
'''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()
lowercase					:							Union[str, Any]   =      logging.get_logger(__name__)







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

	return new_state_dict







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			Tuple:
	# 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)
			_snake_case     		=	state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.weight'			)
			_snake_case     		=	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
			_snake_case     		=	kv_weight[
			    : config.hidden_sizes[i], :
			]
			_snake_case     		=	kv_bias[: config.hidden_sizes[i]]
			_snake_case     		=	kv_weight[
			    config.hidden_sizes[i] :, :
			]
			_snake_case     		=	kv_bias[config.hidden_sizes[i] :]







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

	return image







@torch.no_grad()
def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A=False    ,       __A=None			) ->			str:
	_snake_case     		=	GLPNConfig(hidden_sizes=[64, 128, 320, 512]    ,       decoder_hidden_size=64    ,       depths=[3, 8, 27, 3]			)

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

	# prepare image
	_snake_case     		=	prepare_img()
	_snake_case     		=	image_processor(images=__A    ,       return_tensors='pt'			).pixel_values

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

	# load original state dict
	_snake_case     		=	torch.load(__A    ,       map_location=torch.device('cpu'			)			)

	# rename keys
	_snake_case     		=	rename_keys(__A			)

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

	# create HuggingFace model and load state dict
	_snake_case     		=	GLPNForDepthEstimation(__A			)
	model.load_state_dict(__A			)
	model.eval()

	# forward pass
	_snake_case     		=	model(__A			)
	_snake_case     		=	outputs.predicted_depth

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

		_snake_case     		=	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__":
					lowercase					:							List[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.",
					)
					lowercase					:							Union[str, Any]   =      parser.parse_args()
					convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)



'''simple docstring'''







from ..utils import DummyObject, requires_backends


class 		__UpperCAmelCase   (      metaclass=_lowerCamelCase		):
							__lowercase   	= ["""torch""", """torchsde"""]


							def __init__( self					,     *lowerCAmelCase_					,     **lowerCAmelCase_						):





								"""simple docstring"""






								requires_backends(self					,     ['torch', 'torchsde']						)


							@classmethod
							def 				lowerCamelCase       ( cls					,     *lowerCAmelCase_					,     **lowerCAmelCase_						):





								"""simple docstring"""






								requires_backends(cls					,     ['torch', 'torchsde']						)




							@classmethod
							def 				lowerCamelCase       ( cls					,     *lowerCAmelCase_					,     **lowerCAmelCase_						):





								"""simple docstring"""






								requires_backends(cls					,     ['torch', 'torchsde']						)

