






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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__a :    Dict								=				logging.get_logger(__name__)


if is_vision_available():
  import PIL


class  __lowercase (		lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			["pixel_values"]




     def __init__(			self    :     List[str]	,		UpperCamelCase_    :     bool = True	,		UpperCamelCase_    :     Dict[str, int] = None	,		UpperCamelCase_    :     PILImageResampling = PILImageResampling.BICUBIC	,		UpperCamelCase_    :     bool = True	,		UpperCamelCase_    :     Dict[str, int] = None	,		UpperCamelCase_    :     bool = True	,		UpperCamelCase_    :     Union[int, float] = 1 / 255	,		UpperCamelCase_    :     bool = True	,		UpperCamelCase_    :     Optional[Union[float, List[float]]] = None	,		UpperCamelCase_    :     Optional[Union[float, List[float]]] = None	,		UpperCamelCase_    :     bool = True	,		**UpperCamelCase_    :     Optional[int]	,		):


         """simple docstring"""



         super().__init__(**UpperCamelCase_			)
         __A             =		size if size is not None else {"""shortest_edge""": 224}
         __A             =		get_size_dict(UpperCamelCase_	,		default_to_square=UpperCamelCase_			)
         __A             =		crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
         __A             =		get_size_dict(UpperCamelCase_			)

         __A             =		do_resize
         __A             =		size
         __A             =		resample
         __A             =		do_center_crop
         __A             =		crop_size
         __A             =		do_rescale
         __A             =		rescale_factor
         __A             =		do_normalize
         __A             =		image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         __A             =		image_std if image_std is not None else OPENAI_CLIP_STD
         __A             =		do_convert_rgb




     def 						lowerCAmelCase_       (			self    :     Union[str, Any]	,		UpperCamelCase_    :     np.ndarray	,		UpperCamelCase_    :     Dict[str, int]	,		UpperCamelCase_    :     PILImageResampling = PILImageResampling.BICUBIC	,		UpperCamelCase_    :     Optional[Union[str, ChannelDimension]] = None	,		**UpperCamelCase_    :     Tuple	,		):


         """simple docstring"""



         __A             =		get_size_dict(UpperCamelCase_	,		default_to_square=UpperCamelCase_			)
         __A             =		get_resize_output_image_size(
             UpperCamelCase_	,		size=(size["""height"""], size["""width"""])	,		default_to_square=UpperCamelCase_			)
         return resize(UpperCamelCase_	,		size=UpperCamelCase_	,		resample=UpperCamelCase_	,		data_format=UpperCamelCase_	,		**UpperCamelCase_			)




     def 						lowerCAmelCase_       (			self    :     int	,		UpperCamelCase_    :     np.ndarray	,		UpperCamelCase_    :     Dict[str, int]	,		UpperCamelCase_    :     Optional[Union[str, ChannelDimension]] = None	,		**UpperCamelCase_    :     List[Any]	,		):


         """simple docstring"""



         __A             =		get_size_dict(UpperCamelCase_			)
         return center_crop(UpperCamelCase_	,		size=(size["""height"""], size["""width"""])	,		data_format=UpperCamelCase_	,		**UpperCamelCase_			)




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


         """simple docstring"""



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




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


         """simple docstring"""



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





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


         """simple docstring"""



         __A             =		do_resize if do_resize is not None else self.do_resize
         __A             =		size if size is not None else self.size
         __A             =		get_size_dict(UpperCamelCase_	,		default_to_square=UpperCamelCase_			)
         __A             =		resample if resample is not None else self.resample
         __A             =		do_center_crop if do_center_crop is not None else self.do_center_crop
         __A             =		crop_size if crop_size is not None else self.crop_size
         __A             =		get_size_dict(UpperCamelCase_			)
         __A             =		do_rescale if do_rescale is not None else self.do_rescale
         __A             =		rescale_factor if rescale_factor is not None else self.rescale_factor
         __A             =		do_normalize if do_normalize is not None else self.do_normalize
         __A             =		image_mean if image_mean is not None else self.image_mean
         __A             =		image_std if image_std is not None else self.image_std
         __A             =		do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         __A             =		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."""			)

         # PIL RGBA images are converted to RGB
         if do_convert_rgb:
             __A             =		[convert_to_rgb(UpperCamelCase_			) for image in images]

         # All transformations expect numpy arrays.
         __A             =		[to_numpy_array(UpperCamelCase_			) for image in images]

         if do_resize:
             __A             =		[self.resize(image=UpperCamelCase_	,		size=UpperCamelCase_	,		resample=UpperCamelCase_			) for image in images]

         if do_center_crop:
             __A             =		[self.center_crop(image=UpperCamelCase_	,		size=UpperCamelCase_			) for image in images]

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

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

         __A             =		[to_channel_dimension_format(UpperCamelCase_	,		UpperCamelCase_			) for image in images]

         __A             =		{"""pixel_values""": images}
         return BatchFeature(data=UpperCamelCase_	,		tensor_type=UpperCamelCase_			)












def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     list[int]      )  -> list[int]:





    """simple docstring"""

    if len(__lowercase      ) > 1:
        __A             =		len(__lowercase      ) // 2  # Finds the middle of the array
        __A             =		arr[
            :middle_length
        ]  # Creates an array of the elements in the first half.
        __A             =		arr[
            middle_length:
        ]  # Creates an array of the elements in the second half.
        __A             =		len(__lowercase      )
        __A             =		len(__lowercase      )
        merge(__lowercase      )  # Starts sorting the left.
        merge(__lowercase      )  # Starts sorting the right
        __A             =		0  # Left Counter
        __A             =		0  # Right Counter
        __A             =		0  # Position Counter
        while (
            left_index < left_size and right_index < right_size
        ):  # Runs until the lowers size of the left and right are sorted.
            if left_array[left_index] < right_array[right_index]:
                __A             =		left_array[left_index]
                left_index += 1
            else:
                __A             =		right_array[right_index]
                right_index += 1
            index += 1
        while (
            left_index < left_size
        ):  # Adds the left over elements in the left half of the array
            __A             =		left_array[left_index]
            left_index += 1
            index += 1
        while (
            right_index < right_size
        ):  # Adds the left over elements in the right half of the array
            __A             =		right_array[right_index]
            right_index += 1
            index += 1
    return arr


if __name__ == "__main__":
  import doctest

  doctest.testmod()




