

"""simple docstring"""


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					:str    = logging.get_logger(__name__)




def  _lowercase					(    __lowerCAmelCase							)       ->				Union[str, Any]:
      SCREAMING_SNAKE_CASE__   :      Dict      		=	DPTConfig(embedding_type="""hybrid"""							)

      if "large" in checkpoint_url:
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	1024
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	4096
            SCREAMING_SNAKE_CASE__   :      str      		=	24
            SCREAMING_SNAKE_CASE__   :      Any      		=	16
            SCREAMING_SNAKE_CASE__   :      str      		=	[5, 11, 17, 23]
            SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	[256, 512, 1024, 1024]
            SCREAMING_SNAKE_CASE__   :      int      		=	(1, 384, 384)

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

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

      return config, expected_shape




def  _lowercase					(    __lowerCAmelCase							)       ->				List[str]:
      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
      for k in ignore_keys:
            state_dict.pop(__lowerCAmelCase  ,					__lowerCAmelCase							)




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

      if ".." in name:
            SCREAMING_SNAKE_CASE__   :      List[str]      		=	name.replace(""".."""  ,					"""."""							)

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




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Tuple:
      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__   :      Optional[Any]      		=	state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight'''							)
            SCREAMING_SNAKE_CASE__   :      Dict      		=	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__   :      str      		=	in_proj_weight[: config.hidden_size, :]
            SCREAMING_SNAKE_CASE__   :      int      		=	in_proj_bias[: config.hidden_size]
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            SCREAMING_SNAKE_CASE__   :      List[str]      		=	in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	in_proj_weight[
                -config.hidden_size :, :
            ]
            SCREAMING_SNAKE_CASE__   :      Any      		=	in_proj_bias[-config.hidden_size :]




def  _lowercase					(    )       ->				Tuple:
      SCREAMING_SNAKE_CASE__   :      Any      		=	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      SCREAMING_SNAKE_CASE__   :      str      		=	Image.open(requests.get(__lowerCAmelCase  ,					stream=__lowerCAmelCase							).raw							)
      return im




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

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

      # Check outputs on an image
      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	480 if """ade""" in checkpoint_url else 384
      SCREAMING_SNAKE_CASE__   :      str      		=	DPTImageProcessor(size=__lowerCAmelCase							)

      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	prepare_img()
      SCREAMING_SNAKE_CASE__   :      Dict      		=	image_processor(__lowerCAmelCase  ,					return_tensors="""pt"""							)

      # forward pass
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	model(**__lowerCAmelCase							).logits if """ade""" in checkpoint_url else model(**__lowerCAmelCase							).predicted_depth

      if show_prediction:
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	(
                torch.nn.functional.interpolate(
                    outputs.unsqueeze(1							)  ,					size=(image.size[1], image.size[0])  ,					mode="""bicubic"""  ,					align_corners=__lowerCAmelCase  ,					)
                .squeeze()
                .cpu()
                .numpy()
            )

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

      if pytorch_dump_folder_path is not None:
            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:
            model.push_to_hub("""ybelkada/dpt-hybrid-midas"""							)
            image_processor.push_to_hub("""ybelkada/dpt-hybrid-midas"""							)


if __name__ == "__main__":
  a					:str    = 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",
  )

  a					: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"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


a					:List[Any]    = logging.get_logger(__name__)

a					:Optional[int]    = {
    "microsoft/focalnet-tiny": "https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json",
}

class      __a							(UpperCamelCase_   ,	UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Any       = """focalnet"""

     def __init__(						self	,    _a=224	,    _a=4	,    _a=3	,    _a=96	,    _a=False	,    _a=[192, 384, 768, 768]	,    _a=[2, 2, 6, 2]	,    _a=[2, 2, 2, 2]	,    _a=[3, 3, 3, 3]	,    _a="gelu"	,    _a=4.0	,    _a=0.0	,    _a=0.1	,    _a=False	,    _a=1E-4	,    _a=False	,    _a=False	,    _a=False	,    _a=0.02	,    _a=1E-5	,    _a=32	,    _a=None	,    _a=None	,    **_a	,    )  ->      Optional[Any]:


           """simple docstring"""


           super().__init__(**_a  )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	image_size
           SCREAMING_SNAKE_CASE__   :      str      		=	patch_size
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	num_channels
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	embed_dim
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	use_conv_embed
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	hidden_sizes
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	depths
           SCREAMING_SNAKE_CASE__   :      Any      		=	focal_levels
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	focal_windows
           SCREAMING_SNAKE_CASE__   :      Any      		=	hidden_act
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	mlp_ratio
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	hidden_dropout_prob
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	drop_path_rate
           SCREAMING_SNAKE_CASE__   :      str      		=	use_layerscale
           SCREAMING_SNAKE_CASE__   :      int      		=	layerscale_value
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	use_post_layernorm
           SCREAMING_SNAKE_CASE__   :      Any      		=	use_post_layernorm_in_modulation
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	normalize_modulator
           SCREAMING_SNAKE_CASE__   :      str      		=	initializer_range
           SCREAMING_SNAKE_CASE__   :      Any      		=	layer_norm_eps
           SCREAMING_SNAKE_CASE__   :      Any      		=	encoder_stride
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	["""stem"""] + [f'''stage{idx}''' for idx in range(1	,    len(self.depths  ) + 1  )]
           SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Dict      		=	get_aligned_output_features_output_indices(
               out_features=_a	,    out_indices=_a	,    stage_names=self.stage_names  )




