


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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
   from ...tokenization_utils_base import TextInput
from ...utils import logging


lowerCamelCase__							:						int							     =   logging.get_logger(__name__)

lowerCamelCase__							:						str							     =   {"""vocab_file""": """spiece.model"""}

lowerCamelCase__							:						Optional[Any]							     =   {
    """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""",
    }
}


# TODO(PVP) - this should be removed in Transformers v5
lowerCamelCase__							:						str							     =   {
    """t5-small""": 5_1_2,
    """t5-base""": 5_1_2,
    """t5-large""": 5_1_2,
    """t5-3b""": 5_1_2,
    """t5-11b""": 5_1_2,
}

lowerCamelCase__							:						Union[str, Any]							     =   """▁"""
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							Optional[Any]	   =       VOCAB_FILES_NAMES
     __lowercase				:							str	   =       PRETRAINED_VOCAB_FILES_MAP
     __lowercase				:							List[str]	   =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __lowercase				:							Dict	   =       ['input_ids', 'attention_mask']

     def __init__(					self:int     ,		_a:Any     ,		_a:List[str]="</s>"     ,		_a:Union[str, Any]="<unk>"     ,		_a:List[Any]="<pad>"     ,		_a:Optional[Any]=1_00     ,		_a:List[str]=None     ,		_a:Optional[Dict[str, Any]] = None     ,		_a:int=True     ,		**_a:int     ,		):
       # Add extra_ids to the special token list
       if extra_ids > 0 and additional_special_tokens is None:
         snake_case__						= [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_id special tokens
         snake_case__						= 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'''   )
       if legacy:
         logger.warning_once(
             F"""You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"""
             ''' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'''   )

       snake_case__						= legacy
       snake_case__						= {} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           eos_token=_a     ,		unk_token=_a     ,		pad_token=_a     ,		extra_ids=_a     ,		additional_special_tokens=_a     ,		sp_model_kwargs=self.sp_model_kwargs     ,		legacy=_a     ,		**_a     ,		)

       snake_case__						= vocab_file
       snake_case__						= extra_ids

       snake_case__						= spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.Load(_a   )

     @staticmethod
     def 	SCREAMING_SNAKE_CASE__						(					_a:Optional[int]     ,		_a:Union[str, Any]     ,		_a:str   ):
       if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
         snake_case__						= TaTokenizer.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

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       return self.sp_model.get_piece_size() + self._extra_ids

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       snake_case__						= {self.convert_ids_to_tokens(_a   ): i for i in range(self.vocab_size   )}
       vocab.update(self.added_tokens_encoder   )
       return vocab

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]     ,		_a:List[int]     ,		_a:Optional[List[int]] = None     ,		_a:bool = False   ):
       if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=_a     ,		token_ids_a=_a     ,		already_has_special_tokens=_a   )

       # normal case: some special tokens
       if token_ids_a is None:
         return ([0] * len(_a   )) + [1]
       return ([0] * len(_a   )) + [1] + ([0] * len(_a   )) + [1]

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       return list(
           set(filter(lambda _a   : bool(re.search(r'''<extra_id_\d+>'''     ,		_a   )   ) is not None     ,		self.additional_special_tokens   )   )   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       return [self._convert_token_to_id(_a   ) for token in self.get_sentinel_tokens()]

     def 	SCREAMING_SNAKE_CASE__						(					self:int     ,		_a:List[int]   ):
       if len(_a   ) > 0 and token_ids[-1] == self.eos_token_id:
         warnings.warn(
             F"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
             ''' eos tokens being added.'''   )
         return token_ids
       else:
         return token_ids + [self.eos_token_id]

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple     ,		_a:List[int]     ,		_a:Optional[List[int]] = None   ):
       snake_case__						= [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:List[str]     ,		_a:List[int]     ,		_a:Optional[List[int]] = None   ):
       snake_case__						= self._add_eos_if_not_present(_a   )
       if token_ids_a is None:
         return token_ids_a
       else:
         snake_case__						= self._add_eos_if_not_present(_a   )
         return token_ids_a + token_ids_a

     def __getstate__(					self:Dict   ):
       snake_case__						= self.__dict__.copy()
       snake_case__						= None
       return state

     def __setstate__(					self:Tuple     ,		_a:Union[str, Any]   ):
       snake_case__						= d

       # for backward compatibility
       if not hasattr(self     ,		'''sp_model_kwargs'''   ):
         snake_case__						= {}

       snake_case__						= spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.Load(self.vocab_file   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]     ,		_a:"TextInput"     ,		**_a:Tuple   ):
       # Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
       # the beginning of the text
       if not self.legacy:
         snake_case__						= SPIECE_UNDERLINE + text.replace(_a     ,		''' '''   )
       return super().tokenize(_a     ,		**_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]     ,		_a:Optional[int]     ,		**_a:str   ):
       if not self.legacy:
         snake_case__						= text.startswith(_a   )
         if is_first:
           snake_case__						= text[1:]

       snake_case__						= self.sp_model.encode(_a     ,		out_type=_a   )

       if not self.legacy and not is_first and not text.startswith(''' '''   ) and tokens[0].startswith(_a   ):
         snake_case__						= ([tokens[0][1:]] if len(tokens[0]   ) > 1 else []) + tokens[1:]
       return tokens

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict     ,		_a:int   ):
       if token.startswith('''<extra_id_'''   ):
         snake_case__						= re.match(r'''<extra_id_(\d+)>'''     ,		_a   )
         snake_case__						= int(match.group(1   )   )
         return self.vocab_size - num - 1
       return self.sp_model.piece_to_id(_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple     ,		_a:List[Any]   ):
       if index < self.sp_model.get_piece_size():
         snake_case__						= self.sp_model.IdToPiece(_a   )
       else:
         snake_case__						= F"""<extra_id_{self.vocab_size - 1 - index}>"""
       return token

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple     ,		_a:Dict   ):
       snake_case__						= []
       snake_case__						= ''''''
       snake_case__						= False
       for token in tokens:
         # make sure that special tokens are not decoded using sentencepiece model
         if token in self.all_special_tokens:
           if not prev_is_special:
             out_string += " "
           out_string += self.sp_model.decode(_a   ) + token
           snake_case__						= True
           snake_case__						= []
         else:
           current_sub_tokens.append(_a   )
           snake_case__						= False
       out_string += self.sp_model.decode(_a   )
       return out_string.strip()







     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]     ,		_a:str     ,		_a:Optional[str] = None   ):
       if not os.path.isdir(_a   ):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""   )
         return
       snake_case__						= 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   ) and os.path.isfile(self.vocab_file   ):
         copyfile(self.vocab_file     ,		_a   )
       elif not os.path.isfile(self.vocab_file   ):
         with open(_a     ,		'''wb'''   ) as fi:
           snake_case__						= self.sp_model.serialized_model_proto()
           fi.write(_a   )

       return (out_vocab_file,)









import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger
class     lowerCamelCase_					(   lowerCamelCase			):







				def __init__(    self							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Optional[int]   =   params

								__magic_name__		:Any   =   np.array(__lowerCAmelCase  )
								__magic_name__		:Optional[Any]   =   np.array([len(__lowerCAmelCase  ) for t in data]  )

								self.check()
								self.remove_long_sequences()
								self.remove_empty_sequences()
								self.remove_unknown_sequences()
								self.check()
								self.print_statistics()







				def __getitem__(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								return (self.token_ids[index], self.lengths[index])







				def __len__(    self  ):
								"""simple docstring"""
								return len(self.lengths  )







				def       A						(    self  ):
								"""simple docstring"""
								assert len(self.token_ids  ) == len(self.lengths  )
								assert all(self.lengths[i] == len(self.token_ids[i]  ) for i in range(len(self.lengths  )  )  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Any   =   self.params.max_model_input_size
								__magic_name__		:int   =   self.lengths > max_len
								logger.info(F'''Splitting {sum(__lowerCAmelCase  )} too long sequences.'''  )

								def divide_chunks(__lowerCAmelCase							,       __lowerCAmelCase  ):
												return [l[i : i + n] for i in range(0							,       len(__lowerCAmelCase  )							,       __lowerCAmelCase  )]

								__magic_name__		:Optional[int]   =   []
								__magic_name__		:List[Any]   =   []
								if self.params.mlm:
												__magic_name__  ,					__magic_name__		:Optional[Any]   =   self.params.special_tok_ids['''cls_token'''], self.params.special_tok_ids['''sep_token''']
								else:
												__magic_name__  ,					__magic_name__		:Tuple   =   self.params.special_tok_ids['''bos_token'''], self.params.special_tok_ids['''eos_token''']

								for seq_, len_ in zip(self.token_ids							,       self.lengths  ):
												assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
												if len_ <= max_len:
																new_tok_ids.append(seq_  )
																new_lengths.append(len_  )
												else:
																__magic_name__		:int   =   []
																for sub_s in divide_chunks(seq_							,       max_len - 2  ):
																				if sub_s[0] != cls_id:
																								__magic_name__		:List[Any]   =   np.insert(__lowerCAmelCase							,       0							,       __lowerCAmelCase  )
																				if sub_s[-1] != sep_id:
																								__magic_name__		:Union[str, Any]   =   np.insert(__lowerCAmelCase							,       len(__lowerCAmelCase  )							,       __lowerCAmelCase  )
																				assert len(__lowerCAmelCase  ) <= max_len
																				assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
																				sub_seqs.append(__lowerCAmelCase  )

																new_tok_ids.extend(__lowerCAmelCase  )
																new_lengths.extend([len(__lowerCAmelCase  ) for l in sub_seqs]  )

								__magic_name__		:Tuple   =   np.array(__lowerCAmelCase  )
								__magic_name__		:Optional[int]   =   np.array(__lowerCAmelCase  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   len(self  )
								__magic_name__		:int   =   self.lengths > 1_1
								__magic_name__		:List[str]   =   self.token_ids[indices]
								__magic_name__		:Union[str, Any]   =   self.lengths[indices]
								__magic_name__		:List[str]   =   len(self  )
								logger.info(F'''Remove {init_size - new_size} too short (<=11 tokens) sequences.'''  )







				def       A						(    self  ):
								"""simple docstring"""
								if "unk_token" not in self.params.special_tok_ids:
												return
								else:
												__magic_name__		:Tuple   =   self.params.special_tok_ids['''unk_token''']
								__magic_name__		:Dict   =   len(self  )
								__magic_name__		:Tuple   =   np.array([np.count_nonzero(a == unk_token_id  ) for a in self.token_ids]  )
								__magic_name__		:int   =   (unk_occs / self.lengths) < 0.5
								__magic_name__		:str   =   self.token_ids[indices]
								__magic_name__		:str   =   self.lengths[indices]
								__magic_name__		:Any   =   len(self  )
								logger.info(F'''Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).'''  )







				def       A						(    self  ):
								"""simple docstring"""
								if not self.params.is_master:
												return
								logger.info(F'''{len(self  )} sequences'''  )
								# data_len = sum(self.lengths)
								# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
								# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

								# unk_idx = self.params.special_tok_ids['unk_token']
								# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
								# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')







				def       A						(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   [t[0] for t in batch]
								__magic_name__		:List[Any]   =   [t[1] for t in batch]
								assert len(__lowerCAmelCase  ) == len(__lowerCAmelCase  )

								# Max for paddings
								__magic_name__		:Tuple   =   max(__lowerCAmelCase  )

								# Pad token ids
								if self.params.mlm:
												__magic_name__		:Any   =   self.params.special_tok_ids['''pad_token''']
								else:
												__magic_name__		:str   =   self.params.special_tok_ids['''unk_token''']
								__magic_name__		:Any   =   [list(t.astype(__lowerCAmelCase  )  ) + [pad_idx] * (max_seq_len_ - len(__lowerCAmelCase  )) for t in token_ids]
								assert len(tk_  ) == len(__lowerCAmelCase  )
								assert all(len(__lowerCAmelCase  ) == max_seq_len_ for t in tk_  )

								__magic_name__		:Optional[int]   =   torch.tensor(tk_  )  # (bs, max_seq_len_)
								__magic_name__		:Optional[int]   =   torch.tensor(__lowerCAmelCase  )  # (bs)
								return tk_t, lg_t






