



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()
a__      :				Dict   	=			logging.get_logger(__name__)

def       _lowerCAmelCase   (						A__	):
      lowercase__     =  DPTConfig()

      if "large" in checkpoint_url:
            lowercase__     =  1_024
            lowercase__     =  4_096
            lowercase__     =  24
            lowercase__     =  16
            lowercase__     =  [5, 11, 17, 23]
            lowercase__     =  [256, 512, 1_024, 1_024]
            lowercase__     =  (1, 384, 384)

      if "ade" in checkpoint_url:
            lowercase__     =  True

            lowercase__     =  150
            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, 150, 480, 480]

      return config, expected_shape

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

def       _lowerCAmelCase   (						A__	):
      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'						,  'patch_embeddings'	)
      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:
            lowercase__     =  name.replace('norm1'						,  'layernorm_before'	)
      if "norm2" 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'	)

      return name

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

@torch.no_grad()
def       _lowerCAmelCase   (						A__						,  A__						,  A__						,  A__	):
      lowercase__     =  get_dpt_config(A__	)
      # load original state_dict from URL
      lowercase__     =  torch.hub.load_state_dict_from_url(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__     =  480 if """ade""" in checkpoint_url else 384
      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

      # Assert logits
      lowercase__     =  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:
            lowercase__     =  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(A__	)
      assert (
          torch.allclose(outputs[0, 0, :3, :3]						,  A__						,  atol=1E-4	)
          if "ade" in checkpoint_url
          else torch.allclose(outputs[0, :3, :3]						,  A__	)
      )

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


if __name__ == "__main__":
       a__      :				Optional[int]   	=			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.",
       )

       a__      :				int   	=			parser.parse_args()
       convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)





from __future__ import annotations

def   _lowerCAmelCase   (						A__						,  A__	):
      if b == 0:
            return (1, 0)
      ((lowercase__),	(lowercase__))     =  extended_euclid(A__						,  a % b	)
      lowercase__     =  a // b
      return (y, x - k * y)

def   _lowerCAmelCase   (						A__						,  A__						,  A__						,  A__	):
      ((lowercase__),	(lowercase__))     =  extended_euclid(A__						,  A__	)
      lowercase__     =  na * na
      lowercase__     =  ra * x * na + ra * y * na
      return (n % m + m) % m

def   _lowerCAmelCase   (						A__						,  A__	):
      ((lowercase__),	(lowercase__))     =  extended_euclid(A__						,  A__	)
      if b < 0:
            lowercase__     =  (b % n + n) % n
      return b

def   _lowerCAmelCase   (						A__						,  A__						,  A__						,  A__	):
      lowercase__,	lowercase__     =  invert_modulo(A__						,  A__	), invert_modulo(A__						,  A__	)
      lowercase__     =  na * na
      lowercase__     =  ra * x * na + ra * y * na
      return (n % m + m) % m


if __name__ == "__main__":
       from doctest import testmod

       testmod(name="chinese_remainder_theorem", verbose=True)
       testmod(name="chinese_remainder_theorem2", verbose=True)
       testmod(name="invert_modulo", verbose=True)
       testmod(name="extended_euclid", verbose=True)
