
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)



import warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_A   =			logging.get_logger(__name__)

_A   =			{
    "nvidia/segformer-b0-finetuned-ade-512-512": (
        "https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}







class 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							List[Any]									=		"segformer"





     def __init__(self				:   List[Any]    ,   _A				:   int=3    ,   _A				:   List[Any]=4    ,   _A				:   Any=[2, 2, 2, 2]    ,   _A				:   Dict=[8, 4, 2, 1]    ,   _A				:   List[Any]=[3_2, 6_4, 1_6_0, 2_5_6]    ,   _A				:   Tuple=[7, 3, 3, 3]    ,   _A				:   Optional[int]=[4, 2, 2, 2]    ,   _A				:   Dict=[1, 2, 5, 8]    ,   _A				:   int=[4, 4, 4, 4]    ,   _A				:   Dict="gelu"    ,   _A				:   Tuple=0.0    ,   _A				:   Optional[Any]=0.0    ,   _A				:   List[Any]=0.1    ,   _A				:   Union[str, Any]=0.02    ,   _A				:   Dict=0.1    ,   _A				:   List[Any]=1E-6    ,   _A				:   List[str]=2_5_6    ,   _A				:   Optional[Any]=2_5_5    ,   **_A				:   str    ,   )						->						Tuple:
         super().__init__(**_A       )

         if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
             warnings.warn(
                 "Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"
                 " removed, as the behaviour will default to that of reshape_last_stage = True."    ,   _A    ,   )

         snake_case								= num_channels
         snake_case								= num_encoder_blocks
         snake_case								= depths
         snake_case								= sr_ratios
         snake_case								= hidden_sizes
         snake_case								= patch_sizes
         snake_case								= strides
         snake_case								= mlp_ratios
         snake_case								= num_attention_heads
         snake_case								= hidden_act
         snake_case								= hidden_dropout_prob
         snake_case								= attention_probs_dropout_prob
         snake_case								= classifier_dropout_prob
         snake_case								= initializer_range
         snake_case								= drop_path_rate
         snake_case								= layer_norm_eps
         snake_case								= decoder_hidden_size
         snake_case								= kwargs.get("reshape_last_stage"    ,   _A       )
         snake_case								= semantic_loss_ignore_index







class 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							Optional[Any]									=		version.parse("1.11"  )





     @property
     def 		UpperCAmelCase(self				:   Tuple       )						->						Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
             ]       )





     @property
     def 		UpperCAmelCase(self				:   Tuple       )						->						float:
         return 1E-4





     @property
     def 		UpperCAmelCase(self				:   List[str]       )						->						int:
         return 1_2

