



import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest





class 					__lowerCAmelCase (     lowerCamelCase__       ):
				__lowerCamelCase =				(PNDMScheduler,)
				__lowerCamelCase =				(('''num_inference_steps''', 50),)




				def 							snake_case   (							self						,				**_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase						=    {
									    """num_train_timesteps""": 1000,
									    """beta_start""": 0.0001,
									    """beta_end""": 0.02,
									    """beta_schedule""": """linear""",
									}

									config.update(**_snake_case		)
									return config




				def 							snake_case   (							self						,				_snake_case=0						,				**_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase						=    dict(self.forward_default_kwargs		)
									_lowerCAmelCase						=    kwargs.pop("""num_inference_steps"""						,				_snake_case		)
									_lowerCAmelCase						=    self.dummy_sample
									_lowerCAmelCase						=    0.1 * sample
									_lowerCAmelCase						=    [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

									for scheduler_class in self.scheduler_classes:
														_lowerCAmelCase						=    self.get_scheduler_config(**_snake_case		)
														_lowerCAmelCase						=    scheduler_class(**_snake_case		)
														scheduler.set_timesteps(_snake_case		)
														# copy over dummy past residuals
														_lowerCAmelCase						=    dummy_past_residuals[:]

														with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(_snake_case		)
																			_lowerCAmelCase						=    scheduler_class.from_pretrained(_snake_case		)
																			new_scheduler.set_timesteps(_snake_case		)
																			# copy over dummy past residuals
																			_lowerCAmelCase						=    dummy_past_residuals[:]

														_lowerCAmelCase						=    scheduler.step_prk(_snake_case						,				_snake_case						,				_snake_case						,				**_snake_case		).prev_sample
														_lowerCAmelCase						=    new_scheduler.step_prk(_snake_case						,				_snake_case						,				_snake_case						,				**_snake_case		).prev_sample

														assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"

														_lowerCAmelCase						=    scheduler.step_plms(_snake_case						,				_snake_case						,				_snake_case						,				**_snake_case		).prev_sample
														_lowerCAmelCase						=    new_scheduler.step_plms(_snake_case						,				_snake_case						,				_snake_case						,				**_snake_case		).prev_sample

														assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"




				def 							snake_case   (							self		):



									"""simple docstring"""





									pass




				def 							snake_case   (							self						,				_snake_case=0						,				**_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase						=    dict(self.forward_default_kwargs		)
									_lowerCAmelCase						=    kwargs.pop("""num_inference_steps"""						,				_snake_case		)
									_lowerCAmelCase						=    self.dummy_sample
									_lowerCAmelCase						=    0.1 * sample
									_lowerCAmelCase						=    [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

									for scheduler_class in self.scheduler_classes:
														_lowerCAmelCase						=    self.get_scheduler_config()
														_lowerCAmelCase						=    scheduler_class(**_snake_case		)
														scheduler.set_timesteps(_snake_case		)

														# copy over dummy past residuals (must be after setting timesteps)
														_lowerCAmelCase						=    dummy_past_residuals[:]

														with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(_snake_case		)
																			_lowerCAmelCase						=    scheduler_class.from_pretrained(_snake_case		)
																			# copy over dummy past residuals
																			new_scheduler.set_timesteps(_snake_case		)

																			# copy over dummy past residual (must be after setting timesteps)
																			_lowerCAmelCase						=    dummy_past_residuals[:]

														_lowerCAmelCase						=    scheduler.step_prk(_snake_case						,				_snake_case						,				_snake_case						,				**_snake_case		).prev_sample
														_lowerCAmelCase						=    new_scheduler.step_prk(_snake_case						,				_snake_case						,				_snake_case						,				**_snake_case		).prev_sample

														assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"

														_lowerCAmelCase						=    scheduler.step_plms(_snake_case						,				_snake_case						,				_snake_case						,				**_snake_case		).prev_sample
														_lowerCAmelCase						=    new_scheduler.step_plms(_snake_case						,				_snake_case						,				_snake_case						,				**_snake_case		).prev_sample

														assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"




				def 							snake_case   (							self						,				**_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.scheduler_classes[0]
									_lowerCAmelCase						=    self.get_scheduler_config(**_snake_case		)
									_lowerCAmelCase						=    scheduler_class(**_snake_case		)

									_lowerCAmelCase						=    10
									_lowerCAmelCase						=    self.dummy_model()
									_lowerCAmelCase						=    self.dummy_sample_deter
									scheduler.set_timesteps(_snake_case		)

									for i, t in enumerate(scheduler.prk_timesteps		):
														_lowerCAmelCase						=    model(_snake_case						,				_snake_case		)
														_lowerCAmelCase						=    scheduler.step_prk(_snake_case						,				_snake_case						,				_snake_case		).prev_sample

									for i, t in enumerate(scheduler.plms_timesteps		):
														_lowerCAmelCase						=    model(_snake_case						,				_snake_case		)
														_lowerCAmelCase						=    scheduler.step_plms(_snake_case						,				_snake_case						,				_snake_case		).prev_sample

									return sample




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    dict(self.forward_default_kwargs		)

									_lowerCAmelCase						=    kwargs.pop("""num_inference_steps"""						,				_snake_case		)

									for scheduler_class in self.scheduler_classes:
														_lowerCAmelCase						=    self.get_scheduler_config()
														_lowerCAmelCase						=    scheduler_class(**_snake_case		)

														_lowerCAmelCase						=    self.dummy_sample
														_lowerCAmelCase						=    0.1 * sample

														if num_inference_steps is not None and hasattr(_snake_case						,				"""set_timesteps"""		):
																			scheduler.set_timesteps(_snake_case		)
														elif num_inference_steps is not None and not hasattr(_snake_case						,				"""set_timesteps"""		):
																			_lowerCAmelCase						=    num_inference_steps

														# copy over dummy past residuals (must be done after set_timesteps)
														_lowerCAmelCase						=    [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
														_lowerCAmelCase						=    dummy_past_residuals[:]

														_lowerCAmelCase						=    scheduler.step_prk(_snake_case						,				0						,				_snake_case						,				**_snake_case		).prev_sample
														_lowerCAmelCase						=    scheduler.step_prk(_snake_case						,				1						,				_snake_case						,				**_snake_case		).prev_sample

														self.assertEqual(output_a.shape						,				sample.shape		)
														self.assertEqual(output_a.shape						,				output_a.shape		)

														_lowerCAmelCase						=    scheduler.step_plms(_snake_case						,				0						,				_snake_case						,				**_snake_case		).prev_sample
														_lowerCAmelCase						=    scheduler.step_plms(_snake_case						,				1						,				_snake_case						,				**_snake_case		).prev_sample

														self.assertEqual(output_a.shape						,				sample.shape		)
														self.assertEqual(output_a.shape						,				output_a.shape		)




				def 							snake_case   (							self		):



									"""simple docstring"""





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




				def 							snake_case   (							self		):



									"""simple docstring"""





									for steps_offset in [0, 1]:
														self.check_over_configs(steps_offset=_snake_case		)

									_lowerCAmelCase						=    self.scheduler_classes[0]
									_lowerCAmelCase						=    self.get_scheduler_config(steps_offset=1		)
									_lowerCAmelCase						=    scheduler_class(**_snake_case		)
									scheduler.set_timesteps(10		)
									assert torch.equal(
									    scheduler.timesteps						,				torch.LongTensor(
									        [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]		)						,				)




				def 							snake_case   (							self		):



									"""simple docstring"""





									for beta_start, beta_end in zip([0.0001, 0.001]						,				[0.002, 0.02]		):
														self.check_over_configs(beta_start=_snake_case						,				beta_end=_snake_case		)




				def 							snake_case   (							self		):



									"""simple docstring"""





									for schedule in ["linear", "squaredcos_cap_v2"]:
														self.check_over_configs(beta_schedule=_snake_case		)




				def 							snake_case   (							self		):



									"""simple docstring"""





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




				def 							snake_case   (							self		):



									"""simple docstring"""





									for t in [1, 5, 10]:
														self.check_over_forward(time_step=_snake_case		)




				def 							snake_case   (							self		):



									"""simple docstring"""





									for t, num_inference_steps in zip([1, 5, 10]						,				[10, 50, 100]		):
														self.check_over_forward(num_inference_steps=_snake_case		)




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    27

									for scheduler_class in self.scheduler_classes:
														_lowerCAmelCase						=    self.dummy_sample
														_lowerCAmelCase						=    0.1 * sample

														_lowerCAmelCase						=    self.get_scheduler_config()
														_lowerCAmelCase						=    scheduler_class(**_snake_case		)

														scheduler.set_timesteps(_snake_case		)

														# before power of 3 fix, would error on first step, so we only need to do two
														for i, t in enumerate(scheduler.prk_timesteps[:2]		):
																			_lowerCAmelCase						=    scheduler.step_prk(_snake_case						,				_snake_case						,				_snake_case		).prev_sample




				def 							snake_case   (							self		):



									"""simple docstring"""





									with self.assertRaises(_snake_case		):
														_lowerCAmelCase						=    self.scheduler_classes[0]
														_lowerCAmelCase						=    self.get_scheduler_config()
														_lowerCAmelCase						=    scheduler_class(**_snake_case		)

														scheduler.step_plms(self.dummy_sample						,				1						,				self.dummy_sample		).prev_sample




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.full_loop()
									_lowerCAmelCase						=    torch.sum(torch.abs(_snake_case		)		)
									_lowerCAmelCase						=    torch.mean(torch.abs(_snake_case		)		)

									assert abs(result_sum.item() - 198.1318		) < 1e-2
									assert abs(result_mean.item() - 0.2580		) < 1e-3




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.full_loop(prediction_type="""v_prediction"""		)
									_lowerCAmelCase						=    torch.sum(torch.abs(_snake_case		)		)
									_lowerCAmelCase						=    torch.mean(torch.abs(_snake_case		)		)

									assert abs(result_sum.item() - 67.3986		) < 1e-2
									assert abs(result_mean.item() - 0.0878		) < 1e-3




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.full_loop(set_alpha_to_one=_snake_case						,				beta_start=0.01		)
									_lowerCAmelCase						=    torch.sum(torch.abs(_snake_case		)		)
									_lowerCAmelCase						=    torch.mean(torch.abs(_snake_case		)		)

									assert abs(result_sum.item() - 230.0399		) < 1e-2
									assert abs(result_mean.item() - 0.2995		) < 1e-3




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.full_loop(set_alpha_to_one=_snake_case						,				beta_start=0.01		)
									_lowerCAmelCase						=    torch.sum(torch.abs(_snake_case		)		)
									_lowerCAmelCase						=    torch.mean(torch.abs(_snake_case		)		)

									assert abs(result_sum.item() - 186.9482		) < 1e-2
									assert abs(result_mean.item() - 0.2434		) < 1e-3





from typing import Dict

from .base import GenericTensor, Pipeline







class 			__A( a			):




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case=None		,     _snake_case=None		,     _snake_case=None		,     **_snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




								if tokenize_kwargs is None:
												__a										=    {}

								if truncation is not None:
												if "truncation" in tokenize_kwargs:
																raise ValueError(
																    '''truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'''				)
												__a										=    truncation

								__a										=    tokenize_kwargs

								__a										=    {}
								if return_tensors is not None:
												__a										=    return_tensors

								return preprocess_params, {}, postprocess_params




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     **_snake_case				)    ->      Dict[str, GenericTensor]:


								'''simple docstring'''




								__a										=    self.framework
								__a										=    self.tokenizer(_snake_case		,     return_tensors=_snake_case		,     **_snake_case				)
								return model_inputs




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    self.model(**_snake_case				)
								return model_outputs




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case=False				)    ->      Optional[int]:


								'''simple docstring'''




								if return_tensors:
												return model_outputs[0]
								if self.framework == "pt":
												return model_outputs[0].tolist()
								elif self.framework == "tf":
												return model_outputs[0].numpy().tolist()




				def __call__(     self		,     *_snake_case		,     **_snake_case				)    ->      Any:


								'''simple docstring'''




								return super().__call__(*_snake_case		,     **_snake_case				)