





import numpy as np

_A				:							List[Any]							= [
    ["""a""", """b""", """c""", """d""", """e"""],
    ["""f""", """g""", """h""", """i""", """k"""],
    ["""l""", """m""", """n""", """o""", """p"""],
    ["""q""", """r""", """s""", """t""", """u"""],
    ["""v""", """w""", """x""", """y""", """z"""],
]



class 				__snake_case  :


    '''simple docstring'''







    def __init__(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= np.array(A_    )



    def    lowercase_				(					self      ,							A_    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= np.where(letter == self.SQUARE    )
        SCREAMING_SNAKE_CASE__       							= np.concatenate([indexa + 1, indexa + 1]    )
        return indexes



    def    lowercase_				(					self      ,							A_      ,							A_    ):





        '''simple docstring'''

        return self.SQUARE[indexa - 1, indexa - 1]



    def    lowercase_				(					self      ,							A_    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= message.lower()
        SCREAMING_SNAKE_CASE__       							= message.replace('''j'''      ,							'''i'''    )

        SCREAMING_SNAKE_CASE__       							= ''''''
        for letter_index in range(len(A_    )    ):
            if message[letter_index] != " ":
                SCREAMING_SNAKE_CASE__       							= self.letter_to_numbers(message[letter_index]    )
                SCREAMING_SNAKE_CASE__       							= encoded_message + str(numbers[0]    ) + str(numbers[1]    )
            elif message[letter_index] == " ":
                SCREAMING_SNAKE_CASE__       							= encoded_message + ''' '''

        return encoded_message




    def    lowercase_				(					self      ,							A_    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= message.replace(''' '''      ,							'''  '''    )
        SCREAMING_SNAKE_CASE__       							= ''''''
        for numbers_index in range(int(len(A_    ) / 2    )    ):
            if message[numbers_index * 2] != " ":
                SCREAMING_SNAKE_CASE__       							= message[numbers_index * 2]
                SCREAMING_SNAKE_CASE__       							= message[numbers_index * 2 + 1]

                SCREAMING_SNAKE_CASE__       							= self.numbers_to_letter(int(A_    )      ,							int(A_    )    )
                SCREAMING_SNAKE_CASE__       							= decoded_message + letter
            elif message[numbers_index * 2] == " ":
                SCREAMING_SNAKE_CASE__       							= decoded_message + ''' '''

        return decoded_message







import torch

from transformers import AutoTokenizer



class 				__snake_case  (     __SCREAMING_SNAKE_CASE						):


    '''simple docstring'''







    def __init__(					self      ,							A_    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= AutoTokenizer.from_pretrained(A_    )



    def    lowercase_				(					self      ,							A_    ):





        '''simple docstring'''

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

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

        elif isinstance(A_      ,							A_    ) and all(isinstance(_x      ,							A_    ) for _x in x    ):
            SCREAMING_SNAKE_CASE__       							= 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    lowercase_				(					self      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_=0.70    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= []
        for idx in range(len(W_query['''input_ids''']    )    ):
            SCREAMING_SNAKE_CASE__       							= SCREAMING_SNAKE_CASE__       							= range(p_start.shape[1]    )

            SCREAMING_SNAKE_CASE__       							= []
            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_    )
            SCREAMING_SNAKE_CASE__       							= []
            for k in range(len(A_    )    ):
                if output[k][2] * output[k][3] >= thresh:
                    SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= output[k][0], output[k][1]
                    SCREAMING_SNAKE_CASE__       							= 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
