


'''simple docstring'''

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


a_  :				Optional[int]						= logging.get_logger(__name__)

a_  :				List[Any]						= {
    """vinvino02/glpn-kitti""": """https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json""",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}





class      snake_case    (    lowercase			):





  """simple docstring"""




  _lowerCamelCase		    =  "glpn"






  def __init__(    self			,  UpperCamelCase=3			,  UpperCamelCase=4			,  UpperCamelCase=[2, 2, 2, 2]			,  UpperCamelCase=[8, 4, 2, 1]			,  UpperCamelCase=[32, 64, 160, 256]			,  UpperCamelCase=[7, 3, 3, 3]			,  UpperCamelCase=[4, 2, 2, 2]			,  UpperCamelCase=[1, 2, 5, 8]			,  UpperCamelCase=[4, 4, 4, 4]			,  UpperCamelCase="gelu"			,  UpperCamelCase=0.0			,  UpperCamelCase=0.0			,  UpperCamelCase=0.02			,  UpperCamelCase=0.1			,  UpperCamelCase=1e-6			,  UpperCamelCase=64			,  UpperCamelCase=10			,  UpperCamelCase=-1			,  **UpperCamelCase			,  ):







   """simple docstring"""
   super().__init__(**UpperCamelCase     )

   lowerCamelCase_      =  num_channels
   lowerCamelCase_      =  num_encoder_blocks
   lowerCamelCase_      =  depths
   lowerCamelCase_      =  sr_ratios
   lowerCamelCase_      =  hidden_sizes
   lowerCamelCase_      =  patch_sizes
   lowerCamelCase_      =  strides
   lowerCamelCase_      =  mlp_ratios
   lowerCamelCase_      =  num_attention_heads
   lowerCamelCase_      =  hidden_act
   lowerCamelCase_      =  hidden_dropout_prob
   lowerCamelCase_      =  attention_probs_dropout_prob
   lowerCamelCase_      =  initializer_range
   lowerCamelCase_      =  drop_path_rate
   lowerCamelCase_      =  layer_norm_eps
   lowerCamelCase_      =  decoder_hidden_size
   lowerCamelCase_      =  max_depth
   lowerCamelCase_      =  head_in_index







'''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      snake_case    (    lowercase			):





  """simple docstring"""




  _lowerCamelCase		    =  42
  _lowerCamelCase		    =  42






  def __init__(    self			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   super().__init__()
   self.register_modules(unet=UpperCamelCase			,  scheduler=UpperCamelCase     )







  @torch.no_grad()
  def __call__(    self			,  UpperCamelCase = 1			,  UpperCamelCase = 2000			,  UpperCamelCase = None			,  UpperCamelCase = "pil"			,  UpperCamelCase = True			,  **UpperCamelCase			,  ):







   """simple docstring"""
   lowerCamelCase_      =  self.unet.config.sample_size
   lowerCamelCase_      =  (batch_size, 3, img_size, img_size)

   lowerCamelCase_      =  self.unet

   lowerCamelCase_      =  randn_tensor(UpperCamelCase			,  generator=UpperCamelCase     ) * self.scheduler.init_noise_sigma
   lowerCamelCase_      =  sample.to(self.device     )

   self.scheduler.set_timesteps(UpperCamelCase     )
   self.scheduler.set_sigmas(UpperCamelCase     )

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

    # correction step
    for _ in range(self.scheduler.config.correct_steps     ):
     lowerCamelCase_      =  self.unet(UpperCamelCase			,  UpperCamelCase     ).sample
     lowerCamelCase_      =  self.scheduler.step_correct(UpperCamelCase			,  UpperCamelCase			,  generator=UpperCamelCase     ).prev_sample

    # prediction step
    lowerCamelCase_      =  model(UpperCamelCase			,  UpperCamelCase     ).sample
    lowerCamelCase_      =  self.scheduler.step_pred(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  generator=UpperCamelCase     )

    lowerCamelCase_ ,lowerCamelCase_      =  output.prev_sample, output.prev_sample_mean

   lowerCamelCase_      =  sample_mean.clamp(0			,  1     )
   lowerCamelCase_      =  sample.cpu().permute(0			,  2			,  3			,  1     ).numpy()
   if output_type == "pil":
    lowerCamelCase_      =  self.numpy_to_pil(UpperCamelCase     )

   if not return_dict:
    return (sample,)

   return ImagePipelineOutput(images=UpperCamelCase     )



