


'''simple docstring'''



def 				lowerCamelCase__				(	A       :      list[list]					):




						'''simple docstring'''






						UpperCAmelCase					    =     current_set.copy()
						for row_index, row in enumerate(A					):
												UpperCAmelCase					    =     row[0]
												for column_index, column in enumerate(A					):
																		if magnitude == 0:
																								UpperCAmelCase					    =     column
																								continue
																		UpperCAmelCase					    =     column / magnitude
    # Subtract to cancel term
						UpperCAmelCase					    =     current_set[0]
						UpperCAmelCase					    =     [first_row]
						UpperCAmelCase					    =     current_set[1::]
						for row in current_set:
												UpperCAmelCase					    =     []
												# If first term is 0, it is already in form we want, so we preserve it
												if row[0] == 0:
																		final_set.append(A					)
																		continue
												for column_index in range(len(A					)					):
																		temp_row.append(first_row[column_index] - row[column_index]					)
												final_set.append(A					)
						# Create next recursion iteration set
						if len(final_set[0]					) != 3:
												UpperCAmelCase					    =     final_set[0]
												UpperCAmelCase					    =     []
												UpperCAmelCase					    =     []
												for row in final_set[1::]:
																		current_first_column.append(row[0]					)
																		next_iteration.append(row[1::]					)
												UpperCAmelCase					    =     simplify(A					)
												for i in range(len(A					)					):
																		resultant[i].insert(0				,       current_first_column[i]					)
												resultant.insert(0				,       A					)
												UpperCAmelCase					    =     resultant
						return final_set


def 				lowerCamelCase__				(	A       :      list[list]					):




						'''simple docstring'''






						if len(A					) == 0:
												raise IndexError('''solve_simultaneous() requires n lists of length n+1'''					)
						UpperCAmelCase					    =     len(A					) + 1
						if any(len(A					) != _length for item in equations					):
												raise IndexError('''solve_simultaneous() requires n lists of length n+1'''					)
						for row in equations:
												if any(not isinstance(A				,       (int, float)					) for column in row					):
																		raise ValueError('''solve_simultaneous() requires lists of integers'''					)
						if len(A					) == 1:
												return [equations[0][-1] / equations[0][0]]
						UpperCAmelCase					    =     equations.copy()
						if any(0 in row for row in data_set					):
												UpperCAmelCase					    =     data_set.copy()
												UpperCAmelCase					    =     []
												for row_index, row in enumerate(A					):
																		if 0 not in row:
																								UpperCAmelCase					    =     data_set.pop(A					)
																								break
												if not full_row:
																		raise ValueError('''solve_simultaneous() requires at least 1 full equation'''					)
												data_set.insert(0				,       A					)
						UpperCAmelCase					    =     data_set.copy()
						UpperCAmelCase					    =     simplify(A					)
						UpperCAmelCase					    =     simplified[::-1]
						UpperCAmelCase					    =     []
						for row in simplified:
												UpperCAmelCase					    =     row[-1]
												if not solutions:
																		if row[-2] == 0:
																								solutions.append(0					)
																								continue
																		solutions.append(current_solution / row[-2]					)
																		continue
												UpperCAmelCase					    =     row.copy()[: len(A					) - 1 :]
												while temp_row[0] == 0:
																		temp_row.pop(0					)
												if len(A					) == 0:
																		solutions.append(0					)
																		continue
												UpperCAmelCase					    =     temp_row[1::]
												UpperCAmelCase					    =     temp_row[::-1]
												for column_index, column in enumerate(A					):
																		current_solution -= column * solutions[column_index]
												solutions.append(A					)
						UpperCAmelCase					    =     []
						for item in solutions:
												final.append(float(round(A				,       5					)					)					)
						return final[::-1]


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	_lowercase      :      str            =					[
	    [2, 1, 1, 1, 1, 4],
	    [1, 2, 1, 1, 1, 5],
	    [1, 1, 2, 1, 1, 6],
	    [1, 1, 1, 2, 1, 7],
	    [1, 1, 1, 1, 2, 8],
	]
	print(solve_simultaneous(eq))
	print(solve_simultaneous([[4, 2]]))









'''simple docstring'''



from typing import TYPE_CHECKING

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


_lowercase      :      Union[str, Any]            =					{
    """configuration_poolformer""": [
        """POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """PoolFormerConfig""",
        """PoolFormerOnnxConfig""",
    ]
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase      :      List[Any]            =					["""PoolFormerFeatureExtractor"""]
	_lowercase      :      Any            =					["""PoolFormerImageProcessor"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase      :      Tuple            =					[
	    """POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """PoolFormerForImageClassification""",
	    """PoolFormerModel""",
	    """PoolFormerPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_poolformer import (
	    POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    PoolFormerConfig,
	    PoolFormerOnnxConfig,
	)

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_poolformer import PoolFormerFeatureExtractor
		from .image_processing_poolformer import PoolFormerImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_poolformer import (
		    POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    PoolFormerForImageClassification,
		    PoolFormerModel,
		    PoolFormerPreTrainedModel,
		)


else:
	import sys

	_lowercase      :      Dict            =					_LazyModule(__name__, globals()["""__file__"""], _import_structure)





