
from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
    import PIL

if is_torch_available():
    import torch


lowerCamelCase  =logging.get_logger(__name__)




class 					_lowerCamelCase			(   UpperCamelCase_			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	['''pixel_values''']






     def __init__(				self    ,    __SCREAMING_SNAKE_CASE = True    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR    ,    __SCREAMING_SNAKE_CASE = True    ,    __SCREAMING_SNAKE_CASE = 1 / 2_5_5    ,    __SCREAMING_SNAKE_CASE = True    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = True    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> None:




           """simple docstring"""







           super().__init__(**__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : str											=					size if size is not None else {'''shortest_edge''': 2_2_4}
           UpperCamelCase__     : Optional[int]											=					get_size_dict(__SCREAMING_SNAKE_CASE    ,    default_to_square=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Any											=					crop_size if crop_size is not None else {'''height''': 2_5_6, '''width''': 2_5_6}
           UpperCamelCase__     : Dict											=					get_size_dict(__SCREAMING_SNAKE_CASE    ,    param_name='''crop_size'''	)

           UpperCamelCase__     : Any											=					do_resize
           UpperCamelCase__     : str											=					size
           UpperCamelCase__     : Union[str, Any]											=					resample
           UpperCamelCase__     : List[Any]											=					do_rescale
           UpperCamelCase__     : Any											=					rescale_factor
           UpperCamelCase__     : Optional[int]											=					do_center_crop
           UpperCamelCase__     : Any											=					crop_size
           UpperCamelCase__     : Tuple											=					do_flip_channel_order






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = PIL.Image.BILINEAR    ,    __SCREAMING_SNAKE_CASE = None    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> np.ndarray:




           """simple docstring"""







           UpperCamelCase__     : Optional[Any]											=					get_size_dict(__SCREAMING_SNAKE_CASE    ,    default_to_square=__SCREAMING_SNAKE_CASE	)
           if "shortest_edge" not in size:
                 raise ValueError(F'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'''	)
           UpperCamelCase__     : Union[str, Any]											=					get_resize_output_image_size(__SCREAMING_SNAKE_CASE    ,    size=size['''shortest_edge''']    ,    default_to_square=__SCREAMING_SNAKE_CASE	)
           return resize(__SCREAMING_SNAKE_CASE    ,    size=__SCREAMING_SNAKE_CASE    ,    resample=__SCREAMING_SNAKE_CASE    ,    data_format=__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> np.ndarray:




           """simple docstring"""







           UpperCamelCase__     : str											=					get_size_dict(__SCREAMING_SNAKE_CASE	)
           if "height" not in size or "width" not in size:
                 raise ValueError(F'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''	)
           return center_crop(__SCREAMING_SNAKE_CASE    ,    size=(size['''height'''], size['''width'''])    ,    data_format=__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> Optional[Any]:




           """simple docstring"""







           return rescale(__SCREAMING_SNAKE_CASE    ,    scale=__SCREAMING_SNAKE_CASE    ,    data_format=__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None	)	-> np.ndarray:




           """simple docstring"""







           return flip_channel_order(__SCREAMING_SNAKE_CASE    ,    data_format=__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = ChannelDimension.FIRST    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> PIL.Image.Image:




           """simple docstring"""







           UpperCamelCase__     : List[str]											=					do_resize if do_resize is not None else self.do_resize
           UpperCamelCase__     : str											=					resample if resample is not None else self.resample
           UpperCamelCase__     : Any											=					do_rescale if do_rescale is not None else self.do_rescale
           UpperCamelCase__     : Union[str, Any]											=					rescale_factor if rescale_factor is not None else self.rescale_factor
           UpperCamelCase__     : Optional[int]											=					do_center_crop if do_center_crop is not None else self.do_center_crop
           UpperCamelCase__     : Dict											=					(
               do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
           )

           UpperCamelCase__     : List[str]											=					size if size is not None else self.size
           UpperCamelCase__     : Optional[int]											=					get_size_dict(__SCREAMING_SNAKE_CASE    ,    default_to_square=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : List[Any]											=					crop_size if crop_size is not None else self.crop_size
           UpperCamelCase__     : Union[str, Any]											=					get_size_dict(__SCREAMING_SNAKE_CASE    ,    param_name='''crop_size'''	)

           UpperCamelCase__     : Tuple											=					make_list_of_images(__SCREAMING_SNAKE_CASE	)

           if not valid_images(__SCREAMING_SNAKE_CASE	):
                 raise ValueError(
                     '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                     '''torch.Tensor, tf.Tensor or jax.ndarray.'''	)

           if do_resize and size is None:
                 raise ValueError('''Size must be specified if do_resize is True.'''	)

           if do_rescale and rescale_factor is None:
                 raise ValueError('''Rescale factor must be specified if do_rescale is True.'''	)

           if do_center_crop and crop_size is None:
                 raise ValueError('''Crop size must be specified if do_center_crop is True.'''	)

           # All transformations expect numpy arrays.
           UpperCamelCase__     : Optional[int]											=					[to_numpy_array(__SCREAMING_SNAKE_CASE	) for image in images]

           if do_resize:
                 UpperCamelCase__     : Tuple											=					[self.resize(image=__SCREAMING_SNAKE_CASE    ,    size=__SCREAMING_SNAKE_CASE    ,    resample=__SCREAMING_SNAKE_CASE	) for image in images]

           if do_center_crop:
                 UpperCamelCase__     : Any											=					[self.center_crop(image=__SCREAMING_SNAKE_CASE    ,    size=__SCREAMING_SNAKE_CASE	) for image in images]

           if do_rescale:
                 UpperCamelCase__     : Optional[Any]											=					[self.rescale(image=__SCREAMING_SNAKE_CASE    ,    scale=__SCREAMING_SNAKE_CASE	) for image in images]

           # the pretrained checkpoints assume images are BGR, not RGB
           if do_flip_channel_order:
                 UpperCamelCase__     : int											=					[self.flip_channel_order(image=__SCREAMING_SNAKE_CASE	) for image in images]

           UpperCamelCase__     : Dict											=					[to_channel_dimension_format(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	) for image in images]

           UpperCamelCase__     : Optional[Any]											=					{'''pixel_values''': images}
           return BatchFeature(data=__SCREAMING_SNAKE_CASE    ,    tensor_type=__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None	)	-> Tuple:




           """simple docstring"""







           UpperCamelCase__     : Optional[int]											=					outputs.logits

           # Resize logits and compute semantic segmentation maps
           if target_sizes is not None:
                 if len(__SCREAMING_SNAKE_CASE	) != len(__SCREAMING_SNAKE_CASE	):
                       raise ValueError(
                           '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''	)

                 if is_torch_tensor(__SCREAMING_SNAKE_CASE	):
                       UpperCamelCase__     : Dict											=					target_sizes.numpy()

                 UpperCamelCase__     : Tuple											=					[]

                 for idx in range(len(__SCREAMING_SNAKE_CASE	)	):
                       UpperCamelCase__     : Optional[Any]											=					torch.nn.functional.interpolate(
                           logits[idx].unsqueeze(dim=0	)    ,    size=target_sizes[idx]    ,    mode='''bilinear'''    ,    align_corners=__SCREAMING_SNAKE_CASE	)
                       UpperCamelCase__     : Dict											=					resized_logits[0].argmax(dim=0	)
                       semantic_segmentation.append(__SCREAMING_SNAKE_CASE	)
           else:
                 UpperCamelCase__     : List[Any]											=					logits.argmax(dim=1	)
                 UpperCamelCase__     : Tuple											=					[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]	)]

           return semantic_segmentation




from typing import Any


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

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

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

                  UpperCamelCase__     : Optional[Any]											=					arg_max

    # The final observation
      UpperCamelCase__     : List[str]											=					observations_space[len(UpperCamelCase__		) - 1]

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

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

      return result


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		):
      _validate_not_empty(
          UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		)
      _validate_lists(UpperCamelCase__			,		UpperCamelCase__		)
      _validate_dicts(
          UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__		)


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


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__		):
      _validate_list(UpperCamelCase__			,		'''observations_space'''		)
      _validate_list(UpperCamelCase__			,		'''states_space'''		)


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__		):
      if not isinstance(_object			,		UpperCamelCase__		):
            UpperCamelCase__     : List[Any]											=					f'''{var_name} must be a list'''
            raise ValueError(UpperCamelCase__		)
      else:
            for x in _object:
                  if not isinstance(UpperCamelCase__			,		UpperCamelCase__		):
                        UpperCamelCase__     : List[Any]											=					f'''{var_name} must be a list of strings'''
                        raise ValueError(UpperCamelCase__		)


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		):
      _validate_dict(UpperCamelCase__			,		'''initial_probabilities'''			,		UpperCamelCase__		)
      _validate_nested_dict(UpperCamelCase__			,		'''transition_probabilities'''		)
      _validate_nested_dict(UpperCamelCase__			,		'''emission_probabilities'''		)


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__		):
      _validate_dict(_object			,		UpperCamelCase__			,		UpperCamelCase__		)
      for x in _object.values():
            _validate_dict(UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__		)


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


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

    testmod()


