
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   =			logging.get_logger(__name__)



def 						lowercase_      (       A__		)							->							Optional[Any]:
    """simple docstring"""

    snake_case								= DPTConfig()

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

    if "ade" in checkpoint_url:
        snake_case								= True

        snake_case								= 150
        snake_case								= "huggingface/label-files"
        snake_case								= "ade20k-id2label.json"
        snake_case								= json.load(open(cached_download(hf_hub_url(A__			,		A__			,		repo_type="dataset"		)		)			,		"r"		)		)
        snake_case								= {int(A__		): v for k, v in idalabel.items()}
        snake_case								= idalabel
        snake_case								= {v: k for k, v in idalabel.items()}
        snake_case								= [1, 150, 480, 480]

    return config, expected_shape



def 						lowercase_      (       A__		)							->							List[Any]:
    """simple docstring"""

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



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

    return name



def 						lowercase_      (       A__			,		A__		)							->							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)
        snake_case								= state_dict.pop(F'dpt.encoder.layer.{i}.attn.qkv.weight'		)
        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
        snake_case								= in_proj_weight[: config.hidden_size, :]
        snake_case								= in_proj_bias[: config.hidden_size]
        snake_case								= in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        snake_case								= in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        snake_case								= in_proj_weight[
            -config.hidden_size :, :
        ]
        snake_case								= in_proj_bias[-config.hidden_size :]



def 						lowercase_      (       )							->							List[Any]:
    """simple docstring"""

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



@torch.no_grad()
def 						lowercase_      (       A__			,		A__			,		A__			,		A__		)							->							Union[str, Any]:
    """simple docstring"""

    snake_case					,							snake_case								= get_dpt_config(A__		)
    # load original state_dict from URL
    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():
        snake_case								= state_dict.pop(A__		)
        snake_case								= val
    # read in qkv matrices
    read_in_q_k_v(A__			,		A__		)

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

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

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

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

    # Assert logits
    snake_case								= torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]		)
    if "ade" in checkpoint_url:
        snake_case								= torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]		)
    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   =			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   =			parser.parse_args()
  convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)



from __future__ import annotations

from decimal import Decimal

from numpy import array



def 						lowercase_      (       A__		)							->							list[list[float]]:
    """simple docstring"""

    snake_case								= Decimal

    # Check if the provided matrix has 2 rows and 2 columns
    # since this implementation only works for 2x2 matrices
    if len(A__		) == 2 and len(matrix[0]		) == 2 and len(matrix[1]		) == 2:
        # Calculate the determinant of the matrix
        snake_case								= float(
            d(matrix[0][0]		) * d(matrix[1][1]		) - d(matrix[1][0]		) * d(matrix[0][1]		)		)
        if determinant == 0:
            raise ValueError("This matrix has no inverse."		)

        # Creates a copy of the matrix with swapped positions of the elements
        snake_case								= [[0.0, 0.0], [0.0, 0.0]]
        snake_case					,							snake_case								= matrix[1][1], matrix[0][0]
        snake_case					,							snake_case								= -matrix[1][0], -matrix[0][1]

        # Calculate the inverse of the matrix
        return [
            [(float(d(A__		)		) / determinant) or 0.0 for n in row] for row in swapped_matrix
        ]
    elif (
        len(A__		) == 3
        and len(matrix[0]		) == 3
        and len(matrix[1]		) == 3
        and len(matrix[2]		) == 3
    ):
        # Calculate the determinant of the matrix using Sarrus rule
        snake_case								= float(
            (
                (d(matrix[0][0]		) * d(matrix[1][1]		) * d(matrix[2][2]		))
                + (d(matrix[0][1]		) * d(matrix[1][2]		) * d(matrix[2][0]		))
                + (d(matrix[0][2]		) * d(matrix[1][0]		) * d(matrix[2][1]		))
            )
            - (
                (d(matrix[0][2]		) * d(matrix[1][1]		) * d(matrix[2][0]		))
                + (d(matrix[0][1]		) * d(matrix[1][0]		) * d(matrix[2][2]		))
                + (d(matrix[0][0]		) * d(matrix[1][2]		) * d(matrix[2][1]		))
            )		)
        if determinant == 0:
            raise ValueError("This matrix has no inverse."		)

        # Creating cofactor matrix
        snake_case								= [
            [d(0.0		), d(0.0		), d(0.0		)],
            [d(0.0		), d(0.0		), d(0.0		)],
            [d(0.0		), d(0.0		), d(0.0		)],
        ]
        snake_case								= (d(matrix[1][1]		) * d(matrix[2][2]		)) - (
            d(matrix[1][2]		) * d(matrix[2][1]		)
        )
        snake_case								= -(
            (d(matrix[1][0]		) * d(matrix[2][2]		)) - (d(matrix[1][2]		) * d(matrix[2][0]		))
        )
        snake_case								= (d(matrix[1][0]		) * d(matrix[2][1]		)) - (
            d(matrix[1][1]		) * d(matrix[2][0]		)
        )
        snake_case								= -(
            (d(matrix[0][1]		) * d(matrix[2][2]		)) - (d(matrix[0][2]		) * d(matrix[2][1]		))
        )
        snake_case								= (d(matrix[0][0]		) * d(matrix[2][2]		)) - (
            d(matrix[0][2]		) * d(matrix[2][0]		)
        )
        snake_case								= -(
            (d(matrix[0][0]		) * d(matrix[2][1]		)) - (d(matrix[0][1]		) * d(matrix[2][0]		))
        )
        snake_case								= (d(matrix[0][1]		) * d(matrix[1][2]		)) - (
            d(matrix[0][2]		) * d(matrix[1][1]		)
        )
        snake_case								= -(
            (d(matrix[0][0]		) * d(matrix[1][2]		)) - (d(matrix[0][2]		) * d(matrix[1][0]		))
        )
        snake_case								= (d(matrix[0][0]		) * d(matrix[1][1]		)) - (
            d(matrix[0][1]		) * d(matrix[1][0]		)
        )

        # Transpose the cofactor matrix (Adjoint matrix)
        snake_case								= array(A__		)
        for i in range(3		):
            for j in range(3		):
                snake_case								= cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
        snake_case								= array(A__		)
        for i in range(3		):
            for j in range(3		):
                inverse_matrix[i][j] /= d(A__		)

        # Calculate the inverse of the matrix
        return [[float(d(A__		)		) or 0.0 for n in row] for row in inverse_matrix]
    raise ValueError("Please provide a matrix of size 2x2 or 3x3."		)

