


"""simple docstring"""



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


__snake_case         =					logging.get_logger(__name__)

__snake_case         =					{
    """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       (   snake_case_			):
   __UpperCAmelCase				: Tuple													=						'''mctct'''







   def __init__(			self		,			UpperCamelCase__=8065		,			UpperCamelCase__=1536		,			UpperCamelCase__=36		,			UpperCamelCase__=6144		,			UpperCamelCase__=4		,			UpperCamelCase__=384		,			UpperCamelCase__=920		,			UpperCamelCase__=1e-5		,			UpperCamelCase__=0.3		,			UpperCamelCase__="relu"		,			UpperCamelCase__=0.02		,			UpperCamelCase__=0.3		,			UpperCamelCase__=0.3		,			UpperCamelCase__=1		,			UpperCamelCase__=0		,			UpperCamelCase__=2		,			UpperCamelCase__=1		,			UpperCamelCase__=0.3		,			UpperCamelCase__=1		,			UpperCamelCase__=(7,)		,			UpperCamelCase__=(3,)		,			UpperCamelCase__=80		,			UpperCamelCase__=1		,			UpperCamelCase__=None		,			UpperCamelCase__="sum"		,			UpperCamelCase__=False		,			**UpperCamelCase__		,			)			->	Union[str, Any]:

       '''simple docstring'''




       super().__init__(**UpperCamelCase__		,			pad_token_id=UpperCamelCase__		,			bos_token_id=UpperCamelCase__		,			eos_token_id=UpperCamelCase__		)
       snake_case					:   Any         =					vocab_size
       snake_case					:   str         =					hidden_size
       snake_case					:   Any         =					num_hidden_layers
       snake_case					:   Union[str, Any]         =					intermediate_size
       snake_case					:   int         =					num_attention_heads
       snake_case					:   Optional[int]         =					attention_head_dim
       snake_case					:   Dict         =					max_position_embeddings
       snake_case					:   Optional[int]         =					layer_norm_eps
       snake_case					:   List[Any]         =					layerdrop
       snake_case					:   int         =					hidden_act
       snake_case					:   List[str]         =					initializer_range
       snake_case					:   Optional[int]         =					hidden_dropout_prob
       snake_case					:   int         =					attention_probs_dropout_prob
       snake_case					:   List[str]         =					pad_token_id
       snake_case					:   Tuple         =					bos_token_id
       snake_case					:   Any         =					eos_token_id
       snake_case					:   Union[str, Any]         =					conv_glu_dim
       snake_case					:   Union[str, Any]         =					conv_dropout
       snake_case					:   Optional[int]         =					num_conv_layers
       snake_case					:   Optional[Any]         =					input_feat_per_channel
       snake_case					:   Dict         =					input_channels
       snake_case					:   Dict         =					conv_channels
       snake_case					:   int         =					ctc_loss_reduction
       snake_case					:   int         =					ctc_zero_infinity

       # prevents config testing fail with exporting to json
       snake_case					:   Tuple         =					list(UpperCamelCase__		)
       snake_case					:   Optional[int]         =					list(UpperCamelCase__		)

       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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__snake_case         =					{
    """configuration_distilbert""": [
        """DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """DistilBertConfig""",
        """DistilBertOnnxConfig""",
    ],
    """tokenization_distilbert""": ["""DistilBertTokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case         =					["""DistilBertTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case         =					[
      """DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """DistilBertForMaskedLM""",
      """DistilBertForMultipleChoice""",
      """DistilBertForQuestionAnswering""",
      """DistilBertForSequenceClassification""",
      """DistilBertForTokenClassification""",
      """DistilBertModel""",
      """DistilBertPreTrainedModel""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case         =					[
      """TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFDistilBertForMaskedLM""",
      """TFDistilBertForMultipleChoice""",
      """TFDistilBertForQuestionAnswering""",
      """TFDistilBertForSequenceClassification""",
      """TFDistilBertForTokenClassification""",
      """TFDistilBertMainLayer""",
      """TFDistilBertModel""",
      """TFDistilBertPreTrainedModel""",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case         =					[
      """FlaxDistilBertForMaskedLM""",
      """FlaxDistilBertForMultipleChoice""",
      """FlaxDistilBertForQuestionAnswering""",
      """FlaxDistilBertForSequenceClassification""",
      """FlaxDistilBertForTokenClassification""",
      """FlaxDistilBertModel""",
      """FlaxDistilBertPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_distilbert import (
      DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      DistilBertConfig,
      DistilBertOnnxConfig,
  )
  from .tokenization_distilbert import DistilBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_distilbert_fast import DistilBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_distilbert import (
        DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        DistilBertForMaskedLM,
        DistilBertForMultipleChoice,
        DistilBertForQuestionAnswering,
        DistilBertForSequenceClassification,
        DistilBertForTokenClassification,
        DistilBertModel,
        DistilBertPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_distilbert import (
        TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFDistilBertForMaskedLM,
        TFDistilBertForMultipleChoice,
        TFDistilBertForQuestionAnswering,
        TFDistilBertForSequenceClassification,
        TFDistilBertForTokenClassification,
        TFDistilBertMainLayer,
        TFDistilBertModel,
        TFDistilBertPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_distilbert import (
        FlaxDistilBertForMaskedLM,
        FlaxDistilBertForMultipleChoice,
        FlaxDistilBertForQuestionAnswering,
        FlaxDistilBertForSequenceClassification,
        FlaxDistilBertForTokenClassification,
        FlaxDistilBertModel,
        FlaxDistilBertPreTrainedModel,
    )

else:
  import sys

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





