


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:
 SCREAMING_SNAKE_CASE			: Optional[int]				   =					None


SCREAMING_SNAKE_CASE			: Optional[Any]				   =					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			: List[Any]				   =					{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

SCREAMING_SNAKE_CASE			: int				   =					{
    "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
SCREAMING_SNAKE_CASE			: List[Any]				   =					{
    "t5-small": 512,
    "t5-base": 512,
    "t5-large": 512,
    "t5-3b": 512,
    "t5-11b": 512,
}






class _lowerCamelCase(		_a  ):
       lowercase_					:      Tuple       					=     VOCAB_FILES_NAMES
       lowercase_					:      Any       					=     PRETRAINED_VOCAB_FILES_MAP
       lowercase_					:      Optional[int]       					=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowercase_					:      Any       					=     ["""input_ids""", """attention_mask"""]
       lowercase_					:      List[str]       					=     TaTokenizer

       lowercase_					:      List[int]       					=     []
       def __init__(   self, lowerCamelCase=None, lowerCamelCase=None, lowerCamelCase="</s>", lowerCamelCase="<unk>", lowerCamelCase="<pad>", lowerCamelCase=1_00, lowerCamelCase=None, **lowerCamelCase, )			->  Optional[int]:




           """simple docstring"""

           if extra_ids > 0 and additional_special_tokens is None:
               _lowercase			:				Tuple 						=						[F'''<extra_id_{i}>''' for i in range(lowerCamelCase)]
           elif extra_ids > 0 and additional_special_tokens is not None:
               # Check that we have the right number of extra special tokens
               _lowercase			:				Optional[int] 						=						len(set(filter(lambda lowerCamelCase: bool('extra_id_' in str(lowerCamelCase)), lowerCamelCase)))
               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__(
               lowerCamelCase, tokenizer_file=lowerCamelCase, eos_token=lowerCamelCase, unk_token=lowerCamelCase, pad_token=lowerCamelCase, extra_ids=lowerCamelCase, additional_special_tokens=lowerCamelCase, **lowerCamelCase, )

           _lowercase			:				Any 						=						vocab_file
           _lowercase			:				Dict 						=						False if not self.vocab_file else True
           _lowercase			:				Dict 						=						extra_ids
       @staticmethod
       def      UpperCamelCase	(   lowerCamelCase, lowerCamelCase, lowerCamelCase)			->  Dict:




           """simple docstring"""

           if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
               _lowercase			:				Dict 						=						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.', lowerCamelCase, )

           return max_model_length
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = None)			->  Tuple[str]:




           """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(lowerCamelCase):
               logger.error(F'''Vocabulary path ({save_directory}) should be a directory''')
               return
           _lowercase			:				List[Any] 						=						os.path.join(
               lowerCamelCase, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

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

           return (out_vocab_file,)
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = None)			->  List[int]:




           """simple docstring"""

           _lowercase			:				List[Any] 						=						token_ids_a + [self.eos_token_id]
           if token_ids_a is None:
               return self.prefix_tokens + token_ids_a
           else:
               _lowercase			:				Tuple 						=						token_ids_a + [self.eos_token_id]
               return self.prefix_tokens + token_ids_a + token_ids_a
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = None)			->  List[int]:




           """simple docstring"""

           _lowercase			:				List[Any] 						=						[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      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

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




       def      UpperCamelCase	(   self)			->  List[str]:




           """simple docstring"""

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






import os

try:
 from .build_directory_md import good_file_paths
except ImportError:
 from build_directory_md import good_file_paths  # type: ignore

SCREAMING_SNAKE_CASE			: int				   =					list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

SCREAMING_SNAKE_CASE			: Dict				   =					[file for file in filepaths if file != file.lower()]
if upper_files:
 print(F"{len(upper_files)} files contain uppercase characters:")
 print("\n".join(upper_files) + "\n")

SCREAMING_SNAKE_CASE			: List[Any]				   =					[file for file in filepaths if " " in file]
if space_files:
 print(F"{len(space_files)} files contain space characters:")
 print("\n".join(space_files) + "\n")

SCREAMING_SNAKE_CASE			: Any				   =					[file for file in filepaths if "-" in file]
if hyphen_files:
 print(F"{len(hyphen_files)} files contain hyphen characters:")
 print("\n".join(hyphen_files) + "\n")

SCREAMING_SNAKE_CASE			: str				   =					[file for file in filepaths if os.sep not in file]
if nodir_files:
 print(F"{len(nodir_files)} files are not in a directory:")
 print("\n".join(nodir_files) + "\n")

SCREAMING_SNAKE_CASE			: Tuple				   =					len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
 import sys

 sys.exit(bad_files)


