




import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_					    = logging.get_logger()



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase = True      )     ->	Tuple:
  print(F"""Converting {name}..."""      )

  with torch.no_grad():
    if hidden_sizes == 128:
      if name[-1] == "S":
        lowerCAmelCase__				:  List[Any]		       =     timm.create_model('''levit_128s'''	,      pretrained=_UpperCamelCase      )
      else:
        lowerCAmelCase__				:  Union[str, Any]		       =     timm.create_model('''levit_128'''	,      pretrained=_UpperCamelCase      )
    if hidden_sizes == 192:
      lowerCAmelCase__				:  List[Any]		       =     timm.create_model('''levit_192'''	,      pretrained=_UpperCamelCase      )
    if hidden_sizes == 256:
      lowerCAmelCase__				:  Dict		       =     timm.create_model('''levit_256'''	,      pretrained=_UpperCamelCase      )
    if hidden_sizes == 384:
      lowerCAmelCase__				:  Tuple		       =     timm.create_model('''levit_384'''	,      pretrained=_UpperCamelCase      )

    from_model.eval()
    lowerCAmelCase__				:  Tuple		       =     LevitForImageClassificationWithTeacher(_UpperCamelCase      ).eval()
    lowerCAmelCase__				:  Union[str, Any]		       =     OrderedDict()

    lowerCAmelCase__				:  Optional[Any]		       =     from_model.state_dict()
    lowerCAmelCase__				:  Optional[Any]		       =     list(from_model.state_dict().keys()      )
    lowerCAmelCase__				:  List[str]		       =     list(our_model.state_dict().keys()      )
    print(len(_UpperCamelCase      )	,      len(_UpperCamelCase      )      )
    for i in range(len(_UpperCamelCase      )      ):
      lowerCAmelCase__				:  Union[str, Any]		       =     weights[og_keys[i]]
    our_model.load_state_dict(_UpperCamelCase      )

    lowerCAmelCase__				:  str		       =     torch.randn((2, 3, 224, 224)      )
    lowerCAmelCase__				:  List[str]		       =     from_model(_UpperCamelCase      )
    lowerCAmelCase__				:  List[str]		       =     our_model(_UpperCamelCase      ).logits

  assert torch.allclose(_UpperCamelCase	,      _UpperCamelCase      ), "The model logits don't match the original one."

  lowerCAmelCase__				:  List[Any]		       =     name
  print(_UpperCamelCase      )

  if push_to_hub:
    our_model.save_pretrained(save_directory / checkpoint_name      )
    lowerCAmelCase__				:  int		       =     LevitImageProcessor()
    image_processor.save_pretrained(save_directory / checkpoint_name      )

    print(F"""Pushed {checkpoint_name}"""      )



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase = None	,      UpperCamelCase = True      )     ->	Optional[Any]:
  lowerCAmelCase__				:  Any		       =     '''imagenet-1k-id2label.json'''
  lowerCAmelCase__				:  Dict		       =     1000
  lowerCAmelCase__				:  List[Any]		       =     (1, num_labels)

  lowerCAmelCase__				:  str		       =     '''huggingface/label-files'''
  lowerCAmelCase__				:  Tuple		       =     num_labels
  lowerCAmelCase__				:  List[Any]		       =     json.load(open(hf_hub_download(_UpperCamelCase	,      _UpperCamelCase	,      repo_type='''dataset'''      )	,      '''r'''      )      )
  lowerCAmelCase__				:  Optional[Any]		       =     {int(_UpperCamelCase      ): v for k, v in idalabel.items()}

  lowerCAmelCase__				:  Any		       =     idalabel
  lowerCAmelCase__				:  int		       =     {v: k for k, v in idalabel.items()}

  lowerCAmelCase__				:  Optional[Any]		       =     partial(_UpperCamelCase	,      num_labels=_UpperCamelCase	,      idalabel=_UpperCamelCase	,      labelaid=_UpperCamelCase      )

  lowerCAmelCase__				:  List[Any]		       =     {
      '''levit-128S''': 128,
      '''levit-128''': 128,
      '''levit-192''': 192,
      '''levit-256''': 256,
      '''levit-384''': 384,
  }

  lowerCAmelCase__				:  Union[str, Any]		       =     {
      '''levit-128S''': ImageNetPreTrainedConfig(
          hidden_sizes=[128, 256, 384]	,      num_attention_heads=[4, 6, 8]	,      depths=[2, 3, 4]	,      key_dim=[16, 16, 16]	,      drop_path_rate=0	,      ),
      '''levit-128''': ImageNetPreTrainedConfig(
          hidden_sizes=[128, 256, 384]	,      num_attention_heads=[4, 8, 12]	,      depths=[4, 4, 4]	,      key_dim=[16, 16, 16]	,      drop_path_rate=0	,      ),
      '''levit-192''': ImageNetPreTrainedConfig(
          hidden_sizes=[192, 288, 384]	,      num_attention_heads=[3, 5, 6]	,      depths=[4, 4, 4]	,      key_dim=[32, 32, 32]	,      drop_path_rate=0	,      ),
      '''levit-256''': ImageNetPreTrainedConfig(
          hidden_sizes=[256, 384, 512]	,      num_attention_heads=[4, 6, 8]	,      depths=[4, 4, 4]	,      key_dim=[32, 32, 32]	,      drop_path_rate=0	,      ),
      '''levit-384''': ImageNetPreTrainedConfig(
          hidden_sizes=[384, 512, 768]	,      num_attention_heads=[6, 9, 12]	,      depths=[4, 4, 4]	,      key_dim=[32, 32, 32]	,      drop_path_rate=0.1	,      ),
  }

  if model_name:
    convert_weight_and_push(
        names_to_hidden_sizes[model_name]	,      _UpperCamelCase	,      names_to_config[model_name]	,      _UpperCamelCase	,      _UpperCamelCase      )
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(names_to_hidden_sizes[model_name]	,      _UpperCamelCase	,      _UpperCamelCase	,      _UpperCamelCase	,      _UpperCamelCase      )
  return config, expected_shape


if __name__ == "__main__":
     lowerCAmelCase_					    = argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--model_name""",
         default=None,
         type=str,
         help="""The name of the model you wish to convert, it must be one of the supported Levit* architecture,""",
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""",
         default="""levit-dump-folder/""",
         type=Path,
         required=False,
         help="""Path to the output PyTorch model directory.""",
     )
     parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
     parser.add_argument(
         """--no-push_to_hub""",
         dest="""push_to_hub""",
         action="""store_false""",
         help="""Do not push model and image processor to the hub""",
     )

     lowerCAmelCase_					    = parser.parse_args()
     lowerCAmelCase_					    = args.pytorch_dump_folder_path
     pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
     convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)







from __future__ import annotations



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	list[int]:  # This function is recursive
  lowerCAmelCase__				:  Tuple		       =     len(UpperCamelCase      )
  # If the array contains only one element, we return it (it's the stop condition of
  # recursion)
  if array_length <= 1:
    return array
    # Else
  lowerCAmelCase__				:  Optional[int]		       =     array[0]
  lowerCAmelCase__				:  Union[str, Any]		       =     False
  lowerCAmelCase__				:  List[str]		       =     1
  lowerCAmelCase__				:  list[int]		       =     []
  while not is_found and i < array_length:
    if array[i] < pivot:
      lowerCAmelCase__				:  List[Any]		       =     True
      lowerCAmelCase__				:  Optional[int]		       =     [element for element in array[i:] if element >= array[i]]
      lowerCAmelCase__				:  Any		       =     longest_subsequence(UpperCamelCase      )
      if len(UpperCamelCase      ) > len(UpperCamelCase      ):
        lowerCAmelCase__				:  Optional[int]		       =     temp_array
    else:
      i += 1

  lowerCAmelCase__				:  Optional[Any]		       =     [element for element in array[1:] if element >= pivot]
  lowerCAmelCase__				:  List[Any]		       =     [pivot, *longest_subsequence(UpperCamelCase      )]
  if len(UpperCamelCase      ) > len(UpperCamelCase      ):
    return temp_array
  else:
    return longest_subseq


if __name__ == "__main__":
     import doctest

     doctest.testmod()

