
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 		__magic_name__   (						snake_case    ):
		_lowerCAmelCase		=	42
		_lowerCAmelCase		=	42





		def __init__(							self    :	Dict						,  lowerCamelCase__    :	UNetaDModel						,  lowerCamelCase__    :	PNDMScheduler				):
							super().__init__()

							lowerCAmelCase		:					Any  	=     PNDMScheduler.from_config(scheduler.config				)

							self.register_modules(unet=lowerCamelCase__						,  scheduler=lowerCamelCase__				)





		@torch.no_grad()
		def __call__(							self    :	Dict						,  lowerCamelCase__    :	int = 1						,  lowerCamelCase__    :	int = 5_0						,  lowerCamelCase__    :	Optional[Union[torch.Generator, List[torch.Generator]]] = None						,  lowerCamelCase__    :	Optional[str] = "pil"						,  lowerCamelCase__    :	bool = True						,  **lowerCamelCase__    :	Any						,  ):
							lowerCAmelCase		:					List[str]  	=     randn_tensor(
							    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,  generator=lowerCamelCase__						,  device=self.device						,  )

							self.scheduler.set_timesteps(lowerCamelCase__				)
							for t in self.progress_bar(self.scheduler.timesteps				):
												lowerCAmelCase		:					Tuple  	=     self.unet(lowerCamelCase__						,  lowerCamelCase__				).sample

												lowerCAmelCase		:					str  	=     self.scheduler.step(lowerCamelCase__						,  lowerCamelCase__						,  lowerCamelCase__				).prev_sample

							lowerCAmelCase		:					Dict  	=     (image / 2 + 0.5).clamp(0						,  1				)
							lowerCAmelCase		:					Optional[int]  	=     image.cpu().permute(0						,  2						,  3						,  1				).numpy()
							if output_type == "pil":
												lowerCAmelCase		:					List[Any]  	=     self.numpy_to_pil(lowerCamelCase__				)

							if not return_dict:
												return (image,)

							return ImagePipelineOutput(images=lowerCamelCase__				)


import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard


class 		__magic_name__   (						unittest.TestCase    ):





		def       _A							(							self    :	Dict				):
							lowerCAmelCase		:					List[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''': 5_5.1, '''ROUGE-1''': 7_6},
							}





		def       _A							(							self    :	str				):
							lowerCAmelCase		:					Dict  	=     ModelCard.from_dict(self.inputs_dict				)
							self.assertTrue(hasattr(lowerCamelCase__						,  '''model_details'''				)				)
							self.assertTrue(hasattr(lowerCamelCase__						,  '''intended_use'''				)				)
							self.assertTrue(hasattr(lowerCamelCase__						,  '''factors'''				)				)
							self.assertTrue(hasattr(lowerCamelCase__						,  '''metrics'''				)				)
							self.assertTrue(hasattr(lowerCamelCase__						,  '''evaluation_data'''				)				)
							self.assertTrue(hasattr(lowerCamelCase__						,  '''training_data'''				)				)
							self.assertTrue(hasattr(lowerCamelCase__						,  '''quantitative_analyses'''				)				)
							self.assertTrue(hasattr(lowerCamelCase__						,  '''ethical_considerations'''				)				)
							self.assertTrue(hasattr(lowerCamelCase__						,  '''caveats_and_recommendations'''				)				)





		def       _A							(							self    :	Optional[Any]				):
							lowerCAmelCase		:					Tuple  	=     ModelCard.from_dict(self.inputs_dict				)
							lowerCAmelCase		:					Dict  	=     json.loads(modelcard.to_json_string()				)
							for key, value in self.inputs_dict.items():
												self.assertEqual(obj[key]						,  lowerCamelCase__				)





		def       _A							(							self    :	Tuple				):
							lowerCAmelCase		:					Tuple  	=     ModelCard.from_dict(self.inputs_dict				)

							with tempfile.TemporaryDirectory() as tmpdirname:
												lowerCAmelCase		:					str  	=     os.path.join(lowerCamelCase__						,  '''modelcard.json'''				)
												model_card_first.to_json_file(lowerCamelCase__				)
												lowerCAmelCase		:					Dict  	=     ModelCard.from_json_file(lowerCamelCase__				)

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





		def       _A							(							self    :	Union[str, Any]				):
							lowerCAmelCase		:					int  	=     ModelCard.from_dict(self.inputs_dict				)

							with tempfile.TemporaryDirectory() as tmpdirname:
												model_card_first.save_pretrained(lowerCamelCase__				)
												lowerCAmelCase		:					Any  	=     ModelCard.from_pretrained(lowerCamelCase__				)

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