
import logging

# standard libraries
import sys

import numpy as np
import pytest  # type: ignore

# Custom/local libraries
from matrix import matrix_operation as matop

snake_case_      :					Union[str, Any]				      =     [[1_2, 1_0], [3, 9]]
snake_case_      :					Union[str, Any]				      =     [[3, 4], [7, 4]]
snake_case_      :					Optional[Any]				      =     [[3, 0, 2], [2, 0, -2], [0, 1, 1]]
snake_case_      :					Dict				      =     [[3, 0, -2], [2, 0, 2], [0, 1, 1]]
snake_case_      :					int				      =     [[3, 0, 2], [2, 0, -2], [0, 1, 1], [2, 0, -2]]
snake_case_      :					Union[str, Any]				      =     [1]
snake_case_      :					str				      =     [2]

snake_case_      :					Union[str, Any]				      =     logging.getLogger()
snake_case_      :					Dict				      =     logging.DEBUG
snake_case_      :					List[str]				      =     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      __UpperCAmelCase    (  snake_case_							:		Dict				,					snake_case_							:		List[Any]				):



	'''simple docstring'''



	if (np.array(snake_case_				)).shape < (2, 2) or (np.array(snake_case_				)).shape < (2, 2):
		with pytest.raises(snake_case_				):
			logger.info(F'\n\t{test_addition.__name__} returned integer'				)
			matop.add(snake_case_				,					snake_case_				)
	elif (np.array(snake_case_				)).shape == (np.array(snake_case_				)).shape:
		logger.info(F'\n\t{test_addition.__name__} with same matrix dims'				)
		UpperCAmelCase:					List[Any] 						=     (np.array(snake_case_				) + np.array(snake_case_				)).tolist()
		UpperCAmelCase:					List[str] 						=     matop.add(snake_case_				,					snake_case_				)
		assert theo == act
	else:
		with pytest.raises(snake_case_				):
			logger.info(F'\n\t{test_addition.__name__} with different matrix dims'				)
			matop.add(snake_case_				,					snake_case_				)



@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      __UpperCAmelCase    (  snake_case_							:		Union[str, Any]				,					snake_case_							:		Tuple				):



	'''simple docstring'''



	if (np.array(snake_case_				)).shape < (2, 2) or (np.array(snake_case_				)).shape < (2, 2):
		with pytest.raises(snake_case_				):
			logger.info(F'\n\t{test_subtraction.__name__} returned integer'				)
			matop.subtract(snake_case_				,					snake_case_				)
	elif (np.array(snake_case_				)).shape == (np.array(snake_case_				)).shape:
		logger.info(F'\n\t{test_subtraction.__name__} with same matrix dims'				)
		UpperCAmelCase:					List[str] 						=     (np.array(snake_case_				) - np.array(snake_case_				)).tolist()
		UpperCAmelCase:					Dict 						=     matop.subtract(snake_case_				,					snake_case_				)
		assert theo == act
	else:
		with pytest.raises(snake_case_				):
			logger.info(F'\n\t{test_subtraction.__name__} with different matrix dims'				)
			assert matop.subtract(snake_case_				,					snake_case_				)



@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      __UpperCAmelCase    (  snake_case_							:		List[Any]				,					snake_case_							:		Dict				):



	'''simple docstring'''



	if (np.array(snake_case_				)).shape < (2, 2) or (np.array(snake_case_				)).shape < (2, 2):
		logger.info(F'\n\t{test_multiplication.__name__} returned integer'				)
		with pytest.raises(snake_case_				):
			matop.add(snake_case_				,					snake_case_				)
	elif (np.array(snake_case_				)).shape == (np.array(snake_case_				)).shape:
		logger.info(F'\n\t{test_multiplication.__name__} meets dim requirements'				)
		UpperCAmelCase:					List[Any] 						=     (np.matmul(snake_case_				,					snake_case_				)).tolist()
		UpperCAmelCase:					Optional[int] 						=     matop.multiply(snake_case_				,					snake_case_				)
		assert theo == act
	else:
		with pytest.raises(snake_case_				):
			logger.info(
			    F'\n\t{test_multiplication.__name__} does not meet dim requirements'				)
			assert matop.subtract(snake_case_				,					snake_case_				)



@pytest.mark.mat_ops
def      __UpperCAmelCase    (  ):



	'''simple docstring'''



	UpperCAmelCase:					int 						=     (3.5 * np.array(snake_case_				)).tolist()
	UpperCAmelCase:					Tuple 						=     matop.scalar_multiply(snake_case_				,					3.5				)
	assert theo == act



@pytest.mark.mat_ops
def      __UpperCAmelCase    (  ):



	'''simple docstring'''



	UpperCAmelCase:					Union[str, Any] 						=     (np.identity(5				)).tolist()
	UpperCAmelCase:					int 						=     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      __UpperCAmelCase    (  snake_case_							:		Optional[Any]				):



	'''simple docstring'''



	if (np.array(snake_case_				)).shape < (2, 2):
		with pytest.raises(snake_case_				):
			logger.info(F'\n\t{test_transpose.__name__} returned integer'				)
			matop.transpose(snake_case_				)
	else:
		UpperCAmelCase:					Union[str, Any] 						=     (np.transpose(snake_case_				)).tolist()
		UpperCAmelCase:					Tuple 						=     matop.transpose(snake_case_				,					return_map=snake_case_				)
		assert theo == act


from typing import TYPE_CHECKING

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


snake_case_      :					Any				      =     {
    'configuration_imagegpt': ['IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ImageGPTConfig', 'ImageGPTOnnxConfig']
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					str				      =     ['ImageGPTFeatureExtractor']
	snake_case_      :					Optional[int]				      =     ['ImageGPTImageProcessor']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					List[str]				      =     [
	    'IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'ImageGPTForCausalImageModeling',
	    'ImageGPTForImageClassification',
	    'ImageGPTModel',
	    'ImageGPTPreTrainedModel',
	    'load_tf_weights_in_imagegpt',
	]


if TYPE_CHECKING:
	from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
		from .image_processing_imagegpt import ImageGPTImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_imagegpt import (
		    IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ImageGPTForCausalImageModeling,
		    ImageGPTForImageClassification,
		    ImageGPTModel,
		    ImageGPTPreTrainedModel,
		    load_tf_weights_in_imagegpt,
		)

else:
	import sys

	snake_case_      :					Dict				      =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
