




import os
import sys

_lowerCAmelCase						:   Optional[int]		=   os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


_lowerCAmelCase						:   Optional[Any]		=   [
    "torch",
    "numpy",
    "tokenizers",
    "filelock",
    "requests",
    "tqdm",
    "regex",
    "sentencepiece",
    "sacremoses",
    "importlib_metadata",
    "huggingface_hub",
]



@add_start_docstrings(AutoConfig.__doc__	)
def 						lowerCAmelCase		(		*_lowerCAmelCase							:   Tuple						,							**_lowerCAmelCase							:   Union[str, Any]	):




 """simple docstring"""







 return AutoConfig.from_pretrained(*_lowerCAmelCase						,							**_lowerCAmelCase	)



@add_start_docstrings(AutoTokenizer.__doc__	)
def 						lowerCAmelCase		(		*_lowerCAmelCase							:   List[Any]						,							**_lowerCAmelCase							:   Optional[Any]	):




 """simple docstring"""







 return AutoTokenizer.from_pretrained(*_lowerCAmelCase						,							**_lowerCAmelCase	)



@add_start_docstrings(AutoModel.__doc__	)
def 						lowerCAmelCase		(		*_lowerCAmelCase							:   Tuple						,							**_lowerCAmelCase							:   Union[str, Any]	):




 """simple docstring"""







 return AutoModel.from_pretrained(*_lowerCAmelCase						,							**_lowerCAmelCase	)



@add_start_docstrings(AutoModelForCausalLM.__doc__	)
def 						lowerCAmelCase		(		*_lowerCAmelCase							:   List[Any]						,							**_lowerCAmelCase							:   int	):




 """simple docstring"""







 return AutoModelForCausalLM.from_pretrained(*_lowerCAmelCase						,							**_lowerCAmelCase	)



@add_start_docstrings(AutoModelForMaskedLM.__doc__	)
def 						lowerCAmelCase		(		*_lowerCAmelCase							:   int						,							**_lowerCAmelCase							:   List[str]	):




 """simple docstring"""







 return AutoModelForMaskedLM.from_pretrained(*_lowerCAmelCase						,							**_lowerCAmelCase	)



@add_start_docstrings(AutoModelForSequenceClassification.__doc__	)
def 						lowerCAmelCase		(		*_lowerCAmelCase							:   Union[str, Any]						,							**_lowerCAmelCase							:   Optional[int]	):




 """simple docstring"""







 return AutoModelForSequenceClassification.from_pretrained(*_lowerCAmelCase						,							**_lowerCAmelCase	)



@add_start_docstrings(AutoModelForQuestionAnswering.__doc__	)
def 						lowerCAmelCase		(		*_lowerCAmelCase							:   List[str]						,							**_lowerCAmelCase							:   Tuple	):




 """simple docstring"""







 return AutoModelForQuestionAnswering.from_pretrained(*_lowerCAmelCase						,							**_lowerCAmelCase	)











import torch

from transformers import AutoModel
class 			_UpperCamelCase      (				torch.nn.Module		):
      def __init__(			self   :str ,      lowerCamelCase   :Tuple="sayef/fsner-bert-base-uncased"      )     ->			int:
       super(lowerCamelCase ,      self      ).__init__()

       UpperCAmelCase__							 =						AutoModel.from_pretrained(lowerCamelCase ,      return_dict=lowerCamelCase      )
       UpperCAmelCase__							 =						torch.nn.CosineSimilarity(3 ,      1e-08      )
       UpperCAmelCase__							 =						torch.nn.Softmax(dim=1      )
      def 	UpperCAmelCase_						(			self   :Union[str, Any] ,      **lowerCamelCase   :Tuple      )     ->			Dict:
       return self.bert(**lowerCamelCase      ).last_hidden_state
      def 	UpperCAmelCase_						(			self   :Any ,      lowerCamelCase   :Union[str, Any]      )     ->			Union[str, Any]:
       return token_embeddings.sum(2 ,      keepdim=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Dict ,      lowerCamelCase   :List[Any] ,      lowerCamelCase   :int ,      lowerCamelCase   :Union[str, Any]=1      )     ->			Dict:
       return self.softmax(T * self.cos(lowerCamelCase ,      lowerCamelCase      )      )







      def 	UpperCAmelCase_						(			self   :Union[str, Any] ,      lowerCamelCase   :Any ,      lowerCamelCase   :Any      )     ->			Union[str, Any]:
       UpperCAmelCase__							 =						W_supports["sizes"].tolist()
       UpperCAmelCase__							 =						W_supports["start_token_id"].item()
       UpperCAmelCase__							 =						W_supports["end_token_id"].item()

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

       UpperCAmelCase__							 =						self.BERT(**lowerCamelCase      )
       UpperCAmelCase__							 =						self.BERT(**lowerCamelCase      )

       UpperCAmelCase__							 =						None
       UpperCAmelCase__							 =						None

       UpperCAmelCase__							 =						W_supports["input_ids"] == start_token_id
       UpperCAmelCase__							 =						W_supports["input_ids"] == end_token_id

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

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

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

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

       return p_starts, p_ends





