'''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    lowerCamelCase__  (   __snake_case       ,				__snake_case       ,				__snake_case     ):



   @register_to_config
   def __init__(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__ = False					,      )				->     Dict:
        """simple docstring"""





        super().__init__()

        _UpperCamelCase  :List[Any]       =nn.Linear(lowerCAmelCase__					,      lowerCAmelCase__					,      bias=lowerCAmelCase__     )

        _UpperCamelCase  :List[str]       =nn.Embedding(lowerCAmelCase__					,      lowerCAmelCase__     )
        _UpperCamelCase  :Any       =False

        _UpperCamelCase  :Optional[Any]       =nn.Dropout(p=lowerCAmelCase__     )

        _UpperCamelCase  :List[str]       =TaConfig(
            d_model=lowerCAmelCase__					,      num_heads=lowerCAmelCase__					,      d_kv=lowerCAmelCase__					,      d_ff=lowerCAmelCase__					,      feed_forward_proj=lowerCAmelCase__					,      dropout_rate=lowerCAmelCase__					,      is_decoder=lowerCAmelCase__					,      is_encoder_decoder=lowerCAmelCase__					,      )
        _UpperCamelCase  :Dict       =nn.ModuleList()
        for lyr_num in range(lowerCAmelCase__     ):
             _UpperCamelCase  :List[str]       =TaBlock(lowerCAmelCase__     )
             self.encoders.append(lowerCAmelCase__     )

        _UpperCamelCase  :str       =TaLayerNorm(lowerCAmelCase__     )
        _UpperCamelCase  :List[str]       =nn.Dropout(p=lowerCAmelCase__     )




   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     Any:
        """simple docstring"""





        _UpperCamelCase  :str       =self.input_proj(lowerCAmelCase__     )

        # terminal relative positional encodings
        _UpperCamelCase  :List[str]       =encoder_inputs.shape[1]
        _UpperCamelCase  :Union[str, Any]       =torch.arange(lowerCAmelCase__					,      device=encoder_inputs.device     )

        _UpperCamelCase  :Dict       =encoder_inputs_mask.sum(-1     )
        _UpperCamelCase  :Optional[Any]       =torch.roll(input_positions.unsqueeze(0     )					,      tuple(seq_lens.tolist()     )					,      dims=0     )
        x += self.position_encoding(lowerCAmelCase__     )

        _UpperCamelCase  :List[str]       =self.dropout_pre(lowerCAmelCase__     )

        # inverted the attention mask
        _UpperCamelCase  :Dict       =encoder_inputs.size()
        _UpperCamelCase  :Any       =self.get_extended_attention_mask(lowerCAmelCase__					,      lowerCAmelCase__     )

        for lyr in self.encoders:
             _UpperCamelCase  :Union[str, Any]       =lyr(lowerCAmelCase__					,      lowerCAmelCase__     )[0]
        _UpperCamelCase  :str       =self.layer_norm(lowerCAmelCase__     )

        return self.dropout_post(lowerCAmelCase__     ), encoder_inputs_mask



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_A									=		{
    'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
    'tokenization_flaubert': ['FlaubertTokenizer'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A									=		[
       '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:
   _A									=		[
       '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

   _A									=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





