




'''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()
_SCREAMING_SNAKE_CASE			=						logging.get_logger(__name__)





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

			return new_state_dict





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		Optional[Any]:
			# for each of the encoder blocks:
			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)
									A_									= state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.weight'     )
									A_									= 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
									A_									= kv_weight[
									    : config.hidden_sizes[i], :
									]
									A_									= kv_bias[: config.hidden_sizes[i]]
									A_									= kv_weight[
									    config.hidden_sizes[i] :, :
									]
									A_									= kv_bias[config.hidden_sizes[i] :]





def 			lowerCamelCase(							)		->		Tuple:
			A_									= 'http://images.cocodataset.org/val2017/000000039769.jpg'
			A_									= Image.open(requests.get(SCREAMING_SNAKE_CASE_       ,stream=SCREAMING_SNAKE_CASE_     ).raw     )

			return image





@torch.no_grad()
def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_=False       ,SCREAMING_SNAKE_CASE_=None     )		->		List[Any]:
			A_									= GLPNConfig(hidden_sizes=[64, 128, 320, 512]       ,decoder_hidden_size=64       ,depths=[3, 8, 27, 3]     )

			# load image processor (only resize + rescale)
			A_									= GLPNImageProcessor()

			# prepare image
			A_									= prepare_img()
			A_									= image_processor(images=SCREAMING_SNAKE_CASE_       ,return_tensors='pt'     ).pixel_values

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

			# load original state dict
			A_									= torch.load(SCREAMING_SNAKE_CASE_       ,map_location=torch.device('cpu'     )     )

			# rename keys
			A_									= rename_keys(SCREAMING_SNAKE_CASE_     )

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

			# create HuggingFace model and load state dict
			A_									= GLPNForDepthEstimation(SCREAMING_SNAKE_CASE_     )
			model.load_state_dict(SCREAMING_SNAKE_CASE_     )
			model.eval()

			# forward pass
			A_									= model(SCREAMING_SNAKE_CASE_     )
			A_									= outputs.predicted_depth

			# verify output
			if model_name is not None:
						if "nyu" in model_name:
									A_									= 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:
									A_									= 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}'     )

						A_									= torch.Size([1, 480, 640]     )

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


if __name__ == "__main__":
							_SCREAMING_SNAKE_CASE			=						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.",
							)
							_SCREAMING_SNAKE_CASE			=						parser.parse_args()
							convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)










'''simple docstring'''
import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class   __UpperCAmelCase				(     lowerCAmelCase			,lowerCAmelCase				):



					'''simple docstring'''





					_UpperCamelCase							=		1



					@register_to_config
					def __init__(	self     :						str      ,       _lowercase     :						int = 1_000      ,       _lowercase     :						Optional[Union[np.ndarray, List[float]]] = None)    ->							List[str]:
								# set `betas`, `alphas`, `timesteps`
								self.set_timesteps(_lowercase)

								# standard deviation of the initial noise distribution
								A_									= 1.0

								# For now we only support F-PNDM, i.e. the runge-kutta method
								# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
								# mainly at formula (9), (12), (13) and the Algorithm 2.
								A_									= 4

								# running values
								A_									= []



					def     __snake_case						(	self     :						int      ,       _lowercase     :						int      ,       _lowercase     :						Union[str, torch.device] = None)    ->							Any:
								A_									= num_inference_steps
								A_									= torch.linspace(1      ,       0      ,       num_inference_steps + 1)[:-1]
								A_									= torch.cat([steps, torch.tensor([0.0])])

								if self.config.trained_betas is not None:
											A_									= torch.tensor(self.config.trained_betas      ,       dtype=torch.floataa)
								else:
											A_									= torch.sin(steps * math.pi / 2) ** 2

								A_									= (1.0 - self.betas**2) ** 0.5

								A_									= (torch.atana(self.betas      ,       self.alphas) / math.pi * 2)[:-1]
								A_									= timesteps.to(_lowercase)

								A_									= []



					def     __snake_case						(	self     :						Dict      ,       _lowercase     :						torch.FloatTensor      ,       _lowercase     :						int      ,       _lowercase     :						torch.FloatTensor      ,       _lowercase     :						bool = True      ,       )    ->							Union[SchedulerOutput, Tuple]:
								if self.num_inference_steps is None:
											raise ValueError(
											    'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler')

								A_									= (self.timesteps == timestep).nonzero().item()
								A_									= timestep_index + 1

								A_									= sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
								self.ets.append(_lowercase)

								if len(self.ets) == 1:
											A_									= self.ets[-1]
								elif len(self.ets) == 2:
											A_									= (3 * self.ets[-1] - self.ets[-2]) / 2
								elif len(self.ets) == 3:
											A_									= (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
								else:
											A_									= (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

								A_									= self._get_prev_sample(_lowercase      ,       _lowercase      ,       _lowercase      ,       _lowercase)

								if not return_dict:
											return (prev_sample,)

								return SchedulerOutput(prev_sample=_lowercase)



					def     __snake_case						(	self     :						Dict      ,       _lowercase     :						torch.FloatTensor      ,       *_lowercase     :						Optional[Any]      ,       **_lowercase     :						int)    ->							torch.FloatTensor:
								return sample



					def     __snake_case						(	self     :						Optional[int]      ,       _lowercase     :						Optional[Any]      ,       _lowercase     :						Dict      ,       _lowercase     :						Union[str, Any]      ,       _lowercase     :						Union[str, Any])    ->							Union[str, Any]:
								A_									= self.alphas[timestep_index]
								A_									= self.betas[timestep_index]

								A_									= self.alphas[prev_timestep_index]
								A_									= self.betas[prev_timestep_index]

								A_									= (sample - sigma * ets) / max(_lowercase      ,       1E-8)
								A_									= next_alpha * pred + ets * next_sigma

								return prev_sample



					def __len__(	self     :						List[str])    ->							Union[str, Any]:
								return self.config.num_train_timesteps




