


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						=		logging.get_logger(__name__)






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






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

		return new_state_dict






def 					lowerCamelCase_   (	_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__       :      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__       :      Tuple   					= 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 					lowerCamelCase_   (	):




		"""simple docstring"""






		lowerCAmelCase__       :      Optional[int]   					= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		lowerCAmelCase__       :      int   					= Image.open(requests.get(_a     ,       stream=_a	).raw	)

		return image






@torch.no_grad()
def 					lowerCamelCase_   (	_a     ,       _a     ,       _a=False     ,       _a=None	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[Any]   					= GLPNConfig(hidden_sizes=[64, 128, 320, 512]     ,       decoder_hidden_size=64     ,       depths=[3, 8, 27, 3]	)

		# load image processor (only resize + rescale)
		lowerCAmelCase__       :      Optional[int]   					= GLPNImageProcessor()

		# prepare image
		lowerCAmelCase__       :      Any   					= prepare_img()
		lowerCAmelCase__       :      Optional[int]   					= image_processor(images=_a     ,       return_tensors='''pt'''	).pixel_values

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

		# load original state dict
		lowerCAmelCase__       :      List[Any]   					= torch.load(_a     ,       map_location=torch.device('''cpu'''	)	)

		# rename keys
		lowerCAmelCase__       :      Dict   					= 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[Any]   					= GLPNForDepthEstimation(_a	)
		model.load_state_dict(_a	)
		model.eval()

		# forward pass
		lowerCAmelCase__       :      str   					= model(_a	)
		lowerCAmelCase__       :      Tuple   					= outputs.predicted_depth

		# verify output
		if model_name is not None:
				if "nyu" in model_name:
						lowerCAmelCase__       :      Optional[int]   					= torch.tensor(
						    [[4.41_47, 4.08_73, 4.06_73], [3.78_90, 3.28_81, 3.15_25], [3.76_74, 3.54_23, 3.49_13]]	)
				elif "kitti" in model_name:
						lowerCAmelCase__       :      Optional[Any]   					= torch.tensor(
						    [[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]]	)
				else:
						raise ValueError(f'Unknown model name: {model_name}'	)

				lowerCAmelCase__       :      Any   					= 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__":
		lowerCamelCase						=		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						=		parser.parse_args()
		convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)








def 					lowerCamelCase_   (	_a = 4_000_000	):




		"""simple docstring"""






		lowerCAmelCase__       :      str   					= []
		lowerCAmelCase__					,     lowerCAmelCase__       :      Optional[Any]   					= 0, 1
		while b <= n:
				if b % 2 == 0:
						even_fibs.append(_a	)
				lowerCAmelCase__					,     lowerCAmelCase__       :      Union[str, Any]   					= b, a + b
		return sum(_a	)


if __name__ == "__main__":
		print(f'''{solution() = }''')




