





'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case_     =	logging.get_logger(__name__)

snake_case_     =	{
    """facebook/data2vec-text-base""": """https://huggingface.co/data2vec/resolve/main/config.json""",
}


class   _lowercase						(							a				):
   _UpperCamelCase       		=   """data2vec-text"""


   def __init__( self    ,   _UpperCAmelCase=30_522    ,   _UpperCAmelCase=768    ,   _UpperCAmelCase=12    ,   _UpperCAmelCase=12    ,   _UpperCAmelCase=3_072    ,   _UpperCAmelCase="gelu"    ,   _UpperCAmelCase=0.1    ,   _UpperCAmelCase=0.1    ,   _UpperCAmelCase=512    ,   _UpperCAmelCase=2    ,   _UpperCAmelCase=0.02    ,   _UpperCAmelCase=1E-1_2    ,   _UpperCAmelCase=1    ,   _UpperCAmelCase=0    ,   _UpperCAmelCase=2    ,   _UpperCAmelCase="absolute"    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=None    ,   **_UpperCAmelCase    ,   ):
       super().__init__(pad_token_id=_UpperCAmelCase    ,   bos_token_id=_UpperCAmelCase    ,   eos_token_id=_UpperCAmelCase    ,   **_UpperCAmelCase					)

       A       :       int							=						vocab_size
       A       :       List[str]							=						hidden_size
       A       :       Dict							=						num_hidden_layers
       A       :       Tuple							=						num_attention_heads
       A       :       Union[str, Any]							=						hidden_act
       A       :       Tuple							=						intermediate_size
       A       :       str							=						hidden_dropout_prob
       A       :       Optional[int]							=						attention_probs_dropout_prob
       A       :       str							=						max_position_embeddings
       A       :       Optional[int]							=						type_vocab_size
       A       :       Optional[Any]							=						initializer_range
       A       :       List[str]							=						layer_norm_eps
       A       :       Optional[Any]							=						position_embedding_type
       A       :       Dict							=						use_cache
       A       :       Any							=						classifier_dropout







class   _lowercase						(							a				):


   @property
   def 					snake_case				( self					):
       if self.task == "multiple-choice":
           A       :       Union[str, Any]							=						{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
       else:
           A       :       List[str]							=						{0: '''batch''', 1: '''sequence'''}
       return OrderedDict(
           [
               ('''input_ids''', dynamic_axis),
               ('''attention_mask''', dynamic_axis),
           ]					)







'''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__)
