





from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
      from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore







class        __snake_case			(				__lowerCamelCase     ):





       '''simple docstring'''







       lowerCAmelCase__ 						=	c_float




       @property
       def 				UpperCAmelCase__					(    self					:       List[str]						):
         return sizeof(self.value						)







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__ = 0)  ->       Optional[int]:
  # < 0 is possible if using a dynamic axis
  if dimension <= 0:
    __snake_case:		Union[str, Any]					    =		fixed_dimension

  dimension -= num_token_to_add
  return dimension







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       Dict:
  return num_parameters * dtype.size







def      A__				(       SCREAMING_SNAKE_CASE__)  ->       Any:
  from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

  try:
    return AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE__)
  except (ValueError, OSError, KeyError):
    __snake_case:		Any					    =		None, None
    try:
      __snake_case:		List[str]					    =		AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__)
    except (OSError, KeyError):
      pass
    try:
      __snake_case:		Optional[int]					    =		AutoFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE__)
    except (OSError, KeyError):
      pass

    if tokenizer is not None and feature_extractor is not None:
      raise ValueError(
          F'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.''')
    elif tokenizer is None and feature_extractor is None:
      return None
    elif tokenizer is not None:
      return tokenizer
    else:
      return feature_extractor












from unittest.mock import patch

import datasets
from datasets import Dataset



def    UpperCAmelCase__	(  ):
  lowercase     :Any   						=    Dataset.from_dict({"col_1": [3, 2, 0, 1]} )

  with patch("tqdm.auto.tqdm" ) as mock_tqdm:
    datasets.disable_progress_bar()
    dset.map(lambda lowerCamelCase : {"col_2": x["col_1"] + 1} )
    mock_tqdm.assert_not_called()

    mock_tqdm.reset_mock()

    datasets.enable_progress_bar()
    dset.map(lambda lowerCamelCase : {"col_2": x["col_1"] + 1} )
    mock_tqdm.assert_called()



