

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			:					Optional[Any]						=			logging.get_logger(__name__)





def 	__a     (      A__			:  int       ):
    SCREAMING_SNAKE_CASE										=   DPTConfig()

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

    if "ade" in checkpoint_url:
        SCREAMING_SNAKE_CASE										=   True

        SCREAMING_SNAKE_CASE										=   150
        SCREAMING_SNAKE_CASE										=   "huggingface/label-files"
        SCREAMING_SNAKE_CASE										=   "ade20k-id2label.json"
        SCREAMING_SNAKE_CASE										=   json.load(open(cached_download(hf_hub_url(A__			,  A__			,  repo_type="dataset"       )       )			,  "r"       )       )
        SCREAMING_SNAKE_CASE										=   {int(A__       ): 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, 150, 480, 480]

    return config, expected_shape





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





def 	__a     (      A__			:  Tuple       ):
    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 	__a     (      A__			:  Dict			,  A__			:  List[Any]       ):
    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 	__a     (      ):
    SCREAMING_SNAKE_CASE										=   "http://images.cocodataset.org/val2017/000000039769.jpg"
    SCREAMING_SNAKE_CASE										=   Image.open(requests.get(A__			,  stream=A__       ).raw       )
    return im





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

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

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

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

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

    # Assert logits
    SCREAMING_SNAKE_CASE										=   torch.tensor([[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]]       )
    if "ade" in checkpoint_url:
        SCREAMING_SNAKE_CASE										=   torch.tensor([[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]]       )
    assert outputs.shape == torch.Size(A__       )
    assert (
        torch.allclose(outputs[0, 0, :3, :3]			,  A__			,  atol=1E-4       )
        if "ade" in checkpoint_url
        else torch.allclose(outputs[0, :3, :3]			,  A__       )
    )

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

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


if __name__ == "__main__":
    __A			:					Optional[int]						=			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			:					Optional[int]						=			parser.parse_args()
    convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)


def 	__a     (      A__			:  float			,  A__			:  float       ):
    if mass < 0:
        raise ValueError("The mass of a body cannot be negative"       )
    return 0.5 * mass * abs(A__       ) * abs(A__       )


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)