

"""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()
__lowerCamelCase										=		logging.get_logger(__name__)





def 	lowercase      (   __UpperCamelCase						) ->      Tuple:
 __magic_name__     =      DPTConfig(embedding_type='''hybrid'''						)

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

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

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

 return config, expected_shape





def 	lowercase      (   __UpperCamelCase						) ->      str:
 __magic_name__     =      ['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
 for k in ignore_keys:
  state_dict.pop(__UpperCamelCase      ,   __UpperCamelCase						)





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

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

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

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





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      int:
 for i in range(config.num_hidden_layers						):
  # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
  __magic_name__     =      state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.weight'''						)
  __magic_name__     =      state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.bias'''						)
  # next, add query, keys and values (in that order) to the state dict
  __magic_name__     =      in_proj_weight[: config.hidden_size, :]
  __magic_name__     =      in_proj_bias[: config.hidden_size]
  __magic_name__     =      in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  __magic_name__     =      in_proj_bias[
      config.hidden_size : config.hidden_size * 2
  ]
  __magic_name__     =      in_proj_weight[
      -config.hidden_size :, :
  ]
  __magic_name__     =      in_proj_bias[-config.hidden_size :]





def 	lowercase      (   ) ->      Any:
 __magic_name__     =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
 __magic_name__     =      Image.open(requests.get(__UpperCamelCase      ,   stream=__UpperCamelCase						).raw						)
 return im





@torch.no_grad()
def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase						) ->      Union[str, Any]:
 __magic_name__   ,						__magic_name__     =      get_dpt_config(__UpperCamelCase						)
 # load original state_dict from URL
 # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
 __magic_name__     =      torch.load(__UpperCamelCase      ,   map_location='''cpu'''						)
 # remove certain keys
 remove_ignore_keys_(__UpperCamelCase						)
 # rename keys
 for key in state_dict.copy().keys():
  __magic_name__     =      state_dict.pop(__UpperCamelCase						)
  __magic_name__     =      val
 # read in qkv matrices
 read_in_q_k_v(__UpperCamelCase      ,   __UpperCamelCase						)

 # load HuggingFace model
 __magic_name__     =      DPTForSemanticSegmentation(__UpperCamelCase						) if '''ade''' in checkpoint_url else DPTForDepthEstimation(__UpperCamelCase						)
 model.load_state_dict(__UpperCamelCase						)
 model.eval()

 # Check outputs on an image
 __magic_name__     =      480 if '''ade''' in checkpoint_url else 384
 __magic_name__     =      DPTImageProcessor(size=__UpperCamelCase						)

 __magic_name__     =      prepare_img()
 __magic_name__     =      image_processor(__UpperCamelCase      ,   return_tensors='''pt'''						)

 # forward pass
 __magic_name__     =      model(**__UpperCamelCase						).logits if '''ade''' in checkpoint_url else model(**__UpperCamelCase						).predicted_depth

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

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

 if pytorch_dump_folder_path is not None:
  Path(__UpperCamelCase						).mkdir(exist_ok=__UpperCamelCase						)
  print(f'''Saving model to {pytorch_dump_folder_path}'''						)
  model.save_pretrained(__UpperCamelCase						)
  print(f'''Saving image processor to {pytorch_dump_folder_path}'''						)
  image_processor.save_pretrained(__UpperCamelCase						)

 if push_to_hub:
  model.push_to_hub('''ybelkada/dpt-hybrid-midas'''						)
  image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas'''						)


if __name__ == "__main__":
 __lowerCamelCase										=		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",
 )

 __lowerCamelCase										=		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"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__lowerCamelCase										=		{
    "configuration_m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config", "M2M100OnnxConfig"],
    "tokenization_m2m_100": ["M2M100Tokenizer"],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowerCamelCase										=		[
     "M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST",
     "M2M100ForConditionalGeneration",
     "M2M100Model",
     "M2M100PreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
 from .tokenization_mam_aaa import MaMaaaTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mam_aaa import (
      M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
      MaMaaaForConditionalGeneration,
      MaMaaaModel,
      MaMaaaPreTrainedModel,
  )


else:
 import sys

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

