





'''simple docstring'''






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
     from .tokenization_fnet import FNetTokenizer
else:
     snake_case_     =	None

snake_case_     =	logging.get_logger(__name__)
snake_case_     =	{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

snake_case_     =	{
    """vocab_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/spiece.model""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json""",
    },
}

snake_case_     =	{
    """google/fnet-base""": 5_12,
    """google/fnet-large""": 5_12,
}

snake_case_     =	"""▁"""


class   _lowercase						(							a				):
   _UpperCamelCase       		=   VOCAB_FILES_NAMES
   _UpperCamelCase       		=   PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase       		=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase       		=   ["""input_ids""", """token_type_ids"""]
   _UpperCamelCase       		=   FNetTokenizer


   def __init__( self    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=False    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase="<unk>"    ,   _UpperCAmelCase="[SEP]"    ,   _UpperCAmelCase="<pad>"    ,   _UpperCAmelCase="[CLS]"    ,   _UpperCAmelCase="[MASK]"    ,   **_UpperCAmelCase    ,   ):
       # Mask token behave like a normal word, i.e. include the space before it and
       # is included in the raw text, there should be a match in a non-normalized sentence.
       A       :       Any							=						(
           AddedToken(_UpperCAmelCase    ,   lstrip=_UpperCAmelCase    ,   rstrip=_UpperCAmelCase    ,   normalized=_UpperCAmelCase					)
           if isinstance(_UpperCAmelCase    ,   _UpperCAmelCase					)
           else mask_token
       )

       super().__init__(
           _UpperCAmelCase    ,   tokenizer_file=_UpperCAmelCase    ,   do_lower_case=_UpperCAmelCase    ,   remove_space=_UpperCAmelCase    ,   keep_accents=_UpperCAmelCase    ,   unk_token=_UpperCAmelCase    ,   sep_token=_UpperCAmelCase    ,   pad_token=_UpperCAmelCase    ,   cls_token=_UpperCAmelCase    ,   mask_token=_UpperCAmelCase    ,   **_UpperCAmelCase    ,   )

       A       :       int							=						do_lower_case
       A       :       List[Any]							=						remove_space
       A       :       str							=						keep_accents
       A       :       int							=						vocab_file
       A       :       str							=						False if not self.vocab_file else True


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None					):
       A       :       List[Any]							=						[self.sep_token_id]
       A       :       Tuple							=						[self.cls_token_id]
       if token_ids_a is None:
           return cls + token_ids_a + sep
       return cls + token_ids_a + sep + token_ids_a + sep


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None					):
       A       :       Optional[Any]							=						[self.sep_token_id]
       A       :       str							=						[self.cls_token_id]

       if token_ids_a is None:
           return len(cls + token_ids_a + sep					) * [0]
       return len(cls + token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1]


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None					):
       if not os.path.isdir(_UpperCAmelCase					):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''					)
           return
       A       :       Tuple							=						os.path.join(
           _UpperCAmelCase    ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)

       if os.path.abspath(self.vocab_file					) != os.path.abspath(_UpperCAmelCase					):
           copyfile(self.vocab_file    ,   _UpperCAmelCase					)

       return (out_vocab_file,)







'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


snake_case_     =	{
    """configuration_speecht5""": [
        """SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP""",
        """SpeechT5Config""",
        """SpeechT5HifiGanConfig""",
    ],
    """feature_extraction_speecht5""": ["""SpeechT5FeatureExtractor"""],
    """processing_speecht5""": ["""SpeechT5Processor"""],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case_     =	["""SpeechT5Tokenizer"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case_     =	[
         """SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """SpeechT5ForSpeechToText""",
         """SpeechT5ForSpeechToSpeech""",
         """SpeechT5ForTextToSpeech""",
         """SpeechT5Model""",
         """SpeechT5PreTrainedModel""",
         """SpeechT5HifiGan""",
     ]

if TYPE_CHECKING:
     from .configuration_speechta import (
         SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
         SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
         SpeechTaConfig,
         SpeechTaHifiGanConfig,
     )
     from .feature_extraction_speechta import SpeechTaFeatureExtractor
     from .processing_speechta import SpeechTaProcessor

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_speechta import SpeechTaTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_speechta import (
              SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
              SpeechTaForSpeechToSpeech,
              SpeechTaForSpeechToText,
              SpeechTaForTextToSpeech,
              SpeechTaHifiGan,
              SpeechTaModel,
              SpeechTaPreTrainedModel,
          )

else:
     import sys

     snake_case_     =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
