'''simple docstring'''






import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class     A__							(       UpperCAmelCase_   ):





   """simple docstring"""





   UpperCamelCase_       :						Any    				=     ["""image_processor""", """tokenizer"""]
   UpperCamelCase_       :						Optional[Any]    				=     """ViltImageProcessor"""
   UpperCamelCase_       :						Optional[int]    				=     ("""BertTokenizer""", """BertTokenizerFast""")




   def __init__( self     :		Tuple						,     lowerCAmelCase__     :		Any=None						,     lowerCAmelCase__     :		Optional[int]=None						,     **lowerCAmelCase__     :		Optional[int]			)       ->    List[str]:







      """simple docstring"""




      _UpperCAmelCase							:	Union[str, 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."						,     __lowercase						,     )
         _UpperCAmelCase							:	Tuple							=   kwargs.pop("feature_extractor"			)

      _UpperCAmelCase							:	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__(__lowercase						,     __lowercase			)
      _UpperCAmelCase							:	Optional[Any]							=   self.image_processor




   def __call__( self     :		Dict						,     lowerCAmelCase__     :		Tuple						,     lowerCAmelCase__     :		Tuple = None						,     lowerCAmelCase__     :		str = True						,     lowerCAmelCase__     :		Any = False						,     lowerCAmelCase__     :		int = None						,     lowerCAmelCase__     :		int = None						,     lowerCAmelCase__     :		Tuple = 0						,     lowerCAmelCase__     :		List[str] = None						,     lowerCAmelCase__     :		Tuple = None						,     lowerCAmelCase__     :		int = None						,     lowerCAmelCase__     :		Tuple = False						,     lowerCAmelCase__     :		Optional[Any] = False						,     lowerCAmelCase__     :		Optional[int] = False						,     lowerCAmelCase__     :		str = False						,     lowerCAmelCase__     :		Dict = True						,     lowerCAmelCase__     :		List[Any] = None						,     **lowerCAmelCase__     :		str						,     )       ->    BatchEncoding:







      """simple docstring"""




      _UpperCAmelCase							:	Tuple							=   self.tokenizer(
          text=__lowercase						,     add_special_tokens=__lowercase						,     padding=__lowercase						,     truncation=__lowercase						,     max_length=__lowercase						,     stride=__lowercase						,     pad_to_multiple_of=__lowercase						,     return_token_type_ids=__lowercase						,     return_attention_mask=__lowercase						,     return_overflowing_tokens=__lowercase						,     return_special_tokens_mask=__lowercase						,     return_offsets_mapping=__lowercase						,     return_length=__lowercase						,     verbose=__lowercase						,     return_tensors=__lowercase						,     **__lowercase						,     )
      # add pixel_values + pixel_mask
      _UpperCAmelCase							:	str							=   self.image_processor(__lowercase						,     return_tensors=__lowercase			)
      encoding.update(__lowercase			)

      return encoding




   def 			_lowerCAmelCase		( self     :		List[Any]						,     *lowerCAmelCase__     :		Optional[int]						,     **lowerCAmelCase__     :		int			)       ->    str:







      """simple docstring"""




      return self.tokenizer.batch_decode(*__lowercase						,     **__lowercase			)




   def 			_lowerCAmelCase		( self     :		Optional[Any]						,     *lowerCAmelCase__     :		Optional[Any]						,     **lowerCAmelCase__     :		Any			)       ->    Optional[int]:







      """simple docstring"""




      return self.tokenizer.decode(*__lowercase						,     **__lowercase			)




   @property
   def 			_lowerCAmelCase		( self     :		List[Any]			)       ->    List[str]:







      """simple docstring"""




      _UpperCAmelCase							:	int							=   self.tokenizer.model_input_names
      _UpperCAmelCase							:	int							=   self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)




   @property
   def 			_lowerCAmelCase		( self     :		List[str]			)       ->    Union[str, Any]:







      """simple docstring"""




      warnings.warn(
          "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."						,     __lowercase						,     )
      return self.image_processor_class




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







      """simple docstring"""




      warnings.warn(
          "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."						,     __lowercase						,     )
      return self.image_processor
import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :List[Any]			  =		AlbertConfig.from_json_file(SCREAMING_SNAKE_CASE	)
    print(f"""Building PyTorch model from configuration: {config}"""	)
    __UpperCamelCase     :List[str]			  =		AlbertForPreTraining(SCREAMING_SNAKE_CASE	)

    # Load weights from tf checkpoint
    load_tf_weights_in_albert(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

    # Save pytorch-model
    print(f"""Save PyTorch model to {pytorch_dump_path}"""	)
    torch.save(model.state_dict()  ,			SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
    __lowercase							 =					argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
    )
    parser.add_argument(
        '''--albert_config_file''',
        default=None,
        type=str,
        required=True,
        help=(
            '''The config json file corresponding to the pre-trained ALBERT model. \n'''
            '''This specifies the model architecture.'''
        ),
    )
    parser.add_argument(
        '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    __lowercase							 =					parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)





