





'''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:
 lowerCAmelCase :					List[Any] 							=None


lowerCAmelCase :					List[Any] 							=logging.get_logger(__name__)

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

lowerCAmelCase :					int 							={
    '''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
lowerCAmelCase :					Union[str, Any] 							={
    '''t5-small''': 512,
    '''t5-base''': 512,
    '''t5-large''': 512,
    '''t5-3b''': 512,
    '''t5-11b''': 512,
}
class 				a_       (    _lowerCAmelCase							):
     __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		: List[Any]     ,				lowercase		: Any=None     ,				lowercase		: int=None     ,				lowercase		: Any="</s>"     ,				lowercase		: int="<unk>"     ,				lowercase		: Tuple="<pad>"     ,				lowercase		: str=100     ,				lowercase		: Tuple=None     ,				**lowercase		: str     ,				):





        """simple docstring"""




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

        lowercase_      :Union[str, Any]         =			vocab_file
        lowercase_      :List[Any]         =			False if not self.vocab_file else True
        lowercase_      :Union[str, Any]         =			extra_ids





     @staticmethod
     def 							lowercase__				(			lowercase		: Tuple     ,				lowercase		: int     ,				lowercase		: Optional[int]						):





        """simple docstring"""




        if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
           lowercase_      :Optional[Any]         =			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."     ,				lowercase     ,				)

        return max_model_length





     def 							lowercase__				(			self		: List[Any]     ,				lowercase		: str     ,				lowercase		: Optional[str] = None						):





        """simple docstring"""




        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(lowercase						):
           logger.error(F'Vocabulary path ({save_directory}) should be a directory'						)
           return
        lowercase_      :Tuple         =			os.path.join(
            lowercase     ,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

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

        return (out_vocab_file,)





     def 							lowercase__				(			self		: List[Any]     ,				lowercase		: List[int]     ,				lowercase		: Optional[List[int]] = None						):





        """simple docstring"""




        lowercase_      :Union[str, Any]         =			token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
           return self.prefix_tokens + token_ids_a
        else:
           lowercase_      :List[Any]         =			token_ids_a + [self.eos_token_id]
           return self.prefix_tokens + token_ids_a + token_ids_a





     def 							lowercase__				(			self		: Tuple     ,				lowercase		: List[int]     ,				lowercase		: Optional[List[int]] = None						):





        """simple docstring"""




        lowercase_      :List[str]         =			[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 							lowercase__				(			self		: List[Any]						):





        """simple docstring"""




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







     def 							lowercase__				(			self		: Dict						):





        """simple docstring"""




        return [self.convert_tokens_to_ids(lowercase						) for token in self.get_sentinel_tokens()]









'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase :					Optional[int] 							={'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase :					Optional[Any] 							=[
     '''WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''WavLMForAudioFrameClassification''',
     '''WavLMForCTC''',
     '''WavLMForSequenceClassification''',
     '''WavLMForXVector''',
     '''WavLMModel''',
     '''WavLMPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_wavlm import (
      WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      WavLMForAudioFrameClassification,
      WavLMForCTC,
      WavLMForSequenceClassification,
      WavLMForXVector,
      WavLMModel,
      WavLMPreTrainedModel,
  )

else:
 import sys

 lowerCAmelCase :					Union[str, Any] 							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


