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()
lowercase__          =logging.get_logger(__name__)




def        __UpperCamelCase					(    lowerCAmelCase__							:    int  ):
 __a			:  str		    =  DPTConfig()

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

 if "ade" in checkpoint_url:
  __a			:  List[Any]		    =  True

  __a			:  Tuple		    =  1_5_0
  __a			:  List[str]		    =  'huggingface/label-files'
  __a			:  Any		    =  'ade20k-id2label.json'
  __a			:  int		    =  json.load(open(cached_download(hf_hub_url(_A  ,       _A  ,       repo_type='''dataset'''  )  )  ,       '''r'''  )  )
  __a			:  List[Any]		    =  {int(_A  ): v for k, v in idalabel.items()}
  __a			:  Dict		    =  idalabel
  __a			:  Optional[int]		    =  {v: k for k, v in idalabel.items()}
  __a			:  int		    =  [1, 1_5_0, 4_8_0, 4_8_0]

 return config, expected_shape




def        __UpperCamelCase					(    lowerCAmelCase__							:    str  ):
 __a			:  List[str]		    =  ['pretrained.model.head.weight', 'pretrained.model.head.bias']
 for k in ignore_keys:
  state_dict.pop(_A  ,       _A  )




def        __UpperCamelCase					(    lowerCAmelCase__							:    List[Any]  ):







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

 return name




def        __UpperCamelCase					(    lowerCAmelCase__							:    List[Any]  ,       lowerCAmelCase__							:    int  ):







 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			:  str		    =  state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.weight"  )
  __a			:  Optional[int]		    =  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			:  str		    =  in_proj_weight[: config.hidden_size, :]
  __a			:  Any		    =  in_proj_bias[: config.hidden_size]
  __a			:  str		    =  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			:  Optional[Any]		    =  in_proj_weight[
      -config.hidden_size :, :
  ]
  __a			:  Optional[Any]		    =  in_proj_bias[-config.hidden_size :]




def        __UpperCamelCase					(    ):
 __a			:  Optional[int]		    =  'http://images.cocodataset.org/val2017/000000039769.jpg'
 __a			:  Tuple		    =  Image.open(requests.get(_A  ,       stream=_A  ).raw  )
 return im




@torch.no_grad()
def        __UpperCamelCase					(    lowerCAmelCase__							:    Optional[int]  ,       lowerCAmelCase__							:    Optional[Any]  ,       lowerCAmelCase__							:    Optional[int]  ,       lowerCAmelCase__							:    str  ):
 __a			:  List[Any]		    =  get_dpt_config(_A  )
 # load original state_dict from URL
 __a			:  Tuple		    =  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():
  __a			:  Any		    =  state_dict.pop(_A  )
  __a			:  Any		    =  val
 # read in qkv matrices
 read_in_q_k_v(_A  ,       _A  )

 # load HuggingFace model
 __a			:  Dict		    =  DPTForSemanticSegmentation(_A  ) if 'ade' in checkpoint_url else DPTForDepthEstimation(_A  )
 model.load_state_dict(_A  )
 model.eval()

 # Check outputs on an image
 __a			:  Any		    =  4_8_0 if 'ade' in checkpoint_url else 3_8_4
 __a			:  List[str]		    =  DPTImageProcessor(size=_A  )

 __a			:  List[Any]		    =  prepare_img()
 __a			:  Dict		    =  image_processor(_A  ,       return_tensors='''pt'''  )

 # forward pass
 __a			:  Union[str, Any]		    =  model(**_A  ).logits if 'ade' in checkpoint_url else model(**_A  ).predicted_depth

 # Assert logits
 __a			:  Optional[Any]		    =  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:
  __a			:  Union[str, Any]		    =  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__":
      lowercase__          =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.',
      )

      lowercase__          =parser.parse_args()
      convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)


import argparse
import os
import re

import packaging.version


lowercase__          ='examples/'
lowercase__          ={
    'examples': (re.compile(R'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
    'init': (re.compile(R'^__version__\s+=\s+"([^"]+)"\s*$', re.MULTILINE), '__version__ = "VERSION"\n'),
    'setup': (re.compile(R'^(\s*)version\s*=\s*"[^"]+",', re.MULTILINE), R'\1version="VERSION",'),
    'doc': (re.compile(R'^(\s*)release\s*=\s*"[^"]+"$', re.MULTILINE), 'release = "VERSION"\n'),
}
lowercase__          ={
    'init': 'src/transformers/__init__.py',
    'setup': 'setup.py',
}
lowercase__          ='README.md'




def        __UpperCamelCase					(    lowerCAmelCase__							:    List[Any]  ,       lowerCAmelCase__							:    Dict  ,       lowerCAmelCase__							:    Tuple  ):
 with open(lowerCAmelCase__  ,       '''r'''  ,       encoding='''utf-8'''  ,       newline='''\n'''  ) as f:
  __a			:  Tuple		    =  f.read()
 __a    ,	__a			:  Optional[int]		    =  REPLACE_PATTERNS[pattern]
 __a			:  List[Any]		    =  replace.replace('''VERSION'''  ,       lowerCAmelCase__  )
 __a			:  Any		    =  re_pattern.sub(lowerCAmelCase__  ,       lowerCAmelCase__  )
 with open(lowerCAmelCase__  ,       '''w'''  ,       encoding='''utf-8'''  ,       newline='''\n'''  ) as f:
  f.write(lowerCAmelCase__  )




def        __UpperCamelCase					(    lowerCAmelCase__							:    Tuple  ):
 for folder, directories, fnames in os.walk(lowerCAmelCase__  ):
  # Removing some of the folders with non-actively maintained examples from the walk
  if "research_projects" in directories:
   directories.remove('''research_projects'''  )
  if "legacy" in directories:
   directories.remove('''legacy'''  )
  for fname in fnames:
   if fname.endswith('''.py'''  ):
    update_version_in_file(os.path.join(lowerCAmelCase__  ,       lowerCAmelCase__  )  ,       lowerCAmelCase__  ,       pattern='''examples'''  )




def        __UpperCamelCase					(    lowerCAmelCase__							:    Optional[int]  ,       lowerCAmelCase__							:    Dict=False  ):
 for pattern, fname in REPLACE_FILES.items():
  update_version_in_file(lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )
 if not patch:
  update_version_in_examples(lowerCAmelCase__  )




def        __UpperCamelCase					(    ):
 __a			:  Optional[int]		    =  '''🤗 Transformers currently provides the following architectures'''
 __a			:  int		    =  '''1. Want to contribute a new model?'''
 with open(lowerCAmelCase__  ,       '''r'''  ,       encoding='''utf-8'''  ,       newline='''\n'''  ) as f:
  __a			:  Tuple		    =  f.readlines()

 # Find the start of the list.
 __a			:  Optional[int]		    =  0
 while not lines[start_index].startswith(_start_prompt  ):
  start_index += 1
 start_index += 1

 __a			:  Any		    =  start_index
 # Update the lines in the model list.
 while not lines[index].startswith(_end_prompt  ):
  if lines[index].startswith('''1.'''  ):
   __a			:  str		    =  lines[index].replace(
       '''https://huggingface.co/docs/transformers/main/model_doc'''  ,       '''https://huggingface.co/docs/transformers/model_doc'''  ,       )
  index += 1

 with open(lowerCAmelCase__  ,       '''w'''  ,       encoding='''utf-8'''  ,       newline='''\n'''  ) as f:
  f.writelines(lowerCAmelCase__  )




def        __UpperCamelCase					(    ):
 with open(REPLACE_FILES['''init''']  ,       '''r'''  ) as f:
  __a			:  Optional[int]		    =  f.read()
 __a			:  str		    =  REPLACE_PATTERNS['''init'''][0].search(lowerCAmelCase__  ).groups()[0]
 return packaging.version.parse(lowerCAmelCase__  )




def        __UpperCamelCase					(    lowerCAmelCase__							:    Union[str, Any]=False  ):
 __a			:  str		    =  get_version()
 if patch and default_version.is_devrelease:
  raise ValueError('''Can\'t create a patch version from the dev branch, checkout a released version!'''  )
 if default_version.is_devrelease:
  __a			:  Union[str, Any]		    =  default_version.base_version
 elif patch:
  __a			:  Tuple		    =  f"{default_version.major}.{default_version.minor}.{default_version.micro + 1}"
 else:
  __a			:  List[str]		    =  f"{default_version.major}.{default_version.minor + 1}.0"

 # Now let's ask nicely if that's the right one.
 __a			:  List[str]		    =  input(f"Which version are you releasing? [{default_version}]"  )
 if len(lowerCAmelCase__  ) == 0:
  __a			:  Tuple		    =  default_version

 print(f"Updating version to {version}."  )
 global_version_update(lowerCAmelCase__  ,       patch=lowerCAmelCase__  )
 if not patch:
  print('''Cleaning main README, don\'t forget to run `make fix-copies`.'''  )
  clean_main_ref_in_model_list()




def        __UpperCamelCase					(    ):
 __a			:  Dict		    =  get_version()
 __a			:  str		    =  f"{current_version.major}.{current_version.minor + 1}.0.dev0"
 __a			:  Any		    =  current_version.base_version

 # Check with the user we got that right.
 __a			:  Any		    =  input(f"Which version are we developing now? [{dev_version}]"  )
 if len(lowerCAmelCase__  ) == 0:
  __a			:  Any		    =  dev_version

 print(f"Updating version to {version}."  )
 global_version_update(lowerCAmelCase__  )
 print('''Cleaning main README, don\'t forget to run `make fix-copies`.'''  )
 clean_main_ref_in_model_list()


if __name__ == "__main__":
      lowercase__          =argparse.ArgumentParser()
      parser.add_argument('--post_release', action='store_true', help='Whether this is pre or post release.')
      parser.add_argument('--patch', action='store_true', help='Whether or not this is a patch release.')
      lowercase__          =parser.parse_args()
      if not args.post_release:
            pre_release_work(patch=args.patch)
      elif args.patch:
            print('Nothing to do after a patch :-)')
      else:
            post_release_work()
