
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()
_A		     =    logging.get_logger(__name__)




def  lowerCamelCase__ (  a__     :     Optional[int]   )						->		Optional[int]:
       UpperCamelCase_											=				DPTConfig()

       if "large" in checkpoint_url:
              UpperCamelCase_											=				1024
              UpperCamelCase_											=				4096
              UpperCamelCase_											=				24
              UpperCamelCase_											=				16
              UpperCamelCase_											=				[5, 11, 17, 23]
              UpperCamelCase_											=				[256, 512, 1024, 1024]
              UpperCamelCase_											=				(1, 384, 384)

       if "ade" in checkpoint_url:
              UpperCamelCase_											=				True

              UpperCamelCase_											=				150
              UpperCamelCase_											=				"huggingface/label-files"
              UpperCamelCase_											=				"ade20k-id2label.json"
              UpperCamelCase_											=				json.load(open(cached_download(hf_hub_url(__lowerCAmelCase		,					__lowerCAmelCase		,					repo_type="""dataset"""   )   )		,					"""r"""   )   )
              UpperCamelCase_											=				{int(__lowerCAmelCase   ): v for k, v in idalabel.items()}
              UpperCamelCase_											=				idalabel
              UpperCamelCase_											=				{v: k for k, v in idalabel.items()}
              UpperCamelCase_											=				[1, 150, 480, 480]

       return config, expected_shape




def  lowerCamelCase__ (  a__     :     Optional[int]   )						->		Optional[int]:
       UpperCamelCase_											=				["pretrained.model.head.weight", "pretrained.model.head.bias"]
       for k in ignore_keys:
              state_dict.pop(__lowerCAmelCase		,					__lowerCAmelCase   )




def  lowerCamelCase__ (  a__     :     Dict   )						->		Dict:
       if (
           "pretrained.model" in name
           and "cls_token" not in name
           and "pos_embed" not in name
           and "patch_embed" not in name
       ):
              UpperCamelCase_											=				name.replace("""pretrained.model"""		,					"""dpt.encoder"""   )
       if "pretrained.model" in name:
              UpperCamelCase_											=				name.replace("""pretrained.model"""		,					"""dpt.embeddings"""   )
       if "patch_embed" in name:
              UpperCamelCase_											=				name.replace("""patch_embed"""		,					"""patch_embeddings"""   )
       if "pos_embed" in name:
              UpperCamelCase_											=				name.replace("""pos_embed"""		,					"""position_embeddings"""   )
       if "attn.proj" in name:
              UpperCamelCase_											=				name.replace("""attn.proj"""		,					"""attention.output.dense"""   )
       if "proj" in name and "project" not in name:
              UpperCamelCase_											=				name.replace("""proj"""		,					"""projection"""   )
       if "blocks" in name:
              UpperCamelCase_											=				name.replace("""blocks"""		,					"""layer"""   )
       if "mlp.fc1" in name:
              UpperCamelCase_											=				name.replace("""mlp.fc1"""		,					"""intermediate.dense"""   )
       if "mlp.fc2" in name:
              UpperCamelCase_											=				name.replace("""mlp.fc2"""		,					"""output.dense"""   )
       if "norm1" in name:
              UpperCamelCase_											=				name.replace("""norm1"""		,					"""layernorm_before"""   )
       if "norm2" in name:
              UpperCamelCase_											=				name.replace("""norm2"""		,					"""layernorm_after"""   )
       if "scratch.output_conv" in name:
              UpperCamelCase_											=				name.replace("""scratch.output_conv"""		,					"""head"""   )
       if "scratch" in name:
              UpperCamelCase_											=				name.replace("""scratch"""		,					"""neck"""   )
       if "layer1_rn" in name:
              UpperCamelCase_											=				name.replace("""layer1_rn"""		,					"""convs.0"""   )
       if "layer2_rn" in name:
              UpperCamelCase_											=				name.replace("""layer2_rn"""		,					"""convs.1"""   )
       if "layer3_rn" in name:
              UpperCamelCase_											=				name.replace("""layer3_rn"""		,					"""convs.2"""   )
       if "layer4_rn" in name:
              UpperCamelCase_											=				name.replace("""layer4_rn"""		,					"""convs.3"""   )
       if "refinenet" in name:
              UpperCamelCase_											=				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
              UpperCamelCase_											=				name.replace(f'''refinenet{layer_idx}'''		,					f'''fusion_stage.layers.{abs(layer_idx-4   )}'''   )
       if "out_conv" in name:
              UpperCamelCase_											=				name.replace("""out_conv"""		,					"""projection"""   )
       if "resConfUnit1" in name:
              UpperCamelCase_											=				name.replace("""resConfUnit1"""		,					"""residual_layer1"""   )
       if "resConfUnit2" in name:
              UpperCamelCase_											=				name.replace("""resConfUnit2"""		,					"""residual_layer2"""   )
       if "conv1" in name:
              UpperCamelCase_											=				name.replace("""conv1"""		,					"""convolution1"""   )
       if "conv2" in name:
              UpperCamelCase_											=				name.replace("""conv2"""		,					"""convolution2"""   )
       # readout blocks
       if "pretrained.act_postprocess1.0.project.0" in name:
              UpperCamelCase_											=				name.replace("""pretrained.act_postprocess1.0.project.0"""		,					"""neck.reassemble_stage.readout_projects.0.0"""   )
       if "pretrained.act_postprocess2.0.project.0" in name:
              UpperCamelCase_											=				name.replace("""pretrained.act_postprocess2.0.project.0"""		,					"""neck.reassemble_stage.readout_projects.1.0"""   )
       if "pretrained.act_postprocess3.0.project.0" in name:
              UpperCamelCase_											=				name.replace("""pretrained.act_postprocess3.0.project.0"""		,					"""neck.reassemble_stage.readout_projects.2.0"""   )
       if "pretrained.act_postprocess4.0.project.0" in name:
              UpperCamelCase_											=				name.replace("""pretrained.act_postprocess4.0.project.0"""		,					"""neck.reassemble_stage.readout_projects.3.0"""   )
       # resize blocks
       if "pretrained.act_postprocess1.3" in name:
              UpperCamelCase_											=				name.replace("""pretrained.act_postprocess1.3"""		,					"""neck.reassemble_stage.layers.0.projection"""   )
       if "pretrained.act_postprocess1.4" in name:
              UpperCamelCase_											=				name.replace("""pretrained.act_postprocess1.4"""		,					"""neck.reassemble_stage.layers.0.resize"""   )
       if "pretrained.act_postprocess2.3" in name:
              UpperCamelCase_											=				name.replace("""pretrained.act_postprocess2.3"""		,					"""neck.reassemble_stage.layers.1.projection"""   )
       if "pretrained.act_postprocess2.4" in name:
              UpperCamelCase_											=				name.replace("""pretrained.act_postprocess2.4"""		,					"""neck.reassemble_stage.layers.1.resize"""   )
       if "pretrained.act_postprocess3.3" in name:
              UpperCamelCase_											=				name.replace("""pretrained.act_postprocess3.3"""		,					"""neck.reassemble_stage.layers.2.projection"""   )
       if "pretrained.act_postprocess4.3" in name:
              UpperCamelCase_											=				name.replace("""pretrained.act_postprocess4.3"""		,					"""neck.reassemble_stage.layers.3.projection"""   )
       if "pretrained.act_postprocess4.4" in name:
              UpperCamelCase_											=				name.replace("""pretrained.act_postprocess4.4"""		,					"""neck.reassemble_stage.layers.3.resize"""   )
       if "pretrained" in name:
              UpperCamelCase_											=				name.replace("""pretrained"""		,					"""dpt"""   )
       if "bn" in name:
              UpperCamelCase_											=				name.replace("""bn"""		,					"""batch_norm"""   )
       if "head" in name:
              UpperCamelCase_											=				name.replace("""head"""		,					"""head.head"""   )
       if "encoder.norm" in name:
              UpperCamelCase_											=				name.replace("""encoder.norm"""		,					"""layernorm"""   )
       if "auxlayer" in name:
              UpperCamelCase_											=				name.replace("""auxlayer"""		,					"""auxiliary_head.head"""   )

       return name




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




def  lowerCamelCase__ (  )						->		List[Any]:
       UpperCamelCase_											=				"http://images.cocodataset.org/val2017/000000039769.jpg"
       UpperCamelCase_											=				Image.open(requests.get(__lowerCAmelCase		,					stream=__lowerCAmelCase   ).raw   )
       return im




@torch.no_grad()
def  lowerCamelCase__ (  a__     :     int		,					a__     :     Any		,					a__     :     Optional[Any]		,					a__     :     Optional[Any]   )						->		List[Any]:
       UpperCamelCase_											=				get_dpt_config(__lowerCAmelCase   )
       # load original state_dict from URL
       UpperCamelCase_											=				torch.hub.load_state_dict_from_url(__lowerCAmelCase		,					map_location="""cpu"""   )
       # remove certain keys
       remove_ignore_keys_(__lowerCAmelCase   )
       # rename keys
       for key in state_dict.copy().keys():
              UpperCamelCase_											=				state_dict.pop(__lowerCAmelCase   )
              UpperCamelCase_											=				val
       # read in qkv matrices
       read_in_q_k_v(__lowerCAmelCase		,					__lowerCAmelCase   )

       # load HuggingFace model
       UpperCamelCase_											=				DPTForSemanticSegmentation(__lowerCAmelCase   ) if "ade" in checkpoint_url else DPTForDepthEstimation(__lowerCAmelCase   )
       model.load_state_dict(__lowerCAmelCase   )
       model.eval()

       # Check outputs on an image
       UpperCamelCase_											=				480 if "ade" in checkpoint_url else 384
       UpperCamelCase_											=				DPTImageProcessor(size=__lowerCAmelCase   )

       UpperCamelCase_											=				prepare_img()
       UpperCamelCase_											=				image_processor(__lowerCAmelCase		,					return_tensors="""pt"""   )

       # forward pass
       UpperCamelCase_											=				model(**__lowerCAmelCase   ).logits if "ade" in checkpoint_url else model(**__lowerCAmelCase   ).predicted_depth

       # Assert logits
       UpperCamelCase_											=				torch.tensor([[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]]   )
       if "ade" in checkpoint_url:
              UpperCamelCase_											=				torch.tensor([[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]]   )
       assert outputs.shape == torch.Size(__lowerCAmelCase   )
       assert (
           torch.allclose(outputs[0, 0, :3, :3]		,					__lowerCAmelCase		,					atol=1e-4   )
           if "ade" in checkpoint_url
           else torch.allclose(outputs[0, :3, :3]		,					__lowerCAmelCase   )
       )

       Path(__lowerCAmelCase   ).mkdir(exist_ok=__lowerCAmelCase   )
       print(f'''Saving model to {pytorch_dump_folder_path}'''   )
       model.save_pretrained(__lowerCAmelCase   )
       print(f'''Saving image processor to {pytorch_dump_folder_path}'''   )
       image_processor.save_pretrained(__lowerCAmelCase   )

       if push_to_hub:
              print("""Pushing model to 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__":
      _A		     =    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=True,
          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.''',
      )

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







'''simple docstring'''


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       (__lowerCAmelCase      ):
		_UpperCAmelCase						:				Dict	    =					filter(lambda __lowerCAmelCase      : p.requires_grad					,       model.parameters()      )
		_UpperCAmelCase						:				Optional[Any]	    =					sum([np.prod(p.size()      ) for p in model_parameters]      )
		return params


lowerCamelCase__					=   logging.getLogger(__name__)






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase      ):
		if metric == "rouge2":
				_UpperCAmelCase						:				List[str]	    =					"{val_avg_rouge2:.4f}-{step_count}"
		elif metric == "bleu":
				_UpperCAmelCase						:				int	    =					"{val_avg_bleu:.4f}-{step_count}"
		elif metric == "em":
				_UpperCAmelCase						:				str	    =					"{val_avg_em:.4f}-{step_count}"
		elif metric == "loss":
				_UpperCAmelCase						:				List[Any]	    =					"{val_avg_loss:.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."      )

		_UpperCAmelCase						:				Any	    =					ModelCheckpoint(
		    dirpath=__lowerCAmelCase					,       filename=__lowerCAmelCase					,       monitor=F"""val_{metric}"""					,       mode="max"					,       save_top_k=1					,       every_n_epochs=1					,       )

		return checkpoint_callback






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase      ):
		return EarlyStopping(
		    monitor=F"""val_{metric}"""					,       mode="min" if "loss" in metric else "max"					,       patience=__lowerCAmelCase					,       verbose=__lowerCAmelCase					,       )






class 		lowerCAmelCase__	(	pl.Callback		):




			def 							lowerCAmelCase__			(						self     :   Union[str, Any]		,     lowerCamelCase__     :   Optional[int]		,     lowerCamelCase__     :   Optional[int]       )      ->int:



					'''simple docstring'''







					_UpperCAmelCase						:				Dict	    =					{F"""lr_group_{i}""": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups       )}
					pl_module.logger.log_metrics(lowerCamelCase__       )




			@rank_zero_only
			def 							lowerCAmelCase__			(						self     :   Optional[Any]		,     lowerCamelCase__     :   pl.Trainer		,     lowerCamelCase__     :   pl.LightningModule		,     lowerCamelCase__     :   str		,     lowerCamelCase__     :   Union[str, Any]=True       )      ->None:



					'''simple docstring'''







					logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""       )
					_UpperCAmelCase						:				str	    =					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
					_UpperCAmelCase						:				Any	    =					Path(pl_module.hparams.output_dir       )
					if type_path == "test":
							_UpperCAmelCase						:				List[Any]	    =					od / "test_results.txt"
							_UpperCAmelCase						:				Union[str, Any]	    =					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.
							_UpperCAmelCase						:				Optional[Any]	    =					od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
							_UpperCAmelCase						:				Dict	    =					od / F"""{type_path}_generations/{trainer.global_step:05d}.txt"""
							results_file.parent.mkdir(exist_ok=lowerCamelCase__       )
							generations_file.parent.mkdir(exist_ok=lowerCamelCase__       )
					with open(lowerCamelCase__		,     "a+"       ) as writer:
							for key in sorted(lowerCamelCase__       ):
									if key in ["log", "progress_bar", "preds"]:
											continue
									_UpperCAmelCase						:				Dict	    =					metrics[key]
									if isinstance(lowerCamelCase__		,     torch.Tensor       ):
											_UpperCAmelCase						:				Tuple	    =					val.item()
									_UpperCAmelCase						:				str	    =					F"""{key}: {val:.6f}\n"""
									writer.write(lowerCamelCase__       )

					if not save_generations:
							return

					if "preds" in metrics:
							_UpperCAmelCase						:				Optional[int]	    =					"\n".join(metrics["preds"]       )
							generations_file.open("w+"       ).write(lowerCamelCase__       )




			@rank_zero_only
			def 							lowerCAmelCase__			(						self     :   Dict		,     lowerCamelCase__     :   Optional[int]		,     lowerCamelCase__     :   Optional[int]       )      ->Any:



					'''simple docstring'''







					try:
							_UpperCAmelCase						:				Tuple	    =					pl_module.model.model.num_parameters()
					except AttributeError:
							_UpperCAmelCase						:				int	    =					pl_module.model.num_parameters()

					_UpperCAmelCase						:				int	    =					count_trainable_parameters(lowerCamelCase__       )
					# 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 							lowerCAmelCase__			(						self     :   Dict		,     lowerCamelCase__     :   pl.Trainer		,     lowerCamelCase__     :   pl.LightningModule       )      ->int:



					'''simple docstring'''







					save_json(pl_module.metrics		,     pl_module.metrics_save_path       )
					return self._write_logs(lowerCamelCase__		,     lowerCamelCase__		,     "test"       )






			@rank_zero_only
			def 							lowerCAmelCase__			(						self     :   Tuple		,     lowerCamelCase__     :   pl.Trainer		,     lowerCamelCase__     :   Union[str, Any]       )      ->Union[str, Any]:



					'''simple docstring'''







					save_json(pl_module.metrics		,     pl_module.metrics_save_path       )
					# Uncommenting this will save val generations
					# return self._write_logs(trainer, pl_module, "valid")



