code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
_lowerCAmelCase : Optional[Any] ="""\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
author={Wang... | 113 |
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
_lowerCAmelCase : int ="""scheduler_config.json"""
class __UpperCamelCase ( _a ):
'''simple docstring'''
... | 113 | 1 |
import os
def _A ( ):
UpperCAmelCase__: Any = os.path.dirname(os.path.realpath(SCREAMING_SNAKE_CASE ) )
UpperCAmelCase__: List[Any] = os.path.join(SCREAMING_SNAKE_CASE ,"triangle.txt" )
with open(SCREAMING_SNAKE_CASE ) as f:
UpperCAmelCase__: Union[str, Any] ... | 113 |
import unittest
from transformers import DonutProcessor
_lowerCAmelCase : str ="""naver-clova-ix/donut-base"""
class __UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def _UpperCAmelCase ( self ):
UpperCAmelCase__: Any = DonutProcessor.f... | 113 | 1 |
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
_lowerCAmelCase : str =numpy.array([0, 0])
_lowerCAmelCase : str =numpy.array([0.5, 0.866_0254])
_lowerCAmelCase : Optional[int] =numpy.array([1, 0])
_lower... | 113 |
from __future__ import annotations
from typing import Any
class __UpperCamelCase :
'''simple docstring'''
def __init__( self , lowerCamelCase__ ):
UpperCAmelCase__: Optional[int] = num_of_nodes
UpperCAmelCase__: list[list[int]] = []
UpperCAmel... | 113 | 1 |
from cva import destroyAllWindows, imread, imshow, waitKey
def _A ( SCREAMING_SNAKE_CASE ):
# getting number of pixels in the image
UpperCAmelCase__ , UpperCAmelCase__: str = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i in range(SCREAMING_SNA... | 113 |
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_accelera... | 113 | 1 |
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def _A ( ):
print("Making key files..." )
make_key_files("rsa" ,1_0_2_4 )
print("Key files generation successful." )
def _A ( ... | 113 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : Union[str, Any] ={
"""configuration_clap""": [
"""CLAP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ClapAudioConfig""",
"""ClapConfig""",
"""C... | 113 | 1 |
def _A ( SCREAMING_SNAKE_CASE ):
if num <= 0:
raise ValueError("Input must be a positive integer" )
UpperCAmelCase__: Optional[Any] = [True] * (num + 1)
UpperCAmelCase__: Any = 2
while p * p <= num:
if primes[p]:
for i in range(p * p ,num + 1 ,SCREAMING_SNAKE_CASE ... | 113 |
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import Arr... | 113 | 1 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
_lowerCAmelCase : Any ={"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}
try:
if n... | 113 |
import fire
from utils import calculate_rouge, save_json
def _A ( SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE=None ,**SCREAMING_SNAKE_CASE ):
UpperCAmelCase__: Tuple = [x.strip() for x in open(SCREAMING_SNAKE_CASE ).readlines()]
UpperCAmelCase__: Dict = [x.... | 113 | 1 |
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
_lowerCAmelCase : Tuple ="""%20""".join(argv[1:]) if len(argv) > 1 else quote(str(input("""Search: """)))
... | 113 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class __UpperCamelCase ( _a ,_a ):
'''simple docstring'''
@register_to_config
def __init__( self , *,
lowerCamelCase__ = 4 ... | 113 | 1 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __UpperCamelCase ( _a ):
'''simple docstring'''
__magic_name__ = (IPNDMScheduler,)
__magic_name__ = (("num_inference_steps", 5_0),)
d... | 113 |
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class __UpperCamelCase ( _a ):
'''simple do... | 113 | 1 |
def _A ( SCREAMING_SNAKE_CASE ):
if not isinstance(SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ):
raise ValueError("check_bouncy() accepts only integer arguments" )
UpperCAmelCase__: List[Any] = str(SCREAMING_SNAKE_CASE )
UpperCAmelCase__: List[str] = "".join(sorted(SCR... | 113 |
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="""%(message)s""")
def _A ( SCREAMING_SNAKE_CASE ):
return input_array.reshape((input_array.size, 1) )
def _A ( SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SC... | 113 | 1 |
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert ... | 113 |
import inspect
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_config_docstrings.py
_lowerCAmelCase : int ="""src/transformers"""
# This is to make sure the ... | 113 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dime... | 113 |
def _A ( SCREAMING_SNAKE_CASE ):
UpperCAmelCase__ , UpperCAmelCase__: int = [], []
while len(SCREAMING_SNAKE_CASE ) > 1:
UpperCAmelCase__ , UpperCAmelCase__: str = min(SCREAMING_SNAKE_CASE ), max(SCREAMING_SNAKE_CASE )
start.append(SCREAMING_SNAK... | 113 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : Tuple ={
"""configuration_table_transformer""": [
"""TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""TableTransformerConfig""",
"""Tabl... | 113 |
def _A ( SCREAMING_SNAKE_CASE ): # noqa: E741
UpperCAmelCase__: int = len(SCREAMING_SNAKE_CASE )
UpperCAmelCase__: Dict = 0
UpperCAmelCase__: Optional[int] = [0] * n
UpperCAmelCase__: List[str] = [False] * n
UpperCAmelCase__: List[str] = [False] * n
de... | 113 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowerCAmelCase : Any ={
"""configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
"""tokenization_mvp""": ... | 113 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_a... | 113 | 1 |
def _A ( SCREAMING_SNAKE_CASE ):
if not numbers:
return 0
if not isinstance(SCREAMING_SNAKE_CASE ,(list, tuple) ) or not all(
isinstance(SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ) for number in numbers ):
raise ValueError("numbers must be an iterable of integers" )
Upp... | 113 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowerCAmelCase : Tuple ={
"""configuration_mobilenet_v2""": [
"""MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""MobileNetV2Config""",
"... | 113 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowerCAmelCase : Dict ={
"""configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}
try:
if not is_torch_available():
... | 113 |
_lowerCAmelCase : int ="""
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.g... | 113 | 1 |
from __future__ import annotations
from math import pi, sqrt
def _A ( SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ):
if inductance <= 0:
raise ValueError("Inductance cannot be 0 or negative" )
elif capacitance <= 0:
raise ValueError("Capacitance cannot be 0 or negative" )
else:
r... | 113 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoT... | 113 | 1 |
def _A ( SCREAMING_SNAKE_CASE ):
if edge <= 0 or not isinstance(SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ):
raise ValueError("Length must be a positive." )
return 3 * ((2_5 + 1_0 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
def _A ( SCREAMING_SNAKE_CASE ):
if edge <= 0 or ... | 113 |
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils import AddedToken
log... | 113 | 1 |
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertTokenizer,
AutoTokenizer,... | 113 |
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_EX... | 113 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 113 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def _A ( SCREAMING_SNAKE_CASE ):
# A local function to see if a dot lands in the circle.
def is_in_circle(SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ) -> bool:
UpperCAmelCase... | 113 | 1 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class __UpperCamelCase :
'''simple docstring'''
__magic_name__ = 42
__magic_name__ = None
__magic_name__ = None
_lowerCAmelCase : Any ... | 113 |
def _A ( SCREAMING_SNAKE_CASE ):
stooge(SCREAMING_SNAKE_CASE ,0 ,len(SCREAMING_SNAKE_CASE ) - 1 )
return arr
def _A ( SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ):
if i >= h:
return
# If first element is smaller than the last then swap them
if arr[i... | 113 | 1 |
from __future__ import annotations
import os
from typing import Any
import requests
_lowerCAmelCase : Optional[Any] ="""https://api.github.com"""
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
_lowerCAmelCase : Optional[Any] =BASE_URL + """/user... | 113 |
def _A ( SCREAMING_SNAKE_CASE ):
UpperCAmelCase__: Tuple = int(SCREAMING_SNAKE_CASE )
if decimal in (0, 1): # Exit cases for the recursion
return str(SCREAMING_SNAKE_CASE )
UpperCAmelCase__ , UpperCAmelCase__: Union[str, Any] = divmod(SCREAMING_SNAKE_CASE ,2 ... | 113 | 1 |
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
EfficientFormerIma... | 113 |
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
_lowerCAmelCase : int ="""scheduler_config.json"""
class __UpperCamelCase ( _a ):
'''simple docstring'''
... | 113 | 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,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import TaTo... | 113 |
import unittest
from transformers import DonutProcessor
_lowerCAmelCase : str ="""naver-clova-ix/donut-base"""
class __UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def _UpperCAmelCase ( self ):
UpperCAmelCase__: Any = DonutProcessor.f... | 113 | 1 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
_lowerCAmelCase : Optio... | 113 |
from __future__ import annotations
from typing import Any
class __UpperCamelCase :
'''simple docstring'''
def __init__( self , lowerCamelCase__ ):
UpperCAmelCase__: Optional[int] = num_of_nodes
UpperCAmelCase__: list[list[int]] = []
UpperCAmel... | 113 | 1 |
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelT... | 113 |
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_accelera... | 113 | 1 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def _A ( SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ):
UpperCAmelCase__: Optional[int] = f"{sampling_rate}"
UpperCAmelCase__: Optional[Any] = "1"
UpperCAmelCase__: List[st... | 113 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : Union[str, Any] ={
"""configuration_clap""": [
"""CLAP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ClapAudioConfig""",
"""ClapConfig""",
"""C... | 113 | 1 |
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
warnings.warn(
"""The converted token... | 113 |
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import Arr... | 113 | 1 |
from pathlib import Path
import fire
from tqdm import tqdm
def _A ( SCREAMING_SNAKE_CASE="ro" ,SCREAMING_SNAKE_CASE="en" ,SCREAMING_SNAKE_CASE="wmt16" ,SCREAMING_SNAKE_CASE=None ):
try:
import datasets
except (ModuleNotFoundError, ImportError):
raise ImportError("run pip install datasets" ... | 113 |
import fire
from utils import calculate_rouge, save_json
def _A ( SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE=None ,**SCREAMING_SNAKE_CASE ):
UpperCAmelCase__: Tuple = [x.strip() for x in open(SCREAMING_SNAKE_CASE ).readlines()]
UpperCAmelCase__: Dict = [x.... | 113 | 1 |
from timeit import timeit
_lowerCAmelCase : Dict ={
"""MALAYALAM""": True,
"""String""": False,
"""rotor""": True,
"""level""": True,
"""A""": True,
"""BB""": True,
"""ABC""": False,
"""amanaplanacanalpanama""": True, # "a man a plan a canal panama"
}
# Ensure our test data ... | 113 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class __UpperCamelCase ( _a ,_a ):
'''simple docstring'''
@register_to_config
def __init__( self , *,
lowerCamelCase__ = 4 ... | 113 | 1 |
def _A ( SCREAMING_SNAKE_CASE ):
UpperCAmelCase__ , UpperCAmelCase__: int = [], []
while len(SCREAMING_SNAKE_CASE ) > 1:
UpperCAmelCase__ , UpperCAmelCase__: str = min(SCREAMING_SNAKE_CASE ), max(SCREAMING_SNAKE_CASE )
start.append(SCREAMING_SNAK... | 113 |
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class __UpperCamelCase ( _a ):
'''simple do... | 113 | 1 |
from timeit import timeit
def _A ( SCREAMING_SNAKE_CASE ):
if number < 0:
raise ValueError("the value of input must not be negative" )
UpperCAmelCase__: List[Any] = 0
while number:
number &= number - 1
result += 1
return result
def _A ( SCREAMING_SNAKE_CASE ):
i... | 113 |
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="""%(message)s""")
def _A ( SCREAMING_SNAKE_CASE ):
return input_array.reshape((input_array.size, 1) )
def _A ( SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SC... | 113 | 1 |
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_proces... | 113 |
import inspect
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_config_docstrings.py
_lowerCAmelCase : int ="""src/transformers"""
# This is to make sure the ... | 113 | 1 |
import unittest
from transformers import DonutProcessor
_lowerCAmelCase : str ="""naver-clova-ix/donut-base"""
class __UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def _UpperCAmelCase ( self ):
UpperCAmelCase__: Any = DonutProcessor.f... | 113 |
def _A ( SCREAMING_SNAKE_CASE ):
UpperCAmelCase__ , UpperCAmelCase__: int = [], []
while len(SCREAMING_SNAKE_CASE ) > 1:
UpperCAmelCase__ , UpperCAmelCase__: str = min(SCREAMING_SNAKE_CASE ), max(SCREAMING_SNAKE_CASE )
start.append(SCREAMING_SNAK... | 113 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : int ={"""configuration_vit_msn""": ["""VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMSNConfig"""]}
try:
if not is_torch_available():
raise OptionalDepe... | 113 |
def _A ( SCREAMING_SNAKE_CASE ): # noqa: E741
UpperCAmelCase__: int = len(SCREAMING_SNAKE_CASE )
UpperCAmelCase__: Dict = 0
UpperCAmelCase__: Optional[int] = [0] * n
UpperCAmelCase__: List[str] = [False] * n
UpperCAmelCase__: List[str] = [False] * n
de... | 113 | 1 |
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
_lowerCAmelCase : Dict =logging.get_logger(__name__)
class __UpperCamelCase ( _a ):
'''simple docstring'''
def __init__( self , *lowerCamelCase__ , **lowerCam... | 113 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_a... | 113 | 1 |
import fire
from utils import calculate_rouge, save_json
def _A ( SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE=None ,**SCREAMING_SNAKE_CASE ):
UpperCAmelCase__: Tuple = [x.strip() for x in open(SCREAMING_SNAKE_CASE ).readlines()]
UpperCAmelCase__: Dict = [x.... | 113 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowerCAmelCase : Tuple ={
"""configuration_mobilenet_v2""": [
"""MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""MobileNetV2Config""",
"... | 113 | 1 |
import functools
from typing import Any
def _A ( SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ):
# Validation
if not isinstance(SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ) or len(SCREAMING_SNAKE_CASE ) == 0:
raise ValueError("the string should be not empty string" )
if not isinsta... | 113 |
_lowerCAmelCase : int ="""
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.g... | 113 | 1 |
def _A ( SCREAMING_SNAKE_CASE ):
return 1 if digit in (0, 1) else (digit * factorial(digit - 1 ))
def _A ( SCREAMING_SNAKE_CASE ):
UpperCAmelCase__: Optional[Any] = 0
UpperCAmelCase__: List[str] = number
while duplicate > 0:
UpperCAmelCase__ , UpperCA... | 113 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoT... | 113 | 1 |
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def _A ( SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE = None ):
if version.parse(hfh.__version__ ).release < version.parse("0.11.0" ).release:
# old ... | 113 |
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils import AddedToken
log... | 113 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : List[str] = {
"""facebook/timesformer""": """https://huggingface.co/facebook/timesformer/resolve/main/config.json""",
... | 0 |
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_EX... | 113 | 0 |
__snake_case = {
'''a''': '''AAAAA''',
'''b''': '''AAAAB''',
'''c''': '''AAABA''',
'''d''': '''AAABB''',
'''e''': '''AABAA''',
'''f''': '''AABAB''',
'''g''': '''AABBA''',
'''h''': '''AABBB''',
'''i''': '''ABAAA''',
'''j''': '''BBBAA''',
'''k''':... | 1 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def _A ( SCREAMING_SNAKE_CASE ):
# A local function to see if a dot lands in the circle.
def is_in_circle(SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ) -> bool:
UpperCAmelCase... | 113 | 0 |
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeat... | 2 |
def _A ( SCREAMING_SNAKE_CASE ):
stooge(SCREAMING_SNAKE_CASE ,0 ,len(SCREAMING_SNAKE_CASE ) - 1 )
return arr
def _A ( SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ):
if i >= h:
return
# If first element is smaller than the last then swap them
if arr[i... | 113 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase : Tuple = logging.get_logger(__name__)
lowerCAmelCase : List[Any] ... | 3 |
def _A ( SCREAMING_SNAKE_CASE ):
UpperCAmelCase__: Tuple = int(SCREAMING_SNAKE_CASE )
if decimal in (0, 1): # Exit cases for the recursion
return str(SCREAMING_SNAKE_CASE )
UpperCAmelCase__ , UpperCAmelCase__: Union[str, Any] = divmod(SCREAMING_SNAKE_CASE ,2 ... | 113 | 0 |
"""simple docstring"""
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixi... | 4 |
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
_lowerCAmelCase : int ="""scheduler_config.json"""
class __UpperCamelCase ( _a ):
'''simple docstring'''
... | 113 | 0 |
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : List[Any] = '''Speech2TextFeatureExtractor'''
... | 5 |
import unittest
from transformers import DonutProcessor
_lowerCAmelCase : str ="""naver-clova-ix/donut-base"""
class __UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def _UpperCAmelCase ( self ):
UpperCAmelCase__: Any = DonutProcessor.f... | 113 | 0 |
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: Tuple , UpperCamelCase__: str , U... | 6 |
from __future__ import annotations
from typing import Any
class __UpperCamelCase :
'''simple docstring'''
def __init__( self , lowerCamelCase__ ):
UpperCAmelCase__: Optional[int] = num_of_nodes
UpperCAmelCase__: list[list[int]] = []
UpperCAmel... | 113 | 0 |
"""simple docstring"""
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : Optional[int] = (KDPMaDis... | 7 |
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_accelera... | 113 | 0 |
'''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,
)
fr... | 8 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : Union[str, Any] ={
"""configuration_clap""": [
"""CLAP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ClapAudioConfig""",
"""ClapConfig""",
"""C... | 113 | 0 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
... | 9 |
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import Arr... | 113 | 0 |
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
_lowerCAmelCase = [
# tf -> hf
("/", "."),
("layer_", "layers."),
("kernel", "weight"),
("beta", "bias")... | 10 |
import fire
from utils import calculate_rouge, save_json
def _A ( SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE=None ,**SCREAMING_SNAKE_CASE ):
UpperCAmelCase__: Tuple = [x.strip() for x in open(SCREAMING_SNAKE_CASE ).readlines()]
UpperCAmelCase__: Dict = [x.... | 113 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __A ( unittest.Test... | 11 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class __UpperCamelCase ( _a ,_a ):
'''simple docstring'''
@register_to_config
def __init__( self , *,
lowerCamelCase__ = 4 ... | 113 | 0 |
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class _snake_case ( UpperCAmelCase_ ):
__lowerCAmelCase : str = (EulerDiscreteScheduler,)
__lowerCAmelCase : Any = 10
... | 12 |
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class __UpperCamelCase ( _a ):
'''simple do... | 113 | 0 |
'''simple docstring'''
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
... | 13 |
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="""%(message)s""")
def _A ( SCREAMING_SNAKE_CASE ):
return input_array.reshape((input_array.size, 1) )
def _A ( SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SC... | 113 | 0 |
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def __UpperCAmelCase ( __a : Any ) -> Dict:
"""simple... | 14 |
import inspect
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_config_docstrings.py
_lowerCAmelCase : int ="""src/transformers"""
# This is to make sure the ... | 113 | 0 |
from __future__ import annotations
A : Optional[Any] = 'Muhammad Umer Farooq'
A : str = 'MIT'
A : Any = '1.0.0'
A : List[Any] = 'Muhammad Umer Farooq'
A : Optional[Any] = 'contact@muhammadumerfarooq.me'
A : Optional[Any] ... | 15 |
def _A ( SCREAMING_SNAKE_CASE ):
UpperCAmelCase__ , UpperCAmelCase__: int = [], []
while len(SCREAMING_SNAKE_CASE ) > 1:
UpperCAmelCase__ , UpperCAmelCase__: str = min(SCREAMING_SNAKE_CASE ), max(SCREAMING_SNAKE_CASE )
start.append(SCREAMING_SNAK... | 113 | 0 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSe... | 16 |
def _A ( SCREAMING_SNAKE_CASE ): # noqa: E741
UpperCAmelCase__: int = len(SCREAMING_SNAKE_CASE )
UpperCAmelCase__: Dict = 0
UpperCAmelCase__: Optional[int] = [0] * n
UpperCAmelCase__: List[str] = [False] * n
UpperCAmelCase__: List[str] = [False] * n
de... | 113 | 0 |
from __future__ import annotations
from typing import Any
class lowerCamelCase_ ( _lowercase ):
pass
class lowerCamelCase_ :
def __init__( self : Optional[int] , __A : Any ):
__A : Any = data
__A : Node | None = None
... | 17 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_a... | 113 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_ful... | 18 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowerCAmelCase : Tuple ={
"""configuration_mobilenet_v2""": [
"""MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""MobileNetV2Config""",
"... | 113 | 0 |
"""simple docstring"""
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
_a = (
"""This metric will be removed fr... | 19 |
_lowerCAmelCase : int ="""
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.g... | 113 | 0 |
import os
from collections.abc import Iterator
def _lowercase( __a : str = "." ):
for dir_path, dir_names, filenames in os.walk(__a ):
a__ =[d for d in dir_names if d != 'scripts' and d[0] not in '._']
for filename in filenames:
... | 20 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoT... | 113 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from trans... | 21 |
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils import AddedToken
log... | 113 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=_a )
class A ( _a ):
lowercase_ = field(default='language... | 22 |
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_EX... | 113 | 0 |
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_se... | 23 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def _A ( SCREAMING_SNAKE_CASE ):
# A local function to see if a dot lands in the circle.
def is_in_circle(SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ) -> bool:
UpperCAmelCase... | 113 | 0 |
'''simple docstring'''
import math
from numpy import inf
from scipy.integrate import quad
def _UpperCamelCase (_lowerCamelCase : float )-> float:
'''simple docstring'''
if num <= 0:
raise ValueError('''math domain error''' )
return quad(_lowerCame... | 24 |
def _A ( SCREAMING_SNAKE_CASE ):
stooge(SCREAMING_SNAKE_CASE ,0 ,len(SCREAMING_SNAKE_CASE ) - 1 )
return arr
def _A ( SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ):
if i >= h:
return
# If first element is smaller than the last then swap them
if arr[i... | 113 | 0 |
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __UpperCamelCase ( self : Any ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str... | 25 |
def _A ( SCREAMING_SNAKE_CASE ):
UpperCAmelCase__: Tuple = int(SCREAMING_SNAKE_CASE )
if decimal in (0, 1): # Exit cases for the recursion
return str(SCREAMING_SNAKE_CASE )
UpperCAmelCase__ , UpperCAmelCase__: Union[str, Any] = divmod(SCREAMING_SNAKE_CASE ,2 ... | 113 | 0 |
'''simple docstring'''
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_t... | 26 |
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
_lowerCAmelCase : int ="""scheduler_config.json"""
class __UpperCamelCase ( _a ):
'''simple docstring'''
... | 113 | 0 |
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 |
import unittest
from transformers import DonutProcessor
_lowerCAmelCase : str ="""naver-clova-ix/donut-base"""
class __UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def _UpperCAmelCase ( self ):
UpperCAmelCase__: Any = DonutProcessor.f... | 113 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
UpperCamelCase_ = argparse.ArgumentParser(
description=(
"Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM f... | 28 |
from __future__ import annotations
from typing import Any
class __UpperCamelCase :
'''simple docstring'''
def __init__( self , lowerCamelCase__ ):
UpperCAmelCase__: Optional[int] = num_of_nodes
UpperCAmelCase__: list[list[int]] = []
UpperCAmel... | 113 | 0 |
"""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
A_ = 4
A_ = 3
class __lowerCamelCase ( lowerCAmelCase ):
... | 29 |
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_accelera... | 113 | 0 |
import math
import sys
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
UpperCAmelCase_ : List[str] = ''''''
try:
with open(_lowercase , '''rb''' ) as binary_file:
UpperCAmelCase_ : Dict = binary_file.read()
fo... | 30 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : Union[str, Any] ={
"""configuration_clap""": [
"""CLAP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ClapAudioConfig""",
"""ClapConfig""",
"""C... | 113 | 0 |
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common i... | 31 |
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import Arr... | 113 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"tiiuae/falcon-40b": "https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json",
"tiiuae/falcon-7b": "https://huggingface.co/t... | 32 |
import fire
from utils import calculate_rouge, save_json
def _A ( SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE=None ,**SCREAMING_SNAKE_CASE ):
UpperCAmelCase__: Tuple = [x.strip() for x in open(SCREAMING_SNAKE_CASE ).readlines()]
UpperCAmelCase__: Dict = [x.... | 113 | 0 |
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,... | 33 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class __UpperCamelCase ( _a ,_a ):
'''simple docstring'''
@register_to_config
def __init__( self , *,
lowerCamelCase__ = 4 ... | 113 | 0 |
"""simple docstring"""
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
SCREAMING_SNAKE_CASE_ = datasets.utils.logging.get_logger(__name__)
class snake_case_ ( folder_based_builder.FolderB... | 34 |
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class __UpperCamelCase ( _a ):
'''simple do... | 113 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
class... | 35 |
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="""%(message)s""")
def _A ( SCREAMING_SNAKE_CASE ):
return input_array.reshape((input_array.size, 1) )
def _A ( SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SC... | 113 | 0 |
import os
import pytest
from attr import dataclass
__lowercase : Optional[int] = '''us-east-1''' # defaults region
@dataclass
class _A :
'''simple docstring'''
__lowerCamelCase : str
__lowerCamelCase : Dict = '''arn:aws:iam::558105141721:role/sagemaker... | 36 |
import inspect
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_config_docstrings.py
_lowerCAmelCase : int ="""src/transformers"""
# This is to make sure the ... | 113 | 0 |
def UpperCamelCase_ ( __a , __a , __a=False ) -> Optional[int]:
if isinstance(__a , __a ) and isinstance(__a , __a ):
a__ : Union[str, Any] = len(set_a.intersection(__a ) )
if alternative_union:
a__ : List[... | 37 |
def _A ( SCREAMING_SNAKE_CASE ):
UpperCAmelCase__ , UpperCAmelCase__: int = [], []
while len(SCREAMING_SNAKE_CASE ) > 1:
UpperCAmelCase__ , UpperCAmelCase__: str = min(SCREAMING_SNAKE_CASE ), max(SCREAMING_SNAKE_CASE )
start.append(SCREAMING_SNAK... | 113 | 0 |
'''simple docstring'''
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version imp... | 38 |
def _A ( SCREAMING_SNAKE_CASE ): # noqa: E741
UpperCAmelCase__: int = len(SCREAMING_SNAKE_CASE )
UpperCAmelCase__: Dict = 0
UpperCAmelCase__: Optional[int] = [0] * n
UpperCAmelCase__: List[str] = [False] * n
UpperCAmelCase__: List[str] = [False] * n
de... | 113 | 0 |
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 FlaxGenerationTes... | 39 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_a... | 113 | 0 |
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 ...test_pipel... | 40 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowerCAmelCase : Tuple ={
"""configuration_mobilenet_v2""": [
"""MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""MobileNetV2Config""",
"... | 113 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class lowercase_ (lowerCamelCase__ ):
... | 41 |
_lowerCAmelCase : int ="""
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.g... | 113 | 0 |
'''simple docstring'''
import unittest
from knapsack import knapsack as k
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
lowerCamelCase_ = 0
lowerCamelCase_... | 42 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoT... | 113 | 0 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transforme... | 43 |
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils import AddedToken
log... | 113 | 0 |
'''simple docstring'''
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
UpperCAmelCase_ : Optional[Any] = datasets.logging.get_logger(__name__)
UpperCAmelCase_ : int = ... | 44 |
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_EX... | 113 | 0 |
import unittest
from knapsack import greedy_knapsack as kp
class lowerCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def __a ( self :Tuple ):
UpperCamelCase__ :Optional[int] = [10, 20, 30, 40, 50, 60]
UpperCamelCase__ :str = ... | 45 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def _A ( SCREAMING_SNAKE_CASE ):
# A local function to see if a dot lands in the circle.
def is_in_circle(SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ) -> bool:
UpperCAmelCase... | 113 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available... | 46 |
def _A ( SCREAMING_SNAKE_CASE ):
stooge(SCREAMING_SNAKE_CASE ,0 ,len(SCREAMING_SNAKE_CASE ) - 1 )
return arr
def _A ( SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ):
if i >= h:
return
# If first element is smaller than the last then swap them
if arr[i... | 113 | 0 |
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
SCREAMING_SNAKE_CASE__ = 3
def UpperCAmelCase__ ( lowerCamelCase_ : int ):
print('Generating primitive root of p' )
while True:
__... | 47 |
def _A ( SCREAMING_SNAKE_CASE ):
UpperCAmelCase__: Tuple = int(SCREAMING_SNAKE_CASE )
if decimal in (0, 1): # Exit cases for the recursion
return str(SCREAMING_SNAKE_CASE )
UpperCAmelCase__ , UpperCAmelCase__: Union[str, Any] = divmod(SCREAMING_SNAKE_CASE ,2 ... | 113 | 0 |
'''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_FILES_... | 48 |
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
_lowerCAmelCase : int ="""scheduler_config.json"""
class __UpperCamelCase ( _a ):
'''simple docstring'''
... | 113 | 0 |
"""simple docstring"""
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
_lowercase : Dict = ... | 49 |
import unittest
from transformers import DonutProcessor
_lowerCAmelCase : str ="""naver-clova-ix/donut-base"""
class __UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def _UpperCAmelCase ( self ):
UpperCAmelCase__: Any = DonutProcessor.f... | 113 | 0 |
'''simple docstring'''
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def A__ ( __lowerCAmelCase : Optional[int] ):
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
class UpperCamelCa... | 50 |
from __future__ import annotations
from typing import Any
class __UpperCamelCase :
'''simple docstring'''
def __init__( self , lowerCamelCase__ ):
UpperCAmelCase__: Optional[int] = num_of_nodes
UpperCAmelCase__: list[list[int]] = []
UpperCAmel... | 113 | 0 |
'''simple docstring'''
def __snake_case ( SCREAMING_SNAKE_CASE_ : list[int] ) -> list[list[int]]:
"""simple docstring"""
UpperCAmelCase = []
if len(SCREAMING_SNAKE_CASE_ ) == 1:
return [nums.copy()]
for _ in range(len(SCREAMING_SNAKE_CASE_ ) ... | 51 |
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_accelera... | 113 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A = {
'''configuration_squeezebert''': [
'''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
... | 52 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : Union[str, Any] ={
"""configuration_clap""": [
"""CLAP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ClapAudioConfig""",
"""ClapConfig""",
"""C... | 113 | 0 |
from math import ceil
def a_ ( lowerCAmelCase_ : Tuple, lowerCAmelCase_ : Dict ):
__lowerCAmelCase = list(range(0, lowerCAmelCase_ ) )
__lowerCAmelCase = [item for sublist in list(device_map.values() ) for item in sublist]
... | 53 |
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import Arr... | 113 | 0 |
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, Imag... | 54 |
import fire
from utils import calculate_rouge, save_json
def _A ( SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE=None ,**SCREAMING_SNAKE_CASE ):
UpperCAmelCase__: Tuple = [x.strip() for x in open(SCREAMING_SNAKE_CASE ).readlines()]
UpperCAmelCase__: Dict = [x.... | 113 | 0 |
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def UpperCAmelCase ( a_ ) -> List[Any]:
"""simple docstring"""
__A = os.path.join(args.tf_model_dir , "parameters.json" )
__A... | 55 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class __UpperCamelCase ( _a ,_a ):
'''simple docstring'''
@register_to_config
def __init__( self , *,
lowerCamelCase__ = 4 ... | 113 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import (
UniSpeechSatConfig,
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
WavaVecaFeatureExtractor,
logging,
)
logging.set_verbosity_info()
_a : str ... | 56 |
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class __UpperCamelCase ( _a ):
'''simple do... | 113 | 0 |
import argparse
import json
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... | 57 |
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="""%(message)s""")
def _A ( SCREAMING_SNAKE_CASE ):
return input_array.reshape((input_array.size, 1) )
def _A ( SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SC... | 113 | 0 |
"""simple docstring"""
import numpy
# List of input, output pairs
__lowerCAmelCase : List[str] = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
__lowerCAmelCase : List[Any] = ... | 58 |
import inspect
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_config_docstrings.py
_lowerCAmelCase : int ="""src/transformers"""
# This is to make sure the ... | 113 | 0 |
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
__A = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CA... | 59 |
def _A ( SCREAMING_SNAKE_CASE ):
UpperCAmelCase__ , UpperCAmelCase__: int = [], []
while len(SCREAMING_SNAKE_CASE ) > 1:
UpperCAmelCase__ , UpperCAmelCase__: str = min(SCREAMING_SNAKE_CASE ), max(SCREAMING_SNAKE_CASE )
start.append(SCREAMING_SNAK... | 113 | 0 |
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