"""simple docstring"""


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()
UpperCAmelCase__  		=						logging.get_logger()






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase   ,lowercase   ,lowercase = True		):







  """simple docstring"""
  print(f'''Converting {name}...'''		)

  with torch.no_grad():
    if hidden_sizes == 1_28:
      if name[-1] == "S":
        _UpperCAmelCase						 =      timm.create_model("""levit_128s"""   ,pretrained=lowercase		)
      else:
        _UpperCAmelCase						 =      timm.create_model("""levit_128"""   ,pretrained=lowercase		)
    if hidden_sizes == 1_92:
      _UpperCAmelCase						 =      timm.create_model("""levit_192"""   ,pretrained=lowercase		)
    if hidden_sizes == 2_56:
      _UpperCAmelCase						 =      timm.create_model("""levit_256"""   ,pretrained=lowercase		)
    if hidden_sizes == 3_84:
      _UpperCAmelCase						 =      timm.create_model("""levit_384"""   ,pretrained=lowercase		)

    from_model.eval()
    _UpperCAmelCase						 =      LevitForImageClassificationWithTeacher(lowercase		).eval()
    _UpperCAmelCase						 =      OrderedDict()

    _UpperCAmelCase						 =      from_model.state_dict()
    _UpperCAmelCase						 =      list(from_model.state_dict().keys()		)
    _UpperCAmelCase						 =      list(our_model.state_dict().keys()		)
    print(len(lowercase		)   ,len(lowercase		)		)
    for i in range(len(lowercase		)		):
      _UpperCAmelCase						 =      weights[og_keys[i]]
    our_model.load_state_dict(lowercase		)

    _UpperCAmelCase						 =      torch.randn((2, 3, 2_24, 2_24)		)
    _UpperCAmelCase						 =      from_model(lowercase		)
    _UpperCAmelCase						 =      our_model(lowercase		).logits

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

  _UpperCAmelCase						 =      name
  print(lowercase		)

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

    print(f'''Pushed {checkpoint_name}'''		)






def     __UpperCAmelCase					(			lowercase   ,lowercase = None   ,lowercase = True		):







  """simple docstring"""
  _UpperCAmelCase						 =      """imagenet-1k-id2label.json"""
  _UpperCAmelCase						 =      10_00
  _UpperCAmelCase						 =      (1, num_labels)

  _UpperCAmelCase						 =      """huggingface/label-files"""
  _UpperCAmelCase						 =      num_labels
  _UpperCAmelCase						 =      json.load(open(hf_hub_download(lowercase   ,lowercase   ,repo_type="""dataset"""		)   ,"""r"""		)		)
  _UpperCAmelCase						 =      {int(lowercase		): v for k, v in idalabel.items()}

  _UpperCAmelCase						 =      idalabel
  _UpperCAmelCase						 =      {v: k for k, v in idalabel.items()}

  _UpperCAmelCase						 =      partial(lowercase   ,num_labels=lowercase   ,idalabel=lowercase   ,labelaid=lowercase		)

  _UpperCAmelCase						 =      {
      """levit-128S""": 1_28,
      """levit-128""": 1_28,
      """levit-192""": 1_92,
      """levit-256""": 2_56,
      """levit-384""": 3_84,
  }

  _UpperCAmelCase						 =      {
      """levit-128S""": ImageNetPreTrainedConfig(
          hidden_sizes=[1_28, 2_56, 3_84]   ,num_attention_heads=[4, 6, 8]   ,depths=[2, 3, 4]   ,key_dim=[16, 16, 16]   ,drop_path_rate=0   ,),
      """levit-128""": ImageNetPreTrainedConfig(
          hidden_sizes=[1_28, 2_56, 3_84]   ,num_attention_heads=[4, 8, 12]   ,depths=[4, 4, 4]   ,key_dim=[16, 16, 16]   ,drop_path_rate=0   ,),
      """levit-192""": ImageNetPreTrainedConfig(
          hidden_sizes=[1_92, 2_88, 3_84]   ,num_attention_heads=[3, 5, 6]   ,depths=[4, 4, 4]   ,key_dim=[32, 32, 32]   ,drop_path_rate=0   ,),
      """levit-256""": ImageNetPreTrainedConfig(
          hidden_sizes=[2_56, 3_84, 5_12]   ,num_attention_heads=[4, 6, 8]   ,depths=[4, 4, 4]   ,key_dim=[32, 32, 32]   ,drop_path_rate=0   ,),
      """levit-384""": ImageNetPreTrainedConfig(
          hidden_sizes=[3_84, 5_12, 7_68]   ,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]   ,lowercase   ,names_to_config[model_name]   ,lowercase   ,lowercase		)
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(names_to_hidden_sizes[model_name]   ,lowercase   ,lowercase   ,lowercase   ,lowercase		)
  return config, expected_shape


if __name__ == "__main__":
    UpperCAmelCase__  		=						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""",
    )

    UpperCAmelCase__  		=						parser.parse_args()
    UpperCAmelCase__  		=						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)



"""simple docstring"""


from __future__ import annotations

from random import choice






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  return choice(lowercase		)






def     __UpperCAmelCase					(			lowercase   ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      random_pivot(lowercase		)

  # partition based on pivot
  # linear time
  _UpperCAmelCase						 =      [e for e in lst if e < pivot]
  _UpperCAmelCase						 =      [e for e in lst if e > pivot]

  # if we get lucky, pivot might be the element we want.
  # we can easily see this:
  # small (elements smaller than k)
  # + pivot (kth element)
  # + big (elements larger than k)
  if len(lowercase		) == k - 1:
    return pivot
  # pivot is in elements bigger than k
  elif len(lowercase		) < k - 1:
    return kth_number(lowercase   ,k - len(lowercase		) - 1		)
  # pivot is in elements smaller than k
  else:
    return kth_number(lowercase   ,lowercase		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
