

import torch

from transformers import AutoTokenizer


class     lowerCamelCase_   ( _lowerCAmelCase):




 def __init__( self     ,     _UpperCAmelCase       ):
   __UpperCAmelCase  :     Optional[int]		=    AutoTokenizer.from_pretrained(_UpperCAmelCase       )




 def 						a					( self     ,     _UpperCAmelCase       ):

   if isinstance(_UpperCAmelCase     ,     _UpperCAmelCase       ) and all(isinstance(_x     ,     _UpperCAmelCase       ) for _x in x       ):
     __UpperCAmelCase  :     List[Any]		=    None
     for l in x:
       __UpperCAmelCase  :     Any		=    self.tokenizer(
           _UpperCAmelCase     ,     padding='max_length'     ,     max_length=384     ,     truncation=_UpperCAmelCase     ,     return_tensors='pt'     ,     )
       __UpperCAmelCase  :     List[Any]		=    torch.tensor([len(_UpperCAmelCase       )]       )
       if d is not None:
         for k in d.keys():
           __UpperCAmelCase  :     Any		=    torch.cat((d[k], t[k])     ,     0       )
       else:
         __UpperCAmelCase  :     Union[str, Any]		=    t

     __UpperCAmelCase  :     str		=    torch.tensor(self.tokenizer.convert_tokens_to_ids('[E]'       )       )
     __UpperCAmelCase  :     Tuple		=    torch.tensor(self.tokenizer.convert_tokens_to_ids('[/E]'       )       )

   elif isinstance(_UpperCAmelCase     ,     _UpperCAmelCase       ) and all(isinstance(_x     ,     _UpperCAmelCase       ) for _x in x       ):
     __UpperCAmelCase  :     str		=    self.tokenizer(
         _UpperCAmelCase     ,     padding='max_length'     ,     max_length=384     ,     truncation=_UpperCAmelCase     ,     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     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase=0.7_0       ):
   __UpperCAmelCase  :     Tuple		=    []
   for idx in range(len(W_query['input_ids']       )       ):
     __UpperCAmelCase  :     List[str]		=    range(p_start.shape[1]       )

     __UpperCAmelCase  :     Optional[Any]		=    []
     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 _UpperCAmelCase       : (tup[2] * tup[3])     ,     reverse=_UpperCAmelCase       )
     __UpperCAmelCase  :     str		=    []
     for k in range(len(_UpperCAmelCase       )       ):
       if output[k][2] * output[k][3] >= thresh:
         __UpperCAmelCase, __UpperCAmelCase  :     Union[str, Any]		=    output[k][0], output[k][1]
         __UpperCAmelCase  :     List[str]		=    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(_UpperCAmelCase       )

   return final_outputs








from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


_SCREAMING_SNAKE_CASE          = logging.get_logger(__name__)


class     lowerCamelCase_   ( _lowerCAmelCase):
 _lowerCAmelCase       		=  '''mt5'''
 _lowerCAmelCase       		=  ['''past_key_values''']




 def __init__( self     ,     _UpperCAmelCase=250_112     ,     _UpperCAmelCase=512     ,     _UpperCAmelCase=64     ,     _UpperCAmelCase=1_024     ,     _UpperCAmelCase=8     ,     _UpperCAmelCase=None     ,     _UpperCAmelCase=6     ,     _UpperCAmelCase=32     ,     _UpperCAmelCase=128     ,     _UpperCAmelCase=0.1     ,     _UpperCAmelCase=1E-6     ,     _UpperCAmelCase=1.0     ,     _UpperCAmelCase="gated-gelu"     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase="T5Tokenizer"     ,     _UpperCAmelCase=False     ,     _UpperCAmelCase=0     ,     _UpperCAmelCase=1     ,     _UpperCAmelCase=0     ,     **_UpperCAmelCase     ,     ):
   super().__init__(
       is_encoder_decoder=_UpperCAmelCase     ,     tokenizer_class=_UpperCAmelCase     ,     tie_word_embeddings=_UpperCAmelCase     ,     pad_token_id=_UpperCAmelCase     ,     eos_token_id=_UpperCAmelCase     ,     decoder_start_token_id=_UpperCAmelCase     ,     **_UpperCAmelCase     ,     )
   __UpperCAmelCase  :     Dict		=    vocab_size
   __UpperCAmelCase  :     List[str]		=    d_model
   __UpperCAmelCase  :     str		=    d_kv
   __UpperCAmelCase  :     List[str]		=    d_ff
   __UpperCAmelCase  :     int		=    num_layers
   __UpperCAmelCase  :     List[Any]		=    (
       num_decoder_layers if num_decoder_layers is not None else self.num_layers
   )  # default = symmetry
   __UpperCAmelCase  :     List[Any]		=    num_heads
   __UpperCAmelCase  :     Tuple		=    relative_attention_num_buckets
   __UpperCAmelCase  :     Optional[int]		=    relative_attention_max_distance
   __UpperCAmelCase  :     Dict		=    dropout_rate
   __UpperCAmelCase  :     Union[str, Any]		=    layer_norm_epsilon
   __UpperCAmelCase  :     Tuple		=    initializer_factor
   __UpperCAmelCase  :     Union[str, Any]		=    feed_forward_proj
   __UpperCAmelCase  :     Optional[Any]		=    use_cache

   __UpperCAmelCase  :     Any		=    self.feed_forward_proj.split('-'       )
   __UpperCAmelCase  :     Dict		=    act_info[-1]
   __UpperCAmelCase  :     Any		=    act_info[0] == 'gated'

   if len(_UpperCAmelCase       ) > 1 and act_info[0] != "gated" or len(_UpperCAmelCase       ) > 2:
     raise ValueError(
         F"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
         'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
         '\'gated-gelu\' or \'relu\''       )

   # for backwards compatibility
   if feed_forward_proj == "gated-gelu":
     __UpperCAmelCase  :     List[str]		=    'gelu_new'




 @property
 def 						a					( self       ):
   return self.d_model




 @property
 def 						a					( self       ):
   return self.num_heads




 @property
 def 						a					( self       ):
   return self.num_layers



class     lowerCamelCase_   ( _lowerCAmelCase):




 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
 def 						a					( self       ):
   __UpperCAmelCase  :     Optional[int]		=    {
       'input_ids': {0: 'batch', 1: 'encoder_sequence'},
       'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
   }
   if self.use_past:
     __UpperCAmelCase  :     Optional[Any]		=    'past_encoder_sequence + sequence'
     __UpperCAmelCase  :     Tuple		=    {0: 'batch'}
     __UpperCAmelCase  :     List[Any]		=    {0: 'batch', 1: 'past_decoder_sequence + sequence'}
   else:
     __UpperCAmelCase  :     Tuple		=    {0: 'batch', 1: 'decoder_sequence'}
     __UpperCAmelCase  :     int		=    {0: 'batch', 1: 'decoder_sequence'}

   if self.use_past:
     self.fill_with_past_key_values_(_UpperCAmelCase     ,     direction='inputs'       )

   return common_inputs




 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
 def 						a					( self       ):
   return 13




 @property
 def 						a					( self       ):
   return 5E-4





