
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 			UpperCamelCase					(    __lowerCamelCase     : Tuple    ):
       snake_case       :   List[str]							=      DPTConfig(embedding_type="hybrid"    )

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

       if "nyu" or "midas" in checkpoint_url:
              snake_case       :   List[str]							=      768
              snake_case       :   Tuple							=      [1, 1, 1, 0.5]
              snake_case       :   Any							=      [256, 512, 768, 768]
              snake_case       :   Dict							=      150
              snake_case       :   Optional[int]							=      16
              snake_case       :   str							=      (1, 384, 384)
              snake_case       :   List[Any]							=      False
              snake_case       :   List[str]							=      "project"

       if "ade" in checkpoint_url:
              snake_case       :   Any							=      True
              snake_case       :   Dict							=      768
              snake_case       :   Dict							=      [1, 1, 1, 0.5]
              snake_case       :   int							=      150
              snake_case       :   Union[str, Any]							=      16
              snake_case       :   int							=      "huggingface/label-files"
              snake_case       :   Optional[Any]							=      "ade20k-id2label.json"
              snake_case       :   Optional[int]							=      json.load(open(cached_download(hf_hub_url(__lowerCamelCase				,					__lowerCamelCase				,					repo_type="dataset"    )    )				,					"r"    )    )
              snake_case       :   Optional[Any]							=      {int(__lowerCamelCase    ): v for k, v in idalabel.items()}
              snake_case       :   Tuple							=      idalabel
              snake_case       :   Dict							=      {v: k for k, v in idalabel.items()}
              snake_case       :   List[str]							=      [1, 150, 480, 480]

       return config, expected_shape


def 			UpperCamelCase					(    __lowerCamelCase     : str    ):
       snake_case       :   Optional[Any]							=      ["pretrained.model.head.weight", "pretrained.model.head.bias"]
       for k in ignore_keys:
              state_dict.pop(__lowerCamelCase				,					__lowerCamelCase    )


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

       # resize blocks
       if "pretrained.act_postprocess1.3" in name:
              snake_case       :   List[Any]							=      name.replace("pretrained.act_postprocess1.3"				,					"neck.reassemble_stage.layers.0.projection"    )
       if "pretrained.act_postprocess1.4" in name:
              snake_case       :   List[Any]							=      name.replace("pretrained.act_postprocess1.4"				,					"neck.reassemble_stage.layers.0.resize"    )
       if "pretrained.act_postprocess2.3" in name:
              snake_case       :   List[str]							=      name.replace("pretrained.act_postprocess2.3"				,					"neck.reassemble_stage.layers.1.projection"    )
       if "pretrained.act_postprocess2.4" in name:
              snake_case       :   Any							=      name.replace("pretrained.act_postprocess2.4"				,					"neck.reassemble_stage.layers.1.resize"    )
       if "pretrained.act_postprocess3.3" in name:
              snake_case       :   Optional[int]							=      name.replace("pretrained.act_postprocess3.3"				,					"neck.reassemble_stage.layers.2.projection"    )
       if "pretrained.act_postprocess4.3" in name:
              snake_case       :   Dict							=      name.replace("pretrained.act_postprocess4.3"				,					"neck.reassemble_stage.layers.3.projection"    )
       if "pretrained.act_postprocess4.4" in name:
              snake_case       :   Optional[int]							=      name.replace("pretrained.act_postprocess4.4"				,					"neck.reassemble_stage.layers.3.resize"    )
       if "pretrained" in name:
              snake_case       :   List[str]							=      name.replace("pretrained"				,					"dpt"    )
       if "bn" in name:
              snake_case       :   Any							=      name.replace("bn"				,					"batch_norm"    )
       if "head" in name:
              snake_case       :   str							=      name.replace("head"				,					"head.head"    )
       if "encoder.norm" in name:
              snake_case       :   Dict							=      name.replace("encoder.norm"				,					"layernorm"    )
       if "auxlayer" in name:
              snake_case       :   Dict							=      name.replace("auxlayer"				,					"auxiliary_head.head"    )
       if "backbone" in name:
              snake_case       :   Dict							=      name.replace("backbone"				,					"backbone.bit.encoder"    )

       if ".." in name:
              snake_case       :   int							=      name.replace(".."				,					"."    )

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


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


def 			UpperCamelCase					(    ):
       snake_case       :   Any							=      "http://images.cocodataset.org/val2017/000000039769.jpg"
       snake_case       :   Union[str, Any]							=      Image.open(requests.get(__lowerCamelCase				,					stream=__lowerCamelCase    ).raw    )
       return im


@torch.no_grad()
def 			UpperCamelCase					(    __lowerCamelCase     : str				,					__lowerCamelCase     : Union[str, Any]				,					__lowerCamelCase     : int				,					__lowerCamelCase     : int				,					__lowerCamelCase     : List[str]    ):
       snake_case				,					snake_case       :   Any							=      get_dpt_config(__lowerCamelCase    )
       # load original state_dict from URL
       # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
       snake_case       :   str							=      torch.load(__lowerCamelCase				,					map_location="cpu"    )
       # remove certain keys
       remove_ignore_keys_(__lowerCamelCase    )
       # rename keys
       for key in state_dict.copy().keys():
              snake_case       :   Optional[Any]							=      state_dict.pop(__lowerCamelCase    )
              snake_case       :   Optional[int]							=      val
       # read in qkv matrices
       read_in_q_k_v(__lowerCamelCase				,					__lowerCamelCase    )

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

       # Check outputs on an image
       snake_case       :   Tuple							=      480 if "ade" in checkpoint_url else 384
       snake_case       :   Tuple							=      DPTImageProcessor(size=__lowerCamelCase    )

       snake_case       :   int							=      prepare_img()
       snake_case       :   Tuple							=      image_processor(__lowerCamelCase				,					return_tensors="pt"    )

       # forward pass
       snake_case       :   Tuple							=      model(**__lowerCamelCase    ).logits if "ade" in checkpoint_url else model(**__lowerCamelCase    ).predicted_depth

       if show_prediction:
              snake_case       :   List[Any]							=      (
                  torch.nn.functional.interpolate(
                      outputs.unsqueeze(1    )				,					size=(image.size[1], image.size[0])				,					mode="bicubic"				,					align_corners=__lowerCamelCase				,					)
                  .squeeze()
                  .cpu()
                  .numpy()
              )

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

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

       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
   )













from maths.prime_check import is_prime
def 					snake_case_						(   lowerCAmelCase_				:  int					):
							if not isinstance(lowerCAmelCase_				,				lowerCAmelCase_					):
														__lowercase					:							Dict			=   F"Input value of [number={number}] must be an integer"
														raise TypeError(lowerCAmelCase_					)
							if is_prime(lowerCAmelCase_					) and is_prime(number + 2					):
														return number + 2
							else:
														return -1


if __name__ == "__main__":
	import doctest

	doctest.testmod()