





'''simple docstring'''

import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()





def 						UpperCamelCase    (							_lowerCamelCase : List[str]						,	_lowerCamelCase : int						,	_lowerCamelCase : List[Any]	):
    # Initialise PyTorch model
    A__    =      TaConfig.from_json_file(_lowerCamelCase	)
    print(F"Building PyTorch model from configuration: {config}"	)
    A__    =      TaForConditionalGeneration(_lowerCamelCase	)

    # Load weights from tf checkpoint
    load_tf_weights_in_ta(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	)

    # Save pytorch-model
    print(F"Save PyTorch model to {pytorch_dump_path}"	)
    model.save_pretrained(_lowerCamelCase	)


if __name__ == "__main__":
  __lowerCAmelCase							:     Any		     =argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
  )
  parser.add_argument(
      "--config_file",
      default=None,
      type=str,
      required=True,
      help=(
          "The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."
      ),
  )
  parser.add_argument(
      "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )
  __lowerCAmelCase							:     Optional[Any]		     =parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)










'''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()
__lowerCAmelCase							:     Tuple		     =logging.get_logger(__name__)





def 						UpperCamelCase    (							_lowerCamelCase : Tuple	):
    A__    =      DPTConfig(embedding_type="hybrid"	)

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

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

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

    return config, expected_shape





def 						UpperCamelCase    (							_lowerCamelCase : Optional[Any]	):
    A__    =      ["pretrained.model.head.weight", "pretrained.model.head.bias"]
    for k in ignore_keys:
        state_dict.pop(_lowerCamelCase						,	_lowerCamelCase	)





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

    # resize blocks
    if "pretrained.act_postprocess1.3" in name:
        A__    =      name.replace("pretrained.act_postprocess1.3"						,	"neck.reassemble_stage.layers.0.projection"	)
    if "pretrained.act_postprocess1.4" in name:
        A__    =      name.replace("pretrained.act_postprocess1.4"						,	"neck.reassemble_stage.layers.0.resize"	)
    if "pretrained.act_postprocess2.3" in name:
        A__    =      name.replace("pretrained.act_postprocess2.3"						,	"neck.reassemble_stage.layers.1.projection"	)
    if "pretrained.act_postprocess2.4" in name:
        A__    =      name.replace("pretrained.act_postprocess2.4"						,	"neck.reassemble_stage.layers.1.resize"	)
    if "pretrained.act_postprocess3.3" in name:
        A__    =      name.replace("pretrained.act_postprocess3.3"						,	"neck.reassemble_stage.layers.2.projection"	)
    if "pretrained.act_postprocess4.3" in name:
        A__    =      name.replace("pretrained.act_postprocess4.3"						,	"neck.reassemble_stage.layers.3.projection"	)
    if "pretrained.act_postprocess4.4" in name:
        A__    =      name.replace("pretrained.act_postprocess4.4"						,	"neck.reassemble_stage.layers.3.resize"	)
    if "pretrained" in name:
        A__    =      name.replace("pretrained"						,	"dpt"	)
    if "bn" in name:
        A__    =      name.replace("bn"						,	"batch_norm"	)
    if "head" in name:
        A__    =      name.replace("head"						,	"head.head"	)
    if "encoder.norm" in name:
        A__    =      name.replace("encoder.norm"						,	"layernorm"	)
    if "auxlayer" in name:
        A__    =      name.replace("auxlayer"						,	"auxiliary_head.head"	)
    if "backbone" in name:
        A__    =      name.replace("backbone"						,	"backbone.bit.encoder"	)

    if ".." in name:
        A__    =      name.replace(".."						,	"."	)

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





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





def 						UpperCamelCase    (							):
    A__    =      "http://images.cocodataset.org/val2017/000000039769.jpg"
    A__    =      Image.open(requests.get(_lowerCamelCase						,	stream=_lowerCamelCase	).raw	)
    return im





@torch.no_grad()
def 						UpperCamelCase    (							_lowerCamelCase : List[str]						,	_lowerCamelCase : Tuple						,	_lowerCamelCase : Optional[int]						,	_lowerCamelCase : Any						,	_lowerCamelCase : str	):
    A__,     A__    =      get_dpt_config(_lowerCamelCase	)
    # load original state_dict from URL
    # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
    A__    =      torch.load(_lowerCamelCase						,	map_location="cpu"	)
    # remove certain keys
    remove_ignore_keys_(_lowerCamelCase	)
    # rename keys
    for key in state_dict.copy().keys():
        A__    =      state_dict.pop(_lowerCamelCase	)
        A__    =      val
    # read in qkv matrices
    read_in_q_k_v(_lowerCamelCase						,	_lowerCamelCase	)

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

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

    A__    =      prepare_img()
    A__    =      image_processor(_lowerCamelCase						,	return_tensors="pt"	)

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

    if show_prediction:
        A__    =      (
            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()) * 2_55	).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__":
  __lowerCAmelCase							:     Tuple		     =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							:     List[Any]		     =parser.parse_args()
  convert_dpt_checkpoint(
      args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
  )



