
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


_UpperCAmelCase					: Dict      =       logging.get_logger(__name__)


class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):
		__lowercase       :       Any	     =				["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_				,      )  ->					None:
				"""simple docstring"""
				super().__init__(**A_       )
				UpperCamelCase		=     size if size is not None else {'shortest_edge': 256}
				UpperCamelCase		=     get_size_dict(A_				,      default_to_square=A_       )
				UpperCamelCase		=     crop_size if crop_size is not None else {'height': 224, 'width': 224}
				UpperCamelCase		=     get_size_dict(A_				,      param_name='crop_size'       )
				UpperCamelCase		=     do_resize
				UpperCamelCase		=     size
				UpperCamelCase		=     resample
				UpperCamelCase		=     do_center_crop
				UpperCamelCase		=     crop_size
				UpperCamelCase		=     do_rescale
				UpperCamelCase		=     rescale_factor
				UpperCamelCase		=     do_normalize
				UpperCamelCase		=     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
				UpperCamelCase		=     image_std if image_std is not None else IMAGENET_STANDARD_STD



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_ = PILImageResampling.BICUBIC				,      A_ = None				,      **A_				,      )  ->					np.ndarray:
				"""simple docstring"""
				UpperCamelCase		=     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		=     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_				,      )  ->					np.ndarray:
				"""simple docstring"""
				UpperCamelCase		=     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_       )  ->					np.ndarray:
				"""simple docstring"""
				return rescale(A_				,      scale=A_				,      data_format=A_				,      **A_       )



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_				,      A_ = None				,      **A_				,      )  ->					np.ndarray:
				"""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_				,      )  ->					Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase		=     do_resize if do_resize is not None else self.do_resize
				UpperCamelCase		=     size if size is not None else self.size
				UpperCamelCase		=     get_size_dict(A_				,      default_to_square=A_       )
				UpperCamelCase		=     resample if resample is not None else self.resample
				UpperCamelCase		=     do_center_crop if do_center_crop is not None else self.do_center_crop
				UpperCamelCase		=     crop_size if crop_size is not None else self.crop_size
				UpperCamelCase		=     get_size_dict(A_				,      param_name='crop_size'       )
				UpperCamelCase		=     do_rescale if do_rescale is not None else self.do_rescale
				UpperCamelCase		=     rescale_factor if rescale_factor is not None else self.rescale_factor
				UpperCamelCase		=     do_normalize if do_normalize is not None else self.do_normalize
				UpperCamelCase		=     image_mean if image_mean is not None else self.image_mean
				UpperCamelCase		=     image_std if image_std is not None else self.image_std

				UpperCamelCase		=     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		=     [to_numpy_array(A_       ) for image in images]

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

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

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

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

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

				UpperCamelCase		=     {'pixel_values': images}
				return BatchFeature(data=A_				,      tensor_type=A_       )



		def __UpperCamelCase ( self				,      A_				,      A_ = None       )  ->					Optional[int]:
				"""simple docstring"""
				UpperCamelCase		=     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		=     target_sizes.numpy()

						UpperCamelCase		=     []

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

				return semantic_segmentation


def       A       (					lowercase )   ->  str:
		'''simple docstring'''

		return " ".join(
		    ''.join(word[::-1] ) if len(lowercase ) > 4 else word for word in sentence.split() )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					print(reverse_long_words("Hey wollef sroirraw"))
