





import numpy as np


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	str:
    SCREAMING_SNAKE_CASE__       							= n_draws * class_counts / class_counts.sum()
    # floored means we don't overshoot n_samples, but probably undershoot
    SCREAMING_SNAKE_CASE__       							= np.floor(lowerCAmelCase_  )
    # we add samples according to how much "left over" probability
    # they had, until we arrive at n_samples
    SCREAMING_SNAKE_CASE__       							= int(n_draws - floored.sum()  )
    if need_to_add > 0:
        SCREAMING_SNAKE_CASE__       							= continuous - floored
        SCREAMING_SNAKE_CASE__       							= np.sort(np.unique(lowerCAmelCase_  )  )[::-1]
        # add according to remainder, but break ties
        # randomly to avoid biases
        for value in values:
            ((SCREAMING_SNAKE_CASE__)							,		)       							= np.where(remainder == value  )
            # if we need_to_add less than what's in inds
            # we draw randomly from them.
            # if we need to add more, we add them all and
            # go to the next value
            SCREAMING_SNAKE_CASE__       							= min(len(lowerCAmelCase_  )   ,       lowerCAmelCase_  )
            SCREAMING_SNAKE_CASE__       							= rng.choice(lowerCAmelCase_   ,       size=lowerCAmelCase_   ,       replace=lowerCAmelCase_  )
            floored[inds] += 1
            need_to_add -= add_now
            if need_to_add == 0:
                break
    return floored.astype(np.intaa  )



def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_=1_0  )							->	List[Any]:
    SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= np.unique(lowerCAmelCase_   ,       return_inverse=lowerCAmelCase_  )
    SCREAMING_SNAKE_CASE__       							= classes.shape[0]
    SCREAMING_SNAKE_CASE__       							= np.bincount(lowerCAmelCase_  )
    if np.min(lowerCAmelCase_  ) < 2:
        raise ValueError('''Minimum class count error'''  )
    if n_train < n_classes:
        raise ValueError(
            '''The train_size = %d should be greater or ''' '''equal to the number of classes = %d''' % (n_train, n_classes)  )
    if n_test < n_classes:
        raise ValueError(
            '''The test_size = %d should be greater or ''' '''equal to the number of classes = %d''' % (n_test, n_classes)  )
    SCREAMING_SNAKE_CASE__       							= np.split(np.argsort(lowerCAmelCase_   ,       kind='''mergesort'''  )   ,       np.cumsum(lowerCAmelCase_  )[:-1]  )
    for _ in range(lowerCAmelCase_  ):
        SCREAMING_SNAKE_CASE__       							= approximate_mode(lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )
        SCREAMING_SNAKE_CASE__       							= class_counts - n_i
        SCREAMING_SNAKE_CASE__       							= approximate_mode(lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )

        SCREAMING_SNAKE_CASE__       							= []
        SCREAMING_SNAKE_CASE__       							= []

        for i in range(lowerCAmelCase_  ):
            SCREAMING_SNAKE_CASE__       							= rng.permutation(class_counts[i]  )
            SCREAMING_SNAKE_CASE__       							= class_indices[i].take(lowerCAmelCase_   ,       mode='''clip'''  )
            train.extend(perm_indices_class_i[: n_i[i]]  )
            test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]  )
        SCREAMING_SNAKE_CASE__       							= rng.permutation(lowerCAmelCase_  )
        SCREAMING_SNAKE_CASE__       							= rng.permutation(lowerCAmelCase_  )

        yield train, test







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
