"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

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


_UpperCamelCase       :						Optional[int]     =   logging.get_logger(__name__)

_UpperCamelCase       :						List[Any]     =   {
    "flaubert/flaubert_small_cased": "https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json",
    "flaubert/flaubert_base_uncased": "https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json",
    "flaubert/flaubert_base_cased": "https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json",
    "flaubert/flaubert_large_cased": "https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json",
}


class 					UpperCAmelCase_     (   _a):
      lowerCamelCase__		:   Optional[Any]			  =  "flaubert"
      lowerCamelCase__		:   Optional[int]			  =  {
          "hidden_size": "emb_dim",
          "num_attention_heads": "n_heads",
          "num_hidden_layers": "n_layers",
          "n_words": "vocab_size",  # For backward compatibility
      }


      def __init__(       self		,      a=False		,      a=0.0		,      a=3_0_1_4_5		,      a=2_0_4_8		,      a=1_2		,      a=1_6		,      a=0.1		,      a=0.1		,      a=True		,      a=False		,      a=False		,      a=False		,      a=1		,      a=True		,      a=5_1_2		,      a=2_0_4_8**-0.5		,      a=1e-12		,      a=0.02		,      a=0		,      a=1		,      a=2		,      a=3		,      a=5		,      a=True		,      a="first"		,      a=True		,      a=None		,      a=True		,      a=0.1		,      a=5		,      a=5		,      a=0		,      a=0		,      a=2		,      a=0		,      **a		,      )	-> Tuple:
             lowercase__      :		Dict          =     pre_norm
             lowercase__      :		Union[str, Any]          =     layerdrop
             lowercase__      :		Optional[int]          =     vocab_size
             lowercase__      :		Any          =     emb_dim
             lowercase__      :		Dict          =     n_layers
             lowercase__      :		int          =     n_heads
             lowercase__      :		str          =     dropout
             lowercase__      :		str          =     attention_dropout
             lowercase__      :		Tuple          =     gelu_activation
             lowercase__      :		Dict          =     sinusoidal_embeddings
             lowercase__      :		Dict          =     causal
             lowercase__      :		Union[str, Any]          =     asm
             lowercase__      :		Tuple          =     n_langs
             lowercase__      :		Optional[Any]          =     use_lang_emb
             lowercase__      :		List[Any]          =     layer_norm_eps
             lowercase__      :		Dict          =     bos_index
             lowercase__      :		List[str]          =     eos_index
             lowercase__      :		Tuple          =     pad_index
             lowercase__      :		Any          =     unk_index
             lowercase__      :		Optional[Any]          =     mask_index
             lowercase__      :		List[str]          =     is_encoder
             lowercase__      :		List[Any]          =     max_position_embeddings
             lowercase__      :		Optional[Any]          =     embed_init_std
             lowercase__      :		int          =     init_std
             lowercase__      :		str          =     summary_type
             lowercase__      :		int          =     summary_use_proj
             lowercase__      :		Optional[Any]          =     summary_activation
             lowercase__      :		Any          =     summary_proj_to_labels
             lowercase__      :		List[str]          =     summary_first_dropout
             lowercase__      :		str          =     start_n_top
             lowercase__      :		str          =     end_n_top
             lowercase__      :		Union[str, Any]          =     mask_token_id
             lowercase__      :		Any          =     lang_id

             if "n_words" in kwargs:
                    lowercase__      :		str          =     kwargs['n_words']

             super().__init__(pad_token_id=a		,      bos_token_id=a		,      **a							)





class 					UpperCAmelCase_     (   _a):


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


"""simple docstring"""





import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig


class 					UpperCAmelCase_     (   _a):
      lowerCamelCase__		:   List[str]			  =  CustomConfig


      def __init__(       self		,      a							)	-> Optional[int]:
             super().__init__(a							)
             lowercase__      :		Any          =     torch.nn.Linear(config.hidden_size		,      config.hidden_size							)


      def 		_UpperCAmelCase	(       self		,      a							)	-> Union[str, Any]:
             return self.linear(a							)


      def 		_UpperCAmelCase	(       self		,      a							)	-> Tuple:
             pass





class 					UpperCAmelCase_     (   _a):
      lowerCamelCase__		:   Optional[int]			  =  NoSuperInitConfig


      def __init__(       self		,      a							)	-> int:
             super().__init__(a							)
             lowercase__      :		List[Any]          =     torch.nn.Linear(config.attribute		,      config.attribute							)


      def 		_UpperCAmelCase	(       self		,      a							)	-> Optional[int]:
             return self.linear(a							)


      def 		_UpperCAmelCase	(       self		,      a							)	-> Optional[Any]:
             pass

