



'''simple docstring'''





from ....configuration_utils import PretrainedConfig
from ....utils import logging


_UpperCamelCase											=						logging.get_logger(__name__)

_UpperCamelCase											=						{
    'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json',
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  a_												="""mctct"""




  def __init__( self      :	str			,	_a      :	str=8065			,	_a      :	Any=1536			,	_a      :	List[str]=36			,	_a      :	Dict=6144			,	_a      :	List[str]=4			,	_a      :	Optional[int]=384			,	_a      :	int=920			,	_a      :	List[Any]=1e-5			,	_a      :	Union[str, Any]=0.3			,	_a      :	List[Any]="relu"			,	_a      :	Dict=0.02			,	_a      :	List[str]=0.3			,	_a      :	List[Any]=0.3			,	_a      :	Tuple=1			,	_a      :	Optional[Any]=0			,	_a      :	Dict=2			,	_a      :	int=1			,	_a      :	str=0.3			,	_a      :	Tuple=1			,	_a      :	str=(7,)			,	_a      :	Union[str, Any]=(3,)			,	_a      :	Optional[int]=80			,	_a      :	int=1			,	_a      :	List[str]=None			,	_a      :	str="sum"			,	_a      :	Union[str, Any]=False			,	**_a      :	Any			,	)					->					Optional[Any]:
    super().__init__(**_a			,	pad_token_id=_a			,	bos_token_id=_a			,	eos_token_id=_a   )
    __lowerCamelCase       :       Union[str, Any]           = vocab_size
    __lowerCamelCase       :       Dict           = hidden_size
    __lowerCamelCase       :       Union[str, Any]           = num_hidden_layers
    __lowerCamelCase       :       List[str]           = intermediate_size
    __lowerCamelCase       :       Union[str, Any]           = num_attention_heads
    __lowerCamelCase       :       Any           = attention_head_dim
    __lowerCamelCase       :       Any           = max_position_embeddings
    __lowerCamelCase       :       Dict           = layer_norm_eps
    __lowerCamelCase       :       List[str]           = layerdrop
    __lowerCamelCase       :       Dict           = hidden_act
    __lowerCamelCase       :       List[Any]           = initializer_range
    __lowerCamelCase       :       List[Any]           = hidden_dropout_prob
    __lowerCamelCase       :       Tuple           = attention_probs_dropout_prob
    __lowerCamelCase       :       Any           = pad_token_id
    __lowerCamelCase       :       Dict           = bos_token_id
    __lowerCamelCase       :       List[Any]           = eos_token_id
    __lowerCamelCase       :       List[Any]           = conv_glu_dim
    __lowerCamelCase       :       Dict           = conv_dropout
    __lowerCamelCase       :       List[Any]           = num_conv_layers
    __lowerCamelCase       :       Optional[int]           = input_feat_per_channel
    __lowerCamelCase       :       int           = input_channels
    __lowerCamelCase       :       Dict           = conv_channels
    __lowerCamelCase       :       List[str]           = ctc_loss_reduction
    __lowerCamelCase       :       Optional[Any]           = ctc_zero_infinity

    # prevents config testing fail with exporting to json
    __lowerCamelCase       :       Tuple           = list(_a   )
    __lowerCamelCase       :       Tuple           = list(_a   )

    if len(self.conv_kernel   ) != self.num_conv_layers:
      raise ValueError(
          'Configuration for convolutional module is incorrect. '
          'It is required that `len(config.conv_kernel)` == `config.num_conv_layers` '
          f'but is `len(config.conv_kernel) = {len(self.conv_kernel   )}`, '
          f'`config.num_conv_layers = {self.num_conv_layers}`.'   )











'''simple docstring'''





from typing import TYPE_CHECKING

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


_UpperCamelCase											=						{
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=						[
       'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'BioGptForCausalLM',
       'BioGptForTokenClassification',
       'BioGptForSequenceClassification',
       'BioGptModel',
       'BioGptPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
   from .tokenization_biogpt import BioGptTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_biogpt import (
          BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          BioGptForCausalLM,
          BioGptForSequenceClassification,
          BioGptForTokenClassification,
          BioGptModel,
          BioGptPreTrainedModel,
      )


else:
   import sys

   _UpperCamelCase											=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






