






"""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()
_UpperCamelCase    :   List[Any]   			=						logging.get_logger(__name__)




def       snake_case  (A_				:int				):







  '''simple docstring'''

  a					:      Optional[Any]    =       DPTConfig(embedding_type='hybrid'				)

  if "large" in checkpoint_url:
    a					:      Optional[int]    =       1_0_2_4
    a					:      List[Any]    =       4_0_9_6
    a					:      Optional[Any]    =       2_4
    a					:      Tuple    =       1_6
    a					:      List[Any]    =       [5, 1_1, 1_7, 2_3]
    a					:      str    =       [2_5_6, 5_1_2, 1_0_2_4, 1_0_2_4]
    a					:      List[Any]    =       (1, 3_8_4, 3_8_4)

  if "nyu" or "midas" in checkpoint_url:
    a					:      List[Any]    =       7_6_8
    a					:      str    =       [1, 1, 1, 0.5]
    a					:      int    =       [2_5_6, 5_1_2, 7_6_8, 7_6_8]
    a					:      int    =       1_5_0
    a					:      List[str]    =       1_6
    a					:      Dict    =       (1, 3_8_4, 3_8_4)
    a					:      Union[str, Any]    =       False
    a					:      Optional[Any]    =       'project'

  if "ade" in checkpoint_url:
    a					:      Tuple    =       True
    a					:      Any    =       7_6_8
    a					:      Any    =       [1, 1, 1, 0.5]
    a					:      Dict    =       1_5_0
    a					:      Optional[Any]    =       1_6
    a					:      int    =       'huggingface/label-files'
    a					:      Union[str, Any]    =       'ade20k-id2label.json'
    a					:      Union[str, Any]    =       json.load(open(cached_download(hf_hub_url(A_     ,      A_     ,      repo_type='dataset'				)				)     ,      'r'				)				)
    a					:      List[str]    =       {int(A_				): v for k, v in idalabel.items()}
    a					:      Union[str, Any]    =       idalabel
    a					:      Any    =       {v: k for k, v in idalabel.items()}
    a					:      Union[str, Any]    =       [1, 1_5_0, 4_8_0, 4_8_0]

  return config, expected_shape




def       snake_case  (A_				:Optional[Any]				):







  '''simple docstring'''

  a					:      str    =       ['pretrained.model.head.weight', 'pretrained.model.head.bias']
  for k in ignore_keys:
    state_dict.pop(A_     ,      A_				)




def       snake_case  (A_				:Union[str, Any]				):







  '''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
  ):
    a					:      str    =       name.replace('pretrained.model'     ,      'dpt.encoder'				)
  if "pretrained.model" in name:
    a					:      Any    =       name.replace('pretrained.model'     ,      'dpt.embeddings'				)
  if "patch_embed" in name:
    a					:      Optional[Any]    =       name.replace('patch_embed'     ,      ''				)
  if "pos_embed" in name:
    a					:      Optional[int]    =       name.replace('pos_embed'     ,      'position_embeddings'				)
  if "attn.proj" in name:
    a					:      Dict    =       name.replace('attn.proj'     ,      'attention.output.dense'				)
  if "proj" in name and "project" not in name:
    a					:      Any    =       name.replace('proj'     ,      'projection'				)
  if "blocks" in name:
    a					:      Union[str, Any]    =       name.replace('blocks'     ,      'layer'				)
  if "mlp.fc1" in name:
    a					:      Union[str, Any]    =       name.replace('mlp.fc1'     ,      'intermediate.dense'				)
  if "mlp.fc2" in name:
    a					:      Optional[Any]    =       name.replace('mlp.fc2'     ,      'output.dense'				)
  if "norm1" in name and "backbone" not in name:
    a					:      List[Any]    =       name.replace('norm1'     ,      'layernorm_before'				)
  if "norm2" in name and "backbone" not in name:
    a					:      List[Any]    =       name.replace('norm2'     ,      'layernorm_after'				)
  if "scratch.output_conv" in name:
    a					:      int    =       name.replace('scratch.output_conv'     ,      'head'				)
  if "scratch" in name:
    a					:      Union[str, Any]    =       name.replace('scratch'     ,      'neck'				)
  if "layer1_rn" in name:
    a					:      Any    =       name.replace('layer1_rn'     ,      'convs.0'				)
  if "layer2_rn" in name:
    a					:      Optional[int]    =       name.replace('layer2_rn'     ,      'convs.1'				)
  if "layer3_rn" in name:
    a					:      Any    =       name.replace('layer3_rn'     ,      'convs.2'				)
  if "layer4_rn" in name:
    a					:      Optional[int]    =       name.replace('layer4_rn'     ,      'convs.3'				)
  if "refinenet" in name:
    a					:      Union[str, Any]    =       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
    a					:      str    =       name.replace(f'''refinenet{layer_idx}'''     ,      f'''fusion_stage.layers.{abs(layer_idx-4				)}'''				)
  if "out_conv" in name:
    a					:      Union[str, Any]    =       name.replace('out_conv'     ,      'projection'				)
  if "resConfUnit1" in name:
    a					:      str    =       name.replace('resConfUnit1'     ,      'residual_layer1'				)
  if "resConfUnit2" in name:
    a					:      str    =       name.replace('resConfUnit2'     ,      'residual_layer2'				)
  if "conv1" in name:
    a					:      Tuple    =       name.replace('conv1'     ,      'convolution1'				)
  if "conv2" in name:
    a					:      List[Any]    =       name.replace('conv2'     ,      'convolution2'				)
  # readout blocks
  if "pretrained.act_postprocess1.0.project.0" in name:
    a					:      Optional[int]    =       name.replace('pretrained.act_postprocess1.0.project.0'     ,      'neck.reassemble_stage.readout_projects.0.0'				)
  if "pretrained.act_postprocess2.0.project.0" in name:
    a					:      str    =       name.replace('pretrained.act_postprocess2.0.project.0'     ,      'neck.reassemble_stage.readout_projects.1.0'				)
  if "pretrained.act_postprocess3.0.project.0" in name:
    a					:      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:
    a					:      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:
    a					:      Tuple    =       name.replace('pretrained.act_postprocess1.3'     ,      'neck.reassemble_stage.layers.0.projection'				)
  if "pretrained.act_postprocess1.4" in name:
    a					:      Tuple    =       name.replace('pretrained.act_postprocess1.4'     ,      'neck.reassemble_stage.layers.0.resize'				)
  if "pretrained.act_postprocess2.3" in name:
    a					:      Tuple    =       name.replace('pretrained.act_postprocess2.3'     ,      'neck.reassemble_stage.layers.1.projection'				)
  if "pretrained.act_postprocess2.4" in name:
    a					:      List[str]    =       name.replace('pretrained.act_postprocess2.4'     ,      'neck.reassemble_stage.layers.1.resize'				)
  if "pretrained.act_postprocess3.3" in name:
    a					:      Dict    =       name.replace('pretrained.act_postprocess3.3'     ,      'neck.reassemble_stage.layers.2.projection'				)
  if "pretrained.act_postprocess4.3" in name:
    a					:      Any    =       name.replace('pretrained.act_postprocess4.3'     ,      'neck.reassemble_stage.layers.3.projection'				)
  if "pretrained.act_postprocess4.4" in name:
    a					:      int    =       name.replace('pretrained.act_postprocess4.4'     ,      'neck.reassemble_stage.layers.3.resize'				)
  if "pretrained" in name:
    a					:      Dict    =       name.replace('pretrained'     ,      'dpt'				)
  if "bn" in name:
    a					:      Any    =       name.replace('bn'     ,      'batch_norm'				)
  if "head" in name:
    a					:      Dict    =       name.replace('head'     ,      'head.head'				)
  if "encoder.norm" in name:
    a					:      Union[str, Any]    =       name.replace('encoder.norm'     ,      'layernorm'				)
  if "auxlayer" in name:
    a					:      List[str]    =       name.replace('auxlayer'     ,      'auxiliary_head.head'				)
  if "backbone" in name:
    a					:      Any    =       name.replace('backbone'     ,      'backbone.bit.encoder'				)

  if ".." in name:
    a					:      Optional[int]    =       name.replace('..'     ,      '.'				)

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




def       snake_case  (A_				:Any     ,      A_				:str				):







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




def       snake_case  ():







  '''simple docstring'''

  a					:      Optional[int]    =       'http://images.cocodataset.org/val2017/000000039769.jpg'
  a					:      List[Any]    =       Image.open(requests.get(A_     ,      stream=A_				).raw				)
  return im




@torch.no_grad()
def       snake_case  (A_				:Union[str, Any]     ,      A_				:List[str]     ,      A_				:Optional[int]     ,      A_				:Dict     ,      A_				:Tuple				):







  '''simple docstring'''

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

  # load HuggingFace model
  a					:      str    =       DPTForSemanticSegmentation(A_				) if 'ade' in checkpoint_url else DPTForDepthEstimation(A_				)
  model.load_state_dict(A_				)
  model.eval()

  # Check outputs on an image
  a					:      Dict    =       4_8_0 if 'ade' in checkpoint_url else 3_8_4
  a					:      str    =       DPTImageProcessor(size=A_				)

  a					:      str    =       prepare_img()
  a					:      Any    =       image_processor(A_     ,      return_tensors='pt'				)

  # forward pass
  a					:      List[Any]    =       model(**A_				).logits if 'ade' in checkpoint_url else model(**A_				).predicted_depth

  if show_prediction:
    a					:      Optional[int]    =       (
        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_5_5				).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__":
   _UpperCamelCase    :   List[str]   			=						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',
   )

   _UpperCamelCase    :   Optional[Any]   			=						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 .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels




