from collections import OrderedDict
from typing import Mapping

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


__magic_name__								=logging.get_logger(__name__)

__magic_name__								={
    '''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     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	Optional[int]						="camembert"






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





       '''simple docstring'''





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

       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     _A   (    __UpperCamelCase							):






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





       '''simple docstring'''





       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),
           ]	)




from typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin



class     _A   (    __UpperCamelCase	, __UpperCamelCase							):






  @register_to_config
  def __init__(self   ,				SCREAMING_SNAKE_CASE_ = 768   ,				)		->      Tuple:





       '''simple docstring'''





       super().__init__()

       UpperCamelCase__										=      nn.Parameter(torch.zeros(1   ,				SCREAMING_SNAKE_CASE_	)	)
       UpperCamelCase__										=      nn.Parameter(torch.ones(1   ,				SCREAMING_SNAKE_CASE_	)	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				)		->      Tuple:





       '''simple docstring'''





       UpperCamelCase__										=      nn.Parameter(self.mean.to(SCREAMING_SNAKE_CASE_	).to(SCREAMING_SNAKE_CASE_	)	)
       UpperCamelCase__										=      nn.Parameter(self.std.to(SCREAMING_SNAKE_CASE_	).to(SCREAMING_SNAKE_CASE_	)	)
       return self






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      int:





       '''simple docstring'''





       UpperCamelCase__										=      (embeds - self.mean) * 1.0 / self.std
       return embeds






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      int:





       '''simple docstring'''





       UpperCamelCase__										=      (embeds * self.std) + self.mean
       return embeds



