



'''simple docstring'''







import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor

class    lowerCamelCase_				(						unittest.TestCase	):






   '''simple docstring'''

   @property
   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      1
       UpperCamelCase__	     =      3
       UpperCamelCase__	     =      8
       UpperCamelCase__	     =      8

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

       return sample


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase			):



       if isinstance(image[0]  ,    PIL.Image.Image			):
           return np.stack([np.array(A_			) for i in image]  ,    axis=0			)
       elif isinstance(A_  ,    torch.Tensor			):
           return image.cpu().numpy().transpose(0  ,    2  ,    3  ,    1			)
       return image


   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      VaeImageProcessor(do_resize=A_  ,    do_normalize=A_			)

       UpperCamelCase__	     =      self.dummy_sample
       UpperCamelCase__	     =      self.to_np(A_			)

       for output_type in ["pt", "np", "pil"]:
           UpperCamelCase__	     =      image_processor.postprocess(
               image_processor.preprocess(A_			)  ,    output_type=A_  ,    )
           UpperCamelCase__	     =      self.to_np(A_			)
           UpperCamelCase__	     =      (input_np * 2_55).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 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      VaeImageProcessor(do_resize=A_  ,    do_normalize=A_			)
       UpperCamelCase__	     =      self.dummy_sample.cpu().numpy().transpose(0  ,    2  ,    3  ,    1			)

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

           UpperCamelCase__	     =      self.to_np(A_			)
           UpperCamelCase__	     =      (input_np * 2_55).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 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      VaeImageProcessor(do_resize=A_  ,    do_normalize=A_			)

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

       for output_type in ["pt", "np", "pil"]:
           UpperCamelCase__	     =      image_processor.postprocess(image_processor.preprocess(A_			)  ,    output_type=A_			)
           for i, o in zip(A_  ,    A_			):
               UpperCamelCase__	     =      np.array(A_			)
               UpperCamelCase__	     =      self.to_np(A_			) if output_type == "pil" else (self.to_np(A_			) * 2_55).round()
               assert (
                   np.abs(in_np - out_np			).max() < 1e-6
               ), f'decoded output does not match input for output_type {output_type}'


   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      VaeImageProcessor(do_resize=A_  ,    do_normalize=A_			)

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

       UpperCamelCase__	     =      image_processor.postprocess(
           image_processor.preprocess(A_			)  ,    output_type="np"  ,    )
       UpperCamelCase__	     =      image_processor.postprocess(
           image_processor.preprocess(A_			)  ,    output_type="np"  ,    )

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

       UpperCamelCase__	     =      image_processor.postprocess(
           image_processor.preprocess(A_			)  ,    output_type="np"  ,    )
       UpperCamelCase__	     =      image_processor.postprocess(
           image_processor.preprocess(A_			)  ,    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 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      VaeImageProcessor(do_resize=A_  ,    do_normalize=A_			)

       UpperCamelCase__	     =      self.dummy_sample
       UpperCamelCase__	     =      list(A_			)

       UpperCamelCase__	     =      image_processor.postprocess(
           image_processor.preprocess(A_			)  ,    output_type="np"  ,    )

       UpperCamelCase__	     =      image_processor.postprocess(
           image_processor.preprocess(A_			)  ,    output_type="np"  ,    )

       UpperCamelCase__	     =      self.to_np(self.dummy_sample			)
       list(A_			)

       UpperCamelCase__	     =      image_processor.postprocess(
           image_processor.preprocess(A_			)  ,    output_type="np"  ,    )

       UpperCamelCase__	     =      image_processor.postprocess(
           image_processor.preprocess(A_			)  ,    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"""






from transformers import ProcessorMixin




class    __snake_case(					__A       ):
    _A       = '''AutoFeatureExtractor'''
    _A       = '''AutoTokenizer'''
