




"""simple docstring"""






from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
			import torch

if is_tf_available():
			import tensorflow as tf






class lowerCAmelCase__      (	lowercase		):






			'''simple docstring'''



			lowerCamelCase__ =     ["""image_processor"""]
			lowerCamelCase__ =     """SamImageProcessor"""
			def __init__(    self    , lowercase    ):
							super().__init__(lowercase    )
							_lowerCamelCase	:     Union[str, Any]       =       self.image_processor
							_lowerCamelCase	:     int       =       -10
							_lowerCamelCase	:     Union[str, Any]       =       self.image_processor.size['longest_edge']
			def __call__(    self    , lowercase=None    , lowercase=None    , lowercase=None    , lowercase=None    , lowercase = None    , **lowercase    , ):
							_lowerCamelCase	:     Tuple       =       self.image_processor(
							    lowercase    , return_tensors=lowercase    , **lowercase    , )

							# pop arguments that are not used in the foward but used nevertheless
							_lowerCamelCase	:     Dict       =       encoding_image_processor['original_sizes']

							if hasattr(lowercase    , 'numpy'    ):  # Checks if Torch or TF tensor
											_lowerCamelCase	:     int       =       original_sizes.numpy()

							_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase	:     Dict       =       self._check_and_preprocess_points(
							    input_points=lowercase    , input_labels=lowercase    , input_boxes=lowercase    , )

							_lowerCamelCase	:     List[Any]       =       self._normalize_and_convert(
							    lowercase    , lowercase    , input_points=lowercase    , input_labels=lowercase    , input_boxes=lowercase    , return_tensors=lowercase    , )

							return encoding_image_processor
			def    A_       (    self    , lowercase    , lowercase    , lowercase=None    , lowercase=None    , lowercase=None    , lowercase="pt"    , ):
							if input_points is not None:
											if len(lowercase    ) != len(lowercase    ):
															_lowerCamelCase	:     Dict       =       [
															    self._normalize_coordinates(self.target_size    , lowercase    , original_sizes[0]    ) for point in input_points
															]
											else:
															_lowerCamelCase	:     Any       =       [
															    self._normalize_coordinates(self.target_size    , lowercase    , lowercase    )
															    for point, original_size in zip(lowercase    , lowercase    )
															]
											# check that all arrays have the same shape
											if not all(point.shape == input_points[0].shape for point in input_points    ):
															if input_labels is not None:
																			_lowerCamelCase,					_lowerCamelCase	:     str       =       self._pad_points_and_labels(lowercase    , lowercase    )

											_lowerCamelCase	:     Any       =       np.array(lowercase    )

							if input_labels is not None:
											_lowerCamelCase	:     int       =       np.array(lowercase    )

							if input_boxes is not None:
											if len(lowercase    ) != len(lowercase    ):
															_lowerCamelCase	:     List[Any]       =       [
															    self._normalize_coordinates(self.target_size    , lowercase    , original_sizes[0]    , is_bounding_box=lowercase    )
															    for box in input_boxes
															]
											else:
															_lowerCamelCase	:     int       =       [
															    self._normalize_coordinates(self.target_size    , lowercase    , lowercase    , is_bounding_box=lowercase    )
															    for box, original_size in zip(lowercase    , lowercase    )
															]
											_lowerCamelCase	:     Union[str, Any]       =       np.array(lowercase    )

							if input_boxes is not None:
											if return_tensors == "pt":
															_lowerCamelCase	:     Dict       =       torch.from_numpy(lowercase    )
															# boxes batch size of 1 by default
															_lowerCamelCase	:     Tuple       =       input_boxes.unsqueeze(1    ) if len(input_boxes.shape    ) != 3 else input_boxes
											elif return_tensors == "tf":
															_lowerCamelCase	:     Any       =       tf.convert_to_tensor(lowercase    )
															# boxes batch size of 1 by default
															_lowerCamelCase	:     Union[str, Any]       =       tf.expand_dims(lowercase    , 1    ) if len(input_boxes.shape    ) != 3 else input_boxes
											encoding_image_processor.update({'input_boxes': input_boxes}    )
							if input_points is not None:
											if return_tensors == "pt":
															_lowerCamelCase	:     str       =       torch.from_numpy(lowercase    )
															# point batch size of 1 by default
															_lowerCamelCase	:     Tuple       =       input_points.unsqueeze(1    ) if len(input_points.shape    ) != 4 else input_points
											elif return_tensors == "tf":
															_lowerCamelCase	:     int       =       tf.convert_to_tensor(lowercase    )
															# point batch size of 1 by default
															_lowerCamelCase	:     List[Any]       =       tf.expand_dims(lowercase    , 1    ) if len(input_points.shape    ) != 4 else input_points
											encoding_image_processor.update({'input_points': input_points}    )
							if input_labels is not None:
											if return_tensors == "pt":
															_lowerCamelCase	:     Any       =       torch.from_numpy(lowercase    )
															# point batch size of 1 by default
															_lowerCamelCase	:     str       =       input_labels.unsqueeze(1    ) if len(input_labels.shape    ) != 3 else input_labels
											elif return_tensors == "tf":
															_lowerCamelCase	:     str       =       tf.convert_to_tensor(lowercase    )
															# point batch size of 1 by default
															_lowerCamelCase	:     Dict       =       tf.expand_dims(lowercase    , 1    ) if len(input_labels.shape    ) != 3 else input_labels
											encoding_image_processor.update({'input_labels': input_labels}    )

							return encoding_image_processor
			def    A_       (    self    , lowercase    , lowercase    ):
							_lowerCamelCase	:     List[Any]       =       max([point.shape[0] for point in input_points]    )
							_lowerCamelCase	:     Union[str, Any]       =       []
							for i, point in enumerate(lowercase    ):
											if point.shape[0] != expected_nb_points:
															_lowerCamelCase	:     Optional[int]       =       np.concatenate(
															    [point, np.zeros((expected_nb_points - point.shape[0], 2)    ) + self.point_pad_value]    , axis=0    )
															_lowerCamelCase	:     Tuple       =       np.append(input_labels[i]    , [self.point_pad_value]    )
											processed_input_points.append(lowercase    )
							_lowerCamelCase	:     int       =       processed_input_points
							return input_points, input_labels
			def    A_       (    self    , lowercase    , lowercase    , lowercase    , lowercase=False    ):
							_lowerCamelCase,					_lowerCamelCase	:     Any       =       original_size
							_lowerCamelCase,					_lowerCamelCase	:     Any       =       self.image_processor._get_preprocess_shape(lowercase    , longest_edge=lowercase    )
							_lowerCamelCase	:     str       =       deepcopy(lowercase    ).astype(lowercase    )

							if is_bounding_box:
											_lowerCamelCase	:     List[Any]       =       coords.reshape(-1    , 2    , 2    )

							_lowerCamelCase	:     str       =       coords[..., 0] * (new_w / old_w)
							_lowerCamelCase	:     str       =       coords[..., 1] * (new_h / old_h)

							if is_bounding_box:
											_lowerCamelCase	:     List[Any]       =       coords.reshape(-1    , 4    )

							return coords
			def    A_       (    self    , lowercase=None    , lowercase=None    , lowercase=None    , ):
							if input_points is not None:
											if hasattr(lowercase    , 'numpy'    ):  # Checks for TF or Torch tensor
															_lowerCamelCase	:     List[Any]       =       input_points.numpy().tolist()

											if not isinstance(lowercase    , lowercase    ) or not isinstance(input_points[0]    , lowercase    ):
															raise ValueError('Input points must be a list of list of floating points.'    )
											_lowerCamelCase	:     int       =       [np.array(lowercase    ) for input_point in input_points]
							else:
											_lowerCamelCase	:     List[Any]       =       None

							if input_labels is not None:
											if hasattr(lowercase    , 'numpy'    ):
															_lowerCamelCase	:     str       =       input_labels.numpy().tolist()

											if not isinstance(lowercase    , lowercase    ) or not isinstance(input_labels[0]    , lowercase    ):
															raise ValueError('Input labels must be a list of list integers.'    )
											_lowerCamelCase	:     Optional[int]       =       [np.array(lowercase    ) for label in input_labels]
							else:
											_lowerCamelCase	:     Union[str, Any]       =       None

							if input_boxes is not None:
											if hasattr(lowercase    , 'numpy'    ):
															_lowerCamelCase	:     Any       =       input_boxes.numpy().tolist()

											if (
											    not isinstance(lowercase    , lowercase    )
											    or not isinstance(input_boxes[0]    , lowercase    )
											    or not isinstance(input_boxes[0][0]    , lowercase    )
											):
															raise ValueError('Input boxes must be a list of list of list of floating points.'    )
											_lowerCamelCase	:     Union[str, Any]       =       [np.array(lowercase    ).astype(np.floataa    ) for box in input_boxes]
							else:
											_lowerCamelCase	:     Tuple       =       None

							return input_points, input_labels, input_boxes
			@property
			def    A_       (    self    ):
							_lowerCamelCase	:     Dict       =       self.image_processor.model_input_names
							return list(dict.fromkeys(lowercase    )    )






			def    A_       (    self    , *lowercase    , **lowercase    ):
							return self.image_processor.post_process_masks(*lowercase    , **lowercase    )





"""simple docstring"""






from typing import Any







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       ):
				_validation(
				    lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       )
				# Creates data structures and fill initial step
				_lowerCamelCase	:     dict       =       {}
				_lowerCamelCase	:     dict       =       {}
				for state in states_space:
								_lowerCamelCase	:     Optional[Any]       =       observations_space[0]
								_lowerCamelCase	:     int       =       (
								    initial_probabilities[state] * emission_probabilities[state][observation]
								)
								_lowerCamelCase	:     List[Any]       =       None

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

            # Update probabilities and pointers dicts
												_lowerCamelCase	:     List[str]       =       (
												    probabilities[(arg_max, prior_observation)]
												    * transition_probabilities[arg_max][state]
												    * emission_probabilities[state][observation]
												)

												_lowerCamelCase	:     Union[str, Any]       =       arg_max

    # The final observation
				_lowerCamelCase	:     Tuple       =       observations_space[len(lowercase__						) - 1]

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

				# Process pointers backwards
				_lowerCamelCase	:     Optional[Any]       =       last_state
				_lowerCamelCase	:     Union[str, Any]       =       []
				for o in range(len(lowercase__						) - 1				,       -1				,       -1						):
								result.append(lowercase__						)
								_lowerCamelCase	:     Dict       =       pointers[previous, observations_space[o]]
				result.reverse()

				return result







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       ):
				_validate_not_empty(
				    lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       )
				_validate_lists(lowercase__				,       lowercase__						)
				_validate_dicts(
				    lowercase__				,       lowercase__				,       lowercase__						)







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







def 	_snake_case     (							lowercase__				,       lowercase__						):
				_validate_list(lowercase__				,       'observations_space'						)
				_validate_list(lowercase__				,       'states_space'						)







def 	_snake_case     (							lowercase__				,       lowercase__						):
				if not isinstance(_object				,       lowercase__						):
								_lowerCamelCase	:     Tuple       =       f'''{var_name} must be a list'''
								raise ValueError(lowercase__						)
				else:
								for x in _object:
												if not isinstance(lowercase__				,       lowercase__						):
																_lowerCamelCase	:     int       =       f'''{var_name} must be a list of strings'''
																raise ValueError(lowercase__						)







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       ):
				_validate_dict(lowercase__				,       'initial_probabilities'				,       lowercase__						)
				_validate_nested_dict(lowercase__				,       'transition_probabilities'						)
				_validate_nested_dict(lowercase__				,       'emission_probabilities'						)







def 	_snake_case     (							lowercase__				,       lowercase__						):
				_validate_dict(_object				,       lowercase__				,       lowercase__						)
				for x in _object.values():
								_validate_dict(lowercase__				,       lowercase__				,       lowercase__				,       lowercase__						)







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       lowercase__ = False						):
				if not isinstance(_object				,       lowercase__						):
								_lowerCamelCase	:     Tuple       =       f'''{var_name} must be a dict'''
								raise ValueError(lowercase__						)
				if not all(isinstance(lowercase__				,       lowercase__						) for x in _object						):
								_lowerCamelCase	:     Optional[int]       =       f'''{var_name} all keys must be strings'''
								raise ValueError(lowercase__						)
				if not all(isinstance(lowercase__				,       lowercase__						) for x in _object.values()						):
								_lowerCamelCase	:     Optional[int]       =       'nested dictionary ' if nested else ''
								_lowerCamelCase	:     Optional[Any]       =       f'''{var_name} {nested_text}all values must be {value_type.__name__}'''
								raise ValueError(lowercase__						)


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

			testmod()