



"""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, is_vision_available, logging


if is_vision_available():
     import PIL


snake_case   					=				logging.get_logger(__name__)



class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):
   A__		:	Optional[Any]           =  ['''pixel_values''']



   def __init__(       self    :	Optional[Any]							,       __lowerCamelCase    :	bool = True							,       __lowerCamelCase    :	Dict[str, int] = None							,       __lowerCamelCase    :	float = None							,       __lowerCamelCase    :	PILImageResampling = PILImageResampling.BILINEAR							,       __lowerCamelCase    :	bool = True							,       __lowerCamelCase    :	Union[int, float] = 1 / 2_5_5							,       __lowerCamelCase    :	bool = True							,       __lowerCamelCase    :	Optional[Union[float, List[float]]] = None							,       __lowerCamelCase    :	Optional[Union[float, List[float]]] = None							,       **__lowerCamelCase    :	Union[str, Any]							,       ):



     """simple docstring"""
     super().__init__(**__lowerCamelCase				)
     _snake_case   	=      size if size is not None else {'''shortest_edge''': 3_8_4}
     _snake_case   	=      get_size_dict(__lowerCamelCase							,       default_to_square=__lowerCamelCase				)

     _snake_case   	=      do_resize
     _snake_case   	=      size
     # Default value set here for backwards compatibility where the value in config is None
     _snake_case   	=      crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
     _snake_case   	=      resample
     _snake_case   	=      do_rescale
     _snake_case   	=      rescale_factor
     _snake_case   	=      do_normalize
     _snake_case   	=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     _snake_case   	=      image_std if image_std is not None else IMAGENET_STANDARD_STD



   def    __UpperCAmelCase    (       self    :	List[Any]							,       __lowerCamelCase    :	np.ndarray							,       __lowerCamelCase    :	Dict[str, int]							,       __lowerCamelCase    :	float							,       __lowerCamelCase    :	PILImageResampling = PILImageResampling.BICUBIC							,       __lowerCamelCase    :	Optional[Union[str, ChannelDimension]] = None							,       **__lowerCamelCase    :	List[Any]							,       ):



     """simple docstring"""
     _snake_case   	=      get_size_dict(__lowerCamelCase							,       default_to_square=__lowerCamelCase				)
     if "shortest_edge" not in size:
       raise ValueError(f"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"""				)
     _snake_case   	=      size['''shortest_edge''']

     if shortest_edge < 3_8_4:
       # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
       _snake_case   	=      int(shortest_edge / crop_pct				)
       _snake_case   	=      get_resize_output_image_size(__lowerCamelCase							,       size=__lowerCamelCase							,       default_to_square=__lowerCamelCase				)
       _snake_case   	=      resize(image=__lowerCamelCase							,       size=__lowerCamelCase							,       resample=__lowerCamelCase							,       data_format=__lowerCamelCase							,       **__lowerCamelCase				)
       # then crop to (shortest_edge, shortest_edge)
       return center_crop(image=__lowerCamelCase							,       size=(shortest_edge, shortest_edge)							,       data_format=__lowerCamelCase							,       **__lowerCamelCase				)
     else:
       # warping (no cropping) when evaluated at 384 or larger
       return resize(
           __lowerCamelCase							,       size=(shortest_edge, shortest_edge)							,       resample=__lowerCamelCase							,       data_format=__lowerCamelCase							,       **__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Tuple							,       __lowerCamelCase    :	np.ndarray							,       __lowerCamelCase    :	Union[int, float]							,       __lowerCamelCase    :	Optional[Union[str, ChannelDimension]] = None							,       **__lowerCamelCase    :	int							,       ):



     """simple docstring"""
     return rescale(__lowerCamelCase							,       scale=__lowerCamelCase							,       data_format=__lowerCamelCase							,       **__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Union[str, Any]							,       __lowerCamelCase    :	np.ndarray							,       __lowerCamelCase    :	Union[float, List[float]]							,       __lowerCamelCase    :	Union[float, List[float]]							,       __lowerCamelCase    :	Optional[Union[str, ChannelDimension]] = None							,       **__lowerCamelCase    :	Optional[int]							,       ):



     """simple docstring"""
     return normalize(__lowerCamelCase							,       mean=__lowerCamelCase							,       std=__lowerCamelCase							,       data_format=__lowerCamelCase							,       **__lowerCamelCase				)





   def    __UpperCAmelCase    (       self    :	List[str]							,       __lowerCamelCase    :	ImageInput							,       __lowerCamelCase    :	bool = None							,       __lowerCamelCase    :	Dict[str, int] = None							,       __lowerCamelCase    :	float = None							,       __lowerCamelCase    :	PILImageResampling = None							,       __lowerCamelCase    :	bool = None							,       __lowerCamelCase    :	float = None							,       __lowerCamelCase    :	bool = None							,       __lowerCamelCase    :	Optional[Union[float, List[float]]] = None							,       __lowerCamelCase    :	Optional[Union[float, List[float]]] = None							,       __lowerCamelCase    :	Optional[Union[str, TensorType]] = None							,       __lowerCamelCase    :	ChannelDimension = ChannelDimension.FIRST							,       **__lowerCamelCase    :	Optional[Any]							,       ):



     """simple docstring"""
     _snake_case   	=      do_resize if do_resize is not None else self.do_resize
     _snake_case   	=      crop_pct if crop_pct is not None else self.crop_pct
     _snake_case   	=      resample if resample is not None else self.resample
     _snake_case   	=      do_rescale if do_rescale is not None else self.do_rescale
     _snake_case   	=      rescale_factor if rescale_factor is not None else self.rescale_factor
     _snake_case   	=      do_normalize if do_normalize is not None else self.do_normalize
     _snake_case   	=      image_mean if image_mean is not None else self.image_mean
     _snake_case   	=      image_std if image_std is not None else self.image_std

     _snake_case   	=      size if size is not None else self.size
     _snake_case   	=      get_size_dict(__lowerCamelCase							,       default_to_square=__lowerCamelCase				)

     _snake_case   	=      make_list_of_images(__lowerCamelCase				)

     if not valid_images(__lowerCamelCase				):
       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 or resample is None:
       raise ValueError('''Size and resample must be specified if do_resize is True.'''				)

     if do_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
       raise ValueError('''crop_pct must be specified if size < 384.'''				)

     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   	=      [to_numpy_array(__lowerCamelCase				) for image in images]

     if do_resize:
       _snake_case   	=      [self.resize(image=__lowerCamelCase							,       size=__lowerCamelCase							,       crop_pct=__lowerCamelCase							,       resample=__lowerCamelCase				) for image in images]

     if do_rescale:
       _snake_case   	=      [self.rescale(image=__lowerCamelCase							,       scale=__lowerCamelCase				) for image in images]

     if do_normalize:
       _snake_case   	=      [self.normalize(image=__lowerCamelCase							,       mean=__lowerCamelCase							,       std=__lowerCamelCase				) for image in images]

     _snake_case   	=      [to_channel_dimension_format(__lowerCamelCase							,       __lowerCamelCase				) for image in images]

     _snake_case   	=      {'''pixel_values''': images}
     return BatchFeature(data=__lowerCamelCase							,       tensor_type=__lowerCamelCase				)












"""simple docstring"""



def 						__lowercase	(  snake_case_    :    list		)  ->float:



    '''simple docstring'''







    __A       :		Tuple							=	0
    while len(snake_case_		) > 1:
        __A       :		List[Any]							=	0
        # Consider two files with minimum cost to be merged
        for _ in range(2		):
            __A       :		Dict							=	files.index(min(snake_case_		)		)
            temp += files[min_index]
            files.pop(snake_case_		)
        files.append(snake_case_		)
        optimal_merge_cost += temp
    return optimal_merge_cost


if __name__ == "__main__":
       import doctest

       doctest.testmod()




