'''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()
snake_case__					:      Union[str, Any]				      =  logging.get_logger()


def  _lowerCamelCase   (				lowerCamelCase_ :							int     ,					lowerCamelCase_ :							str     ,					lowerCamelCase_ :							LevitConfig     ,					lowerCamelCase_ :							Path     ,					lowerCamelCase_ :							bool = True       ):
     print(F'''Converting {name}...'''       )

     with torch.no_grad():
          if hidden_sizes == 128:
               if name[-1] == "S":
                    UpperCAmelCase_							:       int       =				timm.create_model('levit_128s'     ,					pretrained=A__       )
               else:
                    UpperCAmelCase_							:       Dict       =				timm.create_model('levit_128'     ,					pretrained=A__       )
          if hidden_sizes == 192:
               UpperCAmelCase_							:       Optional[int]       =				timm.create_model('levit_192'     ,					pretrained=A__       )
          if hidden_sizes == 256:
               UpperCAmelCase_							:       str       =				timm.create_model('levit_256'     ,					pretrained=A__       )
          if hidden_sizes == 384:
               UpperCAmelCase_							:       Optional[int]       =				timm.create_model('levit_384'     ,					pretrained=A__       )

          from_model.eval()
          UpperCAmelCase_							:       List[Any]       =				LevitForImageClassificationWithTeacher(A__       ).eval()
          UpperCAmelCase_							:       int       =				OrderedDict()

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

          UpperCAmelCase_							:       str       =				torch.randn((2, 3, 224, 224)       )
          UpperCAmelCase_							:       Dict       =				from_model(A__       )
          UpperCAmelCase_							:       List[Any]       =				our_model(A__       ).logits

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

     UpperCAmelCase_							:       Optional[int]       =				name
     print(A__       )

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

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


def  _lowerCamelCase   (				lowerCamelCase_ :							Path     ,					lowerCamelCase_ :							str = None     ,					lowerCamelCase_ :							bool = True       ):
     UpperCAmelCase_							:       int       =				"""imagenet-1k-id2label.json"""
     UpperCAmelCase_							:       str       =				1000
     UpperCAmelCase_							:       List[Any]       =				(1, num_labels)

     UpperCAmelCase_							:       str       =				"""huggingface/label-files"""
     UpperCAmelCase_							:       Optional[int]       =				num_labels
     UpperCAmelCase_							:       Dict       =				json.load(open(hf_hub_download(A__     ,					A__     ,					repo_type='dataset'       )     ,					'r'       )       )
     UpperCAmelCase_							:       int       =				{int(A__       ): v for k, v in idalabel.items()}

     UpperCAmelCase_							:       Any       =				idalabel
     UpperCAmelCase_							:       Tuple       =				{v: k for k, v in idalabel.items()}

     UpperCAmelCase_							:       int       =				partial(A__     ,					num_labels=A__     ,					idalabel=A__     ,					labelaid=A__       )

     UpperCAmelCase_							:       Dict       =				{
         """levit-128S""": 128,
         """levit-128""": 128,
         """levit-192""": 192,
         """levit-256""": 256,
         """levit-384""": 384,
     }

     UpperCAmelCase_							:       int       =				{
         """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]     ,					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__					:      Optional[Any]				      =  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__					:      Any				      =  parser.parse_args()
 snake_case__					:      Path				      =  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'''


def  _lowerCamelCase   (				lowerCamelCase_ :							str       ):



     """simple docstring"""





     UpperCAmelCase_							:       Optional[int]       =				[0] * len(lowerCamelCase_       )

     for i in range(1     ,					len(lowerCamelCase_       )       ):
          # use last results for better performance - dynamic programming
          UpperCAmelCase_							:       List[Any]       =				prefix_result[i - 1]
          while j > 0 and input_string[i] != input_string[j]:
               UpperCAmelCase_							:       str       =				prefix_result[j - 1]

          if input_string[i] == input_string[j]:
               j += 1
          UpperCAmelCase_							:       Any       =				j

     return prefix_result


def  _lowerCamelCase   (				lowerCamelCase_ :							str       ):



     """simple docstring"""





     return max(prefix_function(lowerCamelCase_       )       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()






