


'''simple docstring'''







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


lowerCAmelCase     :  List[str]											=    logging.get_logger(__name__)

lowerCAmelCase     :  Any											=    {
    """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 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     __magic_name__           =     "mctct"







     def __init__(			self   ,						snake_case__=8065   ,						snake_case__=1536   ,						snake_case__=36   ,						snake_case__=6144   ,						snake_case__=4   ,						snake_case__=384   ,						snake_case__=920   ,						snake_case__=1E-5   ,						snake_case__=0.3   ,						snake_case__="relu"   ,						snake_case__=0.02   ,						snake_case__=0.3   ,						snake_case__=0.3   ,						snake_case__=1   ,						snake_case__=0   ,						snake_case__=2   ,						snake_case__=1   ,						snake_case__=0.3   ,						snake_case__=1   ,						snake_case__=(7,)   ,						snake_case__=(3,)   ,						snake_case__=80   ,						snake_case__=1   ,						snake_case__=None   ,						snake_case__="sum"   ,						snake_case__=False   ,						**snake_case__   ,						):

            '''simple docstring'''

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

            # prevents config testing fail with exporting to json
            _lowerCAmelCase     :				Optional[Any]       =							list(snake_case__ )
            _lowerCAmelCase     :				int       =							list(snake_case__ )

            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


lowerCAmelCase     :  Any											=    {
    """configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
    """tokenization_canine""": ["""CanineTokenizer"""],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  Tuple											=    [
           """CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """CanineForMultipleChoice""",
           """CanineForQuestionAnswering""",
           """CanineForSequenceClassification""",
           """CanineForTokenClassification""",
           """CanineLayer""",
           """CanineModel""",
           """CaninePreTrainedModel""",
           """load_tf_weights_in_canine""",
       ]


if TYPE_CHECKING:
       from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
       from .tokenization_canine import CanineTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_canine import (
                  CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CanineForMultipleChoice,
                  CanineForQuestionAnswering,
                  CanineForSequenceClassification,
                  CanineForTokenClassification,
                  CanineLayer,
                  CanineModel,
                  CaninePreTrainedModel,
                  load_tf_weights_in_canine,
              )


else:
       import sys

       lowerCAmelCase     :  Optional[Any]											=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

