



"""simple docstring"""






from typing import Any




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

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

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

      _snake_case   	=      arg_max

    # The final observation
  _snake_case   	=      observations_space[len(lowerCAmelCase_	) - 1]

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

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

  return result




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						)						->				None:
  _validate_not_empty(
      lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						)
  _validate_lists(lowerCAmelCase_  ,						lowerCAmelCase_	)
  _validate_dicts(
      lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						)						->				None:
  if not all(
      [
          observations_space,
          states_space,
          initial_probabilities,
          transition_probabilities,
          emission_probabilities,
      ]	):
    raise ValueError('''There\'s an empty parameter'''	)




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_	)						->				None:
  _validate_list(lowerCAmelCase_  ,						'''observations_space'''	)
  _validate_list(lowerCAmelCase_  ,						'''states_space'''	)




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_	)						->				None:
  if not isinstance(_object  ,						lowerCAmelCase_	):
    _snake_case   	=      f"""{var_name} must be a list"""
    raise ValueError(lowerCAmelCase_	)
  else:
    for x in _object:
      if not isinstance(lowerCAmelCase_  ,						lowerCAmelCase_	):
        _snake_case   	=      f"""{var_name} must be a list of strings"""
        raise ValueError(lowerCAmelCase_	)




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						)						->				None:
  _validate_dict(lowerCAmelCase_  ,						'''initial_probabilities'''  ,						lowerCAmelCase_	)
  _validate_nested_dict(lowerCAmelCase_  ,						'''transition_probabilities'''	)
  _validate_nested_dict(lowerCAmelCase_  ,						'''emission_probabilities'''	)




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_	)						->				None:
  _validate_dict(_object  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)
  for x in _object.values():
    _validate_dict(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_ = False	)						->				None:
  if not isinstance(_object  ,						lowerCAmelCase_	):
    _snake_case   	=      f"""{var_name} must be a dict"""
    raise ValueError(lowerCAmelCase_	)
  if not all(isinstance(lowerCAmelCase_  ,						lowerCAmelCase_	) for x in _object	):
    _snake_case   	=      f"""{var_name} all keys must be strings"""
    raise ValueError(lowerCAmelCase_	)
  if not all(isinstance(lowerCAmelCase_  ,						lowerCAmelCase_	) for x in _object.values()	):
    _snake_case   	=      '''nested dictionary ''' if nested else ''''''
    _snake_case   	=      f"""{var_name} {nested_text}all values must be {value_type.__name__}"""
    raise ValueError(lowerCAmelCase_	)


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

     testmod()









from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							_UpperCAmelCase      =						["""image_processor""", """tokenizer"""]
							_UpperCAmelCase      =						"""Pix2StructImageProcessor"""
							_UpperCAmelCase      =						("""T5Tokenizer""", """T5TokenizerFast""")






							def __init__(      self			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Dict					=					False
										super().__init__(lowerCAmelCase__			,						lowerCAmelCase__       )






							def __call__(      self			,						lowerCAmelCase__=None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = True			,						lowerCAmelCase__ = False			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = 2_0_4_8			,						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 either images or text.'       )

										# Get only text
										if images is None and not self.image_processor.is_vqa:
													SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					self.tokenizer
													SCREAMING_SNAKE_CASE_		:     str					=					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__			,						)
													return text_encoding

										if not self.image_processor.is_vqa:
													# add pixel_values
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.image_processor(
													    lowerCAmelCase__			,						return_tensors=lowerCAmelCase__			,						max_patches=lowerCAmelCase__			,						**lowerCAmelCase__       )
										else:
													# add pixel_values and bbox
													SCREAMING_SNAKE_CASE_		:     Any					=					self.image_processor(
													    lowerCAmelCase__			,						return_tensors=lowerCAmelCase__			,						max_patches=lowerCAmelCase__			,						header_text=lowerCAmelCase__			,						**lowerCAmelCase__       )

										if text is not None and not self.image_processor.is_vqa:
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					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__			,						)

													if "attention_mask" in text_encoding:
																SCREAMING_SNAKE_CASE_		:     Any					=					text_encoding.pop('attention_mask'       )
													if "input_ids" in text_encoding:
																SCREAMING_SNAKE_CASE_		:     List[str]					=					text_encoding.pop('input_ids'       )
										else:
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					None

										if text_encoding is not None:
													encoding_image_processor.update(lowerCAmelCase__       )

										return encoding_image_processor






							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
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[str]					=					self.tokenizer.model_input_names
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )





