





'''simple docstring'''


import warnings
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       lowerCAmelCase_						(						UpperCAmelCase__						):
 __lowerCamelCase  :							Tuple				     =    ["image_processor", "tokenizer"]
 __lowerCamelCase  :							Dict				     =    "LayoutLMv2ImageProcessor"
 __lowerCamelCase  :							List[str]				     =    ("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")




 def __init__(		self	,   _lowerCAmelCase=None	,   _lowerCAmelCase=None	,   **_lowerCAmelCase						)			->							Optional[int]:
     if "feature_extractor" in kwargs:
         warnings.warn(
             "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
             " instead."	,   _a	,   )
         _lowerCAmelCase			=					kwargs.pop("feature_extractor"						)

     _lowerCAmelCase			=					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__(_a	,   _a						)




 def __call__(		self	,   _lowerCAmelCase	,   _lowerCAmelCase = None	,   _lowerCAmelCase = None	,   _lowerCAmelCase = None	,   _lowerCAmelCase = None	,   _lowerCAmelCase = True	,   _lowerCAmelCase = False	,   _lowerCAmelCase = None	,   _lowerCAmelCase = None	,   _lowerCAmelCase = 0	,   _lowerCAmelCase = None	,   _lowerCAmelCase = None	,   _lowerCAmelCase = None	,   _lowerCAmelCase = False	,   _lowerCAmelCase = False	,   _lowerCAmelCase = False	,   _lowerCAmelCase = False	,   _lowerCAmelCase = True	,   _lowerCAmelCase = None	,   **_lowerCAmelCase	,   )			->							int:
     # verify input
     if self.image_processor.apply_ocr and (boxes is not None):
         raise ValueError(
             "You cannot provide bounding boxes "
             "if you initialized the image processor with apply_ocr set to True."						)

     if self.image_processor.apply_ocr and (word_labels is not None):
         raise ValueError(
             "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."						)

     if return_overflowing_tokens is True and return_offsets_mapping is False:
         raise ValueError("You cannot return overflowing tokens without returning the offsets mapping."						)

     # first, apply the image processor
     _lowerCAmelCase			=					self.image_processor(images=_a	,   return_tensors=_a						)

     # second, apply the tokenizer
     if text is not None and self.image_processor.apply_ocr and text_pair is None:
         if isinstance(_a	,   _a						):
             _lowerCAmelCase			=					[text]  # add batch dimension (as the image processor always adds a batch dimension)
         _lowerCAmelCase			=					features["words"]

     _lowerCAmelCase			=					self.tokenizer(
         text=text if text is not None else features["words"]	,   text_pair=text_pair if text_pair is not None else None	,   boxes=boxes if boxes is not None else features["boxes"]	,   word_labels=_a	,   add_special_tokens=_a	,   padding=_a	,   truncation=_a	,   max_length=_a	,   stride=_a	,   pad_to_multiple_of=_a	,   return_token_type_ids=_a	,   return_attention_mask=_a	,   return_overflowing_tokens=_a	,   return_special_tokens_mask=_a	,   return_offsets_mapping=_a	,   return_length=_a	,   verbose=_a	,   return_tensors=_a	,   **_a	,   )

     # add pixel values
     _lowerCAmelCase			=					features.pop("pixel_values"						)
     if return_overflowing_tokens is True:
         _lowerCAmelCase			=					self.get_overflowing_images(_a	,   encoded_inputs["overflow_to_sample_mapping"]						)
     _lowerCAmelCase			=					images

     return encoded_inputs




 def 							_snake_case      (		self	,   _lowerCAmelCase	,   _lowerCAmelCase						)			->							Optional[int]:
     _lowerCAmelCase			=					[]
     for sample_idx in overflow_to_sample_mapping:
         images_with_overflow.append(images[sample_idx]						)

     if len(_a						) != len(_a						):
         raise ValueError(
             "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
             f''' {len(_a						)} and {len(_a						)}'''						)

     return images_with_overflow




 def 							_snake_case      (		self	,   *_lowerCAmelCase	,   **_lowerCAmelCase						)			->							Dict:
     return self.tokenizer.batch_decode(*_a	,   **_a						)




 def 							_snake_case      (		self	,   *_lowerCAmelCase	,   **_lowerCAmelCase						)			->							Any:
     return self.tokenizer.decode(*_a	,   **_a						)




 @property
 def 							_snake_case      (		self						)			->							List[Any]:
     return ["input_ids", "bbox", "attention_mask", "image"]




 @property
 def 							_snake_case      (		self						)			->							List[str]:
     warnings.warn(
         "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."	,   _a	,   )
     return self.image_processor_class





 @property
 def 							_snake_case      (		self						)			->							List[Any]:
     warnings.warn(
         "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."	,   _a	,   )
     return self.image_processor








"""simple docstring"""




from typing import Any



def     a__ (				snake_case__       , snake_case__       , snake_case__       , snake_case__       , snake_case__       , )						->					list:
			_validation(
			    snake_case__       , snake_case__       , snake_case__       , snake_case__       , snake_case__       , )
			# 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(snake_case__				)				):
						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(snake_case__				) - 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(snake_case__				) - 1       , -1       , -1				):
						result.append(snake_case__				)
						lowerCamelCase			      =					pointers[previous, observations_space[o]]
			result.reverse()

			return result



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



def     a__ (				snake_case__       , snake_case__				)						->					None:
			_validate_list(snake_case__       , """observations_space"""				)
			_validate_list(snake_case__       , """states_space"""				)



def     a__ (				snake_case__       , snake_case__				)						->					None:
			if not isinstance(_object       , snake_case__				):
						lowerCamelCase			      =					F'{var_name} must be a list'
						raise ValueError(snake_case__				)
			else:
						for x in _object:
									if not isinstance(snake_case__       , snake_case__				):
												lowerCamelCase			      =					F'{var_name} must be a list of strings'
												raise ValueError(snake_case__				)



def     a__ (				snake_case__       , snake_case__       , snake_case__       , )						->					None:
			_validate_dict(snake_case__       , """initial_probabilities"""       , snake_case__				)
			_validate_nested_dict(snake_case__       , """transition_probabilities"""				)
			_validate_nested_dict(snake_case__       , """emission_probabilities"""				)



def     a__ (				snake_case__       , snake_case__				)						->					None:
			_validate_dict(_object       , snake_case__       , snake_case__				)
			for x in _object.values():
						_validate_dict(snake_case__       , snake_case__       , snake_case__       , snake_case__				)



def     a__ (				snake_case__       , snake_case__       , snake_case__       , snake_case__ = False				)						->					None:
			if not isinstance(_object       , snake_case__				):
						lowerCamelCase			      =					F'{var_name} must be a dict'
						raise ValueError(snake_case__				)
			if not all(isinstance(snake_case__       , snake_case__				) for x in _object				):
						lowerCamelCase			      =					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()				):
						lowerCamelCase			      =					"""nested dictionary """ if nested else """"""
						lowerCamelCase			      =					F'{var_name} {nested_text}all values must be {value_type.__name__}'
						raise ValueError(snake_case__				)


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

					testmod()



