
"""simple docstring"""

from dataclasses import dataclass
from typing import Optional

import numpy as np
import torch
import torch.nn as nn

from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block






@dataclass
class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     42






class 	_lowercase       (    nn.Module		):




	def __init__(			self		,			UpperCAmelCase_=3		,			UpperCAmelCase_=3		,			UpperCAmelCase_=("DownEncoderBlock2D",)		,			UpperCAmelCase_=(64,)		,			UpperCAmelCase_=2		,			UpperCAmelCase_=32		,			UpperCAmelCase_="silu"		,			UpperCAmelCase_=True		,			)		->							List[str]:
			super().__init__()
			lowerCamelCase					:      Union[str, Any]          =   layers_per_block

			lowerCamelCase					:      Any          =   torch.nn.Convad(
			    UpperCAmelCase_		,			block_out_channels[0]		,			kernel_size=3		,			stride=1		,			padding=1		,			)

			lowerCamelCase					:      int          =   None
			lowerCamelCase					:      Dict          =   nn.ModuleList([] )

			# down
			lowerCamelCase					:      List[Any]          =   block_out_channels[0]
			for i, down_block_type in enumerate(UpperCAmelCase_ ):
					lowerCamelCase					:      Union[str, Any]          =   output_channel
					lowerCamelCase					:      List[str]          =   block_out_channels[i]
					lowerCamelCase					:      Optional[int]          =   i == len(UpperCAmelCase_ ) - 1

					lowerCamelCase					:      int          =   get_down_block(
					    UpperCAmelCase_		,			num_layers=self.layers_per_block		,			in_channels=UpperCAmelCase_		,			out_channels=UpperCAmelCase_		,			add_downsample=not is_final_block		,			resnet_eps=1E-6		,			downsample_padding=0		,			resnet_act_fn=UpperCAmelCase_		,			resnet_groups=UpperCAmelCase_		,			attention_head_dim=UpperCAmelCase_		,			temb_channels=UpperCAmelCase_		,			)
					self.down_blocks.append(UpperCAmelCase_ )

			# mid
			lowerCamelCase					:      Tuple          =   UNetMidBlockaD(
			    in_channels=block_out_channels[-1]		,			resnet_eps=1E-6		,			resnet_act_fn=UpperCAmelCase_		,			output_scale_factor=1		,			resnet_time_scale_shift='default'		,			attention_head_dim=block_out_channels[-1]		,			resnet_groups=UpperCAmelCase_		,			temb_channels=UpperCAmelCase_		,			)

			# out
			lowerCamelCase					:      str          =   nn.GroupNorm(num_channels=block_out_channels[-1]		,			num_groups=UpperCAmelCase_		,			eps=1E-6 )
			lowerCamelCase					:      List[Any]          =   nn.SiLU()

			lowerCamelCase					:      Optional[int]          =   2 * out_channels if double_z else out_channels
			lowerCamelCase					:      Any          =   nn.Convad(block_out_channels[-1]		,			UpperCAmelCase_		,			3		,			padding=1 )

			lowerCamelCase					:      Optional[Any]          =   False




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							str:
			lowerCamelCase					:      Tuple          =   x
			lowerCamelCase					:      Any          =   self.conv_in(UpperCAmelCase_ )

			if self.training and self.gradient_checkpointing:

					def create_custom_forward(UpperCAmelCase_ ):
							def custom_forward(*UpperCAmelCase_ ):
									return module(*UpperCAmelCase_ )

							return custom_forward

					# down
					if is_torch_version('>='		,			'1.11.0' ):
							for down_block in self.down_blocks:
									lowerCamelCase					:      List[str]          =   torch.utils.checkpoint.checkpoint(
									    create_custom_forward(UpperCAmelCase_ )		,			UpperCAmelCase_		,			use_reentrant=UpperCAmelCase_ )
							# middle
							lowerCamelCase					:      List[str]          =   torch.utils.checkpoint.checkpoint(
							    create_custom_forward(self.mid_block )		,			UpperCAmelCase_		,			use_reentrant=UpperCAmelCase_ )
					else:
							for down_block in self.down_blocks:
									lowerCamelCase					:      Union[str, Any]          =   torch.utils.checkpoint.checkpoint(create_custom_forward(UpperCAmelCase_ )		,			UpperCAmelCase_ )
							# middle
							lowerCamelCase					:      List[str]          =   torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block )		,			UpperCAmelCase_ )

			else:
					# down
					for down_block in self.down_blocks:
							lowerCamelCase					:      List[Any]          =   down_block(UpperCAmelCase_ )

					# middle
					lowerCamelCase					:      str          =   self.mid_block(UpperCAmelCase_ )

			# post-process
			lowerCamelCase					:      Any          =   self.conv_norm_out(UpperCAmelCase_ )
			lowerCamelCase					:      str          =   self.conv_act(UpperCAmelCase_ )
			lowerCamelCase					:      Union[str, Any]          =   self.conv_out(UpperCAmelCase_ )

			return sample






class 	_lowercase       (    nn.Module		):




	def __init__(			self		,			UpperCAmelCase_=3		,			UpperCAmelCase_=3		,			UpperCAmelCase_=("UpDecoderBlock2D",)		,			UpperCAmelCase_=(64,)		,			UpperCAmelCase_=2		,			UpperCAmelCase_=32		,			UpperCAmelCase_="silu"		,			UpperCAmelCase_="group"		,			)		->							List[str]:
			super().__init__()
			lowerCamelCase					:      Union[str, Any]          =   layers_per_block

			lowerCamelCase					:      Dict          =   nn.Convad(
			    UpperCAmelCase_		,			block_out_channels[-1]		,			kernel_size=3		,			stride=1		,			padding=1		,			)

			lowerCamelCase					:      int          =   None
			lowerCamelCase					:      List[str]          =   nn.ModuleList([] )

			lowerCamelCase					:      Optional[Any]          =   in_channels if norm_type == 'spatial' else None

			# mid
			lowerCamelCase					:      Dict          =   UNetMidBlockaD(
			    in_channels=block_out_channels[-1]		,			resnet_eps=1E-6		,			resnet_act_fn=UpperCAmelCase_		,			output_scale_factor=1		,			resnet_time_scale_shift='default' if norm_type == 'group' else norm_type		,			attention_head_dim=block_out_channels[-1]		,			resnet_groups=UpperCAmelCase_		,			temb_channels=UpperCAmelCase_		,			)

			# up
			lowerCamelCase					:      Tuple          =   list(reversed(UpperCAmelCase_ ) )
			lowerCamelCase					:      List[Any]          =   reversed_block_out_channels[0]
			for i, up_block_type in enumerate(UpperCAmelCase_ ):
					lowerCamelCase					:      int          =   output_channel
					lowerCamelCase					:      Union[str, Any]          =   reversed_block_out_channels[i]

					lowerCamelCase					:      List[str]          =   i == len(UpperCAmelCase_ ) - 1

					lowerCamelCase					:      Optional[Any]          =   get_up_block(
					    UpperCAmelCase_		,			num_layers=self.layers_per_block + 1		,			in_channels=UpperCAmelCase_		,			out_channels=UpperCAmelCase_		,			prev_output_channel=UpperCAmelCase_		,			add_upsample=not is_final_block		,			resnet_eps=1E-6		,			resnet_act_fn=UpperCAmelCase_		,			resnet_groups=UpperCAmelCase_		,			attention_head_dim=UpperCAmelCase_		,			temb_channels=UpperCAmelCase_		,			resnet_time_scale_shift=UpperCAmelCase_		,			)
					self.up_blocks.append(UpperCAmelCase_ )
					lowerCamelCase					:      Dict          =   output_channel

			# out
			if norm_type == "spatial":
					lowerCamelCase					:      int          =   SpatialNorm(block_out_channels[0]		,			UpperCAmelCase_ )
			else:
					lowerCamelCase					:      List[str]          =   nn.GroupNorm(num_channels=block_out_channels[0]		,			num_groups=UpperCAmelCase_		,			eps=1E-6 )
			lowerCamelCase					:      Any          =   nn.SiLU()
			lowerCamelCase					:      List[str]          =   nn.Convad(block_out_channels[0]		,			UpperCAmelCase_		,			3		,			padding=1 )

			lowerCamelCase					:      Union[str, Any]          =   False




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_=None )		->							Optional[int]:
			lowerCamelCase					:      Union[str, Any]          =   z
			lowerCamelCase					:      str          =   self.conv_in(UpperCAmelCase_ )

			lowerCamelCase					:      Any          =   next(iter(self.up_blocks.parameters() ) ).dtype
			if self.training and self.gradient_checkpointing:

					def create_custom_forward(UpperCAmelCase_ ):
							def custom_forward(*UpperCAmelCase_ ):
									return module(*UpperCAmelCase_ )

							return custom_forward

					if is_torch_version('>='		,			'1.11.0' ):
							# middle
							lowerCamelCase					:      Dict          =   torch.utils.checkpoint.checkpoint(
							    create_custom_forward(self.mid_block )		,			UpperCAmelCase_		,			UpperCAmelCase_		,			use_reentrant=UpperCAmelCase_ )
							lowerCamelCase					:      Optional[Any]          =   sample.to(UpperCAmelCase_ )

							# up
							for up_block in self.up_blocks:
									lowerCamelCase					:      Tuple          =   torch.utils.checkpoint.checkpoint(
									    create_custom_forward(UpperCAmelCase_ )		,			UpperCAmelCase_		,			UpperCAmelCase_		,			use_reentrant=UpperCAmelCase_ )
					else:
							# middle
							lowerCamelCase					:      Union[str, Any]          =   torch.utils.checkpoint.checkpoint(
							    create_custom_forward(self.mid_block )		,			UpperCAmelCase_		,			UpperCAmelCase_ )
							lowerCamelCase					:      Optional[Any]          =   sample.to(UpperCAmelCase_ )

							# up
							for up_block in self.up_blocks:
									lowerCamelCase					:      Any          =   torch.utils.checkpoint.checkpoint(create_custom_forward(UpperCAmelCase_ )		,			UpperCAmelCase_		,			UpperCAmelCase_ )
			else:
					# middle
					lowerCamelCase					:      Any          =   self.mid_block(UpperCAmelCase_		,			UpperCAmelCase_ )
					lowerCamelCase					:      Union[str, Any]          =   sample.to(UpperCAmelCase_ )

					# up
					for up_block in self.up_blocks:
							lowerCamelCase					:      Tuple          =   up_block(UpperCAmelCase_		,			UpperCAmelCase_ )

        # post-process
			if latent_embeds is None:
					lowerCamelCase					:      int          =   self.conv_norm_out(UpperCAmelCase_ )
			else:
					lowerCamelCase					:      Optional[Any]          =   self.conv_norm_out(UpperCAmelCase_		,			UpperCAmelCase_ )
			lowerCamelCase					:      List[Any]          =   self.conv_act(UpperCAmelCase_ )
			lowerCamelCase					:      Any          =   self.conv_out(UpperCAmelCase_ )

			return sample






class 	_lowercase       (    nn.Module		):




	def __init__(			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_=None		,			UpperCAmelCase_="random"		,			UpperCAmelCase_=False		,			UpperCAmelCase_=True )		->							Optional[int]:
			super().__init__()
			lowerCamelCase					:      List[Any]          =   n_e
			lowerCamelCase					:      Optional[Any]          =   vq_embed_dim
			lowerCamelCase					:      int          =   beta
			lowerCamelCase					:      str          =   legacy

			lowerCamelCase					:      Tuple          =   nn.Embedding(self.n_e		,			self.vq_embed_dim )
			self.embedding.weight.data.uniform_(-1.0 / self.n_e		,			1.0 / self.n_e )

			lowerCamelCase					:      List[str]          =   remap
			if self.remap is not None:
					self.register_buffer('used'		,			torch.tensor(np.load(self.remap ) ) )
					lowerCamelCase					:      List[Any]          =   self.used.shape[0]
					lowerCamelCase					:      Optional[Any]          =   unknown_index  # "random" or "extra" or integer
					if self.unknown_index == "extra":
							lowerCamelCase					:      int          =   self.re_embed
							lowerCamelCase					:      List[Any]          =   self.re_embed + 1
					print(
					    F"""Remapping {self.n_e} indices to {self.re_embed} indices. """
					    F"""Using {self.unknown_index} for unknown indices.""" )
			else:
					lowerCamelCase					:      List[Any]          =   n_e

			lowerCamelCase					:      Optional[Any]          =   sane_index_shape




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							List[Any]:
			lowerCamelCase					:      str          =   inds.shape
			assert len(UpperCAmelCase_ ) > 1
			lowerCamelCase					:      Dict          =   inds.reshape(ishape[0]		,			-1 )
			lowerCamelCase					:      List[Any]          =   self.used.to(UpperCAmelCase_ )
			lowerCamelCase					:      int          =   (inds[:, :, None] == used[None, None, ...]).long()
			lowerCamelCase					:      Tuple          =   match.argmax(-1 )
			lowerCamelCase					:      Dict          =   match.sum(2 ) < 1
			if self.unknown_index == "random":
					lowerCamelCase					:      List[str]          =   torch.randint(0		,			self.re_embed		,			size=new[unknown].shape ).to(device=new.device )
			else:
					lowerCamelCase					:      Tuple          =   self.unknown_index
			return new.reshape(UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							List[Any]:
			lowerCamelCase					:      Optional[int]          =   inds.shape
			assert len(UpperCAmelCase_ ) > 1
			lowerCamelCase					:      Union[str, Any]          =   inds.reshape(ishape[0]		,			-1 )
			lowerCamelCase					:      Union[str, Any]          =   self.used.to(UpperCAmelCase_ )
			if self.re_embed > self.used.shape[0]:  # extra token
					lowerCamelCase					:      Tuple          =   0  # simply set to zero
			lowerCamelCase					:      Dict          =   torch.gather(used[None, :][inds.shape[0] * [0], :]		,			1		,			UpperCAmelCase_ )
			return back.reshape(UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							List[str]:
			# reshape z -> (batch, height, width, channel) and flatten
			lowerCamelCase					:      Union[str, Any]          =   z.permute(0		,			2		,			3		,			1 ).contiguous()
			lowerCamelCase					:      Union[str, Any]          =   z.view(-1		,			self.vq_embed_dim )

			# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
			lowerCamelCase					:      Union[str, Any]          =   torch.argmin(torch.cdist(UpperCAmelCase_		,			self.embedding.weight )		,			dim=1 )

			lowerCamelCase					:      Any          =   self.embedding(UpperCAmelCase_ ).view(z.shape )
			lowerCamelCase					:      List[str]          =   None
			lowerCamelCase					:      int          =   None

			# compute loss for embedding
			if not self.legacy:
					lowerCamelCase					:      Optional[int]          =   self.beta * torch.mean((z_q.detach() - z) ** 2 ) + torch.mean((z_q - z.detach()) ** 2 )
			else:
					lowerCamelCase					:      str          =   torch.mean((z_q.detach() - z) ** 2 ) + self.beta * torch.mean((z_q - z.detach()) ** 2 )

			# preserve gradients
			lowerCamelCase					:      str          =   z + (z_q - z).detach()

			# reshape back to match original input shape
			lowerCamelCase					:      str          =   z_q.permute(0		,			3		,			1		,			2 ).contiguous()

			if self.remap is not None:
					lowerCamelCase					:      Tuple          =   min_encoding_indices.reshape(z.shape[0]		,			-1 )  # add batch axis
					lowerCamelCase					:      Optional[int]          =   self.remap_to_used(UpperCAmelCase_ )
					lowerCamelCase					:      int          =   min_encoding_indices.reshape(-1		,			1 )  # flatten

			if self.sane_index_shape:
					lowerCamelCase					:      int          =   min_encoding_indices.reshape(z_q.shape[0]		,			z_q.shape[2]		,			z_q.shape[3] )

			return z_q, loss, (perplexity, min_encodings, min_encoding_indices)




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							int:
			# shape specifying (batch, height, width, channel)
			if self.remap is not None:
					lowerCamelCase					:      Optional[int]          =   indices.reshape(shape[0]		,			-1 )  # add batch axis
					lowerCamelCase					:      Dict          =   self.unmap_to_all(UpperCAmelCase_ )
					lowerCamelCase					:      Union[str, Any]          =   indices.reshape(-1 )  # flatten again

			# get quantized latent vectors
			lowerCamelCase					:      Union[str, Any]          =   self.embedding(UpperCAmelCase_ )

			if shape is not None:
					lowerCamelCase					:      List[str]          =   z_q.view(UpperCAmelCase_ )
					# reshape back to match original input shape
					lowerCamelCase					:      int          =   z_q.permute(0		,			3		,			1		,			2 ).contiguous()

			return z_q






class 	_lowercase       (    __UpperCAmelCase		):




	def __init__(			self		,			UpperCAmelCase_		,			UpperCAmelCase_=False )		->							List[Any]:
			lowerCamelCase					:      Tuple          =   parameters
			lowerCamelCase     ,   lowerCamelCase					:      Optional[int]          =   torch.chunk(UpperCAmelCase_		,			2		,			dim=1 )
			lowerCamelCase					:      str          =   torch.clamp(self.logvar		,			-30.0		,			20.0 )
			lowerCamelCase					:      int          =   deterministic
			lowerCamelCase					:      Union[str, Any]          =   torch.exp(0.5 * self.logvar )
			lowerCamelCase					:      Tuple          =   torch.exp(self.logvar )
			if self.deterministic:
					lowerCamelCase					:      Any          =   torch.zeros_like(
					    self.mean		,			device=self.parameters.device		,			dtype=self.parameters.dtype )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ = None )		->							torch.FloatTensor:
			# make sure sample is on the same device as the parameters and has same dtype
			lowerCamelCase					:      str          =   randn_tensor(
			    self.mean.shape		,			generator=UpperCAmelCase_		,			device=self.parameters.device		,			dtype=self.parameters.dtype )
			lowerCamelCase					:      Optional[Any]          =   self.mean + self.std * sample
			return x




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_=None )		->							Optional[int]:
			if self.deterministic:
					return torch.Tensor([0.0] )
			else:
					if other is None:
							return 0.5 * torch.sum(torch.pow(self.mean		,			2 ) + self.var - 1.0 - self.logvar		,			dim=[1, 2, 3] )
					else:
							return 0.5 * torch.sum(
							    torch.pow(self.mean - other.mean		,			2 ) / other.var
							    + self.var / other.var
							    - 1.0
							    - self.logvar
							    + other.logvar		,			dim=[1, 2, 3]		,			)




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_=[1, 2, 3] )		->							Union[str, Any]:
			if self.deterministic:
					return torch.Tensor([0.0] )
			lowerCamelCase					:      int          =   np.log(2.0 * np.pi )
			return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean		,			2 ) / self.var		,			dim=UpperCAmelCase_ )




	def       _UpperCamelCase   (			self )		->							str:
			return self.mean








"""simple docstring"""

import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_A						=			logging.get_logger(__name__)
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		lowerCamelCase					:      int          =   OrderedDict()
		for key, value in state_dict.items():
				if key.startswith('module.encoder'      ):
						lowerCamelCase					:      Tuple          =   key.replace('module.encoder',						'glpn.encoder'      )
				if key.startswith('module.decoder'      ):
						lowerCamelCase					:      str          =   key.replace('module.decoder',						'decoder.stages'      )
				if "patch_embed" in key:
						# replace for example patch_embed1 by patch_embeddings.0
						lowerCamelCase					:      Any          =   key[key.find('patch_embed'      ) + len('patch_embed'      )]
						lowerCamelCase					:      Dict          =   key.replace(F"""patch_embed{idx}""",						F"""patch_embeddings.{int(a_      )-1}"""      )
				if "norm" in key:
						lowerCamelCase					:      Optional[int]          =   key.replace('norm',						'layer_norm'      )
				if "glpn.encoder.layer_norm" in key:
						# replace for example layer_norm1 by layer_norm.0
						lowerCamelCase					:      List[str]          =   key[key.find('glpn.encoder.layer_norm'      ) + len('glpn.encoder.layer_norm'      )]
						lowerCamelCase					:      List[str]          =   key.replace(F"""layer_norm{idx}""",						F"""layer_norm.{int(a_      )-1}"""      )
				if "layer_norm1" in key:
						lowerCamelCase					:      List[Any]          =   key.replace('layer_norm1',						'layer_norm_1'      )
				if "layer_norm2" in key:
						lowerCamelCase					:      str          =   key.replace('layer_norm2',						'layer_norm_2'      )
				if "block" in key:
						# replace for example block1 by block.0
						lowerCamelCase					:      Union[str, Any]          =   key[key.find('block'      ) + len('block'      )]
						lowerCamelCase					:      List[str]          =   key.replace(F"""block{idx}""",						F"""block.{int(a_      )-1}"""      )
				if "attn.q" in key:
						lowerCamelCase					:      Union[str, Any]          =   key.replace('attn.q',						'attention.self.query'      )
				if "attn.proj" in key:
						lowerCamelCase					:      Dict          =   key.replace('attn.proj',						'attention.output.dense'      )
				if "attn" in key:
						lowerCamelCase					:      int          =   key.replace('attn',						'attention.self'      )
				if "fc1" in key:
						lowerCamelCase					:      Any          =   key.replace('fc1',						'dense1'      )
				if "fc2" in key:
						lowerCamelCase					:      List[Any]          =   key.replace('fc2',						'dense2'      )
				if "linear_pred" in key:
						lowerCamelCase					:      Optional[Any]          =   key.replace('linear_pred',						'classifier'      )
				if "linear_fuse" in key:
						lowerCamelCase					:      Union[str, Any]          =   key.replace('linear_fuse.conv',						'linear_fuse'      )
						lowerCamelCase					:      Optional[int]          =   key.replace('linear_fuse.bn',						'batch_norm'      )
				if "linear_c" in key:
						# replace for example linear_c4 by linear_c.3
						lowerCamelCase					:      str          =   key[key.find('linear_c'      ) + len('linear_c'      )]
						lowerCamelCase					:      List[Any]          =   key.replace(F"""linear_c{idx}""",						F"""linear_c.{int(a_      )-1}"""      )
				if "bot_conv" in key:
						lowerCamelCase					:      int          =   key.replace('bot_conv',						'0.convolution'      )
				if "skip_conv1" in key:
						lowerCamelCase					:      Any          =   key.replace('skip_conv1',						'1.convolution'      )
				if "skip_conv2" in key:
						lowerCamelCase					:      Optional[Any]          =   key.replace('skip_conv2',						'2.convolution'      )
				if "fusion1" in key:
						lowerCamelCase					:      str          =   key.replace('fusion1',						'1.fusion'      )
				if "fusion2" in key:
						lowerCamelCase					:      Optional[Any]          =   key.replace('fusion2',						'2.fusion'      )
				if "fusion3" in key:
						lowerCamelCase					:      List[str]          =   key.replace('fusion3',						'3.fusion'      )
				if "fusion" in key and "conv" in key:
						lowerCamelCase					:      Optional[int]          =   key.replace('conv',						'convolutional_layer'      )
				if key.startswith('module.last_layer_depth'      ):
						lowerCamelCase					:      Tuple          =   key.replace('module.last_layer_depth',						'head.head'      )
				lowerCamelCase					:      List[Any]          =   value

		return new_state_dict
def 		UpperCAmelCase (		a_,						a_      ):





		'''simple docstring'''


		for i in range(config.num_encoder_blocks      ):
				for j in range(config.depths[i]      ):
						# read in weights + bias of keys and values (which is a single matrix in the original implementation)
						lowerCamelCase					:      Any          =   state_dict.pop(F"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight"""      )
						lowerCamelCase					:      Optional[Any]          =   state_dict.pop(F"""glpn.encoder.block.{i}.{j}.attention.self.kv.bias"""      )
						# next, add keys and values (in that order) to the state dict
						lowerCamelCase					:      Any          =   kv_weight[
						    : config.hidden_sizes[i], :
						]
						lowerCamelCase					:      List[Any]          =   kv_bias[: config.hidden_sizes[i]]
						lowerCamelCase					:      Dict          =   kv_weight[
						    config.hidden_sizes[i] :, :
						]
						lowerCamelCase					:      List[Any]          =   kv_bias[config.hidden_sizes[i] :]
def 		UpperCAmelCase (		):





		'''simple docstring'''


		lowerCamelCase					:      Dict          =   'http://images.cocodataset.org/val2017/000000039769.jpg'
		lowerCamelCase					:      List[Any]          =   Image.open(requests.get(a_,						stream=a_      ).raw      )

		return image
@torch.no_grad()
def 		UpperCAmelCase (		a_,						a_,						a_=False,						a_=None      ):





		'''simple docstring'''


		lowerCamelCase					:      int          =   GLPNConfig(hidden_sizes=[64, 128, 320, 512],						decoder_hidden_size=64,						depths=[3, 8, 27, 3]      )

		# load image processor (only resize + rescale)
		lowerCamelCase					:      Any          =   GLPNImageProcessor()

		# prepare image
		lowerCamelCase					:      int          =   prepare_img()
		lowerCamelCase					:      Tuple          =   image_processor(images=a_,						return_tensors='pt'      ).pixel_values

		logger.info('Converting model...'      )

		# load original state dict
		lowerCamelCase					:      Optional[Any]          =   torch.load(a_,						map_location=torch.device('cpu'      )      )

		# rename keys
		lowerCamelCase					:      Any          =   rename_keys(a_      )

		# key and value matrices need special treatment
		read_in_k_v(a_,						a_      )

		# create HuggingFace model and load state dict
		lowerCamelCase					:      Optional[int]          =   GLPNForDepthEstimation(a_      )
		model.load_state_dict(a_      )
		model.eval()

		# forward pass
		lowerCamelCase					:      str          =   model(a_      )
		lowerCamelCase					:      Any          =   outputs.predicted_depth

		# verify output
		if model_name is not None:
				if "nyu" in model_name:
						lowerCamelCase					:      Any          =   torch.tensor(
						    [[4.4_1_4_7, 4.0_8_7_3, 4.0_6_7_3], [3.7_8_9_0, 3.2_8_8_1, 3.1_5_2_5], [3.7_6_7_4, 3.5_4_2_3, 3.4_9_1_3]]      )
				elif "kitti" in model_name:
						lowerCamelCase					:      str          =   torch.tensor(
						    [[3.4_2_9_1, 2.7_8_6_5, 2.5_1_5_1], [3.2_8_4_1, 2.7_0_2_1, 2.3_5_0_2], [3.1_1_4_7, 2.4_6_2_5, 2.2_4_8_1]]      )
				else:
						raise ValueError(F"""Unknown model name: {model_name}"""      )

				lowerCamelCase					:      int          =   torch.Size([1, 480, 640]      )

				assert predicted_depth.shape == expected_shape
				assert torch.allclose(predicted_depth[0, :3, :3],						a_,						atol=1E-4      )
				print('Looks ok!'      )

		# finally, push to hub if required
		if push_to_hub:
				logger.info('Pushing model and image processor to the hub...'      )
				model.push_to_hub(
				    repo_path_or_name=Path(a_,						a_      ),						organization='nielsr',						commit_message='Add model',						use_temp_dir=a_,						)
				image_processor.push_to_hub(
				    repo_path_or_name=Path(a_,						a_      ),						organization='nielsr',						commit_message='Add image processor',						use_temp_dir=a_,						)


if __name__ == "__main__":
					_A						=			argparse.ArgumentParser()

					parser.add_argument(
					    '--checkpoint_path',
					    default=None,
					    type=str,
					    help='Path to the original PyTorch checkpoint (.pth file).',
					)
					parser.add_argument(
					    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
					)
					parser.add_argument(
					    '--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
					)
					parser.add_argument(
					    '--model_name',
					    default='glpn-kitti',
					    type=str,
					    help='Name of the model in case you\'re pushing to the hub.',
					)
					_A						=			parser.parse_args()
					convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)






