





'''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 				UpperCAmelCase  (    snake_case_							,	snake_case_							,	snake_case_     ):




						@register_to_config
						def __init__(    self    :				Dict    ,    __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    ,    )       ->   Optional[int]:
												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        lowercase__					(    self    :				str    ,    __snake_case    :				Dict    ,    __snake_case    :				List[Any]						)       ->   Optional[int]:
												_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













'''simple docstring'''






from typing import TYPE_CHECKING

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


A__       :     Tuple          ={
    '''configuration_mpnet''': ['''MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MPNetConfig'''],
    '''tokenization_mpnet''': ['''MPNetTokenizer'''],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__       :     Tuple          =['''MPNetTokenizerFast''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__       :     List[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:
      A__       :     str          =[
          '''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

      A__       :     List[Any]          =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






