"""simple docstring"""




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


_a      :		List[str]           =   logging.get_logger(__name__)

_a      :		Dict           =   {
    '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 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			str							=		"glpn"






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

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









# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput



class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						super().__init__()
						self.register_modules(unet=lowerCamelCase_		,       scheduler=lowerCamelCase_		)




	@torch.no_grad()
	def __call__(self		,       lowerCamelCase_ = 1		,       lowerCamelCase_ = None		,       lowerCamelCase_ = 50		,       lowerCamelCase_ = "pil"		,       lowerCamelCase_ = True		,       **lowerCamelCase_		,       ):
						"""simple docstring"""

						a					     =   torch.randn(
						    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)		,       generator=lowerCamelCase_		,       )
						a					     =   image.to(self.device		)

						# set step values
						self.scheduler.set_timesteps(lowerCamelCase_		)

						for t in self.progress_bar(self.scheduler.timesteps		):
											# 1. predict noise model_output
											a					     =   self.unet(lowerCamelCase_		,       lowerCamelCase_		).sample

											# 2. predict previous mean of image x_t-1 and add variance depending on eta
											# eta corresponds to η in paper and should be between [0, 1]
											# do x_t -> x_t-1
											a					     =   self.scheduler.step(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		).prev_sample

						a					     =   (image / 2 + 0.5).clamp(0		,       1		)
						a					     =   image.cpu().permute(0		,       2		,       3		,       1		).numpy()
						if output_type == "pil":
											a					     =   self.numpy_to_pil(lowerCamelCase_		)

						if not return_dict:
											return (image,), "This is a local test"

						return ImagePipelineOutput(images=lowerCamelCase_		), "This is a local test"
