

"""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:
     SCREAMING_SNAKE_CASE_     :			List[str]	  =			None


SCREAMING_SNAKE_CASE_     :			str	  =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_     :			Optional[int]	  =			{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

SCREAMING_SNAKE_CASE_     :			List[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_     :			List[str]	  =			{
    '''t5-small''': 512,
    '''t5-base''': 512,
    '''t5-large''': 512,
    '''t5-3b''': 512,
    '''t5-11b''': 512,
}


class 		_A       (			__a					):
      __a								=	VOCAB_FILES_NAMES
      __a								=	PRETRAINED_VOCAB_FILES_MAP
      __a								=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __a								=	['input_ids', 'attention_mask']
      __a								=	TaTokenizer

      __a								=	[]





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__="</s>"    ,						SCREAMING_SNAKE_CASE__="<unk>"    ,						SCREAMING_SNAKE_CASE__="<pad>"    ,						SCREAMING_SNAKE_CASE__=100    ,						SCREAMING_SNAKE_CASE__=None    ,						**SCREAMING_SNAKE_CASE__    ,						)   ->					Tuple:
         # Add extra_ids to the special token list
         if extra_ids > 0 and additional_special_tokens is None:
            lowerCamelCase__						=			[f'<extra_id_{i}>' for i in range(SCREAMING_SNAKE_CASE__		)]
         elif extra_ids > 0 and additional_special_tokens is not None:
            # Check that we have the right number of extra special tokens
            lowerCamelCase__						=			len(set(filter(lambda SCREAMING_SNAKE_CASE__		: bool("extra_id_" in str(SCREAMING_SNAKE_CASE__		)		)    ,						SCREAMING_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__(
             SCREAMING_SNAKE_CASE__    ,						tokenizer_file=SCREAMING_SNAKE_CASE__    ,						eos_token=SCREAMING_SNAKE_CASE__    ,						unk_token=SCREAMING_SNAKE_CASE__    ,						pad_token=SCREAMING_SNAKE_CASE__    ,						extra_ids=SCREAMING_SNAKE_CASE__    ,						additional_special_tokens=SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__    ,						)

         lowerCamelCase__						=			vocab_file
         lowerCamelCase__						=			False if not self.vocab_file else True
         lowerCamelCase__						=			extra_ids





      @staticmethod
      def 					_lowerCamelCase  (  SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					Any:
         if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
            lowerCamelCase__						=			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."    ,						SCREAMING_SNAKE_CASE__    ,						)

         return max_model_length





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = None		)   ->					Tuple[str]:
         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(SCREAMING_SNAKE_CASE__		):
            logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
            return
         lowerCamelCase__						=			os.path.join(
             SCREAMING_SNAKE_CASE__    ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

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

         return (out_vocab_file,)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = None		)   ->					List[int]:
         lowerCamelCase__						=			token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
            return self.prefix_tokens + token_ids_a
         else:
            lowerCamelCase__						=			token_ids_a + [self.eos_token_id]
            return self.prefix_tokens + token_ids_a + token_ids_a





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = None		)   ->					List[int]:
         lowerCamelCase__						=			[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		)   ->					Optional[int]:
         return list(
             set(filter(lambda SCREAMING_SNAKE_CASE__		: bool(re.search(r"<extra_id_\d+>"    ,						SCREAMING_SNAKE_CASE__		)		) is not None    ,						self.additional_special_tokens		)		)		)





      def 					_lowerCamelCase  (  self		)   ->					Optional[int]:
         return [self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__		) for token in self.get_sentinel_tokens()]







"""simple docstring"""




from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_     :			List[Any]	  =			{
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_     :			Union[str, Any]	  =			[
         '''RagModel''',
         '''RagPreTrainedModel''',
         '''RagSequenceForGeneration''',
         '''RagTokenForGeneration''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_     :			str	  =			[
         '''TFRagModel''',
         '''TFRagPreTrainedModel''',
         '''TFRagSequenceForGeneration''',
         '''TFRagTokenForGeneration''',
     ]


if TYPE_CHECKING:
     from .configuration_rag import RagConfig
     from .retrieval_rag import RagRetriever
     from .tokenization_rag import RagTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_rag import (
              TFRagModel,
              TFRagPreTrainedModel,
              TFRagSequenceForGeneration,
              TFRagTokenForGeneration,
          )

else:
     import sys

     SCREAMING_SNAKE_CASE_     :			List[str]	  =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




