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()
__snake_case							=      logging.get_logger()




def       __lowerCAmelCase      (						A_   : int						,	A_   : str						,	A_   : LevitConfig						,	A_   : Path						,	A_   : bool = True					)    ->  str:
  print(F'''Converting {name}...'''					)

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

    from_model.eval()
    __UpperCAmelCase						    =       LevitForImageClassificationWithTeacher(A_					).eval()
    __UpperCAmelCase						    =       OrderedDict()

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

    __UpperCAmelCase						    =       torch.randn((2, 3, 2_24, 2_24)					)
    __UpperCAmelCase						    =       from_model(A_					)
    __UpperCAmelCase						    =       our_model(A_					).logits

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

  __UpperCAmelCase						    =       name
  print(A_					)

  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       __lowerCAmelCase      (						A_   : Path						,	A_   : str = None						,	A_   : bool = True					)    ->  List[Any]:
  __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(A_						,	A_						,	repo_type="dataset"					)						,	"r"					)					)
  __UpperCAmelCase						    =       {int(A_					): v for k, v in idalabel.items()}

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

  __UpperCAmelCase						    =       partial(A_						,	num_labels=A_						,	idalabel=A_						,	labelaid=A_					)

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


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

       __snake_case							=      parser.parse_args()
       __snake_case							=      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)




def       __lowerCAmelCase      (						A_   : str					)    ->  bool:
  if not all(x.isalpha() for x in string					):
    raise ValueError("String must only contain alphabetic characters."					)

  __UpperCAmelCase						    =       sorted(string.lower()					)
  return len(A_					) == len(set(A_					)					)


if __name__ == "__main__":
       a_							=      input("""Enter a string """).strip()

       a_							=      is_isogram(input_str)
       print(F"{input_str} is {'an' if isogram else 'not an'} isogram.")



