

import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						(UniPCMultistepScheduler,)
			SCREAMING_SNAKE_CASE							   =						(('num_inference_steps', 2_5),)





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




						'''simple docstring'''
						__a        ={
						    'num_train_timesteps': 1000,
						    'beta_start': 0.0001,
						    'beta_end': 0.02,
						    'beta_schedule': 'linear',
						    'solver_order': 2,
						    'solver_type': 'bh2',
						}

						config.update(**__snake_case					)
						return config





			def 		__magic_name__    (		self					,  __snake_case=0					,  **__snake_case					)   -> Optional[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 + 0.10]

						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[: scheduler.config.solver_order]

									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[: new_scheduler.config.solver_order]

									__a      ,    __a        =sample, sample
									for t in range(__snake_case					,  time_step + scheduler.config.solver_order + 1					):
												__a        =scheduler.step(__snake_case					,  __snake_case					,  __snake_case					,  **__snake_case					).prev_sample
												__a        =new_scheduler.step(__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 		__magic_name__    (		self					,  __snake_case=0					,  **__snake_case					)   -> List[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 + 0.10]

						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[: scheduler.config.solver_order]

									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[: new_scheduler.config.solver_order]

									__a        =scheduler.step(__snake_case					,  __snake_case					,  __snake_case					,  **__snake_case					).prev_sample
									__a        =new_scheduler.step(__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 		__magic_name__    (		self					,  __snake_case=None					,  **__snake_case					)   -> List[Any]:




						'''simple docstring'''
						if scheduler is None:
									__a        =self.scheduler_classes[0]
									__a        =self.get_scheduler_config(**__snake_case					)
									__a        =scheduler_class(**__snake_case					)

						__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.timesteps					):
									__a        =model(__snake_case					,  __snake_case					)
									__a        =scheduler.step(__snake_case					,  __snake_case					,  __snake_case					).prev_sample

						return sample





			def 		__magic_name__    (		self					)   -> Optional[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 + 0.10]
									__a        =dummy_past_residuals[: scheduler.config.solver_order]

									__a        =scheduler.timesteps[5]
									__a        =scheduler.timesteps[6]

									__a        =scheduler.step(__snake_case					,  __snake_case					,  __snake_case					,  **__snake_case					).prev_sample
									__a        =scheduler.step(__snake_case					,  __snake_case					,  __snake_case					,  **__snake_case					).prev_sample

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





			def 		__magic_name__    (		self					)   -> Any:




						'''simple docstring'''
						# make sure that iterating over schedulers with same config names gives same results
						# for defaults
						__a        =UniPCMultistepScheduler(**self.get_scheduler_config()					)
						__a        =self.full_loop(scheduler=__snake_case					)
						__a        =torch.mean(torch.abs(__snake_case					)					)

						assert abs(result_mean.item() - 0.2464					) < 1e-3

						__a        =DPMSolverSinglestepScheduler.from_config(scheduler.config					)
						__a        =DEISMultistepScheduler.from_config(scheduler.config					)
						__a        =DPMSolverMultistepScheduler.from_config(scheduler.config					)
						__a        =UniPCMultistepScheduler.from_config(scheduler.config					)

						__a        =self.full_loop(scheduler=__snake_case					)
						__a        =torch.mean(torch.abs(__snake_case					)					)

						assert abs(result_mean.item() - 0.2464					) < 1e-3





			def 		__magic_name__    (		self					)   -> Dict:




						'''simple docstring'''
						for timesteps in [25, 50, 100, 999, 1000]:
									self.check_over_configs(num_train_timesteps=__snake_case					)





			def 		__magic_name__    (		self					)   -> int:




						'''simple docstring'''
						self.check_over_configs(thresholding=__snake_case					)
						for order in [1, 2, 3]:
									for solver_type in ["bh1", "bh2"]:
												for threshold in [0.5, 1.0, 2.0]:
															for prediction_type in ["epsilon", "sample"]:
																		self.check_over_configs(
																		    thresholding=__snake_case					,  prediction_type=__snake_case					,  sample_max_value=__snake_case					,  solver_order=__snake_case					,  solver_type=__snake_case					,  )





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




						'''simple docstring'''
						for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(prediction_type=__snake_case					)





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




						'''simple docstring'''
						for solver_type in ["bh1", "bh2"]:
									for order in [1, 2, 3]:
												for prediction_type in ["epsilon", "sample"]:
															self.check_over_configs(
															    solver_order=__snake_case					,  solver_type=__snake_case					,  prediction_type=__snake_case					,  )
															__a        =self.full_loop(
															    solver_order=__snake_case					,  solver_type=__snake_case					,  prediction_type=__snake_case					,  )
															assert not torch.isnan(__snake_case					).any(), "Samples have nan numbers"





			def 		__magic_name__    (		self					)   -> int:




						'''simple docstring'''
						self.check_over_configs(lower_order_final=__snake_case					)
						self.check_over_configs(lower_order_final=__snake_case					)





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




						'''simple docstring'''
						for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
									self.check_over_forward(num_inference_steps=__snake_case					,  time_step=0					)





			def 		__magic_name__    (		self					)   -> str:




						'''simple docstring'''
						__a        =self.full_loop()
						__a        =torch.mean(torch.abs(__snake_case					)					)

						assert abs(result_mean.item() - 0.2464					) < 1e-3





			def 		__magic_name__    (		self					)   -> Dict:




						'''simple docstring'''
						__a        =self.full_loop(prediction_type='v_prediction'					)
						__a        =torch.mean(torch.abs(__snake_case					)					)

						assert abs(result_mean.item() - 0.1014					) < 1e-3





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




						'''simple docstring'''
						__a        =self.scheduler_classes[0]
						__a        =self.get_scheduler_config(thresholding=__snake_case					,  dynamic_thresholding_ratio=0					)
						__a        =scheduler_class(**__snake_case					)

						__a        =10
						__a        =self.dummy_model()
						__a        =self.dummy_sample_deter.half()
						scheduler.set_timesteps(__snake_case					)

						for i, t in enumerate(scheduler.timesteps					):
									__a        =model(__snake_case					,  __snake_case					)
									__a        =scheduler.step(__snake_case					,  __snake_case					,  __snake_case					).prev_sample

						assert sample.dtype == torch.floataa





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




						'''simple docstring'''
						for scheduler_class in self.scheduler_classes:
									__a        =self.get_scheduler_config(**__snake_case					)
									__a        =scheduler_class(**__snake_case					)

									scheduler.set_timesteps(scheduler.config.num_train_timesteps					)
									assert len(scheduler.timesteps.unique()					) == scheduler.num_inference_steps









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


_lowerCAmelCase					:			Tuple  				=			logging.get_logger(__name__)

_lowerCAmelCase					:			Union[str, Any]  				=			{"ctrl": "https://huggingface.co/ctrl/resolve/main/config.json"}






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						'ctrl'
			SCREAMING_SNAKE_CASE							   =						['past_key_values']
			SCREAMING_SNAKE_CASE							   =						{
			    'max_position_embeddings': 'n_positions',
			    'hidden_size': 'n_embd',
			    'num_attention_heads': 'n_head',
			    'num_hidden_layers': 'n_layer',
			}





			def __init__(		self					,  __snake_case=24_6534					,  __snake_case=256					,  __snake_case=1280					,  __snake_case=8192					,  __snake_case=48					,  __snake_case=16					,  __snake_case=0.1					,  __snake_case=0.1					,  __snake_case=1e-6					,  __snake_case=0.02					,  __snake_case=True					,  **__snake_case					,  )   -> Union[str, Any]:




						'''simple docstring'''
						__a        =vocab_size
						__a        =n_positions
						__a        =n_embd
						__a        =n_layer
						__a        =n_head
						__a        =dff
						__a        =resid_pdrop
						__a        =embd_pdrop
						__a        =layer_norm_epsilon
						__a        =initializer_range

						__a        =use_cache

						super().__init__(**__snake_case					)






