



import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

snake_case    :						str  =						logging.get_logger(__name__)


snake_case    :						Union[str, Any]  =						{name: getattr(transformers, name + 'Fast') for name in SLOW_TO_FAST_CONVERTERS}
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase							,     __lowercase       )		->		List[Any]:







      """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:
            A__	:							Dict						    = TOKENIZER_CLASSES
      else:
            A__	:							Tuple						    = {tokenizer_name: getattr(__lowercase							,     tokenizer_name + "Fast"       )}

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

      for tokenizer_name in tokenizer_names:
            A__	:							Optional[int]						    = TOKENIZER_CLASSES[tokenizer_name]

            A__	:							str						    = True
            if checkpoint_name is None:
                  A__	:							str						    = list(tokenizer_class.max_model_input_sizes.keys()       )
            else:
                  A__	:							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
                  A__	:							Optional[int]						    = tokenizer_class.from_pretrained(__lowercase							,     force_download=__lowercase       )

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

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

                  A__	:							List[str]						    = tokenizer.save_pretrained(
                      __lowercase							,     legacy_format=__lowercase							,     filename_prefix=__lowercase       )
                  logger.info(F'=> File names {file_names}'       )

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


if __name__ == "__main__":
  snake_case    :						Dict  =						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.',
  )
  snake_case    :						Union[str, Any]  =						parser.parse_args()

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




from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset




class 							lowerCAmelCase__  (	UpperCamelCase						):







					def  _lowercase			(       self      :     List[Any]):
											return [
											    {"col_1": 3, "col_2": "a"},
											    {"col_1": 2, "col_2": "b"},
											    {"col_1": 1, "col_2": "c"},
											    {"col_1": 0, "col_2": "d"},
											]







					def  _lowercase			(       self      :     Tuple):
											A__	:							Any						    = {"col_1": [3, 2, 1, 0], "col_2": ["a", "b", "c", "d"]}
											return Dataset.from_dict(_A)







					def  _lowercase			(       self      :     Union[str, Any]):
											A__	:							str						    = self._create_example_records()
											A__	:							Dict						    = Dataset.from_list(_A)
											self.assertListEqual(dset.column_names			,		["col_1", "col_2"])
											for i, r in enumerate(_A):
																	self.assertDictEqual(_A			,		example_records[i])







					def  _lowercase			(       self      :     List[str]):
											A__	:							List[str]						    = self._create_example_records()
											A__	:							Any						    = Dataset.from_list(_A)
											A__	:							Optional[int]						    = Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]})
											self.assertEqual(dset.info			,		dset_from_dict.info)







					def  _lowercase			(       self      :     Any):  # checks what happens with missing columns
											A__	:							Dict						    = [{"col_1": 1}, {"col_2": "x"}]
											A__	:							Any						    = Dataset.from_list(_A)
											self.assertDictEqual(dset[0]			,		{"col_1": 1})
											self.assertDictEqual(dset[1]			,		{"col_1": None})  # NB: first record is used for columns







					def  _lowercase			(       self      :     str):  # checks if the type can be inferred from the second record
											A__	:							Dict						    = [{"col_1": []}, {"col_1": [1, 2]}]
											A__	:							Dict						    = Dataset.from_list(_A)
											self.assertEqual(dset.info.features["col_1"]			,		Sequence(Value("int64")))







					def  _lowercase			(       self      :     Union[str, Any]):
											A__	:							Dict						    = Dataset.from_list([])
											self.assertEqual(len(_A)			,		0)
											self.assertListEqual(dset.column_names			,		[])