


'''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

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

lowerCamelCase							:       str  						=     logging.getLogger()
lowerCamelCase							:       List[Any]  						=     logging.DEBUG
lowerCamelCase							:       Dict  						=     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 _SCREAMING_SNAKE_CASE				(A  ,			A	)	->	Optional[int]:






							"""simple docstring"""







							if (np.array(UpperCamelCase_	)).shape < (2, 2) or (np.array(UpperCamelCase_	)).shape < (2, 2):
														with pytest.raises(UpperCamelCase_	):
																					logger.info(f"\n\t{test_addition.__name__} returned integer"	)
																					matop.add(UpperCamelCase_  ,			UpperCamelCase_	)
							elif (np.array(UpperCamelCase_	)).shape == (np.array(UpperCamelCase_	)).shape:
														logger.info(f"\n\t{test_addition.__name__} with same matrix dims"	)
														lowercase__				      =			(np.array(UpperCamelCase_	) + np.array(UpperCamelCase_	)).tolist()
														lowercase__				      =			matop.add(UpperCamelCase_  ,			UpperCamelCase_	)
														assert theo == act
							else:
														with pytest.raises(UpperCamelCase_	):
																					logger.info(f"\n\t{test_addition.__name__} with different matrix dims"	)
																					matop.add(UpperCamelCase_  ,			UpperCamelCase_	)

@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 _SCREAMING_SNAKE_CASE				(A  ,			A	)	->	Union[str, Any]:






							"""simple docstring"""







							if (np.array(UpperCamelCase_	)).shape < (2, 2) or (np.array(UpperCamelCase_	)).shape < (2, 2):
														with pytest.raises(UpperCamelCase_	):
																					logger.info(f"\n\t{test_subtraction.__name__} returned integer"	)
																					matop.subtract(UpperCamelCase_  ,			UpperCamelCase_	)
							elif (np.array(UpperCamelCase_	)).shape == (np.array(UpperCamelCase_	)).shape:
														logger.info(f"\n\t{test_subtraction.__name__} with same matrix dims"	)
														lowercase__				      =			(np.array(UpperCamelCase_	) - np.array(UpperCamelCase_	)).tolist()
														lowercase__				      =			matop.subtract(UpperCamelCase_  ,			UpperCamelCase_	)
														assert theo == act
							else:
														with pytest.raises(UpperCamelCase_	):
																					logger.info(f"\n\t{test_subtraction.__name__} with different matrix dims"	)
																					assert matop.subtract(UpperCamelCase_  ,			UpperCamelCase_	)

@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 _SCREAMING_SNAKE_CASE				(A  ,			A	)	->	Optional[int]:






							"""simple docstring"""







							if (np.array(UpperCamelCase_	)).shape < (2, 2) or (np.array(UpperCamelCase_	)).shape < (2, 2):
														logger.info(f"\n\t{test_multiplication.__name__} returned integer"	)
														with pytest.raises(UpperCamelCase_	):
																					matop.add(UpperCamelCase_  ,			UpperCamelCase_	)
							elif (np.array(UpperCamelCase_	)).shape == (np.array(UpperCamelCase_	)).shape:
														logger.info(f"\n\t{test_multiplication.__name__} meets dim requirements"	)
														lowercase__				      =			(np.matmul(UpperCamelCase_  ,			UpperCamelCase_	)).tolist()
														lowercase__				      =			matop.multiply(UpperCamelCase_  ,			UpperCamelCase_	)
														assert theo == act
							else:
														with pytest.raises(UpperCamelCase_	):
																					logger.info(
																					    f"\n\t{test_multiplication.__name__} does not meet dim requirements"	)
																					assert matop.subtract(UpperCamelCase_  ,			UpperCamelCase_	)

@pytest.mark.mat_ops
def _SCREAMING_SNAKE_CASE				()	->	str:






							"""simple docstring"""







							lowercase__				      =			(3.5 * np.array(UpperCamelCase_	)).tolist()
							lowercase__				      =			matop.scalar_multiply(UpperCamelCase_  ,			3.5	)
							assert theo == act

@pytest.mark.mat_ops
def _SCREAMING_SNAKE_CASE				()	->	List[str]:






							"""simple docstring"""







							lowercase__				      =			(np.identity(5	)).tolist()
							lowercase__				      =			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 _SCREAMING_SNAKE_CASE				(A	)	->	List[Any]:






							"""simple docstring"""







							if (np.array(UpperCamelCase_	)).shape < (2, 2):
														with pytest.raises(UpperCamelCase_	):
																					logger.info(f"\n\t{test_transpose.__name__} returned integer"	)
																					matop.transpose(UpperCamelCase_	)
							else:
														lowercase__				      =			(np.transpose(UpperCamelCase_	)).tolist()
														lowercase__				      =			matop.transpose(UpperCamelCase_  ,			return_map=UpperCamelCase_	)
														assert theo == act








'''simple docstring'''

import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
 import torch_xla.core.xla_model as xm

def _SCREAMING_SNAKE_CASE				(A  ,			A = False	)	->	Tuple:






       """simple docstring"""







       if device_specific:
              seed += AcceleratorState().process_index
       random.seed(A	)
       np.random.seed(A	)
       torch.manual_seed(A	)
       if is_xpu_available():
              torch.xpu.manual_seed_all(A	)
       elif is_npu_available():
              torch.npu.manual_seed_all(A	)
       else:
              torch.cuda.manual_seed_all(A	)
       # ^^ safe to call this function even if cuda is not available
       if is_tpu_available():
              xm.set_rng_state(A	)

def _SCREAMING_SNAKE_CASE				(A = None  ,			A = None	)	->	Tuple:






       """simple docstring"""







       if rng_type == RNGType.TORCH:
              lowercase__				      =			torch.get_rng_state()
       elif rng_type == RNGType.CUDA:
              lowercase__				      =			torch.cuda.get_rng_state()
       elif rng_type == RNGType.XLA:
              assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
              lowercase__				      =			torch.tensor(xm.get_rng_state()	)
       elif rng_type == RNGType.NPU:
              assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
              lowercase__				      =			torch.npu.get_rng_state()
       elif rng_type == RNGType.XPU:
              assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
              lowercase__				      =			torch.xpu.get_rng_state()
       elif rng_type == RNGType.GENERATOR:
              assert generator is not None, "Need a generator to synchronize its seed."
              lowercase__				      =			generator.get_state()

       # Broadcast the rng state from device 0 to other devices
       lowercase__				      =			AcceleratorState()
       if state.distributed_type == DistributedType.TPU:
              lowercase__				      =			rng_state.to(xm.xla_device()	)
              xm.collective_broadcast([rng_state]	)
              xm.mark_step()
              lowercase__				      =			rng_state.cpu()
       elif (
           state.distributed_type in CUDA_DISTRIBUTED_TYPES
           or state.distributed_type == DistributedType.MULTI_NPU
           or state.distributed_type == DistributedType.MULTI_XPU
       ):
              lowercase__				      =			rng_state.to(state.device	)
              torch.distributed.broadcast(A  ,			0	)
              lowercase__				      =			rng_state.cpu()
       elif state.distributed_type == DistributedType.MULTI_CPU:
              torch.distributed.broadcast(A  ,			0	)

       # Set the broadcast rng state
       if rng_type == RNGType.TORCH:
              torch.set_rng_state(A	)
       elif rng_type == RNGType.CUDA:
              torch.cuda.set_rng_state(A	)
       elif rng_type == RNGType.NPU:
              torch.npu.set_rng_state(A	)
       elif rng_type == RNGType.XPU:
              torch.xpu.set_rng_state(A	)
       elif rng_type == RNGType.XLA:
              xm.set_rng_state(rng_state.item()	)
       elif rng_type == RNGType.GENERATOR:
              generator.set_state(A	)

def _SCREAMING_SNAKE_CASE				(A  ,			A = None	)	->	int:






       """simple docstring"""







       for rng_type in rng_types:
              synchronize_rng_state(RNGType(A	)  ,			generator=A	)




