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


snake_case__  							=							logging.get_logger(__name__)

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







class 							UpperCAmelCase	(   __lowerCamelCase					):
					a__: List[str]			  =				"""mpnet"""

					def __init__(			self      :					Union[str, Any] ,      lowerCAmelCase      :					Optional[int]=3_0527 ,      lowerCAmelCase      :					Tuple=768 ,      lowerCAmelCase      :					Tuple=12 ,      lowerCAmelCase      :					Union[str, Any]=12 ,      lowerCAmelCase      :					Any=3072 ,      lowerCAmelCase      :					List[str]="gelu" ,      lowerCAmelCase      :					Dict=0.1 ,      lowerCAmelCase      :					Dict=0.1 ,      lowerCAmelCase      :					str=512 ,      lowerCAmelCase      :					Optional[Any]=0.02 ,      lowerCAmelCase      :					Union[str, Any]=1E-12 ,      lowerCAmelCase      :					Optional[int]=32 ,      lowerCAmelCase      :					Optional[Any]=1 ,      lowerCAmelCase      :					Tuple=0 ,      lowerCAmelCase      :					Dict=2 ,      **lowerCAmelCase      :					int ,      ):
											super().__init__(pad_token_id=lowerCAmelCase ,      bos_token_id=lowerCAmelCase ,      eos_token_id=lowerCAmelCase ,      **lowerCAmelCase					)

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




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 							UpperCAmelCase	(   __lowerCamelCase					):
					a__: UNetaDModel
					a__: PNDMScheduler

					def __init__(			self      :					List[str] ,      lowerCAmelCase      :					UNetaDModel ,      lowerCAmelCase      :					PNDMScheduler					):
											super().__init__()

											lowercase       :    int											=  PNDMScheduler.from_config(scheduler.config					)

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




					@torch.no_grad()
					def __call__(			self      :					List[str] ,      lowerCAmelCase      :					int = 1 ,      lowerCAmelCase      :					int = 50 ,      lowerCAmelCase      :					Optional[Union[torch.Generator, List[torch.Generator]]] = None ,      lowerCAmelCase      :					Optional[str] = "pil" ,      lowerCAmelCase      :					bool = True ,      **lowerCAmelCase      :					int ,      ):
											lowercase       :    Tuple											=  randn_tensor(
											    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,      generator=lowerCAmelCase ,      device=self.device ,      )

											self.scheduler.set_timesteps(lowerCAmelCase					)
											for t in self.progress_bar(self.scheduler.timesteps					):
																	lowercase       :    Dict											=  self.unet(lowerCAmelCase ,      lowerCAmelCase					).sample

																	lowercase       :    Tuple											=  self.scheduler.step(lowerCAmelCase ,      lowerCAmelCase ,      lowerCAmelCase					).prev_sample

											lowercase       :    Optional[Any]											=  (image / 2 + 0.5).clamp(0 ,      1					)
											lowercase       :    Optional[int]											=  image.cpu().permute(0 ,      2 ,      3 ,      1					).numpy()
											if output_type == "pil":
																	lowercase       :    Union[str, Any]											=  self.numpy_to_pil(lowerCAmelCase					)

											if not return_dict:
																	return (image,)

											return ImagePipelineOutput(images=lowerCAmelCase					)


