




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()
snake_case_  :      Optional[int]		=		logging.get_logger(__name__)



def        __a							(  __UpperCAmelCase		:						Tuple      ) ->					Tuple:
							"""simple docstring"""




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

							return new_state_dict



def        __a							(  __UpperCAmelCase		:						int   ,		__UpperCAmelCase		:						List[str]      ) ->					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)
																					lowerCamelCase_			:    Optional[int]   						=      state_dict.pop(f"glpn.encoder.block.{i}.{j}.attention.self.kv.weight"      )
																					lowerCamelCase_			:    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
																					lowerCamelCase_			:    str   						=      kv_weight[
																					    : config.hidden_sizes[i], :
																					]
																					lowerCamelCase_			:    Dict   						=      kv_bias[: config.hidden_sizes[i]]
																					lowerCamelCase_			:    List[Any]   						=      kv_weight[
																					    config.hidden_sizes[i] :, :
																					]
																					lowerCamelCase_			:    List[str]   						=      kv_bias[config.hidden_sizes[i] :]



def        __a							(  ) ->					Union[str, Any]:
							"""simple docstring"""




							lowerCamelCase_			:    Optional[int]   						=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
							lowerCamelCase_			:    str   						=      Image.open(requests.get(_lowerCamelCase   ,		stream=_lowerCamelCase      ).raw      )

							return image



@torch.no_grad()
def        __a							(  __UpperCAmelCase		:						Optional[int]   ,		__UpperCAmelCase		:						str   ,		__UpperCAmelCase		:						Optional[int]=False   ,		__UpperCAmelCase		:						Tuple=None      ) ->					List[Any]:
							"""simple docstring"""




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

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

							# prepare image
							lowerCamelCase_			:    str   						=      prepare_img()
							lowerCamelCase_			:    str   						=      image_processor(images=_lowerCamelCase   ,		return_tensors="pt"      ).pixel_values

							logger.info("Converting model..."      )

							# load original state dict
							lowerCamelCase_			:    Union[str, Any]   						=      torch.load(_lowerCamelCase   ,		map_location=torch.device("cpu"      )      )

							# rename keys
							lowerCamelCase_			:    Any   						=      rename_keys(_lowerCamelCase      )

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

							# create HuggingFace model and load state dict
							lowerCamelCase_			:    Any   						=      GLPNForDepthEstimation(_lowerCamelCase      )
							model.load_state_dict(_lowerCamelCase      )
							model.eval()

							# forward pass
							lowerCamelCase_			:    Optional[Any]   						=      model(_lowerCamelCase      )
							lowerCamelCase_			:    Any   						=      outputs.predicted_depth

							# verify output
							if model_name is not None:
														if "nyu" in model_name:
																					lowerCamelCase_			:    Any   						=      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:
																					lowerCamelCase_			:    Tuple   						=      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}"      )

														lowerCamelCase_			:    List[Any]   						=      torch.Size([1, 480, 640]      )

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


if __name__ == "__main__":
	snake_case_  :      Tuple		=		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.",
	)
	snake_case_  :      Optional[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 __future__ import annotations

from typing import Any




class  _A :

		def __init__(   self					:   str  ,      __magic_name__					:   int  ,      __magic_name__					:   int  ,      __magic_name__					:   float = 0			)					->    None:


					"""simple docstring"""

					__snake_case      ,     __snake_case					:      Optional[Any]							      =     row, column
					__snake_case					:      Dict							      =     [[default_value for c in range(__magic_name__			)] for r in range(__magic_name__			)]

		def __str__(   self					:   List[Any]			)					->    str:


					"""simple docstring"""

					__snake_case					:      Dict							      =     f'''Matrix consist of {self.row} rows and {self.column} columns\n'''

					# Make string identifier
					__snake_case					:      Optional[int]							      =     0
					for row_vector in self.array:
								for obj in row_vector:
											__snake_case					:      Optional[int]							      =     max(__magic_name__  ,      len(str(__magic_name__			)			)			)
					__snake_case					:      str							      =     f'''%{max_element_length}s'''

					# Make string and return
					def single_line(__magic_name__					:   list[float]			) -> str:
								nonlocal string_format_identifier
								__snake_case					:      Union[str, Any]							      =     """["""
								line += ", ".join(string_format_identifier % (obj,) for obj in row_vector			)
								line += "]"
								return line

					s += "\n".join(single_line(__magic_name__			) for row_vector in self.array			)
					return s

		def __repr__(   self					:   Optional[int]			)					->    str:


					"""simple docstring"""

					return str(self			)

		def 	lowercase__     (   self					:   Dict  ,      __magic_name__					:   tuple[int, int]			)					->    bool:


					"""simple docstring"""

					if not (isinstance(__magic_name__  ,      (list, tuple)			) and len(__magic_name__			) == 2):
								return False
					elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
								return False
					else:
								return True

		def __getitem__(   self					:   int  ,      __magic_name__					:   tuple[int, int]			)					->    Any:


					"""simple docstring"""

					assert self.validate_indicies(__magic_name__			)
					return self.array[loc[0]][loc[1]]

		def __setitem__(   self					:   List[str]  ,      __magic_name__					:   tuple[int, int]  ,      __magic_name__					:   float			)					->    None:


					"""simple docstring"""

					assert self.validate_indicies(__magic_name__			)
					__snake_case					:      Optional[int]							      =     value

		def __add__(   self					:   Any  ,      __magic_name__					:   Matrix			)					->    Matrix:


					"""simple docstring"""

					assert isinstance(__magic_name__  ,      __magic_name__			)
					assert self.row == another.row and self.column == another.column

					# Add
					__snake_case					:      Union[str, Any]							      =     Matrix(self.row  ,      self.column			)
					for r in range(self.row			):
								for c in range(self.column			):
											__snake_case					:      List[Any]							      =     self[r, c] + another[r, c]
					return result

		def __neg__(   self					:   Tuple			)					->    Matrix:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     Matrix(self.row  ,      self.column			)
					for r in range(self.row			):
								for c in range(self.column			):
											__snake_case					:      List[Any]							      =     -self[r, c]
					return result

		def __sub__(   self					:   Optional[int]  ,      __magic_name__					:   Matrix			)					->    Matrix:


					"""simple docstring"""

					return self + (-another)

		def __mul__(   self					:   List[Any]  ,      __magic_name__					:   int | float | Matrix			)					->    Matrix:


					"""simple docstring"""

					if isinstance(__magic_name__  ,      (int, float)			):  # Scalar multiplication
								__snake_case					:      Optional[int]							      =     Matrix(self.row  ,      self.column			)
								for r in range(self.row			):
											for c in range(self.column			):
														__snake_case					:      Tuple							      =     self[r, c] * another
								return result
					elif isinstance(__magic_name__  ,      __magic_name__			):  # Matrix multiplication
								assert self.column == another.row
								__snake_case					:      Dict							      =     Matrix(self.row  ,      another.column			)
								for r in range(self.row			):
											for c in range(another.column			):
														for i in range(self.column			):
																	result[r, c] += self[r, i] * another[i, c]
								return result
					else:
								__snake_case					:      Optional[int]							      =     f'''Unsupported type given for another ({type(__magic_name__			)})'''
								raise TypeError(__magic_name__			)

		def 	lowercase__     (   self					:   str			)					->    Matrix:


					"""simple docstring"""

					__snake_case					:      Any							      =     Matrix(self.column  ,      self.row			)
					for r in range(self.row			):
								for c in range(self.column			):
											__snake_case					:      str							      =     self[r, c]
					return result

		def 	lowercase__     (   self					:   Union[str, Any]  ,      __magic_name__					:   Matrix  ,      __magic_name__					:   Matrix			)					->    Any:


					"""simple docstring"""

					assert isinstance(__magic_name__  ,      __magic_name__			) and isinstance(__magic_name__  ,      __magic_name__			)
					assert self.row == self.column == u.row == v.row  # u, v should be column vector
					assert u.column == v.column == 1  # u, v should be column vector

					# Calculate
					__snake_case					:      List[str]							      =     v.transpose()
					__snake_case					:      Tuple							      =     (v_t * self * u)[0, 0] + 1
					if numerator_factor == 0:
								return None  # It's not invertable
					return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":







	def 					_a						(       ) ->				None:



				"""simple docstring"""


				__snake_case					:      Tuple							      =     Matrix(3    ,	3    ,	0 )
				for i in range(3 ):
							__snake_case					:      Any							      =     1
				print(F'''a^(-1) is {ainv}''' )
				# u, v
				__snake_case					:      Dict							      =     Matrix(3    ,	1    ,	0 )
				__snake_case      ,     __snake_case      ,     __snake_case					:      Union[str, Any]							      =     1, 2, -3
				__snake_case					:      str							      =     Matrix(3    ,	1    ,	0 )
				__snake_case      ,     __snake_case      ,     __snake_case					:      Tuple							      =     4, -2, 5
				print(F'''u is {u}''' )
				print(F'''v is {v}''' )
				print(F'''uv^T is {u * v.transpose()}''' )
				# Sherman Morrison
				print(F'''(a + uv^T)^(-1) is {ainv.sherman_morrison(_lowerCamelCase    ,	_lowerCamelCase )}''' )







	def 					_a						(       ) ->				None:



				"""simple docstring"""


				import doctest

				doctest.testmod()

	testa()


