



"""simple docstring"""







import logging

from transformers import PretrainedConfig


UpperCamelCase     =							logging.getLogger(__name__)


UpperCamelCase     =							{
    """bertabs-finetuned-cnndm""": """https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json""",
}






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					List[str]							  =     "bertabs"


			def __init__(      self						,	SCREAMING_SNAKE_CASE__=30522						,	SCREAMING_SNAKE_CASE__=512						,	SCREAMING_SNAKE_CASE__=6						,	SCREAMING_SNAKE_CASE__=512						,	SCREAMING_SNAKE_CASE__=8						,	SCREAMING_SNAKE_CASE__=512						,	SCREAMING_SNAKE_CASE__=0.2						,	SCREAMING_SNAKE_CASE__=6						,	SCREAMING_SNAKE_CASE__=768						,	SCREAMING_SNAKE_CASE__=8						,	SCREAMING_SNAKE_CASE__=2048						,	SCREAMING_SNAKE_CASE__=0.2						,	**SCREAMING_SNAKE_CASE__						,	)      ->	Union[str, Any]:
				super().__init__(**SCREAMING_SNAKE_CASE__		)

				A__											=					vocab_size
				A__											=					max_pos

				A__											=					enc_layers
				A__											=					enc_hidden_size
				A__											=					enc_heads
				A__											=					enc_ff_size
				A__											=					enc_dropout

				A__											=					dec_layers
				A__											=					dec_hidden_size
				A__											=					dec_heads
				A__											=					dec_ff_size
				A__											=					dec_dropout









"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase     =							{"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCamelCase     =							[
			    """WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """WavLMForAudioFrameClassification""",
			    """WavLMForCTC""",
			    """WavLMForSequenceClassification""",
			    """WavLMForXVector""",
			    """WavLMModel""",
			    """WavLMPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_wavlm import (
						    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    WavLMForAudioFrameClassification,
						    WavLMForCTC,
						    WavLMForSequenceClassification,
						    WavLMForXVector,
						    WavLMModel,
						    WavLMPreTrainedModel,
						)

else:
			import sys

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




