
'''simple docstring'''

import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
     from .tokenization_ta import TaTokenizer
else:
     snake_case									=    None


snake_case									=    logging.get_logger(__name__)

snake_case									=    {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

snake_case									=    {
    """vocab_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/tokenizer.json""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/tokenizer.json""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/tokenizer.json""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/tokenizer.json""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/tokenizer.json""",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
snake_case									=    {
    """t5-small""": 5_12,
    """t5-base""": 5_12,
    """t5-large""": 5_12,
    """t5-3b""": 5_12,
    """t5-11b""": 5_12,
}




class    lowerCAmelCase       (     UpperCamelCase_  ):
 A_				:				Optional[Any]	=			VOCAB_FILES_NAMES
 A_				:				Dict	=			PRETRAINED_VOCAB_FILES_MAP
 A_				:				Dict	=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 A_				:				str	=			["""input_ids""", """attention_mask"""]
 A_				:				List[str]	=			TaTokenizer

 A_				:				List[int]	=			[]


 def __init__(    self  : List[str]			,							a__  : Union[str, Any]=None			,							a__  : Any=None			,							a__  : Optional[int]="</s>"			,							a__  : List[Any]="<unk>"			,							a__  : Union[str, Any]="<pad>"			,							a__  : str=100			,							a__  : List[str]=None			,							**a__  : Optional[Any]			,							):
  '''simple docstring'''

  if extra_ids > 0 and additional_special_tokens is None:
   lowerCAmelCase__     :   int     		=	[F'''<extra_id_{i}>''' for i in range(a__    )]
  elif extra_ids > 0 and additional_special_tokens is not None:
   # Check that we have the right number of extra special tokens
   lowerCAmelCase__     :   str     		=	len(set(filter(lambda a__    : bool("extra_id_" in str(a__    )    )			,							a__    )    )    )
   if extra_tokens != extra_ids:
    raise ValueError(
        F'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
        " provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"
        " tokens"    )

  super().__init__(
      a__			,							tokenizer_file=a__			,							eos_token=a__			,							unk_token=a__			,							pad_token=a__			,							extra_ids=a__			,							additional_special_tokens=a__			,							**a__			,							)

  lowerCAmelCase__     :   int     		=	vocab_file
  lowerCAmelCase__     :   Optional[int]     		=	False if not self.vocab_file else True
  lowerCAmelCase__     :   Any     		=	extra_ids


 @staticmethod
 def    _A	(    a__  : Optional[int]			,							a__  : Optional[Any]			,							a__  : Tuple    ):
  '''simple docstring'''

  if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
   lowerCAmelCase__     :   str     		=	TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
   if init_max_model_length is not None and init_max_model_length != max_model_length:
    return init_max_model_length
   elif init_max_model_length is None:
    warnings.warn(
        "This tokenizer was incorrectly instantiated with a model max length of"
        F''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
        " behavior is kept to avoid breaking backwards compatibility when padding/encoding with"
        " `truncation is True`.\n- Be aware that you SHOULD NOT rely on"
        F''' {pretrained_model_name_or_path} automatically truncating your input to'''
        F''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
        F''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
        " `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"
        " instantiate this tokenizer with `model_max_length` set to your preferred value."			,							a__			,							)

  return max_model_length


 def    _A	(    self  : List[Any]			,							a__  : str			,							a__  : Optional[str] = None    ):
  '''simple docstring'''

  if not self.can_save_slow_tokenizer:
   raise ValueError(
       "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
       "tokenizer."    )

  if not os.path.isdir(a__    ):
   logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''    )
   return
  lowerCAmelCase__     :   Optional[Any]     		=	os.path.join(
      a__			,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]    )

  if os.path.abspath(self.vocab_file    ) != os.path.abspath(a__    ):
   copyfile(self.vocab_file			,							a__    )
   logger.info(F'''Copy vocab file to {out_vocab_file}'''    )

  return (out_vocab_file,)


 def    _A	(    self  : str			,							a__  : List[int]			,							a__  : Optional[List[int]] = None    ):
  '''simple docstring'''

  lowerCAmelCase__     :   int     		=	token_ids_a + [self.eos_token_id]
  if token_ids_a is None:
   return self.prefix_tokens + token_ids_a
  else:
   lowerCAmelCase__     :   Union[str, Any]     		=	token_ids_a + [self.eos_token_id]
   return self.prefix_tokens + token_ids_a + token_ids_a


 def    _A	(    self  : Dict			,							a__  : List[int]			,							a__  : Optional[List[int]] = None    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[str]     		=	[self.eos_token_id]

  if token_ids_a is None:
   return len(token_ids_a + eos    ) * [0]
  return len(token_ids_a + eos + token_ids_a + eos    ) * [0]


 def    _A	(    self  : int    ):
  '''simple docstring'''

  return list(
      set(filter(lambda a__    : bool(re.search(r"<extra_id_\d+>"			,							a__    )    ) is not None			,							self.additional_special_tokens    )    )    )


 def    _A	(    self  : Tuple    ):
  '''simple docstring'''

  return [self.convert_tokens_to_ids(a__    ) for token in self.get_sentinel_tokens()]







'''simple docstring'''

import unittest

from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


snake_case									=    get_tests_dir("""fixtures/spiece.model""")




@require_sentencepiece
@require_tokenizers
class    lowerCAmelCase       (     UpperCamelCase_    ,  unittest.TestCase  ):
 A_				:				List[str]	=			AlbertTokenizer
 A_				:				List[Any]	=			AlbertTokenizerFast
 A_				:				List[str]	=			True
 A_				:				Any	=			True
 A_				:				List[Any]	=			True


 def    _A	(    self  : Optional[int]    ):
  '''simple docstring'''

  super().setUp()

  # We have a SentencePiece fixture for testing
  lowerCAmelCase__     :   int     		=	AlbertTokenizer(a__    )
  tokenizer.save_pretrained(self.tmpdirname    )


 def    _A	(    self  : Tuple			,							a__  : Optional[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Union[str, Any]     		=	"this is a test"
  lowerCAmelCase__     :   Optional[Any]     		=	"this is a test"
  return input_text, output_text


 def    _A	(    self  : Optional[int]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Union[str, Any]     		=	"<pad>"
  lowerCAmelCase__     :   Dict     		=	0

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


 def    _A	(    self  : Tuple    ):
  '''simple docstring'''

  lowerCAmelCase__     :   int     		=	list(self.get_tokenizer().get_vocab().keys()    )

  self.assertEqual(vocab_keys[0]			,							"<pad>"    )
  self.assertEqual(vocab_keys[1]			,							"<unk>"    )
  self.assertEqual(vocab_keys[-1]			,							"▁eloquent"    )
  self.assertEqual(len(a__    )			,							3_0000    )


 def    _A	(    self  : Optional[int]    ):
  '''simple docstring'''

  self.assertEqual(self.get_tokenizer().vocab_size			,							3_0000    )


 def    _A	(    self  : int    ):
  '''simple docstring'''

  if not self.test_rust_tokenizer:
   return

  lowerCAmelCase__     :   List[Any]     		=	self.get_tokenizer()
  lowerCAmelCase__     :   Optional[int]     		=	self.get_rust_tokenizer()

  lowerCAmelCase__     :   List[str]     		=	"I was born in 92000, and this is falsé."

  lowerCAmelCase__     :   Dict     		=	tokenizer.tokenize(a__    )
  lowerCAmelCase__     :   str     		=	rust_tokenizer.tokenize(a__    )
  self.assertListEqual(a__			,							a__    )

  lowerCAmelCase__     :   List[Any]     		=	tokenizer.encode(a__			,							add_special_tokens=a__    )
  lowerCAmelCase__     :   Tuple     		=	rust_tokenizer.encode(a__			,							add_special_tokens=a__    )
  self.assertListEqual(a__			,							a__    )

  lowerCAmelCase__     :   Optional[int]     		=	self.get_rust_tokenizer()
  lowerCAmelCase__     :   str     		=	tokenizer.encode(a__    )
  lowerCAmelCase__     :   Optional[int]     		=	rust_tokenizer.encode(a__    )
  self.assertListEqual(a__			,							a__    )


 def    _A	(    self  : Optional[int]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[Any]     		=	AlbertTokenizer(a__			,							keep_accents=a__    )

  lowerCAmelCase__     :   Optional[Any]     		=	tokenizer.tokenize("This is a test"    )
  self.assertListEqual(a__			,							["▁this", "▁is", "▁a", "▁test"]    )

  self.assertListEqual(tokenizer.convert_tokens_to_ids(a__    )			,							[48, 25, 21, 1289]    )

  lowerCAmelCase__     :   Optional[int]     		=	tokenizer.tokenize("I was born in 92000, and this is falsé."    )
  self.assertListEqual(
      a__			,							["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", "."]    )
  lowerCAmelCase__     :   Tuple     		=	tokenizer.convert_tokens_to_ids(a__    )
  self.assertListEqual(a__			,							[31, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]    )

  lowerCAmelCase__     :   Optional[int]     		=	tokenizer.convert_ids_to_tokens(a__    )
  self.assertListEqual(
      a__			,							["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "."]			,							)


 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Tuple     		=	AlbertTokenizer(a__    )

  lowerCAmelCase__     :   List[str]     		=	tokenizer.encode("sequence builders"    )
  lowerCAmelCase__     :   Optional[int]     		=	tokenizer.encode("multi-sequence build"    )

  lowerCAmelCase__     :   Tuple     		=	tokenizer.build_inputs_with_special_tokens(a__    )
  lowerCAmelCase__     :   Optional[Any]     		=	tokenizer.build_inputs_with_special_tokens(a__			,							a__    )

  assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
  assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
      tokenizer.sep_token_id
  ]


 @slow
 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Any     		=	{"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, 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, 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, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "input_ids": [[2, 2_1970, 13, 5, 6092, 167, 28, 7103, 2153, 673, 8, 7028, 1_2051, 18, 17, 7103, 2153, 673, 8, 3515, 1_8684, 8, 4461, 6, 1927, 297, 8, 1_2060, 2607, 18, 13, 5, 4461, 15, 1_0538, 38, 8, 135, 15, 822, 58, 15, 993, 1_0363, 15, 1460, 8005, 4461, 15, 993, 255, 2328, 9, 9, 9, 6, 26, 1112, 816, 3260, 13, 5, 103, 2377, 6, 17, 1112, 816, 2782, 13, 5, 103, 1_0641, 6, 29, 84, 2512, 2430, 782, 1_8684, 2761, 19, 808, 2430, 2556, 17, 855, 1480, 9477, 4091, 128, 1_1712, 15, 7103, 2153, 673, 17, 2_4883, 9990, 9, 3], [2, 1_1502, 25, 1006, 20, 782, 8, 1_1809, 855, 1732, 1_9393, 1_8667, 37, 367, 2_1018, 69, 1854, 34, 1_1860, 1_9124, 27, 156, 225, 17, 193, 4141, 19, 65, 9124, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 14, 2231, 886, 2385, 1_7659, 84, 14, 1_6792, 1952, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "token_type_ids": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
  # fmt: on

  self.tokenizer_integration_test_util(
      expected_encoding=a__			,							model_name="albert-base-v2"			,							revision="6b6560eaf5ff2e250b00c50f380c5389a9c2d82e"			,							)





