



"""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()
lowercase__					:						Any												=			logging.get_logger(__name__)




def __lowercase    (							_a					):
    snake_case_				:  str       =			DPTConfig(embedding_type='''hybrid'''					)

    if "large" in checkpoint_url:
        snake_case_				:  int       =			1_024
        snake_case_				:  Union[str, Any]       =			4_096
        snake_case_				:  Any       =			24
        snake_case_				:  Dict       =			16
        snake_case_				:  Any       =			[5, 11, 17, 23]
        snake_case_				:  str       =			[256, 512, 1_024, 1_024]
        snake_case_				:  Dict       =			(1, 384, 384)

    if "nyu" or "midas" in checkpoint_url:
        snake_case_				:  Any       =			768
        snake_case_				:  Union[str, Any]       =			[1, 1, 1, 0.5]
        snake_case_				:  str       =			[256, 512, 768, 768]
        snake_case_				:  str       =			150
        snake_case_				:  int       =			16
        snake_case_				:  Optional[Any]       =			(1, 384, 384)
        snake_case_				:  Optional[Any]       =			False
        snake_case_				:  int       =			'''project'''

    if "ade" in checkpoint_url:
        snake_case_				:  Optional[Any]       =			True
        snake_case_				:  Union[str, Any]       =			768
        snake_case_				:  Union[str, Any]       =			[1, 1, 1, 0.5]
        snake_case_				:  Tuple       =			150
        snake_case_				:  Optional[Any]       =			16
        snake_case_				:  Optional[int]       =			'''huggingface/label-files'''
        snake_case_				:  Any       =			'''ade20k-id2label.json'''
        snake_case_				:  Any       =			json.load(open(cached_download(hf_hub_url(_a	,    _a	,    repo_type='''dataset'''					)					)	,    '''r'''					)					)
        snake_case_				:  int       =			{int(_a					): v for k, v in idalabel.items()}
        snake_case_				:  Optional[Any]       =			idalabel
        snake_case_				:  Any       =			{v: k for k, v in idalabel.items()}
        snake_case_				:  Dict       =			[1, 150, 480, 480]

    return config, expected_shape




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




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

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

    if "stem.conv" in name:
        snake_case_				:  Optional[int]       =			name.replace('''stem.conv'''	,    '''bit.embedder.convolution'''					)
    if "blocks" in name:
        snake_case_				:  int       =			name.replace('''blocks'''	,    '''layers'''					)
    if "convolution" in name and "backbone" in name:
        snake_case_				:  Optional[Any]       =			name.replace('''convolution'''	,    '''conv'''					)
    if "layer" in name and "backbone" in name:
        snake_case_				:  List[str]       =			name.replace('''layer'''	,    '''layers'''					)
    if "backbone.bit.encoder.bit" in name:
        snake_case_				:  Optional[Any]       =			name.replace('''backbone.bit.encoder.bit'''	,    '''backbone.bit'''					)
    if "embedder.conv" in name:
        snake_case_				:  Optional[Any]       =			name.replace('''embedder.conv'''	,    '''embedder.convolution'''					)
    if "backbone.bit.encoder.stem.norm" in name:
        snake_case_				:  Union[str, Any]       =			name.replace('''backbone.bit.encoder.stem.norm'''	,    '''backbone.bit.embedder.norm'''					)
    return name




def __lowercase    (							_a	,    _a					):
    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_				:  Tuple       =			state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.weight"					)
        snake_case_				:  int       =			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_				:  List[Any]       =			in_proj_weight[: config.hidden_size, :]
        snake_case_				:  Optional[Any]       =			in_proj_bias[: config.hidden_size]
        snake_case_				:  Dict       =			in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        snake_case_				:  int       =			in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        snake_case_				:  List[Any]       =			in_proj_weight[
            -config.hidden_size :, :
        ]
        snake_case_				:  int       =			in_proj_bias[-config.hidden_size :]




def __lowercase    (							):
    snake_case_				:  List[Any]       =			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    snake_case_				:  Tuple       =			Image.open(requests.get(_a	,    stream=_a					).raw					)
    return im




@torch.no_grad()
def __lowercase    (							_a	,    _a	,    _a	,    _a	,    _a					):
    snake_case_,			snake_case_				:  List[Any]       =			get_dpt_config(_a					)
    # load original state_dict from URL
    # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
    snake_case_				:  Tuple       =			torch.load(_a	,    map_location='''cpu'''					)
    # remove certain keys
    remove_ignore_keys_(_a					)
    # rename keys
    for key in state_dict.copy().keys():
        snake_case_				:  List[str]       =			state_dict.pop(_a					)
        snake_case_				:  Optional[Any]       =			val
    # read in qkv matrices
    read_in_q_k_v(_a	,    _a					)

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

    # Check outputs on an image
    snake_case_				:  List[Any]       =			480 if '''ade''' in checkpoint_url else 384
    snake_case_				:  Optional[Any]       =			DPTImageProcessor(size=_a					)

    snake_case_				:  Any       =			prepare_img()
    snake_case_				:  Tuple       =			image_processor(_a	,    return_tensors='''pt'''					)

    # forward pass
    snake_case_				:  Optional[int]       =			model(**_a					).logits if '''ade''' in checkpoint_url else model(**_a					).predicted_depth

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

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

    if pytorch_dump_folder_path is not None:
        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:
        model.push_to_hub('''ybelkada/dpt-hybrid-midas'''					)
        image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas'''					)


if __name__ == "__main__":
     lowercase__					:						Tuple												=			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''',
     )

     lowercase__					:						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
     )










"""simple docstring"""

import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
     import torch

     from transformers import AutoModelForImageClassification

if is_vision_available():
     from transformers import AutoImageProcessor




@require_torch
@require_vision
class  _UpperCAmelCase (    unittest.TestCase):

 @slow
 def 		_snake_case      (  self	: List[Any]       ):
     snake_case_				:  List[Any]       =			AutoImageProcessor.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''       )
     snake_case_				:  List[Any]       =			AutoModelForImageClassification.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''       )
     model.to(lowercase_       )

     from datasets import load_dataset

     snake_case_				:  str       =			load_dataset('''nielsr/rvlcdip-demo'''       )

     snake_case_				:  Tuple       =			dataset['''train'''][0]['''image'''].convert('''RGB'''       )

     snake_case_				:  Union[str, Any]       =			image_processor(lowercase_						,  return_tensors='''pt'''       ).to(lowercase_       )

     # forward pass
     with torch.no_grad():
         snake_case_				:  List[Any]       =			model(**lowercase_       )
         snake_case_				:  int       =			outputs.logits

     snake_case_				:  Any       =			torch.Size((1, 16)       )
     self.assertEqual(logits.shape						,  lowercase_       )

     snake_case_				:  Any       =			torch.tensor(
         [-0.41_58, -0.40_92, -0.43_47]						,  device=lowercase_						,  dtype=torch.float						,  )
     self.assertTrue(torch.allclose(logits[0, :3]						,  lowercase_						,  atol=1E-4       )       )





