




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:
  _lowerCamelCase     :		Dict								=       None


_lowerCamelCase     :		Union[str, Any]								=       logging.get_logger(__name__)

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

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


class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    UpperCAmelCase__							  =				VOCAB_FILES_NAMES
    UpperCAmelCase__							  =				PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__							  =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__							  =				['''input_ids''', '''attention_mask''']
    UpperCAmelCase__							  =				TaTokenizer

    UpperCAmelCase__							  =				[]


    def __init__(      self				:     Optional[int] ,			UpperCAmelCase__				:     Any=None ,			UpperCAmelCase__				:     Optional[Any]=None ,			UpperCAmelCase__				:     Optional[int]="</s>" ,			UpperCAmelCase__				:     Tuple="<unk>" ,			UpperCAmelCase__				:     Any="<pad>" ,			UpperCAmelCase__				:     Optional[Any]=100 ,			UpperCAmelCase__				:     Optional[int]=None ,			**UpperCAmelCase__				:     Optional[Any] ,			)						->Union[str, Any]:





           '''simple docstring'''

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

           A__							=   vocab_file
           A__							=   False if not self.vocab_file else True
           A__							=   extra_ids


    @staticmethod
    def 	SCREAMING_SNAKE_CASE							(      UpperCAmelCase__				:     Dict ,			UpperCAmelCase__				:     List[str] ,			UpperCAmelCase__				:     int)						->Optional[int]:





           '''simple docstring'''

           if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
                  A__							=   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.''' ,			UpperCAmelCase__ ,			)

           return max_model_length


    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any] ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     Optional[str] = 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(UpperCAmelCase__):
                  logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
                  return
           A__							=   os.path.join(
               UpperCAmelCase__ ,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])

           if os.path.abspath(self.vocab_file) != os.path.abspath(UpperCAmelCase__):
                  copyfile(self.vocab_file ,			UpperCAmelCase__)
                  logger.info(f"""Copy vocab file to {out_vocab_file}""")

           return (out_vocab_file,)


    def 	SCREAMING_SNAKE_CASE							(      self				:     str ,			UpperCAmelCase__				:     List[int] ,			UpperCAmelCase__				:     Optional[List[int]] = None)						->List[int]:





           '''simple docstring'''

           A__							=   token_ids_a + [self.eos_token_id]
           if token_ids_a is None:
                  return self.prefix_tokens + token_ids_a
           else:
                  A__							=   token_ids_a + [self.eos_token_id]
                  return self.prefix_tokens + token_ids_a + token_ids_a


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any ,			UpperCAmelCase__				:     List[int] ,			UpperCAmelCase__				:     Optional[List[int]] = None)						->List[int]:





           '''simple docstring'''

           A__							=   [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 	SCREAMING_SNAKE_CASE							(      self				:     Tuple)						->Optional[int]:





           '''simple docstring'''

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





    def 	SCREAMING_SNAKE_CASE							(      self				:     List[Any])						->Optional[int]:





           '''simple docstring'''

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








from __future__ import annotations

from typing import Any







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	None:



       """simple docstring"""

       create_state_space_tree(lowercase_			,    []			,    0						)







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	None:



       """simple docstring"""

       if index == len(lowercase_						):
              print(lowercase_						)
              return

       create_state_space_tree(lowercase_			,    lowercase_			,    index + 1						)
       current_subsequence.append(sequence[index]						)
       create_state_space_tree(lowercase_			,    lowercase_			,    index + 1						)
       current_subsequence.pop()


if __name__ == "__main__":
  _lowerCamelCase     :		list[Any]								=       [3, 1, 2, 4]
  generate_all_subsequences(seq)

  seq.clear()
  seq.extend(["""A""", """B""", """C"""])
  generate_all_subsequences(seq)


