



'''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()
A__    :   Optional[Any]           =					logging.get_logger()





def   a_   (		_UpperCAmelCase    :      int ,_UpperCAmelCase    :      str ,_UpperCAmelCase    :      LevitConfig ,_UpperCAmelCase    :      Path ,_UpperCAmelCase    :      bool = True				)    ->  int:
      print(f'''Converting {name}...'''				)

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

            from_model.eval()
            __snake_case :   Any													=    LevitForImageClassificationWithTeacher(_UpperCAmelCase				).eval()
            __snake_case :   Dict													=    OrderedDict()

            __snake_case :   Any													=    from_model.state_dict()
            __snake_case :   str													=    list(from_model.state_dict().keys()				)
            __snake_case :   Dict													=    list(our_model.state_dict().keys()				)
            print(len(_UpperCAmelCase				) ,len(_UpperCAmelCase				)				)
            for i in range(len(_UpperCAmelCase				)				):
                  __snake_case :   Optional[Any]													=    weights[og_keys[i]]
            our_model.load_state_dict(_UpperCAmelCase				)

            __snake_case :   Optional[Any]													=    torch.randn((2, 3, 2_24, 2_24)				)
            __snake_case :   Union[str, Any]													=    from_model(_UpperCAmelCase				)
            __snake_case :   int													=    our_model(_UpperCAmelCase				).logits

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

      __snake_case :   Optional[Any]													=    name
      print(_UpperCAmelCase				)

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

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





def   a_   (		_UpperCAmelCase    :      Path ,_UpperCAmelCase    :      str = None ,_UpperCAmelCase    :      bool = True				)    ->  List[Any]:
      __snake_case :   Optional[Any]													=    'imagenet-1k-id2label.json'
      __snake_case :   Optional[Any]													=    10_00
      __snake_case :   str													=    (1, num_labels)

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

      __snake_case :   Tuple													=    idalabel
      __snake_case :   str													=    {v: k for k, v in idalabel.items()}

      __snake_case :   List[Any]													=    partial(_UpperCAmelCase ,num_labels=_UpperCAmelCase ,idalabel=_UpperCAmelCase ,labelaid=_UpperCAmelCase				)

      __snake_case :   Union[str, Any]													=    {
          'levit-128S': 1_28,
          'levit-128': 1_28,
          'levit-192': 1_92,
          'levit-256': 2_56,
          'levit-384': 3_84,
      }

      __snake_case :   Union[str, Any]													=    {
          '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] ,_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__":
    A__    :   Union[str, 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''',
    )

    A__    :   int           =					parser.parse_args()
    A__    :   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'''





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 A__				= ['''image_processor''', '''tokenizer''']
 A__				= '''CLIPImageProcessor'''
 A__				= ('''CLIPTokenizer''', '''CLIPTokenizerFast''')
 def __init__(   self						:							Any      ,     __a						:							str=None      ,     __a						:							List[Any]=None      ,     **__a						:							List[str]  )      ->   Optional[Any]:







       '''simple docstring'''
       __snake_case :   Optional[Any]													=    None
       if "feature_extractor" in kwargs:
             warnings.warn(
                 'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                 ' instead.'      ,     __a      ,     )
             __snake_case :   List[str]													=    kwargs.pop('feature_extractor'  )

       __snake_case :   List[Any]													=    image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
             raise ValueError('You need to specify an `image_processor`.'  )
       if tokenizer is None:
             raise ValueError('You need to specify a `tokenizer`.'  )

       super().__init__(__a      ,     __a  )
 def __call__(   self						:							List[Any]      ,     __a						:							Optional[int]=None      ,     __a						:							Optional[int]=None      ,     __a						:							Union[str, Any]=None      ,     **__a						:							Union[str, Any]  )      ->   List[Any]:







       '''simple docstring'''

       if text is None and images is None:
             raise ValueError('You have to specify either text or images. Both cannot be none.'  )

       if text is not None:
             __snake_case :   Any													=    self.tokenizer(__a      ,     return_tensors=__a      ,     **__a  )

       if images is not None:
             __snake_case :   str													=    self.image_processor(__a      ,     return_tensors=__a      ,     **__a  )

       if text is not None and images is not None:
             __snake_case :   Optional[Any]													=    image_features.pixel_values
             return encoding
       elif text is not None:
             return encoding
       else:
             return BatchEncoding(data=dict(**__a  )      ,     tensor_type=__a  )
 def A_   (   self						:							List[Any]      ,     *__a						:							Dict      ,     **__a						:							Dict  )      ->   Union[str, Any]:







       '''simple docstring'''
       return self.tokenizer.batch_decode(*__a      ,     **__a  )
 def A_   (   self						:							str      ,     *__a						:							Tuple      ,     **__a						:							List[str]  )      ->   int:







       '''simple docstring'''
       return self.tokenizer.decode(*__a      ,     **__a  )
 @property
 def A_   (   self						:							Union[str, Any]  )      ->   Optional[Any]:







       '''simple docstring'''
       __snake_case :   Tuple													=    self.tokenizer.model_input_names
       __snake_case :   Tuple													=    self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )
 @property
 def A_   (   self						:							Any  )      ->   Any:







       '''simple docstring'''
       warnings.warn(
           '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'      ,     __a      ,     )
       return self.image_processor_class





 @property
 def A_   (   self						:							Optional[int]  )      ->   Optional[Any]:







       '''simple docstring'''
       warnings.warn(
           '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'      ,     __a      ,     )
       return self.image_processor



