






'''simple docstring'''





import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard


class 				snake_case__     (			unittest.TestCase):




					def 						A     (       self       :						List[Any]     )  ->    Dict:
								UpperCAmelCase_     :   Any       =   {
								    '''model_details''': {
								        '''Organization''': '''testing''',
								        '''Model date''': '''today''',
								        '''Model version''': '''v2.1, Developed by Test Corp in 2019.''',
								        '''Architecture''': '''Convolutional Neural Network.''',
								    },
								    '''metrics''': '''BLEU and ROUGE-1''',
								    '''evaluation_data''': {
								        '''Datasets''': {'''BLEU''': '''My-great-dataset-v1''', '''ROUGE-1''': '''My-short-dataset-v2.1'''},
								        '''Preprocessing''': '''See details on https://arxiv.org/pdf/1810.03993.pdf''',
								    },
								    '''training_data''': {
								        '''Dataset''': '''English Wikipedia dump dated 2018-12-01''',
								        '''Preprocessing''': (
								            '''Using SentencePiece vocabulary of size 52k tokens. See details on'''
								            ''' https://arxiv.org/pdf/1810.03993.pdf'''
								        ),
								    },
								    '''quantitative_analyses''': {'''BLEU''': 55.1, '''ROUGE-1''': 76},
								}




					def 						A     (       self       :						Union[str, Any]     )  ->    Optional[int]:
								UpperCAmelCase_     :   Optional[Any]       =   ModelCard.from_dict(self.inputs_dict     )
								self.assertTrue(hasattr(_A							,				'''model_details'''     )     )
								self.assertTrue(hasattr(_A							,				'''intended_use'''     )     )
								self.assertTrue(hasattr(_A							,				'''factors'''     )     )
								self.assertTrue(hasattr(_A							,				'''metrics'''     )     )
								self.assertTrue(hasattr(_A							,				'''evaluation_data'''     )     )
								self.assertTrue(hasattr(_A							,				'''training_data'''     )     )
								self.assertTrue(hasattr(_A							,				'''quantitative_analyses'''     )     )
								self.assertTrue(hasattr(_A							,				'''ethical_considerations'''     )     )
								self.assertTrue(hasattr(_A							,				'''caveats_and_recommendations'''     )     )




					def 						A     (       self       :						List[Any]     )  ->    List[str]:
								UpperCAmelCase_     :   Any       =   ModelCard.from_dict(self.inputs_dict     )
								UpperCAmelCase_     :   str       =   json.loads(modelcard.to_json_string()     )
								for key, value in self.inputs_dict.items():
											self.assertEqual(obj[key]							,				_A     )




					def 						A     (       self       :						List[Any]     )  ->    Optional[Any]:
								UpperCAmelCase_     :   Dict       =   ModelCard.from_dict(self.inputs_dict     )

								with tempfile.TemporaryDirectory() as tmpdirname:
											UpperCAmelCase_     :   List[Any]       =   os.path.join(_A							,				'''modelcard.json'''     )
											model_card_first.to_json_file(_A     )
											UpperCAmelCase_     :   Tuple       =   ModelCard.from_json_file(_A     )

								self.assertEqual(model_card_second.to_dict()							,				model_card_first.to_dict()     )





					def 						A     (       self       :						Optional[Any]     )  ->    List[Any]:
								UpperCAmelCase_     :   Dict       =   ModelCard.from_dict(self.inputs_dict     )

								with tempfile.TemporaryDirectory() as tmpdirname:
											model_card_first.save_pretrained(_A     )
											UpperCAmelCase_     :   Optional[Any]       =   ModelCard.from_pretrained(_A     )

								self.assertEqual(model_card_second.to_dict()							,				model_card_first.to_dict()     )










'''simple docstring'''





import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor


class 				snake_case__     (			unittest.TestCase):




					@property
					def 						A     (       self       :						Optional[Any]     )  ->    int:
								UpperCAmelCase_     :   Optional[int]       =   1
								UpperCAmelCase_     :   List[str]       =   3
								UpperCAmelCase_     :   Optional[int]       =   8
								UpperCAmelCase_     :   Optional[Any]       =   8

								UpperCAmelCase_     :   Any       =   torch.rand((batch_size, num_channels, height, width)     )

								return sample




					def 						A     (       self       :						Dict							,				_A       :						str     )  ->    List[Any]:
								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 						A     (       self       :						Tuple     )  ->    Optional[Any]:
								UpperCAmelCase_     :   int       =   VaeImageProcessor(do_resize=_A							,				do_normalize=_A     )

								UpperCAmelCase_     :   List[Any]       =   self.dummy_sample
								UpperCAmelCase_     :   Optional[int]       =   self.to_np(_A     )

								for output_type in ["pt", "np", "pil"]:
											UpperCAmelCase_     :   Any       =   image_processor.postprocess(
											    image_processor.preprocess(_A     )							,				output_type=_A							,				)
											UpperCAmelCase_     :   Union[str, Any]       =   self.to_np(_A     )
											UpperCAmelCase_     :   Union[str, Any]       =   (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 						A     (       self       :						Optional[Any]     )  ->    int:
								UpperCAmelCase_     :   Union[str, Any]       =   VaeImageProcessor(do_resize=_A							,				do_normalize=_A     )
								UpperCAmelCase_     :   List[Any]       =   self.dummy_sample.cpu().numpy().transpose(0							,				2							,				3							,				1     )

								for output_type in ["pt", "np", "pil"]:
											UpperCAmelCase_     :   Union[str, Any]       =   image_processor.postprocess(image_processor.preprocess(_A     )							,				output_type=_A     )

											UpperCAmelCase_     :   List[Any]       =   self.to_np(_A     )
											UpperCAmelCase_     :   Any       =   (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 						A     (       self       :						Any     )  ->    Optional[Any]:
								UpperCAmelCase_     :   Tuple       =   VaeImageProcessor(do_resize=_A							,				do_normalize=_A     )

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

								for output_type in ["pt", "np", "pil"]:
											UpperCAmelCase_     :   str       =   image_processor.postprocess(image_processor.preprocess(_A     )							,				output_type=_A     )
											for i, o in zip(_A							,				_A     ):
														UpperCAmelCase_     :   Optional[int]       =   np.array(_A     )
														UpperCAmelCase_     :   Dict       =   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 						A     (       self       :						Optional[Any]     )  ->    Optional[Any]:
								UpperCAmelCase_     :   int       =   VaeImageProcessor(do_resize=_A							,				do_normalize=_A     )

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

								UpperCAmelCase_     :   Tuple       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)
								UpperCAmelCase_     :   Any       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								UpperCAmelCase_     :   str       =   self.to_np(self.dummy_sample     )
								UpperCAmelCase_     :   Tuple       =   input_np_ad.squeeze(0     )

								UpperCAmelCase_     :   Optional[int]       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)
								UpperCAmelCase_     :   Optional[Any]       =   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 						A     (       self       :						Dict     )  ->    Dict:
								UpperCAmelCase_     :   List[Any]       =   VaeImageProcessor(do_resize=_A							,				do_normalize=_A     )

								UpperCAmelCase_     :   Optional[int]       =   self.dummy_sample
								UpperCAmelCase_     :   Dict       =   list(_A     )

								UpperCAmelCase_     :   str       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								UpperCAmelCase_     :   Optional[Any]       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								UpperCAmelCase_     :   Dict       =   self.to_np(self.dummy_sample     )
								list(_A     )

								UpperCAmelCase_     :   str       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								UpperCAmelCase_     :   Optional[int]       =   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


