
'''simple docstring'''


from typing import Any



def  __snake_case     (    _UpperCAmelCase					:       list,      _UpperCAmelCase					:       list,      _UpperCAmelCase					:       dict,      _UpperCAmelCase					:       dict,      _UpperCAmelCase					:       dict,      ):
   _validation(
       __snake_case,      __snake_case,      __snake_case,      __snake_case,      __snake_case,      )
   # 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(__snake_case)):
      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(__snake_case) - 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(__snake_case) - 1,      -1,      -1):
      result.append(__snake_case)
      UpperCamelCase       =  pointers[previous, observations_space[o]]
   result.reverse()

   return result



def  __snake_case     (    _UpperCAmelCase					:       Any,      _UpperCAmelCase					:       Any,      _UpperCAmelCase					:       Any,      _UpperCAmelCase					:       Any,      _UpperCAmelCase					:       Any,      ):
   _validate_not_empty(
       __snake_case,      __snake_case,      __snake_case,      __snake_case,      __snake_case,      )
   _validate_lists(__snake_case,      __snake_case)
   _validate_dicts(
       __snake_case,      __snake_case,      __snake_case)



def  __snake_case     (    _UpperCAmelCase					:       Any,      _UpperCAmelCase					:       Any,      _UpperCAmelCase					:       Any,      _UpperCAmelCase					:       Any,      _UpperCAmelCase					:       Any,      ):


   if not all(
       [
           observations_space,
           states_space,
           initial_probabilities,
           transition_probabilities,
           emission_probabilities,
       ]):
      raise ValueError('''There\'s an empty parameter''')



def  __snake_case     (    _UpperCAmelCase					:       Any,      _UpperCAmelCase					:       Any):
   _validate_list(__snake_case,      '''observations_space''')
   _validate_list(__snake_case,      '''states_space''')



def  __snake_case     (    _UpperCAmelCase					:       Any,      _UpperCAmelCase					:       str):


   if not isinstance(_object,      __snake_case):
      UpperCamelCase       =  f'{var_name} must be a list'
      raise ValueError(__snake_case)
   else:
      for x in _object:
         if not isinstance(__snake_case,      __snake_case):
            UpperCamelCase       =  f'{var_name} must be a list of strings'
            raise ValueError(__snake_case)



def  __snake_case     (    _UpperCAmelCase					:       Any,      _UpperCAmelCase					:       Any,      _UpperCAmelCase					:       Any,      ):
   _validate_dict(__snake_case,      '''initial_probabilities''',      __snake_case)
   _validate_nested_dict(__snake_case,      '''transition_probabilities''')
   _validate_nested_dict(__snake_case,      '''emission_probabilities''')



def  __snake_case     (    _UpperCAmelCase					:       Any,      _UpperCAmelCase					:       str):
   _validate_dict(_object,      __snake_case,      __snake_case)
   for x in _object.values():
      _validate_dict(__snake_case,      __snake_case,      __snake_case,      __snake_case)



def  __snake_case     (    _UpperCAmelCase					:       Any,      _UpperCAmelCase					:       str,      _UpperCAmelCase					:       type,      _UpperCAmelCase					:       bool = False):


   if not isinstance(_object,      __snake_case):
      UpperCamelCase       =  f'{var_name} must be a dict'
      raise ValueError(__snake_case)
   if not all(isinstance(__snake_case,      __snake_case) for x in _object):
      UpperCamelCase       =  f'{var_name} all keys must be strings'
      raise ValueError(__snake_case)
   if not all(isinstance(__snake_case,      __snake_case) 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(__snake_case)


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

   testmod()







'''simple docstring'''


import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer


class 						lowercase__	(  snake_case_   ):






   '''simple docstring'''







   _snake_case		   =							['''image_processor''', '''tokenizer''']
   _snake_case		   =							'''BlipImageProcessor'''
   _snake_case		   =							'''AutoTokenizer'''







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







      super().__init__(lowerCamelCase__ ,   lowerCamelCase__   )

      # add QFormer tokenizer
      UpperCamelCase       =  qformer_tokenizer







   def __call__(			self ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = True ,   lowerCamelCase__ = False ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = 0 ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = False ,   lowerCamelCase__ = False ,   lowerCamelCase__ = False ,   lowerCamelCase__ = False ,   lowerCamelCase__ = False ,   lowerCamelCase__ = True ,   lowerCamelCase__ = None ,   **lowerCamelCase__ ,   ):

      '''simple docstring'''







      if images is None and text is None:
         raise ValueError('''You have to specify at least images or text.'''   )

      UpperCamelCase       =  BatchFeature()

      if text is not None:
         UpperCamelCase       =  self.tokenizer(
             text=lowerCamelCase__ ,   add_special_tokens=lowerCamelCase__ ,   padding=lowerCamelCase__ ,   truncation=lowerCamelCase__ ,   max_length=lowerCamelCase__ ,   stride=lowerCamelCase__ ,   pad_to_multiple_of=lowerCamelCase__ ,   return_attention_mask=lowerCamelCase__ ,   return_overflowing_tokens=lowerCamelCase__ ,   return_special_tokens_mask=lowerCamelCase__ ,   return_offsets_mapping=lowerCamelCase__ ,   return_token_type_ids=lowerCamelCase__ ,   return_length=lowerCamelCase__ ,   verbose=lowerCamelCase__ ,   return_tensors=lowerCamelCase__ ,   **lowerCamelCase__ ,   )
         encoding.update(lowerCamelCase__   )
         UpperCamelCase       =  self.qformer_tokenizer(
             text=lowerCamelCase__ ,   add_special_tokens=lowerCamelCase__ ,   padding=lowerCamelCase__ ,   truncation=lowerCamelCase__ ,   max_length=lowerCamelCase__ ,   stride=lowerCamelCase__ ,   pad_to_multiple_of=lowerCamelCase__ ,   return_attention_mask=lowerCamelCase__ ,   return_overflowing_tokens=lowerCamelCase__ ,   return_special_tokens_mask=lowerCamelCase__ ,   return_offsets_mapping=lowerCamelCase__ ,   return_token_type_ids=lowerCamelCase__ ,   return_length=lowerCamelCase__ ,   verbose=lowerCamelCase__ ,   return_tensors=lowerCamelCase__ ,   **lowerCamelCase__ ,   )
         UpperCamelCase       =  qformer_text_encoding.pop('''input_ids'''   )
         UpperCamelCase       =  qformer_text_encoding.pop('''attention_mask'''   )

      if images is not None:
         UpperCamelCase       =  self.image_processor(lowerCamelCase__ ,   return_tensors=lowerCamelCase__   )
         encoding.update(lowerCamelCase__   )

      return encoding







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

      '''simple docstring'''







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







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

      '''simple docstring'''







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







   @property
   # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
   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   )   )







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

      '''simple docstring'''







      if os.path.isfile(lowerCamelCase__   ):
         raise ValueError(f'Provided path ({save_directory}) should be a directory, not a file'   )
      os.makedirs(lowerCamelCase__ ,   exist_ok=lowerCamelCase__   )
      UpperCamelCase       =  os.path.join(lowerCamelCase__ ,   '''qformer_tokenizer'''   )
      self.qformer_tokenizer.save_pretrained(lowerCamelCase__   )
      return super().save_pretrained(lowerCamelCase__ ,   **lowerCamelCase__   )







   @classmethod
   def       UpperCAmelCase				(			cls ,   lowerCamelCase__ ,   **lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  AutoTokenizer.from_pretrained(lowerCamelCase__ ,   subfolder='''qformer_tokenizer'''   )
      UpperCamelCase       =  cls._get_arguments_from_pretrained(lowerCamelCase__ ,   **lowerCamelCase__   )
      args.append(lowerCamelCase__   )
      return cls(*lowerCamelCase__   )





