



"""simple docstring"""
import torch

from transformers import AutoModel


class    SCREAMING_SNAKE_CASE__ (							torch.nn.Module   ):

    def __init__(				self			:				Optional[Any] ,  lowerCAmelCase_			:				int="sayef/fsner-bert-base-uncased"):







      """simple docstring"""







      super(_snake_case ,  self).__init__()

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

    def 					_UpperCAmelCase	(				self			:				Any ,  **lowerCAmelCase_			:				Dict):







      """simple docstring"""







      return self.bert(**_snake_case).last_hidden_state

    def 					_UpperCAmelCase	(				self			:				Optional[int] ,  lowerCAmelCase_			:				int):







      """simple docstring"""







      return token_embeddings.sum(2 ,  keepdim=_snake_case)

    def 					_UpperCAmelCase	(				self			:				str ,  lowerCAmelCase_			:				Tuple ,  lowerCAmelCase_			:				Tuple ,  lowerCAmelCase_			:				List[Any]=1):







      """simple docstring"""







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




    def 					_UpperCAmelCase	(				self			:				Any ,  lowerCAmelCase_			:				int ,  lowerCAmelCase_			:				Union[str, Any]):







      """simple docstring"""







      lowercase_								= W_supports["""sizes"""].tolist()
      lowercase_								= W_supports["""start_token_id"""].item()
      lowercase_								= W_supports["""end_token_id"""].item()

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

      lowercase_								= self.BERT(**_snake_case)
      lowercase_								= self.BERT(**_snake_case)

      lowercase_								= None
      lowercase_								= None

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

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

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

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

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

      return p_starts, p_ends






"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase						:		List[Any]			= logging.get_logger(__name__)

UpperCAmelCase						:		Optional[Any]			= {
    "alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json",
}


class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):
    lowercase__      =   "mgp-str"

    def __init__(				self			:				Optional[Any] ,  lowerCAmelCase_			:				Optional[Any]=[3_2, 1_2_8] ,  lowerCAmelCase_			:				Union[str, Any]=4 ,  lowerCAmelCase_			:				Dict=3 ,  lowerCAmelCase_			:				List[str]=2_7 ,  lowerCAmelCase_			:				str=3_8 ,  lowerCAmelCase_			:				Optional[int]=5_0_2_5_7 ,  lowerCAmelCase_			:				Any=3_0_5_2_2 ,  lowerCAmelCase_			:				str=7_6_8 ,  lowerCAmelCase_			:				Any=1_2 ,  lowerCAmelCase_			:				Optional[Any]=1_2 ,  lowerCAmelCase_			:				Tuple=4.0 ,  lowerCAmelCase_			:				str=True ,  lowerCAmelCase_			:				List[str]=False ,  lowerCAmelCase_			:				List[Any]=1E-5 ,  lowerCAmelCase_			:				Any=0.0 ,  lowerCAmelCase_			:				int=0.0 ,  lowerCAmelCase_			:				str=0.0 ,  lowerCAmelCase_			:				Dict=False ,  lowerCAmelCase_			:				List[str]=0.02 ,  **lowerCAmelCase_			:				str ,  ):







      """simple docstring"""







      super().__init__(**lowerCAmelCase_)

      lowercase_								= image_size
      lowercase_								= patch_size
      lowercase_								= num_channels
      lowercase_								= max_token_length
      lowercase_								= num_character_labels
      lowercase_								= num_bpe_labels
      lowercase_								= num_wordpiece_labels
      lowercase_								= hidden_size
      lowercase_								= num_hidden_layers
      lowercase_								= num_attention_heads
      lowercase_								= mlp_ratio
      lowercase_								= distilled
      lowercase_								= layer_norm_eps
      lowercase_								= drop_rate
      lowercase_								= qkv_bias
      lowercase_								= attn_drop_rate
      lowercase_								= drop_path_rate
      lowercase_								= output_aa_attentions
      lowercase_								= initializer_range

