




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    (      A ,       A ,       A ):






   @register_to_config
   def __init__(						self :							str    ,       UpperCamelCase_ :							int    ,       UpperCamelCase_ :							int    ,       UpperCamelCase_ :							int    ,       UpperCamelCase_ :							float    ,       UpperCamelCase_ :							int    ,       UpperCamelCase_ :							int    ,       UpperCamelCase_ :							int    ,       UpperCamelCase_ :							int    ,       UpperCamelCase_ :							str    ,       UpperCamelCase_ :							bool = False    ,       )->      str:
     super().__init__()

     _lowercase:			Optional[int]      =     nn.Linear(UpperCamelCase_    ,       UpperCamelCase_    ,       bias=UpperCamelCase_  )

     _lowercase:			Optional[int]      =     nn.Embedding(UpperCamelCase_    ,       UpperCamelCase_  )
     _lowercase:			str      =     False

     _lowercase:			Dict      =     nn.Dropout(p=UpperCamelCase_  )

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

     _lowercase:			int      =     TaLayerNorm(UpperCamelCase_  )
     _lowercase:			int      =     nn.Dropout(p=UpperCamelCase_  )






   def 			_a    (						self :							str    ,       UpperCamelCase_ :							List[Any]    ,       UpperCamelCase_ :							int  )->      str:
     _lowercase:			int      =     self.input_proj(UpperCamelCase_  )

     # terminal relative positional encodings
     _lowercase:			List[Any]      =     encoder_inputs.shape[1]
     _lowercase:			Dict      =     torch.arange(UpperCamelCase_    ,       device=encoder_inputs.device  )

     _lowercase:			Optional[int]      =     encoder_inputs_mask.sum(-1  )
     _lowercase:			int      =     torch.roll(input_positions.unsqueeze(0  )    ,       tuple(seq_lens.tolist()  )    ,       dims=0  )
     x += self.position_encoding(UpperCamelCase_  )

     _lowercase:			int      =     self.dropout_pre(UpperCamelCase_  )

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

     for lyr in self.encoders:
       _lowercase:			List[Any]      =     lyr(UpperCamelCase_    ,       UpperCamelCase_  )[0]
     _lowercase:			List[str]      =     self.layer_norm(UpperCamelCase_  )

     return self.dropout_post(UpperCamelCase_  ), encoder_inputs_mask






from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


UpperCAmelCase__    :							List[str]       		=     {
    'configuration_ernie_m': ['ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ErnieMConfig'],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__    :							Any       		=     ['ErnieMTokenizer']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__    :							Dict       		=     [
       'ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ErnieMForMultipleChoice',
       'ErnieMForQuestionAnswering',
       'ErnieMForSequenceClassification',
       'ErnieMForTokenClassification',
       'ErnieMModel',
       'ErnieMPreTrainedModel',
       'ErnieMForInformationExtraction',
   ]


if TYPE_CHECKING:
   from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_ernie_m import ErnieMTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_ernie_m import (
          ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
          ErnieMForInformationExtraction,
          ErnieMForMultipleChoice,
          ErnieMForQuestionAnswering,
          ErnieMForSequenceClassification,
          ErnieMForTokenClassification,
          ErnieMModel,
          ErnieMPreTrainedModel,
      )


else:
   import sys

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