




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





def 						snake_case_							(_a	:							str     ):
    UpperCAmelCase			       =		DPTConfig()

    if "large" in checkpoint_url:
        UpperCAmelCase			       =		1_0_2_4
        UpperCAmelCase			       =		4_0_9_6
        UpperCAmelCase			       =		2_4
        UpperCAmelCase			       =		1_6
        UpperCAmelCase			       =		[5, 1_1, 1_7, 2_3]
        UpperCAmelCase			       =		[2_5_6, 5_1_2, 1_0_2_4, 1_0_2_4]
        UpperCAmelCase			       =		(1, 3_8_4, 3_8_4)

    if "ade" in checkpoint_url:
        UpperCAmelCase			       =		True

        UpperCAmelCase			       =		1_5_0
        UpperCAmelCase			       =		'''huggingface/label-files'''
        UpperCAmelCase			       =		'''ade20k-id2label.json'''
        UpperCAmelCase			       =		json.load(open(cached_download(hf_hub_url(_a    ,   _a    ,   repo_type='''dataset'''     )     )    ,   '''r'''     )     )
        UpperCAmelCase			       =		{int(_a     ): v for k, v in idalabel.items()}
        UpperCAmelCase			       =		idalabel
        UpperCAmelCase			       =		{v: k for k, v in idalabel.items()}
        UpperCAmelCase			       =		[1, 1_5_0, 4_8_0, 4_8_0]

    return config, expected_shape





def 						snake_case_							(_a	:							List[str]     ):
    UpperCAmelCase			       =		['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
    for k in ignore_keys:
        state_dict.pop(_a    ,   _a     )





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

    return name





def 						snake_case_							(_a	:							Optional[int]    ,   _a	:							Optional[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)
        UpperCAmelCase			       =		state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.weight"     )
        UpperCAmelCase			       =		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			       =		in_proj_weight[: config.hidden_size, :]
        UpperCAmelCase			       =		in_proj_bias[: config.hidden_size]
        UpperCAmelCase			       =		in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        UpperCAmelCase			       =		in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        UpperCAmelCase			       =		in_proj_weight[
            -config.hidden_size :, :
        ]
        UpperCAmelCase			       =		in_proj_bias[-config.hidden_size :]





def 						snake_case_							():
    UpperCAmelCase			       =		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    UpperCAmelCase			       =		Image.open(requests.get(_a    ,   stream=_a     ).raw     )
    return im





@torch.no_grad()
def 						snake_case_							(_a	:							Optional[int]    ,   _a	:							Dict    ,   _a	:							List[str]    ,   _a	:							int     ):
    UpperCAmelCase       ,     UpperCAmelCase			       =		get_dpt_config(_a     )
    # load original state_dict from URL
    UpperCAmelCase			       =		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():
        UpperCAmelCase			       =		state_dict.pop(_a     )
        UpperCAmelCase			       =		val
    # read in qkv matrices
    read_in_q_k_v(_a    ,   _a     )

    # load HuggingFace model
    UpperCAmelCase			       =		DPTForSemanticSegmentation(_a     ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(_a     )
    model.load_state_dict(_a     )
    model.eval()

    # Check outputs on an image
    UpperCAmelCase			       =		4_8_0 if '''ade''' in checkpoint_url else 3_8_4
    UpperCAmelCase			       =		DPTImageProcessor(size=_a     )

    UpperCAmelCase			       =		prepare_img()
    UpperCAmelCase			       =		image_processor(_a    ,   return_tensors='''pt'''     )

    # forward pass
    UpperCAmelCase			       =		model(**_a     ).logits if '''ade''' in checkpoint_url else model(**_a     ).predicted_depth

    # Assert logits
    UpperCAmelCase			       =		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			       =		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)









'''simple docstring'''


_lowerCamelCase	:       int      	=	"0.21.0"

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
   from .utils import rich

