





import logging

# standard libraries
import sys

import numpy as np
import pytest  # type: ignore

# Custom/local libraries
from matrix import matrix_operation as matop

_A				:							List[Any]							= [[12, 10], [3, 9]]
_A				:							List[str]							= [[3, 4], [7, 4]]
_A				:							List[str]							= [[3, 0, 2], [2, 0, -2], [0, 1, 1]]
_A				:							Optional[Any]							= [[3, 0, -2], [2, 0, 2], [0, 1, 1]]
_A				:							List[Any]							= [[3, 0, 2], [2, 0, -2], [0, 1, 1], [2, 0, -2]]
_A				:							Union[str, Any]							= [1]
_A				:							Dict							= [2]

_A				:							Union[str, Any]							= logging.getLogger()
_A				:							Union[str, Any]							= logging.DEBUG
_A				:							Any							= logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)


@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ('''mat1''', '''mat2''')   ,       [(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]  )
def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_  )							->	Dict:
    if (np.array(lowerCAmelCase_  )).shape < (2, 2) or (np.array(lowerCAmelCase_  )).shape < (2, 2):
        with pytest.raises(lowerCAmelCase_  ):
            logger.info(f'''\n\t{test_addition.__name__} returned integer'''  )
            matop.add(lowerCAmelCase_   ,       lowerCAmelCase_  )
    elif (np.array(lowerCAmelCase_  )).shape == (np.array(lowerCAmelCase_  )).shape:
        logger.info(f'''\n\t{test_addition.__name__} with same matrix dims'''  )
        SCREAMING_SNAKE_CASE__       							= (np.array(lowerCAmelCase_  ) + np.array(lowerCAmelCase_  )).tolist()
        SCREAMING_SNAKE_CASE__       							= matop.add(lowerCAmelCase_   ,       lowerCAmelCase_  )
        assert theo == act
    else:
        with pytest.raises(lowerCAmelCase_  ):
            logger.info(f'''\n\t{test_addition.__name__} with different matrix dims'''  )
            matop.add(lowerCAmelCase_   ,       lowerCAmelCase_  )


@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ('''mat1''', '''mat2''')   ,       [(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]  )
def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_  )							->	List[str]:
    if (np.array(lowerCAmelCase_  )).shape < (2, 2) or (np.array(lowerCAmelCase_  )).shape < (2, 2):
        with pytest.raises(lowerCAmelCase_  ):
            logger.info(f'''\n\t{test_subtraction.__name__} returned integer'''  )
            matop.subtract(lowerCAmelCase_   ,       lowerCAmelCase_  )
    elif (np.array(lowerCAmelCase_  )).shape == (np.array(lowerCAmelCase_  )).shape:
        logger.info(f'''\n\t{test_subtraction.__name__} with same matrix dims'''  )
        SCREAMING_SNAKE_CASE__       							= (np.array(lowerCAmelCase_  ) - np.array(lowerCAmelCase_  )).tolist()
        SCREAMING_SNAKE_CASE__       							= matop.subtract(lowerCAmelCase_   ,       lowerCAmelCase_  )
        assert theo == act
    else:
        with pytest.raises(lowerCAmelCase_  ):
            logger.info(f'''\n\t{test_subtraction.__name__} with different matrix dims'''  )
            assert matop.subtract(lowerCAmelCase_   ,       lowerCAmelCase_  )


@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ('''mat1''', '''mat2''')   ,       [(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]  )
def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_  )							->	int:
    if (np.array(lowerCAmelCase_  )).shape < (2, 2) or (np.array(lowerCAmelCase_  )).shape < (2, 2):
        logger.info(f'''\n\t{test_multiplication.__name__} returned integer'''  )
        with pytest.raises(lowerCAmelCase_  ):
            matop.add(lowerCAmelCase_   ,       lowerCAmelCase_  )
    elif (np.array(lowerCAmelCase_  )).shape == (np.array(lowerCAmelCase_  )).shape:
        logger.info(f'''\n\t{test_multiplication.__name__} meets dim requirements'''  )
        SCREAMING_SNAKE_CASE__       							= (np.matmul(lowerCAmelCase_   ,       lowerCAmelCase_  )).tolist()
        SCREAMING_SNAKE_CASE__       							= matop.multiply(lowerCAmelCase_   ,       lowerCAmelCase_  )
        assert theo == act
    else:
        with pytest.raises(lowerCAmelCase_  ):
            logger.info(
                f'''\n\t{test_multiplication.__name__} does not meet dim requirements'''  )
            assert matop.subtract(lowerCAmelCase_   ,       lowerCAmelCase_  )


@pytest.mark.mat_ops
def        __snake_case       (						)							->	List[str]:
    SCREAMING_SNAKE_CASE__       							= (3.5 * np.array(lowerCAmelCase_  )).tolist()
    SCREAMING_SNAKE_CASE__       							= matop.scalar_multiply(lowerCAmelCase_   ,       3.5  )
    assert theo == act


@pytest.mark.mat_ops
def        __snake_case       (						)							->	Optional[Any]:
    SCREAMING_SNAKE_CASE__       							= (np.identity(5  )).tolist()
    SCREAMING_SNAKE_CASE__       							= matop.identity(5  )
    assert theo == act



@pytest.mark.mat_ops
@pytest.mark.parametrize('''mat'''   ,       [mat_a, mat_b, mat_c, mat_d, mat_e, mat_f]  )
def        __snake_case       (						lowerCAmelCase_  )							->	Any:
    if (np.array(lowerCAmelCase_  )).shape < (2, 2):
        with pytest.raises(lowerCAmelCase_  ):
            logger.info(f'''\n\t{test_transpose.__name__} returned integer'''  )
            matop.transpose(lowerCAmelCase_  )
    else:
        SCREAMING_SNAKE_CASE__       							= (np.transpose(lowerCAmelCase_  )).tolist()
        SCREAMING_SNAKE_CASE__       							= matop.transpose(lowerCAmelCase_   ,       return_map=lowerCAmelCase_  )
        assert theo == act







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
