

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__  :      Union[str, Any]			=    logging.get_logger(__name__)


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Union[str, Any]       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						DPTConfig(embedding_type='hybrid'       )

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

    if "nyu" or "midas" in checkpoint_url:
        SCREAMING_SNAKE_CASE_									=						768
        SCREAMING_SNAKE_CASE_									=						[1, 1, 1, 0.5]
        SCREAMING_SNAKE_CASE_									=						[256, 512, 768, 768]
        SCREAMING_SNAKE_CASE_									=						150
        SCREAMING_SNAKE_CASE_									=						16
        SCREAMING_SNAKE_CASE_									=						(1, 384, 384)
        SCREAMING_SNAKE_CASE_									=						False
        SCREAMING_SNAKE_CASE_									=						'project'

    if "ade" in checkpoint_url:
        SCREAMING_SNAKE_CASE_									=						True
        SCREAMING_SNAKE_CASE_									=						768
        SCREAMING_SNAKE_CASE_									=						[1, 1, 1, 0.5]
        SCREAMING_SNAKE_CASE_									=						150
        SCREAMING_SNAKE_CASE_									=						16
        SCREAMING_SNAKE_CASE_									=						'huggingface/label-files'
        SCREAMING_SNAKE_CASE_									=						'ade20k-id2label.json'
        SCREAMING_SNAKE_CASE_									=						json.load(open(cached_download(hf_hub_url(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   repo_type='dataset'       )       ) ,   'r'       )       )
        SCREAMING_SNAKE_CASE_									=						{int(_SCREAMING_SNAKE_CASE       ): 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 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		str       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						['pretrained.model.head.weight', 'pretrained.model.head.bias']
    for k in ignore_keys:
        state_dict.pop(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		int       ):





    """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
    ):
        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' ,   ''       )
    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 and "backbone" not in name:
        SCREAMING_SNAKE_CASE_									=						name.replace('norm1' ,   'layernorm_before'       )
    if "norm2" in name and "backbone" not 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'       )
    if "backbone" in name:
        SCREAMING_SNAKE_CASE_									=						name.replace('backbone' ,   'backbone.bit.encoder'       )

    if ".." in name:
        SCREAMING_SNAKE_CASE_									=						name.replace('..' ,   '.'       )

    if "stem.conv" in name:
        SCREAMING_SNAKE_CASE_									=						name.replace('stem.conv' ,   'bit.embedder.convolution'       )
    if "blocks" in name:
        SCREAMING_SNAKE_CASE_									=						name.replace('blocks' ,   'layers'       )
    if "convolution" in name and "backbone" in name:
        SCREAMING_SNAKE_CASE_									=						name.replace('convolution' ,   'conv'       )
    if "layer" in name and "backbone" in name:
        SCREAMING_SNAKE_CASE_									=						name.replace('layer' ,   'layers'       )
    if "backbone.bit.encoder.bit" in name:
        SCREAMING_SNAKE_CASE_									=						name.replace('backbone.bit.encoder.bit' ,   'backbone.bit'       )
    if "embedder.conv" in name:
        SCREAMING_SNAKE_CASE_									=						name.replace('embedder.conv' ,   'embedder.convolution'       )
    if "backbone.bit.encoder.stem.norm" in name:
        SCREAMING_SNAKE_CASE_									=						name.replace('backbone.bit.encoder.stem.norm' ,   'backbone.bit.embedder.norm'       )
    return name


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Union[str, Any] ,   _SCREAMING_SNAKE_CASE					:		int       ):





    """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)
        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 					_UpperCAmelCase       (  ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						'http://images.cocodataset.org/val2017/000000039769.jpg'
    SCREAMING_SNAKE_CASE_									=						Image.open(requests.get(_SCREAMING_SNAKE_CASE ,   stream=_SCREAMING_SNAKE_CASE       ).raw       )
    return im


@torch.no_grad()
def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Optional[Any] ,   _SCREAMING_SNAKE_CASE					:		Optional[int] ,   _SCREAMING_SNAKE_CASE					:		Union[str, Any] ,   _SCREAMING_SNAKE_CASE					:		Tuple ,   _SCREAMING_SNAKE_CASE					:		Optional[Any]       ):





    """simple docstring"""


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

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

    # Check outputs on an image
    SCREAMING_SNAKE_CASE_									=						480 if 'ade' in checkpoint_url else 384
    SCREAMING_SNAKE_CASE_									=						DPTImageProcessor(size=_SCREAMING_SNAKE_CASE       )

    SCREAMING_SNAKE_CASE_									=						prepare_img()
    SCREAMING_SNAKE_CASE_									=						image_processor(_SCREAMING_SNAKE_CASE ,   return_tensors='pt'       )

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

    if show_prediction:
        SCREAMING_SNAKE_CASE_									=						(
            torch.nn.functional.interpolate(
                outputs.unsqueeze(1       ) ,   size=(image.size[1], image.size[0]) ,   mode='bicubic' ,   align_corners=_SCREAMING_SNAKE_CASE ,   )
            .squeeze()
            .cpu()
            .numpy()
        )

        Image.fromarray((prediction / prediction.max()) * 255       ).show()

    if pytorch_dump_folder_path is not None:
        Path(_SCREAMING_SNAKE_CASE       ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE       )
        print(f"""Saving model to {pytorch_dump_folder_path}"""       )
        model.save_pretrained(_SCREAMING_SNAKE_CASE       )
        print(f"""Saving image processor to {pytorch_dump_folder_path}"""       )
        image_processor.save_pretrained(_SCREAMING_SNAKE_CASE       )

    if push_to_hub:
        model.push_to_hub('ybelkada/dpt-hybrid-midas'       )
        image_processor.push_to_hub('ybelkada/dpt-hybrid-midas'       )


if __name__ == "__main__":
       UpperCamelCase__  :      Any			=    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=False,
           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.",
       )
       parser.add_argument(
           "--show_prediction",
           action="store_true",
       )

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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCamelCase__  :      Any			=    {
    "configuration_mvp": ["MVP_PRETRAINED_CONFIG_ARCHIVE_MAP", "MvpConfig", "MvpOnnxConfig"],
    "tokenization_mvp": ["MvpTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__  :      Optional[int]			=    ["MvpTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__  :      str			=    [
           "MVP_PRETRAINED_MODEL_ARCHIVE_LIST",
           "MvpForCausalLM",
           "MvpForConditionalGeneration",
           "MvpForQuestionAnswering",
           "MvpForSequenceClassification",
           "MvpModel",
           "MvpPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
       from .tokenization_mvp import MvpTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_mvp_fast import MvpTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mvp import (
                  MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MvpForCausalLM,
                  MvpForConditionalGeneration,
                  MvpForQuestionAnswering,
                  MvpForSequenceClassification,
                  MvpModel,
                  MvpPreTrainedModel,
              )

else:
       import sys

       UpperCamelCase__  :      Dict			=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


