



'''simple docstring'''
import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest


class 	SCREAMING_SNAKE_CASE__					( lowercase_       ):
	"""simple docstring"""


	A__     :     Optional[Any]    =					(DEISMultistepScheduler,)
	A__     :     Optional[int]    =					(('''num_inference_steps''', 25),)


	def   a__			(		self       , **A      )			->				List[Any]:
				A:   Any								= {
				    """num_train_timesteps""": 10_00,
				    """beta_start""": 0.0001,
				    """beta_end""": 0.02,
				    """beta_schedule""": """linear""",
				    """solver_order""": 2,
				}

				config.update(**A      )
				return config


	def   a__			(		self       , A=0       , **A      )			->				Dict:
				A:   List[str]								= dict(self.forward_default_kwargs      )
				A:   List[str]								= kwargs.pop("""num_inference_steps"""       , A      )
				A:   str								= self.dummy_sample
				A:   Tuple								= 0.1 * sample
				A:   int								= [residual + 0.2, residual + 0.15, residual + 0.10]

				for scheduler_class in self.scheduler_classes:
							A:   Optional[int]								= self.get_scheduler_config(**A      )
							A:   Union[str, Any]								= scheduler_class(**A      )
							scheduler.set_timesteps(A      )
							# copy over dummy past residuals
							A:   List[Any]								= dummy_past_residuals[: scheduler.config.solver_order]

							with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(A      )
										A:   Any								= scheduler_class.from_pretrained(A      )
										new_scheduler.set_timesteps(A      )
										# copy over dummy past residuals
										A:   Dict								= dummy_past_residuals[: new_scheduler.config.solver_order]

							A				,		A:   int								= sample, sample
							for t in range(A       , time_step + scheduler.config.solver_order + 1      ):
										A:   Optional[Any]								= scheduler.step(A       , A       , A       , **A      ).prev_sample
										A:   int								= new_scheduler.step(A       , A       , A       , **A      ).prev_sample

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


	def   a__			(		self      )			->				List[str]:
				pass


	def   a__			(		self       , A=0       , **A      )			->				Optional[Any]:
				A:   str								= dict(self.forward_default_kwargs      )
				A:   Optional[Any]								= kwargs.pop("""num_inference_steps"""       , A      )
				A:   Tuple								= self.dummy_sample
				A:   int								= 0.1 * sample
				A:   Optional[Any]								= [residual + 0.2, residual + 0.15, residual + 0.10]

				for scheduler_class in self.scheduler_classes:
							A:   int								= self.get_scheduler_config()
							A:   Union[str, Any]								= scheduler_class(**A      )
							scheduler.set_timesteps(A      )

							# copy over dummy past residuals (must be after setting timesteps)
							A:   List[Any]								= dummy_past_residuals[: scheduler.config.solver_order]

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

										# copy over dummy past residual (must be after setting timesteps)
										A:   List[str]								= dummy_past_residuals[: new_scheduler.config.solver_order]

							A:   int								= scheduler.step(A       , A       , A       , **A      ).prev_sample
							A:   int								= new_scheduler.step(A       , A       , A       , **A      ).prev_sample

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


	def   a__			(		self       , A=None       , **A      )			->				Optional[int]:
				if scheduler is None:
							A:   List[Any]								= self.scheduler_classes[0]
							A:   int								= self.get_scheduler_config(**A      )
							A:   int								= scheduler_class(**A      )

				A:   Optional[Any]								= self.scheduler_classes[0]
				A:   List[str]								= self.get_scheduler_config(**A      )
				A:   Tuple								= scheduler_class(**A      )

				A:   Any								= 10
				A:   Tuple								= self.dummy_model()
				A:   int								= self.dummy_sample_deter
				scheduler.set_timesteps(A      )

				for i, t in enumerate(scheduler.timesteps      ):
							A:   Any								= model(A       , A      )
							A:   int								= scheduler.step(A       , A       , A      ).prev_sample

				return sample


	def   a__			(		self      )			->				Dict:
				A:   Tuple								= dict(self.forward_default_kwargs      )

				A:   List[Any]								= kwargs.pop("""num_inference_steps"""       , A      )

				for scheduler_class in self.scheduler_classes:
							A:   List[Any]								= self.get_scheduler_config()
							A:   Optional[int]								= scheduler_class(**A      )

							A:   List[str]								= self.dummy_sample
							A:   Union[str, Any]								= 0.1 * sample

							if num_inference_steps is not None and hasattr(A       , """set_timesteps"""      ):
										scheduler.set_timesteps(A      )
							elif num_inference_steps is not None and not hasattr(A       , """set_timesteps"""      ):
										A:   List[str]								= num_inference_steps

							# copy over dummy past residuals (must be done after set_timesteps)
							A:   Union[str, Any]								= [residual + 0.2, residual + 0.15, residual + 0.10]
							A:   Dict								= dummy_past_residuals[: scheduler.config.solver_order]

							A:   str								= scheduler.timesteps[5]
							A:   Any								= scheduler.timesteps[6]

							A:   List[Any]								= scheduler.step(A       , A       , A       , **A      ).prev_sample
							A:   Union[str, Any]								= scheduler.step(A       , A       , A       , **A      ).prev_sample

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


	def   a__			(		self      )			->				List[Any]:
				# make sure that iterating over schedulers with same config names gives same results
				# for defaults
				A:   Optional[Any]								= DEISMultistepScheduler(**self.get_scheduler_config()      )
				A:   Optional[Any]								= self.full_loop(scheduler=A      )
				A:   Tuple								= torch.mean(torch.abs(A      )      )

				assert abs(result_mean.item() - 0.23916      ) < 1e-3

				A:   Union[str, Any]								= DPMSolverSinglestepScheduler.from_config(scheduler.config      )
				A:   Tuple								= DPMSolverMultistepScheduler.from_config(scheduler.config      )
				A:   str								= UniPCMultistepScheduler.from_config(scheduler.config      )
				A:   Optional[Any]								= DEISMultistepScheduler.from_config(scheduler.config      )

				A:   List[Any]								= self.full_loop(scheduler=A      )
				A:   List[str]								= torch.mean(torch.abs(A      )      )

				assert abs(result_mean.item() - 0.23916      ) < 1e-3


	def   a__			(		self      )			->				Optional[int]:
				for timesteps in [25, 50, 1_00, 9_99, 10_00]:
							self.check_over_configs(num_train_timesteps=A      )


	def   a__			(		self      )			->				Optional[Any]:
				self.check_over_configs(thresholding=A      )
				for order in [1, 2, 3]:
							for solver_type in ["logrho"]:
										for threshold in [0.5, 1.0, 2.0]:
													for prediction_type in ["epsilon", "sample"]:
																self.check_over_configs(
																    thresholding=A       , prediction_type=A       , sample_max_value=A       , algorithm_type="""deis"""       , solver_order=A       , solver_type=A       , )


	def   a__			(		self      )			->				Tuple:
				for prediction_type in ["epsilon", "v_prediction"]:
							self.check_over_configs(prediction_type=A      )


	def   a__			(		self      )			->				Any:
				for algorithm_type in ["deis"]:
							for solver_type in ["logrho"]:
										for order in [1, 2, 3]:
													for prediction_type in ["epsilon", "sample"]:
																self.check_over_configs(
																    solver_order=A       , solver_type=A       , prediction_type=A       , algorithm_type=A       , )
																A:   Any								= self.full_loop(
																    solver_order=A       , solver_type=A       , prediction_type=A       , algorithm_type=A       , )
																assert not torch.isnan(A      ).any(), "Samples have nan numbers"


	def   a__			(		self      )			->				Tuple:
				self.check_over_configs(lower_order_final=A      )
				self.check_over_configs(lower_order_final=A      )


	def   a__			(		self      )			->				List[str]:
				for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
							self.check_over_forward(num_inference_steps=A       , time_step=0      )


	def   a__			(		self      )			->				Tuple:
				A:   int								= self.full_loop()
				A:   Union[str, Any]								= torch.mean(torch.abs(A      )      )

				assert abs(result_mean.item() - 0.23916      ) < 1e-3


	def   a__			(		self      )			->				Union[str, Any]:
				A:   str								= self.full_loop(prediction_type="""v_prediction"""      )
				A:   List[Any]								= torch.mean(torch.abs(A      )      )

				assert abs(result_mean.item() - 0.091      ) < 1e-3





	def   a__			(		self      )			->				Dict:
				A:   List[str]								= self.scheduler_classes[0]
				A:   Any								= self.get_scheduler_config(thresholding=A       , dynamic_thresholding_ratio=0      )
				A:   Tuple								= scheduler_class(**A      )

				A:   Dict								= 10
				A:   str								= self.dummy_model()
				A:   str								= self.dummy_sample_deter.half()
				scheduler.set_timesteps(A      )

				for i, t in enumerate(scheduler.timesteps      ):
							A:   List[Any]								= model(A       , A      )
							A:   Any								= scheduler.step(A       , A       , A      ).prev_sample

				assert sample.dtype == torch.floataa








import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad







class 					lowercase							(			unittest.TestCase     ):




				def        a				(   self			):
											snake_case_     							=       10




				def        a				(   self			):
											snake_case_     							=       [1, 2, 3, 4]
											snake_case_     							=       [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
											self.assertEqual(truncate_or_pad(snake_case ,  self.block_size ,  0			) ,  snake_case			)




				def        a				(   self			):
											snake_case_     							=       [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
											snake_case_     							=       [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
											self.assertEqual(truncate_or_pad(snake_case ,  self.block_size ,  0			) ,  snake_case			)




				def        a				(   self			):
											snake_case_     							=       [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
											snake_case_     							=       [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
											self.assertEqual(truncate_or_pad(snake_case ,  self.block_size ,  0			) ,  snake_case			)




				def        a				(   self			):
											snake_case_     							=       'It was the year of Our Lord one thousand seven hundred and\n        seventy-five.\n\nSpiritual revelations were conceded to England at that\n        favoured period, as at this.'
											snake_case_							,       snake_case_     							=       process_story(snake_case			)
											self.assertEqual(snake_case ,  []			)




				def        a				(   self			):
											snake_case_     							=       ''
											snake_case_							,       snake_case_     							=       process_story(snake_case			)
											self.assertEqual(snake_case ,  []			)
											self.assertEqual(snake_case ,  []			)




				def        a				(   self			):
											snake_case_     							=       (
											    'It was the year of Our Lord one thousand seven hundred and '
											    'seventy-five\n\nSpiritual revelations were conceded to England '
											    'at that favoured period, as at this.\n@highlight\n\nIt was the best of times'
											)
											snake_case_							,       snake_case_     							=       process_story(snake_case			)

											snake_case_     							=       [
											    'It was the year of Our Lord one thousand seven hundred and seventy-five.',
											    'Spiritual revelations were conceded to England at that favoured period, as at this.',
											]
											self.assertEqual(snake_case ,  snake_case			)

											snake_case_     							=       ['It was the best of times.']
											self.assertEqual(snake_case ,  snake_case			)




				def        a				(   self			):
											snake_case_     							=       torch.tensor([1, 2, 3, 4]			)
											snake_case_     							=       torch.tensor([1, 1, 1, 1]			)
											np.testing.assert_array_equal(build_mask(snake_case ,  0			).numpy() ,  expected.numpy()			)




				def        a				(   self			):
											snake_case_     							=       torch.tensor([1, 2, 3, 4, 23, 23, 23]			)
											snake_case_     							=       torch.tensor([1, 1, 1, 1, 0, 0, 0]			)
											np.testing.assert_array_equal(build_mask(snake_case ,  23			).numpy() ,  expected.numpy()			)




				def        a				(   self			):
											snake_case_     							=       torch.tensor([8, 2, 3, 4, 1, 1, 1]			)
											snake_case_     							=       torch.tensor([1, 1, 1, 1, 0, 0, 0]			)
											np.testing.assert_array_equal(build_mask(snake_case ,  1			).numpy() ,  expected.numpy()			)




				def        a				(   self			):
											snake_case_     							=       101
											snake_case_     							=       torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]]			)
											snake_case_     							=       torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]			)

											snake_case_     							=       compute_token_type_ids(snake_case ,  snake_case			)
											np.testing.assert_array_equal(snake_case ,  snake_case			)




