






"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


UpperCAmelCase_     :				Tuple         =				{
    """configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TrOCRConfig"""],
    """processing_trocr""": ["""TrOCRProcessor"""],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_     :				Optional[int]         =				[
      """TROCR_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TrOCRForCausalLM""",
      """TrOCRPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
  from .processing_trocr import TrOCRProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
  import sys

  UpperCAmelCase_     :				Union[str, Any]         =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)










"""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()
UpperCAmelCase_     :				List[Any]         =				logging.get_logger(__name__)



def       _A    (__a							)				-> Union[str, Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	DPTConfig(embedding_type='''hybrid'''							)

    if "large" in checkpoint_url:
        SCREAMING_SNAKE_CASE_   :						int							=	10_24
        SCREAMING_SNAKE_CASE_   :						Dict							=	40_96
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	24
        SCREAMING_SNAKE_CASE_   :						Any							=	16
        SCREAMING_SNAKE_CASE_   :						int							=	[5, 11, 17, 23]
        SCREAMING_SNAKE_CASE_   :						List[str]							=	[2_56, 5_12, 10_24, 10_24]
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	(1, 3_84, 3_84)

    if "nyu" or "midas" in checkpoint_url:
        SCREAMING_SNAKE_CASE_   :						List[str]							=	7_68
        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	[1, 1, 1, 0.5]
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	[2_56, 5_12, 7_68, 7_68]
        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	1_50
        SCREAMING_SNAKE_CASE_   :						str							=	16
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	(1, 3_84, 3_84)
        SCREAMING_SNAKE_CASE_   :						int							=	False
        SCREAMING_SNAKE_CASE_   :						Tuple							=	'''project'''

    if "ade" in checkpoint_url:
        SCREAMING_SNAKE_CASE_   :						str							=	True
        SCREAMING_SNAKE_CASE_   :						Dict							=	7_68
        SCREAMING_SNAKE_CASE_   :						Tuple							=	[1, 1, 1, 0.5]
        SCREAMING_SNAKE_CASE_   :						List[str]							=	1_50
        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	16
        SCREAMING_SNAKE_CASE_   :						Tuple							=	'''huggingface/label-files'''
        SCREAMING_SNAKE_CASE_   :						List[Any]							=	'''ade20k-id2label.json'''
        SCREAMING_SNAKE_CASE_   :						List[str]							=	json.load(open(cached_download(hf_hub_url(__a					,       __a					,       repo_type='''dataset'''							)							)					,       '''r'''							)							)
        SCREAMING_SNAKE_CASE_   :						Dict							=	{int(__a							): v for k, v in idalabel.items()}
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	idalabel
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	{v: k for k, v in idalabel.items()}
        SCREAMING_SNAKE_CASE_   :						Any							=	[1, 1_50, 4_80, 4_80]

    return config, expected_shape



def       _A    (__a							)				-> List[str]:
    """simple docstring"""






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



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

    if ".." in name:
        SCREAMING_SNAKE_CASE_   :						List[str]							=	name.replace('''..'''					,       '''.'''							)

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



def       _A    (__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)
        SCREAMING_SNAKE_CASE_   :						Dict							=	state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.weight'							)
        SCREAMING_SNAKE_CASE_   :						Optional[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
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	in_proj_weight[: config.hidden_size, :]
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	in_proj_bias[: config.hidden_size]
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        SCREAMING_SNAKE_CASE_   :						int							=	in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        SCREAMING_SNAKE_CASE_   :						List[str]							=	in_proj_weight[
            -config.hidden_size :, :
        ]
        SCREAMING_SNAKE_CASE_   :						Any							=	in_proj_bias[-config.hidden_size :]



def       _A    ()				-> Dict:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Dict							=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	Image.open(requests.get(__a					,       stream=__a							).raw							)
    return im



@torch.no_grad()
def       _A    (__a					,       __a					,       __a					,       __a					,       __a							)				-> List[str]:
    """simple docstring"""






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

    # load HuggingFace model
    SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	DPTForSemanticSegmentation(__a							) if '''ade''' in checkpoint_url else DPTForDepthEstimation(__a							)
    model.load_state_dict(__a							)
    model.eval()

    # Check outputs on an image
    SCREAMING_SNAKE_CASE_   :						Dict							=	4_80 if '''ade''' in checkpoint_url else 3_84
    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	DPTImageProcessor(size=__a							)

    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	prepare_img()
    SCREAMING_SNAKE_CASE_   :						Tuple							=	image_processor(__a					,       return_tensors='''pt'''							)

    # forward pass
    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	model(**__a							).logits if '''ade''' in checkpoint_url else model(**__a							).predicted_depth

    if show_prediction:
        SCREAMING_SNAKE_CASE_   :						List[Any]							=	(
            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()) * 2_55							).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_     :				List[str]         =				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_     :				List[Any]         =				parser.parse_args()
  convert_dpt_checkpoint(
      args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
  )


