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       A__   (						snake_case_			:		Union[str, Any]   ):
 SCREAMING_SNAKE_CASE__: Tuple=   DPTConfig()

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

 if "ade" in checkpoint_url:
  SCREAMING_SNAKE_CASE__: List[Any]=   True

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

 return config, expected_shape







def       A__   (						snake_case_			:		Union[str, Any]   ):
 SCREAMING_SNAKE_CASE__: str=   ['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
 for k in ignore_keys:
  state_dict.pop(snake_case_							,					snake_case_   )







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

 return name







def       A__   (						snake_case_			:		Optional[int]							,					snake_case_			:		List[str]   ):
 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__: Optional[int]=   state_dict.pop(F'dpt.encoder.layer.{i}.attn.qkv.weight'   )
  SCREAMING_SNAKE_CASE__: Dict=   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__: Union[str, Any]=   in_proj_weight[: config.hidden_size, :]
  SCREAMING_SNAKE_CASE__: Optional[int]=   in_proj_bias[: config.hidden_size]
  SCREAMING_SNAKE_CASE__: List[str]=   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[Any]=   in_proj_weight[
      -config.hidden_size :, :
  ]
  SCREAMING_SNAKE_CASE__: Union[str, Any]=   in_proj_bias[-config.hidden_size :]







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







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

 # load HuggingFace model
 SCREAMING_SNAKE_CASE__: List[str]=   DPTForSemanticSegmentation(snake_case_   ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(snake_case_   )
 model.load_state_dict(snake_case_   )
 model.eval()

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

 SCREAMING_SNAKE_CASE__: Optional[int]=   prepare_img()
 SCREAMING_SNAKE_CASE__: Dict=   image_processor(snake_case_							,					return_tensors='''pt'''   )

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

 # Assert logits
 SCREAMING_SNAKE_CASE__: List[str]=   torch.tensor([[6.31_99, 6.36_29, 6.41_48], [6.38_50, 6.36_15, 6.41_66], [6.35_19, 6.31_76, 6.35_75]]   )
 if "ade" in checkpoint_url:
  SCREAMING_SNAKE_CASE__: int=   torch.tensor([[4.04_80, 4.24_20, 4.43_60], [4.31_24, 4.56_93, 4.82_61], [4.57_68, 4.89_65, 5.21_63]]   )
 assert outputs.shape == torch.Size(snake_case_   )
 assert (
     torch.allclose(outputs[0, 0, :3, :3]							,					snake_case_							,					atol=1E-4   )
     if "ade" in checkpoint_url
     else torch.allclose(outputs[0, :3, :3]							,					snake_case_   )
 )

 Path(snake_case_   ).mkdir(exist_ok=snake_case_   )
 print(F'Saving model to {pytorch_dump_folder_path}'   )
 model.save_pretrained(snake_case_   )
 print(F'Saving image processor to {pytorch_dump_folder_path}'   )
 image_processor.save_pretrained(snake_case_   )

 if push_to_hub:
  print('''Pushing model to hub...'''   )
  model.push_to_hub(
      repo_path_or_name=Path(snake_case_							,					snake_case_   )							,					organization='''nielsr'''							,					commit_message='''Add model'''							,					use_temp_dir=snake_case_							,					)
  image_processor.push_to_hub(
      repo_path_or_name=Path(snake_case_							,					snake_case_   )							,					organization='''nielsr'''							,					commit_message='''Add image processor'''							,					use_temp_dir=snake_case_							,					)


if __name__ == "__main__":
      lowercase_  : 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=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.',
      )

      lowercase_  : List[Any]								=					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_tf_available, is_torch_available


__magic_name__       :					int						=			{
    """configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
    """configuration_data2vec_text""": [
        """DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Data2VecTextConfig""",
        """Data2VecTextOnnxConfig""",
    ],
    """configuration_data2vec_vision""": [
        """DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Data2VecVisionConfig""",
        """Data2VecVisionOnnxConfig""",
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__       :					List[Any]						=			[
		    """DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """Data2VecAudioForAudioFrameClassification""",
		    """Data2VecAudioForCTC""",
		    """Data2VecAudioForSequenceClassification""",
		    """Data2VecAudioForXVector""",
		    """Data2VecAudioModel""",
		    """Data2VecAudioPreTrainedModel""",
		]
		__magic_name__       :					Optional[int]						=			[
		    """DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """Data2VecTextForCausalLM""",
		    """Data2VecTextForMaskedLM""",
		    """Data2VecTextForMultipleChoice""",
		    """Data2VecTextForQuestionAnswering""",
		    """Data2VecTextForSequenceClassification""",
		    """Data2VecTextForTokenClassification""",
		    """Data2VecTextModel""",
		    """Data2VecTextPreTrainedModel""",
		]
		__magic_name__       :					str						=			[
		    """DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """Data2VecVisionForImageClassification""",
		    """Data2VecVisionForMaskedImageModeling""",
		    """Data2VecVisionForSemanticSegmentation""",
		    """Data2VecVisionModel""",
		    """Data2VecVisionPreTrainedModel""",
		]

if is_tf_available():
		__magic_name__       :					Optional[Any]						=			[
		    """TFData2VecVisionForImageClassification""",
		    """TFData2VecVisionForSemanticSegmentation""",
		    """TFData2VecVisionModel""",
		    """TFData2VecVisionPreTrainedModel""",
		]

if TYPE_CHECKING:
		from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
		from .configuration_dataavec_text import (
		    DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    DataaVecTextConfig,
		    DataaVecTextOnnxConfig,
		)
		from .configuration_dataavec_vision import (
		    DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    DataaVecVisionConfig,
		    DataaVecVisionOnnxConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_dataavec_audio import (
				    DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DataaVecAudioForAudioFrameClassification,
				    DataaVecAudioForCTC,
				    DataaVecAudioForSequenceClassification,
				    DataaVecAudioForXVector,
				    DataaVecAudioModel,
				    DataaVecAudioPreTrainedModel,
				)
				from .modeling_dataavec_text import (
				    DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DataaVecTextForCausalLM,
				    DataaVecTextForMaskedLM,
				    DataaVecTextForMultipleChoice,
				    DataaVecTextForQuestionAnswering,
				    DataaVecTextForSequenceClassification,
				    DataaVecTextForTokenClassification,
				    DataaVecTextModel,
				    DataaVecTextPreTrainedModel,
				)
				from .modeling_dataavec_vision import (
				    DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DataaVecVisionForImageClassification,
				    DataaVecVisionForMaskedImageModeling,
				    DataaVecVisionForSemanticSegmentation,
				    DataaVecVisionModel,
				    DataaVecVisionPreTrainedModel,
				)
		if is_tf_available():
				from .modeling_tf_dataavec_vision import (
				    TFDataaVecVisionForImageClassification,
				    TFDataaVecVisionForSemanticSegmentation,
				    TFDataaVecVisionModel,
				    TFDataaVecVisionPreTrainedModel,
				)

else:
		import sys

		__magic_name__       :					Optional[Any]						=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



