import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest


class   lowercase      (       a       ):
							lowercase__  :    int									=							(PNDMScheduler,)
							lowercase__  :    int									=							(("""num_inference_steps""", 50),)




							def 		__snake_case(  self				:   List[Any]		,							**_UpperCamelCase				:   int )   ->			int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    {
												    "num_train_timesteps": 1_000,
												    "beta_start": 0.0_0_0_1,
												    "beta_end": 0.0_2,
												    "beta_schedule": "linear",
												}

												config.update(**_UpperCamelCase )
												return config




							def 		__snake_case(  self				:   Optional[int]		,							_UpperCamelCase				:   str=0		,							**_UpperCamelCase				:   Any )   ->			Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    dict(self.forward_default_kwargs )
												SCREAMING_SNAKE_CASE           =    kwargs.pop("num_inference_steps"		,							_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    self.dummy_sample
												SCREAMING_SNAKE_CASE           =    0.1 * sample
												SCREAMING_SNAKE_CASE           =    [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

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

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

																	SCREAMING_SNAKE_CASE           =    scheduler.step_prk(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							**_UpperCamelCase ).prev_sample
																	SCREAMING_SNAKE_CASE           =    new_scheduler.step_prk(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							**_UpperCamelCase ).prev_sample

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

																	SCREAMING_SNAKE_CASE           =    scheduler.step_plms(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							**_UpperCamelCase ).prev_sample
																	SCREAMING_SNAKE_CASE           =    new_scheduler.step_plms(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							**_UpperCamelCase ).prev_sample

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




							def 		__snake_case(  self				:   str )   ->			str:



												'''simple docstring'''

												pass




							def 		__snake_case(  self				:   Any		,							_UpperCamelCase				:   Tuple=0		,							**_UpperCamelCase				:   Union[str, Any] )   ->			Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    dict(self.forward_default_kwargs )
												SCREAMING_SNAKE_CASE           =    kwargs.pop("num_inference_steps"		,							_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    self.dummy_sample
												SCREAMING_SNAKE_CASE           =    0.1 * sample
												SCREAMING_SNAKE_CASE           =    [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

												for scheduler_class in self.scheduler_classes:
																	SCREAMING_SNAKE_CASE           =    self.get_scheduler_config()
																	SCREAMING_SNAKE_CASE           =    scheduler_class(**_UpperCamelCase )
																	scheduler.set_timesteps(_UpperCamelCase )

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

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

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

																	SCREAMING_SNAKE_CASE           =    scheduler.step_prk(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							**_UpperCamelCase ).prev_sample
																	SCREAMING_SNAKE_CASE           =    new_scheduler.step_prk(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							**_UpperCamelCase ).prev_sample

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

																	SCREAMING_SNAKE_CASE           =    scheduler.step_plms(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							**_UpperCamelCase ).prev_sample
																	SCREAMING_SNAKE_CASE           =    new_scheduler.step_plms(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							**_UpperCamelCase ).prev_sample

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




							def 		__snake_case(  self				:   List[str]		,							**_UpperCamelCase				:   str )   ->			int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE           =    self.get_scheduler_config(**_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    scheduler_class(**_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    10
												SCREAMING_SNAKE_CASE           =    self.dummy_model()
												SCREAMING_SNAKE_CASE           =    self.dummy_sample_deter
												scheduler.set_timesteps(_UpperCamelCase )

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

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

												return sample




							def 		__snake_case(  self				:   str )   ->			int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    dict(self.forward_default_kwargs )

												SCREAMING_SNAKE_CASE           =    kwargs.pop("num_inference_steps"		,							_UpperCamelCase )

												for scheduler_class in self.scheduler_classes:
																	SCREAMING_SNAKE_CASE           =    self.get_scheduler_config()
																	SCREAMING_SNAKE_CASE           =    scheduler_class(**_UpperCamelCase )

																	SCREAMING_SNAKE_CASE           =    self.dummy_sample
																	SCREAMING_SNAKE_CASE           =    0.1 * sample

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

																	# copy over dummy past residuals (must be done after set_timesteps)
																	SCREAMING_SNAKE_CASE           =    [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
																	SCREAMING_SNAKE_CASE           =    dummy_past_residuals[:]

																	SCREAMING_SNAKE_CASE           =    scheduler.step_prk(_UpperCamelCase		,							0		,							_UpperCamelCase		,							**_UpperCamelCase ).prev_sample
																	SCREAMING_SNAKE_CASE           =    scheduler.step_prk(_UpperCamelCase		,							1		,							_UpperCamelCase		,							**_UpperCamelCase ).prev_sample

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

																	SCREAMING_SNAKE_CASE           =    scheduler.step_plms(_UpperCamelCase		,							0		,							_UpperCamelCase		,							**_UpperCamelCase ).prev_sample
																	SCREAMING_SNAKE_CASE           =    scheduler.step_plms(_UpperCamelCase		,							1		,							_UpperCamelCase		,							**_UpperCamelCase ).prev_sample

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




							def 		__snake_case(  self				:   Optional[Any] )   ->			Dict:



												'''simple docstring'''

												for timesteps in [100, 1_000]:
																	self.check_over_configs(num_train_timesteps=_UpperCamelCase )




							def 		__snake_case(  self				:   Any )   ->			List[str]:



												'''simple docstring'''

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

												SCREAMING_SNAKE_CASE           =    self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE           =    self.get_scheduler_config(steps_offset=1 )
												SCREAMING_SNAKE_CASE           =    scheduler_class(**_UpperCamelCase )
												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				:   int )   ->			Tuple:



												'''simple docstring'''

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




							def 		__snake_case(  self				:   str )   ->			Dict:



												'''simple docstring'''

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




							def 		__snake_case(  self				:   List[Any] )   ->			Union[str, Any]:



												'''simple docstring'''

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




							def 		__snake_case(  self				:   Dict )   ->			Optional[Any]:



												'''simple docstring'''

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




							def 		__snake_case(  self				:   List[str] )   ->			str:



												'''simple docstring'''

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




							def 		__snake_case(  self				:   Optional[int] )   ->			Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    27

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

																	SCREAMING_SNAKE_CASE           =    self.get_scheduler_config()
																	SCREAMING_SNAKE_CASE           =    scheduler_class(**_UpperCamelCase )

																	scheduler.set_timesteps(_UpperCamelCase )

																	# 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] ):
																						SCREAMING_SNAKE_CASE           =    scheduler.step_prk(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase ).prev_sample




							def 		__snake_case(  self				:   Dict )   ->			Dict:



												'''simple docstring'''

												with self.assertRaises(_UpperCamelCase ):
																	SCREAMING_SNAKE_CASE           =    self.scheduler_classes[0]
																	SCREAMING_SNAKE_CASE           =    self.get_scheduler_config()
																	SCREAMING_SNAKE_CASE           =    scheduler_class(**_UpperCamelCase )

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




							def 		__snake_case(  self				:   Any )   ->			List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.full_loop()
												SCREAMING_SNAKE_CASE           =    torch.sum(torch.abs(_UpperCamelCase ) )
												SCREAMING_SNAKE_CASE           =    torch.mean(torch.abs(_UpperCamelCase ) )

												assert abs(result_sum.item() - 1_9_8.1_3_1_8 ) < 1e-2
												assert abs(result_mean.item() - 0.2_5_8_0 ) < 1e-3




							def 		__snake_case(  self				:   Tuple )   ->			Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.full_loop(prediction_type="v_prediction" )
												SCREAMING_SNAKE_CASE           =    torch.sum(torch.abs(_UpperCamelCase ) )
												SCREAMING_SNAKE_CASE           =    torch.mean(torch.abs(_UpperCamelCase ) )

												assert abs(result_sum.item() - 6_7.3_9_8_6 ) < 1e-2
												assert abs(result_mean.item() - 0.0_8_7_8 ) < 1e-3




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.full_loop(set_alpha_to_one=_UpperCamelCase		,							beta_start=0.0_1 )
												SCREAMING_SNAKE_CASE           =    torch.sum(torch.abs(_UpperCamelCase ) )
												SCREAMING_SNAKE_CASE           =    torch.mean(torch.abs(_UpperCamelCase ) )

												assert abs(result_sum.item() - 2_3_0.0_3_9_9 ) < 1e-2
												assert abs(result_mean.item() - 0.2_9_9_5 ) < 1e-3





							def 		__snake_case(  self				:   Union[str, Any] )   ->			Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.full_loop(set_alpha_to_one=_UpperCamelCase		,							beta_start=0.0_1 )
												SCREAMING_SNAKE_CASE           =    torch.sum(torch.abs(_UpperCamelCase ) )
												SCREAMING_SNAKE_CASE           =    torch.mean(torch.abs(_UpperCamelCase ) )

												assert abs(result_sum.item() - 1_8_6.9_4_8_2 ) < 1e-2
												assert abs(result_mean.item() - 0.2_4_3_4 ) < 1e-3





def 		__lowerCamelCase							(UpperCAmelCase__       :							str ,	UpperCAmelCase__       :							str = " "					):
					SCREAMING_SNAKE_CASE           =    []

					SCREAMING_SNAKE_CASE           =    0
					for index, char in enumerate(UpperCAmelCase__					):
										if char == separator:
															split_words.append(string[last_index:index]					)
															SCREAMING_SNAKE_CASE           =    index + 1
										elif index + 1 == len(UpperCAmelCase__					):
															split_words.append(string[last_index : index + 1]					)
					return split_words


if __name__ == "__main__":
							from doctest import testmod

							testmod()




