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


lowerCamelCase_    :		int							=	logging.get_logger(__name__)


class 						a__							(       UpperCAmelCase_		):
	A__    :       Dict        =      ["""pixel_values"""]



	def __init__(						self				,						UpperCAmelCase = True				,						UpperCAmelCase = None				,						UpperCAmelCase = PILImageResampling.BILINEAR				,						UpperCAmelCase = True				,						UpperCAmelCase = 1 / 2_5_5				,						UpperCAmelCase = True				,						UpperCAmelCase = None				,						UpperCAmelCase = True				,						**UpperCAmelCase				,						)			->   None:
						super().__init__(**__lowercase   )
						__a		 =       size if size is not None else {'''shortest_edge''': 2_2_4}
						__a		 =       get_size_dict(__lowercase				,						default_to_square=__lowercase   )
						__a		 =       crop_size if crop_size is not None else {'''height''': 2_5_6, '''width''': 2_5_6}
						__a		 =       get_size_dict(__lowercase				,						param_name='crop_size'   )

						__a		 =       do_resize
						__a		 =       size
						__a		 =       resample
						__a		 =       do_rescale
						__a		 =       rescale_factor
						__a		 =       do_center_crop
						__a		 =       crop_size
						__a		 =       do_flip_channel_order



	def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = PIL.Image.BILINEAR				,						UpperCAmelCase = None				,						**UpperCAmelCase				,						)			->   np.ndarray:
						__a		 =       get_size_dict(__lowercase				,						default_to_square=__lowercase   )
						if "shortest_edge" not in size:
											raise ValueError(f'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'''   )
						__a		 =       get_resize_output_image_size(__lowercase				,						size=size['shortest_edge']				,						default_to_square=__lowercase   )
						return resize(__lowercase				,						size=__lowercase				,						resample=__lowercase				,						data_format=__lowercase				,						**__lowercase   )



	def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = None				,						**UpperCAmelCase				,						)			->   np.ndarray:
						__a		 =       get_size_dict(__lowercase   )
						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(__lowercase				,						size=(size['height'], size['width'])				,						data_format=__lowercase				,						**__lowercase   )



	def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = None				,						**UpperCAmelCase				,						)			->   List[str]:
						return rescale(__lowercase				,						scale=__lowercase				,						data_format=__lowercase				,						**__lowercase   )



	def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = None   )			->   np.ndarray:
						return flip_channel_order(__lowercase				,						data_format=__lowercase   )



	def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = ChannelDimension.FIRST				,						**UpperCAmelCase				,						)			->   PIL.Image.Image:
						__a		 =       do_resize if do_resize is not None else self.do_resize
						__a		 =       resample if resample is not None else self.resample
						__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_center_crop if do_center_crop is not None else self.do_center_crop
						__a		 =       (
						    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
						)

						__a		 =       size if size is not None else self.size
						__a		 =       get_size_dict(__lowercase				,						default_to_square=__lowercase   )
						__a		 =       crop_size if crop_size is not None else self.crop_size
						__a		 =       get_size_dict(__lowercase				,						param_name='crop_size'   )

						__a		 =       make_list_of_images(__lowercase   )

						if not valid_images(__lowercase   ):
											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.
						__a		 =       [to_numpy_array(__lowercase   ) for image in images]

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

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

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

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

						__a		 =       [to_channel_dimension_format(__lowercase				,						__lowercase   ) for image in images]

						__a		 =       {'''pixel_values''': images}
						return BatchFeature(data=__lowercase				,						tensor_type=__lowercase   )







	def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = None   )			->   str:
						__a		 =       outputs.logits

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

											if is_torch_tensor(__lowercase   ):
																__a		 =       target_sizes.numpy()

											__a		 =       []

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

						return semantic_segmentation





def 							lowerCAmelCase(     __lowerCamelCase						):
					if not all(char in '01' for char in bin_string						):
										raise ValueError('Non-binary value was passed to the function'						)
					if not bin_string:
										raise ValueError('Empty string was passed to the function'						)
					__a		 =       ''
					while len(__lowerCamelCase						) % 3 != 0:
										__a		 =       '0' + bin_string
					__a		 =       [
					    bin_string[index : index + 3]
					    for index in range(len(__lowerCamelCase						)						)
					    if index % 3 == 0
					]
					for bin_group in bin_string_in_3_list:
										__a		 =       0
										for index, val in enumerate(__lowerCamelCase						):
															oct_val += int(2 ** (2 - index) * int(__lowerCamelCase						)						)
										oct_string += str(__lowerCamelCase						)
					return oct_string


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

	testmod()




