





'''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					:		Tuple		=							logging.get_logger(__name__)


UpperCAmelCase					:		Union[str, Any]		=							{name: getattr(transformers, name + 'Fast') for name in SLOW_TO_FAST_CONVERTERS}




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      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:
            _snake_case		:	Dict					     =     TOKENIZER_CLASSES
      else:
            _snake_case		:	Any					     =     {tokenizer_name: getattr(lowerCAmelCase_			,      tokenizer_name + '''Fast'''	)}

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

      for tokenizer_name in tokenizer_names:
            _snake_case		:	Any					     =     TOKENIZER_CLASSES[tokenizer_name]

            _snake_case		:	Optional[int]					     =     True
            if checkpoint_name is None:
                  _snake_case		:	List[Any]					     =     list(tokenizer_class.max_model_input_sizes.keys()	)
            else:
                  _snake_case		:	Union[str, Any]					     =     [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
                  _snake_case		:	Dict					     =     tokenizer_class.from_pretrained(lowerCAmelCase_			,      force_download=lowerCAmelCase_	)

                  # 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:
                        _snake_case		:	Dict					     =     checkpoint.split('''/'''	)
                        _snake_case		:	Any					     =     os.path.join(lowerCAmelCase_			,      lowerCAmelCase_	)
                  elif add_prefix:
                        _snake_case		:	Any					     =     checkpoint
                        _snake_case		:	List[str]					     =     dump_path
                  else:
                        _snake_case		:	Union[str, Any]					     =     None
                        _snake_case		:	Any					     =     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]:
                        _snake_case		:	str					     =     list(tokenizer.pretrained_vocab_files_map.values()	)[0][checkpoint]
                        _snake_case		:	List[str]					     =     file_path.split(lowerCAmelCase_	)[-1][0]
                        if next_char == "/":
                              _snake_case		:	Optional[int]					     =     os.path.join(lowerCAmelCase_			,      lowerCAmelCase_	)
                              _snake_case		:	Optional[int]					     =     None

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

                  _snake_case		:	List[str]					     =     tokenizer.save_pretrained(
                      lowerCAmelCase_			,      legacy_format=lowerCAmelCase_			,      filename_prefix=lowerCAmelCase_	)
                  logger.info(f'''=> File names {file_names}'''	)

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


if __name__ == "__main__":
   UpperCAmelCase					:		Optional[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					:		Tuple		=							parser.parse_args()

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









'''simple docstring'''

import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
   import optax
   from flax.training.common_utils import onehot

   from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
   from transformers.models.ta.modeling_flax_ta import shift_tokens_right





@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class lowerCamelCase						(unittest.TestCase					):







  @slow
  def 				UpperCAmelCase_				(		self		)		->						int:






        """simple docstring"""





        _snake_case		:	Tuple					     =     FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small'''		)
        _snake_case		:	Any					     =     AutoTokenizer.from_pretrained('''google/mt5-small'''		)

        _snake_case		:	List[str]					     =     tokenizer('''Hello there'''     ,     return_tensors='''np'''		).input_ids
        _snake_case		:	Dict					     =     tokenizer('''Hi I am'''     ,     return_tensors='''np'''		).input_ids

        _snake_case		:	Any					     =     shift_tokens_right(lowercase__     ,     model.config.pad_token_id     ,     model.config.decoder_start_token_id		)

        _snake_case		:	Any					     =     model(lowercase__     ,     decoder_input_ids=lowercase__		).logits
        _snake_case		:	Tuple					     =     optax.softmax_cross_entropy(lowercase__     ,     onehot(lowercase__     ,     logits.shape[-1]		)		).mean()

        _snake_case		:	Tuple					     =     -(labels.shape[-1] * loss.item())

        _snake_case		:	Union[str, Any]					     =     -84.9_127
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 1E-4		)


