





'''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:
  __lowerCAmelCase							:	Optional[int]			=None


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

__lowerCAmelCase							:	Optional[Any]			={"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							:	Any			={
    "t5-small": 512,
    "t5-base": 512,
    "t5-large": 512,
    "t5-3b": 512,
    "t5-11b": 512,
}






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				VOCAB_FILES_NAMES
 __lowercase   		=				PRETRAINED_VOCAB_FILES_MAP
 __lowercase   		=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowercase   		=				["""input_ids""", """attention_mask"""]
 __lowercase   		=				TaTokenizer

 __lowercase   		=				[]
 def __init__(				self      :Optional[Any]						,       lowercase_      :Tuple=None						,       lowercase_      :Optional[int]=None						,       lowercase_      :Optional[Any]="</s>"						,       lowercase_      :Tuple="<unk>"						,       lowercase_      :Optional[Any]="<pad>"						,       lowercase_      :Union[str, Any]=1_00						,       lowercase_      :Any=None						,       **lowercase_      :List[str]						,       )->							Dict:
     # Add extra_ids to the special token list
     if extra_ids > 0 and additional_special_tokens is None:
         A__				=			[F"<extra_id_{i}>" for i in range(lowercase_							)]
     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 lowercase_							: bool("extra_id_" in str(lowercase_							)							)						,       lowercase_							)							)							)
         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__(
         lowercase_						,       tokenizer_file=lowercase_						,       eos_token=lowercase_						,       unk_token=lowercase_						,       pad_token=lowercase_						,       extra_ids=lowercase_						,       additional_special_tokens=lowercase_						,       **lowercase_						,       )

     A__				=			vocab_file
     A__				=			False if not self.vocab_file else True
     A__				=			extra_ids
 @staticmethod
 def      UpperCAmelCase_							(				lowercase_      :Optional[int]						,       lowercase_      :Any						,       lowercase_      :Optional[Any]							)->							str:
     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."						,       lowercase_						,       )

     return max_model_length
 def      UpperCAmelCase_							(				self      :Optional[int]						,       lowercase_      :str						,       lowercase_      :Optional[str] = None							)->							Tuple[str]:
     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(lowercase_							):
         logger.error(F"Vocabulary path ({save_directory}) should be a directory"							)
         return
     A__				=			os.path.join(
         lowercase_						,       (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)

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

     return (out_vocab_file,)
 def      UpperCAmelCase_							(				self      :List[Any]						,       lowercase_      :List[int]						,       lowercase_      :Optional[List[int]] = None							)->							List[int]:
     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      UpperCAmelCase_							(				self      :List[Any]						,       lowercase_      :List[int]						,       lowercase_      :Optional[List[int]] = None							)->							List[int]:
     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      UpperCAmelCase_							(				self      :Any							)->							Any:
     return list(
         set(filter(lambda lowercase_							: bool(re.search(R"<extra_id_\d+>"						,       lowercase_							)							) is not None						,       self.additional_special_tokens							)							)							)



 def      UpperCAmelCase_							(				self      :Tuple							)->							Union[str, Any]:
     return [self.convert_tokens_to_ids(lowercase_							) for token in self.get_sentinel_tokens()]










'''simple docstring'''

from __future__ import annotations





def 						UpperCamelCase				(							_lowerCamelCase					:					list[int | str]       ):
    create_state_space_tree(_lowerCamelCase						,       []						,       0						,       [0 for i in range(len(_lowerCamelCase       )       )]       )





def 						UpperCamelCase				(							_lowerCamelCase					:					list[int | str]						,       _lowerCamelCase					:					list[int | str]						,       _lowerCamelCase					:					int						,       _lowerCamelCase					:					list[int]						,       ):

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

    for i in range(len(_lowerCamelCase       )       ):
        if not index_used[i]:
            current_sequence.append(sequence[i]       )
            A__				=			True
            create_state_space_tree(_lowerCamelCase						,       _lowerCamelCase						,       index + 1						,       _lowerCamelCase       )
            current_sequence.pop()
            A__				=			False
__lowerCAmelCase							:	list[int | str]			=[3, 1, 2, 4]
generate_all_permutations(sequence)

__lowerCAmelCase							:	list[int | str]			=["A", "B", "C"]



generate_all_permutations(sequence_a)



