'''simple docstring'''






import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

_UpperCAmelCase    :     List[str]         =       logging.get_logger(__name__)


_UpperCAmelCase    :     Any         =       {name: getattr(transformers, name + '''Fast''') for name in SLOW_TO_FAST_CONVERTERS}





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Optional[int]   ,		__snake_case							:			Optional[Any]   ,		__snake_case							:			Optional[Any]   ,		__snake_case							:			Any				):
     if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
          raise ValueError(F'Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys()				)}.'				)

     if tokenizer_name is None:
          _A    		=  TOKENIZER_CLASSES
     else:
          _A    		=  {tokenizer_name: getattr(__snake_case   ,		tokenizer_name + 'Fast'				)}

     logger.info(F'Loading tokenizer classes: {tokenizer_names}'				)

     for tokenizer_name in tokenizer_names:
          _A    		=  TOKENIZER_CLASSES[tokenizer_name]

          _A    		=  True
          if checkpoint_name is None:
               _A    		=  list(tokenizer_class.max_model_input_sizes.keys()				)
          else:
               _A    		=  [checkpoint_name]

          logger.info(F'For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}'				)

          for checkpoint in checkpoint_names:
               logger.info(F'Loading {tokenizer_class.__class__.__name__} {checkpoint}'				)

               # Load tokenizer
               _A    		=  tokenizer_class.from_pretrained(__snake_case   ,		force_download=__snake_case				)

               # Save fast tokenizer
               logger.info(F'Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}'				)

               # For organization names we create sub-directories
               if "/" in checkpoint:
                    _A		,       _A    		=  checkpoint.split('/'				)
                    _A    		=  os.path.join(__snake_case   ,		__snake_case				)
               elif add_prefix:
                    _A    		=  checkpoint
                    _A    		=  dump_path
               else:
                    _A    		=  None
                    _A    		=  dump_path

               logger.info(F'=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}'				)

               if checkpoint in list(tokenizer.pretrained_vocab_files_map.values()				)[0]:
                    _A    		=  list(tokenizer.pretrained_vocab_files_map.values()				)[0][checkpoint]
                    _A    		=  file_path.split(__snake_case				)[-1][0]
                    if next_char == "/":
                         _A    		=  os.path.join(__snake_case   ,		__snake_case				)
                         _A    		=  None

                    logger.info(F'=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}'				)

               _A    		=  tokenizer.save_pretrained(
                   __snake_case   ,		legacy_format=__snake_case   ,		filename_prefix=__snake_case				)
               logger.info(F'=> File names {file_names}'				)

               for file_name in file_names:
                    if not file_name.endswith('tokenizer.json'				):
                         os.remove(__snake_case				)
                         logger.info(F'=> removing {file_name}'				)


if __name__ == "__main__":
   _UpperCAmelCase    :     int         =       argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--dump_path''', default=None, type=str, required=True, help='''Path to output generated fast tokenizer files.'''
   )
   parser.add_argument(
       '''--tokenizer_name''',
       default=None,
       type=str,
       help=(
           F'''Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will '''
           '''download and convert all the checkpoints from AWS.'''
       ),
   )
   parser.add_argument(
       '''--checkpoint_name''',
       default=None,
       type=str,
       help='''Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.''',
   )
   parser.add_argument(
       '''--force_download''',
       action='''store_true''',
       help='''Re-download checkpoints.''',
   )
   _UpperCAmelCase    :     int         =       parser.parse_args()

   convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)











'''simple docstring'''



import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
      import torch
class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      UpperCamelCase_             =      '''char'''
      UpperCamelCase_             =      '''bpe'''
      UpperCamelCase_             =      '''wp'''





SCREAMING_SNAKE_CASE							=						(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      UpperCamelCase_             =      ['''image_processor''', '''char_tokenizer''']
      UpperCamelCase_             =      '''ViTImageProcessor'''
      UpperCamelCase_             =      '''MgpstrTokenizer'''






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


       '''simple docstring'''

       lowercase       : str						=None
       if "feature_extractor" in kwargs:
        warnings.warn(
            '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
            ''' instead.'''  ,   UpperCAmelCase  ,   )
        lowercase       : int						=kwargs.pop('''feature_extractor'''		)

       lowercase       : Optional[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`.'''		)

       lowercase       : Any						=tokenizer
       lowercase       : Dict						=AutoTokenizer.from_pretrained('''gpt2'''		)
       lowercase       : Optional[int]						=AutoTokenizer.from_pretrained('''bert-base-uncased'''		)

       super().__init__(UpperCAmelCase  ,   UpperCAmelCase		)






      def __call__(			self      :		List[Any]  ,   UpperCAmelCase      :		Union[str, Any]=None  ,   UpperCAmelCase      :		List[str]=None  ,   UpperCAmelCase      :		Dict=None  ,   **UpperCAmelCase      :		Union[str, Any]		)	->	int:


       '''simple docstring'''

       if images is None and text is None:
        raise ValueError('''You need to specify either an `images` or `text` input to process.'''		)

       if images is not None:
        lowercase       : str						=self.image_processor(UpperCAmelCase  ,   return_tensors=UpperCAmelCase  ,   **UpperCAmelCase		)
       if text is not None:
        lowercase       : Any						=self.char_tokenizer(UpperCAmelCase  ,   return_tensors=UpperCAmelCase  ,   **UpperCAmelCase		)

       if text is None:
        return inputs
       elif images is None:
        return encodings
       else:
        lowercase       : List[str]						=encodings['''input_ids''']
        return inputs






      def 					A__   (			self      :		List[Any]  ,   UpperCAmelCase      :		Optional[Any]		)	->	Optional[Any]:


       '''simple docstring'''

       lowercase	,		lowercase	,		lowercase       : Union[str, Any]						=sequences
       lowercase       : Dict						=char_preds.size(0		)

       lowercase	,		lowercase       : Any						=self._decode_helper(UpperCAmelCase  ,   '''char'''		)
       lowercase	,		lowercase       : int						=self._decode_helper(UpperCAmelCase  ,   '''bpe'''		)
       lowercase	,		lowercase       : Optional[Any]						=self._decode_helper(UpperCAmelCase  ,   '''wp'''		)

       lowercase       : Dict						=[]
       lowercase       : List[Any]						=[]
       for i in range(UpperCAmelCase		):
        lowercase       : List[str]						=[char_scores[i], bpe_scores[i], wp_scores[i]]
        lowercase       : Union[str, Any]						=[char_strs[i], bpe_strs[i], wp_strs[i]]
        lowercase       : List[str]						=scores.index(max(UpperCAmelCase		)		)
        final_strs.append(strs[max_score_index]		)
        final_scores.append(scores[max_score_index]		)

       lowercase       : List[Any]						={}
       lowercase       : Optional[Any]						=final_strs
       lowercase       : Optional[Any]						=final_scores
       lowercase       : str						=char_strs
       lowercase       : Tuple						=bpe_strs
       lowercase       : List[str]						=wp_strs
       return out






      def 					A__   (			self      :		str  ,   UpperCAmelCase      :		Dict  ,   UpperCAmelCase      :		Union[str, Any]		)	->	Optional[int]:


       '''simple docstring'''

       if format == DecodeType.CHARACTER:
        lowercase       : Union[str, Any]						=self.char_decode
        lowercase       : List[str]						=1
        lowercase       : Tuple						='''[s]'''
       elif format == DecodeType.BPE:
        lowercase       : Union[str, Any]						=self.bpe_decode
        lowercase       : Optional[int]						=2
        lowercase       : Optional[Any]						='''#'''
       elif format == DecodeType.WORDPIECE:
        lowercase       : List[str]						=self.wp_decode
        lowercase       : Union[str, Any]						=102
        lowercase       : int						='''[SEP]'''
       else:
        raise ValueError(f'Format {format} is not supported.'		)

       lowercase	,		lowercase       : Union[str, Any]						=[], []
       lowercase       : Optional[int]						=pred_logits.size(0		)
       lowercase       : List[str]						=pred_logits.size(1		)
       lowercase	,		lowercase       : Optional[int]						=pred_logits.topk(1  ,   dim=-1  ,   largest=UpperCAmelCase  ,   sorted=UpperCAmelCase		)
       lowercase       : Tuple						=preds_index.view(-1  ,   UpperCAmelCase		)[:, 1:]
       lowercase       : Any						=decoder(UpperCAmelCase		)
       lowercase	,		lowercase       : List[str]						=torch.nn.functional.softmax(UpperCAmelCase  ,   dim=2		).max(dim=2		)
       lowercase       : List[Any]						=preds_max_prob[:, 1:]

       for index in range(UpperCAmelCase		):
        lowercase       : List[Any]						=preds_str[index].find(UpperCAmelCase		)
        lowercase       : Any						=preds_str[index][:pred_eos]
        lowercase       : Any						=preds_index[index].cpu().tolist()
        lowercase       : Optional[int]						=pred_index.index(UpperCAmelCase		) if eos_token in pred_index else -1
        lowercase       : int						=preds_max_prob[index][: pred_eos_index + 1]
        lowercase       : Tuple						=pred_max_prob.cumprod(dim=0		)[-1] if pred_max_prob.nelement() != 0 else 0.0
        dec_strs.append(UpperCAmelCase		)
        conf_scores.append(UpperCAmelCase		)

       return dec_strs, conf_scores






      def 					A__   (			self      :		List[str]  ,   UpperCAmelCase      :		Optional[int]		)	->	Dict:


       '''simple docstring'''

       lowercase       : Union[str, Any]						=[seq.replace(''' '''  ,   ''''''		) for seq in self.char_tokenizer.batch_decode(UpperCAmelCase		)]
       return decode_strs






      def 					A__   (			self      :		Optional[int]  ,   UpperCAmelCase      :		Optional[Any]		)	->	str:


       '''simple docstring'''

       return self.bpe_tokenizer.batch_decode(UpperCAmelCase		)






      def 					A__   (			self      :		Tuple  ,   UpperCAmelCase      :		Dict		)	->	List[str]:


       '''simple docstring'''

       lowercase       : str						=[seq.replace(''' '''  ,   ''''''		) for seq in self.wp_tokenizer.batch_decode(UpperCAmelCase		)]
       return decode_strs



