
from typing import List, Optional, Tuple, Union

import torch

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





class 							UpperCamelCase__						(     __SCREAMING_SNAKE_CASE     ):






       """simple docstring"""


       UpperCAmelCase_							    =42
       UpperCAmelCase_							    =42



       def __init__(				self      ,			_A      ,			_A     )							->							Optional[int]:
          super().__init__()
          self.register_modules(unet=_SCREAMING_SNAKE_CASE      ,			scheduler=_SCREAMING_SNAKE_CASE     )




       @torch.no_grad()
       def __call__(				self      ,			_A = 1      ,			_A = 50      ,			_A = None      ,			_A = "pil"      ,			_A = True      ,			**_A      ,			)							->							Union[Tuple, ImagePipelineOutput]:
          SCREAMING_SNAKE_CASE_     =	self.unet.config.sample_size
          SCREAMING_SNAKE_CASE_     =	(batch_size, 3, img_size, img_size)

          SCREAMING_SNAKE_CASE_     =	self.unet

          # sample x_0 ~ N(0, sigma_0^2 * I)
          SCREAMING_SNAKE_CASE_     =	randn_tensor(_SCREAMING_SNAKE_CASE      ,			generator=_SCREAMING_SNAKE_CASE      ,			device=self.device     ) * self.scheduler.init_noise_sigma

          self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE     )

          for t in self.progress_bar(self.scheduler.timesteps     ):
             # here sigma_t == t_i from the paper
             SCREAMING_SNAKE_CASE_     =	self.scheduler.schedule[t]
             SCREAMING_SNAKE_CASE_     =	self.scheduler.schedule[t - 1] if t > 0 else 0

             # 1. Select temporarily increased noise level sigma_hat
             # 2. Add new noise to move from sample_i to sample_hat
             SCREAMING_SNAKE_CASE_     =	self.scheduler.add_noise_to_input(_SCREAMING_SNAKE_CASE      ,			_SCREAMING_SNAKE_CASE      ,			generator=_SCREAMING_SNAKE_CASE     )

             # 3. Predict the noise residual given the noise magnitude `sigma_hat`
             # The model inputs and output are adjusted by following eq. (213) in [1].
             SCREAMING_SNAKE_CASE_     =	(sigma_hat / 2) * model((sample_hat + 1) / 2      ,			sigma_hat / 2     ).sample

             # 4. Evaluate dx/dt at sigma_hat
             # 5. Take Euler step from sigma to sigma_prev
             SCREAMING_SNAKE_CASE_     =	self.scheduler.step(_SCREAMING_SNAKE_CASE      ,			_SCREAMING_SNAKE_CASE      ,			_SCREAMING_SNAKE_CASE      ,			_SCREAMING_SNAKE_CASE     )

             if sigma_prev != 0:
                # 6. Apply 2nd order correction
                # The model inputs and output are adjusted by following eq. (213) in [1].
                SCREAMING_SNAKE_CASE_     =	(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2      ,			sigma_prev / 2     ).sample
                SCREAMING_SNAKE_CASE_     =	self.scheduler.step_correct(
                    _SCREAMING_SNAKE_CASE      ,			_SCREAMING_SNAKE_CASE      ,			_SCREAMING_SNAKE_CASE      ,			_SCREAMING_SNAKE_CASE      ,			step_output.prev_sample      ,			step_output['''derivative''']      ,			)
             SCREAMING_SNAKE_CASE_     =	step_output.prev_sample

          SCREAMING_SNAKE_CASE_     =	(sample / 2 + 0.5).clamp(0      ,			1     )
          SCREAMING_SNAKE_CASE_     =	sample.cpu().permute(0      ,			2      ,			3      ,			1     ).numpy()
          if output_type == "pil":
             SCREAMING_SNAKE_CASE_     =	self.numpy_to_pil(_SCREAMING_SNAKE_CASE     )

          if not return_dict:
             return (image,)

          return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE     )






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


UpperCamelCase								=	logging.get_logger(__name__)

UpperCamelCase								=	{
    """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   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""
       snake_case      =    "glpn"



       def __init__(      self				,       _SCREAMING_SNAKE_CASE=3				,       _SCREAMING_SNAKE_CASE=4				,       _SCREAMING_SNAKE_CASE=[2, 2, 2, 2]				,       _SCREAMING_SNAKE_CASE=[8, 4, 2, 1]				,       _SCREAMING_SNAKE_CASE=[32, 64, 160, 256]				,       _SCREAMING_SNAKE_CASE=[7, 3, 3, 3]				,       _SCREAMING_SNAKE_CASE=[4, 2, 2, 2]				,       _SCREAMING_SNAKE_CASE=[1, 2, 5, 8]				,       _SCREAMING_SNAKE_CASE=[4, 4, 4, 4]				,       _SCREAMING_SNAKE_CASE="gelu"				,       _SCREAMING_SNAKE_CASE=0.0				,       _SCREAMING_SNAKE_CASE=0.0				,       _SCREAMING_SNAKE_CASE=0.0_2				,       _SCREAMING_SNAKE_CASE=0.1				,       _SCREAMING_SNAKE_CASE=1e-6				,       _SCREAMING_SNAKE_CASE=64				,       _SCREAMING_SNAKE_CASE=10				,       _SCREAMING_SNAKE_CASE=-1				,       **_SCREAMING_SNAKE_CASE				,       )->Any:




         '''simple docstring'''





         super().__init__(**_SCREAMING_SNAKE_CASE		)

         A_      :				Optional[int]		     =					num_channels
         A_      :				Union[str, Any]		     =					num_encoder_blocks
         A_      :				int		     =					depths
         A_      :				Dict		     =					sr_ratios
         A_      :				Any		     =					hidden_sizes
         A_      :				int		     =					patch_sizes
         A_      :				Optional[int]		     =					strides
         A_      :				str		     =					mlp_ratios
         A_      :				List[str]		     =					num_attention_heads
         A_      :				str		     =					hidden_act
         A_      :				int		     =					hidden_dropout_prob
         A_      :				List[Any]		     =					attention_probs_dropout_prob
         A_      :				Optional[Any]		     =					initializer_range
         A_      :				Tuple		     =					drop_path_rate
         A_      :				Optional[int]		     =					layer_norm_eps
         A_      :				List[str]		     =					decoder_hidden_size
         A_      :				List[Any]		     =					max_depth
         A_      :				List[Any]		     =					head_in_index




