
import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest







class 			__A( a			):
				snake_case_						   =    (PNDMScheduler,)
				snake_case_						   =    (('''num_inference_steps''', 5_0),)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     **_snake_case				)    ->      str:


								'''simple docstring'''




								__a										=    {
								    '''num_train_timesteps''': 1_000,
								    '''beta_start''': 0.0001,
								    '''beta_end''': 0.02,
								    '''beta_schedule''': '''linear''',
								}

								config.update(**_snake_case				)
								return config




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case=0		,     **_snake_case				)    ->      Any:


								'''simple docstring'''




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

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

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

												__a										=    scheduler.step_prk(_snake_case		,     _snake_case		,     _snake_case		,     **_snake_case				).prev_sample
												__a										=    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"

												__a										=    scheduler.step_plms(_snake_case		,     _snake_case		,     _snake_case		,     **_snake_case				).prev_sample
												__a										=    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 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[Any]:


								'''simple docstring'''




								pass




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case=0		,     **_snake_case				)    ->      Union[str, Any]:


								'''simple docstring'''




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

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

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

												with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(_snake_case				)
																__a										=    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)
																__a										=    dummy_past_residuals[:]

												__a										=    scheduler.step_prk(_snake_case		,     _snake_case		,     _snake_case		,     **_snake_case				).prev_sample
												__a										=    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"

												__a										=    scheduler.step_plms(_snake_case		,     _snake_case		,     _snake_case		,     **_snake_case				).prev_sample
												__a										=    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 				SCREAMING_SNAKE_CASE_		(     self		,     **_snake_case				)    ->      Tuple:


								'''simple docstring'''




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

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

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

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

								return sample




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


								'''simple docstring'''




								__a										=    dict(self.forward_default_kwargs				)

								__a										=    kwargs.pop('''num_inference_steps'''		,     _snake_case				)

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

												__a										=    self.dummy_sample
												__a										=    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'''				):
																__a										=    num_inference_steps

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

												__a										=    scheduler.step_prk(_snake_case		,     0		,     _snake_case		,     **_snake_case				).prev_sample
												__a										=    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				)

												__a										=    scheduler.step_plms(_snake_case		,     0		,     _snake_case		,     **_snake_case				).prev_sample
												__a										=    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 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[Any]:


								'''simple docstring'''




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




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


								'''simple docstring'''




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

								__a										=    self.scheduler_classes[0]
								__a										=    self.get_scheduler_config(steps_offset=1				)
								__a										=    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 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Union[str, Any]:


								'''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 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Tuple:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Any:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[int]:


								'''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 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Any:


								'''simple docstring'''




								__a										=    27

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

												__a										=    self.get_scheduler_config()
												__a										=    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]				):
																__a										=    scheduler.step_prk(_snake_case		,     _snake_case		,     _snake_case				).prev_sample




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


								'''simple docstring'''




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

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




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


								'''simple docstring'''




								__a										=    self.full_loop()
								__a										=    torch.sum(torch.abs(_snake_case				)				)
								__a										=    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 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict:


								'''simple docstring'''




								__a										=    self.full_loop(prediction_type='''v_prediction'''				)
								__a										=    torch.sum(torch.abs(_snake_case				)				)
								__a										=    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 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Union[str, Any]:


								'''simple docstring'''




								__a										=    self.full_loop(set_alpha_to_one=_snake_case		,     beta_start=0.01				)
								__a										=    torch.sum(torch.abs(_snake_case				)				)
								__a										=    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 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict:


								'''simple docstring'''




								__a										=    self.full_loop(set_alpha_to_one=_snake_case		,     beta_start=0.01				)
								__a										=    torch.sum(torch.abs(_snake_case				)				)
								__a										=    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 dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate







@dataclass(frozen=a			)
class 			__A( a			):
				snake_case_						   =    field(default='''language-modeling'''  ,   metadata={'''include_in_asdict_even_if_is_default''': True}			)

				snake_case_						   =    Features({'''text''': Value('''string'''			)}			)
				snake_case_						   =    Features({}			)
				snake_case_						   =    "text"




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict[str, str]:


								'''simple docstring'''




								return {self.text_column: "text"}