from typing import Any




def       __lowerCAmelCase      (						A_   : list						,	A_   : list						,	A_   : dict						,	A_   : dict						,	A_   : dict						,	)    ->  Tuple:
  _validation(
      A_						,	A_						,	A_						,	A_						,	A_						,	)
  # Creates data structures and fill initial step
  __UpperCAmelCase						    =       {}
  __UpperCAmelCase						    =       {}
  for state in states_space:
    __UpperCAmelCase						    =       observations_space[0]
    __UpperCAmelCase						    =       (
        initial_probabilities[state] * emission_probabilities[state][observation]
    )
    __UpperCAmelCase						    =       None

  # Fills the data structure with the probabilities of
  # different transitions and pointers to previous states
  for o in range(1						,	len(A_					)					):
    __UpperCAmelCase						    =       observations_space[o]
    __UpperCAmelCase						    =       observations_space[o - 1]
    for state in states_space:
      # Calculates the argmax for probability function
      __UpperCAmelCase						    =       ""
      __UpperCAmelCase						    =       -1
      for k_state in states_space:
        __UpperCAmelCase						    =       (
            probabilities[(k_state, prior_observation)]
            * transition_probabilities[k_state][state]
            * emission_probabilities[state][observation]
        )
        if probability > max_probability:
          __UpperCAmelCase						    =       probability
          __UpperCAmelCase						    =       k_state

            # Update probabilities and pointers dicts
      __UpperCAmelCase						    =       (
          probabilities[(arg_max, prior_observation)]
          * transition_probabilities[arg_max][state]
          * emission_probabilities[state][observation]
      )

      __UpperCAmelCase						    =       arg_max

    # The final observation
  __UpperCAmelCase						    =       observations_space[len(A_					) - 1]

  # argmax for given final observation
  __UpperCAmelCase						    =       ""
  __UpperCAmelCase						    =       -1
  for k_state in states_space:
    __UpperCAmelCase						    =       probabilities[(k_state, final_observation)]
    if probability > max_probability:
      __UpperCAmelCase						    =       probability
      __UpperCAmelCase						    =       k_state
  __UpperCAmelCase						    =       arg_max

  # Process pointers backwards
  __UpperCAmelCase						    =       last_state
  __UpperCAmelCase						    =       []
  for o in range(len(A_					) - 1						,	-1						,	-1					):
    result.append(A_					)
    __UpperCAmelCase						    =       pointers[previous, observations_space[o]]
  result.reverse()

  return result




def       __lowerCAmelCase      (						A_   : Any						,	A_   : Any						,	A_   : Any						,	A_   : Any						,	A_   : Any						,	)    ->  str:
  _validate_not_empty(
      A_						,	A_						,	A_						,	A_						,	A_						,	)
  _validate_lists(A_						,	A_					)
  _validate_dicts(
      A_						,	A_						,	A_					)




def       __lowerCAmelCase      (						A_   : Any						,	A_   : Any						,	A_   : Any						,	A_   : Any						,	A_   : Any						,	)    ->  List[Any]:
  if not all(
      [
          observations_space,
          states_space,
          initial_probabilities,
          transition_probabilities,
          emission_probabilities,
      ]					):
    raise ValueError("There's an empty parameter"					)




def       __lowerCAmelCase      (						A_   : Any						,	A_   : Any					)    ->  Union[str, Any]:
  _validate_list(A_						,	"observations_space"					)
  _validate_list(A_						,	"states_space"					)




def       __lowerCAmelCase      (						A_   : Any						,	A_   : str					)    ->  Any:
  if not isinstance(_object						,	A_					):
    __UpperCAmelCase						    =       F'''{var_name} must be a list'''
    raise ValueError(A_					)
  else:
    for x in _object:
      if not isinstance(A_						,	A_					):
        __UpperCAmelCase						    =       F'''{var_name} must be a list of strings'''
        raise ValueError(A_					)




def       __lowerCAmelCase      (						A_   : Any						,	A_   : Any						,	A_   : Any						,	)    ->  Dict:
  _validate_dict(A_						,	"initial_probabilities"						,	A_					)
  _validate_nested_dict(A_						,	"transition_probabilities"					)
  _validate_nested_dict(A_						,	"emission_probabilities"					)




def       __lowerCAmelCase      (						A_   : Any						,	A_   : str					)    ->  List[Any]:
  _validate_dict(_object						,	A_						,	A_					)
  for x in _object.values():
    _validate_dict(A_						,	A_						,	A_						,	A_					)




def       __lowerCAmelCase      (						A_   : Any						,	A_   : str						,	A_   : type						,	A_   : bool = False					)    ->  List[str]:
  if not isinstance(_object						,	A_					):
    __UpperCAmelCase						    =       F'''{var_name} must be a dict'''
    raise ValueError(A_					)
  if not all(isinstance(A_						,	A_					) for x in _object					):
    __UpperCAmelCase						    =       F'''{var_name} all keys must be strings'''
    raise ValueError(A_					)
  if not all(isinstance(A_						,	A_					) for x in _object.values()					):
    __UpperCAmelCase						    =       "nested dictionary " if nested else ""
    __UpperCAmelCase						    =       F'''{var_name} {nested_text}all values must be {value_type.__name__}'''
    raise ValueError(A_					)


if __name__ == "__main__":
       from doctest import testmod

       testmod()
import warnings

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


class 			UpperCAmelCase__    (		snake_case					):



  """simple docstring"""






  lowerCAmelCase__		:	Optional[int]    		= ['image_processor', 'tokenizer']
  lowerCAmelCase__		:	Any    		= 'CLIPImageProcessor'
  lowerCAmelCase__		:	Any    		= ('CLIPTokenizer', 'CLIPTokenizerFast')






  def __init__(				self:			str				,				__lowerCAmelCase:			List[Any]=None				,				__lowerCAmelCase:			Any=None				,				**__lowerCAmelCase:			str   ) -> Optional[int]:




    '''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."				,				__lowerCAmelCase				,				)
      __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 need to specify an `image_processor`."   )
    if tokenizer is None:
      raise ValueError("You need to specify a `tokenizer`."   )

    super().__init__(__lowerCAmelCase				,				__lowerCAmelCase   )






  def __call__(				self:			Optional[int]				,				__lowerCAmelCase:			Union[str, Any]=None				,				__lowerCAmelCase:			Dict=None				,				__lowerCAmelCase:			Optional[int]=None				,				**__lowerCAmelCase:			Optional[int]   ) -> Dict:




    '''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(__lowerCAmelCase				,				return_tensors=__lowerCAmelCase				,				**__lowerCAmelCase   )

    if images is not None:
      __UpperCAmelCase						    =       self.image_processor(__lowerCAmelCase				,				return_tensors=__lowerCAmelCase				,				**__lowerCAmelCase   )

    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(**__lowerCAmelCase   )				,				tensor_type=__lowerCAmelCase   )






  def 	_UpperCAmelCase				(				self:			str				,				*__lowerCAmelCase:			str				,				**__lowerCAmelCase:			List[Any]   ) -> Optional[Any]:




    '''simple docstring'''




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






  def 	_UpperCAmelCase				(				self:			Any				,				*__lowerCAmelCase:			List[Any]				,				**__lowerCAmelCase:			Dict   ) -> Union[str, Any]:




    '''simple docstring'''




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






  @property
  def 	_UpperCAmelCase				(				self:			Tuple   ) -> List[Any]:




    '''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:			Optional[int]   ) -> str:




    '''simple docstring'''




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






  @property
  def 	_UpperCAmelCase				(				self:			List[Any]   ) -> List[str]:




    '''simple docstring'''




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



