"""simple docstring"""






import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest
class        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					snake_case__       					=				(PNDMScheduler,)
					snake_case__       					=				(("num_inference_steps", 50),)







					def 	__lowerCAmelCase		(       self     :			List[Any]					,**lowerCamelCase__     :			List[Any]    ):
									UpperCAmelCase__    =							{
									    'num_train_timesteps': 1_000,
									    'beta_start': 0.0_0_0_1,
									    'beta_end': 0.0_2,
									    'beta_schedule': 'linear',
									}

									config.update(**lowerCamelCase__    )
									return config







					def 	__lowerCAmelCase		(       self     :			Optional[Any]					,lowerCamelCase__     :			List[str]=0					,**lowerCamelCase__     :			str    ):
									UpperCAmelCase__    =							dict(self.forward_default_kwargs    )
									UpperCAmelCase__    =							kwargs.pop('num_inference_steps'					,lowerCamelCase__    )
									UpperCAmelCase__    =							self.dummy_sample
									UpperCAmelCase__    =							0.1 * sample
									UpperCAmelCase__    =							[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

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

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

													UpperCAmelCase__    =							scheduler.step_prk(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					,**lowerCamelCase__    ).prev_sample
													UpperCAmelCase__    =							new_scheduler.step_prk(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					,**lowerCamelCase__    ).prev_sample

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

													UpperCAmelCase__    =							scheduler.step_plms(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					,**lowerCamelCase__    ).prev_sample
													UpperCAmelCase__    =							new_scheduler.step_plms(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					,**lowerCamelCase__    ).prev_sample

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







					def 	__lowerCAmelCase		(       self     :			str    ):
									pass







					def 	__lowerCAmelCase		(       self     :			Optional[int]					,lowerCamelCase__     :			int=0					,**lowerCamelCase__     :			List[Any]    ):
									UpperCAmelCase__    =							dict(self.forward_default_kwargs    )
									UpperCAmelCase__    =							kwargs.pop('num_inference_steps'					,lowerCamelCase__    )
									UpperCAmelCase__    =							self.dummy_sample
									UpperCAmelCase__    =							0.1 * sample
									UpperCAmelCase__    =							[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

									for scheduler_class in self.scheduler_classes:
													UpperCAmelCase__    =							self.get_scheduler_config()
													UpperCAmelCase__    =							scheduler_class(**lowerCamelCase__    )
													scheduler.set_timesteps(lowerCamelCase__    )

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

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

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

													UpperCAmelCase__    =							scheduler.step_prk(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					,**lowerCamelCase__    ).prev_sample
													UpperCAmelCase__    =							new_scheduler.step_prk(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					,**lowerCamelCase__    ).prev_sample

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

													UpperCAmelCase__    =							scheduler.step_plms(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					,**lowerCamelCase__    ).prev_sample
													UpperCAmelCase__    =							new_scheduler.step_plms(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					,**lowerCamelCase__    ).prev_sample

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







					def 	__lowerCAmelCase		(       self     :			str					,**lowerCamelCase__     :			Any    ):
									UpperCAmelCase__    =							self.scheduler_classes[0]
									UpperCAmelCase__    =							self.get_scheduler_config(**lowerCamelCase__    )
									UpperCAmelCase__    =							scheduler_class(**lowerCamelCase__    )

									UpperCAmelCase__    =							10
									UpperCAmelCase__    =							self.dummy_model()
									UpperCAmelCase__    =							self.dummy_sample_deter
									scheduler.set_timesteps(lowerCamelCase__    )

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

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

									return sample







					def 	__lowerCAmelCase		(       self     :			str    ):
									UpperCAmelCase__    =							dict(self.forward_default_kwargs    )

									UpperCAmelCase__    =							kwargs.pop('num_inference_steps'					,lowerCamelCase__    )

									for scheduler_class in self.scheduler_classes:
													UpperCAmelCase__    =							self.get_scheduler_config()
													UpperCAmelCase__    =							scheduler_class(**lowerCamelCase__    )

													UpperCAmelCase__    =							self.dummy_sample
													UpperCAmelCase__    =							0.1 * sample

													if num_inference_steps is not None and hasattr(lowerCamelCase__					,'set_timesteps'    ):
																	scheduler.set_timesteps(lowerCamelCase__    )
													elif num_inference_steps is not None and not hasattr(lowerCamelCase__					,'set_timesteps'    ):
																	UpperCAmelCase__    =							num_inference_steps

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

													UpperCAmelCase__    =							scheduler.step_prk(lowerCamelCase__					,0					,lowerCamelCase__					,**lowerCamelCase__    ).prev_sample
													UpperCAmelCase__    =							scheduler.step_prk(lowerCamelCase__					,1					,lowerCamelCase__					,**lowerCamelCase__    ).prev_sample

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

													UpperCAmelCase__    =							scheduler.step_plms(lowerCamelCase__					,0					,lowerCamelCase__					,**lowerCamelCase__    ).prev_sample
													UpperCAmelCase__    =							scheduler.step_plms(lowerCamelCase__					,1					,lowerCamelCase__					,**lowerCamelCase__    ).prev_sample

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







					def 	__lowerCAmelCase		(       self     :			List[str]    ):
									for timesteps in [100, 1_000]:
													self.check_over_configs(num_train_timesteps=lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Dict    ):
									for steps_offset in [0, 1]:
													self.check_over_configs(steps_offset=lowerCamelCase__    )

									UpperCAmelCase__    =							self.scheduler_classes[0]
									UpperCAmelCase__    =							self.get_scheduler_config(steps_offset=1    )
									UpperCAmelCase__    =							scheduler_class(**lowerCamelCase__    )
									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 	__lowerCAmelCase		(       self     :			List[Any]    ):
									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=lowerCamelCase__					,beta_end=lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Tuple    ):
									for schedule in ["linear", "squaredcos_cap_v2"]:
													self.check_over_configs(beta_schedule=lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Union[str, Any]    ):
									for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Optional[Any]    ):
									for t in [1, 5, 10]:
													self.check_over_forward(time_step=lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			List[Any]    ):
									for t, num_inference_steps in zip([1, 5, 10]					,[10, 50, 100]    ):
													self.check_over_forward(num_inference_steps=lowerCamelCase__    )







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

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

													UpperCAmelCase__    =							self.get_scheduler_config()
													UpperCAmelCase__    =							scheduler_class(**lowerCamelCase__    )

													scheduler.set_timesteps(lowerCamelCase__    )

													# 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]    ):
																	UpperCAmelCase__    =							scheduler.step_prk(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__    ).prev_sample







					def 	__lowerCAmelCase		(       self     :			Union[str, Any]    ):
									with self.assertRaises(lowerCamelCase__    ):
													UpperCAmelCase__    =							self.scheduler_classes[0]
													UpperCAmelCase__    =							self.get_scheduler_config()
													UpperCAmelCase__    =							scheduler_class(**lowerCamelCase__    )

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







					def 	__lowerCAmelCase		(       self     :			Any    ):
									UpperCAmelCase__    =							self.full_loop()
									UpperCAmelCase__    =							torch.sum(torch.abs(lowerCamelCase__    )    )
									UpperCAmelCase__    =							torch.mean(torch.abs(lowerCamelCase__    )    )

									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 	__lowerCAmelCase		(       self     :			Optional[Any]    ):
									UpperCAmelCase__    =							self.full_loop(prediction_type='v_prediction'    )
									UpperCAmelCase__    =							torch.sum(torch.abs(lowerCamelCase__    )    )
									UpperCAmelCase__    =							torch.mean(torch.abs(lowerCamelCase__    )    )

									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 	__lowerCAmelCase		(       self     :			int    ):
									# We specify different beta, so that the first alpha is 0.99
									UpperCAmelCase__    =							self.full_loop(set_alpha_to_one=lowerCamelCase__					,beta_start=0.0_1    )
									UpperCAmelCase__    =							torch.sum(torch.abs(lowerCamelCase__    )    )
									UpperCAmelCase__    =							torch.mean(torch.abs(lowerCamelCase__    )    )

									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 	__lowerCAmelCase		(       self     :			Dict    ):
									# We specify different beta, so that the first alpha is 0.99
									UpperCAmelCase__    =							self.full_loop(set_alpha_to_one=lowerCamelCase__					,beta_start=0.0_1    )
									UpperCAmelCase__    =							torch.sum(torch.abs(lowerCamelCase__    )    )
									UpperCAmelCase__    =							torch.mean(torch.abs(lowerCamelCase__    )    )

									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




"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__						:						Any		  =			logging.get_logger(__name__)

lowerCAmelCase__						:						str		  =			{'ctrl': 'https://huggingface.co/ctrl/resolve/main/config.json'}
class        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					snake_case__       					=				"ctrl"
					snake_case__       					=				["past_key_values"]
					snake_case__       					=				{
					    "max_position_embeddings": "n_positions",
					    "hidden_size": "n_embd",
					    "num_attention_heads": "n_head",
					    "num_hidden_layers": "n_layer",
					}







					def __init__(       self     :			Any					,lowerCamelCase__     :			str=246_534					,lowerCamelCase__     :			List[str]=256					,lowerCamelCase__     :			Optional[int]=1_280					,lowerCamelCase__     :			Any=8_192					,lowerCamelCase__     :			int=48					,lowerCamelCase__     :			Optional[Any]=16					,lowerCamelCase__     :			Union[str, Any]=0.1					,lowerCamelCase__     :			Dict=0.1					,lowerCamelCase__     :			List[str]=1e-6					,lowerCamelCase__     :			List[str]=0.0_2					,lowerCamelCase__     :			Tuple=True					,**lowerCamelCase__     :			Optional[Any]					,):
									UpperCAmelCase__    =							vocab_size
									UpperCAmelCase__    =							n_positions
									UpperCAmelCase__    =							n_embd
									UpperCAmelCase__    =							n_layer
									UpperCAmelCase__    =							n_head
									UpperCAmelCase__    =							dff
									UpperCAmelCase__    =							resid_pdrop
									UpperCAmelCase__    =							embd_pdrop
									UpperCAmelCase__    =							layer_norm_epsilon
									UpperCAmelCase__    =							initializer_range

									UpperCAmelCase__    =							use_cache

									super().__init__(**lowerCamelCase__    )



