



'''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()
SCREAMING_SNAKE_CASE__          =  logging.get_logger(__name__)




def 							lowerCamelCase	(					_snake_case	:    List[str]							):





    '''simple docstring'''


    lowercase__									=    DPTConfig()

    if "large" in checkpoint_url:
        lowercase__									=    1_024
        lowercase__									=    4_096
        lowercase__									=    24
        lowercase__									=    16
        lowercase__									=    [5, 11, 17, 23]
        lowercase__									=    [256, 512, 1_024, 1_024]
        lowercase__									=    (1, 384, 384)

    if "ade" in checkpoint_url:
        lowercase__									=    True

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

    return config, expected_shape




def 							lowerCamelCase	(					_snake_case	:    List[Any]							):





    '''simple docstring'''


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




def 							lowerCamelCase	(					_snake_case	:    Dict							):





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

    return name




def 							lowerCamelCase	(					_snake_case	:    str  ,_snake_case	:    Optional[Any]							):





    '''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)
        lowercase__									=    state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.weight'''							)
        lowercase__									=    state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.bias'''							)
        # next, add query, keys and values (in that order) to the state dict
        lowercase__									=    in_proj_weight[: config.hidden_size, :]
        lowercase__									=    in_proj_bias[: config.hidden_size]
        lowercase__									=    in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        lowercase__									=    in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        lowercase__									=    in_proj_weight[
            -config.hidden_size :, :
        ]
        lowercase__									=    in_proj_bias[-config.hidden_size :]




def 							lowerCamelCase	(					):





    '''simple docstring'''


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




@torch.no_grad()
def 							lowerCamelCase	(					_snake_case	:    Optional[Any]  ,_snake_case	:    Optional[Any]  ,_snake_case	:    Dict  ,_snake_case	:    Dict							):





    '''simple docstring'''


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

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

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

    lowercase__									=    prepare_img()
    lowercase__									=    image_processor(_snake_case  ,return_tensors="pt"							)

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

    # Assert logits
    lowercase__									=    torch.tensor([[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]]							)
    if "ade" in checkpoint_url:
        lowercase__									=    torch.tensor([[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]]							)
    assert outputs.shape == torch.Size(_snake_case							)
    assert (
        torch.allclose(outputs[0, 0, :3, :3]  ,_snake_case  ,atol=1e-4							)
        if "ade" in checkpoint_url
        else torch.allclose(outputs[0, :3, :3]  ,_snake_case							)
    )

    Path(_snake_case							).mkdir(exist_ok=_snake_case							)
    print(f'''Saving model to {pytorch_dump_folder_path}'''							)
    model.save_pretrained(_snake_case							)
    print(f'''Saving image processor to {pytorch_dump_folder_path}'''							)
    image_processor.save_pretrained(_snake_case							)

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


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__          =  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.",
 )

 SCREAMING_SNAKE_CASE__          =  parser.parse_args()
 convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)









'''simple docstring'''





from __future__ import annotations

import math




def 							lowerCamelCase	(					_snake_case	:    float  ,_snake_case	:    int							):





    '''simple docstring'''


    lowercase__									=    u
    for i in range(1  ,_snake_case							):
        lowercase__									=    temp * (u - i)
    return temp




def 							lowerCamelCase	(					):





    '''simple docstring'''


    lowercase__									=    int(input("enter the numbers of values: "							)							)
    lowercase__									=    []
    for _ in range(_snake_case							):
        y.append([]							)
    for i in range(_snake_case							):
        for j in range(_snake_case							):
            y[i].append(_snake_case							)
            lowercase__									=    0

    print("enter the values of parameters in a list: "							)
    lowercase__									=    list(map(_snake_case  ,input().split()							)							)

    print("enter the values of corresponding parameters: "							)
    for i in range(_snake_case							):
        lowercase__									=    float(input()							)

    lowercase__									=    int(input("enter the value to interpolate: "							)							)
    lowercase__									=    (value - x[0]) / (x[1] - x[0])

    # for calculating forward difference table

    for i in range(1  ,_snake_case							):
        for j in range(n - i							):
            lowercase__									=    y[j + 1][i - 1] - y[j][i - 1]

    lowercase__									=    y[0][0]
    for i in range(1  ,_snake_case							):
        summ += (ucal(_snake_case  ,_snake_case							) * y[0][i]) / math.factorial(_snake_case							)

    print(f'''the value at {value} is {summ}'''							)


if __name__ == "__main__":
 main()




