
"""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()
UpperCamelCase__          =	logging.get_logger(__name__)
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__	:      Any     							=					DPTConfig()

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

    if "ade" in checkpoint_url:
        UpperCAmelCase__	:      Optional[int]     							=					True

        UpperCAmelCase__	:      Optional[int]     							=					150
        UpperCAmelCase__	:      Dict     							=					'huggingface/label-files'
        UpperCAmelCase__	:      List[str]     							=					'ade20k-id2label.json'
        UpperCAmelCase__	:      Any     							=					json.load(open(cached_download(hf_hub_url(_snake_case    ,_snake_case    ,repo_type='dataset' ) )    ,'r' ) )
        UpperCAmelCase__	:      Dict     							=					{int(_snake_case ): v for k, v in idalabel.items()}
        UpperCAmelCase__	:      Dict     							=					idalabel
        UpperCAmelCase__	:      Union[str, Any]     							=					{v: k for k, v in idalabel.items()}
        UpperCAmelCase__	:      List[str]     							=					[1, 150, 480, 480]

    return config, expected_shape
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__	:      List[Any]     							=					['pretrained.model.head.weight', 'pretrained.model.head.bias']
    for k in ignore_keys:
        state_dict.pop(_snake_case    ,_snake_case )
def 					lowerCamelCase   (     _snake_case ):
    if (
        "pretrained.model" in name
        and "cls_token" not in name
        and "pos_embed" not in name
        and "patch_embed" not in name
    ):
        UpperCAmelCase__	:      List[str]     							=					name.replace('pretrained.model'    ,'dpt.encoder' )
    if "pretrained.model" in name:
        UpperCAmelCase__	:      List[str]     							=					name.replace('pretrained.model'    ,'dpt.embeddings' )
    if "patch_embed" in name:
        UpperCAmelCase__	:      int     							=					name.replace('patch_embed'    ,'patch_embeddings' )
    if "pos_embed" in name:
        UpperCAmelCase__	:      Tuple     							=					name.replace('pos_embed'    ,'position_embeddings' )
    if "attn.proj" in name:
        UpperCAmelCase__	:      Union[str, Any]     							=					name.replace('attn.proj'    ,'attention.output.dense' )
    if "proj" in name and "project" not in name:
        UpperCAmelCase__	:      Optional[Any]     							=					name.replace('proj'    ,'projection' )
    if "blocks" in name:
        UpperCAmelCase__	:      Optional[int]     							=					name.replace('blocks'    ,'layer' )
    if "mlp.fc1" in name:
        UpperCAmelCase__	:      Optional[Any]     							=					name.replace('mlp.fc1'    ,'intermediate.dense' )
    if "mlp.fc2" in name:
        UpperCAmelCase__	:      str     							=					name.replace('mlp.fc2'    ,'output.dense' )
    if "norm1" in name:
        UpperCAmelCase__	:      Union[str, Any]     							=					name.replace('norm1'    ,'layernorm_before' )
    if "norm2" in name:
        UpperCAmelCase__	:      Any     							=					name.replace('norm2'    ,'layernorm_after' )
    if "scratch.output_conv" in name:
        UpperCAmelCase__	:      str     							=					name.replace('scratch.output_conv'    ,'head' )
    if "scratch" in name:
        UpperCAmelCase__	:      List[Any]     							=					name.replace('scratch'    ,'neck' )
    if "layer1_rn" in name:
        UpperCAmelCase__	:      Optional[Any]     							=					name.replace('layer1_rn'    ,'convs.0' )
    if "layer2_rn" in name:
        UpperCAmelCase__	:      Union[str, Any]     							=					name.replace('layer2_rn'    ,'convs.1' )
    if "layer3_rn" in name:
        UpperCAmelCase__	:      Union[str, Any]     							=					name.replace('layer3_rn'    ,'convs.2' )
    if "layer4_rn" in name:
        UpperCAmelCase__	:      Tuple     							=					name.replace('layer4_rn'    ,'convs.3' )
    if "refinenet" in name:
        UpperCAmelCase__	:      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
        UpperCAmelCase__	:      Any     							=					name.replace(F'''refinenet{layer_idx}'''    ,F'''fusion_stage.layers.{abs(layer_idx-4 )}''' )
    if "out_conv" in name:
        UpperCAmelCase__	:      Tuple     							=					name.replace('out_conv'    ,'projection' )
    if "resConfUnit1" in name:
        UpperCAmelCase__	:      Union[str, Any]     							=					name.replace('resConfUnit1'    ,'residual_layer1' )
    if "resConfUnit2" in name:
        UpperCAmelCase__	:      Dict     							=					name.replace('resConfUnit2'    ,'residual_layer2' )
    if "conv1" in name:
        UpperCAmelCase__	:      List[Any]     							=					name.replace('conv1'    ,'convolution1' )
    if "conv2" in name:
        UpperCAmelCase__	:      Optional[int]     							=					name.replace('conv2'    ,'convolution2' )
    # readout blocks
    if "pretrained.act_postprocess1.0.project.0" in name:
        UpperCAmelCase__	:      List[Any]     							=					name.replace('pretrained.act_postprocess1.0.project.0'    ,'neck.reassemble_stage.readout_projects.0.0' )
    if "pretrained.act_postprocess2.0.project.0" in name:
        UpperCAmelCase__	:      Union[str, Any]     							=					name.replace('pretrained.act_postprocess2.0.project.0'    ,'neck.reassemble_stage.readout_projects.1.0' )
    if "pretrained.act_postprocess3.0.project.0" in name:
        UpperCAmelCase__	:      Any     							=					name.replace('pretrained.act_postprocess3.0.project.0'    ,'neck.reassemble_stage.readout_projects.2.0' )
    if "pretrained.act_postprocess4.0.project.0" in name:
        UpperCAmelCase__	:      Tuple     							=					name.replace('pretrained.act_postprocess4.0.project.0'    ,'neck.reassemble_stage.readout_projects.3.0' )
    # resize blocks
    if "pretrained.act_postprocess1.3" in name:
        UpperCAmelCase__	:      Any     							=					name.replace('pretrained.act_postprocess1.3'    ,'neck.reassemble_stage.layers.0.projection' )
    if "pretrained.act_postprocess1.4" in name:
        UpperCAmelCase__	:      Optional[int]     							=					name.replace('pretrained.act_postprocess1.4'    ,'neck.reassemble_stage.layers.0.resize' )
    if "pretrained.act_postprocess2.3" in name:
        UpperCAmelCase__	:      int     							=					name.replace('pretrained.act_postprocess2.3'    ,'neck.reassemble_stage.layers.1.projection' )
    if "pretrained.act_postprocess2.4" in name:
        UpperCAmelCase__	:      Optional[int]     							=					name.replace('pretrained.act_postprocess2.4'    ,'neck.reassemble_stage.layers.1.resize' )
    if "pretrained.act_postprocess3.3" in name:
        UpperCAmelCase__	:      Union[str, Any]     							=					name.replace('pretrained.act_postprocess3.3'    ,'neck.reassemble_stage.layers.2.projection' )
    if "pretrained.act_postprocess4.3" in name:
        UpperCAmelCase__	:      str     							=					name.replace('pretrained.act_postprocess4.3'    ,'neck.reassemble_stage.layers.3.projection' )
    if "pretrained.act_postprocess4.4" in name:
        UpperCAmelCase__	:      Optional[Any]     							=					name.replace('pretrained.act_postprocess4.4'    ,'neck.reassemble_stage.layers.3.resize' )
    if "pretrained" in name:
        UpperCAmelCase__	:      Dict     							=					name.replace('pretrained'    ,'dpt' )
    if "bn" in name:
        UpperCAmelCase__	:      Optional[int]     							=					name.replace('bn'    ,'batch_norm' )
    if "head" in name:
        UpperCAmelCase__	:      Optional[Any]     							=					name.replace('head'    ,'head.head' )
    if "encoder.norm" in name:
        UpperCAmelCase__	:      Any     							=					name.replace('encoder.norm'    ,'layernorm' )
    if "auxlayer" in name:
        UpperCAmelCase__	:      Tuple     							=					name.replace('auxlayer'    ,'auxiliary_head.head' )

    return name
def 					lowerCamelCase   (     _snake_case    ,_snake_case ):
    for i in range(config.num_hidden_layers ):
        # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
        UpperCAmelCase__	:      Tuple     							=					state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight''' )
        UpperCAmelCase__	:      List[Any]     							=					state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.bias''' )
        # next, add query, keys and values (in that order) to the state dict
        UpperCAmelCase__	:      Optional[Any]     							=					in_proj_weight[: config.hidden_size, :]
        UpperCAmelCase__	:      List[Any]     							=					in_proj_bias[: config.hidden_size]
        UpperCAmelCase__	:      Any     							=					in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        UpperCAmelCase__	:      Tuple     							=					in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        UpperCAmelCase__	:      Tuple     							=					in_proj_weight[
            -config.hidden_size :, :
        ]
        UpperCAmelCase__	:      Union[str, Any]     							=					in_proj_bias[-config.hidden_size :]
def 					lowerCamelCase   (     ):
    UpperCAmelCase__	:      int     							=					'http://images.cocodataset.org/val2017/000000039769.jpg'
    UpperCAmelCase__	:      int     							=					Image.open(requests.get(_snake_case    ,stream=_snake_case ).raw )
    return im
@torch.no_grad()
def 					lowerCamelCase   (     _snake_case    ,_snake_case    ,_snake_case    ,_snake_case ):
    UpperCAmelCase__							,							UpperCAmelCase__	:      int     							=					get_dpt_config(_snake_case )
    # load original state_dict from URL
    UpperCAmelCase__	:      List[Any]     							=					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():
        UpperCAmelCase__	:      str     							=					state_dict.pop(_snake_case )
        UpperCAmelCase__	:      List[Any]     							=					val
    # read in qkv matrices
    read_in_q_k_v(_snake_case    ,_snake_case )

    # load HuggingFace model
    UpperCAmelCase__	:      List[str]     							=					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
    UpperCAmelCase__	:      str     							=					480 if 'ade' in checkpoint_url else 384
    UpperCAmelCase__	:      Union[str, Any]     							=					DPTImageProcessor(size=_snake_case )

    UpperCAmelCase__	:      List[Any]     							=					prepare_img()
    UpperCAmelCase__	:      Tuple     							=					image_processor(_snake_case    ,return_tensors='pt' )

    # forward pass
    UpperCAmelCase__	:      Optional[Any]     							=					model(**_snake_case ).logits if 'ade' in checkpoint_url else model(**_snake_case ).predicted_depth

    # Assert logits
    UpperCAmelCase__	:      Union[str, Any]     							=					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:
        UpperCAmelCase__	:      Optional[Any]     							=					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(_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__":
    UpperCamelCase__          =	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.',
    )

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



"""simple docstring"""



UpperCamelCase__          =	{
    'meter': 'm',
    'kilometer': 'km',
    'megametre': 'Mm',
    'gigametre': 'Gm',
    'terametre': 'Tm',
    'petametre': 'Pm',
    'exametre': 'Em',
    'zettametre': 'Zm',
    'yottametre': 'Ym',
}
# Exponent of the factor(meter)
UpperCamelCase__          =	{
    'm': 0,
    'km': 3,
    'Mm': 6,
    'Gm': 9,
    'Tm': 12,
    'Pm': 15,
    'Em': 18,
    'Zm': 21,
    'Ym': 24,
}
def 					lowerCamelCase   (     _snake_case    ,_snake_case    ,_snake_case ):
    UpperCAmelCase__	:      int     							=					from_type.lower().strip('s' )
    UpperCAmelCase__	:      List[Any]     							=					to_type.lower().strip('s' )

    UpperCAmelCase__	:      Optional[int]     							=					UNIT_SYMBOL.get(_snake_case    ,_snake_case )
    UpperCAmelCase__	:      Optional[Any]     							=					UNIT_SYMBOL.get(_snake_case    ,_snake_case )

    if from_sanitized not in METRIC_CONVERSION:
        UpperCAmelCase__	:      Optional[int]     							=					(
            F'''Invalid \'from_type\' value: {from_type!r}.\n'''
            F'''Conversion abbreviations are: {', '.join(_snake_case )}'''
        )
        raise ValueError(_snake_case )
    if to_sanitized not in METRIC_CONVERSION:
        UpperCAmelCase__	:      int     							=					(
            F'''Invalid \'to_type\' value: {to_type!r}.\n'''
            F'''Conversion abbreviations are: {', '.join(_snake_case )}'''
        )
        raise ValueError(_snake_case )
    UpperCAmelCase__	:      str     							=					METRIC_CONVERSION[from_sanitized]
    UpperCAmelCase__	:      Optional[int]     							=					METRIC_CONVERSION[to_sanitized]
    UpperCAmelCase__	:      int     							=					1

    if from_exponent > to_exponent:
        UpperCAmelCase__	:      List[str]     							=					from_exponent - to_exponent
    else:
        UpperCAmelCase__	:      int     							=					-(to_exponent - from_exponent)

    return value * pow(10    ,_snake_case )


if __name__ == "__main__":
    from doctest import testmod

    testmod()

