






'''simple docstring'''

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


UpperCamelCase							=logging.get_logger(__name__)

UpperCamelCase							={'''vocab_file''': '''spiece.model'''}

UpperCamelCase							={
    '''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
UpperCamelCase							={
    '''t5-small''': 512,
    '''t5-base''': 512,
    '''t5-large''': 512,
    '''t5-3b''': 512,
    '''t5-11b''': 512,
}

UpperCamelCase							='''▁'''




class     A    (      UpperCAmelCase__						):






 """simple docstring"""





 __a			:						Optional[Any]   =						VOCAB_FILES_NAMES
 __a			:						List[Any]   =						PRETRAINED_VOCAB_FILES_MAP
 __a			:						Optional[int]   =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __a			:						Dict   =						['''input_ids''', '''attention_mask''']
 def __init__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase="</s>"			,     __lowerCAmelCase="<unk>"			,     __lowerCAmelCase="<pad>"			,     __lowerCAmelCase=1_00			,     __lowerCAmelCase=None			,     __lowerCAmelCase = None			,     __lowerCAmelCase=True			,     **__lowerCAmelCase			,     ):






       if extra_ids > 0 and additional_special_tokens is None:
             UpperCamelCase_				:   Optional[int]					    =							[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_id special tokens
             UpperCamelCase_				:   List[Any]					    =							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""" )
       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""" )

       UpperCamelCase_				:   Union[str, Any]					    =							legacy
       UpperCamelCase_				:   str					    =							{} if sp_model_kwargs is None else sp_model_kwargs

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

       UpperCamelCase_				:   Dict					    =							vocab_file
       UpperCamelCase_				:   Dict					    =							extra_ids

       UpperCamelCase_				:   Dict					    =							spm.SentencePieceProcessor(**self.sp_model_kwargs )
       self.sp_model.Load(__lowerCAmelCase )
 @staticmethod
 def   _UpperCAmelCase       (							__lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase ):






       if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
             UpperCamelCase_				:   Any					    =							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."""			,     __lowerCAmelCase			,     )

       return max_model_length
 @property
 def   _UpperCAmelCase       (							self ):
       return self.sp_model.get_piece_size() + self._extra_ids
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Union[str, Any]					    =							{self.convert_ids_to_tokens(__lowerCAmelCase ): i for i in range(self.vocab_size )}
       vocab.update(self.added_tokens_encoder )
       return vocab
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase = None			,     __lowerCAmelCase = False ):






       if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=__lowerCAmelCase			,     token_ids_a=__lowerCAmelCase			,     already_has_special_tokens=__lowerCAmelCase )

       # normal case: some special tokens
       if token_ids_a is None:
             return ([0] * len(__lowerCAmelCase )) + [1]
       return ([0] * len(__lowerCAmelCase )) + [1] + ([0] * len(__lowerCAmelCase )) + [1]
 def   _UpperCAmelCase       (							self ):
       return list(
           set(filter(lambda __lowerCAmelCase : bool(re.search(R"""<extra_id_\d+>"""			,     __lowerCAmelCase ) ) is not None			,     self.additional_special_tokens ) ) )
 def   _UpperCAmelCase       (							self ):
       return [self._convert_token_to_id(__lowerCAmelCase ) for token in self.get_sentinel_tokens()]
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):






       if len(__lowerCAmelCase ) > 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   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase = None ):
       UpperCamelCase_				:   Optional[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			,     __lowerCAmelCase			,     __lowerCAmelCase = None ):
       UpperCamelCase_				:   Optional[int]					    =							self._add_eos_if_not_present(__lowerCAmelCase )
       if token_ids_a is None:
             return token_ids_a
       else:
             UpperCamelCase_				:   int					    =							self._add_eos_if_not_present(__lowerCAmelCase )
             return token_ids_a + token_ids_a
 def __getstate__(							self ):
       UpperCamelCase_				:   str					    =							self.__dict__.copy()
       UpperCamelCase_				:   List[str]					    =							None
       return state
 def __setstate__(							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   List[str]					    =							d

       # for backward compatibility
       if not hasattr(self			,     """sp_model_kwargs""" ):
             UpperCamelCase_				:   List[str]					    =							{}

       UpperCamelCase_				:   List[str]					    =							spm.SentencePieceProcessor(**self.sp_model_kwargs )
       self.sp_model.Load(self.vocab_file )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     **__lowerCAmelCase ):






       if not self.legacy:
             UpperCamelCase_				:   Optional[Any]					    =							SPIECE_UNDERLINE + text.replace(__lowerCAmelCase			,     """ """ )
       return super().tokenize(__lowerCAmelCase			,     **__lowerCAmelCase )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     **__lowerCAmelCase ):






       if not self.legacy:
             UpperCamelCase_				:   Dict					    =							text.startswith(__lowerCAmelCase )
             if is_first:
                   UpperCamelCase_				:   Tuple					    =							text[1:]

       UpperCamelCase_				:   str					    =							self.sp_model.encode(__lowerCAmelCase			,     out_type=__lowerCAmelCase )

       if not self.legacy and not is_first and not text.startswith(""" """ ) and tokens[0].startswith(__lowerCAmelCase ):
             UpperCamelCase_				:   List[str]					    =							([tokens[0][1:]] if len(tokens[0] ) > 1 else []) + tokens[1:]
       return tokens
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):






       if token.startswith("""<extra_id_""" ):
             UpperCamelCase_				:   int					    =							re.match(R"""<extra_id_(\d+)>"""			,     __lowerCAmelCase )
             UpperCamelCase_				:   Dict					    =							int(match.group(1 ) )
             return self.vocab_size - num - 1
       return self.sp_model.piece_to_id(__lowerCAmelCase )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):






       if index < self.sp_model.get_piece_size():
             UpperCamelCase_				:   Optional[Any]					    =							self.sp_model.IdToPiece(__lowerCAmelCase )
       else:
             UpperCamelCase_				:   Optional[int]					    =							F"<extra_id_{self.vocab_size - 1 - index}>"
       return token
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Tuple					    =							[]
       UpperCamelCase_				:   int					    =							""""""
       UpperCamelCase_				:   Optional[Any]					    =							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(__lowerCAmelCase ) + token
                   UpperCamelCase_				:   List[Any]					    =							True
                   UpperCamelCase_				:   str					    =							[]
             else:
                   current_sub_tokens.append(__lowerCAmelCase )
                   UpperCamelCase_				:   Tuple					    =							False
       out_string += self.sp_model.decode(__lowerCAmelCase )
       return out_string.strip()





 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase = None ):






       if not os.path.isdir(__lowerCAmelCase ):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
             return
       UpperCamelCase_				:   Optional[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 ) and os.path.isfile(self.vocab_file ):
             copyfile(self.vocab_file			,     __lowerCAmelCase )
       elif not os.path.isfile(self.vocab_file ):
             with open(__lowerCAmelCase			,     """wb""" ) as fi:
                   UpperCamelCase_				:   int					    =							self.sp_model.serialized_model_proto()
                   fi.write(__lowerCAmelCase )

       return (out_vocab_file,)














'''simple docstring'''

from datetime import datetime

import matplotlib.pyplot as plt
import torch



def 							snake_case					(     a_							: List[Any]	)       ->				Any:





      """simple docstring"""

      for param in module.parameters():
            UpperCamelCase_				:   Dict					    =							False



def 							snake_case					(     )       ->				List[str]:





      """simple docstring"""

      UpperCamelCase_				:   List[str]					    =							"""cuda""" if torch.cuda.is_available() else """cpu"""
      if torch.backends.mps.is_available() and torch.backends.mps.is_built():
            UpperCamelCase_				:   int					    =							"""mps"""
      if device == "mps":
            print(
                """WARNING: MPS currently doesn't seem to work, and messes up backpropagation without any visible torch"""
                """ errors. I recommend using CUDA on a colab notebook or CPU instead if you're facing inexplicable issues"""
                """ with generations."""	)
      return device



def 							snake_case					(     a_							: str	)       ->				Dict:





      """simple docstring"""

      UpperCamelCase_				:   Optional[int]					    =							plt.imshow(a_	)
      fig.axes.get_xaxis().set_visible(a_	)
      fig.axes.get_yaxis().set_visible(a_	)
      plt.show()



def 							snake_case					(     )       ->				Optional[Any]:





      """simple docstring"""

      UpperCamelCase_				:   Optional[Any]					    =							datetime.now()
      UpperCamelCase_				:   int					    =							current_time.strftime("""%H:%M:%S"""	)
      return timestamp






