




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__       (						A__       :					int	):





       '''simple docstring'''



       __lowerCamelCase   =						DPTConfig(embedding_type="""hybrid"""	)

       if "large" in checkpoint_url:
              __lowerCamelCase   =						1024
              __lowerCamelCase   =						4096
              __lowerCamelCase   =						24
              __lowerCamelCase   =						16
              __lowerCamelCase   =						[5, 11, 17, 23]
              __lowerCamelCase   =						[256, 512, 1024, 1024]
              __lowerCamelCase   =						(1, 384, 384)

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

       if "ade" in checkpoint_url:
              __lowerCamelCase   =						True
              __lowerCamelCase   =						768
              __lowerCamelCase   =						[1, 1, 1, 0.5]
              __lowerCamelCase   =						150
              __lowerCamelCase   =						16
              __lowerCamelCase   =						"""huggingface/label-files"""
              __lowerCamelCase   =						"""ade20k-id2label.json"""
              __lowerCamelCase   =						json.load(open(cached_download(hf_hub_url(A__  ,			A__  ,			repo_type="""dataset"""	)	)  ,			"""r"""	)	)
              __lowerCamelCase   =						{int(A__	): v for k, v in idalabel.items()}
              __lowerCamelCase   =						idalabel
              __lowerCamelCase   =						{v: k for k, v in idalabel.items()}
              __lowerCamelCase   =						[1, 150, 480, 480]

       return config, expected_shape




def 					lowerCamelCase__       (						A__       :					Optional[Any]	):





       '''simple docstring'''



       __lowerCamelCase   =						["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
       for k in ignore_keys:
              state_dict.pop(A__  ,			A__	)




def 					lowerCamelCase__       (						A__       :					Optional[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
       ):
              __lowerCamelCase   =						name.replace("""pretrained.model"""  ,			"""dpt.encoder"""	)
       if "pretrained.model" in name:
              __lowerCamelCase   =						name.replace("""pretrained.model"""  ,			"""dpt.embeddings"""	)
       if "patch_embed" in name:
              __lowerCamelCase   =						name.replace("""patch_embed"""  ,			""""""	)
       if "pos_embed" in name:
              __lowerCamelCase   =						name.replace("""pos_embed"""  ,			"""position_embeddings"""	)
       if "attn.proj" in name:
              __lowerCamelCase   =						name.replace("""attn.proj"""  ,			"""attention.output.dense"""	)
       if "proj" in name and "project" not in name:
              __lowerCamelCase   =						name.replace("""proj"""  ,			"""projection"""	)
       if "blocks" in name:
              __lowerCamelCase   =						name.replace("""blocks"""  ,			"""layer"""	)
       if "mlp.fc1" in name:
              __lowerCamelCase   =						name.replace("""mlp.fc1"""  ,			"""intermediate.dense"""	)
       if "mlp.fc2" in name:
              __lowerCamelCase   =						name.replace("""mlp.fc2"""  ,			"""output.dense"""	)
       if "norm1" in name and "backbone" not in name:
              __lowerCamelCase   =						name.replace("""norm1"""  ,			"""layernorm_before"""	)
       if "norm2" in name and "backbone" not in name:
              __lowerCamelCase   =						name.replace("""norm2"""  ,			"""layernorm_after"""	)
       if "scratch.output_conv" in name:
              __lowerCamelCase   =						name.replace("""scratch.output_conv"""  ,			"""head"""	)
       if "scratch" in name:
              __lowerCamelCase   =						name.replace("""scratch"""  ,			"""neck"""	)
       if "layer1_rn" in name:
              __lowerCamelCase   =						name.replace("""layer1_rn"""  ,			"""convs.0"""	)
       if "layer2_rn" in name:
              __lowerCamelCase   =						name.replace("""layer2_rn"""  ,			"""convs.1"""	)
       if "layer3_rn" in name:
              __lowerCamelCase   =						name.replace("""layer3_rn"""  ,			"""convs.2"""	)
       if "layer4_rn" in name:
              __lowerCamelCase   =						name.replace("""layer4_rn"""  ,			"""convs.3"""	)
       if "refinenet" in name:
              __lowerCamelCase   =						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
              __lowerCamelCase   =						name.replace(f'refinenet{layer_idx}'  ,			f'fusion_stage.layers.{abs(layer_idx-4	)}'	)
       if "out_conv" in name:
              __lowerCamelCase   =						name.replace("""out_conv"""  ,			"""projection"""	)
       if "resConfUnit1" in name:
              __lowerCamelCase   =						name.replace("""resConfUnit1"""  ,			"""residual_layer1"""	)
       if "resConfUnit2" in name:
              __lowerCamelCase   =						name.replace("""resConfUnit2"""  ,			"""residual_layer2"""	)
       if "conv1" in name:
              __lowerCamelCase   =						name.replace("""conv1"""  ,			"""convolution1"""	)
       if "conv2" in name:
              __lowerCamelCase   =						name.replace("""conv2"""  ,			"""convolution2"""	)
       # readout blocks
       if "pretrained.act_postprocess1.0.project.0" in name:
              __lowerCamelCase   =						name.replace("""pretrained.act_postprocess1.0.project.0"""  ,			"""neck.reassemble_stage.readout_projects.0.0"""	)
       if "pretrained.act_postprocess2.0.project.0" in name:
              __lowerCamelCase   =						name.replace("""pretrained.act_postprocess2.0.project.0"""  ,			"""neck.reassemble_stage.readout_projects.1.0"""	)
       if "pretrained.act_postprocess3.0.project.0" in name:
              __lowerCamelCase   =						name.replace("""pretrained.act_postprocess3.0.project.0"""  ,			"""neck.reassemble_stage.readout_projects.2.0"""	)
       if "pretrained.act_postprocess4.0.project.0" in name:
              __lowerCamelCase   =						name.replace("""pretrained.act_postprocess4.0.project.0"""  ,			"""neck.reassemble_stage.readout_projects.3.0"""	)

       # resize blocks
       if "pretrained.act_postprocess1.3" in name:
              __lowerCamelCase   =						name.replace("""pretrained.act_postprocess1.3"""  ,			"""neck.reassemble_stage.layers.0.projection"""	)
       if "pretrained.act_postprocess1.4" in name:
              __lowerCamelCase   =						name.replace("""pretrained.act_postprocess1.4"""  ,			"""neck.reassemble_stage.layers.0.resize"""	)
       if "pretrained.act_postprocess2.3" in name:
              __lowerCamelCase   =						name.replace("""pretrained.act_postprocess2.3"""  ,			"""neck.reassemble_stage.layers.1.projection"""	)
       if "pretrained.act_postprocess2.4" in name:
              __lowerCamelCase   =						name.replace("""pretrained.act_postprocess2.4"""  ,			"""neck.reassemble_stage.layers.1.resize"""	)
       if "pretrained.act_postprocess3.3" in name:
              __lowerCamelCase   =						name.replace("""pretrained.act_postprocess3.3"""  ,			"""neck.reassemble_stage.layers.2.projection"""	)
       if "pretrained.act_postprocess4.3" in name:
              __lowerCamelCase   =						name.replace("""pretrained.act_postprocess4.3"""  ,			"""neck.reassemble_stage.layers.3.projection"""	)
       if "pretrained.act_postprocess4.4" in name:
              __lowerCamelCase   =						name.replace("""pretrained.act_postprocess4.4"""  ,			"""neck.reassemble_stage.layers.3.resize"""	)
       if "pretrained" in name:
              __lowerCamelCase   =						name.replace("""pretrained"""  ,			"""dpt"""	)
       if "bn" in name:
              __lowerCamelCase   =						name.replace("""bn"""  ,			"""batch_norm"""	)
       if "head" in name:
              __lowerCamelCase   =						name.replace("""head"""  ,			"""head.head"""	)
       if "encoder.norm" in name:
              __lowerCamelCase   =						name.replace("""encoder.norm"""  ,			"""layernorm"""	)
       if "auxlayer" in name:
              __lowerCamelCase   =						name.replace("""auxlayer"""  ,			"""auxiliary_head.head"""	)
       if "backbone" in name:
              __lowerCamelCase   =						name.replace("""backbone"""  ,			"""backbone.bit.encoder"""	)

       if ".." in name:
              __lowerCamelCase   =						name.replace(""".."""  ,			"""."""	)

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




def 					lowerCamelCase__       (						A__       :					List[str]  ,			A__       :					List[Any]	):





       '''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)
              __lowerCamelCase   =						state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.weight'	)
              __lowerCamelCase   =						state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.bias'	)
              # next, add query, keys and values (in that order) to the state dict
              __lowerCamelCase   =						in_proj_weight[: config.hidden_size, :]
              __lowerCamelCase   =						in_proj_bias[: config.hidden_size]
              __lowerCamelCase   =						in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              __lowerCamelCase   =						in_proj_bias[
                  config.hidden_size : config.hidden_size * 2
              ]
              __lowerCamelCase   =						in_proj_weight[
                  -config.hidden_size :, :
              ]
              __lowerCamelCase   =						in_proj_bias[-config.hidden_size :]




def 					lowerCamelCase__       (						):





       '''simple docstring'''



       __lowerCamelCase   =						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
       __lowerCamelCase   =						Image.open(requests.get(A__  ,			stream=A__	).raw	)
       return im




@torch.no_grad()
def 					lowerCamelCase__       (						A__       :					Any  ,			A__       :					Dict  ,			A__       :					Any  ,			A__       :					Any  ,			A__       :					Union[str, Any]	):





       '''simple docstring'''



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

       # load HuggingFace model
       __lowerCamelCase   =						DPTForSemanticSegmentation(A__	) if """ade""" in checkpoint_url else DPTForDepthEstimation(A__	)
       model.load_state_dict(A__	)
       model.eval()

       # Check outputs on an image
       __lowerCamelCase   =						480 if """ade""" in checkpoint_url else 384
       __lowerCamelCase   =						DPTImageProcessor(size=A__	)

       __lowerCamelCase   =						prepare_img()
       __lowerCamelCase   =						image_processor(A__  ,			return_tensors="""pt"""	)

       # forward pass
       __lowerCamelCase   =						model(**A__	).logits if """ade""" in checkpoint_url else model(**A__	).predicted_depth

       if show_prediction:
              __lowerCamelCase   =						(
                  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__":
       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=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_									=	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_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase_									=	{
    'configuration_vision_encoder_decoder': ['VisionEncoderDecoderConfig', 'VisionEncoderDecoderOnnxConfig']
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase_									=	['VisionEncoderDecoderModel']

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase_									=	['TFVisionEncoderDecoderModel']

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase_									=	['FlaxVisionEncoderDecoderModel']

if TYPE_CHECKING:
       from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
       import sys

       UpperCAmelCase_									=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

