
"""simple docstring"""

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class __lowerCAmelCase						( __SCREAMING_SNAKE_CASE     ):
     lowercase		  =		["image_processor", "tokenizer"]
     lowercase		  =		"AutoImageProcessor"
     lowercase		  =		"AutoTokenizer"





     def __init__(      self      ,	__UpperCAmelCase=None      ,	__UpperCAmelCase=None      ,	**__UpperCAmelCase ):

      '''simple docstring'''




      __UpperCamelCase							    =			None
      if "feature_extractor" in kwargs:
       warnings.warn(
           'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
           ' instead.'      ,	UpperCAmelCase__      ,	)
       __UpperCamelCase							    =			kwargs.pop('feature_extractor' )

      __UpperCamelCase							    =			image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
       raise ValueError('You have to specify an image_processor.' )
      if tokenizer is None:
       raise ValueError('You have to specify a tokenizer.' )

      super().__init__(UpperCAmelCase__      ,	UpperCAmelCase__ )
      __UpperCamelCase							    =			self.image_processor





     def __call__(      self      ,	__UpperCAmelCase=None      ,	__UpperCAmelCase=None      ,	__UpperCAmelCase=None      ,	**__UpperCAmelCase ):

      '''simple docstring'''




      if text is None and images is None:
       raise ValueError('You have to specify either text or images. Both cannot be none.' )

      if text is not None:
       __UpperCamelCase							    =			self.tokenizer(UpperCAmelCase__      ,	return_tensors=UpperCAmelCase__      ,	**UpperCAmelCase__ )

      if images is not None:
       __UpperCamelCase							    =			self.image_processor(UpperCAmelCase__      ,	return_tensors=UpperCAmelCase__      ,	**UpperCAmelCase__ )

      if text is not None and images is not None:
       __UpperCamelCase							    =			image_features.pixel_values
       return encoding
      elif text is not None:
       return encoding
      else:
       return BatchEncoding(data=dict(**UpperCAmelCase__ )      ,	tensor_type=UpperCAmelCase__ )





     def 		UpperCAmelCase   (      self      ,	*__UpperCAmelCase      ,	**__UpperCAmelCase ):

      '''simple docstring'''




      return self.tokenizer.batch_decode(*UpperCAmelCase__      ,	**UpperCAmelCase__ )





     def 		UpperCAmelCase   (      self      ,	*__UpperCAmelCase      ,	**__UpperCAmelCase ):

      '''simple docstring'''




      return self.tokenizer.decode(*UpperCAmelCase__      ,	**UpperCAmelCase__ )





     @property
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			self.tokenizer.model_input_names
      __UpperCamelCase							    =			self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )





     @property
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      warnings.warn(
          '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'      ,	UpperCAmelCase__      ,	)
      return self.image_processor_class







     @property
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      warnings.warn(
          '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'      ,	UpperCAmelCase__      ,	)
      return self.image_processor







"""simple docstring"""






from __future__ import annotations

from collections.abc import Generator


def __lowercase  (   lowerCamelCase_	:   int	):
				if not isinstance(lowerCamelCase_    ,       lowerCamelCase_	) or n < 1:
								raise Exception("Sequence only defined for positive integers"	)

				yield n
				while n != 1:
								if n % 2 == 0:
												n //= 2
								else:
												SCREAMING_SNAKE_CASE__  				=					3 * n + 1
								yield n


def __lowercase  (   ):
				SCREAMING_SNAKE_CASE__  				=					43
				SCREAMING_SNAKE_CASE__  				=					tuple(collatz_sequence(lowerCamelCase_	)	)
				print(lowerCamelCase_	)
				print(F'''Collatz sequence from {n} took {len(lowerCamelCase_	)} steps.'''	)


if __name__ == "__main__":
	main()
