


"""simple docstring"""



import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard







class       __a	(   unittest.TestCase       ):







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					lowerCAmelCase_			       = {
					    '''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''': 76},
					}







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					lowerCAmelCase_			       = ModelCard.from_dict(self.inputs_dict      )
					self.assertTrue(hasattr(UpperCAmelCase			,  '''model_details'''      )      )
					self.assertTrue(hasattr(UpperCAmelCase			,  '''intended_use'''      )      )
					self.assertTrue(hasattr(UpperCAmelCase			,  '''factors'''      )      )
					self.assertTrue(hasattr(UpperCAmelCase			,  '''metrics'''      )      )
					self.assertTrue(hasattr(UpperCAmelCase			,  '''evaluation_data'''      )      )
					self.assertTrue(hasattr(UpperCAmelCase			,  '''training_data'''      )      )
					self.assertTrue(hasattr(UpperCAmelCase			,  '''quantitative_analyses'''      )      )
					self.assertTrue(hasattr(UpperCAmelCase			,  '''ethical_considerations'''      )      )
					self.assertTrue(hasattr(UpperCAmelCase			,  '''caveats_and_recommendations'''      )      )







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					lowerCAmelCase_			       = ModelCard.from_dict(self.inputs_dict      )
					lowerCAmelCase_			       = json.loads(modelcard.to_json_string()      )
					for key, value in self.inputs_dict.items():
						self.assertEqual(obj[key]			,  UpperCAmelCase      )







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					lowerCAmelCase_			       = ModelCard.from_dict(self.inputs_dict      )

					with tempfile.TemporaryDirectory() as tmpdirname:
						lowerCAmelCase_			       = os.path.join(UpperCAmelCase			,  '''modelcard.json'''      )
						model_card_first.to_json_file(UpperCAmelCase      )
						lowerCAmelCase_			       = ModelCard.from_json_file(UpperCAmelCase      )

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







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					lowerCAmelCase_			       = ModelCard.from_dict(self.inputs_dict      )

					with tempfile.TemporaryDirectory() as tmpdirname:
						model_card_first.save_pretrained(UpperCAmelCase      )
						lowerCAmelCase_			       = ModelCard.from_pretrained(UpperCAmelCase      )

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



"""simple docstring"""



import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class       __a	(   __snake_case       ):
				lowerCamelCase    :					Union[str, Any] 					=(LMSDiscreteScheduler,)
				lowerCamelCase    :					int 					=10







				def     lowerCamelCase_    (   self			,  **UpperCAmelCase      ):

					'''simple docstring'''






					lowerCAmelCase_			       = {
					    '''num_train_timesteps''': 1100,
					    '''beta_start''': 0.0_0_0_1,
					    '''beta_end''': 0.0_2,
					    '''beta_schedule''': '''linear''',
					}

					config.update(**UpperCAmelCase      )
					return config







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					for timesteps in [10, 50, 100, 1000]:
						self.check_over_configs(num_train_timesteps=UpperCAmelCase      )







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]			,  [0.0_0_0_2, 0.0_0_2, 0.0_2]      ):
						self.check_over_configs(beta_start=UpperCAmelCase			,  beta_end=UpperCAmelCase      )







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					for schedule in ["linear", "scaled_linear"]:
						self.check_over_configs(beta_schedule=UpperCAmelCase      )







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					for prediction_type in ["epsilon", "v_prediction"]:
						self.check_over_configs(prediction_type=UpperCAmelCase      )







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					for t in [0, 500, 800]:
						self.check_over_forward(time_step=UpperCAmelCase      )







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					lowerCAmelCase_			       = self.scheduler_classes[0]
					lowerCAmelCase_			       = self.get_scheduler_config()
					lowerCAmelCase_			       = scheduler_class(**UpperCAmelCase      )

					scheduler.set_timesteps(self.num_inference_steps      )

					lowerCAmelCase_			       = self.dummy_model()
					lowerCAmelCase_			       = self.dummy_sample_deter * scheduler.init_noise_sigma

					for i, t in enumerate(scheduler.timesteps      ):
						lowerCAmelCase_			       = scheduler.scale_model_input(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = model(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = scheduler.step(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase      )
						lowerCAmelCase_			       = output.prev_sample

					lowerCAmelCase_			       = torch.sum(torch.abs(UpperCAmelCase      )      )
					lowerCAmelCase_			       = torch.mean(torch.abs(UpperCAmelCase      )      )

					assert abs(result_sum.item() - 1_0_0_6.3_8_8      ) < 1E-2
					assert abs(result_mean.item() - 1.3_1      ) < 1E-3







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					lowerCAmelCase_			       = self.scheduler_classes[0]
					lowerCAmelCase_			       = self.get_scheduler_config(prediction_type='''v_prediction'''      )
					lowerCAmelCase_			       = scheduler_class(**UpperCAmelCase      )

					scheduler.set_timesteps(self.num_inference_steps      )

					lowerCAmelCase_			       = self.dummy_model()
					lowerCAmelCase_			       = self.dummy_sample_deter * scheduler.init_noise_sigma

					for i, t in enumerate(scheduler.timesteps      ):
						lowerCAmelCase_			       = scheduler.scale_model_input(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = model(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = scheduler.step(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase      )
						lowerCAmelCase_			       = output.prev_sample

					lowerCAmelCase_			       = torch.sum(torch.abs(UpperCAmelCase      )      )
					lowerCAmelCase_			       = torch.mean(torch.abs(UpperCAmelCase      )      )

					assert abs(result_sum.item() - 0.0_0_1_7      ) < 1E-2
					assert abs(result_mean.item() - 2.2_6_7_6E-0_6      ) < 1E-3







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					lowerCAmelCase_			       = self.scheduler_classes[0]
					lowerCAmelCase_			       = self.get_scheduler_config()
					lowerCAmelCase_			       = scheduler_class(**UpperCAmelCase      )

					scheduler.set_timesteps(self.num_inference_steps			,  device=UpperCAmelCase      )

					lowerCAmelCase_			       = self.dummy_model()
					lowerCAmelCase_			       = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
					lowerCAmelCase_			       = sample.to(UpperCAmelCase      )

					for i, t in enumerate(scheduler.timesteps      ):
						lowerCAmelCase_			       = scheduler.scale_model_input(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = model(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = scheduler.step(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase      )
						lowerCAmelCase_			       = output.prev_sample

					lowerCAmelCase_			       = torch.sum(torch.abs(UpperCAmelCase      )      )
					lowerCAmelCase_			       = torch.mean(torch.abs(UpperCAmelCase      )      )

					assert abs(result_sum.item() - 1_0_0_6.3_8_8      ) < 1E-2
					assert abs(result_mean.item() - 1.3_1      ) < 1E-3







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					lowerCAmelCase_			       = self.scheduler_classes[0]
					lowerCAmelCase_			       = self.get_scheduler_config()
					lowerCAmelCase_			       = scheduler_class(**UpperCAmelCase			,  use_karras_sigmas=UpperCAmelCase      )

					scheduler.set_timesteps(self.num_inference_steps			,  device=UpperCAmelCase      )

					lowerCAmelCase_			       = self.dummy_model()
					lowerCAmelCase_			       = self.dummy_sample_deter.to(UpperCAmelCase      ) * scheduler.init_noise_sigma
					lowerCAmelCase_			       = sample.to(UpperCAmelCase      )

					for t in scheduler.timesteps:
						lowerCAmelCase_			       = scheduler.scale_model_input(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = model(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = scheduler.step(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase      )
						lowerCAmelCase_			       = output.prev_sample

					lowerCAmelCase_			       = torch.sum(torch.abs(UpperCAmelCase      )      )
					lowerCAmelCase_			       = torch.mean(torch.abs(UpperCAmelCase      )      )

					assert abs(result_sum.item() - 3_8_1_2.9_9_2_7      ) < 2E-2
					assert abs(result_mean.item() - 4.9_6_4_8      ) < 1E-3