





'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

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


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

UpperCAmelCase					:		int		=							{
    'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/config.json',
    'umberto-commoncrawl-cased-v1': (
        'https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'
    ),
    'umberto-wikipedia-uncased-v1': (
        'https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'
    ),
}





class lowerCamelCase						(a__					):
  _lowercase  :	Any       					=				"""camembert"""







  def __init__(		self     ,     lowercase__=30_522     ,     lowercase__=768     ,     lowercase__=12     ,     lowercase__=12     ,     lowercase__=3_072     ,     lowercase__="gelu"     ,     lowercase__=0.1     ,     lowercase__=0.1     ,     lowercase__=512     ,     lowercase__=2     ,     lowercase__=0.02     ,     lowercase__=1E-1_2     ,     lowercase__=1     ,     lowercase__=0     ,     lowercase__=2     ,     lowercase__="absolute"     ,     lowercase__=True     ,     lowercase__=None     ,     **lowercase__     ,     )		->						List[Any]:






        """simple docstring"""





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

        _snake_case		:	Any					     =     vocab_size
        _snake_case		:	int					     =     hidden_size
        _snake_case		:	Dict					     =     num_hidden_layers
        _snake_case		:	Any					     =     num_attention_heads
        _snake_case		:	Any					     =     hidden_act
        _snake_case		:	int					     =     intermediate_size
        _snake_case		:	List[str]					     =     hidden_dropout_prob
        _snake_case		:	Dict					     =     attention_probs_dropout_prob
        _snake_case		:	int					     =     max_position_embeddings
        _snake_case		:	Tuple					     =     type_vocab_size
        _snake_case		:	Any					     =     initializer_range
        _snake_case		:	Tuple					     =     layer_norm_eps
        _snake_case		:	str					     =     position_embedding_type
        _snake_case		:	Dict					     =     use_cache
        _snake_case		:	Optional[int]					     =     classifier_dropout





class lowerCamelCase						(a__					):







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






        """simple docstring"""





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









'''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()


