





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


__lowercase							:Any							=	logging.get_logger(__name__)

__lowercase							:Tuple							=	{
    "microsoft/mpnet-base": "https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json",
}



class  _a  (					lowercase__				):






							"""simple docstring"""

							snake_case_							=    "mpnet"

							def __init__(   self					:  List[Any]				,						a					:  List[str]=3_05_27				,						a					:  List[Any]=7_68				,						a					:  Optional[int]=12				,						a					:  List[str]=12				,						a					:  List[str]=30_72				,						a					:  Optional[int]="gelu"				,						a					:  List[Any]=0.1				,						a					:  Union[str, Any]=0.1				,						a					:  str=5_12				,						a					:  int=0.02				,						a					:  Union[str, Any]=1E-12				,						a					:  List[Any]=32				,						a					:  List[str]=1				,						a					:  Any=0				,						a					:  Dict=2				,						**a					:  Dict				,						)						->Optional[Any]:
													super().__init__(pad_token_id=a				,						bos_token_id=a				,						eos_token_id=a				,						**a     )

													SCREAMING_SNAKE_CASE__  :					Optional[int] =       vocab_size
													SCREAMING_SNAKE_CASE__  :					Any =       hidden_size
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       num_hidden_layers
													SCREAMING_SNAKE_CASE__  :					Tuple =       num_attention_heads
													SCREAMING_SNAKE_CASE__  :					List[str] =       hidden_act
													SCREAMING_SNAKE_CASE__  :					Dict =       intermediate_size
													SCREAMING_SNAKE_CASE__  :					int =       hidden_dropout_prob
													SCREAMING_SNAKE_CASE__  :					str =       attention_probs_dropout_prob
													SCREAMING_SNAKE_CASE__  :					Any =       max_position_embeddings
													SCREAMING_SNAKE_CASE__  :					List[Any] =       initializer_range
													SCREAMING_SNAKE_CASE__  :					Tuple =       layer_norm_eps
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       relative_attention_num_buckets






from typing import List, Optional, Tuple, Union

import torch

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



class  _a  (					lowercase__				):






							"""simple docstring"""

							def __init__(   self					:  Dict				,						a					:  str				,						a					:  Optional[Any]     )						->Any:
													super().__init__()
													self.register_modules(unet=a				,						scheduler=a     )






							@torch.no_grad()
							def __call__(   self					:  int				,						a					:  int = 1				,						a					:  Optional[Union[torch.Generator, List[torch.Generator]]] = None				,						a					:  int = 10_00				,						a					:  Optional[str] = "pil"				,						a					:  bool = True				,						)						->Union[ImagePipelineOutput, Tuple]:
													# Sample gaussian noise to begin loop
													if isinstance(self.unet.config.sample_size				,						a     ):
																			SCREAMING_SNAKE_CASE__  :					int =       (
																			    batch_size,
																			    self.unet.config.in_channels,
																			    self.unet.config.sample_size,
																			    self.unet.config.sample_size,
																			)
													else:
																			SCREAMING_SNAKE_CASE__  :					Dict =       (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

													if self.device.type == "mps":
																			# randn does not work reproducibly on mps
																			SCREAMING_SNAKE_CASE__  :					List[str] =       randn_tensor(a				,						generator=a     )
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       image.to(self.device     )
													else:
																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       randn_tensor(a				,						generator=a				,						device=self.device     )

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

													for t in self.progress_bar(self.scheduler.timesteps     ):
																			# 1. predict noise model_output
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       self.unet(a				,						a     ).sample

																			# 2. compute previous image: x_t -> x_t-1
																			SCREAMING_SNAKE_CASE__  :					Tuple =       self.scheduler.step(a				,						a				,						a				,						generator=a     ).prev_sample

													SCREAMING_SNAKE_CASE__  :					List[str] =       (image / 2 + 0.5).clamp(0				,						1     )
													SCREAMING_SNAKE_CASE__  :					Tuple =       image.cpu().permute(0				,						2				,						3				,						1     ).numpy()
													if output_type == "pil":
																			SCREAMING_SNAKE_CASE__  :					Dict =       self.numpy_to_pil(a     )

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=a     )