
'''simple docstring'''



from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class   UpperCAmelCase_					(		_a			):






						'''simple docstring'''






						def __init__( self   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												super().__init__()

												# make sure scheduler can always be converted to DDIM
												_lowerCAmelCase  						=			DDIMScheduler.from_config(scheduler.config				)

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




						@torch.no_grad()
						def __call__( self   ,       _lowercase = 1   ,       _lowercase = None   ,       _lowercase = 0.0   ,       _lowercase = 50   ,       _lowercase = None   ,       _lowercase = "pil"   ,       _lowercase = True   ,       ):

												"""simple docstring"""



												if isinstance(self.unet.config.sample_size   ,       __lowerCamelCase				):
																		_lowerCAmelCase  						=			(
																		    batch_size,
																		    self.unet.config.in_channels,
																		    self.unet.config.sample_size,
																		    self.unet.config.sample_size,
																		)
												else:
																		_lowerCAmelCase  						=			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

												if isinstance(__lowerCamelCase   ,       __lowerCamelCase				) and len(__lowerCamelCase				) != batch_size:
																		raise ValueError(
																		    F'You have passed a list of generators of length {len(__lowerCamelCase				)}, but requested an effective batch'
																		    F' size of {batch_size}. Make sure the batch size matches the length of the generators.'				)

												_lowerCAmelCase  						=			randn_tensor(__lowerCamelCase   ,       generator=__lowerCamelCase   ,       device=self.device   ,       dtype=self.unet.dtype				)

												# set step values
												self.scheduler.set_timesteps(__lowerCamelCase				)

												for t in self.progress_bar(self.scheduler.timesteps				):
																		# 1. predict noise model_output
																		_lowerCAmelCase  						=			self.unet(__lowerCamelCase   ,       __lowerCamelCase				).sample

																		# 2. predict previous mean of image x_t-1 and add variance depending on eta
																		# eta corresponds to η in paper and should be between [0, 1]
																		# do x_t -> x_t-1
																		_lowerCAmelCase  						=			self.scheduler.step(
																		    __lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase   ,       eta=__lowerCamelCase   ,       use_clipped_model_output=__lowerCamelCase   ,       generator=__lowerCamelCase				).prev_sample

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

												if not return_dict:
																		return (image,)

												return ImagePipelineOutput(images=__lowerCamelCase				)


'''simple docstring'''



import logging

from transformers import PretrainedConfig


_lowercase    =		logging.getLogger(__name__)


_lowercase    =		{
    """bertabs-finetuned-cnndm""": """https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json""",
}




class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						_lowercase		:				Optional[Any]									=       '''bertabs'''




						def __init__( self   ,       _lowercase=30_522   ,       _lowercase=512   ,       _lowercase=6   ,       _lowercase=512   ,       _lowercase=8   ,       _lowercase=512   ,       _lowercase=0.2   ,       _lowercase=6   ,       _lowercase=768   ,       _lowercase=8   ,       _lowercase=2_048   ,       _lowercase=0.2   ,       **_lowercase   ,       ):

												"""simple docstring"""



												super().__init__(**_lowercase				)

												_lowerCAmelCase  						=			vocab_size
												_lowerCAmelCase  						=			max_pos

												_lowerCAmelCase  						=			enc_layers
												_lowerCAmelCase  						=			enc_hidden_size
												_lowerCAmelCase  						=			enc_heads
												_lowerCAmelCase  						=			enc_ff_size
												_lowerCAmelCase  						=			enc_dropout

												_lowerCAmelCase  						=			dec_layers
												_lowerCAmelCase  						=			dec_hidden_size
												_lowerCAmelCase  						=			dec_heads
												_lowerCAmelCase  						=			dec_ff_size
												_lowerCAmelCase  						=			dec_dropout
