"""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_:					Any     =					logging.get_logger(__name__)







def 					__a   (		A      ):






     '''simple docstring'''



     lowercase__				 =							DPTConfig(embedding_type="hybrid"      )

     if "large" in checkpoint_url:
          lowercase__				 =							10_24
          lowercase__				 =							40_96
          lowercase__				 =							24
          lowercase__				 =							16
          lowercase__				 =							[5, 11, 17, 23]
          lowercase__				 =							[2_56, 5_12, 10_24, 10_24]
          lowercase__				 =							(1, 3_84, 3_84)

     if "nyu" or "midas" in checkpoint_url:
          lowercase__				 =							7_68
          lowercase__				 =							[1, 1, 1, 0.5]
          lowercase__				 =							[2_56, 5_12, 7_68, 7_68]
          lowercase__				 =							1_50
          lowercase__				 =							16
          lowercase__				 =							(1, 3_84, 3_84)
          lowercase__				 =							False
          lowercase__				 =							"project"

     if "ade" in checkpoint_url:
          lowercase__				 =							True
          lowercase__				 =							7_68
          lowercase__				 =							[1, 1, 1, 0.5]
          lowercase__				 =							1_50
          lowercase__				 =							16
          lowercase__				 =							"huggingface/label-files"
          lowercase__				 =							"ade20k-id2label.json"
          lowercase__				 =							json.load(open(cached_download(hf_hub_url(A    ,				A    ,				repo_type="dataset"      )      )    ,				"r"      )      )
          lowercase__				 =							{int(A      ): v for k, v in idalabel.items()}
          lowercase__				 =							idalabel
          lowercase__				 =							{v: k for k, v in idalabel.items()}
          lowercase__				 =							[1, 1_50, 4_80, 4_80]

     return config, expected_shape







def 					__a   (		A      ):






     '''simple docstring'''



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







def 					__a   (		A      ):






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

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

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

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







def 					__a   (		A    ,				A      ):






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







def 					__a   (		):






     '''simple docstring'''



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







@torch.no_grad()
def 					__a   (		A    ,				A    ,				A    ,				A    ,				A      ):






     '''simple docstring'''



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

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

     # Check outputs on an image
     lowercase__				 =							4_80 if "ade" in checkpoint_url else 3_84
     lowercase__				 =							DPTImageProcessor(size=A      )

     lowercase__				 =							prepare_img()
     lowercase__				 =							image_processor(A    ,				return_tensors="pt"      )

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

     if show_prediction:
          lowercase__				 =							(
              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__":
 lowerCAmelCase_:					Tuple     =					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_:					Optional[int]     =					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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase_:					Union[str, Any]     =					{
    "configuration_distilbert": [
        "DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "DistilBertConfig",
        "DistilBertOnnxConfig",
    ],
    "tokenization_distilbert": ["DistilBertTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_:					Union[str, Any]     =					["DistilBertTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_:					Any     =					[
     "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "DistilBertForMaskedLM",
     "DistilBertForMultipleChoice",
     "DistilBertForQuestionAnswering",
     "DistilBertForSequenceClassification",
     "DistilBertForTokenClassification",
     "DistilBertModel",
     "DistilBertPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_:					Tuple     =					[
     "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFDistilBertForMaskedLM",
     "TFDistilBertForMultipleChoice",
     "TFDistilBertForQuestionAnswering",
     "TFDistilBertForSequenceClassification",
     "TFDistilBertForTokenClassification",
     "TFDistilBertMainLayer",
     "TFDistilBertModel",
     "TFDistilBertPreTrainedModel",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_:					Optional[Any]     =					[
     "FlaxDistilBertForMaskedLM",
     "FlaxDistilBertForMultipleChoice",
     "FlaxDistilBertForQuestionAnswering",
     "FlaxDistilBertForSequenceClassification",
     "FlaxDistilBertForTokenClassification",
     "FlaxDistilBertModel",
     "FlaxDistilBertPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_distilbert import (
     DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     DistilBertConfig,
     DistilBertOnnxConfig,
 )
 from .tokenization_distilbert import DistilBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_distilbert_fast import DistilBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_distilbert import (
      DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      DistilBertForMaskedLM,
      DistilBertForMultipleChoice,
      DistilBertForQuestionAnswering,
      DistilBertForSequenceClassification,
      DistilBertForTokenClassification,
      DistilBertModel,
      DistilBertPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_distilbert import (
      TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDistilBertForMaskedLM,
      TFDistilBertForMultipleChoice,
      TFDistilBertForQuestionAnswering,
      TFDistilBertForSequenceClassification,
      TFDistilBertForTokenClassification,
      TFDistilBertMainLayer,
      TFDistilBertModel,
      TFDistilBertPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_distilbert import (
      FlaxDistilBertForMaskedLM,
      FlaxDistilBertForMultipleChoice,
      FlaxDistilBertForQuestionAnswering,
      FlaxDistilBertForSequenceClassification,
      FlaxDistilBertForTokenClassification,
      FlaxDistilBertModel,
      FlaxDistilBertPreTrainedModel,
  )

else:
 import sys

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




