





import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor

class 						UpperCamelCase__					( unittest.TestCase  ):



     '''simple docstring'''

     @property
     def 							SCREAMING_SNAKE_CASE__    (				self		:       int			)	->						Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		1
           SCREAMING_SNAKE_CASE						=		3
           SCREAMING_SNAKE_CASE						=		8
           SCREAMING_SNAKE_CASE						=		8

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

           return sample






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Dict		,lowerCamelCase__		:       Optional[Any]			)	->						Dict:




           '''simple docstring'''


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






     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[Any]			)	->						Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		VaeImageProcessor(do_resize=lowerCamelCase__		,do_normalize=lowerCamelCase__			)

           SCREAMING_SNAKE_CASE						=		self.dummy_sample
           SCREAMING_SNAKE_CASE						=		self.to_np(lowerCamelCase__			)

           for output_type in ["pt", "np", "pil"]:
                 SCREAMING_SNAKE_CASE						=		image_processor.postprocess(
                     image_processor.preprocess(lowerCamelCase__			)		,output_type=lowerCamelCase__		,)
                 SCREAMING_SNAKE_CASE						=		self.to_np(lowerCamelCase__			)
                 SCREAMING_SNAKE_CASE						=		(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 							SCREAMING_SNAKE_CASE__    (				self		:       Any			)	->						Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		VaeImageProcessor(do_resize=lowerCamelCase__		,do_normalize=lowerCamelCase__			)
           SCREAMING_SNAKE_CASE						=		self.dummy_sample.cpu().numpy().transpose(0		,2		,3		,1			)

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

                 SCREAMING_SNAKE_CASE						=		self.to_np(lowerCamelCase__			)
                 SCREAMING_SNAKE_CASE						=		(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 							SCREAMING_SNAKE_CASE__    (				self		:       List[Any]			)	->						Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		VaeImageProcessor(do_resize=lowerCamelCase__		,do_normalize=lowerCamelCase__			)

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

           for output_type in ["pt", "np", "pil"]:
                 SCREAMING_SNAKE_CASE						=		image_processor.postprocess(image_processor.preprocess(lowerCamelCase__			)		,output_type=lowerCamelCase__			)
                 for i, o in zip(lowerCamelCase__		,lowerCamelCase__			):
                       SCREAMING_SNAKE_CASE						=		np.array(lowerCamelCase__			)
                       SCREAMING_SNAKE_CASE						=		self.to_np(lowerCamelCase__			) if output_type == """pil""" else (self.to_np(lowerCamelCase__			) * 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 							SCREAMING_SNAKE_CASE__    (				self		:       Optional[Any]			)	->						Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		VaeImageProcessor(do_resize=lowerCamelCase__		,do_normalize=lowerCamelCase__			)

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

           SCREAMING_SNAKE_CASE						=		image_processor.postprocess(
               image_processor.preprocess(lowerCamelCase__			)		,output_type="""np"""		,)
           SCREAMING_SNAKE_CASE						=		image_processor.postprocess(
               image_processor.preprocess(lowerCamelCase__			)		,output_type="""np"""		,)

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

           SCREAMING_SNAKE_CASE						=		image_processor.postprocess(
               image_processor.preprocess(lowerCamelCase__			)		,output_type="""np"""		,)
           SCREAMING_SNAKE_CASE						=		image_processor.postprocess(
               image_processor.preprocess(lowerCamelCase__			)		,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 							SCREAMING_SNAKE_CASE__    (				self		:       Optional[int]			)	->						Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		VaeImageProcessor(do_resize=lowerCamelCase__		,do_normalize=lowerCamelCase__			)

           SCREAMING_SNAKE_CASE						=		self.dummy_sample
           SCREAMING_SNAKE_CASE						=		list(lowerCamelCase__			)

           SCREAMING_SNAKE_CASE						=		image_processor.postprocess(
               image_processor.preprocess(lowerCamelCase__			)		,output_type="""np"""		,)

           SCREAMING_SNAKE_CASE						=		image_processor.postprocess(
               image_processor.preprocess(lowerCamelCase__			)		,output_type="""np"""		,)

           SCREAMING_SNAKE_CASE						=		self.to_np(self.dummy_sample			)
           list(lowerCamelCase__			)

           SCREAMING_SNAKE_CASE						=		image_processor.postprocess(
               image_processor.preprocess(lowerCamelCase__			)		,output_type="""np"""		,)

           SCREAMING_SNAKE_CASE						=		image_processor.postprocess(
               image_processor.preprocess(lowerCamelCase__			)		,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 unittest
from dataclasses import dataclass
from typing import Optional

from transformers.utils import ModelOutput

@dataclass
class 						UpperCamelCase__					( lowerCAmelCase_  ):



     '''simple docstring'''

     __snake_case       :      float
     __snake_case       :      Optional[float]						      =     None
     __snake_case       :      Optional[float]						      =     None

class 						UpperCamelCase__					( unittest.TestCase  ):



     '''simple docstring'''

     def 							SCREAMING_SNAKE_CASE__    (				self		:       Any			)	->						Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=30			)
           self.assertEqual(x.a		,30			)
           self.assertIsNone(x.b			)
           self.assertIsNone(x.c			)
           with self.assertRaises(lowerCamelCase__			):
                 SCREAMING_SNAKE_CASE						=		x.d






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Optional[Any]			)	->						Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=30		,b=10			)
           self.assertEqual(x[0]		,30			)
           self.assertEqual(x[1]		,10			)
           self.assertEqual(x[:2]		,(30, 10)			)
           self.assertEqual(x[:]		,(30, 10)			)

           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=30		,c=10			)
           self.assertEqual(x[0]		,30			)
           self.assertEqual(x[1]		,10			)
           self.assertEqual(x[:2]		,(30, 10)			)
           self.assertEqual(x[:]		,(30, 10)			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       str			)	->						Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=30		,b=10			)
           self.assertEqual(x["""a"""]		,30			)
           self.assertEqual(x["""b"""]		,10			)
           with self.assertRaises(lowerCamelCase__			):
                 SCREAMING_SNAKE_CASE						=		x["""c"""]

           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=30		,c=10			)
           self.assertEqual(x["""a"""]		,30			)
           self.assertEqual(x["""c"""]		,10			)
           with self.assertRaises(lowerCamelCase__			):
                 SCREAMING_SNAKE_CASE						=		x["""b"""]






     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[Any]			)	->						Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=30			)
           self.assertEqual(list(x.keys()			)		,["""a"""]			)
           self.assertEqual(list(x.values()			)		,[30]			)
           self.assertEqual(list(x.items()			)		,[("""a""", 30)]			)
           self.assertEqual(list(lowerCamelCase__			)		,["""a"""]			)

           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=30		,b=10			)
           self.assertEqual(list(x.keys()			)		,["""a""", """b"""]			)
           self.assertEqual(list(x.values()			)		,[30, 10]			)
           self.assertEqual(list(x.items()			)		,[("""a""", 30), ("""b""", 10)]			)
           self.assertEqual(list(lowerCamelCase__			)		,["""a""", """b"""]			)

           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=30		,c=10			)
           self.assertEqual(list(x.keys()			)		,["""a""", """c"""]			)
           self.assertEqual(list(x.values()			)		,[30, 10]			)
           self.assertEqual(list(x.items()			)		,[("""a""", 30), ("""c""", 10)]			)
           self.assertEqual(list(lowerCamelCase__			)		,["""a""", """c"""]			)

           with self.assertRaises(lowerCamelCase__			):
                 SCREAMING_SNAKE_CASE						=		x.update({"""d""": 20}			)
           with self.assertRaises(lowerCamelCase__			):
                 del x["a"]
           with self.assertRaises(lowerCamelCase__			):
                 SCREAMING_SNAKE_CASE						=		x.pop("""a"""			)
           with self.assertRaises(lowerCamelCase__			):
                 SCREAMING_SNAKE_CASE						=		x.setdefault("""d"""		,32			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       int			)	->						List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=30			)
           SCREAMING_SNAKE_CASE						=		10
           self.assertEqual(x.a		,10			)
           self.assertEqual(x["""a"""]		,10			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[str]			)	->						Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=30			)
           SCREAMING_SNAKE_CASE						=		10
           self.assertEqual(x.a		,10			)
           self.assertEqual(x["""a"""]		,10			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[Any]			)	->						Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		ModelOutputTest({"""a""": 30, """b""": 10}			)
           self.assertEqual(list(x.keys()			)		,["""a""", """b"""]			)
           self.assertEqual(x.a		,30			)
           self.assertEqual(x.b		,10			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Optional[int]			)	->						Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		ModelOutputTest([("""a""", 30), ("""b""", 10)]			)
           self.assertEqual(list(x.keys()			)		,["""a""", """b"""]			)
           self.assertEqual(x.a		,30			)
           self.assertEqual(x.b		,10			)

           with self.assertRaises(lowerCamelCase__			):
                 SCREAMING_SNAKE_CASE						=		ModelOutputTest([("""a""", 30), (10, 10)]			)

           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=(30, 30)			)
           self.assertEqual(list(x.keys()			)		,["""a"""]			)
           self.assertEqual(x.a		,(30, 30)			)


