


'''simple docstring'''



import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor



class       a							(						unittest.TestCase			):


     """simple docstring"""






     @property
     def 					lowerCamelCase__					(							self	:						Optional[Any]     )		->				Any:
          __UpperCAmelCase      :					str    				=     1
          __UpperCAmelCase      :					List[str]    				=     3
          __UpperCAmelCase      :					str    				=     8
          __UpperCAmelCase      :					Union[str, Any]    				=     8

          __UpperCAmelCase      :					Dict    				=     torch.rand((batch_size, num_channels, height, width)     )

          return sample

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

     def 					lowerCamelCase__					(							self	:						List[Any]     )		->				Tuple:
          __UpperCAmelCase      :					List[Any]    				=     VaeImageProcessor(do_resize=snake_case				,					do_normalize=snake_case     )

          __UpperCAmelCase      :					Optional[Any]    				=     self.dummy_sample
          __UpperCAmelCase      :					Union[str, Any]    				=     self.to_np(snake_case     )

          for output_type in ["pt", "np", "pil"]:
               __UpperCAmelCase      :					str    				=     image_processor.postprocess(
                   image_processor.preprocess(snake_case     )				,					output_type=snake_case				,					)
               __UpperCAmelCase      :					Tuple    				=     self.to_np(snake_case     )
               __UpperCAmelCase      :					Dict    				=     (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	:						Any     )		->				Dict:
          __UpperCAmelCase      :					Tuple    				=     VaeImageProcessor(do_resize=snake_case				,					do_normalize=snake_case     )
          __UpperCAmelCase      :					Any    				=     self.dummy_sample.cpu().numpy().transpose(0				,					2				,					3				,					1     )

          for output_type in ["pt", "np", "pil"]:
               __UpperCAmelCase      :					List[str]    				=     image_processor.postprocess(image_processor.preprocess(snake_case     )				,					output_type=snake_case     )

               __UpperCAmelCase      :					int    				=     self.to_np(snake_case     )
               __UpperCAmelCase      :					List[Any]    				=     (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	:						Any     )		->				str:
          __UpperCAmelCase      :					str    				=     VaeImageProcessor(do_resize=snake_case				,					do_normalize=snake_case     )

          __UpperCAmelCase      :					List[Any]    				=     self.dummy_sample.cpu().numpy().transpose(0				,					2				,					3				,					1     )
          __UpperCAmelCase      :					List[Any]    				=     image_processor.numpy_to_pil(snake_case     )

          for output_type in ["pt", "np", "pil"]:
               __UpperCAmelCase      :					int    				=     image_processor.postprocess(image_processor.preprocess(snake_case     )				,					output_type=snake_case     )
               for i, o in zip(snake_case				,					snake_case     ):
                    __UpperCAmelCase      :					Tuple    				=     np.array(snake_case     )
                    __UpperCAmelCase      :					List[Any]    				=     self.to_np(snake_case     ) if output_type == '''pil''' else (self.to_np(snake_case     ) * 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	:						Union[str, Any]     )		->				str:
          __UpperCAmelCase      :					List[Any]    				=     VaeImageProcessor(do_resize=snake_case				,					do_normalize=snake_case     )

          __UpperCAmelCase      :					Optional[Any]    				=     self.dummy_sample
          __UpperCAmelCase      :					int    				=     input_pt_ad.squeeze(0     )

          __UpperCAmelCase      :					Dict    				=     image_processor.postprocess(
              image_processor.preprocess(snake_case     )				,					output_type='''np'''				,					)
          __UpperCAmelCase      :					List[Any]    				=     image_processor.postprocess(
              image_processor.preprocess(snake_case     )				,					output_type='''np'''				,					)

          __UpperCAmelCase      :					str    				=     self.to_np(self.dummy_sample     )
          __UpperCAmelCase      :					Dict    				=     input_np_ad.squeeze(0     )

          __UpperCAmelCase      :					Any    				=     image_processor.postprocess(
              image_processor.preprocess(snake_case     )				,					output_type='''np'''				,					)
          __UpperCAmelCase      :					Optional[Any]    				=     image_processor.postprocess(
              image_processor.preprocess(snake_case     )				,					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	:						Optional[int]     )		->				Any:
          __UpperCAmelCase      :					Any    				=     VaeImageProcessor(do_resize=snake_case				,					do_normalize=snake_case     )

          __UpperCAmelCase      :					Optional[Any]    				=     self.dummy_sample
          __UpperCAmelCase      :					Tuple    				=     list(snake_case     )

          __UpperCAmelCase      :					Dict    				=     image_processor.postprocess(
              image_processor.preprocess(snake_case     )				,					output_type='''np'''				,					)

          __UpperCAmelCase      :					Union[str, Any]    				=     image_processor.postprocess(
              image_processor.preprocess(snake_case     )				,					output_type='''np'''				,					)

          __UpperCAmelCase      :					Dict    				=     self.to_np(self.dummy_sample     )
          list(snake_case     )

          __UpperCAmelCase      :					List[Any]    				=     image_processor.postprocess(
              image_processor.preprocess(snake_case     )				,					output_type='''np'''				,					)

          __UpperCAmelCase      :					Tuple    				=     image_processor.postprocess(
              image_processor.preprocess(snake_case     )				,					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



'''simple docstring'''



import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

__UpperCAmelCase     :Tuple												=  [(1,)]




def       _a      (		):





     '''simple docstring'''






     __UpperCAmelCase      :					Any    				=     bool(os.getenv('''RUN_SLOW'''     ,   0    )    )
     if run_slow:
          assert True
     else:
          assert False, "should have been skipped"




def       _a      (		):





     '''simple docstring'''






     __UpperCAmelCase      :					str    				=     bool(os.getenv('''RUN_SLOW'''     ,   0    )    )
     if run_slow and torch_device == "cuda":
          assert True
     else:
          assert False, "should have been skipped"



@require_torch
class       a							(						unittest.TestCase			):


     """simple docstring"""






     @slow
     @require_torch_gpu
     def 					lowerCamelCase__					(							self	:						Tuple     )		->				str:
          check_slow_torch_cuda()

     @require_torch_gpu
     @slow
     def 					lowerCamelCase__					(							self	:						Any     )		->				List[Any]:
          check_slow_torch_cuda()






     @parameterized.expand(snake_case     )
     @slow
     def 					lowerCamelCase__					(							self	:						Union[str, Any]				,					snake_case	:						int=None     )		->				Union[str, Any]:
          check_slow()




@slow
@require_torch_gpu
def       _a      (		):





     '''simple docstring'''






     check_slow_torch_cuda()




@require_torch_gpu
@slow
def       _a      (		):





     '''simple docstring'''






     check_slow_torch_cuda()




@slow
@pytest.mark.parametrize('''param'''     ,   [1]    )
def       _a      (		_lowercase   :      Union[str, Any]    ):





     '''simple docstring'''






     check_slow()




@pytest.mark.parametrize('''param'''     ,   [1]    )
@slow
def       _a      (		_lowercase   :      Dict    ):





     '''simple docstring'''






     check_slow()