
"""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

A_						       =				[[12, 10], [3, 9]]
A_						       =				[[3, 4], [7, 4]]
A_						       =				[[3, 0, 2], [2, 0, -2], [0, 1, 1]]
A_						       =				[[3, 0, -2], [2, 0, 2], [0, 1, 1]]
A_						       =				[[3, 0, 2], [2, 0, -2], [0, 1, 1], [2, 0, -2]]
A_						       =				[1]
A_						       =				[2]

A_						       =				logging.getLogger()
A_						       =				logging.DEBUG
A_						       =				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    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__	):
			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"	)
						lowerCamelCase_ 		=     (np.array(__lowerCamelCase	) + np.array(__lowerCamelCase	)).tolist()
						lowerCamelCase_ 		=     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    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__	):
			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"	)
						lowerCamelCase_ 		=     (np.array(__lowerCamelCase	) - np.array(__lowerCamelCase	)).tolist()
						lowerCamelCase_ 		=     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    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__	):
			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"	)
						lowerCamelCase_ 		=     (np.matmul(__lowerCamelCase  ,__lowerCamelCase	)).tolist()
						lowerCamelCase_ 		=     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    lowercase	(  ):
			lowerCamelCase_ 		=     (3.5 * np.array(__lowerCamelCase	)).tolist()
			lowerCamelCase_ 		=     matop.scalar_multiply(__lowerCamelCase  ,3.5	)
			assert theo == act






@pytest.mark.mat_ops
def    lowercase	(  ):
			lowerCamelCase_ 		=     (np.identity(5	)).tolist()
			lowerCamelCase_ 		=     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    lowercase	(  lowerCAmelCase__	):
			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:
						lowerCamelCase_ 		=     (np.transpose(__lowerCamelCase	)).tolist()
						lowerCamelCase_ 		=     matop.transpose(__lowerCamelCase  ,return_map=__lowerCamelCase	)
						assert theo == act








"""simple docstring"""


import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()




class    __lowerCamelCase     (   unittest.TestCase ):
				a__:			Union[str, Any]									=						'hf-internal-testing/tiny-stable-diffusion-pipe'
				a__:			Tuple									=						1
				a__:			int									=						77
				a__:			List[str]									=						32
				a__:			List[Any]									=						4
				a__:			str									=						64
				a__:			Optional[Any]									=						torch.manual_seed(0 )




				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase=0.9_9_9_9      ):
							lowerCamelCase_ 		=     UNetaDConditionModel.from_pretrained(self.model_id       ,	subfolder='''unet'''      )
							lowerCamelCase_ 		=     unet.to(UpperCAmelCase      )
							lowerCamelCase_ 		=     EMAModel(unet.parameters()       ,	decay=UpperCAmelCase       ,	model_cls=UpperCAmelCase       ,	model_config=unet.config      )
							return unet, ema_unet




				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     torch.randn(
							    self.batch_size       ,	self.num_in_channels       ,	self.latent_height       ,	self.latent_width       ,	generator=self.generator      ).to(UpperCAmelCase      )
							lowerCamelCase_ 		=     torch.randint(0       ,	1000       ,	size=(self.batch_size,)       ,	generator=self.generator      ).to(UpperCAmelCase      )
							lowerCamelCase_ 		=     torch.randn(
							    self.batch_size       ,	self.prompt_length       ,	self.text_encoder_hidden_dim       ,	generator=self.generator      ).to(UpperCAmelCase      )
							return noisy_latents, timesteps, encoder_hidden_states




				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase      ):
							lowerCamelCase_ 		=     {}
							for k, param in unet.state_dict().items():
										lowerCamelCase_ 		=     torch.randn_like(UpperCAmelCase      ) + (param * torch.randn_like(UpperCAmelCase      ))
										updated_state_dict.update({k: updated_param}      )
							unet.load_state_dict(UpperCAmelCase      )
							return unet




				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_		,   lowerCamelCase_ 		=     self.get_models()
							# Take the first (hypothetical) EMA step.
							ema_unet.step(unet.parameters()      )
							assert ema_unet.optimization_step == 1

							# Take two more.
							for _ in range(2      ):
										ema_unet.step(unet.parameters()      )
							assert ema_unet.optimization_step == 3




				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_		,   lowerCamelCase_ 		=     self.get_models()
							# Since the `unet` is not being updated (i.e., backprop'd)
							# there won't be any difference between the `params` of `unet`
							# and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
							ema_unet.step(unet.parameters()      )
							lowerCamelCase_ 		=     list(unet.parameters()      )
							for s_param, param in zip(ema_unet.shadow_params       ,	UpperCAmelCase      ):
										assert torch.allclose(UpperCAmelCase       ,	UpperCAmelCase      )

							# The above holds true even if we call `ema.step()` multiple times since
							# `unet` params are still not being updated.
							for _ in range(4      ):
										ema_unet.step(unet.parameters()      )
							for s_param, param in zip(ema_unet.shadow_params       ,	UpperCAmelCase      ):
										assert torch.allclose(UpperCAmelCase       ,	UpperCAmelCase      )




				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_		,   lowerCamelCase_ 		=     self.get_models()
							# Here we simulate the parameter updates for `unet`. Since there might
							# be some parameters which are initialized to zero we take extra care to
							# initialize their values to something non-zero before the multiplication.
							lowerCamelCase_ 		=     self.simulate_backprop(UpperCAmelCase      )

							# Take the EMA step.
							ema_unet.step(unet_pseudo_updated_step_one.parameters()      )

							# Now the EMA'd parameters won't be equal to the original model parameters.
							lowerCamelCase_ 		=     list(unet_pseudo_updated_step_one.parameters()      )
							for s_param, param in zip(ema_unet.shadow_params       ,	UpperCAmelCase      ):
										assert ~torch.allclose(UpperCAmelCase       ,	UpperCAmelCase      )

							# Ensure this is the case when we take multiple EMA steps.
							for _ in range(4      ):
										ema_unet.step(unet.parameters()      )
							for s_param, param in zip(ema_unet.shadow_params       ,	UpperCAmelCase      ):
										assert ~torch.allclose(UpperCAmelCase       ,	UpperCAmelCase      )




				def 					UpperCAmelCase__	(							self      ):
							# If we call EMA step after a backpropagation consecutively for two times,
							# the shadow params from those two steps should be different.
							lowerCamelCase_		,   lowerCamelCase_ 		=     self.get_models()

							# First backprop + EMA
							lowerCamelCase_ 		=     self.simulate_backprop(UpperCAmelCase      )
							ema_unet.step(unet_step_one.parameters()      )
							lowerCamelCase_ 		=     ema_unet.shadow_params

							# Second backprop + EMA
							lowerCamelCase_ 		=     self.simulate_backprop(UpperCAmelCase      )
							ema_unet.step(unet_step_two.parameters()      )
							lowerCamelCase_ 		=     ema_unet.shadow_params

							for step_one, step_two in zip(UpperCAmelCase       ,	UpperCAmelCase      ):
										assert ~torch.allclose(UpperCAmelCase       ,	UpperCAmelCase      )




				def 					UpperCAmelCase__	(							self      ):
							# If there's no decay even if there are backprops, EMA steps
							# won't take any effect i.e., the shadow params would remain the
							# same.
							lowerCamelCase_		,   lowerCamelCase_ 		=     self.get_models(decay=0.0      )
							lowerCamelCase_ 		=     self.simulate_backprop(UpperCAmelCase      )
							ema_unet.step(unet_step_one.parameters()      )
							lowerCamelCase_ 		=     ema_unet.shadow_params

							lowerCamelCase_ 		=     self.simulate_backprop(UpperCAmelCase      )
							ema_unet.step(unet_step_two.parameters()      )
							lowerCamelCase_ 		=     ema_unet.shadow_params

							for step_one, step_two in zip(UpperCAmelCase       ,	UpperCAmelCase      ):
										assert torch.allclose(UpperCAmelCase       ,	UpperCAmelCase      )





				@skip_mps
				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_		,   lowerCamelCase_ 		=     self.get_models()
							lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_ 		=     self.get_dummy_inputs()

							with tempfile.TemporaryDirectory() as tmpdir:
										ema_unet.save_pretrained(UpperCAmelCase      )
										lowerCamelCase_ 		=     UNetaDConditionModel.from_pretrained(UpperCAmelCase       ,	model_cls=UpperCAmelCase      )
										lowerCamelCase_ 		=     loaded_unet.to(unet.device      )

							# Since no EMA step has been performed the outputs should match.
							lowerCamelCase_ 		=     unet(UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase      ).sample
							lowerCamelCase_ 		=     loaded_unet(UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase      ).sample

							assert torch.allclose(UpperCAmelCase       ,	UpperCAmelCase       ,	atol=1e-4      )



