




'''simple docstring'''







import torch

from transformers import AutoTokenizer


class       _a  (     __a			):
       def __init__(     self						:     Dict       ,    lowercase						:     Dict    ):



           '''simple docstring'''

           UpperCAmelCase      	=		AutoTokenizer.from_pretrained(lowercase    )
       def    A				(     self						:     List[Any]       ,    lowercase						:     Any    ):



           '''simple docstring'''

           if isinstance(lowercase       ,    lowercase    ) and all(isinstance(_x       ,    lowercase    ) for _x in x    ):
               UpperCAmelCase      	=		None
               for l in x:
                   UpperCAmelCase      	=		self.tokenizer(
                       lowercase       ,    padding='''max_length'''       ,    max_length=384       ,    truncation=lowercase       ,    return_tensors='''pt'''       ,    )
                   UpperCAmelCase      	=		torch.tensor([len(lowercase    )]    )
                   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(lowercase       ,    lowercase    ) and all(isinstance(_x       ,    lowercase    ) for _x in x    ):
               UpperCAmelCase      	=		self.tokenizer(
                   lowercase       ,    padding='''max_length'''       ,    max_length=384       ,    truncation=lowercase       ,    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						:     Optional[int]       ,    lowercase						:     List[Any]       ,    lowercase						:     int       ,    lowercase						:     List[Any]       ,    lowercase						:     Dict       ,    lowercase						:     List[str]=0.70    ):



           '''simple docstring'''

           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 lowercase    : (tup[2] * tup[3])       ,    reverse=lowercase    )
               UpperCAmelCase      	=		[]
               for k in range(len(lowercase    )    ):
                   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(lowercase    )

           return final_outputs










'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A			   ={'configuration_dinat': ['DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DinatConfig']}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A			   =[
       'DINAT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'DinatForImageClassification',
       'DinatModel',
       'DinatPreTrainedModel',
       'DinatBackbone',
   ]

if TYPE_CHECKING:
   from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_dinat import (
          DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
          DinatBackbone,
          DinatForImageClassification,
          DinatModel,
          DinatPreTrainedModel,
      )

else:
   import sys

   A			   =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




