


import logging

from transformers import PretrainedConfig


snake_case_						:			int													=							logging.getLogger(__name__)


snake_case_						:			Optional[Any]													=							{
    '''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}






class 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						"bertabs"




						def __init__(       self					:      List[str]				,    _a					:      List[str]=3_0522				,    _a					:      List[Any]=512				,    _a					:      Union[str, Any]=6				,    _a					:      List[str]=512				,    _a					:      List[Any]=8				,    _a					:      List[str]=512				,    _a					:      List[str]=0.2				,    _a					:      Optional[Any]=6				,    _a					:      Dict=768				,    _a					:      List[Any]=8				,    _a					:      int=2048				,    _a					:      Optional[Any]=0.2				,    **_a					:      Any				,    )							->     int:


										"""simple docstring"""





										super().__init__(**_a  )

										_SCREAMING_SNAKE_CASE			  =vocab_size
										_SCREAMING_SNAKE_CASE			  =max_pos

										_SCREAMING_SNAKE_CASE			  =enc_layers
										_SCREAMING_SNAKE_CASE			  =enc_hidden_size
										_SCREAMING_SNAKE_CASE			  =enc_heads
										_SCREAMING_SNAKE_CASE			  =enc_ff_size
										_SCREAMING_SNAKE_CASE			  =enc_dropout

										_SCREAMING_SNAKE_CASE			  =dec_layers
										_SCREAMING_SNAKE_CASE			  =dec_hidden_size
										_SCREAMING_SNAKE_CASE			  =dec_heads
										_SCREAMING_SNAKE_CASE			  =dec_ff_size
										_SCREAMING_SNAKE_CASE			  =dec_dropout



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case_						:			str													=							logging.get_logger(__name__)

snake_case_						:			str													=							{
    '''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}

class 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						"data2vec-text"




						def __init__(       self					:      Optional[int]				,    _a					:      Union[str, Any]=3_0522				,    _a					:      Any=768				,    _a					:      List[Any]=12				,    _a					:      Any=12				,    _a					:      List[str]=3072				,    _a					:      Optional[Any]="gelu"				,    _a					:      List[str]=0.1				,    _a					:      Optional[Any]=0.1				,    _a					:      Optional[Any]=512				,    _a					:      Any=2				,    _a					:      Optional[Any]=0.02				,    _a					:      Dict=1E-12				,    _a					:      int=1				,    _a					:      Any=0				,    _a					:      List[Any]=2				,    _a					:      Dict="absolute"				,    _a					:      Optional[Any]=True				,    _a					:      Dict=None				,    **_a					:      Optional[Any]				,    )							->     Union[str, Any]:


										"""simple docstring"""





										super().__init__(pad_token_id=_a				,    bos_token_id=_a				,    eos_token_id=_a				,    **_a  )

										_SCREAMING_SNAKE_CASE			  =vocab_size
										_SCREAMING_SNAKE_CASE			  =hidden_size
										_SCREAMING_SNAKE_CASE			  =num_hidden_layers
										_SCREAMING_SNAKE_CASE			  =num_attention_heads
										_SCREAMING_SNAKE_CASE			  =hidden_act
										_SCREAMING_SNAKE_CASE			  =intermediate_size
										_SCREAMING_SNAKE_CASE			  =hidden_dropout_prob
										_SCREAMING_SNAKE_CASE			  =attention_probs_dropout_prob
										_SCREAMING_SNAKE_CASE			  =max_position_embeddings
										_SCREAMING_SNAKE_CASE			  =type_vocab_size
										_SCREAMING_SNAKE_CASE			  =initializer_range
										_SCREAMING_SNAKE_CASE			  =layer_norm_eps
										_SCREAMING_SNAKE_CASE			  =position_embedding_type
										_SCREAMING_SNAKE_CASE			  =use_cache
										_SCREAMING_SNAKE_CASE			  =classifier_dropout

class 						A__							(		UpperCamelCase__  ):




						@property
						def 							__UpperCamelCase				(       self					:      List[str]  )							->     Mapping[str, Mapping[int, str]]:


										"""simple docstring"""





										if self.task == "multiple-choice":
														_SCREAMING_SNAKE_CASE			  ={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
										else:
														_SCREAMING_SNAKE_CASE			  ={0: '''batch''', 1: '''sequence'''}
										return OrderedDict(
										    [
										        ('''input_ids''', dynamic_axis),
										        ('''attention_mask''', dynamic_axis),
										    ]  )