





import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler

class      A_	(unittest.TestCase      ):
	# TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
	UpperCAmelCase__ =      (ScoreSdeVeScheduler,)
	UpperCAmelCase__ =      ()
	@property
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    4
				UpperCAmelCase       		=    3
				UpperCAmelCase       		=    8
				UpperCAmelCase       		=    8

				UpperCAmelCase       		=    torch.rand((batch_size, num_channels, height, width)		)

				return sample
	@property
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    4
				UpperCAmelCase       		=    3
				UpperCAmelCase       		=    8
				UpperCAmelCase       		=    8

				UpperCAmelCase       		=    batch_size * num_channels * height * width
				UpperCAmelCase       		=    torch.arange(_A		)
				UpperCAmelCase       		=    sample.reshape(_A							, _A							, _A							, _A		)
				UpperCAmelCase       		=    sample / num_elems
				UpperCAmelCase       		=    sample.permute(3							, 0							, 1							, 2		)

				return sample
	def 					_lowercase    (   self		):






				'''simple docstring'''







				def model(_A							, _A							, *_A		):
							return sample * t / (t + 1)

				return model
	def 					_lowercase    (   self							, **_A		):






				'''simple docstring'''







				UpperCAmelCase       		=    {
				    '''num_train_timesteps''': 2_0_0_0,
				    '''snr''': 0.15,
				    '''sigma_min''': 0.01,
				    '''sigma_max''': 1_3_4_8,
				    '''sampling_eps''': 1E-5,
				}

				config.update(**_A		)
				return config
	def 					_lowercase    (   self							, _A=0							, **_A		):






				'''simple docstring'''







				UpperCAmelCase       		=    dict(self.forward_default_kwargs		)

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

							UpperCAmelCase       		=    self.get_scheduler_config(**_A		)
							UpperCAmelCase       		=    scheduler_class(**_A		)

							with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(_A		)
										UpperCAmelCase       		=    scheduler_class.from_pretrained(_A		)

							UpperCAmelCase       		=    scheduler.step_pred(
							    _A							, _A							, _A							, generator=torch.manual_seed(0		)							, **_A		).prev_sample
							UpperCAmelCase       		=    new_scheduler.step_pred(
							    _A							, _A							, _A							, generator=torch.manual_seed(0		)							, **_A		).prev_sample

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

							UpperCAmelCase       		=    scheduler.step_correct(_A							, _A							, generator=torch.manual_seed(0		)							, **_A		).prev_sample
							UpperCAmelCase       		=    new_scheduler.step_correct(
							    _A							, _A							, generator=torch.manual_seed(0		)							, **_A		).prev_sample

							assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler correction are not identical"
	def 					_lowercase    (   self							, _A=0							, **_A		):






				'''simple docstring'''







				UpperCAmelCase       		=    dict(self.forward_default_kwargs		)
				kwargs.update(_A		)

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

							UpperCAmelCase       		=    self.get_scheduler_config()
							UpperCAmelCase       		=    scheduler_class(**_A		)

							with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(_A		)
										UpperCAmelCase       		=    scheduler_class.from_pretrained(_A		)

							UpperCAmelCase       		=    scheduler.step_pred(
							    _A							, _A							, _A							, generator=torch.manual_seed(0		)							, **_A		).prev_sample
							UpperCAmelCase       		=    new_scheduler.step_pred(
							    _A							, _A							, _A							, generator=torch.manual_seed(0		)							, **_A		).prev_sample

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

							UpperCAmelCase       		=    scheduler.step_correct(_A							, _A							, generator=torch.manual_seed(0		)							, **_A		).prev_sample
							UpperCAmelCase       		=    new_scheduler.step_correct(
							    _A							, _A							, generator=torch.manual_seed(0		)							, **_A		).prev_sample

							assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler correction are not identical"
	def 					_lowercase    (   self		):






				'''simple docstring'''







				for timesteps in [1_0, 1_0_0, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=_A		)
	def 					_lowercase    (   self		):






				'''simple docstring'''







				for sigma_min, sigma_max in zip([0.00_01, 0.0_01, 0.01]							, [1, 1_0_0, 1_0_0_0]		):
							self.check_over_configs(sigma_min=_A							, sigma_max=_A		)
	def 					_lowercase    (   self		):






				'''simple docstring'''







				for t in [0.1, 0.5, 0.75]:
							self.check_over_forward(time_step=_A		)
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    dict(self.forward_default_kwargs		)

				UpperCAmelCase       		=    self.scheduler_classes[0]
				UpperCAmelCase       		=    self.get_scheduler_config()
				UpperCAmelCase       		=    scheduler_class(**_A		)

				UpperCAmelCase       		=    3

				UpperCAmelCase       		=    self.dummy_model()
				UpperCAmelCase       		=    self.dummy_sample_deter

				scheduler.set_sigmas(_A		)
				scheduler.set_timesteps(_A		)
				UpperCAmelCase       		=    torch.manual_seed(0		)

				for i, t in enumerate(scheduler.timesteps		):
							UpperCAmelCase       		=    scheduler.sigmas[i]

							for _ in range(scheduler.config.correct_steps		):
										with torch.no_grad():
													UpperCAmelCase       		=    model(_A							, _A		)
										UpperCAmelCase       		=    scheduler.step_correct(_A							, _A							, generator=_A							, **_A		).prev_sample

							with torch.no_grad():
										UpperCAmelCase       		=    model(_A							, _A		)

							UpperCAmelCase       		=    scheduler.step_pred(_A							, _A							, _A							, generator=_A							, **_A		)
							UpperCAmelCase		,      UpperCAmelCase       		=    output.prev_sample, output.prev_sample_mean

				UpperCAmelCase       		=    torch.sum(torch.abs(_A		)		)
				UpperCAmelCase       		=    torch.mean(torch.abs(_A		)		)

				assert np.isclose(result_sum.item()							, 1_43_72_75_85_28.0		)
				assert np.isclose(result_mean.item()							, 18_71_45_30.0		)







	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    dict(self.forward_default_kwargs		)

				UpperCAmelCase       		=    kwargs.pop('''num_inference_steps'''							, _A		)

				for scheduler_class in self.scheduler_classes:
							UpperCAmelCase       		=    self.get_scheduler_config()
							UpperCAmelCase       		=    scheduler_class(**_A		)

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

							UpperCAmelCase       		=    scheduler.step_pred(_A							, 0							, _A							, generator=torch.manual_seed(0		)							, **_A		).prev_sample
							UpperCAmelCase       		=    scheduler.step_pred(_A							, 1							, _A							, generator=torch.manual_seed(0		)							, **_A		).prev_sample

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










import copy

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


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

class      A_	(a_      ):
	UpperCAmelCase__ =      '''speech-encoder-decoder'''
	UpperCAmelCase__ =      True
	def __init__(   self							, **_A		):






				'''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}"""		)

				UpperCAmelCase       		=    kwargs.pop('''encoder'''		)
				UpperCAmelCase       		=    encoder_config.pop('''model_type'''		)
				UpperCAmelCase       		=    kwargs.pop('''decoder'''		)
				UpperCAmelCase       		=    decoder_config.pop('''model_type'''		)

				UpperCAmelCase       		=    AutoConfig.for_model(_A							, **_A		)
				UpperCAmelCase       		=    AutoConfig.for_model(_A							, **_A		)
				UpperCAmelCase       		=    True
	@classmethod
	def 					_lowercase    (   cls							, _A							, _A							, **_A		):






				'''simple docstring'''







				logger.info('''Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''		)
				UpperCAmelCase       		=    True
				UpperCAmelCase       		=    True

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







	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    copy.deepcopy(self.__dict__		)
				UpperCAmelCase       		=    self.encoder.to_dict()
				UpperCAmelCase       		=    self.decoder.to_dict()
				UpperCAmelCase       		=    self.__class__.model_type
				return output



