
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      : Tuple       =  None


__SCREAMING_SNAKE_CASE      : str       =  logging.get_logger(__name__)

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

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


class 		__magic_name__ (    a_     ):
		_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     :						List[Any]    ,	lowerCamelCase__     :						List[str]=None    ,	lowerCamelCase__     :						List[Any]=None    ,	lowerCamelCase__     :						List[str]="</s>"    ,	lowerCamelCase__     :						Optional[Any]="<unk>"    ,	lowerCamelCase__     :						str="<pad>"    ,	lowerCamelCase__     :						List[Any]=1_0_0    ,	lowerCamelCase__     :						int=None    ,	**lowerCamelCase__     :						Union[str, Any]    ,	):
							# Add extra_ids to the special token list
							if extra_ids > 0 and additional_special_tokens is None:
												lowerCAmelCase		:       str							=  [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 special tokens
												lowerCAmelCase		:       Optional[Any]							=  len(set(filter(lambda lowerCamelCase__					: 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'''					)

							super().__init__(
							    _a    ,	tokenizer_file=_a    ,	eos_token=_a    ,	unk_token=_a    ,	pad_token=_a    ,	extra_ids=_a    ,	additional_special_tokens=_a    ,	**_a    ,	)

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





		@staticmethod
		def    _A				(				lowerCamelCase__     :						Optional[int]    ,	lowerCamelCase__     :						Optional[Any]    ,	lowerCamelCase__     :						List[str]					):
							if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
												lowerCAmelCase		:       Dict							=  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.'''    ,	_a    ,	)

							return max_model_length





		def    _A				(				self     :						str    ,	lowerCamelCase__     :						str    ,	lowerCamelCase__     :						Any = 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(_a					):
												logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''					)
												return
							lowerCAmelCase		:       Dict							=  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					):
												copyfile(self.vocab_file    ,	_a					)
												logger.info(f'''Copy vocab file to {out_vocab_file}'''					)

							return (out_vocab_file,)





		def    _A				(				self     :						str    ,	lowerCamelCase__     :						Union[str, Any]    ,	lowerCamelCase__     :						Union[str, Any] = None					):
							lowerCAmelCase		:       Tuple							=  token_ids_a + [self.eos_token_id]
							if token_ids_a is None:
												return self.prefix_tokens + token_ids_a
							else:
												lowerCAmelCase		:       Union[str, Any]							=  token_ids_a + [self.eos_token_id]
												return self.prefix_tokens + token_ids_a + token_ids_a





		def    _A				(				self     :						List[Any]    ,	lowerCamelCase__     :						Union[str, Any]    ,	lowerCamelCase__     :						List[str] = None					):
							lowerCAmelCase		:       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    _A				(				self     :						Optional[Any]					):
							return list(
							    set(filter(lambda lowerCamelCase__					: bool(re.search(R'''<extra_id_\d+>'''    ,	_a					)					) is not None    ,	self.additional_special_tokens					)					)					)





		def    _A				(				self     :						Union[str, Any]					):
							return [self.convert_tokens_to_ids(_a					) for token in self.get_sentinel_tokens()]


"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_A     :			Optional[Any]  						=			{
    """configuration_bridgetower""": [
        """BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BridgeTowerConfig""",
        """BridgeTowerTextConfig""",
        """BridgeTowerVisionConfig""",
    ],
    """processing_bridgetower""": ["""BridgeTowerProcessor"""],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A     :			Any  						=			["""BridgeTowerImageProcessor"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A     :			List[Any]  						=			[
       """BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """BridgeTowerForContrastiveLearning""",
       """BridgeTowerForImageAndTextRetrieval""",
       """BridgeTowerForMaskedLM""",
       """BridgeTowerModel""",
       """BridgeTowerPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_bridgetower import (
       BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       BridgeTowerConfig,
       BridgeTowerTextConfig,
       BridgeTowerVisionConfig,
   )
   from .processing_bridgetower import BridgeTowerProcessor

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_bridgetower import BridgeTowerImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_bridgetower import (
          BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
          BridgeTowerForContrastiveLearning,
          BridgeTowerForImageAndTextRetrieval,
          BridgeTowerForMaskedLM,
          BridgeTowerModel,
          BridgeTowerPreTrainedModel,
      )


else:
   import sys

   _A     :			Any  						=			_LazyModule(__name__, globals()["""__file__"""], _import_structure)


