





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,
    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_torch_available, is_torch_tensor, logging


if is_torch_available():
    import torch


__lowerCamelCase       :     Tuple   =     logging.get_logger(__name__)



class 					A__   (       __snake_case			):
      _UpperCAmelCase	:Tuple						    =   ['pixel_values']





      def __init__( self       ,							A_ = True       ,							A_ = None       ,							A_ = PILImageResampling.BILINEAR       ,							A_ = True       ,							A_ = None       ,							A_ = True       ,							A_ = 1 / 255       ,							A_ = True       ,							A_ = None       ,							A_ = None       ,							**A_       ,							):
       '''simple docstring'''




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





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_ = PILImageResampling.BICUBIC       ,							A_ = None       ,							**A_       ,							):
       '''simple docstring'''




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





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_ = None       ,							**A_       ,							):
       '''simple docstring'''




       UpperCamelCase	:					Dict       	=   get_size_dict(A_   )
       if "height" not in size or "width" not in size:
        raise ValueError(F"""The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}"""   )
       return center_crop(A_       ,							size=(size["height"], size["width"])       ,							data_format=A_       ,							**A_   )





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_ = None       ,							**A_   ):
       '''simple docstring'''




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





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_ = None       ,							**A_       ,							):
       '''simple docstring'''




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





      def        __UpperCamelCase( self       ,							A_       ,							A_ = None       ,							A_ = None       ,							A_ = None       ,							A_ = None       ,							A_ = None       ,							A_ = None       ,							A_ = None       ,							A_ = None       ,							A_ = None       ,							A_ = None       ,							A_ = None       ,							A_ = ChannelDimension.FIRST       ,							**A_       ,							):
       '''simple docstring'''




       UpperCamelCase	:					str       	=   do_resize if do_resize is not None else self.do_resize
       UpperCamelCase	:					Tuple       	=   size if size is not None else self.size
       UpperCamelCase	:					Any       	=   get_size_dict(A_       ,							default_to_square=A_   )
       UpperCamelCase	:					Optional[Any]       	=   resample if resample is not None else self.resample
       UpperCamelCase	:					List[str]       	=   do_center_crop if do_center_crop is not None else self.do_center_crop
       UpperCamelCase	:					str       	=   crop_size if crop_size is not None else self.crop_size
       UpperCamelCase	:					List[Any]       	=   get_size_dict(A_       ,							param_name="crop_size"   )
       UpperCamelCase	:					Tuple       	=   do_rescale if do_rescale is not None else self.do_rescale
       UpperCamelCase	:					List[Any]       	=   rescale_factor if rescale_factor is not None else self.rescale_factor
       UpperCamelCase	:					List[Any]       	=   do_normalize if do_normalize is not None else self.do_normalize
       UpperCamelCase	:					List[Any]       	=   image_mean if image_mean is not None else self.image_mean
       UpperCamelCase	:					Optional[Any]       	=   image_std if image_std is not None else self.image_std

       UpperCamelCase	:					List[Any]       	=   make_list_of_images(A_   )

       if not valid_images(A_   ):
        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.
       UpperCamelCase	:					Tuple       	=   [to_numpy_array(A_   ) for image in images]

       if do_resize:
        UpperCamelCase	:					Union[str, Any]       	=   [self.resize(image=A_       ,							size=A_       ,							resample=A_   ) for image in images]

       if do_center_crop:
        UpperCamelCase	:					Optional[Any]       	=   [self.center_crop(image=A_       ,							size=A_   ) for image in images]

       if do_rescale:
        UpperCamelCase	:					int       	=   [self.rescale(image=A_       ,							scale=A_   ) for image in images]

       if do_normalize:
        UpperCamelCase	:					str       	=   [self.normalize(image=A_       ,							mean=A_       ,							std=A_   ) for image in images]

       UpperCamelCase	:					List[str]       	=   [to_channel_dimension_format(A_       ,							A_   ) for image in images]

       UpperCamelCase	:					List[Any]       	=   {"pixel_values": images}
       return BatchFeature(data=A_       ,							tensor_type=A_   )





      def        __UpperCamelCase( self       ,							A_       ,							A_ = None   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   outputs.logits

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

        if is_torch_tensor(A_   ):
         UpperCamelCase	:					Tuple       	=   target_sizes.numpy()

        UpperCamelCase	:					Tuple       	=   []

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

       return semantic_segmentation


"""simple docstring"""





from __future__ import annotations

__magic_name__       =			[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
__magic_name__       =			[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = []
		__SCREAMING_SNAKE_CASE            = len(UpperCamelCase_				)

		for i in range(UpperCamelCase_				):
				__SCREAMING_SNAKE_CASE            = -1
				for j in range(i + 1  ,				UpperCamelCase_				):
						if arr[i] < arr[j]:
								__SCREAMING_SNAKE_CASE            = arr[j]
								break
				result.append(UpperCamelCase_				)
		return result



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = []
		for i, outer in enumerate(UpperCamelCase_				):
				__SCREAMING_SNAKE_CASE            = -1
				for inner in arr[i + 1 :]:
						if outer < inner:
								__SCREAMING_SNAKE_CASE            = inner
								break
				result.append(UpperCamelCase_				)
		return result



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = len(UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = []
		__SCREAMING_SNAKE_CASE            = [-1] * arr_size

		for index in reversed(range(UpperCamelCase_				)				):
				if stack:
						while stack[-1] <= arr[index]:
								stack.pop()
								if not stack:
										break
				if stack:
						__SCREAMING_SNAKE_CASE            = stack[-1]
				stack.append(arr[index]				)
		return result


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

							testmod()
							print(next_greatest_element_slow(arr))
							print(next_greatest_element_fast(arr))
							print(next_greatest_element(arr))

							__magic_name__       =			(
							    "from __main__ import arr, next_greatest_element_slow, "
							    "next_greatest_element_fast, next_greatest_element"
							)
							print(
							    "next_greatest_element_slow():",
							    timeit("next_greatest_element_slow(arr)", setup=setup),
							)
							print(
							    "next_greatest_element_fast():",
							    timeit("next_greatest_element_fast(arr)", setup=setup),
							)
							print(
							    "     next_greatest_element():",
							    timeit("next_greatest_element(arr)", setup=setup),
							)




