





'''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:
       _SCREAMING_SNAKE_CASE	 =							None


_SCREAMING_SNAKE_CASE	 =							logging.get_logger(__name__)

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

_SCREAMING_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
_SCREAMING_SNAKE_CASE	 =							{
    "t5-small": 5_12,
    "t5-base": 5_12,
    "t5-large": 5_12,
    "t5-3b": 5_12,
    "t5-11b": 5_12,
}

class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							Tuple         =					VOCAB_FILES_NAMES
 __lowerCamelCase						:							Any         =					PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase						:							List[Any]         =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase						:							Optional[Any]         =					["input_ids", "attention_mask"]
 __lowerCamelCase						:							int         =					TaTokenizer

 __lowerCamelCase						:							List[int]         =					[]




 def __init__(	self			,     _lowerCAmelCase=None			,     _lowerCAmelCase=None			,     _lowerCAmelCase="</s>"			,     _lowerCAmelCase="<unk>"			,     _lowerCAmelCase="<pad>"			,     _lowerCAmelCase=100			,     _lowerCAmelCase=None			,     **_lowerCAmelCase			,     )    ->							List[str]:
     # Add extra_ids to the special token list
     if extra_ids > 0 and additional_special_tokens is None:
         _lowerCAmelCase				=      [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
         _lowerCAmelCase				=      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			,     )

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




 @staticmethod
 def 							_snake_case   (	_lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							str:
     if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
         _lowerCAmelCase				=      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 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase = 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(_lowerCAmelCase		):
         logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
         return
     _lowerCAmelCase				=      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 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase = None		)    ->							List[int]:
     _lowerCAmelCase				=      token_ids_a + [self.eos_token_id]
     if token_ids_a is None:
         return self.prefix_tokens + token_ids_a
     else:
         _lowerCAmelCase				=      token_ids_a + [self.eos_token_id]
         return self.prefix_tokens + token_ids_a + token_ids_a




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase = None		)    ->							List[int]:
     _lowerCAmelCase				=      [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 							_snake_case   (	self		)    ->							str:
     return list(
         set(filter(lambda _lowerCAmelCase		: bool(re.search(r"<extra_id_\d+>"			,     _lowerCAmelCase		)		) is not None			,     self.additional_special_tokens		)		)		)





 def 							_snake_case   (	self		)    ->							List[str]:
     return [self.convert_tokens_to_ids(_lowerCAmelCase		) for token in self.get_sentinel_tokens()]









'''simple docstring'''


import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


_SCREAMING_SNAKE_CASE	 =							logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	 =							"T5Config"


def 	__a(SCREAMING_SNAKE_CASE_  :					jnp.array		,       SCREAMING_SNAKE_CASE_  :					int		,       SCREAMING_SNAKE_CASE_  :					int    ):



    '''simple docstring'''





    _lowerCAmelCase				=      jnp.zeros_like(SCREAMING_SNAKE_CASE_    )
    _lowerCAmelCase				=      shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]    )
    _lowerCAmelCase				=      shifted_input_ids.at[:, 0].set(SCREAMING_SNAKE_CASE_    )

    _lowerCAmelCase				=      jnp.where(shifted_input_ids == -100		,       SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    )
    return shifted_input_ids

class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							List[str]         =					"mt5"
 __lowerCamelCase						:							Any         =					MTaConfig







class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							List[str]         =					"mt5"
 __lowerCamelCase						:							Dict         =					MTaConfig







class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							Optional[Any]         =					"mt5"
 __lowerCamelCase						:							str         =					MTaConfig


