






"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE						:						Dict					   =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE						:						Union[str, Any]					   =       {
    "microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/config.json",
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}





class       snake_case_       (    _UpperCAmelCase       ):



 """simple docstring"""

 SCREAMING_SNAKE_CASE_:							List[str]   			=					'''biogpt'''


 def __init__(      self  ,					__a=4_2384  ,					__a=1024  ,					__a=24  ,					__a=16  ,					__a=4096  ,					__a="gelu"  ,					__a=0.1  ,					__a=0.1  ,					__a=1024  ,					__a=0.02  ,					__a=1E-12  ,					__a=True  ,					__a=True  ,					__a=0.0  ,					__a=0.0  ,					__a=1  ,					__a=0  ,					__a=2  ,					**__a  ,					):


     """simple docstring"""

     A__							 =       vocab_size
     A__							 =       max_position_embeddings
     A__							 =       hidden_size
     A__							 =       num_hidden_layers
     A__							 =       num_attention_heads
     A__							 =       intermediate_size
     A__							 =       hidden_act
     A__							 =       hidden_dropout_prob
     A__							 =       attention_probs_dropout_prob
     A__							 =       initializer_range
     A__							 =       layer_norm_eps
     A__							 =       scale_embedding
     A__							 =       use_cache
     A__							 =       layerdrop
     A__							 =       activation_dropout
     super().__init__(pad_token_id=A_  ,					bos_token_id=A_  ,					eos_token_id=A_  ,					**A_						)










"""simple docstring"""






import torch

from transformers import AutoModel





class       snake_case_       (    torch.nn.Module       ):



	"""simple docstring"""

	def __init__(      self  ,					__a="sayef/fsner-bert-base-uncased"						):


					"""simple docstring"""

					super(__a  ,					self						).__init__()

					A__							 =       AutoModel.from_pretrained(__a  ,					return_dict=__a						)
					A__							 =       torch.nn.CosineSimilarity(3  ,					1E-08						)
					A__							 =       torch.nn.Softmax(dim=1						)


	def       _UpperCAmelCase						(      self  ,					**__a						):


					"""simple docstring"""

					return self.bert(**__a						).last_hidden_state


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					return token_embeddings.sum(2  ,					keepdim=__a						)


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a  ,					__a=1						):


					"""simple docstring"""

					return self.softmax(T * self.cos(__a  ,					__a						)						)






	def       _UpperCAmelCase						(      self  ,					__a  ,					__a						):


					"""simple docstring"""

					A__							 =       W_supports['sizes'].tolist()
					A__							 =       W_supports['start_token_id'].item()
					A__							 =       W_supports['end_token_id'].item()

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

					A__							 =       self.BERT(**__a						)
					A__							 =       self.BERT(**__a						)

					A__							 =       None
					A__							 =       None

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

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

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

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

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

					return p_starts, p_ends


