





"""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()
UpperCAmelCase_ 							=       logging.get_logger(__name__)





def   lowerCamelCase__       (       UpperCamelCase__     :    List[Any]  )     ->	int:



					'''simple docstring'''




					_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(UpperCamelCase__  )-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(UpperCamelCase__  )-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(UpperCamelCase__  )-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(UpperCamelCase__  )-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   lowerCamelCase__       (       UpperCamelCase__     :    Optional[Any]    ,      UpperCamelCase__     :    Optional[Any]  )     ->	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)
															_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   lowerCamelCase__       (       )     ->	int:



					'''simple docstring'''




					_snake_case													=			'http://images.cocodataset.org/val2017/000000039769.jpg'
					_snake_case													=			Image.open(requests.get(UpperCamelCase__    ,      stream=UpperCamelCase__  ).raw  )

					return image





@torch.no_grad()
def   lowerCamelCase__       (       UpperCamelCase__     :    List[str]    ,      UpperCamelCase__     :    Tuple    ,      UpperCamelCase__     :    int=False    ,      UpperCamelCase__     :    int=None  )     ->	Optional[int]:



					'''simple docstring'''




					_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=UpperCamelCase__    ,      return_tensors='pt'  ).pixel_values

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

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

					# rename keys
					_snake_case													=			rename_keys(UpperCamelCase__  )

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

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

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

					# verify output
					if model_name is not None:
										if "nyu" in model_name:
															_snake_case													=			torch.tensor(
															    [[4.4147, 4.0873, 4.0673], [3.7890, 3.2881, 3.1525], [3.7674, 3.5423, 3.4913]]  )
										elif "kitti" in model_name:
															_snake_case													=			torch.tensor(
															    [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]  )
										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]    ,      UpperCamelCase__    ,      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(UpperCamelCase__    ,      UpperCamelCase__  )    ,      organization='nielsr'    ,      commit_message='Add model'    ,      use_temp_dir=UpperCamelCase__    ,      )
										image_processor.push_to_hub(
										    repo_path_or_name=Path(UpperCamelCase__    ,      UpperCamelCase__  )    ,      organization='nielsr'    ,      commit_message='Add image processor'    ,      use_temp_dir=UpperCamelCase__    ,      )


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










def   lowerCamelCase__       (       UpperCamelCase__     :    list[list[int]]    ,      UpperCamelCase__     :    int    ,      UpperCamelCase__     :    int    ,      UpperCamelCase__     :    list[int]  )     ->	bool:



					'''simple docstring'''




					if graph[path[curr_ind - 1]][next_ver] == 0:
										return False

					# 2. Validate that next vertex is not already in path
					return not any(vertex == next_ver for vertex in path  )





def   lowerCamelCase__       (       UpperCamelCase__     :    list[list[int]]    ,      UpperCamelCase__     :    list[int]    ,      UpperCamelCase__     :    int  )     ->	bool:



					'''simple docstring'''




					if curr_ind == len(UpperCamelCase__  ):
										# return whether path exists between current and starting vertices
										return graph[path[curr_ind - 1]][path[0]] == 1

					# Recursive Step
					for next_ver in range(0    ,      len(UpperCamelCase__  )  ):
										if valid_connection(UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__  ):
															# Insert current vertex  into path as next transition
															_snake_case													=			next_ver
															# Validate created path
															if util_hamilton_cycle(UpperCamelCase__    ,      UpperCamelCase__    ,      curr_ind + 1  ):
																				return True
															# Backtrack
															_snake_case													=			-1
					return False





def   lowerCamelCase__       (       UpperCamelCase__     :    list[list[int]]    ,      UpperCamelCase__     :    int = 0  )     ->	list[int]:



					'''simple docstring'''




					_snake_case													=			[-1] * (len(UpperCamelCase__  ) + 1)
					# initialize start and end of path with starting index
					_snake_case													=			_snake_case													=			start_index
					# evaluate and if we find answer return path either return empty array
					return path if util_hamilton_cycle(UpperCamelCase__    ,      UpperCamelCase__    ,      1  ) else []



