





'''simple docstring'''

import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest


class 			SCREAMING_SNAKE_CASE__	(					snake_case__						):







					"""simple docstring"""






					SCREAMING_SNAKE_CASE				=     (DDIMScheduler,)
					SCREAMING_SNAKE_CASE				=     (('''eta''', 0.0), ('''num_inference_steps''', 50))
					def 						lowerCamelCase_					(					self    :	Union[str, Any]						,     **UpperCAmelCase_    :	Dict						):







							"""simple docstring"""
							__UpperCAmelCase     :				List[Any]					   =			{
							    'num_train_timesteps': 1_000,
							    'beta_start': 0.0001,
							    'beta_end': 0.02,
							    'beta_schedule': 'linear',
							    'clip_sample': True,
							}

							config.update(**UpperCAmelCase_						)
							return config
					def 						lowerCamelCase_					(					self    :	int						,     **UpperCAmelCase_    :	Optional[Any]						):







							"""simple docstring"""
							__UpperCAmelCase     :				List[str]					   =			self.scheduler_classes[0]
							__UpperCAmelCase     :				List[Any]					   =			self.get_scheduler_config(**UpperCAmelCase_						)
							__UpperCAmelCase     :				Tuple					   =			scheduler_class(**UpperCAmelCase_						)

							__UpperCAmelCase     :				Union[str, Any]					   =			10, 0.0

							__UpperCAmelCase     :				Optional[int]					   =			self.dummy_model()
							__UpperCAmelCase     :				Tuple					   =			self.dummy_sample_deter

							scheduler.set_timesteps(UpperCAmelCase_						)

							for t in scheduler.timesteps:
									__UpperCAmelCase     :				Any					   =			model(UpperCAmelCase_						,     UpperCAmelCase_						)
									__UpperCAmelCase     :				Optional[int]					   =			scheduler.step(UpperCAmelCase_						,     UpperCAmelCase_						,     UpperCAmelCase_						,     UpperCAmelCase_						).prev_sample

							return sample
					def 						lowerCamelCase_					(					self    :	Dict						):







							"""simple docstring"""




							for timesteps in [100, 500, 1_000]:
									self.check_over_configs(num_train_timesteps=UpperCAmelCase_						)
					def 						lowerCamelCase_					(					self    :	int						):







							"""simple docstring"""




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

							__UpperCAmelCase     :				Union[str, Any]					   =			self.scheduler_classes[0]
							__UpperCAmelCase     :				Dict					   =			self.get_scheduler_config(steps_offset=1						)
							__UpperCAmelCase     :				Any					   =			scheduler_class(**UpperCAmelCase_						)
							scheduler.set_timesteps(5						)
							assert torch.equal(scheduler.timesteps						,     torch.LongTensor([801, 601, 401, 201, 1]						)						)
					def 						lowerCamelCase_					(					self    :	Any						):







							"""simple docstring"""




							for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]						,     [0.002, 0.02, 0.2, 2]						):
									self.check_over_configs(beta_start=UpperCAmelCase_						,     beta_end=UpperCAmelCase_						)
					def 						lowerCamelCase_					(					self    :	Any						):







							"""simple docstring"""




							for schedule in ["linear", "squaredcos_cap_v2"]:
									self.check_over_configs(beta_schedule=UpperCAmelCase_						)
					def 						lowerCamelCase_					(					self    :	int						):







							"""simple docstring"""




							for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(prediction_type=UpperCAmelCase_						)
					def 						lowerCamelCase_					(					self    :	Dict						):







							"""simple docstring"""




							for clip_sample in [True, False]:
									self.check_over_configs(clip_sample=UpperCAmelCase_						)
					def 						lowerCamelCase_					(					self    :	Dict						):







							"""simple docstring"""




							for timestep_spacing in ["trailing", "leading"]:
									self.check_over_configs(timestep_spacing=UpperCAmelCase_						)
					def 						lowerCamelCase_					(					self    :	Union[str, Any]						):







							"""simple docstring"""




							for rescale_betas_zero_snr in [True, False]:
									self.check_over_configs(rescale_betas_zero_snr=UpperCAmelCase_						)
					def 						lowerCamelCase_					(					self    :	int						):







							"""simple docstring"""
							self.check_over_configs(thresholding=UpperCAmelCase_						)
							for threshold in [0.5, 1.0, 2.0]:
									for prediction_type in ["epsilon", "v_prediction"]:
											self.check_over_configs(
											    thresholding=UpperCAmelCase_						,     prediction_type=UpperCAmelCase_						,     sample_max_value=UpperCAmelCase_						,     )
					def 						lowerCamelCase_					(					self    :	Any						):







							"""simple docstring"""




							for t in [1, 10, 49]:
									self.check_over_forward(time_step=UpperCAmelCase_						)
					def 						lowerCamelCase_					(					self    :	List[Any]						):







							"""simple docstring"""




							for t, num_inference_steps in zip([1, 10, 50]						,     [10, 50, 500]						):
									self.check_over_forward(time_step=UpperCAmelCase_						,     num_inference_steps=UpperCAmelCase_						)
					def 						lowerCamelCase_					(					self    :	Optional[Any]						):







							"""simple docstring"""




							for t, eta in zip([1, 10, 49]						,     [0.0, 0.5, 1.0]						):
									self.check_over_forward(time_step=UpperCAmelCase_						,     eta=UpperCAmelCase_						)
					def 						lowerCamelCase_					(					self    :	str						):







							"""simple docstring"""
							__UpperCAmelCase     :				Tuple					   =			self.scheduler_classes[0]
							__UpperCAmelCase     :				Any					   =			self.get_scheduler_config()
							__UpperCAmelCase     :				Optional[int]					   =			scheduler_class(**UpperCAmelCase_						)

							assert torch.sum(torch.abs(scheduler._get_variance(0						,     0						) - 0.0						)						) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(420						,     400						) - 0.14771						)						) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(980						,     960						) - 0.32460						)						) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(0						,     0						) - 0.0						)						) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(487						,     486						) - 0.00979						)						) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(999						,     998						) - 0.02						)						) < 1e-5
					def 						lowerCamelCase_					(					self    :	str						):







							"""simple docstring"""
							__UpperCAmelCase     :				Optional[Any]					   =			self.full_loop()

							__UpperCAmelCase     :				Optional[Any]					   =			torch.sum(torch.abs(UpperCAmelCase_						)						)
							__UpperCAmelCase     :				Tuple					   =			torch.mean(torch.abs(UpperCAmelCase_						)						)

							assert abs(result_sum.item() - 172.0_067						) < 1e-2
							assert abs(result_mean.item() - 0.223967						) < 1e-3
					def 						lowerCamelCase_					(					self    :	int						):







							"""simple docstring"""
							__UpperCAmelCase     :				Union[str, Any]					   =			self.full_loop(prediction_type="v_prediction"						)

							__UpperCAmelCase     :				Optional[Any]					   =			torch.sum(torch.abs(UpperCAmelCase_						)						)
							__UpperCAmelCase     :				Any					   =			torch.mean(torch.abs(UpperCAmelCase_						)						)

							assert abs(result_sum.item() - 52.5302						) < 1e-2
							assert abs(result_mean.item() - 0.0684						) < 1e-3
					def 						lowerCamelCase_					(					self    :	List[Any]						):







							"""simple docstring"""
							__UpperCAmelCase     :				Any					   =			self.full_loop(set_alpha_to_one=UpperCAmelCase_						,     beta_start=0.01						)
							__UpperCAmelCase     :				int					   =			torch.sum(torch.abs(UpperCAmelCase_						)						)
							__UpperCAmelCase     :				List[Any]					   =			torch.mean(torch.abs(UpperCAmelCase_						)						)

							assert abs(result_sum.item() - 149.8_295						) < 1e-2
							assert abs(result_mean.item() - 0.1951						) < 1e-3






					def 						lowerCamelCase_					(					self    :	Any						):







							"""simple docstring"""
							__UpperCAmelCase     :				Optional[int]					   =			self.full_loop(set_alpha_to_one=UpperCAmelCase_						,     beta_start=0.01						)
							__UpperCAmelCase     :				Optional[Any]					   =			torch.sum(torch.abs(UpperCAmelCase_						)						)
							__UpperCAmelCase     :				List[Any]					   =			torch.mean(torch.abs(UpperCAmelCase_						)						)

							assert abs(result_sum.item() - 149.0_784						) < 1e-2
							assert abs(result_mean.item() - 0.1941						) < 1e-3












"""simple docstring"""


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


_UpperCamelCase				:    Optional[int]     =						logging.get_logger(__name__)






class 						snake_case  (  UpperCAmelCase       ):
    __magic_name__         =     '''speech-encoder-decoder'''
    __magic_name__         =     True





    def __init__(							self	:					Optional[Any]				,		**A	:					List[Any]			):


      '''simple docstring'''




      super().__init__(**A			)
      if "encoder" not in kwargs or "decoder" not in kwargs:
        raise ValueError(
            F'''A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and'''
            F''' `decoder` sub-configurations are passed, but only {kwargs}'''			)

      a     :			Optional[int]				=   kwargs.pop('encoder'			)
      a     :			Any				=   encoder_config.pop('model_type'			)
      a     :			Optional[Any]				=   kwargs.pop('decoder'			)
      a     :			str				=   decoder_config.pop('model_type'			)

      a     :			Tuple				=   AutoConfig.for_model(A				,		**A			)
      a     :			Dict				=   AutoConfig.for_model(A				,		**A			)
      a     :			Optional[Any]				=   True





    @classmethod
    def 				lowerCamelCase__      (							cls	:					List[Any]				,		A	:					PretrainedConfig				,		A	:					PretrainedConfig				,		**A	:					str			):


      '''simple docstring'''




      logger.info('Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'			)
      a     :			Any				=   True
      a     :			List[str]				=   True

      return cls(encoder=encoder_config.to_dict()				,		decoder=decoder_config.to_dict()				,		**A			)





    def 				lowerCamelCase__      (							self	:					Optional[int]			):


      '''simple docstring'''




      a     :			List[Any]				=   copy.deepcopy(self.__dict__			)
      a     :			int				=   self.encoder.to_dict()
      a     :			Optional[int]				=   self.decoder.to_dict()
      a     :			Any				=   self.__class__.model_type
      return output




