





"""simple docstring"""


def        lowercase__     (       lowercase_ = 50      )       ->		int:




      """simple docstring"""

      _UpperCamelCase   :						Optional[int]    =							[1] * (length + 1)

      for row_length in range(length + 1      ):
            for tile_length in range(2		,5      ):
                  for tile_start in range(row_length - tile_length + 1      ):
                        ways_number[row_length] += ways_number[
                            row_length - tile_start - tile_length
                        ]

      return ways_number[length]


if __name__ == "__main__":
  print(f"""{solution() = }""")







"""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__     (       lowercase_      )       ->		int:




      """simple docstring"""

      _UpperCamelCase   :						Optional[Any]    =							DPTConfig(embedding_type="hybrid"      )

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

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

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

      return config, expected_shape




def        lowercase__     (       lowercase_      )       ->		List[str]:




      """simple docstring"""

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




def        lowercase__     (       lowercase_      )       ->		int:




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

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

      if ".." in name:
            _UpperCamelCase   :						List[str]    =							name.replace(".."		,"."      )

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




def        lowercase__     (       lowercase_		,lowercase_      )       ->		Dict:




      """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)
            _UpperCamelCase   :						Any    =							state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight'''      )
            _UpperCamelCase   :						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
            _UpperCamelCase   :						Tuple    =							in_proj_weight[: config.hidden_size, :]
            _UpperCamelCase   :						Dict    =							in_proj_bias[: config.hidden_size]
            _UpperCamelCase   :						Optional[int]    =							in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            _UpperCamelCase   :						Union[str, Any]    =							in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            _UpperCamelCase   :						Dict    =							in_proj_weight[
                -config.hidden_size :, :
            ]
            _UpperCamelCase   :						List[Any]    =							in_proj_bias[-config.hidden_size :]




def        lowercase__     (       )       ->		List[str]:




      """simple docstring"""

      _UpperCamelCase   :						Any    =							"http://images.cocodataset.org/val2017/000000039769.jpg"
      _UpperCamelCase   :						Dict    =							Image.open(requests.get(lowercase_		,stream=lowercase_      ).raw      )
      return im




@torch.no_grad()
def        lowercase__     (       lowercase_		,lowercase_		,lowercase_		,lowercase_		,lowercase_      )       ->		Union[str, Any]:




      """simple docstring"""

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

      # load HuggingFace model
      _UpperCamelCase   :						Dict    =							DPTForSemanticSegmentation(lowercase_      ) if "ade" in checkpoint_url else DPTForDepthEstimation(lowercase_      )
      model.load_state_dict(lowercase_      )
      model.eval()

      # Check outputs on an image
      _UpperCamelCase   :						List[Any]    =							480 if "ade" in checkpoint_url else 384
      _UpperCamelCase   :						List[str]    =							DPTImageProcessor(size=lowercase_      )

      _UpperCamelCase   :						int    =							prepare_img()
      _UpperCamelCase   :						List[str]    =							image_processor(lowercase_		,return_tensors="pt"      )

      # forward pass
      _UpperCamelCase   :						Any    =							model(**lowercase_      ).logits if "ade" in checkpoint_url else model(**lowercase_      ).predicted_depth

      if show_prediction:
            _UpperCamelCase   :						Optional[int]    =							(
                torch.nn.functional.interpolate(
                    outputs.unsqueeze(1      )		,size=(image.size[1], image.size[0])		,mode="bicubic"		,align_corners=lowercase_		,)
                .squeeze()
                .cpu()
                .numpy()
            )

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

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

      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
  )
