






import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor





class        a__      (	unittest.TestCase		):






		@property
		def 					_lowerCamelCase					(      self						)				->				str:
				__A    =							1
				__A    =							3
				__A    =							8
				__A    =							8

				__A    =							torch.rand((batch_size, num_channels, height, width)						)

				return sample






		def 					_lowerCamelCase					(      self							, lowercase__						)				->				Any:
				if isinstance(image[0]							, PIL.Image.Image						):
						return np.stack([np.array(lowercase__						) for i in image]							, axis=0						)
				elif isinstance(lowercase__							, torch.Tensor						):
						return image.cpu().numpy().transpose(0							, 2							, 3							, 1						)
				return image






		def 					_lowerCamelCase					(      self						)				->				Optional[Any]:
				__A    =							VaeImageProcessor(do_resize=lowercase__							, do_normalize=lowercase__						)

				__A    =							self.dummy_sample
				__A    =							self.to_np(lowercase__						)

				for output_type in ["pt", "np", "pil"]:
						__A    =							image_processor.postprocess(
						    image_processor.preprocess(lowercase__						)							, output_type=lowercase__							, )
						__A    =							self.to_np(lowercase__						)
						__A    =							(input_np * 255).round() if output_type == "pil" else input_np
						assert (
						    np.abs(in_np - out_np						).max() < 1e-6
						), F"""decoded output does not match input for output_type {output_type}"""






		def 					_lowerCamelCase					(      self						)				->				List[Any]:
				__A    =							VaeImageProcessor(do_resize=lowercase__							, do_normalize=lowercase__						)
				__A    =							self.dummy_sample.cpu().numpy().transpose(0							, 2							, 3							, 1						)

				for output_type in ["pt", "np", "pil"]:
						__A    =							image_processor.postprocess(image_processor.preprocess(lowercase__						)							, output_type=lowercase__						)

						__A    =							self.to_np(lowercase__						)
						__A    =							(input_np * 255).round() if output_type == "pil" else input_np
						assert (
						    np.abs(in_np - out_np						).max() < 1e-6
						), F"""decoded output does not match input for output_type {output_type}"""






		def 					_lowerCamelCase					(      self						)				->				List[Any]:
				__A    =							VaeImageProcessor(do_resize=lowercase__							, do_normalize=lowercase__						)

				__A    =							self.dummy_sample.cpu().numpy().transpose(0							, 2							, 3							, 1						)
				__A    =							image_processor.numpy_to_pil(lowercase__						)

				for output_type in ["pt", "np", "pil"]:
						__A    =							image_processor.postprocess(image_processor.preprocess(lowercase__						)							, output_type=lowercase__						)
						for i, o in zip(lowercase__							, lowercase__						):
								__A    =							np.array(lowercase__						)
								__A    =							self.to_np(lowercase__						) if output_type == "pil" else (self.to_np(lowercase__						) * 255).round()
								assert (
								    np.abs(in_np - out_np						).max() < 1e-6
								), F"""decoded output does not match input for output_type {output_type}"""






		def 					_lowerCamelCase					(      self						)				->				List[str]:
				__A    =							VaeImageProcessor(do_resize=lowercase__							, do_normalize=lowercase__						)

				__A    =							self.dummy_sample
				__A    =							input_pt_ad.squeeze(0						)

				__A    =							image_processor.postprocess(
				    image_processor.preprocess(lowercase__						)							, output_type="np"							, )
				__A    =							image_processor.postprocess(
				    image_processor.preprocess(lowercase__						)							, output_type="np"							, )

				__A    =							self.to_np(self.dummy_sample						)
				__A    =							input_np_ad.squeeze(0						)

				__A    =							image_processor.postprocess(
				    image_processor.preprocess(lowercase__						)							, output_type="np"							, )
				__A    =							image_processor.postprocess(
				    image_processor.preprocess(lowercase__						)							, output_type="np"							, )

				assert np.abs(out_pt_ad - out_pt_ad						).max() < 1e-6
				assert np.abs(out_np_ad - out_np_ad						).max() < 1e-6






		def 					_lowerCamelCase					(      self						)				->				str:
				__A    =							VaeImageProcessor(do_resize=lowercase__							, do_normalize=lowercase__						)

				__A    =							self.dummy_sample
				__A    =							list(lowercase__						)

				__A    =							image_processor.postprocess(
				    image_processor.preprocess(lowercase__						)							, output_type="np"							, )

				__A    =							image_processor.postprocess(
				    image_processor.preprocess(lowercase__						)							, output_type="np"							, )

				__A    =							self.to_np(self.dummy_sample						)
				list(lowercase__						)

				__A    =							image_processor.postprocess(
				    image_processor.preprocess(lowercase__						)							, output_type="np"							, )

				__A    =							image_processor.postprocess(
				    image_processor.preprocess(lowercase__						)							, output_type="np"							, )

				assert np.abs(out_pt_ad - out_pt_list						).max() < 1e-6
				assert np.abs(out_np_ad - out_np_list						).max() < 1e-6





import os






def  _UpperCAmelCase						( )		->Tuple:


 '''simple docstring'''
 snake_case :			Dict							=   0
 snake_case :			int							=   0
 with open(os.path.dirname(__lowerCamelCase							) + '''/p022_names.txt'''							) as file:
  snake_case :			int							=   str(file.readlines()[0]							)
  snake_case :			Any							=   name.replace('''"'''			,  ''''''							).split(''','''							)

 name.sort()
 for i in range(len(__lowerCamelCase							)							):
  for j in name[i]:
   temp_sum += ord(__lowerCamelCase							) - ord('''A'''							) + 1
  total_sum += (i + 1) * temp_sum
  snake_case :			List[Any]							=   0
 return total_sum


if __name__ == "__main__":
  print(solution())






