




import torch

from transformers import AutoModel





class     SCREAMING_SNAKE_CASE__		(torch.nn.Module			):
     def __init__(						self	:		Dict       ,						__lowerCamelCase	:		int="sayef/fsner-bert-base-uncased"						):



       """simple docstring"""
       super(__lowerCamelCase       ,						self						).__init__()

       lowerCAmelCase__										=      AutoModel.from_pretrained(__lowerCamelCase       ,						return_dict=__lowerCamelCase						)
       lowerCAmelCase__										=      torch.nn.CosineSimilarity(3       ,						1e-08						)
       lowerCAmelCase__										=      torch.nn.Softmax(dim=1						)
     def        A__      (						self	:		int       ,						**__lowerCamelCase	:		Optional[int]						):



       """simple docstring"""
       return self.bert(**__lowerCamelCase						).last_hidden_state
     def        A__      (						self	:		Optional[Any]       ,						__lowerCamelCase	:		Dict						):



       """simple docstring"""
       return token_embeddings.sum(2       ,						keepdim=__lowerCamelCase						)
     def        A__      (						self	:		str       ,						__lowerCamelCase	:		str       ,						__lowerCamelCase	:		int       ,						__lowerCamelCase	:		int=1						):



       """simple docstring"""
       return self.softmax(T * self.cos(__lowerCamelCase       ,						__lowerCamelCase						)						)



     def        A__      (						self	:		Optional[Any]       ,						__lowerCamelCase	:		Tuple       ,						__lowerCamelCase	:		Tuple						):



       """simple docstring"""
       lowerCAmelCase__										=      W_supports['''sizes'''].tolist()
       lowerCAmelCase__										=      W_supports['''start_token_id'''].item()
       lowerCAmelCase__										=      W_supports['''end_token_id'''].item()

       del W_supports["sizes"]
       del W_supports["start_token_id"]
       del W_supports["end_token_id"]

       lowerCAmelCase__										=      self.BERT(**__lowerCamelCase						)
       lowerCAmelCase__										=      self.BERT(**__lowerCamelCase						)

       lowerCAmelCase__										=      None
       lowerCAmelCase__										=      None

       lowerCAmelCase__										=      W_supports['''input_ids'''] == start_token_id
       lowerCAmelCase__										=      W_supports['''input_ids'''] == end_token_id

       for i, size in enumerate(__lowerCamelCase						):
         if i == 0:
           lowerCAmelCase__										=      0
         else:
           lowerCAmelCase__										=      support_sizes[i - 1]

         lowerCAmelCase__										=      S[s : s + size][start_token_masks[s : s + size]]
         lowerCAmelCase__										=      S[s : s + size][end_token_masks[s : s + size]]

         lowerCAmelCase__										=      torch.matmul(q[i]       ,						s_start.T						).sum(1						).softmax(0						)
         lowerCAmelCase__										=      torch.matmul(q[i]       ,						s_end.T						).sum(1						).softmax(0						)

         if p_starts is not None:
           lowerCAmelCase__										=      torch.vstack((p_starts, p_start)						)
           lowerCAmelCase__										=      torch.vstack((p_ends, p_end)						)
         else:
           lowerCAmelCase__										=      p_start
           lowerCAmelCase__										=      p_end

       return p_starts, p_ends







from typing import TYPE_CHECKING

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


__magic_name__  :  Optional[Any]					=       {
    """configuration_informer""": [
        """INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InformerConfig""",
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__  :  int					=       [
          """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

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

