



"""simple docstring"""
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


_UpperCAmelCase     =			logging.get_logger(__name__)
class 					a	(  UpperCAmelCase__       ):
  UpperCamelCase	:				Tuple     						=					['pixel_values']



  def __init__(						self :					Any       ,		lowerCAmelCase :					bool = True       ,		lowerCAmelCase :					Dict[str, int] = None       ,		lowerCAmelCase :					PILImageResampling = PILImageResampling.BILINEAR       ,		lowerCAmelCase :					bool = True       ,		lowerCAmelCase :					Union[int, float] = 1 / 255       ,		lowerCAmelCase :					bool = True       ,		lowerCAmelCase :					Dict[str, int] = None       ,		lowerCAmelCase :					bool = True       ,		**lowerCAmelCase :					str       ,		)     ->					None:


     '''simple docstring'''




     super().__init__(**lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: List[str]     			=size if size is not None else {"""shortest_edge""": 224}
     SCREAMING_SNAKE_CASE_: List[str]     			=get_size_dict(lowerCAmelCase       ,		default_to_square=lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: str     			=crop_size if crop_size is not None else {"""height""": 256, """width""": 256}
     SCREAMING_SNAKE_CASE_: Any     			=get_size_dict(lowerCAmelCase       ,		param_name="""crop_size"""		)

     SCREAMING_SNAKE_CASE_: Any     			=do_resize
     SCREAMING_SNAKE_CASE_: List[Any]     			=size
     SCREAMING_SNAKE_CASE_: str     			=resample
     SCREAMING_SNAKE_CASE_: Tuple     			=do_rescale
     SCREAMING_SNAKE_CASE_: Tuple     			=rescale_factor
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=do_center_crop
     SCREAMING_SNAKE_CASE_: Any     			=crop_size
     SCREAMING_SNAKE_CASE_: List[Any]     			=do_flip_channel_order



  def 	lowerCamelCase__       (						self :					int       ,		lowerCAmelCase :					np.ndarray       ,		lowerCAmelCase :					Dict[str, int]       ,		lowerCAmelCase :					PILImageResampling = PIL.Image.BILINEAR       ,		lowerCAmelCase :					Optional[Union[str, ChannelDimension]] = None       ,		**lowerCAmelCase :					str       ,		)     ->					np.ndarray:


     '''simple docstring'''




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



  def 	lowerCamelCase__       (						self :					List[Any]       ,		lowerCAmelCase :					np.ndarray       ,		lowerCAmelCase :					Dict[str, int]       ,		lowerCAmelCase :					Optional[Union[str, ChannelDimension]] = None       ,		**lowerCAmelCase :					Any       ,		)     ->					np.ndarray:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Tuple     			=get_size_dict(lowerCAmelCase		)
     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(lowerCAmelCase       ,		size=(size["""height"""], size["""width"""])       ,		data_format=lowerCAmelCase       ,		**lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					int       ,		lowerCAmelCase :					np.ndarray       ,		lowerCAmelCase :					Union[int, float]       ,		lowerCAmelCase :					Optional[Union[str, ChannelDimension]] = None       ,		**lowerCAmelCase :					Union[str, Any]       ,		)     ->					Optional[Any]:


     '''simple docstring'''




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



  def 	lowerCamelCase__       (						self :					str       ,		lowerCAmelCase :					np.ndarray       ,		lowerCAmelCase :					Optional[Union[str, ChannelDimension]] = None		)     ->					np.ndarray:


     '''simple docstring'''




     return flip_channel_order(lowerCAmelCase       ,		data_format=lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					List[str]       ,		lowerCAmelCase :					ImageInput       ,		lowerCAmelCase :					bool = None       ,		lowerCAmelCase :					Dict[str, int] = None       ,		lowerCAmelCase :					PILImageResampling = None       ,		lowerCAmelCase :					bool = None       ,		lowerCAmelCase :					float = None       ,		lowerCAmelCase :					bool = None       ,		lowerCAmelCase :					Dict[str, int] = None       ,		lowerCAmelCase :					bool = None       ,		lowerCAmelCase :					Optional[Union[str, TensorType]] = None       ,		lowerCAmelCase :					ChannelDimension = ChannelDimension.FIRST       ,		**lowerCAmelCase :					Dict       ,		)     ->					PIL.Image.Image:


     '''simple docstring'''




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

     SCREAMING_SNAKE_CASE_: List[str]     			=size if size is not None else self.size
     SCREAMING_SNAKE_CASE_: Dict     			=get_size_dict(lowerCAmelCase       ,		default_to_square=lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Dict     			=crop_size if crop_size is not None else self.crop_size
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=get_size_dict(lowerCAmelCase       ,		param_name="""crop_size"""		)

     SCREAMING_SNAKE_CASE_: Tuple     			=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:
        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.
     SCREAMING_SNAKE_CASE_: Dict     			=[to_numpy_array(lowerCAmelCase		) for image in images]

     if do_resize:
        SCREAMING_SNAKE_CASE_: Any     			=[self.resize(image=lowerCAmelCase       ,		size=lowerCAmelCase       ,		resample=lowerCAmelCase		) for image in images]

     if do_center_crop:
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=[self.center_crop(image=lowerCAmelCase       ,		size=lowerCAmelCase		) for image in images]

     if do_rescale:
        SCREAMING_SNAKE_CASE_: str     			=[self.rescale(image=lowerCAmelCase       ,		scale=lowerCAmelCase		) for image in images]

     # the pretrained checkpoints assume images are BGR, not RGB
     if do_flip_channel_order:
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=[self.flip_channel_order(image=lowerCAmelCase		) for image in images]

     SCREAMING_SNAKE_CASE_: int     			=[to_channel_dimension_format(lowerCAmelCase       ,		lowerCAmelCase		) for image in images]

     SCREAMING_SNAKE_CASE_: Optional[Any]     			={"""pixel_values""": images}
     return BatchFeature(data=lowerCAmelCase       ,		tensor_type=lowerCAmelCase		)





  def 	lowerCamelCase__       (						self :					int       ,		lowerCAmelCase :					Optional[Any]       ,		lowerCAmelCase :					List[Tuple] = None		)     ->					List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Dict     			=outputs.logits

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

        if is_torch_tensor(lowerCAmelCase		):
           SCREAMING_SNAKE_CASE_: List[str]     			=target_sizes.numpy()

        SCREAMING_SNAKE_CASE_: int     			=[]

        for idx in range(len(lowerCAmelCase		)		):
           SCREAMING_SNAKE_CASE_: Optional[int]     			=torch.nn.functional.interpolate(
               logits[idx].unsqueeze(dim=0		)       ,		size=target_sizes[idx]       ,		mode="""bilinear"""       ,		align_corners=lowerCAmelCase		)
           SCREAMING_SNAKE_CASE_: str     			=resized_logits[0].argmax(dim=0		)
           semantic_segmentation.append(lowerCAmelCase		)
     else:
        SCREAMING_SNAKE_CASE_: Any     			=logits.argmax(dim=1		)
        SCREAMING_SNAKE_CASE_: Optional[int]     			=[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]		)]

     return semantic_segmentation








"""simple docstring"""
from __future__ import annotations





def      __magic_name__				(							lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: List[Any]     			=sorted(numsa + numsa )
   SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Tuple     			=divmod(len(lowercase )							,			2 )
   if mod == 1:
      return all_numbers[div]
   else:
      return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   _UpperCAmelCase     =			[float(x) for x in input("""Enter the elements of first array: """).split()]
   _UpperCAmelCase     =			[float(x) for x in input("""Enter the elements of second array: """).split()]
   print(f"""The median of two arrays is: {median_of_two_arrays(array_a, array_a)}""")



