






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__      :			Any							=		logging.get_logger(__name__)

def      __lowercase					( snake_case ):







				"""simple docstring"""






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

				return new_state_dict

def      __lowercase					( snake_case,  snake_case ):







				"""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)
												__magic_name__		:str        =      state_dict.pop(f'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight''' )
												__magic_name__		: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
												__magic_name__		:Dict        =      kv_weight[
												    : config.hidden_sizes[i], :
												]
												__magic_name__		:Optional[int]        =      kv_bias[: config.hidden_sizes[i]]
												__magic_name__		:str        =      kv_weight[
												    config.hidden_sizes[i] :, :
												]
												__magic_name__		:Dict        =      kv_bias[config.hidden_sizes[i] :]

def      __lowercase					( ):







				"""simple docstring"""






				__magic_name__		:int        =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				__magic_name__		:List[Any]        =      Image.open(requests.get(snake_case,  stream=snake_case ).raw )

				return image

@torch.no_grad()
def      __lowercase					( snake_case,  snake_case,  snake_case=False,  snake_case=None ):







				"""simple docstring"""






				__magic_name__		:Union[str, Any]        =      GLPNConfig(hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2],  decoder_hidden_size=6_4,  depths=[3, 8, 2_7, 3] )

				# load image processor (only resize + rescale)
				__magic_name__		:List[Any]        =      GLPNImageProcessor()

				# prepare image
				__magic_name__		:Tuple        =      prepare_img()
				__magic_name__		:List[Any]        =      image_processor(images=snake_case,  return_tensors='''pt''' ).pixel_values

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

				# load original state dict
				__magic_name__		:Tuple        =      torch.load(snake_case,  map_location=torch.device('''cpu''' ) )

				# rename keys
				__magic_name__		:List[str]        =      rename_keys(snake_case )

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

				# create HuggingFace model and load state dict
				__magic_name__		:str        =      GLPNForDepthEstimation(snake_case )
				model.load_state_dict(snake_case )
				model.eval()

				# forward pass
				__magic_name__		:str        =      model(snake_case )
				__magic_name__		:Optional[int]        =      outputs.predicted_depth

				# verify output
				if model_name is not None:
								if "nyu" in model_name:
												__magic_name__		:List[Any]        =      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:
												__magic_name__		:str        =      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}''' )

								__magic_name__		:str        =      torch.Size([1, 4_8_0, 6_4_0] )

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


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__      :			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.""",
					)
					SCREAMING_SNAKE_CASE__      :			Optional[int]							=		parser.parse_args()
					convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)














from typing import List

from .keymap import KEYMAP, get_character

def      __lowercase					( snake_case ):







				"""simple docstring"""






				def decorator(snake_case ):
								__magic_name__		:int        =      getattr(snake_case,  '''handle_key''',  [] )
								handle += [key]
								setattr(snake_case,  '''handle_key''',  snake_case )
								return func

				return decorator

def      __lowercase					( *snake_case ):







				"""simple docstring"""






				def decorator(snake_case ):
								__magic_name__		:Union[str, Any]        =      getattr(snake_case,  '''handle_key''',  [] )
								handle += keys
								setattr(snake_case,  '''handle_key''',  snake_case )
								return func

				return decorator
class 						lowerCamelCase_				(      lowerCamelCase ):







				def __new__(						cls       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Optional[int]        =      super().__new__(cls       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     )
								if not hasattr(__lowerCAmelCase       ,					'''key_handler'''     ):
												setattr(__lowerCAmelCase       ,					'''key_handler'''       ,					{}     )
								setattr(__lowerCAmelCase       ,					'''handle_input'''       ,					KeyHandler.handle_input     )

								for value in attrs.values():
												__magic_name__		:Dict        =      getattr(__lowerCAmelCase       ,					'''handle_key'''       ,					[]     )
												for key in handled_keys:
																__magic_name__		:List[Any]        =      value
								return new_cls







				@staticmethod
				def 			A	(						cls     ):
								"""simple docstring"""
								__magic_name__		:Tuple        =      get_character()
								if char != KEYMAP["undefined"]:
												__magic_name__		:Dict        =      ord(__lowerCAmelCase     )
								__magic_name__		:str        =      cls.key_handler.get(__lowerCAmelCase     )
								if handler:
												__magic_name__		:Optional[Any]        =      char
												return handler(cls     )
								else:
												return None







def      __lowercase					( cls ):







				"""simple docstring"""






				return KeyHandler(cls.__name__,  cls.__bases__,  cls.__dict__.copy() )






