


'''simple docstring'''
import logging

# standard libraries
import sys

import numpy as np
import pytest  # type: ignore

# Custom/local libraries
from matrix import matrix_operation as matop

__magic_name__   :   int  							=[[12, 10], [3, 9]]
__magic_name__   :   List[Any]  							=[[3, 4], [7, 4]]
__magic_name__   :   str  							=[[3, 0, 2], [2, 0, -2], [0, 1, 1]]
__magic_name__   :   Optional[Any]  							=[[3, 0, -2], [2, 0, 2], [0, 1, 1]]
__magic_name__   :   int  							=[[3, 0, 2], [2, 0, -2], [0, 1, 1], [2, 0, -2]]
__magic_name__   :   Optional[int]  							=[1]
__magic_name__   :   Optional[int]  							=[2]

__magic_name__   :   int  							=logging.getLogger()
__magic_name__   :   Optional[int]  							=logging.DEBUG
__magic_name__   :   List[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_ :				List[str]	,  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'						)
								__magic_name__	  = (np.array(lowerCamelCase_						) + np.array(lowerCamelCase_						)).tolist()
								__magic_name__	  = 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_ :				Tuple	,  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_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'						)
								__magic_name__	  = (np.array(lowerCamelCase_						) - np.array(lowerCamelCase_						)).tolist()
								__magic_name__	  = 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_ :				List[Any]	,  lowerCamelCase_ :				List[str]						):




				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'						)
								__magic_name__	  = (np.matmul(lowerCamelCase_	,  lowerCamelCase_						)).tolist()
								__magic_name__	  = 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       (       ):
				__magic_name__	  = (3.5 * np.array(lowerCamelCase_						)).tolist()
				__magic_name__	  = matop.scalar_multiply(lowerCamelCase_	,  3.5						)
				assert theo == act




@pytest.mark.mat_ops
def      __snake_case       (       ):
				__magic_name__	  = (np.identity(5						)).tolist()
				__magic_name__	  = 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_ :				Tuple						):




				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:
								__magic_name__	  = (np.transpose(lowerCamelCase_						)).tolist()
								__magic_name__	  = matop.transpose(lowerCamelCase_	,  return_map=lowerCamelCase_						)
								assert theo == act







'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__magic_name__   :   List[Any]  							={'configuration_yoso': ['YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YosoConfig']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__magic_name__   :   Union[str, Any]  							=[
						    'YOSO_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'YosoForMaskedLM',
						    'YosoForMultipleChoice',
						    'YosoForQuestionAnswering',
						    'YosoForSequenceClassification',
						    'YosoForTokenClassification',
						    'YosoLayer',
						    'YosoModel',
						    'YosoPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_yoso import YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP, YosoConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_yoso import (
												    YOSO_PRETRAINED_MODEL_ARCHIVE_LIST,
												    YosoForMaskedLM,
												    YosoForMultipleChoice,
												    YosoForQuestionAnswering,
												    YosoForSequenceClassification,
												    YosoForTokenClassification,
												    YosoLayer,
												    YosoModel,
												    YosoPreTrainedModel,
												)


else:
						import sys

						__magic_name__   :   Optional[int]  							=_LazyModule(__name__, globals()['__file__'], _import_structure)



