





"""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:
		A       	=     None


A       	=     logging.get_logger(__name__)

A       	=     {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

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




class  __lowercase	(	A_							):







					'''simple docstring'''




					__lowerCAmelCase     =     VOCAB_FILES_NAMES
					__lowerCAmelCase     =     PRETRAINED_VOCAB_FILES_MAP
					__lowerCAmelCase     =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__lowerCAmelCase     =     ["input_ids", "attention_mask"]
					__lowerCAmelCase     =     TaTokenizer

					__lowerCAmelCase     =     []







					def __init__(  self		,	_UpperCAmelCase=None		,	_UpperCAmelCase=None		,	_UpperCAmelCase="</s>"		,	_UpperCAmelCase="<unk>"		,	_UpperCAmelCase="<pad>"		,	_UpperCAmelCase=100		,	_UpperCAmelCase=None		,	**_UpperCAmelCase		,	):



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

									__a       :  List[Any]					=     vocab_file
									__a       :  int					=     False if not self.vocab_file else True
									__a       :  List[str]					=     extra_ids







					@staticmethod
					def        _lowerCamelCase						(  _UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase   ):



									if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
													__a       :  int					=     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.'''		,	_snake_case		,	)

									return max_model_length







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



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

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

									return (out_vocab_file,)







					def        _lowerCamelCase						(  self		,	_UpperCAmelCase		,	_UpperCAmelCase = None   ):
									__a       :  Any					=     token_ids_a + [self.eos_token_id]
									if token_ids_a is None:
													return self.prefix_tokens + token_ids_a
									else:
													__a       :  Dict					=     token_ids_a + [self.eos_token_id]
													return self.prefix_tokens + token_ids_a + token_ids_a







					def        _lowerCamelCase						(  self		,	_UpperCAmelCase		,	_UpperCAmelCase = None   ):
									__a       :  Tuple					=     [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        _lowerCamelCase						(  self   ):
									return list(
									    set(filter(lambda _UpperCAmelCase   : bool(re.search(R'''<extra_id_\d+>'''		,	_snake_case   )   ) is not None		,	self.additional_special_tokens   )   )   )







					def        _lowerCamelCase						(  self   ):
									return [self.convert_tokens_to_ids(_snake_case   ) for token in self.get_sentinel_tokens()]




"""simple docstring"""







from ..utils import DummyObject, requires_backends
class        __A       (	metaclass=A_    ):







		'''simple docstring'''





		lowerCAmelCase			:					List[str]   	=				["torch", "torchsde"]







		def __init__(	self						:  Tuple			,*_snake_case						:  Union[str, Any]			,**_snake_case						:  Any						)					->		Union[str, Any]:






				"""simple docstring"""



				requires_backends(self			,['''torch''', '''torchsde''']						)







		@classmethod
		def 					UpperCAmelCase    (	cls						:  List[str]			,*_snake_case						:  int			,**_snake_case						:  Union[str, Any]						)					->		str:






				"""simple docstring"""



				requires_backends(cls			,['''torch''', '''torchsde''']						)







		@classmethod
		def 					UpperCAmelCase    (	cls						:  List[Any]			,*_snake_case						:  List[Any]			,**_snake_case						:  List[str]						)					->		List[Any]:






				"""simple docstring"""



				requires_backends(cls			,['''torch''', '''torchsde''']						)
