






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase								=						{
    'configuration_time_series_transformer': [
        'TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TimeSeriesTransformerConfig',
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase								=						[
		    'TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TimeSeriesTransformerForPrediction',
		    'TimeSeriesTransformerModel',
		    'TimeSeriesTransformerPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_time_series_transformer import (
		    TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    TimeSeriesTransformerConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_time_series_transformer import (
				    TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TimeSeriesTransformerForPrediction,
				    TimeSeriesTransformerModel,
				    TimeSeriesTransformerPreTrainedModel,
				)

else:
		import sys

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









import logging

from transformers import PretrainedConfig


a__				=		logging.getLogger(__name__)


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

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Any	    =    "bertabs"


							def __init__(			self       ,							_a=3_0_5_2_2       ,							_a=5_1_2       ,							_a=6       ,							_a=5_1_2       ,							_a=8       ,							_a=5_1_2       ,							_a=0.2       ,							_a=6       ,							_a=7_6_8       ,							_a=8       ,							_a=2_0_4_8       ,							_a=0.2       ,							**_a       ,							)    ->    Any:
												super().__init__(**_a       )

												_a	:    int         =		vocab_size
												_a	:    List[str]         =		max_pos

												_a	:    Tuple         =		enc_layers
												_a	:    Optional[Any]         =		enc_hidden_size
												_a	:    int         =		enc_heads
												_a	:    Optional[Any]         =		enc_ff_size
												_a	:    List[str]         =		enc_dropout

												_a	:    Tuple         =		dec_layers
												_a	:    Optional[Any]         =		dec_hidden_size
												_a	:    Optional[Any]         =		dec_heads
												_a	:    Optional[Any]         =		dec_ff_size
												_a	:    List[Any]         =		dec_dropout



