


"""simple docstring"""



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__snake_case         =					logging.get_logger(__name__)







class _lowerCAmelCase       (   snake_case_			):
   __UpperCAmelCase				: List[str]													=						["""pixel_values"""]







   def __init__(			self		,			UpperCamelCase__ = True		,			UpperCamelCase__ = None		,			UpperCamelCase__ = PILImageResampling.BILINEAR		,			UpperCamelCase__ = True		,			UpperCamelCase__ = None		,			UpperCamelCase__ = True		,			UpperCamelCase__ = 1 / 255		,			UpperCamelCase__ = True		,			UpperCamelCase__ = None		,			UpperCamelCase__ = None		,			**UpperCamelCase__		,			)			->	Optional[int]:

       '''simple docstring'''




       super().__init__(**UpperCamelCase__		)
       snake_case					:   str         =					size if size is not None else {"shortest_edge": 256}
       snake_case					:   List[str]         =					get_size_dict(UpperCamelCase__		,			default_to_square=UpperCamelCase__		)
       snake_case					:   List[Any]         =					crop_size if crop_size is not None else {"height": 224, "width": 224}
       snake_case					:   str         =					get_size_dict(UpperCamelCase__		)
       snake_case					:   int         =					do_resize
       snake_case					:   List[str]         =					size
       snake_case					:   int         =					resample
       snake_case					:   Optional[int]         =					do_center_crop
       snake_case					:   Any         =					crop_size
       snake_case					:   Any         =					do_rescale
       snake_case					:   Dict         =					rescale_factor
       snake_case					:   Optional[int]         =					do_normalize
       snake_case					:   Optional[Any]         =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       snake_case					:   Optional[Any]         =					image_std if image_std is not None else IMAGENET_STANDARD_STD







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__ = PILImageResampling.BICUBIC		,			UpperCamelCase__ = None		,			**UpperCamelCase__		,			)			->	Optional[Any]:

       '''simple docstring'''




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







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

       '''simple docstring'''




       snake_case					:   Union[str, Any]         =					get_size_dict(UpperCamelCase__		)
       return center_crop(UpperCamelCase__		,			size=(size["height"], size["width"])		,			data_format=UpperCamelCase__		,			**UpperCamelCase__		)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__ = None		,			**UpperCamelCase__		)			->	Dict:

       '''simple docstring'''




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







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

       '''simple docstring'''




       return normalize(UpperCamelCase__		,			mean=UpperCamelCase__		,			std=UpperCamelCase__		,			data_format=UpperCamelCase__		,			**UpperCamelCase__		)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__ = None		,			UpperCamelCase__ = None		,			UpperCamelCase__ = None		,			UpperCamelCase__ = None		,			UpperCamelCase__ = None		,			UpperCamelCase__ = None		,			UpperCamelCase__ = None		,			UpperCamelCase__ = None		,			UpperCamelCase__ = None		,			UpperCamelCase__ = None		,			UpperCamelCase__ = None		,			UpperCamelCase__ = ChannelDimension.FIRST		,			**UpperCamelCase__		,			)			->	Dict:

       '''simple docstring'''




       snake_case					:   List[str]         =					do_resize if do_resize is not None else self.do_resize
       snake_case					:   int         =					size if size is not None else self.size
       snake_case					:   Optional[Any]         =					get_size_dict(UpperCamelCase__		,			default_to_square=UpperCamelCase__		)
       snake_case					:   Tuple         =					resample if resample is not None else self.resample
       snake_case					:   Optional[int]         =					do_center_crop if do_center_crop is not None else self.do_center_crop
       snake_case					:   List[Any]         =					crop_size if crop_size is not None else self.crop_size
       snake_case					:   List[Any]         =					get_size_dict(UpperCamelCase__		)
       snake_case					:   Union[str, Any]         =					do_rescale if do_rescale is not None else self.do_rescale
       snake_case					:   List[Any]         =					rescale_factor if rescale_factor is not None else self.rescale_factor
       snake_case					:   Dict         =					do_normalize if do_normalize is not None else self.do_normalize
       snake_case					:   Tuple         =					image_mean if image_mean is not None else self.image_mean
       snake_case					:   Any         =					image_std if image_std is not None else self.image_std

       snake_case					:   str         =					make_list_of_images(UpperCamelCase__		)

       if not valid_images(UpperCamelCase__		):
           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_center_crop and crop_size is None:
           raise ValueError("Crop size must be specified if do_center_crop is True."		)

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

       if do_normalize and (image_mean is None or image_std is None):
           raise ValueError("Image mean and std must be specified if do_normalize is True."		)

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

       if do_resize:
           snake_case					:   List[str]         =					[self.resize(image=UpperCamelCase__		,			size=UpperCamelCase__		,			resample=UpperCamelCase__		) for image in images]

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

       if do_rescale:
           snake_case					:   Union[str, Any]         =					[self.rescale(image=UpperCamelCase__		,			scale=UpperCamelCase__		) for image in images]

       if do_normalize:
           snake_case					:   Dict         =					[self.normalize(image=UpperCamelCase__		,			mean=UpperCamelCase__		,			std=UpperCamelCase__		) for image in images]

       snake_case					:   Optional[Any]         =					[to_channel_dimension_format(UpperCamelCase__		,			UpperCamelCase__		) for image in images]

       snake_case					:   Optional[int]         =					{"pixel_values": images}
       return BatchFeature(data=UpperCamelCase__		,			tensor_type=UpperCamelCase__		)









"""simple docstring"""



from typing import Any







def 						__lowerCAmelCase		(		lowercase						:						list		,							lowercase						:						list		,							lowercase						:						dict		,							lowercase						:						dict		,							lowercase						:						dict		,							)			->		list:


    """simple docstring"""





    _validation(
        lowercase		,							lowercase		,							lowercase		,							lowercase		,							lowercase		,							)
    # Creates data structures and fill initial step
    snake_case					:   dict         =					{}
    snake_case					:   dict         =					{}
    for state in states_space:
        snake_case					:   int         =					observations_space[0]
        snake_case					:   Any         =					(
            initial_probabilities[state] * emission_probabilities[state][observation]
        )
        snake_case					:   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(lowercase				)				):
        snake_case					:   Optional[Any]         =					observations_space[o]
        snake_case					:   str         =					observations_space[o - 1]
        for state in states_space:
            # Calculates the argmax for probability function
            snake_case					:   str         =					""
            snake_case					:   List[Any]         =					-1
            for k_state in states_space:
                snake_case					:   Tuple         =					(
                    probabilities[(k_state, prior_observation)]
                    * transition_probabilities[k_state][state]
                    * emission_probabilities[state][observation]
                )
                if probability > max_probability:
                    snake_case					:   Optional[Any]         =					probability
                    snake_case					:   int         =					k_state

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

            snake_case					:   List[Any]         =					arg_max

    # The final observation
    snake_case					:   Dict         =					observations_space[len(lowercase				) - 1]

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

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

    return result







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						Any		,							)			->		None:


    """simple docstring"""





    _validate_not_empty(
        lowercase		,							lowercase		,							lowercase		,							lowercase		,							lowercase		,							)
    _validate_lists(lowercase		,							lowercase				)
    _validate_dicts(
        lowercase		,							lowercase		,							lowercase				)







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						Any		,							)			->		None:


    """simple docstring"""





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







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						Any				)			->		None:


    """simple docstring"""





    _validate_list(lowercase		,							"observations_space"				)
    _validate_list(lowercase		,							"states_space"				)







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						str				)			->		None:


    """simple docstring"""





    if not isinstance(_object		,							lowercase				):
        snake_case					:   List[str]         =					F'{var_name} must be a list'
        raise ValueError(lowercase				)
    else:
        for x in _object:
            if not isinstance(lowercase		,							lowercase				):
                snake_case					:   Tuple         =					F'{var_name} must be a list of strings'
                raise ValueError(lowercase				)







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						Any		,							)			->		None:


    """simple docstring"""





    _validate_dict(lowercase		,							"initial_probabilities"		,							lowercase				)
    _validate_nested_dict(lowercase		,							"transition_probabilities"				)
    _validate_nested_dict(lowercase		,							"emission_probabilities"				)







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						str				)			->		None:


    """simple docstring"""





    _validate_dict(_object		,							lowercase		,							lowercase				)
    for x in _object.values():
        _validate_dict(lowercase		,							lowercase		,							lowercase		,							lowercase				)







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						str		,							lowercase						:						type		,							lowercase						:						bool = False				)			->		None:


    """simple docstring"""





    if not isinstance(_object		,							lowercase				):
        snake_case					:   int         =					F'{var_name} must be a dict'
        raise ValueError(lowercase				)
    if not all(isinstance(lowercase		,							lowercase				) for x in _object				):
        snake_case					:   Optional[Any]         =					F'{var_name} all keys must be strings'
        raise ValueError(lowercase				)
    if not all(isinstance(lowercase		,							lowercase				) for x in _object.values()				):
        snake_case					:   Optional[int]         =					"nested dictionary " if nested else ""
        snake_case					:   int         =					F'{var_name} {nested_text}all values must be {value_type.__name__}'
        raise ValueError(lowercase				)


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

  testmod()





