






'''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 fire

from utils import calculate_rouge, save_json

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__=None     ,**__magic_name__						)->    Optional[Any]:



			"""simple docstring"""



			snake_case_				:					int   =       [x.strip() for x in open(__magic_name__						).readlines()]
			snake_case_				:					Optional[int]   =       [x.strip() for x in open(__magic_name__						).readlines()][: len(__magic_name__						)]
			snake_case_				:					List[Any]   =       calculate_rouge(__magic_name__     ,__magic_name__     ,**__magic_name__						)
			if save_path is not None:
						save_json(__magic_name__     ,__magic_name__     ,indent=__magic_name__						)
			return metrics  # these print nicely


if __name__ == "__main__":
		fire.Fire(calculate_rouge_path)




