"""simple docstring"""





import torch

from transformers import AutoTokenizer


class 					UpperCAmelCase_     (   _a):


      def __init__(       self		,      a							)	-> int:
             lowercase__      :		Dict          =     AutoTokenizer.from_pretrained(a							)


      def 		_UpperCAmelCase	(       self		,      a							)	-> Dict:

             if isinstance(a		,      a							) and all(isinstance(_x		,      a							) for _x in x							):
                    lowercase__      :		Union[str, Any]          =     None
                    for l in x:
                           lowercase__      :		str          =     self.tokenizer(
                               a		,      padding='max_length'		,      max_length=3_8_4		,      truncation=a		,      return_tensors='pt'		,      )
                           lowercase__      :		int          =     torch.tensor([len(a							)]							)
                           if d is not None:
                                  for k in d.keys():
                                         lowercase__      :		int          =     torch.cat((d[k], t[k])		,      0							)
                           else:
                                  lowercase__      :		Optional[Any]          =     t

                    lowercase__      :		Optional[Any]          =     torch.tensor(self.tokenizer.convert_tokens_to_ids('[E]'							)							)
                    lowercase__      :		Tuple          =     torch.tensor(self.tokenizer.convert_tokens_to_ids('[/E]'							)							)

             elif isinstance(a		,      a							) and all(isinstance(_x		,      a							) for _x in x							):
                    lowercase__      :		int          =     self.tokenizer(
                        a		,      padding='max_length'		,      max_length=3_8_4		,      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 		_UpperCAmelCase	(       self		,      a		,      a		,      a		,      a		,      a=0.70							)	-> Optional[Any]:
             lowercase__      :		Dict          =     []
             for idx in range(len(W_query['input_ids']							)							):
                    lowercase__      :		str          =     range(p_start.shape[1]							)

                    lowercase__      :		str          =     []
                    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							)
                    lowercase__      :		Tuple          =     []
                    for k in range(len(a							)							):
                           if output[k][2] * output[k][3] >= thresh:
                                  lowercase__   ,				lowercase__      :		Tuple          =     output[k][0], output[k][1]
                                  lowercase__      :		Tuple          =     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 ..utils import DummyObject, requires_backends


class 					UpperCAmelCase_     (   metaclass=_a):
      lowerCamelCase__		:   List[str]			  =  ["torch", "transformers", "k_diffusion"]


      def __init__(       self		,      *a		,      **a							)	-> Dict:
             requires_backends(self		,      ['torch', 'transformers', 'k_diffusion']							)


      @classmethod
      def 		_UpperCAmelCase	(       cls		,      *a		,      **a							)	-> Tuple:
             requires_backends(cls		,      ['torch', 'transformers', 'k_diffusion']							)


      @classmethod
      def 		_UpperCAmelCase	(       cls		,      *a		,      **a							)	-> Any:
             requires_backends(cls		,      ['torch', 'transformers', 'k_diffusion']							)

