






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


_SCREAMING_SNAKE_CASE   =  logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   =  {
    "speechbrain/m-ctc-t-large": "https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json",
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		List[Any]					='''mctct'''





       def __init__(				self	:str,      snake_case	:List[Any]=8065,      snake_case	:List[str]=1536,      snake_case	:str=36,      snake_case	:Dict=6144,      snake_case	:str=4,      snake_case	:Any=384,      snake_case	:Any=920,      snake_case	:Dict=1e-5,      snake_case	:Any=0.3,      snake_case	:str="relu",      snake_case	:List[Any]=0.0_2,      snake_case	:int=0.3,      snake_case	:Tuple=0.3,      snake_case	:List[str]=1,      snake_case	:Dict=0,      snake_case	:List[Any]=2,      snake_case	:str=1,      snake_case	:Any=0.3,      snake_case	:int=1,      snake_case	:Tuple=(7,),      snake_case	:List[str]=(3,),      snake_case	:List[Any]=80,      snake_case	:Union[str, Any]=1,      snake_case	:Any=None,      snake_case	:str="sum",      snake_case	:Union[str, Any]=False,      **snake_case	:int,      ):



          """simple docstring"""

          super().__init__(**snake_case,      pad_token_id=snake_case,      bos_token_id=snake_case,      eos_token_id=snake_case)
          _lowercase              =vocab_size
          _lowercase              =hidden_size
          _lowercase              =num_hidden_layers
          _lowercase              =intermediate_size
          _lowercase              =num_attention_heads
          _lowercase              =attention_head_dim
          _lowercase              =max_position_embeddings
          _lowercase              =layer_norm_eps
          _lowercase              =layerdrop
          _lowercase              =hidden_act
          _lowercase              =initializer_range
          _lowercase              =hidden_dropout_prob
          _lowercase              =attention_probs_dropout_prob
          _lowercase              =pad_token_id
          _lowercase              =bos_token_id
          _lowercase              =eos_token_id
          _lowercase              =conv_glu_dim
          _lowercase              =conv_dropout
          _lowercase              =num_conv_layers
          _lowercase              =input_feat_per_channel
          _lowercase              =input_channels
          _lowercase              =conv_channels
          _lowercase              =ctc_loss_reduction
          _lowercase              =ctc_zero_infinity

          # prevents config testing fail with exporting to json
          _lowercase              =list(snake_case)
          _lowercase              =list(snake_case)

          if len(self.conv_kernel) != self.num_conv_layers:
             raise ValueError(
                 'Configuration for convolutional module is incorrect. '
                 'It is required that `len(config.conv_kernel)` == `config.num_conv_layers` '
                 f'''but is `len(config.conv_kernel) = {len(self.conv_kernel)}`, '''
                 f'''`config.num_conv_layers = {self.num_conv_layers}`.''')










import torch

from transformers import AutoModel






class 					SCREAMING_SNAKE_CASE_  (       torch.nn.Module ):
       """simple docstring"""


       def __init__(				self	:Dict,      snake_case	:str="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-0_8)
          _lowercase              =torch.nn.Softmax(dim=1)





       def 				UpperCamelCase__						(				self	:str,      **snake_case	:int):



          """simple docstring"""

          return self.bert(**snake_case).last_hidden_state





       def 				UpperCamelCase__						(				self	:Union[str, Any],      snake_case	:Optional[Any]):



          """simple docstring"""

          return token_embeddings.sum(2,      keepdim=snake_case)





       def 				UpperCamelCase__						(				self	:List[Any],      snake_case	:int,      snake_case	:Dict,      snake_case	:Dict=1):



          """simple docstring"""

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





       def 				UpperCamelCase__						(				self	:List[str],      snake_case	:int,      snake_case	:List[str]):



          """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


