import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCamelCase      :			Any							       =		get_tests_dir('''fixtures/test_sentencepiece.model''')







@require_sentencepiece
@require_tokenizers
class __snake_case    (						lowerCamelCase_       ,		unittest.TestCase		):
   lowerCAmelCase_														=							ReformerTokenizer
   lowerCAmelCase_														=							ReformerTokenizerFast
   lowerCAmelCase_														=							True
   lowerCAmelCase_														=							False
   lowerCAmelCase_														=							True




   def      __a						(   self      :    Tuple				):



     """simple docstring"""






     super().setUp()

     SCREAMING_SNAKE_CASE__      							=				ReformerTokenizer(_lowercase	,     keep_accents=_lowercase				)
     tokenizer.save_pretrained(self.tmpdirname				)




   def      __a						(   self      :    Any				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				"""<s>"""
     SCREAMING_SNAKE_CASE__      							=				1

     self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowercase				)	,     _lowercase				)
     self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowercase				)	,     _lowercase				)




   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				list(self.get_tokenizer().get_vocab().keys()				)

     self.assertEqual(vocab_keys[0]	,     """<unk>"""				)
     self.assertEqual(vocab_keys[1]	,     """<s>"""				)
     self.assertEqual(vocab_keys[-1]	,     """j"""				)
     self.assertEqual(len(_lowercase				)	,     10_00				)




   def      __a						(   self      :    Dict				):



     """simple docstring"""






     self.assertEqual(self.get_tokenizer().vocab_size	,     10_00				)




   def      __a						(   self      :    List[str]				):



     """simple docstring"""






     if not self.test_rust_tokenizer:
       return

     SCREAMING_SNAKE_CASE__      							=				self.get_tokenizer()
     SCREAMING_SNAKE_CASE__      							=				self.get_rust_tokenizer()

     SCREAMING_SNAKE_CASE__      							=				"""I was born in 92000, and this is falsé."""

     SCREAMING_SNAKE_CASE__      							=				tokenizer.tokenize(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				rust_tokenizer.tokenize(_lowercase				)
     self.assertListEqual(_lowercase	,     _lowercase				)

     SCREAMING_SNAKE_CASE__      							=				tokenizer.encode(_lowercase	,     add_special_tokens=_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				rust_tokenizer.encode(_lowercase	,     add_special_tokens=_lowercase				)
     self.assertListEqual(_lowercase	,     _lowercase				)

     SCREAMING_SNAKE_CASE__      							=				self.get_rust_tokenizer()
     SCREAMING_SNAKE_CASE__      							=				tokenizer.encode(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				rust_tokenizer.encode(_lowercase				)
     self.assertListEqual(_lowercase	,     _lowercase				)




   def      __a						(   self      :    Union[str, Any]	,     _lowercase      :    Optional[Any]=15				):



     """simple docstring"""






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

         # Simple input
         SCREAMING_SNAKE_CASE__      							=				"""This is a simple input"""
         SCREAMING_SNAKE_CASE__      							=				["""This is a simple input 1""", """This is a simple input 2"""]
         SCREAMING_SNAKE_CASE__      							=				("""This is a simple input""", """This is a pair""")
         SCREAMING_SNAKE_CASE__      							=				[
             ("""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(_lowercase	,     tokenizer_r.encode	,     _lowercase	,     max_length=_lowercase	,     padding="""max_length"""				)

         # Simple input
         self.assertRaises(_lowercase	,     tokenizer_r.encode_plus	,     _lowercase	,     max_length=_lowercase	,     padding="""max_length"""				)

         # Simple input
         self.assertRaises(
             _lowercase	,     tokenizer_r.batch_encode_plus	,     _lowercase	,     max_length=_lowercase	,     padding="""max_length"""	,     )

         # Pair input
         self.assertRaises(_lowercase	,     tokenizer_r.encode	,     _lowercase	,     max_length=_lowercase	,     padding="""max_length"""				)

         # Pair input
         self.assertRaises(_lowercase	,     tokenizer_r.encode_plus	,     _lowercase	,     max_length=_lowercase	,     padding="""max_length"""				)

         # Pair input
         self.assertRaises(
             _lowercase	,     tokenizer_r.batch_encode_plus	,     _lowercase	,     max_length=_lowercase	,     padding="""max_length"""	,     )




   def      __a						(   self      :    Dict				):



     """simple docstring"""






     pass




   def      __a						(   self      :    Dict				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				ReformerTokenizer(_lowercase	,     keep_accents=_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				tokenizer.tokenize("""This is a test"""				)
     self.assertListEqual(_lowercase	,     ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]				)

     self.assertListEqual(
         tokenizer.convert_tokens_to_ids(_lowercase				)	,     [2_85, 46, 10, 1_70, 3_82]	,     )

     SCREAMING_SNAKE_CASE__      							=				tokenizer.tokenize("""I was born in 92000, and this is falsé."""				)
     self.assertListEqual(
         _lowercase	,     [
             SPIECE_UNDERLINE + """I""",
             SPIECE_UNDERLINE + """was""",
             SPIECE_UNDERLINE + """b""",
             """or""",
             """n""",
             SPIECE_UNDERLINE + """in""",
             SPIECE_UNDERLINE + """""",
             """9""",
             """2""",
             """0""",
             """0""",
             """0""",
             """,""",
             SPIECE_UNDERLINE + """and""",
             SPIECE_UNDERLINE + """this""",
             SPIECE_UNDERLINE + """is""",
             SPIECE_UNDERLINE + """f""",
             """al""",
             """s""",
             """é""",
             """.""",
         ]	,     )
     SCREAMING_SNAKE_CASE__      							=				tokenizer.convert_tokens_to_ids(_lowercase				)
     self.assertListEqual(
         _lowercase	,     [8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4]	,     )

     SCREAMING_SNAKE_CASE__      							=				tokenizer.convert_ids_to_tokens(_lowercase				)
     self.assertListEqual(
         _lowercase	,     [
             SPIECE_UNDERLINE + """I""",
             SPIECE_UNDERLINE + """was""",
             SPIECE_UNDERLINE + """b""",
             """or""",
             """n""",
             SPIECE_UNDERLINE + """in""",
             SPIECE_UNDERLINE + """""",
             """<unk>""",
             """2""",
             """0""",
             """0""",
             """0""",
             """,""",
             SPIECE_UNDERLINE + """and""",
             SPIECE_UNDERLINE + """this""",
             SPIECE_UNDERLINE + """is""",
             SPIECE_UNDERLINE + """f""",
             """al""",
             """s""",
             """<unk>""",
             """.""",
         ]	,     )




   @cached_property
   def      __a						(   self      :    Union[str, Any]				):



     """simple docstring"""






     return ReformerTokenizer.from_pretrained("""google/reformer-crime-and-punishment"""				)




   @slow
   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				"""Hello World!"""
     SCREAMING_SNAKE_CASE__      							=				[1_26, 32, 2_62, 1_52, 38, 72, 2_87]

     self.assertListEqual(_lowercase	,     self.big_tokenizer.encode(_lowercase				)				)




   @slow
   def      __a						(   self      :    Union[str, Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				(
         """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
         """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
     )
     SCREAMING_SNAKE_CASE__      							=				[
         1_08,
         2_65,
         24,
         1_11,
         4,
         2_58,
         1_56,
         35,
         28,
         2_75,
         3,
         2_59,
         2_97,
         2_60,
         84,
         4,
         35,
         1_10,
         44,
         8,
         2_59,
         91,
         2_68,
         21,
         11,
         2_09,
         2_74,
         1_09,
         2_66,
         2_77,
         1_17,
         86,
         93,
         3_15,
         2_58,
         2_78,
         2_58,
         2_77,
         2_58,
         0,
         2_58,
         2_88,
         2_58,
         3_19,
         2_58,
         0,
         2_58,
         0,
         2_58,
         0,
         2_58,
         0,
         2_58,
         2_87,
         2_58,
         3_15,
         2_58,
         2_89,
         2_58,
         2_78,
         99,
         2_69,
         2_66,
         2_62,
         8,
         2_59,
         2_41,
         4,
         2_17,
         2_30,
         2_68,
         2_66,
         55,
         1_68,
         1_06,
         75,
         1_93,
         2_66,
         2_23,
         27,
         49,
         26,
         2_82,
         25,
         2_64,
         2_99,
         19,
         26,
         0,
         2_58,
         2_77,
         1_17,
         86,
         93,
         1_76,
         1_83,
         2_70,
         11,
         2_62,
         42,
         61,
         2_65,
     ]

     self.assertListEqual(_lowercase	,     self.big_tokenizer.encode(_lowercase				)				)




   @require_torch
   @slow
   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






     import torch

     from transformers import ReformerConfig, ReformerModel

     # Build sequence
     SCREAMING_SNAKE_CASE__      							=				list(self.big_tokenizer.get_vocab().keys()				)[:10]
     SCREAMING_SNAKE_CASE__      							=				""" """.join(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				self.big_tokenizer.encode_plus(_lowercase	,     return_tensors="""pt"""				)
     SCREAMING_SNAKE_CASE__      							=				self.big_tokenizer.batch_encode_plus([sequence, sequence]	,     return_tensors="""pt"""				)

     SCREAMING_SNAKE_CASE__      							=				ReformerConfig()
     # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
     SCREAMING_SNAKE_CASE__      							=				encoded_sequence["""input_ids"""].shape
     SCREAMING_SNAKE_CASE__      							=				ReformerModel(_lowercase				)

     # Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
     assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

     with torch.no_grad():
       model(**_lowercase				)
       model(**_lowercase				)




   @slow
   def      __a						(   self      :    str				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				{"""input_ids""": [[1_08, 2_65, 24, 1_11, 4, 2_58, 1_56, 7, 51, 2_79, 58, 7, 76, 25, 69, 2_78], [1_40, 2_43, 2_64, 1_34, 17, 2_67, 77, 2_63, 22, 2_62, 2_97, 2_58, 3_04, 1_77, 2_79, 2_66, 14, 89, 13, 35, 2_61, 2_99, 2_72, 1_37, 2_75, 2_78]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
     # fmt: on

     # This tokenizer does not know some characters like ")".
     # That is the reason why we use very simple texts here.
     # Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
     SCREAMING_SNAKE_CASE__      							=				[
         """This is a very simple sentence.""",
         """The quick brown fox jumps over the lazy dog.""",
     ]

     self.tokenizer_integration_test_util(
         expected_encoding=_lowercase	,     model_name="""google/reformer-crime-and-punishment"""	,     revision="""0e6c3decb8211d49bf881013425dc8b0448b3f5a"""	,     padding=_lowercase	,     sequences=_lowercase	,     )

import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

__lowerCamelCase      :			int							       =		logging.get_logger(__name__)


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



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	int			,					__UpperCamelCase   :	Optional[int]			,					__UpperCamelCase   :	str			,					__UpperCamelCase   :	Optional[Any]     )							-> Union[str, 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:
    SCREAMING_SNAKE_CASE__      							=				TOKENIZER_CLASSES
  else:
    SCREAMING_SNAKE_CASE__      							=				{tokenizer_name: getattr(__UpperCamelCase			,					tokenizer_name + """Fast"""     )}

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

  for tokenizer_name in tokenizer_names:
    SCREAMING_SNAKE_CASE__      							=				TOKENIZER_CLASSES[tokenizer_name]

    SCREAMING_SNAKE_CASE__      							=				True
    if checkpoint_name is None:
      SCREAMING_SNAKE_CASE__      							=				list(tokenizer_class.max_model_input_sizes.keys()     )
    else:
      SCREAMING_SNAKE_CASE__      							=				[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
      SCREAMING_SNAKE_CASE__      							=				tokenizer_class.from_pretrained(__UpperCamelCase			,					force_download=__UpperCamelCase     )

      # 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:
        SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				checkpoint.split("""/"""     )
        SCREAMING_SNAKE_CASE__      							=				os.path.join(__UpperCamelCase			,					__UpperCamelCase     )
      elif add_prefix:
        SCREAMING_SNAKE_CASE__      							=				checkpoint
        SCREAMING_SNAKE_CASE__      							=				dump_path
      else:
        SCREAMING_SNAKE_CASE__      							=				None
        SCREAMING_SNAKE_CASE__      							=				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]:
        SCREAMING_SNAKE_CASE__      							=				list(tokenizer.pretrained_vocab_files_map.values()     )[0][checkpoint]
        SCREAMING_SNAKE_CASE__      							=				file_path.split(__UpperCamelCase     )[-1][0]
        if next_char == "/":
          SCREAMING_SNAKE_CASE__      							=				os.path.join(__UpperCamelCase			,					__UpperCamelCase     )
          SCREAMING_SNAKE_CASE__      							=				None

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

      SCREAMING_SNAKE_CASE__      							=				tokenizer.save_pretrained(
          __UpperCamelCase			,					legacy_format=__UpperCamelCase			,					filename_prefix=__UpperCamelCase     )
      logger.info(f"""=> File names {file_names}"""     )

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


if __name__ == "__main__":
   __lowerCamelCase      :			str							       =		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      :			Any							       =		parser.parse_args()

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