



import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   A__      : int       =						CustomConfig


   def __init__(    self:     List[str]  ,lowerCamelCase_:     Dict							)	->      Tuple:
       super().__init__(lowerCamelCase_							)
       UpperCAmelCase_     :       List[str]  		=					torch.nn.Linear(config.hidden_size  ,config.hidden_size							)


   def       A__    (    self:     List[Any]  ,lowerCamelCase_:     Union[str, Any]							)	->      str:
       return self.linear(lowerCamelCase_							)






   def       A__    (    self:     int  ,lowerCamelCase_:     Dict							)	->      str:
       pass







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   A__      : str       =						NoSuperInitConfig


   def __init__(    self:     Any  ,lowerCamelCase_:     int							)	->      List[str]:
       super().__init__(lowerCamelCase_							)
       UpperCAmelCase_     :       Any  		=					torch.nn.Linear(config.attribute  ,config.attribute							)


   def       A__    (    self:     int  ,lowerCamelCase_:     int							)	->      List[str]:
       return self.linear(lowerCamelCase_							)






   def       A__    (    self:     Optional[Any]  ,lowerCamelCase_:     List[str]							)	->      str:
       pass





from typing import TYPE_CHECKING

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


__A       ={
    "configuration_speech_to_text_2": ["SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Speech2Text2Config"],
    "processing_speech_to_text_2": ["Speech2Text2Processor"],
    "tokenization_speech_to_text_2": ["Speech2Text2Tokenizer"],
}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A       =[
				    "SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "Speech2Text2ForCausalLM",
				    "Speech2Text2PreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
				from .processing_speech_to_text_a import SpeechaTextaProcessor
				from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_speech_to_text_a import (
								    SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SpeechaTextaForCausalLM,
								    SpeechaTextaPreTrainedModel,
								)

else:
				import sys

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





