rate < 0:
raise ValueError("""Discount rate cannot be negative""" )
if not cash_flows:
raise ValueError("""Cash flows list cannot be empty""" )
__SCREA... | 112 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = {
'''vinvino02/glpn-kitti''': '''https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json''',
# Se... | 312 | """simple docstring"""
from __future__ import annotations
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> list[str]:
if partitions <= 0:
raise ValueError('partitions must be a positive number!' )
if partitions > number_of_bytes:
raise ValueError('partition... | 312 | 1 |
import math
def lowerCAmelCase_ ( __UpperCAmelCase: Union[str, Any] ) -> List[str]:
UpperCamelCase__ : Any = [True] * n
UpperCamelCase__ : List[Any] = False
UpperCamelCase__ : Optional[int] = False... | 201 |
"""simple docstring"""
import functools
from typing import Any
def _snake_case ( lowercase__ , lowercase__ ):
# Validation
if not isinstance(lowercase__ , lowercase__ ) or len(lowercase__ ) == 0:
raise ValueError('the string shou... | 96 | 0 |
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLi... | 131 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__snake_case :Tuple = {
'''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
if not... | 131 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class a ( metaclass=UpperCAmelCase__ ):
UpperCamelCase : Optional[int] = ['torch', 'torchsde']
def __init__( self : Union[str, Any] , *lowerCAmelCase : Any , **lowerCAme... | 173 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENS... | 173 | 1 |
"""simple docstring"""
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_C... | 154 | """simple docstring"""
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNe... | 154 | 1 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transforme... | 41 |
'''simple docstring'''
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
... | 41 | 1 |
"""simple docstring"""
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_snake_case : Any = logging.get_logger(__name__)
_snake_case : int = {
'''post_e... | 371 |
from math import factorial
class _UpperCAmelCase :
"""simple docstring"""
def __init__( self : Dict , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : str ) -> Union[str, Any]:
__lowerCAmelCase = real
if isinstance(lowerCAmelCase_ , lowerCA... | 207 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ : Tuple = logging.get_logger(__name__)
a__ : Optional[int] = {
"faceboo... | 313 |
from __future__ import annotations
_snake_case : Any = "Muhammad Umer Farooq"
_snake_case : Optional[int] = "MIT"
_snake_case : Union[str, Any] = "1.0.0"
_snake_case : Optional[Any] = "Muhammad Umer Farooq"
_snake_case : List[Any] = "contact@muhammadu... | 123 | 0 |
from sklearn.metrics import recall_score
import datasets
UpperCamelCase_ = "\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere TP is the true positives and FN is the false negativ... | 344 |
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def A ( __UpperCAmelCase ) -> Dict[str, torch.Tensor]:
'''simple docstring'''
UpperCAmelCase_ = []
UpperC... | 344 | 1 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
'Salesforce/blip-vqa-base': 'https://huggingface.co/Salesforce/blip-... | 194 |
"""simple docstring"""
import math
from numpy import inf
from scipy.integrate import quad
def lowerCamelCase_ (UpperCamelCase__ : float ):
if num <= 0:
raise ValueError('''math domain error''' )
return quad(UpperCamelCase__ , 0 , UpperCamelCase__ , ... | 263 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase : Any = logging.get_logger(__name__)
UpperCamelCase : Dict = {
"fac... | 263 |
"""simple docstring"""
UpperCamelCase : Union[str, Any] = [
[0, 1_6, 1_3, 0, 0, 0],
[0, 0, 1_0, 1_2, 0, 0],
[0, 4, 0, 0, 1_4, 0],
[0, 0, 9, 0, 0, 2_0],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def A ( snake_case :Dict , snake_case :Tuple , snake... | 263 | 1 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess... | 29 |
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 ..image_utils impor... | 227 | 0 |
"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 53 |
"""simple docstring"""
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, ... | 53 | 1 |
"""simple docstring"""
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
Aut... | 289 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers... | 324 | 0 |
'''simple docstring'''
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
... | 107 |
'''simple docstring'''
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...tes... | 107 | 1 |
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,
)
A_ :Any = {'''configuration_xglm'... | 71 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTok... | 70 | 0 |
"""simple docstring"""
from sklearn.metrics import recall_score
import datasets
__UpperCamelCase : Optional[int] = '''
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 ... | 309 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list[list[float]] ):
lowerCAmelCase = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation on... | 309 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
load_numpy,
... | 36 |
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.rob... | 36 | 1 |
"""simple docstring"""
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB... | 353 |
import string
def __UpperCamelCase ( _A ):
for key in range(len(string.ascii_uppercase ) ):
lowerCAmelCase_ = ''''''
for symbol in message:
if symbol in string.ascii_uppercase:
lowerCAmelCase_ = string.ascii_up... | 167 | 0 |
def lowercase_ (A : list[int] , A : list[int] , A : int ):
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(A ) )
def lowercase_ (A : list[list[int]] , A : int , A : list[int] , ... | 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 argparse
import collections
import os
import re
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_table.py
_lowerCamelCase : Union[str, Any] = """src/tr... | 363 |
def SCREAMING_SNAKE_CASE ( lowercase_ = 50 ) -> int:
"""simple docstring"""
A__ = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in ra... | 231 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Generator
def lowercase_ ( ):
SCREAMING_SNAKE_CASE__ : dict[int, int] = {}
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 2
while True:
... | 25 |
"""simple docstring"""
def lowercase_ ( _snake_case ):
SCREAMING_SNAKE_CASE__ : Optional[int] = [1]
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str = 0, 0, 0
SCREAMING_SNAKE_CASE... | 25 | 1 |
'''simple docstring'''
from pathlib import Path
import numpy as np
from PIL import Image
def _lowerCAmelCase ( __snake_case : np.ndarray ) -> np.ndarray:
__A ,__A ,__A : int = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.2_9... | 190 |
'''simple docstring'''
from __future__ import annotations
from math import gcd
def _lowerCAmelCase ( __snake_case : int , __snake_case : int = 2 , __snake_case : int = 1 , __snake_case : int = 3 , ) -> int | None:
# A ... | 190 | 1 |
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler... | 317 |
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 NestedDataStructu... | 317 | 1 |
"""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 __magic_name__ ( UpperCAmelCas... | 168 |
"""simple docstring"""
from __future__ import annotations
class __magic_name__ :
'''simple docstring'''
def __init__( self , _a ):
"""simple docstring"""
lowerCamelCase = TypeError(
"""Matrices must be formed from a list of zero or m... | 168 | 1 |
'''simple docstring'''
from __future__ import annotations
from statistics import mean
def __snake_case ( UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Any ):
lowerCamelCase_ = [0] * no_of_processes
low... | 55 |
'''simple docstring'''
def __lowercase ( __lowercase = 100 ) -> int:
'''simple docstring'''
_A = n * (n + 1) * (2 * n + 1) / 6
_A = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
p... | 79 | 0 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion impo... | 368 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class A ( UpperCAmelCase__ ):
'''s... | 146 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from tran... | 25 |
"""simple docstring"""
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.m... | 25 | 1 |
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformer... | 341 |
"""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 = logging.get_logger(__name__)
__A = '▁'
... | 341 | 1 |
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': (
'htt... | 205 |
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_START_DOCSTRING,
Be... | 205 | 1 |
"""simple docstring"""
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class UpperCamelCase_ :
_A : Optional[Any] = None
def UpperCamelCase_ ( self ) -> Optional[int]:
"""simple docstrin... | 363 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassificatio... | 248 | 0 |
'''simple docstring'''
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
lowercase__ = ... | 151 | """simple docstring"""
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def UpperCAmelCase__ ( lowerCAmelCase__ :Any , lowerCAmelCase__ :str , lowerCAmelCase__ :... | 197 | 0 |
'''simple docstring'''
from __future__ import annotations
import pandas as pd
def _lowerCAmelCase ( _UpperCamelCase : List[str] , _UpperCamelCase : Any , _UpperCamelCase : Union[str, Any] ) -> list[int]:
"""simple docstring"""
_SCREAMING... | 364 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class A__ ( metaclass=A__ ):
A__ = ['note_seq']
def __init__( self : List[str] , *_a : Any , **_a : Dict ) -> Optional[Any]:
'''simple docstring'''
... | 114 | 0 |
import os
def lowercase_ ( ):
"""simple docstring"""
lowerCamelCase__ : Union[str, Any] = os.path.join(os.path.dirname(_a ) , "num.txt" )
with open(_a ) as file_hand:
return str(sum(int(_a ) for line in file_hand ) ... | 184 |
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A =logging.get_logger(__name__)
A ={
'facebook/encodec_24khz': 'https://huggingface.co/facebook/encodec_24khz/resolve/main/confi... | 34 | 0 |
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
lowercase = TypeVar("""KT""")
lowercase = TypeVar("""VT""")
class __lowercase ( Generic[KT, VT] ):
'''simple docstring'''
def __init__( sel... | 35 | def lowerCamelCase_ ( UpperCamelCase__ : list[int], UpperCamelCase__ : list[int], UpperCamelCase__ : int ):
'''simple docstring'''
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(UpperCamel... | 35 | 1 |
'''simple docstring'''
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
__A = 0b101100111110110010010000011110111011000110011110
# bi... | 164 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__A = logging.get_logger(__name__)
__A = {
"facebook/convnextv2-tiny-1k-224": "https://... | 164 | 1 |
"""simple docstring"""
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"kwargs, expected" , [
({"num_shards": 0, "max_num_jobs": 1}, []),
({"num_shards": 10, "max_num_jobs": 1}, [range(1... | 298 |
"""simple docstring"""
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch... | 298 | 1 |
'''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_m... | 151 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
if partitions <= 0:
raise ValueError('partitions must be a positive number!' )
if partitions > number_of_bytes:
raise ValueError('partitions can not > number_of_b... | 151 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_... | 215 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
lowerCamelCase_ : int = logging.get_logger(__name__)
class __A ( _SCREAMING_... | 215 | 1 |
'''simple docstring'''
class __UpperCAmelCase ( _lowerCamelCase ):
pass
class __UpperCAmelCase ( _lowerCamelCase ):
pass
class __UpperCAmelCase :
def __init__( self ):
"""simple docstring"""
_snake_ca... | 42 |
'''simple docstring'''
from collections import defaultdict
from math import gcd
def SCREAMING_SNAKE_CASE__ ( __A = 1_500_000 ) -> int:
_snake_case = defaultdict(__A )
_snake_case = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for euclid_n in range((euclid_m % 2) + 1 ... | 42 | 1 |
'''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__ = {
... | 322 |
'''simple docstring'''
def __lowerCAmelCase (__lowerCAmelCase ):
if number < 0:
raise ValueError("number must not be negative" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 322 | 1 |
from ..utils import DummyObject, requires_backends
class a__ ( metaclass=snake_case__ ):
_a : List[str] = ["""keras_nlp"""]
def __init__( self , *_A , **_A ):
"""simple docstring"""
requires_backends(self , ["keras_nlp"] )... | 92 |
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
__snake_case : Dict = logging.get_logger(__name__)
__snake_case : Any ... | 248 | 0 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def __a ( __lowerCamelCase ):
UpperCAmelCase_ : Dict = SwinConfig(image_size=192 )
if "base" in model... | 23 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_a = {'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}
try... | 23 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config imp... | 254 |
'''simple docstring'''
from pathlib import Path
import fire
def lowercase_ ( lowerCAmelCase__ : str , lowerCAmelCase__ : str , lowerCAmelCase__ : int ):
"""simple docstring"""
__UpperCAmelCase : List[str] = Path(lowerCAmelCase... | 254 | 1 |
from collections import defaultdict
class a :
"""simple docstring"""
def __init__( self : str , lowerCamelCase : Optional[int] , lowerCamelCase : Any ) -> str:
__snake_case : Union[str, Any] = total # total no of tasks (N)
... | 134 |
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
if index == r:
for j in range(__lowerCamelCase ):
print(data[j] , ... | 134 | 1 |
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 a__ ( datasets.BuilderConfig ):
"""simple docstring"""
U... | 245 |
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall... | 245 | 1 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
_a = logging.get_logger(__name__)
class... | 356 |
"""simple docstring"""
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
# Return True if there is node that has not iterated.
UpperCAmelCase_ : List[Any] = [False] * len(__lowerCamelCase )
UpperCAmelCase_ : Any = []
queue.append... | 23 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from ... | 232 |
def lowerCAmelCase__(__snake_case ) -> str:
'''simple docstring'''
return "".join(chr(ord(__snake_case ) - 32 ) if '''a''' <= char <= '''z''' else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 209 | 0 |
'''simple docstring'''
from __future__ import annotations
__snake_case = [True] * 1000001
__snake_case = 2
while i * i <= 1000000:
if seive[i]:
for j in range(i * i, 1000001, i):
__snake_case = False
i += 1
def a ( __a ) -> bool:
... | 219 |
'''simple docstring'''
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase ( A__ ):
"""simple docstring"""
_a = (DDIMParallelScheduler,)
_a = (('eta', 0.0), ('num_inference_steps', 50))
def lower... | 219 | 1 |
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
"""snap-research/efficientformer-l1-300""": (
"""https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main... | 176 |
def _lowercase ( UpperCamelCase_ , UpperCamelCase_ ) -> str:
'''simple docstring'''
if not isinstance(UpperCamelCase_ , UpperCamelCase_ ):
raise ValueError('iterations must be defined as integers' )
if not isinstance(UpperCamelCase_ , UpperCamelCase_ ) or not number >=... | 176 | 1 |
def UpperCamelCase( lowercase_ ) -> List[str]:
'''simple docstring'''
snake_case_ = []
snake_case_ = set({"""(""", """[""", """{"""} )
snake_case_ = set({""")""", """]""", """}"""} )
snake_case_ = {"""{""": """}""", """[""": """]"... | 34 |
from __future__ import annotations
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ ) -> dict[str, float]:
'''simple docstring'''
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if r... | 34 | 1 |
"""simple docstring"""
def _A ( lowercase , lowercase = " " ):
"""simple docstring"""
a =[]
a =0
for index, char in enumerate(lowercase ):
if char == separator:
split_words.append(string[last_index:index] )
... | 81 |
"""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 ...test_configura... | 60 | 0 |
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_mo... | 366 | def snake_case (__lowercase ) -> bool:
'''simple docstring'''
_snake_case : Dict = 0
for ch in input_str:
_snake_case : int = ord(__lowercase )
_snake_case : List[Any] = pow(2 , __lowercase )
#... | 284 | 0 |
"""simple docstring"""
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pya... | 292 |
"""simple docstring"""
class _UpperCAmelCase :
def __init__( self :List[str] , __UpperCamelCase :Union[str, Any] , __UpperCamelCase :Tuple ):
A = name
A = val
def __str__( self :str ):
return f"{self.__class__.__nam... | 292 | 1 |
"""simple docstring"""
def A__ ( SCREAMING_SNAKE_CASE__) -> bool:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 364 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import Stabl... | 293 | 0 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
__A =list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
__A =[file for file in filepaths if file != file.lower()]
i... | 226 |
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distr... | 226 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowercase : List[str] = {
'configuration_gpt_bigcode': ['GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTBigCodeConfi... | 311 |
'''simple docstring'''
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def lowerCAmelCase_ ( snake_case__ = "laptop" ):
'''simple docstring'''
A : Tuple = F'https://www.amazon.i... | 311 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , ... | 321 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> bool:
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(__UpperCamelCase ) )
def ... | 321 | 1 |
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case : Any =logging.get_logger(__name__)
__snake_case : List[str] ={
'google/e... | 351 |
def lowerCAmelCase__ ( lowerCamelCase_ : int = 1000000):
'''simple docstring'''
lowerCAmelCase__ : int = set(range(3 ,lowerCamelCase_ ,2))
primes.add(2)
for p in range(3 ,lowerCamelCase_ ,2):
if p not in primes:
continue
primes.differ... | 94 | 0 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerat... | 122 |
import colorsys
from PIL import Image # type: ignore
def lowerCamelCase__ ( a__ : float , a__ : float , a__ : int ) -> float:
UpperCamelCase_ = x
UpperCamelCase_ = y
for step in range(a__ ): # noqa: B007
U... | 122 | 1 |
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils im... | 360 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class a :
def __init__( self , A_ = None ):
'''simple docstring'''
if components is None:
_UpperCAmelCase : Dict ... | 189 | 0 |
"""simple docstring"""
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils imp... | 54 |
"""simple docstring"""
from jiwer import compute_measures
import datasets
a__ : Optional[int] = '''\
@inproceedings{inproceedings,
author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
year = {2004},
month = {01},
pages = {},
title = {From WER and RIL to MER a... | 54 | 1 |
"""simple docstring"""
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError("To use the rich extension, install rich with `pip install rich`")
| 368 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...... | 316 | 0 |
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