





from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class      A_	(a_      ):
	UpperCAmelCase__ =      42
	UpperCAmelCase__ =      42
	def __init__(   self							, _A							, _A		):






				'''simple docstring'''







				super().__init__()

				UpperCAmelCase       		=    PNDMScheduler.from_config(scheduler.config		)

				self.register_modules(unet=_A							, scheduler=_A		)







	@torch.no_grad()
	def __call__(   self							, _A = 1							, _A = 5_0							, _A = None							, _A = "pil"							, _A = True							, **_A							, ):






				'''simple docstring'''







				UpperCAmelCase       		=    randn_tensor(
				    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)							, generator=_A							, device=self.device							, )

				self.scheduler.set_timesteps(_A		)
				for t in self.progress_bar(self.scheduler.timesteps		):
							UpperCAmelCase       		=    self.unet(_A							, _A		).sample

							UpperCAmelCase       		=    self.scheduler.step(_A							, _A							, _A		).prev_sample

				UpperCAmelCase       		=    (image / 2 + 0.5).clamp(0							, 1		)
				UpperCAmelCase       		=    image.cpu().permute(0							, 2							, 3							, 1		).numpy()
				if output_type == "pil":
							UpperCAmelCase       		=    self.numpy_to_pil(_A		)

				if not return_dict:
							return (image,)

				return ImagePipelineOutput(images=_A		)










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



