


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase						=		logging.get_logger(__name__)






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[Any]   					= DPTConfig(embedding_type='''hybrid'''	)

		if "large" in checkpoint_url:
				lowerCAmelCase__       :      Any   					= 1_024
				lowerCAmelCase__       :      int   					= 4_096
				lowerCAmelCase__       :      List[str]   					= 24
				lowerCAmelCase__       :      Tuple   					= 16
				lowerCAmelCase__       :      str   					= [5, 11, 17, 23]
				lowerCAmelCase__       :      Any   					= [256, 512, 1_024, 1_024]
				lowerCAmelCase__       :      Union[str, Any]   					= (1, 384, 384)

		if "nyu" or "midas" in checkpoint_url:
				lowerCAmelCase__       :      List[Any]   					= 768
				lowerCAmelCase__       :      Optional[int]   					= [1, 1, 1, 0.5]
				lowerCAmelCase__       :      Optional[Any]   					= [256, 512, 768, 768]
				lowerCAmelCase__       :      List[Any]   					= 150
				lowerCAmelCase__       :      List[Any]   					= 16
				lowerCAmelCase__       :      Any   					= (1, 384, 384)
				lowerCAmelCase__       :      Tuple   					= False
				lowerCAmelCase__       :      int   					= '''project'''

		if "ade" in checkpoint_url:
				lowerCAmelCase__       :      Optional[int]   					= True
				lowerCAmelCase__       :      Dict   					= 768
				lowerCAmelCase__       :      str   					= [1, 1, 1, 0.5]
				lowerCAmelCase__       :      Dict   					= 150
				lowerCAmelCase__       :      Optional[int]   					= 16
				lowerCAmelCase__       :      int   					= '''huggingface/label-files'''
				lowerCAmelCase__       :      Dict   					= '''ade20k-id2label.json'''
				lowerCAmelCase__       :      int   					= json.load(open(cached_download(hf_hub_url(_a     ,       _a     ,       repo_type='''dataset'''	)	)     ,       '''r'''	)	)
				lowerCAmelCase__       :      int   					= {int(_a	): v for k, v in idalabel.items()}
				lowerCAmelCase__       :      List[Any]   					= idalabel
				lowerCAmelCase__       :      Union[str, Any]   					= {v: k for k, v in idalabel.items()}
				lowerCAmelCase__       :      str   					= [1, 150, 480, 480]

		return config, expected_shape






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		lowerCAmelCase__       :      int   					= ['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
		for k in ignore_keys:
				state_dict.pop(_a     ,       _a	)






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		if (
		    "pretrained.model" in name
		    and "cls_token" not in name
		    and "pos_embed" not in name
		    and "patch_embed" not in name
		):
				lowerCAmelCase__       :      List[Any]   					= name.replace('''pretrained.model'''     ,       '''dpt.encoder'''	)
		if "pretrained.model" in name:
				lowerCAmelCase__       :      Any   					= name.replace('''pretrained.model'''     ,       '''dpt.embeddings'''	)
		if "patch_embed" in name:
				lowerCAmelCase__       :      Any   					= name.replace('''patch_embed'''     ,       ''''''	)
		if "pos_embed" in name:
				lowerCAmelCase__       :      List[Any]   					= name.replace('''pos_embed'''     ,       '''position_embeddings'''	)
		if "attn.proj" in name:
				lowerCAmelCase__       :      int   					= name.replace('''attn.proj'''     ,       '''attention.output.dense'''	)
		if "proj" in name and "project" not in name:
				lowerCAmelCase__       :      int   					= name.replace('''proj'''     ,       '''projection'''	)
		if "blocks" in name:
				lowerCAmelCase__       :      Dict   					= name.replace('''blocks'''     ,       '''layer'''	)
		if "mlp.fc1" in name:
				lowerCAmelCase__       :      Optional[int]   					= name.replace('''mlp.fc1'''     ,       '''intermediate.dense'''	)
		if "mlp.fc2" in name:
				lowerCAmelCase__       :      Optional[int]   					= name.replace('''mlp.fc2'''     ,       '''output.dense'''	)
		if "norm1" in name and "backbone" not in name:
				lowerCAmelCase__       :      List[Any]   					= name.replace('''norm1'''     ,       '''layernorm_before'''	)
		if "norm2" in name and "backbone" not in name:
				lowerCAmelCase__       :      Optional[Any]   					= name.replace('''norm2'''     ,       '''layernorm_after'''	)
		if "scratch.output_conv" in name:
				lowerCAmelCase__       :      Any   					= name.replace('''scratch.output_conv'''     ,       '''head'''	)
		if "scratch" in name:
				lowerCAmelCase__       :      List[Any]   					= name.replace('''scratch'''     ,       '''neck'''	)
		if "layer1_rn" in name:
				lowerCAmelCase__       :      Dict   					= name.replace('''layer1_rn'''     ,       '''convs.0'''	)
		if "layer2_rn" in name:
				lowerCAmelCase__       :      Any   					= name.replace('''layer2_rn'''     ,       '''convs.1'''	)
		if "layer3_rn" in name:
				lowerCAmelCase__       :      int   					= name.replace('''layer3_rn'''     ,       '''convs.2'''	)
		if "layer4_rn" in name:
				lowerCAmelCase__       :      int   					= name.replace('''layer4_rn'''     ,       '''convs.3'''	)
		if "refinenet" in name:
				lowerCAmelCase__       :      Dict   					= int(name[len('''neck.refinenet'''	) : len('''neck.refinenet'''	) + 1]	)
				# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
				lowerCAmelCase__       :      List[str]   					= name.replace(f'refinenet{layer_idx}'     ,       f'fusion_stage.layers.{abs(layer_idx-4	)}'	)
		if "out_conv" in name:
				lowerCAmelCase__       :      Any   					= name.replace('''out_conv'''     ,       '''projection'''	)
		if "resConfUnit1" in name:
				lowerCAmelCase__       :      str   					= name.replace('''resConfUnit1'''     ,       '''residual_layer1'''	)
		if "resConfUnit2" in name:
				lowerCAmelCase__       :      Union[str, Any]   					= name.replace('''resConfUnit2'''     ,       '''residual_layer2'''	)
		if "conv1" in name:
				lowerCAmelCase__       :      Dict   					= name.replace('''conv1'''     ,       '''convolution1'''	)
		if "conv2" in name:
				lowerCAmelCase__       :      List[Any]   					= name.replace('''conv2'''     ,       '''convolution2'''	)
		# readout blocks
		if "pretrained.act_postprocess1.0.project.0" in name:
				lowerCAmelCase__       :      str   					= name.replace('''pretrained.act_postprocess1.0.project.0'''     ,       '''neck.reassemble_stage.readout_projects.0.0'''	)
		if "pretrained.act_postprocess2.0.project.0" in name:
				lowerCAmelCase__       :      List[Any]   					= name.replace('''pretrained.act_postprocess2.0.project.0'''     ,       '''neck.reassemble_stage.readout_projects.1.0'''	)
		if "pretrained.act_postprocess3.0.project.0" in name:
				lowerCAmelCase__       :      int   					= name.replace('''pretrained.act_postprocess3.0.project.0'''     ,       '''neck.reassemble_stage.readout_projects.2.0'''	)
		if "pretrained.act_postprocess4.0.project.0" in name:
				lowerCAmelCase__       :      List[str]   					= name.replace('''pretrained.act_postprocess4.0.project.0'''     ,       '''neck.reassemble_stage.readout_projects.3.0'''	)

		# resize blocks
		if "pretrained.act_postprocess1.3" in name:
				lowerCAmelCase__       :      Optional[Any]   					= name.replace('''pretrained.act_postprocess1.3'''     ,       '''neck.reassemble_stage.layers.0.projection'''	)
		if "pretrained.act_postprocess1.4" in name:
				lowerCAmelCase__       :      Optional[int]   					= name.replace('''pretrained.act_postprocess1.4'''     ,       '''neck.reassemble_stage.layers.0.resize'''	)
		if "pretrained.act_postprocess2.3" in name:
				lowerCAmelCase__       :      str   					= name.replace('''pretrained.act_postprocess2.3'''     ,       '''neck.reassemble_stage.layers.1.projection'''	)
		if "pretrained.act_postprocess2.4" in name:
				lowerCAmelCase__       :      Any   					= name.replace('''pretrained.act_postprocess2.4'''     ,       '''neck.reassemble_stage.layers.1.resize'''	)
		if "pretrained.act_postprocess3.3" in name:
				lowerCAmelCase__       :      Any   					= name.replace('''pretrained.act_postprocess3.3'''     ,       '''neck.reassemble_stage.layers.2.projection'''	)
		if "pretrained.act_postprocess4.3" in name:
				lowerCAmelCase__       :      int   					= name.replace('''pretrained.act_postprocess4.3'''     ,       '''neck.reassemble_stage.layers.3.projection'''	)
		if "pretrained.act_postprocess4.4" in name:
				lowerCAmelCase__       :      Any   					= name.replace('''pretrained.act_postprocess4.4'''     ,       '''neck.reassemble_stage.layers.3.resize'''	)
		if "pretrained" in name:
				lowerCAmelCase__       :      List[str]   					= name.replace('''pretrained'''     ,       '''dpt'''	)
		if "bn" in name:
				lowerCAmelCase__       :      Optional[int]   					= name.replace('''bn'''     ,       '''batch_norm'''	)
		if "head" in name:
				lowerCAmelCase__       :      List[Any]   					= name.replace('''head'''     ,       '''head.head'''	)
		if "encoder.norm" in name:
				lowerCAmelCase__       :      Optional[Any]   					= name.replace('''encoder.norm'''     ,       '''layernorm'''	)
		if "auxlayer" in name:
				lowerCAmelCase__       :      List[Any]   					= name.replace('''auxlayer'''     ,       '''auxiliary_head.head'''	)
		if "backbone" in name:
				lowerCAmelCase__       :      Optional[Any]   					= name.replace('''backbone'''     ,       '''backbone.bit.encoder'''	)

		if ".." in name:
				lowerCAmelCase__       :      Any   					= name.replace('''..'''     ,       '''.'''	)

		if "stem.conv" in name:
				lowerCAmelCase__       :      Optional[int]   					= name.replace('''stem.conv'''     ,       '''bit.embedder.convolution'''	)
		if "blocks" in name:
				lowerCAmelCase__       :      Any   					= name.replace('''blocks'''     ,       '''layers'''	)
		if "convolution" in name and "backbone" in name:
				lowerCAmelCase__       :      List[Any]   					= name.replace('''convolution'''     ,       '''conv'''	)
		if "layer" in name and "backbone" in name:
				lowerCAmelCase__       :      List[Any]   					= name.replace('''layer'''     ,       '''layers'''	)
		if "backbone.bit.encoder.bit" in name:
				lowerCAmelCase__       :      Dict   					= name.replace('''backbone.bit.encoder.bit'''     ,       '''backbone.bit'''	)
		if "embedder.conv" in name:
				lowerCAmelCase__       :      Union[str, Any]   					= name.replace('''embedder.conv'''     ,       '''embedder.convolution'''	)
		if "backbone.bit.encoder.stem.norm" in name:
				lowerCAmelCase__       :      Union[str, Any]   					= name.replace('''backbone.bit.encoder.stem.norm'''     ,       '''backbone.bit.embedder.norm'''	)
		return name






def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






		for i in range(config.num_hidden_layers	):
				# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
				lowerCAmelCase__       :      Any   					= state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.weight'	)
				lowerCAmelCase__       :      List[Any]   					= state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.bias'	)
				# next, add query, keys and values (in that order) to the state dict
				lowerCAmelCase__       :      Optional[Any]   					= in_proj_weight[: config.hidden_size, :]
				lowerCAmelCase__       :      Tuple   					= in_proj_bias[: config.hidden_size]
				lowerCAmelCase__       :      Union[str, Any]   					= in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				lowerCAmelCase__       :      List[Any]   					= in_proj_bias[
				    config.hidden_size : config.hidden_size * 2
				]
				lowerCAmelCase__       :      int   					= in_proj_weight[
				    -config.hidden_size :, :
				]
				lowerCAmelCase__       :      List[Any]   					= in_proj_bias[-config.hidden_size :]






def 					lowerCamelCase_   (	):




		"""simple docstring"""






		lowerCAmelCase__       :      Optional[Any]   					= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		lowerCAmelCase__       :      List[Any]   					= Image.open(requests.get(_a     ,       stream=_a	).raw	)
		return im






@torch.no_grad()
def 					lowerCamelCase_   (	_a     ,       _a     ,       _a     ,       _a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__					,     lowerCAmelCase__       :      int   					= get_dpt_config(_a	)
		# load original state_dict from URL
		# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
		lowerCAmelCase__       :      Tuple   					= torch.load(_a     ,       map_location='''cpu'''	)
		# remove certain keys
		remove_ignore_keys_(_a	)
		# rename keys
		for key in state_dict.copy().keys():
				lowerCAmelCase__       :      Any   					= state_dict.pop(_a	)
				lowerCAmelCase__       :      Dict   					= val
		# read in qkv matrices
		read_in_q_k_v(_a     ,       _a	)

		# load HuggingFace model
		lowerCAmelCase__       :      Tuple   					= DPTForSemanticSegmentation(_a	) if '''ade''' in checkpoint_url else DPTForDepthEstimation(_a	)
		model.load_state_dict(_a	)
		model.eval()

		# Check outputs on an image
		lowerCAmelCase__       :      Optional[int]   					= 480 if '''ade''' in checkpoint_url else 384
		lowerCAmelCase__       :      Dict   					= DPTImageProcessor(size=_a	)

		lowerCAmelCase__       :      Union[str, Any]   					= prepare_img()
		lowerCAmelCase__       :      str   					= image_processor(_a     ,       return_tensors='''pt'''	)

		# forward pass
		lowerCAmelCase__       :      Tuple   					= model(**_a	).logits if '''ade''' in checkpoint_url else model(**_a	).predicted_depth

		if show_prediction:
				lowerCAmelCase__       :      Optional[int]   					= (
				    torch.nn.functional.interpolate(
				        outputs.unsqueeze(1	)     ,       size=(image.size[1], image.size[0])     ,       mode='''bicubic'''     ,       align_corners=_a     ,       )
				    .squeeze()
				    .cpu()
				    .numpy()
				)

				Image.fromarray((prediction / prediction.max()) * 255	).show()

		if pytorch_dump_folder_path is not None:
				Path(_a	).mkdir(exist_ok=_a	)
				print(f'Saving model to {pytorch_dump_folder_path}'	)
				model.save_pretrained(_a	)
				print(f'Saving image processor to {pytorch_dump_folder_path}'	)
				image_processor.save_pretrained(_a	)

		if push_to_hub:
				model.push_to_hub('''ybelkada/dpt-hybrid-midas'''	)
				image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas'''	)


if __name__ == "__main__":
		lowerCamelCase						=		argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--checkpoint_url''',
		    default='''https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt''',
		    type=str,
		    help='''URL of the original DPT checkpoint you\'d like to convert.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''',
		    default=None,
		    type=str,
		    required=False,
		    help='''Path to the output PyTorch model directory.''',
		)
		parser.add_argument(
		    '''--push_to_hub''',
		    action='''store_true''',
		)
		parser.add_argument(
		    '''--model_name''',
		    default='''dpt-large''',
		    type=str,
		    help='''Name of the model, in case you\'re pushing to the hub.''',
		)
		parser.add_argument(
		    '''--show_prediction''',
		    action='''store_true''',
		)

		lowerCamelCase						=		parser.parse_args()
		convert_dpt_checkpoint(
		    args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
		)








import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		lowerCAmelCase__       :      Any   					= filter(lambda _a	: p.requires_grad     ,       model.parameters()	)
		lowerCAmelCase__       :      str   					= sum([np.prod(p.size()	) for p in model_parameters]	)
		return params


lowerCamelCase						=		logging.getLogger(__name__)






def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






		if metric == "rouge2":
				lowerCAmelCase__       :      Optional[int]   					= '''{val_avg_rouge2:.4f}-{step_count}'''
		elif metric == "bleu":
				lowerCAmelCase__       :      Optional[int]   					= '''{val_avg_bleu:.4f}-{step_count}'''
		elif metric == "em":
				lowerCAmelCase__       :      List[Any]   					= '''{val_avg_em:.4f}-{step_count}'''
		else:
				raise NotImplementedError(
				    f'seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'
				    ''' function.'''	)

		lowerCAmelCase__       :      Dict   					= ModelCheckpoint(
		    dirpath=_a     ,       filename=_a     ,       monitor=f'val_{metric}'     ,       mode='''max'''     ,       save_top_k=3     ,       every_n_epochs=1     ,       )
		return checkpoint_callback






def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






		return EarlyStopping(
		    monitor=f'val_{metric}'     ,       mode='''min''' if '''loss''' in metric else '''max'''     ,       patience=_a     ,       verbose=_a     ,       )





class  _a							(    pl.Callback):




	def 							UpperCAmelCase__( self						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		int		, _SCREAMING_SNAKE_CASE						:		Any		)->	Optional[int]:
			lowerCAmelCase__       :      Dict   					= {F'lr_group_{i}': param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups		)}
			pl_module.logger.log_metrics(_SCREAMING_SNAKE_CASE		)




	@rank_zero_only
	def 							UpperCAmelCase__( self						:		List[str]		, _SCREAMING_SNAKE_CASE						:		pl.Trainer		, _SCREAMING_SNAKE_CASE						:		pl.LightningModule		, _SCREAMING_SNAKE_CASE						:		str		, _SCREAMING_SNAKE_CASE						:		List[str]=True		)->	None:
			logger.info(F'***** {type_path} results at step {trainer.global_step:05d} *****'		)
			lowerCAmelCase__       :      List[Any]   					= trainer.callback_metrics
			trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['''log''', '''progress_bar''', '''preds''']}		)
			# Log results
			lowerCAmelCase__       :      List[Any]   					= Path(pl_module.hparams.output_dir		)
			if type_path == "test":
					lowerCAmelCase__       :      Optional[int]   					= od / '''test_results.txt'''
					lowerCAmelCase__       :      Tuple   					= od / '''test_generations.txt'''
			else:
					# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
					# If people want this it will be easy enough to add back.
					lowerCAmelCase__       :      int   					= od / F'{type_path}_results/{trainer.global_step:05d}.txt'
					lowerCAmelCase__       :      int   					= od / F'{type_path}_generations/{trainer.global_step:05d}.txt'
					results_file.parent.mkdir(exist_ok=_SCREAMING_SNAKE_CASE		)
					generations_file.parent.mkdir(exist_ok=_SCREAMING_SNAKE_CASE		)
			with open(_SCREAMING_SNAKE_CASE		, '''a+'''		) as writer:
					for key in sorted(_SCREAMING_SNAKE_CASE		):
							if key in ["log", "progress_bar", "preds"]:
									continue
							lowerCAmelCase__       :      Optional[int]   					= metrics[key]
							if isinstance(_SCREAMING_SNAKE_CASE		, torch.Tensor		):
									lowerCAmelCase__       :      List[str]   					= val.item()
							lowerCAmelCase__       :      List[str]   					= F'{key}: {val:.6f}\n'
							writer.write(_SCREAMING_SNAKE_CASE		)

			if not save_generations:
					return

			if "preds" in metrics:
					lowerCAmelCase__       :      Dict   					= '''\n'''.join(metrics['''preds''']		)
					generations_file.open('''w+'''		).write(_SCREAMING_SNAKE_CASE		)




	@rank_zero_only
	def 							UpperCAmelCase__( self						:		str		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		Optional[int]		)->	Optional[int]:
			try:
					lowerCAmelCase__       :      Tuple   					= pl_module.model.model.num_parameters()
			except AttributeError:
					lowerCAmelCase__       :      Optional[Any]   					= pl_module.model.num_parameters()

			lowerCAmelCase__       :      Dict   					= count_trainable_parameters(_SCREAMING_SNAKE_CASE		)
			# mp stands for million parameters
			trainer.logger.log_metrics({'''n_params''': npars, '''mp''': npars / 1E6, '''grad_mp''': n_trainable_pars / 1E6}		)




	@rank_zero_only
	def 							UpperCAmelCase__( self						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		pl.Trainer		, _SCREAMING_SNAKE_CASE						:		pl.LightningModule		)->	Optional[Any]:
			save_json(pl_module.metrics		, pl_module.metrics_save_path		)
			return self._write_logs(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, '''test'''		)




	@rank_zero_only
	def 							UpperCAmelCase__( self						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		pl.Trainer		, _SCREAMING_SNAKE_CASE						:		List[Any]		)->	List[Any]:
			save_json(pl_module.metrics		, pl_module.metrics_save_path		)
			# Uncommenting this will save val generations
			# return self._write_logs(trainer, pl_module, "valid")




