




"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE_	     =       logging.get_logger(__name__)

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

class 				snake_case_			(					lowerCamelCase_			):







   """simple docstring"""




   A_				      =       '''data2vec-text'''







   def __init__(		self       ,					lowerCamelCase_=3_0_5_2_2       ,					lowerCamelCase_=7_6_8       ,					lowerCamelCase_=1_2       ,					lowerCamelCase_=1_2       ,					lowerCamelCase_=3_0_7_2       ,					lowerCamelCase_="gelu"       ,					lowerCamelCase_=0.1       ,					lowerCamelCase_=0.1       ,					lowerCamelCase_=5_1_2       ,					lowerCamelCase_=2       ,					lowerCamelCase_=0.02       ,					lowerCamelCase_=1e-12       ,					lowerCamelCase_=1       ,					lowerCamelCase_=0       ,					lowerCamelCase_=2       ,					lowerCamelCase_="absolute"       ,					lowerCamelCase_=True       ,					lowerCamelCase_=None       ,					**lowerCamelCase_       ,					)				->   List[str]:
       super().__init__(pad_token_id=lowerCamelCase_       ,					bos_token_id=lowerCamelCase_       ,					eos_token_id=lowerCamelCase_       ,					**lowerCamelCase_)

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

class 				snake_case_			(					lowerCamelCase_			):







   """simple docstring"""




   @property
   def   UpperCAmelCase__			(		self)				->   Mapping[str, Mapping[int, str]]:
       if self.task == "multiple-choice":
           UpperCamelCase              =	{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
       else:
           UpperCamelCase              =	{0: '''batch''', 1: '''sequence'''}
       return OrderedDict(
           [
               ('''input_ids''', dynamic_axis),
               ('''attention_mask''', dynamic_axis),
           ])






'''simple docstring'''




import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer






def 			_lowerCAmelCase     (    __snake_case    :					str					,				__snake_case    :					str					,				**__snake_case    :					List[Any]     )	->	Any:
     __A		:					Optional[Any]						=			AutoConfig.from_pretrained(__snake_case					,				**__snake_case     )
     __A		:					int						=			AutoModelForSeqaSeqLM.from_config(__snake_case     )
     model.save_pretrained(__snake_case     )
     AutoTokenizer.from_pretrained(__snake_case     ).save_pretrained(__snake_case     )
     return model


if __name__ == "__main__":
  fire.Fire(save_randomly_initialized_version)