






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


a_		:						Tuple             =    logging.get_logger(__name__)

a_		:						Optional[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 		_snake_case  (  A__					):
			_lowercase		:    Dict			     =							'''mctct'''






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

						# prevents config testing fail with exporting to json
						SCREAMING_SNAKE_CASE						= list(a)
						SCREAMING_SNAKE_CASE						= 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}`.''')












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_		:						List[Any]             =    {
    'configuration_bigbird_pegasus': [
        'BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BigBirdPegasusConfig',
        'BigBirdPegasusOnnxConfig',
    ],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				a_		:						Dict             =    [
				    'BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'BigBirdPegasusForCausalLM',
				    'BigBirdPegasusForConditionalGeneration',
				    'BigBirdPegasusForQuestionAnswering',
				    'BigBirdPegasusForSequenceClassification',
				    'BigBirdPegasusModel',
				    'BigBirdPegasusPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_bigbird_pegasus import (
				    BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    BigBirdPegasusConfig,
				    BigBirdPegasusOnnxConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_bigbird_pegasus import (
								    BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BigBirdPegasusForCausalLM,
								    BigBirdPegasusForConditionalGeneration,
								    BigBirdPegasusForQuestionAnswering,
								    BigBirdPegasusForSequenceClassification,
								    BigBirdPegasusModel,
								    BigBirdPegasusPreTrainedModel,
								)


else:
				import sys

				a_		:						Tuple             =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




