

"""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__   (				SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE    ):



    '''simple docstring'''




    @register_to_config
    def __init__( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = False ,)    ->      Tuple:
          super().__init__()

          A				  =		nn.Embedding(lowerCamelCase_ ,lowerCamelCase_    )

          A				  =		nn.Embedding(lowerCamelCase_ ,lowerCamelCase_    )
          A				  =		False

          A				  =		nn.Dropout(p=lowerCamelCase_    )

          A				  =		TaConfig(
              vocab_size=lowerCamelCase_ ,d_model=lowerCamelCase_ ,num_heads=lowerCamelCase_ ,d_kv=lowerCamelCase_ ,d_ff=lowerCamelCase_ ,dropout_rate=lowerCamelCase_ ,feed_forward_proj=lowerCamelCase_ ,is_decoder=lowerCamelCase_ ,is_encoder_decoder=lowerCamelCase_ ,)

          A				  =		nn.ModuleList()
          for lyr_num in range(lowerCamelCase_    ):
                A				  =		TaBlock(lowerCamelCase_    )
                self.encoders.append(lowerCamelCase_    )

          A				  =		TaLayerNorm(lowerCamelCase_    )
          A				  =		nn.Dropout(p=lowerCamelCase_    )




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_    )    ->      List[Any]:
          A				  =		self.token_embedder(lowerCamelCase_    )

          A				  =		encoder_input_tokens.shape[1]
          A				  =		torch.arange(lowerCamelCase_ ,device=encoder_input_tokens.device    )
          x += self.position_encoding(lowerCamelCase_    )

          A				  =		self.dropout_pre(lowerCamelCase_    )

          # inverted the attention mask
          A				  =		encoder_input_tokens.size()
          A				  =		self.get_extended_attention_mask(lowerCamelCase_ ,lowerCamelCase_    )

          for lyr in self.encoders:
                A				  =		lyr(lowerCamelCase_ ,lowerCamelCase_    )[0]
          A				  =		self.layer_norm(lowerCamelCase_    )

          return self.dropout_post(lowerCamelCase_    ), encoder_inputs_mask







"""simple docstring"""


from typing import TYPE_CHECKING

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


UpperCAmelCase													={
    "configuration_informer": [
        "INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "InformerConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase													=[
      "INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "InformerForPrediction",
      "InformerModel",
      "InformerPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_informer import (
        INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        InformerForPrediction,
        InformerModel,
        InformerPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase													=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




