


'''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()
__lowerCamelCase   :					Any   							=					logging.get_logger(__name__)

def 	__UpperCAmelCase       ( __magic_name__						)->    Tuple:



			"""simple docstring"""



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

			return new_state_dict

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    Tuple:



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

def 	__UpperCAmelCase       ( )->    List[str]:



			"""simple docstring"""



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

			return image

@torch.no_grad()
def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__=False     ,__magic_name__=None						)->    List[str]:



			"""simple docstring"""



			snake_case_				:					str   =       GLPNConfig(hidden_sizes=[64, 128, 320, 512]     ,decoder_hidden_size=64     ,depths=[3, 8, 27, 3]						)

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

			# prepare image
			snake_case_				:					Any   =       prepare_img()
			snake_case_				:					Any   =       image_processor(images=__magic_name__     ,return_tensors="pt"						).pixel_values

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

			# load original state dict
			snake_case_				:					Optional[int]   =       torch.load(__magic_name__     ,map_location=torch.device("cpu"						)						)

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

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

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

			# forward pass
			snake_case_				:					Union[str, Any]   =       model(__magic_name__						)
			snake_case_				:					int   =       outputs.predicted_depth

			# verify output
			if model_name is not None:
						if "nyu" in model_name:
									snake_case_				:					Dict   =       torch.tensor(
									    [[4.4_147, 4.0_873, 4.0_673], [3.7_890, 3.2_881, 3.1_525], [3.7_674, 3.5_423, 3.4_913]]						)
						elif "kitti" in model_name:
									snake_case_				:					List[Any]   =       torch.tensor(
									    [[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]]						)
						else:
									raise ValueError(F'''Unknown model name: {model_name}'''						)

						snake_case_				:					Tuple   =       torch.Size([1, 480, 640]						)

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


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












'''simple docstring'''




import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__=()     ,__magic_name__=None     ,__magic_name__="no"     ,__magic_name__="29500"						)->    Optional[int]:



			"""simple docstring"""



			snake_case_				:					str   =       False
			snake_case_				:					int   =       False
			if any(key.startswith("KAGGLE"						) for key in os.environ.keys()						):
						snake_case_				:					Any   =       True
			elif "IPython" in sys.modules:
						snake_case_				:					Union[str, Any]   =       "google.colab" in str(sys.modules["IPython"].get_ipython()						)

			try:
						snake_case_				:					Any   =       PrecisionType(mixed_precision.lower()						)
			except ValueError:
						raise ValueError(
						    F'''Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.'''						)

			if (in_colab or in_kaggle) and (os.environ.get("TPU_NAME"     ,__magic_name__						) is not None):
						# TPU launch
						import torch_xla.distributed.xla_multiprocessing as xmp

						if len(AcceleratorState._shared_state						) > 0:
									raise ValueError(
									    "To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside "
									    "your training function. Restart your notebook and make sure no cells initializes an "
									    "`Accelerator`."						)
						if num_processes is None:
									snake_case_				:					Tuple   =       8

						snake_case_				:					Optional[int]   =       PrepareForLaunch(__magic_name__     ,distributed_type="TPU"						)
						print(F'''Launching a training on {num_processes} TPU cores.'''						)
						xmp.spawn(__magic_name__     ,args=__magic_name__     ,nprocs=__magic_name__     ,start_method="fork"						)
			elif in_colab:
						# No need for a distributed launch otherwise as it's either CPU or one GPU.
						if torch.cuda.is_available():
									print("Launching training on one GPU."						)
						else:
									print("Launching training on one CPU."						)
						function(*__magic_name__						)
			else:
						if num_processes is None:
									raise ValueError(
									    "You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call."						)

						if num_processes > 1:
									# Multi-GPU launch
									from torch.multiprocessing import start_processes
									from torch.multiprocessing.spawn import ProcessRaisedException

									if len(AcceleratorState._shared_state						) > 0:
												raise ValueError(
												    "To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized "
												    "inside your training function. Restart your notebook and make sure no cells initializes an "
												    "`Accelerator`."						)

									if torch.cuda.is_initialized():
												raise ValueError(
												    "To launch a multi-GPU training from your notebook, you need to avoid running any instruction "
												    "using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA "
												    "function."						)

									# torch.distributed will expect a few environment variable to be here. We set the ones common to each
									# process here (the other ones will be set be the launcher).
									with patch_environment(
									    world_size=__magic_name__     ,master_addr="127.0.01"     ,master_port=__magic_name__     ,mixed_precision=__magic_name__						):
												snake_case_				:					Optional[int]   =       PrepareForLaunch(__magic_name__     ,distributed_type="MULTI_GPU"						)
												print(F'''Launching training on {num_processes} GPUs.'''						)
												try:
															start_processes(__magic_name__     ,args=__magic_name__     ,nprocs=__magic_name__     ,start_method="fork"						)
												except ProcessRaisedException as e:
															if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
																		raise RuntimeError(
																		    "CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. "
																		    "This likely stems from an outside import causing issues once the `notebook_launcher()` is called. "
																		    "Please review your imports and test them when running the `notebook_launcher()` to identify "
																		    "which one is problematic."						) from e

						else:
									# No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
									if is_mps_available():
												snake_case_				:					Any   =       "1"
												print("Launching training on MPS."						)
									elif torch.cuda.is_available():
												print("Launching training on one GPU."						)
									else:
												print("Launching training on CPU."						)
									function(*__magic_name__						)






def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__=()     ,__magic_name__=2						)->    Dict:



			"""simple docstring"""



			from torch.multiprocessing import start_processes

			with tempfile.NamedTemporaryFile() as tmp_file:
						# torch.distributed will expect a few environment variable to be here. We set the ones common to each
						# process here (the other ones will be set be the launcher).
						with patch_environment(
						    world_size=__magic_name__     ,master_addr="127.0.01"     ,master_port="29500"     ,accelerate_mixed_precision="no"     ,accelerate_debug_rdv_file=tmp_file.name     ,accelerate_use_cpu="yes"     ,):
									snake_case_				:					Any   =       PrepareForLaunch(__magic_name__     ,debug=__magic_name__						)
									start_processes(__magic_name__     ,args=__magic_name__     ,nprocs=__magic_name__     ,start_method="fork"						)




