code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
__lowerCamelCase = 5_00_00
__lowerCamelCase = 50_00
__lowerCamelCase , __lowerCamelCase = os.path.split(__file__)
__lowerCamelCase =... | 490 |
"""simple docstring"""
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class __lowercase ( _UpperCAmelCase):
"""simple docstring"""
@require_torch
... | 480 | 0 |
import sys
def UpperCamelCase_( _A :Union[str, Any] )-> Dict:
UpperCamelCase__ = len(lowerCAmelCase_ )
UpperCamelCase__ = [[0 for x in range(lowerCAmelCase_ )] for x in range(lowerCAmelCase_ )]
UpperCamelCase__ = [[0 for x in range(lowerCAmelCase_ )] for x ... | 707 |
from __future__ import annotations
import numpy as np
def UpperCamelCase_( _A :list[float] )-> Union[str, Any]:
return np.maximum(0 , _A )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 185 | 0 |
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_cha... | 144 |
# using dfs for finding eulerian path traversal
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : List[Any] , __UpperCamelCase : int , __UpperCamelCase : List[str] , __UpperCamelCase : List[str]=None ) -> Optional[Any]:
UpperCAmelCase_ = (path or [])... | 144 | 1 |
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from transformers import DPRContext... | 706 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""google/switch-base-8""": """https://huggingface.co/google/switch-base-8/blob/main/config.json""",
}
class __UpperCamelCase ( lowerCAme... | 131 | 0 |
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,
)
lowerCAmelCase : str = {"""configuration_xglm""": ["""XGLM_PR... | 671 |
from __future__ import annotations
from math import ceil, floor, sqrt
def A_ ( _UpperCAmelCase = 2_00_00_00 ):
SCREAMING_SNAKE_CASE_: list[int] = [0]
SCREAMING_SNAKE_CASE_: int
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ... | 671 | 1 |
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from tra... | 718 |
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
UpperCamelCase__ : List[str] = logging.get_logger(... | 486 | 0 |
from ..utils import DummyObject, requires_backends
class lowerCamelCase (metaclass=__UpperCAmelCase ):
"""simple docstring"""
lowerCamelCase__ = ['torch']
def __init__( self : int , *__magic_name__ : Optional[Any] , **__magic_name__ : int ) -> Opt... | 140 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def _UpperCAmelCase ( UpperCamelCase: int ):
"""simple docstring"""
if not isinstance(UpperCamelCase , UpperCamelCase ):
raise TypeError("Undefined for non-integers" )
elif precision < 1:
raise ValueError("Und... | 611 | 0 |
import heapq
import sys
import numpy as np
_UpperCAmelCase : str = tuple[int, int]
class lowercase :
def __init__( self ):
snake_case_ = []
snake_case_ = set()
def a ( self ):
if not self.e... | 108 |
_UpperCAmelCase : str = [0, 2, 4, 6, 8]
_UpperCAmelCase : Any = [1, 3, 5, 7, 9]
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if remaining_len... | 108 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case: Any = {"configuration_plbart": ["PLBART_PRETRAINED_CON... | 577 |
'''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class _UpperCAmelCase ( lowerCAmelCase__ ):
""... | 577 | 1 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
a__ : List[str] = (3, 9, -1_1, 0, 7, 5, 1, -1)
a__ : Optional[int] = (4, 6, 2, 0, 8, 1_0, 3, -2)
@dataclass
class lowercase_ :
__UpperCAmelCase = 42
... | 710 |
'''simple docstring'''
import comet # From: unbabel-comet
import torch
import datasets
a__ : int = datasets.logging.get_logger(__name__)
a__ : Union[str, Any] = '\\n@inproceedings{rei-EtAl:2020:WMT,\n author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and ... | 223 | 0 |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def UpperCamelCase__ ( UpperCAmelCase_ , UpperCAmelCase_ ) -> Tuple:
'''simple docstring'''
_lowercase : List[str] = list(lowercase__ )
... | 322 |
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
__lowercase : Tuple =logging.get... | 54 | 0 |
def _lowerCamelCase ( snake_case = 50_000_000 ):
_lowerCAmelCase = set()
_lowerCAmelCase = int((limit - 24) ** (1 / 2) )
_lowerCAmelCase = set(range(3 , prime_square_limit + 1 , 2 ) )
primes.add(2 )
for p in ra... | 225 | import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ..... | 225 | 1 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
'''simple docstring'''
lowercase_ = [False] * len(__lowerCAmelCase )
lowercase_ = []
qu... | 567 |
"""simple docstring"""
import sys
import turtle
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> tuple[float, float]:
'''simple docstring'''
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def _SCREAMING_SNAKE_CASE (__lowerCAmelC... | 567 | 1 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class a :
"""simple docstring"""
def __init__( self , lowerCAmelCase_ = None ) -> None:
if component... | 704 | def snake_case ( snake_case__ :int = 1_000_000) -> int:
_A = set(range(3 , snake_case__ , 2))
primes.add(2)
for p in range(3 , snake_case__ , 2):
if p not in primes:
continue
primes.difference... | 83 | 0 |
'''simple docstring'''
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
snake_case_ : str = get_logger(__name__)
snake_case_ : List[str] = R'''
Args:
input_ids (`... | 138 |
'''simple docstring'''
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def lowercase__( _UpperCamelCase : Optional[Any] , _UpperCamelCase : Dict , _UpperCamelCase : int , _UpperCamelCase : Optional[int] )-> List[Any]:
"""simple docstring"""
_... | 138 | 1 |
import cmath
import math
def __lowerCAmelCase ( UpperCAmelCase__ : float , UpperCAmelCase__ : float , UpperCAmelCase__ : float , UpperCAmelCase__ : float ) -> str:
lowerCamelCase_ = math.radians(__UpperCamelCase ... | 721 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...tes... | 103 | 0 |
'''simple docstring'''
import functools
from typing import Any
def a_ ( _UpperCAmelCase : str ,_UpperCAmelCase : list[str] ) -> List[str]:
if not isinstance(lowercase__ ,lowercase__ ) or len(lowercase__ ) == 0:
raise ValueError('the string should be no... | 286 | from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
SCREAMING_SNAKE_CASE__ : Any = {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]... | 85 | 0 |
from numpy import exp, pi, sqrt
def __lowerCamelCase ( lowerCamelCase__ , lowerCamelCase__ = 0.0 , lowerCamelCase__ = 1.0 ):
"""simple docstring"""
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
... | 81 |
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
... | 81 | 1 |
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class lowerCamelCase( __sn... | 27 |
"""simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class lowerCAmelCase__ ( _lowerCamelCase ):
'''si... | 265 | 0 |
'''simple docstring'''
def lowerCAmelCase( a__ : str , a__ : int ):
'''simple docstring'''
lowerCamelCase__ = [[] for _ in range(A__ )]
lowerCamelCase__ = key - 1
if key <= 0:
raise ValueError(... | 714 |
'''simple docstring'''
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
lowerCAmelCase_ = 1.0_5457_1817E-34 # unit of ℏ : J * s
lowerCAmelCase_ = 3E8 # unit of c : m * s^-1
... | 426 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_a : Union[str, Any] = {
"""configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
"""feature_extra... | 689 |
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
_a :... | 689 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor,... | 663 | """simple docstring"""
from PIL import Image
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Image ,_lowerCamelCase : int ) -> Image:
_lowerCAmelCase : Any = (259 * (level + 255)) / (255 * (259 - level))
def contrast(_lowerCamelCase : int ) -> int:
return int(128 + facto... | 663 | 1 |
def _a ( UpperCAmelCase ) -> int:
"""simple docstring"""
assert isinstance(UpperCAmelCase , UpperCAmelCase ), f"The input value of [n={number}] is not an integer"
if number == 1:
return 2
elif number < 1:
lowerCamelCase__ : List[str] = f"The in... | 315 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
_A : Any = logging.get_logger(__name__)
_A : str = [
['attention', 'attn'],
['encoder_at... | 315 | 1 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diffusers.pipel... | 716 |
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_available():
from .tokenization_camembert i... | 106 | 0 |
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 __lowerCAmelCas... | 175 |
def lowerCamelCase_ ( UpperCAmelCase__ = 100 ):
"""simple docstring"""
a_ = (n * (n + 1) // 2) ** 2
a_ = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(F'''{solution() = }''') | 483 | 0 |
"""simple docstring"""
def lowerCamelCase_ ( UpperCAmelCase_ ) ->Any:
if not nums: # Makes sure that the list is not empty
raise ValueError('''List is empty''' )
__UpperCAmelCase : str = sum(UpperCAmelCase_ ) / len(UpperCAmelCase_ ) # Calcu... | 714 |
"""simple docstring"""
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
lowercase__ :int = 'sr... | 374 | 0 |
"""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 impor... | 76 |
'''simple docstring'''
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for te... | 688 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCamelCase__ ( UpperCAmelCase ):
"""simple docstring"""
_UpperCamelCase : List[str] = ['image_processor', 'tokenizer']
_UpperCamelCase ... | 185 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class lowerCamelCase__ ( UpperCAmelCase ):
"""simple docstring"""
@staticmethod
@abstractmethod
def snake_case__ ( snake_case ):
'''simple docstring'''
raise NotImp... | 185 | 1 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class snake_case_ :
"""simple docstring"""
def __init__( self , lowerCamelCase_) -> None:
UpperCamelCase = num_of_nodes
UpperCamelCase = []
... | 34 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def __snake_case ( _lowercase ):
"""simple docstring"""
if "cls_token" in name:
UpperCamelCas... | 34 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.json",
}
class UpperCAmelCase_ ( a):
lowerCamelCase__... | 658 |
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
_snake_case = "\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n title = {BLEU: a Method for Automatic Evalua... | 658 | 1 |
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __UpperCAmelC... | 99 |
class UpperCAmelCase_ :
def __init__( self , _lowerCAmelCase ):
# we need a list not a string, so do something to change the type
UpperCAmelCase__ : Dict = arr.split(""",""" )
def __UpperCAmelCase ( self ):
... | 79 | 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
lowerCamelCase : int = logging.get_logger(__name__)
lowerCamelCase : Any = ... | 707 |
def snake_case_ ( lowerCAmelCase_ : int , lowerCAmelCase_ : int ):
return int((input_a, input_a).count(0 ) == 0 )
def snake_case_ ( ):
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 )... | 649 | 0 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.convers... | 427 | '''simple docstring'''
import random
from .binary_exp_mod import bin_exp_mod
def UpperCamelCase_ ( snake_case_ : str , snake_case_ : List[str]=10_00 ) -> Dict:
'''simple docstring'''
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means... | 427 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modelin... | 704 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE__ ( snake_case__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = '''ClapFeatureExtractor'''
SCREAMING_SNAKE_CASE ... | 329 | 0 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
__lowerCamelCase ... | 96 |
from __future__ import annotations
import math
def snake_case__ ( UpperCAmelCase : int ):
if num <= 0:
lowerCAmelCase__ :Optional[Any] = F'''{num}: Invalid input, please enter a positive integer.'''
raise ValueError(UpperCAmelCase )
lowerCAmelCase_... | 145 | 0 |
# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless ... | 356 | from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class lowerCamelCase (yaml.SafeLoader ):
"""simple docstring"""
def __A ( self : str , __magic_name__ : str ) -> str:
SCREAMING_SNAKE_CASE_ = ... | 356 | 1 |
"""simple docstring"""
from __future__ import annotations
def _a ( _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and array[indexa] < array[index... | 341 |
"""simple docstring"""
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class lowerCamelCase__ ( snake_case ):
... | 341 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__A : List[str] = {"""configuration_speech_encoder_decoder""": ["""SpeechEncoderDecoderConfig"""]}
try:
if not is_torch_available():
raise OptionalDep... | 450 |
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
__A : int = {
"""User-Agent""": """Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"""
""" (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582"""
}... | 450 | 1 |
import os
import re
import warnings
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_ta import TaTokenizer
else:
__snake_... | 540 |
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,
BertEncoder,
... | 540 | 1 |
'''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_device... | 721 | '''simple docstring'''
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .atte... | 389 | 0 |
"""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, AutoTokenizer
@require_torch
@require_... | 182 |
"""simple docstring"""
import requests
def a__ ( lowerCAmelCase , lowerCAmelCase ) -> None:
UpperCAmelCase__ : List[str] = {"""Content-Type""": """application/json"""}
UpperCAmelCase__ : List[str] = requests.post(lowerCAmelCase , json={"""text"""... | 182 | 1 |
import argparse
import os
import re
import packaging.version
__UpperCamelCase : Any = "examples/"
__UpperCamelCase : Tuple = {
"examples": (re.compile(r"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
"init": ... | 106 |
__UpperCamelCase : List[Any] = 256
# Modulus to hash a string
__UpperCamelCase : Union[str, Any] = 100_0003
def _a ( SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
UpperCamelCase__ : Optio... | 106 | 1 |
'''simple docstring'''
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transfor... | 330 |
'''simple docstring'''
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... | 330 | 1 |
from math import pow, sqrt
def __A ( *_SCREAMING_SNAKE_CASE : float ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[str] = len(_SCREAMING_SNAKE_CASE ) > 0 and all(value > 0.0 for value in values )
return ... | 700 |
'''simple docstring'''
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
lowercase = logging.get_logger(__name__)
lowercase = '''T5Config'''
class __lowerCamelCase ... | 564 | 0 |
"""simple docstring"""
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
UpperCAmelCase__ = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
" (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582"... | 277 |
"""simple docstring"""
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProces... | 289 | 0 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def __UpperCAmelCase ( __magic_name__ = "AAPL" )-> str:
"""simple docstring"""
snake_case_ : int = F'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
snake_case_ ... | 718 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrFor... | 656 | 0 |
'''simple docstring'''
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def _lowerCAmelCase ( __magic_name__ : List[str] ) -> Union[str, Any]:
# This defines a "chinese character" as anything in the CJK Unicode ... | 92 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
Wava... | 92 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"""facebook/c... | 717 |
"""simple docstring"""
from __future__ import annotations
import math
import random
from typing import Any
class a :
def __init__( self : str ) -> None:
'''simple docstring'''
SCREAMING_SNAKE_CASE_: list[Any] =[]
SCREAMING_SNAKE_CASE_: ... | 36 | 0 |
'''simple docstring'''
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
... | 301 |
"""simple docstring"""
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .ut... | 174 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResa... | 38 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__lowerCamelCase : Union[str, Any] = {
"""configuration_vision_encoder_decoder""": ["""VisionEncoderDecoderConfig""",... | 38 | 1 |
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_common import TokenizerTe... | 454 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
def _lowerCAmelCase (_lowercase ):
"""simple docstring"""
return np.maximum(0 , _lowercase )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, ... | 331 | 0 |
"""simple docstring"""
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... | 616 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torc... | 616 | 1 |
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:
SCREAMI... | 99 |
"""simple docstring"""
import json
from typing import TYPE_CHECKING, 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 ... | 174 | 0 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data... | 720 |
from __future__ import annotations
def lowercase_ ( __snake_case : list ) -> float:
'''simple docstring'''
if not nums:
raise ValueError("List is empty" )
return sum(__snake_case ) / len(__snake_case )
if __nam... | 57 | 0 |
'''simple docstring'''
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_ut... | 430 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LI... | 430 | 1 |
'''simple docstring'''
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, nes... | 411 |
'''simple docstring'''
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
... | 411 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokeniz... | 168 | '''simple docstring'''
import os
import sys
import unittest
_a : Optional[int] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402... | 168 | 1 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since... | 685 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
UpperCamelCase__ : Tuple = {'''tokenization_tapex''': ['''TapexTokenizer''']}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
UpperCamelCase__ : int = _LazyModule... | 685 | 1 |
from __future__ import annotations
__A : str = list[tuple[int, int]]
__A : Optional[int] = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[... | 16 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
__A : str = logging.get_logger(__name__)
_... | 16 | 1 |
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class a ( unittest.TestCase ):
def snake_case_ ( self ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE: Any = [
'safety_checker... | 706 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def lowerCAmelCase ( UpperCamelCase__ : int = 3 ) -> qiskit.result.counts.Counts:
"""simple docstring"""
if isinstance(UpperCamelCase... | 146 | 0 |
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
__magic_name__ = 299_792_458
# Symbols
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ = symbols('''ct x y z''')
def SCREAMING_SNAKE_CASE__ ... | 276 |
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, prepare_image_inputs
if is_tor... | 276 | 1 |
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ... | 194 |
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install... | 194 | 1 |
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def lowercase ( __A : Union[str, Any] ) -> Any:
'''simple docstring'''
if "cls_token" in name:
snake_case : ... | 36 |
"""simple docstring"""
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
lowercase_ = {
"<": operator.lt,
"<=": operator.le,
"==": operator.eq,
"!=": operator.ne,
">=": operator.ge,
">... | 695 | 0 |
'''simple docstring'''
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_tabl... | 581 |
'''simple docstring'''
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
SCREAMING_SNAKE_CASE__ : Any = '''
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Sim... | 581 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
_UpperCamelCase : List[Any] = {
"""configuration_trocr""": ["""TROCR_PR... | 284 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Union[str, Any] = logging.get_logger(__name__)
a_ : Optional[int] = {
"""BAAI/AltCLIP""": """https://huggingface.co/BAAI/AltC... | 676 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
"google/fnet-large": "https://huggingface.co/... | 92 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
P... | 92 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowercase = {
'''configuration_roberta_prelayernorm''': [
'''ROBERTA_PREL... | 211 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def __A ( _SCREAMING_SNAKE_CASE : Tuple ):
"""simple docstring"""
if "img_enco... | 211 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_de... | 716 | '''simple docstring'''
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, id... | 654 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __snake_case (__UpperCAmelCase ):
lowerCAmelCase__ = ["image_processor", "tokenizer"]
lowerCAmelCase__ = "CLIPImageProcessor"
lowerCAmelCase__ ... | 429 |
"""simple docstring"""
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class SCREAMING_SNAKE_CASE__ ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
... | 567 | 0 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ ( snake_case_ : str ) -> list[int]:
SCREAMING_SNAKE_CASE : Dict = [0 for i in range(len(snake_case_ ) )]
# initialize interval's left pointer and right pointer
SCREAMING_SNAKE_CASE , SCREA... | 220 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
__UpperCAmelCase = '\\n\n'
__UpperCAmelCase = '\nPerplexity (PPL) is one of the most comm... | 220 | 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",
"AltCLIPTextConfig",
... | 32 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Ac... | 15 | 0 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversati... | 712 |
"""simple docstring"""
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
__magic_name__ = "\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n author... | 248 | 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__ ( _lowercase ):
... | 30 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_dat... | 154 | 0 |
"""simple docstring"""
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... | 706 |
"""simple docstring"""
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 (
... | 16 | 0 |
"""simple docstring"""
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_tor... | 96 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, g... | 259 | 0 |
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
a : Optional[int] = '''src/diff... | 704 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a : List[str] = {
'''configuration_table_transformer''': [
'''TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''TableTransformerConfig''',
'''TableTransformerO... | 527 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a : Any = {
'configuration_vision_encoder_decoder': ['VisionEncoderDecoderConfig', 'VisionEncoderDe... | 556 |
def lowerCAmelCase_ (lowerCAmelCase__: list ):
"""simple docstring"""
if len(lowerCAmelCase__ ) <= 1:
return [tuple(lowerCAmelCase__ )]
UpperCAmelCase_: List[Any] = []
def generate(lowerCAmelCase__: int , lowerCAmelCase__: list ):
if... | 556 | 1 |
"""simple docstring"""
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def lowercase (SCREAMING_SNAKE_CASE_ : List[A... | 327 |
"""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... | 327 | 1 |
"""simple docstring"""
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 ex... | 564 |
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageResampling,
get_image_siz... | 285 | 0 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase__ ( __snake_case ... | 332 |
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 ... | 332 | 1 |
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
_UpperCAmelCase : Tuple = JukeboxTokenizer
_UpperCAmelCase : int = {
""... | 315 |
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class _lowerCAmelCase ( UpperCAmelCase_ , unittes... | 411 | 0 |
'''simple docstring'''
from string import ascii_uppercase
a__ = {str(ord(c) - 55): c for c in ascii_uppercase}
def snake_case__ ( a , a ) -> str:
'''simple docstring'''
if isinstance(a , a ):
raise TypeError("""int() can't convert non-string w... | 566 |
'''simple docstring'''
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
'''huggingface/informer-tourism-monthly''': (
'''https://huggingface.co/huggingface/in... | 566 | 1 |
# using dfs for finding eulerian path traversal
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=None):
SCREAMING_SNAKE_CASE = (path or []) + [u]
for v in graph[u]:
if visited_edge[u][v] is False:
SC... | 73 |
def lowerCAmelCase ( UpperCAmelCase, UpperCAmelCase ) ->int:
"""simple docstring"""
return abs(UpperCAmelCase ) if a == 0 else greatest_common_divisor(b % a, UpperCAmelCase )
def lowerCAmelCase ( UpperCAmelCase, ... | 154 | 0 |
"""simple docstring"""
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class lowercase( tf.keras.optimiz... | 716 |
"""simple docstring"""
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils impor... | 28 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase_ = {
"""configuration_mobilevit""": ["""MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Mobi... | 2 | class a__ :
def __init__( self : Tuple ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = 0
SCREAMING_SNAKE_CASE_ : Tuple = 0
SCREAMING_SNAKE_CASE_ : Union[str, Any] = {}
def ... | 216 | 0 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.... | 183 |
"""simple docstring"""
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class ... | 183 | 1 |
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_roberta import RobertaTok... | 684 |
'''simple docstring'''
from __future__ import annotations
a__ : Optional[int] = list[tuple[int, int]]
a__ : List[Any] = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
... | 368 | 0 |
'''simple docstring'''
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase : List[str] = name
... | 706 |
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def a ( SCREAMING_SNAKE_CASE_ : bool = True , *SCREAMING_SNAKE_CASE_ : List[str] , **SCREAMING_SNAKE_CASE_ : Tuple ... | 643 | 0 |
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
A = numpy.array([0, 0])
A = numpy.array([0.5, 0.8_6_6_0_2_5_4])
A = numpy.array([1, 0])
A = [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]
def a(low... | 187 |
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def UpperCAmelCase_ ( __UpperCamelCase = 8 ):
SCREAMING_SNAKE_CASE__ =ascii_letters + digits + punctuation
return "".join(secrets.choice(__UpperC... | 151 | 0 |
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase = logging.get_logger(__name__)
... | 720 |
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_score
from t... | 565 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ = {"""configuration_timm_backbone""": ["""TimmBackboneConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except Opt... | 411 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
fr... | 133 | 0 |
"""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
#
... | 261 |
"""simple docstring"""
from typing import Dict, Optional
import numpy as np
import datasets
__UpperCAmelCase ="""
IoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union
between the predicted segmentation and the ground truth. For binary (two c... | 261 | 1 |
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
a_ : Tuple = TypeVar('T')
class _snake_case ( Generic[T] ):
def __init__( se... | 73 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMix... | 324 | 0 |
from __future__ import annotations
from typing import Any
class lowerCAmelCase__:
'''simple docstring'''
def __init__( self , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = 0 ) -> None:
_SCREAMING_SNAKE_CASE , _SCRE... | 718 |
from PIL import Image
def lowerCamelCase__ (__lowerCamelCase ):
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : List[str] = image.size
_SCREAMING_SNAKE_CASE : Tuple = 0
_SCREAMING_SNAKE_CASE : Dict = image.load()
f... | 381 | 0 |
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
fr... | 475 | """simple docstring"""
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 Co... | 473 | 0 |
"""simple docstring"""
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
lowerCAmelCase : Optional[Any] ... | 533 |
"""simple docstring"""
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
lowerCAmelCase : List[str] = 4
lowerCAmelCase : List[str]... | 533 | 1 |
"""simple docstring"""
import os
def a_ ( ):
with open(os.path.dirname(_UpperCamelCase ) + """/p022_names.txt""" ) as file:
__lowerCamelCase = str(file.readlines()[0] )
__lowerCamelCase = names.replace("""\"""", ""... | 281 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
a : Dict = '''2.13.1'''
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse('''3.7'''):
raise ... | 639 | 0 |
'''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_ ( __A ):
"""simple docstring"... | 8 |
'''simple docstring'''
from __future__ import annotations
import math
def lowercase_ ( __A : float , __A : int ) -> float:
"""simple docstring"""
lowercase : str =u
for i in range(1 , __A ):
lowercase : Any =temp * ... | 8 | 1 |
'''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class SCREAMIN... | 685 |
'''simple docstring'''
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
UpperCamelCase__ = ['small', 'medium', 'large']
UpperCamelCase__ = 'lm_head.decoder.weight'
UpperCamelCase__ = 'lm_head.weight'
def __SCREAMING_SNAKE_CASE ( _UpperCamelC... | 620 | 0 |
"""simple docstring"""
import cva
import numpy as np
class UpperCAmelCase_ :
def __init__( self : int , __UpperCamelCase : float , __UpperCamelCase : int ) -> int:
if k in (0.0_4, 0.0_6):
_UpperCamelCase = k
_UpperCamelC... | 342 | """simple docstring"""
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
UpperCAmelCase = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
"""text-cla... | 342 | 1 |
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