






'''simple docstring'''
from typing import List, Optional, Tuple, Union

import torch

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






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):
 snake_case_												=							42
 snake_case_												=							42




 def __init__( self		:							Any   , __lowercase		:							UNetaDModel   , __lowercase		:							ScoreSdeVeScheduler )		->  Any:
       super().__init__()
       self.register_modules(unet=__lowercase   , scheduler=__lowercase )




 @torch.no_grad()
 def __call__( self		:							Optional[Any]   , __lowercase		:							int = 1   , __lowercase		:							int = 20_00   , __lowercase		:							Optional[Union[torch.Generator, List[torch.Generator]]] = None   , __lowercase		:							Optional[str] = "pil"   , __lowercase		:							bool = True   , **__lowercase		:							Optional[Any]   , )		->  Union[ImagePipelineOutput, Tuple]:
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=self.unet.config.sample_size
       SCREAMING_SNAKE_CASE__       :			str													=(batch_size, 3, img_size, img_size)

       SCREAMING_SNAKE_CASE__       :			int													=self.unet

       SCREAMING_SNAKE_CASE__       :			Any													=randn_tensor(__lowercase   , generator=__lowercase ) * self.scheduler.init_noise_sigma
       SCREAMING_SNAKE_CASE__       :			str													=sample.to(self.device )

       self.scheduler.set_timesteps(__lowercase )
       self.scheduler.set_sigmas(__lowercase )

       for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
             SCREAMING_SNAKE_CASE__       :			str													=self.scheduler.sigmas[i] * torch.ones(shape[0]   , device=self.device )

             # correction step
             for _ in range(self.scheduler.config.correct_steps ):
                   SCREAMING_SNAKE_CASE__       :			Tuple													=self.unet(__lowercase   , __lowercase ).sample
                   SCREAMING_SNAKE_CASE__       :			List[str]													=self.scheduler.step_correct(__lowercase   , __lowercase   , generator=__lowercase ).prev_sample

             # prediction step
             SCREAMING_SNAKE_CASE__       :			List[str]													=model(__lowercase   , __lowercase ).sample
             SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=self.scheduler.step_pred(__lowercase   , __lowercase   , __lowercase   , generator=__lowercase )

             SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			Optional[int]													=output.prev_sample, output.prev_sample_mean

       SCREAMING_SNAKE_CASE__       :			Dict													=sample_mean.clamp(0   , 1 )
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=sample.cpu().permute(0   , 2   , 3   , 1 ).numpy()
       if output_type == "pil":
             SCREAMING_SNAKE_CASE__       :			str													=self.numpy_to_pil(__lowercase )

       if not return_dict:
             return (sample,)

       return ImagePipelineOutput(images=__lowercase )







'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_					=	logging.get_logger(__name__)

a_					=	{
    'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):
 snake_case_												=							"""biogpt"""




 def __init__( self		:							str   , __lowercase		:							Union[str, Any]=4_23_84   , __lowercase		:							Union[str, Any]=10_24   , __lowercase		:							Any=24   , __lowercase		:							Any=16   , __lowercase		:							Optional[Any]=40_96   , __lowercase		:							Any="gelu"   , __lowercase		:							Optional[Any]=0.1   , __lowercase		:							List[Any]=0.1   , __lowercase		:							Union[str, Any]=10_24   , __lowercase		:							List[Any]=0.02   , __lowercase		:							Tuple=1e-12   , __lowercase		:							Optional[Any]=True   , __lowercase		:							Optional[Any]=True   , __lowercase		:							Any=0.0   , __lowercase		:							int=0.0   , __lowercase		:							str=1   , __lowercase		:							int=0   , __lowercase		:							Optional[int]=2   , **__lowercase		:							Dict   , )		->  Optional[Any]:
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=vocab_size
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=max_position_embeddings
       SCREAMING_SNAKE_CASE__       :			str													=hidden_size
       SCREAMING_SNAKE_CASE__       :			int													=num_hidden_layers
       SCREAMING_SNAKE_CASE__       :			Tuple													=num_attention_heads
       SCREAMING_SNAKE_CASE__       :			Any													=intermediate_size
       SCREAMING_SNAKE_CASE__       :			int													=hidden_act
       SCREAMING_SNAKE_CASE__       :			List[Any]													=hidden_dropout_prob
       SCREAMING_SNAKE_CASE__       :			Dict													=attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=initializer_range
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=layer_norm_eps
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=scale_embedding
       SCREAMING_SNAKE_CASE__       :			str													=use_cache
       SCREAMING_SNAKE_CASE__       :			str													=layerdrop
       SCREAMING_SNAKE_CASE__       :			Dict													=activation_dropout
       super().__init__(pad_token_id=__lowercase   , bos_token_id=__lowercase   , eos_token_id=__lowercase   , **__lowercase )