
from typing import Any



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

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

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

																					lowerCamelCase_							  =					arg_max

    # The final observation
							lowerCamelCase_							  =					observations_space[len(UpperCAmelCase__       ) - 1]

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

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

							return result



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Any			,				UpperCAmelCase__				:      Any			,				UpperCAmelCase__				:      Any			,				UpperCAmelCase__				:      Any			,				UpperCAmelCase__				:      Any			,				)  ->    None:
							_validate_not_empty(
							    UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__			,				)
							_validate_lists(UpperCAmelCase__			,				UpperCAmelCase__       )
							_validate_dicts(
							    UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__       )



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Any			,				UpperCAmelCase__				:      Any			,				UpperCAmelCase__				:      Any			,				UpperCAmelCase__				:      Any			,				UpperCAmelCase__				:      Any			,				)  ->    None:
							if not all(
							    [
							        observations_space,
							        states_space,
							        initial_probabilities,
							        transition_probabilities,
							        emission_probabilities,
							    ]       ):
														raise ValueError("""There's an empty parameter"""       )



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Any			,				UpperCAmelCase__				:      Any       )  ->    None:
							_validate_list(UpperCAmelCase__			,				"""observations_space"""       )
							_validate_list(UpperCAmelCase__			,				"""states_space"""       )



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Any			,				UpperCAmelCase__				:      str       )  ->    None:
							if not isinstance(_object			,				UpperCAmelCase__       ):
														lowerCamelCase_							  =					F'''{var_name} must be a list'''
														raise ValueError(UpperCAmelCase__       )
							else:
														for x in _object:
																					if not isinstance(UpperCAmelCase__			,				UpperCAmelCase__       ):
																												lowerCamelCase_							  =					F'''{var_name} must be a list of strings'''
																												raise ValueError(UpperCAmelCase__       )



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Any			,				UpperCAmelCase__				:      Any			,				UpperCAmelCase__				:      Any			,				)  ->    None:
							_validate_dict(UpperCAmelCase__			,				"""initial_probabilities"""			,				UpperCAmelCase__       )
							_validate_nested_dict(UpperCAmelCase__			,				"""transition_probabilities"""       )
							_validate_nested_dict(UpperCAmelCase__			,				"""emission_probabilities"""       )



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Any			,				UpperCAmelCase__				:      str       )  ->    None:
							_validate_dict(_object			,				UpperCAmelCase__			,				UpperCAmelCase__       )
							for x in _object.values():
														_validate_dict(UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__       )



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Any			,				UpperCAmelCase__				:      str			,				UpperCAmelCase__				:      type			,				UpperCAmelCase__				:      bool = False       )  ->    None:
							if not isinstance(_object			,				UpperCAmelCase__       ):
														lowerCamelCase_							  =					F'''{var_name} must be a dict'''
														raise ValueError(UpperCAmelCase__       )
							if not all(isinstance(UpperCAmelCase__			,				UpperCAmelCase__       ) for x in _object       ):
														lowerCamelCase_							  =					F'''{var_name} all keys must be strings'''
														raise ValueError(UpperCAmelCase__       )
							if not all(isinstance(UpperCAmelCase__			,				UpperCAmelCase__       ) for x in _object.values()       ):
														lowerCamelCase_							  =					"""nested dictionary """ if nested else """"""
														lowerCamelCase_							  =					F'''{var_name} {nested_text}all values must be {value_type.__name__}'''
														raise ValueError(UpperCAmelCase__       )


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

		testmod()







from typing import List, Optional, Union

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





class       __A(				UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    ['''image_processor''', '''tokenizer''']
					SCREAMING_SNAKE_CASE			    =    '''BlipImageProcessor'''
					SCREAMING_SNAKE_CASE			    =    '''AutoTokenizer'''

					def __init__(	self					:     Union[str, Any]   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     List[str]   ):
												lowerCamelCase_							  =					False
												super().__init__(__UpperCamelCase   ,					__UpperCamelCase   )
												lowerCamelCase_							  =					self.image_processor

					def __call__(	self					:     Any   ,					__UpperCamelCase					:     ImageInput = None   ,					__UpperCamelCase					:     Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,					__UpperCamelCase					:     bool = True   ,					__UpperCamelCase					:     Union[bool, str, PaddingStrategy] = False   ,					__UpperCamelCase					:     Union[bool, str, TruncationStrategy] = None   ,					__UpperCamelCase					:     Optional[int] = None   ,					__UpperCamelCase					:     int = 0   ,					__UpperCamelCase					:     Optional[int] = None   ,					__UpperCamelCase					:     Optional[bool] = None   ,					__UpperCamelCase					:     bool = False   ,					__UpperCamelCase					:     bool = False   ,					__UpperCamelCase					:     bool = False   ,					__UpperCamelCase					:     bool = False   ,					__UpperCamelCase					:     bool = False   ,					__UpperCamelCase					:     bool = True   ,					__UpperCamelCase					:     Optional[Union[str, TensorType]] = None   ,					**__UpperCamelCase					:     Optional[Any]   ,					):
												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:
																			lowerCamelCase_							  =					self.tokenizer
																			lowerCamelCase_							  =					self.tokenizer(
																			    text=__UpperCamelCase   ,					add_special_tokens=__UpperCamelCase   ,					padding=__UpperCamelCase   ,					truncation=__UpperCamelCase   ,					max_length=__UpperCamelCase   ,					stride=__UpperCamelCase   ,					pad_to_multiple_of=__UpperCamelCase   ,					return_attention_mask=__UpperCamelCase   ,					return_overflowing_tokens=__UpperCamelCase   ,					return_special_tokens_mask=__UpperCamelCase   ,					return_offsets_mapping=__UpperCamelCase   ,					return_token_type_ids=__UpperCamelCase   ,					return_length=__UpperCamelCase   ,					verbose=__UpperCamelCase   ,					return_tensors=__UpperCamelCase   ,					**__UpperCamelCase   ,					)
																			return text_encoding

												# add pixel_values
												lowerCamelCase_							  =					self.image_processor(__UpperCamelCase   ,					return_tensors=__UpperCamelCase   )

												if text is not None:
																			lowerCamelCase_							  =					self.tokenizer(
																			    text=__UpperCamelCase   ,					add_special_tokens=__UpperCamelCase   ,					padding=__UpperCamelCase   ,					truncation=__UpperCamelCase   ,					max_length=__UpperCamelCase   ,					stride=__UpperCamelCase   ,					pad_to_multiple_of=__UpperCamelCase   ,					return_attention_mask=__UpperCamelCase   ,					return_overflowing_tokens=__UpperCamelCase   ,					return_special_tokens_mask=__UpperCamelCase   ,					return_offsets_mapping=__UpperCamelCase   ,					return_token_type_ids=__UpperCamelCase   ,					return_length=__UpperCamelCase   ,					verbose=__UpperCamelCase   ,					return_tensors=__UpperCamelCase   ,					**__UpperCamelCase   ,					)
												else:
																			lowerCamelCase_							  =					None

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

												return encoding_image_processor

					def    lowercase__      (	self					:     Any   ,					*__UpperCamelCase					:     Union[str, Any]   ,					**__UpperCamelCase					:     List[Any]   ):
												return self.tokenizer.batch_decode(*__UpperCamelCase   ,					**__UpperCamelCase   )

					def    lowercase__      (	self					:     List[str]   ,					*__UpperCamelCase					:     List[Any]   ,					**__UpperCamelCase					:     int   ):
												return self.tokenizer.decode(*__UpperCamelCase   ,					**__UpperCamelCase   )



					@property
					# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
					def    lowercase__      (	self					:     Optional[int]   ):
												lowerCamelCase_							  =					self.tokenizer.model_input_names
												lowerCamelCase_							  =					self.image_processor.model_input_names
												return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )





