





import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class        _lowerCAmelCase    (			UpperCAmelCase_							,    unittest.TestCase							):


  '''simple docstring'''
  a_							:       Any      					=OpenAIGPTTokenizer
  a_							:       List[Any]      					=OpenAIGPTTokenizerFast
  a_							:       str      					=True
  a_							:       int      					=False







  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    _snake_case   :				Union[str, Any]														=	[
        'l',
        'o',
        'w',
        'e',
        'r',
        's',
        't',
        'i',
        'd',
        'n',
        'w</w>',
        'r</w>',
        't</w>',
        'lo',
        'low',
        'er</w>',
        'low</w>',
        'lowest</w>',
        'newer</w>',
        'wider</w>',
        '<unk>',
    ]
    _snake_case   :				Tuple														=	dict(zip(UpperCamelCase      ,				range(len(UpperCamelCase							)							)							)							)
    _snake_case   :				str														=	['#version: 0.2', 'l o', 'lo w', 'e r</w>', '']

    _snake_case   :				Union[str, Any]														=	os.path.join(self.tmpdirname      ,				VOCAB_FILES_NAMES['vocab_file']							)
    _snake_case   :				str														=	os.path.join(self.tmpdirname      ,				VOCAB_FILES_NAMES['merges_file']							)
    with open(self.vocab_file      ,				'w'							) as fp:
      fp.write(json.dumps(UpperCamelCase							)							)
    with open(self.merges_file      ,				'w'							) as fp:
      fp.write('\n'.join(UpperCamelCase							)							)







  def UpperCamelCase_ (      self      :					Tuple      ,				UpperCamelCase      :					Tuple							):


    '''simple docstring'''





    return "lower newer", "lower newer"







  def UpperCamelCase_ (      self      :					Any							):


    '''simple docstring'''





    _snake_case   :				List[Any]														=	OpenAIGPTTokenizer(self.vocab_file      ,				self.merges_file							)

    _snake_case   :				Any														=	'lower'
    _snake_case   :				Optional[Any]														=	['low', 'er</w>']
    _snake_case   :				Optional[int]														=	tokenizer.tokenize(UpperCamelCase							)
    self.assertListEqual(UpperCamelCase      ,				UpperCamelCase							)

    _snake_case   :				Any														=	tokens + ['<unk>']
    _snake_case   :				Dict														=	[14, 15, 20]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase							)      ,				UpperCamelCase							)







  def UpperCamelCase_ (      self      :					Optional[int]      ,				UpperCamelCase      :					str=15							):


    '''simple docstring'''





    for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
      with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
        _snake_case   :				int														=	self.rust_tokenizer_class.from_pretrained(UpperCamelCase      ,				**UpperCamelCase							)

        # Simple input
        _snake_case   :				Optional[int]														=	'This is a simple input'
        _snake_case   :				Union[str, Any]														=	['This is a simple input 1', 'This is a simple input 2']
        _snake_case   :				str														=	('This is a simple input', 'This is a pair')
        _snake_case   :				Tuple														=	[
            ('This is a simple input 1', 'This is a simple input 2'),
            ('This is a simple pair 1', 'This is a simple pair 2'),
        ]

        # Simple input tests
        self.assertRaises(UpperCamelCase      ,				tokenizer_r.encode      ,				UpperCamelCase      ,				max_length=UpperCamelCase      ,				padding='max_length'							)

        # Simple input
        self.assertRaises(UpperCamelCase      ,				tokenizer_r.encode_plus      ,				UpperCamelCase      ,				max_length=UpperCamelCase      ,				padding='max_length'							)

        # Simple input
        self.assertRaises(
            UpperCamelCase      ,				tokenizer_r.batch_encode_plus      ,				UpperCamelCase      ,				max_length=UpperCamelCase      ,				padding='max_length'      ,				)

        # Pair input
        self.assertRaises(UpperCamelCase      ,				tokenizer_r.encode      ,				UpperCamelCase      ,				max_length=UpperCamelCase      ,				padding='max_length'							)

        # Pair input
        self.assertRaises(UpperCamelCase      ,				tokenizer_r.encode_plus      ,				UpperCamelCase      ,				max_length=UpperCamelCase      ,				padding='max_length'							)

        # Pair input
        self.assertRaises(
            UpperCamelCase      ,				tokenizer_r.batch_encode_plus      ,				UpperCamelCase      ,				max_length=UpperCamelCase      ,				padding='max_length'      ,				)







  def UpperCamelCase_ (      self      :					int							):


    '''simple docstring'''





    pass




@require_ftfy
@require_spacy
@require_tokenizers
class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''
  pass











import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

lowerCAmelCase_						   =    logging.get_logger(__name__)


lowerCAmelCase_						   =    {name: getattr(transformers, name + """Fast""") for name in SLOW_TO_FAST_CONVERTERS}


def     lowerCamelCase_	(      lowerCAmelCase:	List[Any]    ,							lowerCAmelCase:	Optional[Any]    ,							lowerCAmelCase:	Dict    ,							lowerCAmelCase:	Union[str, Any]				)->		Optional[int]:
  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   :				Tuple														=	TOKENIZER_CLASSES
  else:
    _snake_case   :				Union[str, Any]														=	{tokenizer_name: getattr(lowerCAmelCase    ,							tokenizer_name + 'Fast'				)}

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

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

    _snake_case   :				Optional[Any]														=	True
    if checkpoint_name is None:
      _snake_case   :				Union[str, Any]														=	list(tokenizer_class.max_model_input_sizes.keys()				)
    else:
      _snake_case   :				Optional[int]														=	[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   :				str														=	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							,					_snake_case   :				Tuple														=	checkpoint.split('/'				)
        _snake_case   :				int														=	os.path.join(lowerCAmelCase    ,							lowerCAmelCase				)
      elif add_prefix:
        _snake_case   :				Dict														=	checkpoint
        _snake_case   :				Optional[Any]														=	dump_path
      else:
        _snake_case   :				str														=	None
        _snake_case   :				Union[str, 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   :				Optional[Any]														=	list(tokenizer.pretrained_vocab_files_map.values()				)[0][checkpoint]
        _snake_case   :				Optional[int]														=	file_path.split(lowerCAmelCase				)[-1][0]
        if next_char == "/":
          _snake_case   :				Union[str, Any]														=	os.path.join(lowerCAmelCase    ,							lowerCAmelCase				)
          _snake_case   :				str														=	None

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

      _snake_case   :				Optional[int]														=	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__":
      lowerCAmelCase_						   =    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.""",
      )
      lowerCAmelCase_						   =    parser.parse_args()

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




