





import logging

from transformers import PretrainedConfig


UpperCAmelCase					:		Union[str, Any]		=							logging.getLogger(__name__)


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





class lowerCamelCase						(_lowerCAmelCase					):
  _lowercase  :	Tuple       					=				'bertabs'







  def __init__(		self     ,     lowercase__=30_522     ,     lowercase__=512     ,     lowercase__=6     ,     lowercase__=512     ,     lowercase__=8     ,     lowercase__=512     ,     lowercase__=0.2     ,     lowercase__=6     ,     lowercase__=768     ,     lowercase__=8     ,     lowercase__=2_048     ,     lowercase__=0.2     ,     **lowercase__     ,     )		->						Tuple:






        """simple docstring"""





        super().__init__(**_lowerCAmelCase		)

        _snake_case		:	Optional[Any]					     =     vocab_size
        _snake_case		:	Any					     =     max_pos

        _snake_case		:	Optional[int]					     =     enc_layers
        _snake_case		:	Union[str, Any]					     =     enc_hidden_size
        _snake_case		:	Tuple					     =     enc_heads
        _snake_case		:	Optional[Any]					     =     enc_ff_size
        _snake_case		:	List[str]					     =     enc_dropout

        _snake_case		:	int					     =     dec_layers
        _snake_case		:	Dict					     =     dec_hidden_size
        _snake_case		:	Any					     =     dec_heads
        _snake_case		:	Dict					     =     dec_ff_size
        _snake_case		:	List[Any]					     =     dec_dropout









'''simple docstring'''

import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu





@require_cpu
class lowerCamelCase						(unittest.TestCase					):







  def 				UpperCAmelCase_				(		self		)		->						Union[str, Any]:






        """simple docstring"""





        _snake_case		:	Any					     =     torch.nn.Linear(10     ,     10		)
        _snake_case		:	Optional[int]					     =     torch.optim.SGD(model.parameters()     ,     0.1		)
        _snake_case		:	List[str]					     =     Accelerator()
        _snake_case		:	Optional[Any]					     =     accelerator.prepare(lowercase__		)
        try:
              pickle.loads(pickle.dumps(lowercase__		)		)
        except Exception as e:
              self.fail(F'''Accelerated optimizer pickling failed with {e}'''		)
        AcceleratorState._reset_state()


