


'''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       a							(						_a      ,    _a      ,    _a			):


     """simple docstring"""






     @register_to_config
     def __init__(							self	:						Optional[int]				,					snake_case	:						int				,					snake_case	:						int				,					snake_case	:						int				,					snake_case	:						float				,					snake_case	:						int				,					snake_case	:						int				,					snake_case	:						int				,					snake_case	:						int				,					snake_case	:						str				,					snake_case	:						bool = False				,					)		->				Tuple:
          super().__init__()

          __UpperCAmelCase      :					Optional[int]    				=     nn.Linear(snake_case				,					snake_case				,					bias=snake_case     )

          __UpperCAmelCase      :					List[Any]    				=     nn.Embedding(snake_case				,					snake_case     )
          __UpperCAmelCase      :					str    				=     False

          __UpperCAmelCase      :					List[str]    				=     nn.Dropout(p=snake_case     )

          __UpperCAmelCase      :					int    				=     TaConfig(
              d_model=snake_case				,					num_heads=snake_case				,					d_kv=snake_case				,					d_ff=snake_case				,					feed_forward_proj=snake_case				,					dropout_rate=snake_case				,					is_decoder=snake_case				,					is_encoder_decoder=snake_case				,					)
          __UpperCAmelCase      :					Optional[Any]    				=     nn.ModuleList()
          for lyr_num in range(snake_case     ):
               __UpperCAmelCase      :					Union[str, Any]    				=     TaBlock(snake_case     )
               self.encoders.append(snake_case     )

          __UpperCAmelCase      :					int    				=     TaLayerNorm(snake_case     )
          __UpperCAmelCase      :					Union[str, Any]    				=     nn.Dropout(p=snake_case     )






     def 					lowerCamelCase__					(							self	:						List[Any]				,					snake_case	:						Optional[int]				,					snake_case	:						Dict     )		->				Optional[int]:
          __UpperCAmelCase      :					Tuple    				=     self.input_proj(snake_case     )

          # terminal relative positional encodings
          __UpperCAmelCase      :					Optional[Any]    				=     encoder_inputs.shape[1]
          __UpperCAmelCase      :					Tuple    				=     torch.arange(snake_case				,					device=encoder_inputs.device     )

          __UpperCAmelCase      :					Union[str, Any]    				=     encoder_inputs_mask.sum(-1     )
          __UpperCAmelCase      :					Optional[Any]    				=     torch.roll(input_positions.unsqueeze(0     )				,					tuple(seq_lens.tolist()     )				,					dims=0     )
          x += self.position_encoding(snake_case     )

          __UpperCAmelCase      :					str    				=     self.dropout_pre(snake_case     )

          # inverted the attention mask
          __UpperCAmelCase      :					Dict    				=     encoder_inputs.size()
          __UpperCAmelCase      :					Optional[Any]    				=     self.get_extended_attention_mask(snake_case				,					snake_case     )

          for lyr in self.encoders:
               __UpperCAmelCase      :					int    				=     lyr(snake_case				,					snake_case     )[0]
          __UpperCAmelCase      :					Tuple    				=     self.layer_norm(snake_case     )

          return self.dropout_post(snake_case     ), encoder_inputs_mask



'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase     :Union[str, Any]												=  {
    "configuration_mpnet": ["MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "MPNetConfig"],
    "tokenization_mpnet": ["MPNetTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase     :Optional[int]												=  ["MPNetTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase     :Union[str, Any]												=  [
          "MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
          "MPNetForMaskedLM",
          "MPNetForMultipleChoice",
          "MPNetForQuestionAnswering",
          "MPNetForSequenceClassification",
          "MPNetForTokenClassification",
          "MPNetLayer",
          "MPNetModel",
          "MPNetPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase     :Any												=  [
          "TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFMPNetEmbeddings",
          "TFMPNetForMaskedLM",
          "TFMPNetForMultipleChoice",
          "TFMPNetForQuestionAnswering",
          "TFMPNetForSequenceClassification",
          "TFMPNetForTokenClassification",
          "TFMPNetMainLayer",
          "TFMPNetModel",
          "TFMPNetPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
      from .tokenization_mpnet import MPNetTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mpnet_fast import MPNetTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mpnet import (
                MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                MPNetForMaskedLM,
                MPNetForMultipleChoice,
                MPNetForQuestionAnswering,
                MPNetForSequenceClassification,
                MPNetForTokenClassification,
                MPNetLayer,
                MPNetModel,
                MPNetPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mpnet import (
                TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFMPNetEmbeddings,
                TFMPNetForMaskedLM,
                TFMPNetForMultipleChoice,
                TFMPNetForQuestionAnswering,
                TFMPNetForSequenceClassification,
                TFMPNetForTokenClassification,
                TFMPNetMainLayer,
                TFMPNetModel,
                TFMPNetPreTrainedModel,
            )

else:
      import sys

      __UpperCAmelCase     :Optional[Any]												=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)