


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


lowerCAmelCase       		=    logging.get_logger(__name__)

lowerCAmelCase       		=    {
    """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 ):
	_lowerCAmelCase     :		Optional[int]										=						'''glpn'''


	def __init__(    self				,     lowercase__=3				,     lowercase__=4				,     lowercase__=[2, 2, 2, 2]				,     lowercase__=[8, 4, 2, 1]				,     lowercase__=[3_2, 6_4, 1_6_0, 2_5_6]				,     lowercase__=[7, 3, 3, 3]				,     lowercase__=[4, 2, 2, 2]				,     lowercase__=[1, 2, 5, 8]				,     lowercase__=[4, 4, 4, 4]				,     lowercase__="gelu"				,     lowercase__=0.0				,     lowercase__=0.0				,     lowercase__=0.0_2				,     lowercase__=0.1				,     lowercase__=1e-6				,     lowercase__=6_4				,     lowercase__=1_0				,     lowercase__=-1				,     **lowercase__				,     ):
							super().__init__(**lowercase__)

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





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 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		UNetaDModel
	_lowerCAmelCase     :		ScoreSdeVeScheduler


	def __init__(    self				,     lowercase__				,     lowercase__):
							super().__init__()
							self.register_modules(unet=lowercase__				,     scheduler=lowercase__)


	@torch.no_grad()
	def __call__(    self				,     lowercase__ = 1				,     lowercase__ = 2_0_0_0				,     lowercase__ = None				,     lowercase__ = "pil"				,     lowercase__ = True				,     **lowercase__				,     ):
							__UpperCAmelCase			:							int	    =	self.unet.config.sample_size
							__UpperCAmelCase			:							Optional[int]	    =	(batch_size, 3, img_size, img_size)

							__UpperCAmelCase			:							int	    =	self.unet

							__UpperCAmelCase			:							Tuple	    =	randn_tensor(lowercase__				,     generator=lowercase__) * self.scheduler.init_noise_sigma
							__UpperCAmelCase			:							List[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)):
													__UpperCAmelCase			:							List[str]	    =	self.scheduler.sigmas[i] * torch.ones(shape[0]				,     device=self.device)

													# correction step
													for _ in range(self.scheduler.config.correct_steps):
																			__UpperCAmelCase			:							int	    =	self.unet(lowercase__				,     lowercase__).sample
																			__UpperCAmelCase			:							str	    =	self.scheduler.step_correct(lowercase__				,     lowercase__				,     generator=lowercase__).prev_sample

													# prediction step
													__UpperCAmelCase			:							Tuple	    =	model(lowercase__				,     lowercase__).sample
													__UpperCAmelCase			:							Dict	    =	self.scheduler.step_pred(lowercase__				,     lowercase__				,     lowercase__				,     generator=lowercase__)

													__UpperCAmelCase    ,      __UpperCAmelCase			:							Optional[Any]	    =	output.prev_sample, output.prev_sample_mean

							__UpperCAmelCase			:							List[str]	    =	sample_mean.clamp(0				,     1)
							__UpperCAmelCase			:							Union[str, Any]	    =	sample.cpu().permute(0				,     2				,     3				,     1).numpy()
							if output_type == "pil":
													__UpperCAmelCase			:							List[Any]	    =	self.numpy_to_pil(lowercase__)

							if not return_dict:
													return (sample,)

							return ImagePipelineOutput(images=lowercase__)

