





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()
SCREAMING_SNAKE_CASE_						   =       logging.get_logger(__name__)



def    __lowercase   (      _SCREAMING_SNAKE_CASE	) ->   Any:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						DPTConfig()

      if "large" in checkpoint_url:
            SCREAMING_SNAKE_CASE		=						10_24
            SCREAMING_SNAKE_CASE		=						40_96
            SCREAMING_SNAKE_CASE		=						24
            SCREAMING_SNAKE_CASE		=						16
            SCREAMING_SNAKE_CASE		=						[5, 11, 17, 23]
            SCREAMING_SNAKE_CASE		=						[2_56, 5_12, 10_24, 10_24]
            SCREAMING_SNAKE_CASE		=						(1, 3_84, 3_84)

      if "ade" in checkpoint_url:
            SCREAMING_SNAKE_CASE		=						True

            SCREAMING_SNAKE_CASE		=						1_50
            SCREAMING_SNAKE_CASE		=						"""huggingface/label-files"""
            SCREAMING_SNAKE_CASE		=						"""ade20k-id2label.json"""
            SCREAMING_SNAKE_CASE		=						json.load(open(cached_download(hf_hub_url(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						repo_type="""dataset"""	)	) ,						"""r"""	)	)
            SCREAMING_SNAKE_CASE		=						{int(_SCREAMING_SNAKE_CASE	): v for k, v in idalabel.items()}
            SCREAMING_SNAKE_CASE		=						idalabel
            SCREAMING_SNAKE_CASE		=						{v: k for k, v in idalabel.items()}
            SCREAMING_SNAKE_CASE		=						[1, 1_50, 4_80, 4_80]

      return config, expected_shape



def    __lowercase   (      _SCREAMING_SNAKE_CASE	) ->   Optional[int]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
      for k in ignore_keys:
            state_dict.pop(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	)



def    __lowercase   (      _SCREAMING_SNAKE_CASE	) ->   Any:


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

      return name



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   Dict:


      '''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)
            SCREAMING_SNAKE_CASE		=						state_dict.pop(F"""dpt.encoder.layer.{i}.attn.qkv.weight"""	)
            SCREAMING_SNAKE_CASE		=						state_dict.pop(F"""dpt.encoder.layer.{i}.attn.qkv.bias"""	)
            # next, add query, keys and values (in that order) to the state dict
            SCREAMING_SNAKE_CASE		=						in_proj_weight[: config.hidden_size, :]
            SCREAMING_SNAKE_CASE		=						in_proj_bias[: config.hidden_size]
            SCREAMING_SNAKE_CASE		=						in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            SCREAMING_SNAKE_CASE		=						in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            SCREAMING_SNAKE_CASE		=						in_proj_weight[
                -config.hidden_size :, :
            ]
            SCREAMING_SNAKE_CASE		=						in_proj_bias[-config.hidden_size :]



def    __lowercase   (      ) ->   Tuple:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      SCREAMING_SNAKE_CASE		=						Image.open(requests.get(_SCREAMING_SNAKE_CASE ,						stream=_SCREAMING_SNAKE_CASE	).raw	)
      return im



@torch.no_grad()
def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   Union[str, Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE		=						get_dpt_config(_SCREAMING_SNAKE_CASE	)
      # load original state_dict from URL
      SCREAMING_SNAKE_CASE		=						torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE ,						map_location="""cpu"""	)
      # remove certain keys
      remove_ignore_keys_(_SCREAMING_SNAKE_CASE	)
      # rename keys
      for key in state_dict.copy().keys():
            SCREAMING_SNAKE_CASE		=						state_dict.pop(_SCREAMING_SNAKE_CASE	)
            SCREAMING_SNAKE_CASE		=						val
      # read in qkv matrices
      read_in_q_k_v(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	)

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

      # Check outputs on an image
      SCREAMING_SNAKE_CASE		=						4_80 if """ade""" in checkpoint_url else 3_84
      SCREAMING_SNAKE_CASE		=						DPTImageProcessor(size=_SCREAMING_SNAKE_CASE	)

      SCREAMING_SNAKE_CASE		=						prepare_img()
      SCREAMING_SNAKE_CASE		=						image_processor(_SCREAMING_SNAKE_CASE ,						return_tensors="""pt"""	)

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

      # Assert logits
      SCREAMING_SNAKE_CASE		=						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:
            SCREAMING_SNAKE_CASE		=						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(_SCREAMING_SNAKE_CASE	)
      assert (
          torch.allclose(outputs[0, 0, :3, :3] ,						_SCREAMING_SNAKE_CASE ,						atol=1E-4	)
          if "ade" in checkpoint_url
          else torch.allclose(outputs[0, :3, :3] ,						_SCREAMING_SNAKE_CASE	)
      )

      Path(_SCREAMING_SNAKE_CASE	).mkdir(exist_ok=_SCREAMING_SNAKE_CASE	)
      print(F"""Saving model to {pytorch_dump_folder_path}"""	)
      model.save_pretrained(_SCREAMING_SNAKE_CASE	)
      print(F"""Saving image processor to {pytorch_dump_folder_path}"""	)
      image_processor.save_pretrained(_SCREAMING_SNAKE_CASE	)

      if push_to_hub:
            print("""Pushing model to hub..."""	)
            model.push_to_hub(
                repo_path_or_name=Path(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ,						organization="""nielsr""" ,						commit_message="""Add model""" ,						use_temp_dir=_SCREAMING_SNAKE_CASE ,						)
            image_processor.push_to_hub(
                repo_path_or_name=Path(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ,						organization="""nielsr""" ,						commit_message="""Add image processor""" ,						use_temp_dir=_SCREAMING_SNAKE_CASE ,						)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_						   =       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.""",
      )

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









from pathlib import Path

import fire



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   List[str]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						Path(_SCREAMING_SNAKE_CASE	)
      SCREAMING_SNAKE_CASE		=						Path(_SCREAMING_SNAKE_CASE	)
      dest_dir.mkdir(exist_ok=_SCREAMING_SNAKE_CASE	)
      for path in src_dir.iterdir():
            SCREAMING_SNAKE_CASE		=						[x.rstrip() for x in list(path.open().readlines()	)][:n]
            SCREAMING_SNAKE_CASE		=						dest_dir.joinpath(path.name	)
            print(_SCREAMING_SNAKE_CASE	)
            dest_path.open("""w"""	).write("""\n""".join(_SCREAMING_SNAKE_CASE	)	)


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


