





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 	lowerCAmelCase_					(			__a						)	->						int:







  """simple docstring"""





  lowerCamelCase__: Any         =DPTConfig()

  if "large" in checkpoint_url:
    lowerCamelCase__: Optional[int]         =1024
    lowerCamelCase__: Any         =4096
    lowerCamelCase__: Optional[int]         =24
    lowerCamelCase__: Tuple         =16
    lowerCamelCase__: Dict         =[5, 11, 17, 23]
    lowerCamelCase__: List[Any]         =[256, 512, 1024, 1024]
    lowerCamelCase__: str         =(1, 384, 384)

  if "ade" in checkpoint_url:
    lowerCamelCase__: int         =True

    lowerCamelCase__: str         =150
    lowerCamelCase__: str         ="huggingface/label-files"
    lowerCamelCase__: List[str]         ="ade20k-id2label.json"
    lowerCamelCase__: Optional[int]         =json.load(open(cached_download(hf_hub_url(__a      ,       __a      ,       repo_type="dataset"						)						)      ,       "r"						)						)
    lowerCamelCase__: str         ={int(__a						): v for k, v in idalabel.items()}
    lowerCamelCase__: Optional[Any]         =idalabel
    lowerCamelCase__: Tuple         ={v: k for k, v in idalabel.items()}
    lowerCamelCase__: int         =[1, 150, 480, 480]

  return config, expected_shape





def 	lowerCAmelCase_					(			__a						)	->						List[Any]:







  """simple docstring"""





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





def 	lowerCAmelCase_					(			__a						)	->						Dict:







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

  return name





def 	lowerCAmelCase_					(			__a      ,       __a						)	->						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)
    lowerCamelCase__: List[Any]         =state_dict.pop(F"""dpt.encoder.layer.{i}.attn.qkv.weight"""						)
    lowerCamelCase__: Optional[Any]         =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__: Dict         =in_proj_weight[: config.hidden_size, :]
    lowerCamelCase__: Union[str, Any]         =in_proj_bias[: config.hidden_size]
    lowerCamelCase__: Any         =in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    lowerCamelCase__: List[Any]         =in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    lowerCamelCase__: List[str]         =in_proj_weight[
        -config.hidden_size :, :
    ]
    lowerCamelCase__: Union[str, Any]         =in_proj_bias[-config.hidden_size :]





def 	lowerCAmelCase_					(			)	->						Optional[int]:







  """simple docstring"""





  lowerCamelCase__: str         ="http://images.cocodataset.org/val2017/000000039769.jpg"
  lowerCamelCase__: Tuple         =Image.open(requests.get(__a      ,       stream=__a						).raw						)
  return im





@torch.no_grad()
def 	lowerCAmelCase_					(			__a      ,       __a      ,       __a      ,       __a						)	->						List[Any]:







  """simple docstring"""





  lowerCamelCase__    ,   lowerCamelCase__: Optional[Any]         =get_dpt_config(__a						)
  # load original state_dict from URL
  lowerCamelCase__: int         =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():
    lowerCamelCase__: Optional[Any]         =state_dict.pop(__a						)
    lowerCamelCase__: Optional[Any]         =val
  # read in qkv matrices
  read_in_q_k_v(__a      ,       __a						)

  # load HuggingFace model
  lowerCamelCase__: Any         =DPTForSemanticSegmentation(__a						) if "ade" in checkpoint_url else DPTForDepthEstimation(__a						)
  model.load_state_dict(__a						)
  model.eval()

  # Check outputs on an image
  lowerCamelCase__: int         =480 if "ade" in checkpoint_url else 384
  lowerCamelCase__: Union[str, Any]         =DPTImageProcessor(size=__a						)

  lowerCamelCase__: Optional[Any]         =prepare_img()
  lowerCamelCase__: Optional[Any]         =image_processor(__a      ,       return_tensors="pt"						)

  # forward pass
  lowerCamelCase__: Dict         =model(**__a						).logits if "ade" in checkpoint_url else model(**__a						).predicted_depth

  # Assert logits
  lowerCamelCase__: List[str]         =torch.tensor([[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]]						)
  if "ade" in checkpoint_url:
    lowerCamelCase__: Dict         =torch.tensor([[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]]						)
  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)






"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a_							=    {"""configuration_opt""": ["""OPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OPTConfig"""]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    [
	    """OPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """OPTForCausalLM""",
	    """OPTModel""",
	    """OPTPreTrainedModel""",
	    """OPTForSequenceClassification""",
	    """OPTForQuestionAnswering""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    ["""TFOPTForCausalLM""", """TFOPTModel""", """TFOPTPreTrainedModel"""]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    [
	    """FlaxOPTForCausalLM""",
	    """FlaxOPTModel""",
	    """FlaxOPTPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_opt import (
		    OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    OPTForCausalLM,
		    OPTForQuestionAnswering,
		    OPTForSequenceClassification,
		    OPTModel,
		    OPTPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
	import sys

	a_							=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





