

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						:  int									=			logging.get_logger(__name__)

_UpperCAmelCase						:  Union[str, Any]									=			{"""vocab_file""": """spiece.model"""}

_UpperCAmelCase						:  List[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""",
    }
}


# TODO(PVP) - this should be removed in Transformers v5
_UpperCAmelCase						:  Dict									=			{
    """t5-small""": 512,
    """t5-base""": 512,
    """t5-large""": 512,
    """t5-3b""": 512,
    """t5-11b""": 512,
}

_UpperCAmelCase						:  Dict									=			"""▁"""







class 					lowercase							(			lowercase_     ):
				__SCREAMING_SNAKE_CASE   :					Optional[Any]			     =     VOCAB_FILES_NAMES
				__SCREAMING_SNAKE_CASE   :					Dict			     =     PRETRAINED_VOCAB_FILES_MAP
				__SCREAMING_SNAKE_CASE   :					Dict			     =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__SCREAMING_SNAKE_CASE   :					List[Any]			     =     ['''input_ids''', '''attention_mask''']




				def __init__(   self ,  snake_case ,  snake_case="</s>" ,  snake_case="<unk>" ,  snake_case="<pad>" ,  snake_case=100 ,  snake_case=None ,  snake_case = None ,  snake_case=True ,  **snake_case ,  ):
											# 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(snake_case			)]
											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 snake_case			: 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'			)
											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=snake_case ,  unk_token=snake_case ,  pad_token=snake_case ,  extra_ids=snake_case ,  additional_special_tokens=snake_case ,  sp_model_kwargs=self.sp_model_kwargs ,  legacy=snake_case ,  **snake_case ,  )

											snake_case_     							=       vocab_file
											snake_case_     							=       extra_ids

											snake_case_     							=       spm.SentencePieceProcessor(**self.sp_model_kwargs			)
											self.sp_model.Load(snake_case			)




				@staticmethod
				def        a				(   snake_case ,  snake_case ,  snake_case			):
											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.' ,  snake_case ,  )

											return max_model_length




				@property
				def        a				(   self			):
											return self.sp_model.get_piece_size() + self._extra_ids




				def        a				(   self			):
											snake_case_     							=       {self.convert_ids_to_tokens(snake_case			): i for i in range(self.vocab_size			)}
											vocab.update(self.added_tokens_encoder			)
											return vocab




				def        a				(   self ,  snake_case ,  snake_case = None ,  snake_case = False			):
											if already_has_special_tokens:
																		return super().get_special_tokens_mask(
																		    token_ids_a=snake_case ,  token_ids_a=snake_case ,  already_has_special_tokens=snake_case			)

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




				def        a				(   self			):
											return list(
											    set(filter(lambda snake_case			: bool(re.search(R'<extra_id_\d+>' ,  snake_case			)			) is not None ,  self.additional_special_tokens			)			)			)




				def        a				(   self			):
											return [self._convert_token_to_id(snake_case			) for token in self.get_sentinel_tokens()]




				def        a				(   self ,  snake_case			):
											if len(snake_case			) > 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        a				(   self ,  snake_case ,  snake_case = 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        a				(   self ,  snake_case ,  snake_case = None			):
											snake_case_     							=       self._add_eos_if_not_present(snake_case			)
											if token_ids_a is None:
																		return token_ids_a
											else:
																		snake_case_     							=       self._add_eos_if_not_present(snake_case			)
																		return token_ids_a + token_ids_a




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




				def __setstate__(   self ,  snake_case			):
											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        a				(   self ,  snake_case ,  **snake_case			):
											# 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(snake_case ,  ' '			)
											return super().tokenize(snake_case ,  **snake_case			)




				def        a				(   self ,  snake_case ,  **snake_case			):
											if not self.legacy:
																		snake_case_     							=       text.startswith(snake_case			)
																		if is_first:
																									snake_case_     							=       text[1:]

											snake_case_     							=       self.sp_model.encode(snake_case ,  out_type=snake_case			)

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




				def        a				(   self ,  snake_case			):
											if token.startswith('<extra_id_'			):
																		snake_case_     							=       re.match(R'<extra_id_(\d+)>' ,  snake_case			)
																		snake_case_     							=       int(match.group(1			)			)
																		return self.vocab_size - num - 1
											return self.sp_model.piece_to_id(snake_case			)




				def        a				(   self ,  snake_case			):
											if index < self.sp_model.get_piece_size():
																		snake_case_     							=       self.sp_model.IdToPiece(snake_case			)
											else:
																		snake_case_     							=       F'''<extra_id_{self.vocab_size - 1 - index}>'''
											return token




				def        a				(   self ,  snake_case			):
											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(snake_case			) + token
																									snake_case_     							=       True
																									snake_case_     							=       []
																		else:
																									current_sub_tokens.append(snake_case			)
																									snake_case_     							=       False
											out_string += self.sp_model.decode(snake_case			)
											return out_string.strip()




				def        a				(   self ,  snake_case ,  snake_case = None			):
											if not os.path.isdir(snake_case			):
																		logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''			)
																		return
											snake_case_     							=       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			) and os.path.isfile(self.vocab_file			):
																		copyfile(self.vocab_file ,  snake_case			)
											elif not os.path.isfile(self.vocab_file			):
																		with open(snake_case ,  'wb'			) as fi:
																									snake_case_     							=       self.sp_model.serialized_model_proto()
																									fi.write(snake_case			)

											return (out_vocab_file,)







from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class 					lowercase							(			lowercase_     ):
				__SCREAMING_SNAKE_CASE   :					Union[str, Any]			     =     ['''image_processor''', '''tokenizer''']
				__SCREAMING_SNAKE_CASE   :					Tuple			     =     '''AutoImageProcessor'''
				__SCREAMING_SNAKE_CASE   :					Dict			     =     '''AutoTokenizer'''




				def __init__(   self ,  snake_case ,  snake_case			):
											super().__init__(snake_case ,  snake_case			)
											snake_case_     							=       self.image_processor




				def __call__(   self ,  snake_case=None ,  snake_case=None ,  snake_case=None ,  **snake_case			):

											if text is None and images is None:
																		raise ValueError('You have to specify either text or images. Both cannot be none.'			)

											if text is not None:
																		snake_case_     							=       self.tokenizer(snake_case ,  return_tensors=snake_case ,  **snake_case			)

											if images is not None:
																		snake_case_     							=       self.image_processor(snake_case ,  return_tensors=snake_case ,  **snake_case			)

											if text is not None and images is not None:
																		snake_case_     							=       image_features.pixel_values
																		return encoding
											elif text is not None:
																		return encoding
											else:
																		return BatchEncoding(data=dict(**snake_case			) ,  tensor_type=snake_case			)




				def        a				(   self ,  *snake_case ,  **snake_case			):
											return self.tokenizer.batch_decode(*snake_case ,  **snake_case			)




				def        a				(   self ,  *snake_case ,  **snake_case			):
											return self.tokenizer.decode(*snake_case ,  **snake_case			)




				@property
				def        a				(   self			):
											return ["input_ids", "attention_mask", "pixel_values"]




