code: 
  
  
  """simple docstring"""
  
  
  
  
  import torch
  import torch.nn as nn
  from transformers.modeling_utils import ModuleUtilsMixin
  from transformers.models.ta.modeling_ta import (
      TaBlock,
      TaConfig,
      TaLayerNorm,
  )
  
  from ...configuration_utils import ConfigMixin, register_to_config
  from ...models import ModelMixin
  
  
  
  
  class  __snake_case      (lowerCamelCase ,		lowerCamelCase ,		lowerCamelCase     ):
  
  
  
  
  							@register_to_config
  							def __init__(    self:						Dict    ,       A_:						int    ,       A_:						int    ,       A_:						int    ,       A_:						float    ,       A_:						int    ,       A_:						int    ,       A_:						int    ,       A_:						int    ,       A_:						str    ,       A_:						bool = False    ,       ):
  										super().__init__()
  
  										__lowerCamelCase        =			nn.Linear(A_    ,       A_    ,       bias=A_      )
  
  										__lowerCamelCase        =			nn.Embedding(A_    ,       A_      )
  										__lowerCamelCase        =			False
  
  										__lowerCamelCase        =			nn.Dropout(p=A_      )
  
  										__lowerCamelCase        =			TaConfig(
  										    d_model=A_    ,       num_heads=A_    ,       d_kv=A_    ,       d_ff=A_    ,       feed_forward_proj=A_    ,       dropout_rate=A_    ,       is_decoder=A_    ,       is_encoder_decoder=A_    ,       )
  										__lowerCamelCase        =			nn.ModuleList()
  										for lyr_num in range(A_      ):
  													__lowerCamelCase        =			TaBlock(A_      )
  													self.encoders.append(A_      )
  
  										__lowerCamelCase        =			TaLayerNorm(A_      )
  										__lowerCamelCase        =			nn.Dropout(p=A_      )
  
  
  
  
  							def      __a      (    self:						str    ,       A_:						Dict    ,       A_:						List[Any]      ):
  										__lowerCamelCase        =			self.input_proj(A_      )
  
  										# terminal relative positional encodings
  										__lowerCamelCase        =			encoder_inputs.shape[1]
  										__lowerCamelCase        =			torch.arange(A_    ,       device=encoder_inputs.device      )
  
  										__lowerCamelCase        =			encoder_inputs_mask.sum(-1      )
  										__lowerCamelCase        =			torch.roll(input_positions.unsqueeze(0      )    ,       tuple(seq_lens.tolist()      )    ,       dims=0      )
  										x += self.position_encoding(A_      )
  
  										__lowerCamelCase        =			self.dropout_pre(A_      )
  
  										# inverted the attention mask
  										__lowerCamelCase        =			encoder_inputs.size()
  										__lowerCamelCase        =			self.get_extended_attention_mask(A_    ,       A_      )
  
  										for lyr in self.encoders:
  													__lowerCamelCase        =			lyr(A_    ,       A_      )[0]
  										__lowerCamelCase        =			self.layer_norm(A_      )
  
  										return self.dropout_post(A_      ), encoder_inputs_mask
  
  
  
  
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
  
  
  __magic_name__	:   Optional[Any]          =							{
      'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
      'tokenization_flaubert': ['FlaubertTokenizer'],
  }
  
  try:
  					if not is_torch_available():
  										raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  					pass
  else:
  					__magic_name__	:   List[Any]          =							[
  					    'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
  					    'FlaubertForMultipleChoice',
  					    'FlaubertForQuestionAnswering',
  					    'FlaubertForQuestionAnsweringSimple',
  					    'FlaubertForSequenceClassification',
  					    'FlaubertForTokenClassification',
  					    'FlaubertModel',
  					    'FlaubertWithLMHeadModel',
  					    'FlaubertPreTrainedModel',
  					]
  
  try:
  					if not is_tf_available():
  										raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  					pass
  else:
  					__magic_name__	:   Tuple          =							[
  					    'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
  					    'TFFlaubertForMultipleChoice',
  					    'TFFlaubertForQuestionAnsweringSimple',
  					    'TFFlaubertForSequenceClassification',
  					    'TFFlaubertForTokenClassification',
  					    'TFFlaubertModel',
  					    'TFFlaubertPreTrainedModel',
  					    'TFFlaubertWithLMHeadModel',
  					]
  
  
  if TYPE_CHECKING:
  					from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
  					from .tokenization_flaubert import FlaubertTokenizer
  
  					try:
  										if not is_torch_available():
  															raise OptionalDependencyNotAvailable()
  					except OptionalDependencyNotAvailable:
  										pass
  					else:
  										from .modeling_flaubert import (
  										    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
  										    FlaubertForMultipleChoice,
  										    FlaubertForQuestionAnswering,
  										    FlaubertForQuestionAnsweringSimple,
  										    FlaubertForSequenceClassification,
  										    FlaubertForTokenClassification,
  										    FlaubertModel,
  										    FlaubertPreTrainedModel,
  										    FlaubertWithLMHeadModel,
  										)
  
  					try:
  										if not is_tf_available():
  															raise OptionalDependencyNotAvailable()
  					except OptionalDependencyNotAvailable:
  										pass
  					else:
  										from .modeling_tf_flaubert import (
  										    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
  										    TFFlaubertForMultipleChoice,
  										    TFFlaubertForQuestionAnsweringSimple,
  										    TFFlaubertForSequenceClassification,
  										    TFFlaubertForTokenClassification,
  										    TFFlaubertModel,
  										    TFFlaubertPreTrainedModel,
  										    TFFlaubertWithLMHeadModel,
  										)
  
  else:
  					import sys
  
  					__magic_name__	:   List[str]          =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
  