




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()
_lowerCamelCase   :	str       =    logging.get_logger(__name__)




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

				return new_state_dict




def 				_UpperCAmelCase	(UpperCamelCase_			:			Optional[int]   ,     UpperCamelCase_			:			Optional[Any]							):
				'''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     :     Optional[Any]              =   state_dict.pop(F"glpn.encoder.block.{i}.{j}.attention.self.kv.weight"							)
												_lowerCAmelCase     :     Optional[int]              =   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     :     str              =   kv_weight[
												    : config.hidden_sizes[i], :
												]
												_lowerCAmelCase     :     Tuple              =   kv_bias[: config.hidden_sizes[i]]
												_lowerCAmelCase     :     Tuple              =   kv_weight[
												    config.hidden_sizes[i] :, :
												]
												_lowerCAmelCase     :     Optional[Any]              =   kv_bias[config.hidden_sizes[i] :]




def 				_UpperCAmelCase	():
				'''simple docstring'''
				_lowerCAmelCase     :     Tuple              =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				_lowerCAmelCase     :     Optional[Any]              =   Image.open(requests.get(lowercase_   ,     stream=lowercase_							).raw							)

				return image




@torch.no_grad()
def 				_UpperCAmelCase	(UpperCamelCase_			:			Optional[Any]   ,     UpperCamelCase_			:			Any   ,     UpperCamelCase_			:			Optional[int]=False   ,     UpperCamelCase_			:			List[Any]=None							):
				'''simple docstring'''
				_lowerCAmelCase     :     str              =   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     :     Any              =   prepare_img()
				_lowerCAmelCase     :     Any              =   image_processor(images=lowercase_   ,     return_tensors="""pt"""							).pixel_values

				logger.info("""Converting model..."""							)

				# load original state dict
				_lowerCAmelCase     :     List[Any]              =   torch.load(lowercase_   ,     map_location=torch.device("""cpu"""							)							)

				# rename keys
				_lowerCAmelCase     :     str              =   rename_keys(lowercase_							)

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

				# create HuggingFace model and load state dict
				_lowerCAmelCase     :     Union[str, Any]              =   GLPNForDepthEstimation(lowercase_							)
				model.load_state_dict(lowercase_							)
				model.eval()

				# forward pass
				_lowerCAmelCase     :     str              =   model(lowercase_							)
				_lowerCAmelCase     :     Any              =   outputs.predicted_depth

				# verify output
				if model_name is not None:
								if "nyu" in model_name:
												_lowerCAmelCase     :     Optional[int]              =   torch.tensor(
												    [[4.4_147, 4.0_873, 4.0_673], [3.7_890, 3.2_881, 3.1_525], [3.7_674, 3.5_423, 3.4_913]]							)
								elif "kitti" in model_name:
												_lowerCAmelCase     :     Tuple              =   torch.tensor(
												    [[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]]							)
								else:
												raise ValueError(F"Unknown model name: {model_name}"							)

								_lowerCAmelCase     :     List[Any]              =   torch.Size([1, 480, 640]							)

								assert predicted_depth.shape == expected_shape
								assert torch.allclose(predicted_depth[0, :3, :3]   ,     lowercase_   ,     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(lowercase_   ,     lowercase_							)   ,     organization="""nielsr"""   ,     commit_message="""Add model"""   ,     use_temp_dir=lowercase_   ,     )
								image_processor.push_to_hub(
								    repo_path_or_name=Path(lowercase_   ,     lowercase_							)   ,     organization="""nielsr"""   ,     commit_message="""Add image processor"""   ,     use_temp_dir=lowercase_   ,     )


if __name__ == "__main__":
	_lowerCamelCase   :	str       =    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.",
	)
	_lowerCamelCase   :	List[str]       =    parser.parse_args()
	convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin




@dataclass
class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		torch.FloatTensor
	_lowerCAmelCase     :		torch.FloatTensor
	_lowerCAmelCase     :		Optional[torch.FloatTensor]										=						None






class 							lowerCamelCase   (  _UpperCamelCase							,						_UpperCamelCase ):
	_lowerCAmelCase     :		Tuple										=						2


	@register_to_config
	def __init__(    self				,     lowercase__ = 0.0_2				,     lowercase__ = 1_0_0				,     lowercase__ = 1.0_0_7				,     lowercase__ = 8_0				,     lowercase__ = 0.0_5				,     lowercase__ = 5_0				,     ):
							# standard deviation of the initial noise distribution
							__UpperCAmelCase			:							Union[str, Any]	    =	sigma_max

							# setable values
							__UpperCAmelCase			:							int	    =	None
							__UpperCAmelCase			:							np.IntTensor	    =	None
							__UpperCAmelCase			:							torch.FloatTensor	    =	None  # sigma(t_i)


	def  A(    self				,     lowercase__				,     lowercase__ = None):
							return sample


	def  A(    self				,     lowercase__				,     lowercase__ = None):
							__UpperCAmelCase			:							int	    =	num_inference_steps
							__UpperCAmelCase			:							List[str]	    =	np.arange(0				,     self.num_inference_steps)[::-1].copy()
							__UpperCAmelCase			:							Any	    =	torch.from_numpy(lowercase__).to(lowercase__)
							__UpperCAmelCase			:							Union[str, Any]	    =	[
							    (
							        self.config.sigma_max**2
							        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
							    )
							    for i in self.timesteps
							]
							__UpperCAmelCase			:							Tuple	    =	torch.tensor(lowercase__				,     dtype=torch.floataa				,     device=lowercase__)


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__ = None):
							if self.config.s_min <= sigma <= self.config.s_max:
													__UpperCAmelCase			:							int	    =	min(self.config.s_churn / self.num_inference_steps				,     2**0.5 - 1)
							else:
													__UpperCAmelCase			:							int	    =	0

							# sample eps ~ N(0, S_noise^2 * I)
							__UpperCAmelCase			:							List[str]	    =	self.config.s_noise * randn_tensor(sample.shape				,     generator=lowercase__).to(sample.device)
							__UpperCAmelCase			:							Optional[int]	    =	sigma + gamma * sigma
							__UpperCAmelCase			:							List[Any]	    =	sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

							return sample_hat, sigma_hat


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__ = True				,     ):
							__UpperCAmelCase			:							str	    =	sample_hat + sigma_hat * model_output
							__UpperCAmelCase			:							Tuple	    =	(sample_hat - pred_original_sample) / sigma_hat
							__UpperCAmelCase			:							str	    =	sample_hat + (sigma_prev - sigma_hat) * derivative

							if not return_dict:
													return (sample_prev, derivative)

							return KarrasVeOutput(
							    prev_sample=lowercase__				,     derivative=lowercase__				,     pred_original_sample=lowercase__)


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__ = True				,     ):
							__UpperCAmelCase			:							Any	    =	sample_prev + sigma_prev * model_output
							__UpperCAmelCase			:							List[str]	    =	(sample_prev - pred_original_sample) / sigma_prev
							__UpperCAmelCase			:							Union[str, Any]	    =	sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

							if not return_dict:
													return (sample_prev, derivative)

							return KarrasVeOutput(
							    prev_sample=lowercase__				,     derivative=lowercase__				,     pred_original_sample=lowercase__)


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__):
							raise NotImplementedError()

