

import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest







class 			lowercase    (	lowercase_      ):
				__SCREAMING_SNAKE_CASE :			str	      =      (PNDMScheduler,)
				__SCREAMING_SNAKE_CASE :			Union[str, Any]	      =      (('''num_inference_steps''', 50),)




				def    a							( self				,       **snake_case	):
											snake_case_          =   {
											    'num_train_timesteps': 1000,
											    'beta_start': 0.00_01,
											    'beta_end': 0.02,
											    'beta_schedule': 'linear',
											}

											config.update(**snake_case	)
											return config




				def    a							( self				,       snake_case=0				,       **snake_case	):
											snake_case_          =   dict(self.forward_default_kwargs	)
											snake_case_          =   kwargs.pop('num_inference_steps'				,       snake_case	)
											snake_case_          =   self.dummy_sample
											snake_case_          =   0.1 * sample
											snake_case_          =   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

																		snake_case_          =   scheduler.step_prk(snake_case				,       snake_case				,       snake_case				,       **snake_case	).prev_sample
																		snake_case_          =   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"

																		snake_case_          =   scheduler.step_plms(snake_case				,       snake_case				,       snake_case				,       **snake_case	).prev_sample
																		snake_case_          =   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    a							( self	):
											pass




				def    a							( self				,       snake_case=0				,       **snake_case	):
											snake_case_          =   dict(self.forward_default_kwargs	)
											snake_case_          =   kwargs.pop('num_inference_steps'				,       snake_case	)
											snake_case_          =   self.dummy_sample
											snake_case_          =   0.1 * sample
											snake_case_          =   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

											for scheduler_class in self.scheduler_classes:
																		snake_case_          =   self.get_scheduler_config()
																		snake_case_          =   scheduler_class(**snake_case	)
																		scheduler.set_timesteps(snake_case	)

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

																		with tempfile.TemporaryDirectory() as tmpdirname:
																									scheduler.save_config(snake_case	)
																									snake_case_          =   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)
																									snake_case_          =   dummy_past_residuals[:]

																		snake_case_          =   scheduler.step_prk(snake_case				,       snake_case				,       snake_case				,       **snake_case	).prev_sample
																		snake_case_          =   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"

																		snake_case_          =   scheduler.step_plms(snake_case				,       snake_case				,       snake_case				,       **snake_case	).prev_sample
																		snake_case_          =   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    a							( self				,       **snake_case	):
											snake_case_          =   self.scheduler_classes[0]
											snake_case_          =   self.get_scheduler_config(**snake_case	)
											snake_case_          =   scheduler_class(**snake_case	)

											snake_case_          =   10
											snake_case_          =   self.dummy_model()
											snake_case_          =   self.dummy_sample_deter
											scheduler.set_timesteps(snake_case	)

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

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

											return sample




				def    a							( self	):
											snake_case_          =   dict(self.forward_default_kwargs	)

											snake_case_          =   kwargs.pop('num_inference_steps'				,       snake_case	)

											for scheduler_class in self.scheduler_classes:
																		snake_case_          =   self.get_scheduler_config()
																		snake_case_          =   scheduler_class(**snake_case	)

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

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

																		snake_case_          =   scheduler.step_prk(snake_case				,       0				,       snake_case				,       **snake_case	).prev_sample
																		snake_case_          =   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	)

																		snake_case_          =   scheduler.step_plms(snake_case				,       0				,       snake_case				,       **snake_case	).prev_sample
																		snake_case_          =   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    a							( self	):
											for timesteps in [100, 1000]:
																		self.check_over_configs(num_train_timesteps=snake_case	)




				def    a							( self	):
											for steps_offset in [0, 1]:
																		self.check_over_configs(steps_offset=snake_case	)

											snake_case_          =   self.scheduler_classes[0]
											snake_case_          =   self.get_scheduler_config(steps_offset=1	)
											snake_case_          =   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    a							( self	):
											for beta_start, beta_end in zip([0.00_01, 0.0_01]				,       [0.0_02, 0.02]	):
																		self.check_over_configs(beta_start=snake_case				,       beta_end=snake_case	)




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




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




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




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




				def    a							( self	):
											# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
											snake_case_          =   27

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

																		snake_case_          =   self.get_scheduler_config()
																		snake_case_          =   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]	):
																									snake_case_          =   scheduler.step_prk(snake_case				,       snake_case				,       snake_case	).prev_sample




				def    a							( self	):
											with self.assertRaises(snake_case	):
																		snake_case_          =   self.scheduler_classes[0]
																		snake_case_          =   self.get_scheduler_config()
																		snake_case_          =   scheduler_class(**snake_case	)

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




				def    a							( self	):
											snake_case_          =   self.full_loop()
											snake_case_          =   torch.sum(torch.abs(snake_case	)	)
											snake_case_          =   torch.mean(torch.abs(snake_case	)	)

											assert abs(result_sum.item() - 1_98.13_18	) < 1e-2
											assert abs(result_mean.item() - 0.25_80	) < 1e-3




				def    a							( self	):
											snake_case_          =   self.full_loop(prediction_type='v_prediction'	)
											snake_case_          =   torch.sum(torch.abs(snake_case	)	)
											snake_case_          =   torch.mean(torch.abs(snake_case	)	)

											assert abs(result_sum.item() - 67.39_86	) < 1e-2
											assert abs(result_mean.item() - 0.08_78	) < 1e-3




				def    a							( self	):
											# We specify different beta, so that the first alpha is 0.99
											snake_case_          =   self.full_loop(set_alpha_to_one=snake_case				,       beta_start=0.01	)
											snake_case_          =   torch.sum(torch.abs(snake_case	)	)
											snake_case_          =   torch.mean(torch.abs(snake_case	)	)

											assert abs(result_sum.item() - 2_30.03_99	) < 1e-2
											assert abs(result_mean.item() - 0.29_95	) < 1e-3




				def    a							( self	):
											# We specify different beta, so that the first alpha is 0.99
											snake_case_          =   self.full_loop(set_alpha_to_one=snake_case				,       beta_start=0.01	)
											snake_case_          =   torch.sum(torch.abs(snake_case	)	)
											snake_case_          =   torch.mean(torch.abs(snake_case	)	)

											assert abs(result_sum.item() - 1_86.94_82	) < 1e-2
											assert abs(result_mean.item() - 0.24_34	) < 1e-3







from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
					if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
					from .pipeline_unclip import UnCLIPPipeline
					from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
					from .text_proj import UnCLIPTextProjModel




