





"""simple docstring"""






import torch

from transformers import AutoTokenizer



class 						lowerCAmelCase       (   snake_case				):






       def __init__( self     ,	a__  ):
              _UpperCAmelCase			     =  AutoTokenizer.from_pretrained(a__  )






       def 				__A			( self     ,	a__  ):

              if isinstance(a__     ,	a__  ) and all(isinstance(_x     ,	a__  ) for _x in x  ):
                     _UpperCAmelCase			     =  None
                     for l in x:
                            _UpperCAmelCase			     =  self.tokenizer(
                                a__     ,	padding='max_length'     ,	max_length=3_84     ,	truncation=a__     ,	return_tensors='pt'     ,	)
                            _UpperCAmelCase			     =  torch.tensor([len(a__  )]  )
                            if d is not None:
                                   for k in d.keys():
                                          _UpperCAmelCase			     =  torch.cat((d[k], t[k])     ,	0  )
                            else:
                                   _UpperCAmelCase			     =  t

                     _UpperCAmelCase			     =  torch.tensor(self.tokenizer.convert_tokens_to_ids('[E]'  )  )
                     _UpperCAmelCase			     =  torch.tensor(self.tokenizer.convert_tokens_to_ids('[/E]'  )  )

              elif isinstance(a__     ,	a__  ) and all(isinstance(_x     ,	a__  ) for _x in x  ):
                     _UpperCAmelCase			     =  self.tokenizer(
                         a__     ,	padding='max_length'     ,	max_length=3_84     ,	truncation=a__     ,	return_tensors='pt'     ,	)

              else:
                     raise Exception(
                         'Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of'
                         ' strings` for supports are supported.'  )

              return d






       def 				__A			( self     ,	a__     ,	a__     ,	a__     ,	a__     ,	a__=0.70  ):
              _UpperCAmelCase			     =  []
              for idx in range(len(W_query['input_ids']  )  ):
                     _UpperCAmelCase			     =  _UpperCAmelCase			     =  range(p_start.shape[1]  )

                     _UpperCAmelCase			     =  []
                     for start_id in start_indexes:
                            for end_id in end_indexes:
                                   if start_id < end_id:
                                          output.append(
                                              (
                                                  start_id,
                                                  end_id,
                                                  p_start[idx][start_id].item(),
                                                  p_end[idx][end_id].item(),
                                              )  )

                     output.sort(key=lambda a__  : (tup[2] * tup[3])     ,	reverse=a__  )
                     _UpperCAmelCase			     =  []
                     for k in range(len(a__  )  ):
                            if output[k][2] * output[k][3] >= thresh:
                                   _UpperCAmelCase     ,				_UpperCAmelCase			     =  output[k][0], output[k][1]
                                   _UpperCAmelCase			     =  self.tokenizer.decode(W_query['input_ids'][idx][c_start_pos:c_end_pos]  )
                                   temp.append((decoded, output[k][2] * output[k][3])  )

                     final_outputs.append(a__  )

              return final_outputs













"""simple docstring"""






from typing import Callable, Optional, Union

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


lowerCAmelCase_      =				logging.get_logger(__name__)

lowerCAmelCase_      =				{
    '''microsoft/prophetnet-large-uncased''': (
        '''https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'''
    ),
}



class 						lowerCAmelCase       (   snake_case				):
       lowerCAmelCase__    			=	"""prophetnet"""
       lowerCAmelCase__    			=	["""past_key_values"""]
       lowerCAmelCase__    			=	{
           """num_attention_heads""": """num_encoder_attention_heads""",
       }






       def __init__( self     ,	a__ = 0.1     ,	a__ = "gelu"     ,	a__ = 3_05_22     ,	a__ = 10_24     ,	a__ = 40_96     ,	a__ = 12     ,	a__ = 16     ,	a__ = 40_96     ,	a__ = 12     ,	a__ = 16     ,	a__ = 0.1     ,	a__ = 0.1     ,	a__ = 5_12     ,	a__ = 0.02     ,	a__ = True     ,	a__ = True     ,	a__ = 0     ,	a__ = 2     ,	a__ = 32     ,	a__ = 1_28     ,	a__ = False     ,	a__ = 0.0     ,	a__ = True     ,	a__ = 0     ,	a__ = 1     ,	a__ = 2     ,	**a__     ,	):
              _UpperCAmelCase			     =  vocab_size
              _UpperCAmelCase			     =  hidden_size
              _UpperCAmelCase			     =  encoder_ffn_dim
              _UpperCAmelCase			     =  num_encoder_layers
              _UpperCAmelCase			     =  num_encoder_attention_heads
              _UpperCAmelCase			     =  decoder_ffn_dim
              _UpperCAmelCase			     =  num_decoder_layers
              _UpperCAmelCase			     =  num_decoder_attention_heads
              _UpperCAmelCase			     =  max_position_embeddings
              _UpperCAmelCase			     =  init_std  # Normal(0, this parameter)
              _UpperCAmelCase			     =  activation_function

              # parameters for prophetnet
              _UpperCAmelCase			     =  ngram
              _UpperCAmelCase			     =  num_buckets
              _UpperCAmelCase			     =  relative_max_distance
              _UpperCAmelCase			     =  disable_ngram_loss
              _UpperCAmelCase			     =  eps

              # 3 Types of Dropout
              _UpperCAmelCase			     =  attention_dropout
              _UpperCAmelCase			     =  activation_dropout
              _UpperCAmelCase			     =  dropout

              _UpperCAmelCase			     =  use_cache

              super().__init__(
                  pad_token_id=a__     ,	bos_token_id=a__     ,	eos_token_id=a__     ,	is_encoder_decoder=a__     ,	add_cross_attention=a__     ,	decoder_start_token_id=a__     ,	**a__     ,	)






       @property
       def 				__A			( self  ):
              return self.num_encoder_layers + self.num_decoder_layers






       @num_hidden_layers.setter
       def 				__A			( self     ,	a__  ):
              raise NotImplementedError(
                  'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
                  ' `num_decoder_layers`.'  )






