




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							:						int					       =  logging.get_logger(__name__)

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			Union[str, Any]	):


      '''simple docstring'''



      lowerCamelCase_							=       DPTConfig(embedding_type='hybrid'	)

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

      if "nyu" or "midas" in checkpoint_url:
            lowerCamelCase_							=       7_68
            lowerCamelCase_							=       [1, 1, 1, 0.5]
            lowerCamelCase_							=       [2_56, 5_12, 7_68, 7_68]
            lowerCamelCase_							=       1_50
            lowerCamelCase_							=       16
            lowerCamelCase_							=       (1, 3_84, 3_84)
            lowerCamelCase_							=       False
            lowerCamelCase_							=       'project'

      if "ade" in checkpoint_url:
            lowerCamelCase_							=       True
            lowerCamelCase_							=       7_68
            lowerCamelCase_							=       [1, 1, 1, 0.5]
            lowerCamelCase_							=       1_50
            lowerCamelCase_							=       16
            lowerCamelCase_							=       'huggingface/label-files'
            lowerCamelCase_							=       'ade20k-id2label.json'
            lowerCamelCase_							=       json.load(open(cached_download(hf_hub_url(lowercase      ,				lowercase      ,				repo_type='dataset'	)	)      ,				'r'	)	)
            lowerCamelCase_							=       {int(lowercase	): v for k, v in idalabel.items()}
            lowerCamelCase_							=       idalabel
            lowerCamelCase_							=       {v: k for k, v in idalabel.items()}
            lowerCamelCase_							=       [1, 1_50, 4_80, 4_80]

      return config, expected_shape

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			Optional[int]	):


      '''simple docstring'''



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

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

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

      if "stem.conv" in name:
            lowerCamelCase_							=       name.replace('stem.conv'      ,				'bit.embedder.convolution'	)
      if "blocks" in name:
            lowerCamelCase_							=       name.replace('blocks'      ,				'layers'	)
      if "convolution" in name and "backbone" in name:
            lowerCamelCase_							=       name.replace('convolution'      ,				'conv'	)
      if "layer" in name and "backbone" in name:
            lowerCamelCase_							=       name.replace('layer'      ,				'layers'	)
      if "backbone.bit.encoder.bit" in name:
            lowerCamelCase_							=       name.replace('backbone.bit.encoder.bit'      ,				'backbone.bit'	)
      if "embedder.conv" in name:
            lowerCamelCase_							=       name.replace('embedder.conv'      ,				'embedder.convolution'	)
      if "backbone.bit.encoder.stem.norm" in name:
            lowerCamelCase_							=       name.replace('backbone.bit.encoder.stem.norm'      ,				'backbone.bit.embedder.norm'	)
      return name

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			Any      ,				lowercase      :			str	):


      '''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_							=       state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.weight"""	)
            lowerCamelCase_							=       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_							=       in_proj_weight[: config.hidden_size, :]
            lowerCamelCase_							=       in_proj_bias[: config.hidden_size]
            lowerCamelCase_							=       in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            lowerCamelCase_							=       in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            lowerCamelCase_							=       in_proj_weight[
                -config.hidden_size :, :
            ]
            lowerCamelCase_							=       in_proj_bias[-config.hidden_size :]

def      _SCREAMING_SNAKE_CASE     (				):


      '''simple docstring'''



      lowerCamelCase_							=       'http://images.cocodataset.org/val2017/000000039769.jpg'
      lowerCamelCase_							=       Image.open(requests.get(lowercase      ,				stream=lowercase	).raw	)
      return im

@torch.no_grad()
def      _SCREAMING_SNAKE_CASE     (				lowercase      :			Any      ,				lowercase      :			str      ,				lowercase      :			List[Any]      ,				lowercase      :			Any      ,				lowercase      :			Union[str, Any]	):


      '''simple docstring'''



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

      # load HuggingFace model
      lowerCamelCase_							=       DPTForSemanticSegmentation(lowercase	) if 'ade' in checkpoint_url else DPTForDepthEstimation(lowercase	)
      model.load_state_dict(lowercase	)
      model.eval()

      # Check outputs on an image
      lowerCamelCase_							=       4_80 if 'ade' in checkpoint_url else 3_84
      lowerCamelCase_							=       DPTImageProcessor(size=lowercase	)

      lowerCamelCase_							=       prepare_img()
      lowerCamelCase_							=       image_processor(lowercase      ,				return_tensors='pt'	)

      # forward pass
      lowerCamelCase_							=       model(**lowercase	).logits if 'ade' in checkpoint_url else model(**lowercase	).predicted_depth

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

            Image.fromarray((prediction / prediction.max()) * 2_55	).show()

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

      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							:						Union[str, Any]					       =  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							:						int					       =  parser.parse_args()
 convert_dpt_checkpoint(
     args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
 )







"""simple docstring"""



import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification







def     __lowerCAmelCase		(		lowercase    :    Optional[int]					)      ->		Tuple:


    """simple docstring"""





    snake_case       : Optional[Any]    =       SwinConfig()
    snake_case       : Union[str, Any]    =       swin_name.split("_"					)

    snake_case       : str    =       name_split[1]
    snake_case       : Tuple    =       int(name_split[4]					)
    snake_case       : str    =       int(name_split[3][-1]					)

    if model_size == "tiny":
        snake_case       : str    =       96
        snake_case       : List[Any]    =       (2, 2, 6, 2)
        snake_case       : Union[str, Any]    =       (3, 6, 12, 24)
    elif model_size == "small":
        snake_case       : int    =       96
        snake_case       : Optional[Any]    =       (2, 2, 18, 2)
        snake_case       : Any    =       (3, 6, 12, 24)
    elif model_size == "base":
        snake_case       : List[str]    =       128
        snake_case       : int    =       (2, 2, 18, 2)
        snake_case       : Any    =       (4, 8, 16, 32)
    else:
        snake_case       : List[str]    =       192
        snake_case       : str    =       (2, 2, 18, 2)
        snake_case       : Any    =       (6, 12, 24, 48)

    if "in22k" in swin_name:
        snake_case       : List[Any]    =       2_1841
    else:
        snake_case       : str    =       1000
        snake_case       : Any    =       "huggingface/label-files"
        snake_case       : str    =       "imagenet-1k-id2label.json"
        snake_case       : Optional[int]    =       json.load(open(hf_hub_download(lowercase		,				lowercase		,				repo_type="dataset"					)		,				"r"					)					)
        snake_case       : List[Any]    =       {int(lowercase					): v for k, v in idalabel.items()}
        snake_case       : Optional[int]    =       idalabel
        snake_case       : Optional[int]    =       {v: k for k, v in idalabel.items()}

    snake_case       : Tuple    =       img_size
    snake_case       : Any    =       num_classes
    snake_case       : str    =       embed_dim
    snake_case       : Any    =       depths
    snake_case       : Dict    =       num_heads
    snake_case       : List[Any]    =       window_size

    return config







def     __lowerCAmelCase		(		lowercase    :    Optional[Any]					)      ->		Any:


    """simple docstring"""





    if "patch_embed.proj" in name:
        snake_case       : int    =       name.replace("patch_embed.proj"		,				"embeddings.patch_embeddings.projection"					)
    if "patch_embed.norm" in name:
        snake_case       : Union[str, Any]    =       name.replace("patch_embed.norm"		,				"embeddings.norm"					)
    if "layers" in name:
        snake_case       : Optional[Any]    =       "encoder." + name
    if "attn.proj" in name:
        snake_case       : List[Any]    =       name.replace("attn.proj"		,				"attention.output.dense"					)
    if "attn" in name:
        snake_case       : List[str]    =       name.replace("attn"		,				"attention.self"					)
    if "norm1" in name:
        snake_case       : List[str]    =       name.replace("norm1"		,				"layernorm_before"					)
    if "norm2" in name:
        snake_case       : Optional[int]    =       name.replace("norm2"		,				"layernorm_after"					)
    if "mlp.fc1" in name:
        snake_case       : int    =       name.replace("mlp.fc1"		,				"intermediate.dense"					)
    if "mlp.fc2" in name:
        snake_case       : Tuple    =       name.replace("mlp.fc2"		,				"output.dense"					)

    if name == "norm.weight":
        snake_case       : Optional[int]    =       "layernorm.weight"
    if name == "norm.bias":
        snake_case       : Tuple    =       "layernorm.bias"

    if "head" in name:
        snake_case       : Any    =       name.replace("head"		,				"classifier"					)
    else:
        snake_case       : Tuple    =       "swin." + name

    return name







def     __lowerCAmelCase		(		lowercase    :    List[Any]		,				lowercase    :    List[Any]					)      ->		Any:


    """simple docstring"""





    for key in orig_state_dict.copy().keys():
        snake_case       : int    =       orig_state_dict.pop(lowercase					)

        if "mask" in key:
            continue
        elif "qkv" in key:
            snake_case       : List[Any]    =       key.split("."					)
            snake_case       : Dict    =       int(key_split[1]					)
            snake_case       : Dict    =       int(key_split[3]					)
            snake_case       : str    =       model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

            if "weight" in key:
                snake_case       : Dict    =       val[:dim, :]
                snake_case       : Tuple    =       val[
                    dim : dim * 2, :
                ]
                snake_case       : List[str]    =       val[-dim:, :]
            else:
                snake_case       : Dict    =       val[
                    :dim
                ]
                snake_case       : List[str]    =       val[
                    dim : dim * 2
                ]
                snake_case       : Union[str, Any]    =       val[
                    -dim:
                ]
        else:
            snake_case       : Any    =       val

    return orig_state_dict







def     __lowerCAmelCase		(		lowercase    :    Tuple		,				lowercase    :    Any					)      ->		Dict:


    """simple docstring"""





    snake_case       : str    =       timm.create_model(lowercase		,				pretrained=lowercase					)
    timm_model.eval()

    snake_case       : int    =       get_swin_config(lowercase					)
    snake_case       : Optional[int]    =       SwinForImageClassification(lowercase					)
    model.eval()

    snake_case       : int    =       convert_state_dict(timm_model.state_dict()		,				lowercase					)
    model.load_state_dict(lowercase					)

    snake_case       : Dict    =       "http://images.cocodataset.org/val2017/000000039769.jpg"

    snake_case       : Tuple    =       AutoImageProcessor.from_pretrained("microsoft/{}".format(swin_name.replace("_"		,				"-"					)					)					)
    snake_case       : Optional[int]    =       Image.open(requests.get(lowercase		,				stream=lowercase					).raw					)
    snake_case       : Dict    =       image_processor(images=lowercase		,				return_tensors="pt"					)

    snake_case       : str    =       timm_model(inputs["pixel_values"]					)
    snake_case       : Tuple    =       model(**lowercase					).logits

    assert torch.allclose(lowercase		,				lowercase		,				atol=1e-3					)

    print(F'Saving model {swin_name} to {pytorch_dump_folder_path}'					)
    model.save_pretrained(lowercase					)

    print(F'Saving image processor to {pytorch_dump_folder_path}'					)
    image_processor.save_pretrained(lowercase					)


if __name__ == "__main__":
  __snake_case       =       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--swin_name""",
      default="""swin_tiny_patch4_window7_224""",
      type=str,
      help="""Name of the Swin timm model you'd like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
  )

  __snake_case       =       parser.parse_args()
  convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)





