





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 		snake_case_      (       unittest.TestCase	):







	'''simple docstring'''
	__UpperCamelCase        =		'''hf-internal-testing/tiny-stable-diffusion-pipe'''
	__UpperCamelCase        =		1
	__UpperCamelCase        =		77
	__UpperCamelCase        =		32
	__UpperCamelCase        =		4
	__UpperCamelCase        =		__UpperCamelCase        =		64
	__UpperCamelCase        =		torch.manual_seed(0	)




	def       UpperCAmelCase (	self       :    Union[str, Any]				,			__lowerCamelCase       :    Dict=0.9999      )					->   List[str]:

			'''simple docstring'''




			__lowercase					=    UNetaDConditionModel.from_pretrained(self.model_id				,			subfolder='unet'      )
			__lowercase					=    unet.to(__lowerCamelCase      )
			__lowercase					=    EMAModel(unet.parameters()				,			decay=__lowerCamelCase				,			model_cls=__lowerCamelCase				,			model_config=unet.config      )
			return unet, ema_unet




	def       UpperCAmelCase (	self       :    Optional[int]      )					->   List[Any]:

			'''simple docstring'''




			__lowercase					=    torch.randn(
			    self.batch_size				,			self.num_in_channels				,			self.latent_height				,			self.latent_width				,			generator=self.generator      ).to(__lowerCamelCase      )
			__lowercase					=    torch.randint(0				,			1_000				,			size=(self.batch_size,)				,			generator=self.generator      ).to(__lowerCamelCase      )
			__lowercase					=    torch.randn(
			    self.batch_size				,			self.prompt_length				,			self.text_encoder_hidden_dim				,			generator=self.generator      ).to(__lowerCamelCase      )
			return noisy_latents, timesteps, encoder_hidden_states




	def       UpperCAmelCase (	self       :    str				,			__lowerCamelCase       :    Optional[int]      )					->   Union[str, Any]:

			'''simple docstring'''




			__lowercase					=    {}
			for k, param in unet.state_dict().items():
					__lowercase					=    torch.randn_like(__lowerCamelCase      ) + (param * torch.randn_like(__lowerCamelCase      ))
					updated_state_dict.update({k: updated_param}      )
			unet.load_state_dict(__lowerCamelCase      )
			return unet




	def       UpperCAmelCase (	self       :    Optional[int]      )					->   List[str]:

			'''simple docstring'''




			__lowercase	,			__lowercase					=    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       :    str      )					->   Any:

			'''simple docstring'''




			__lowercase	,			__lowercase					=    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()      )
			__lowercase					=    list(unet.parameters()      )
			for s_param, param in zip(ema_unet.shadow_params				,			__lowerCamelCase      ):
					assert torch.allclose(__lowerCamelCase				,			__lowerCamelCase      )

			# 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				,			__lowerCamelCase      ):
					assert torch.allclose(__lowerCamelCase				,			__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    Any      )					->   str:

			'''simple docstring'''




			__lowercase	,			__lowercase					=    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.
			__lowercase					=    self.simulate_backprop(__lowerCamelCase      )

			# 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.
			__lowercase					=    list(unet_pseudo_updated_step_one.parameters()      )
			for s_param, param in zip(ema_unet.shadow_params				,			__lowerCamelCase      ):
					assert ~torch.allclose(__lowerCamelCase				,			__lowerCamelCase      )

			# 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				,			__lowerCamelCase      ):
					assert ~torch.allclose(__lowerCamelCase				,			__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    List[str]      )					->   Optional[int]:

			'''simple docstring'''




			__lowercase	,			__lowercase					=    self.get_models()

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

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

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




	def       UpperCAmelCase (	self       :    List[str]      )					->   Tuple:

			'''simple docstring'''




			__lowercase	,			__lowercase					=    self.get_models(decay=0.0      )
			__lowercase					=    self.simulate_backprop(__lowerCamelCase      )
			ema_unet.step(unet_step_one.parameters()      )
			__lowercase					=    ema_unet.shadow_params

			__lowercase					=    self.simulate_backprop(__lowerCamelCase      )
			ema_unet.step(unet_step_two.parameters()      )
			__lowercase					=    ema_unet.shadow_params

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






	@skip_mps
	def       UpperCAmelCase (	self       :    Optional[int]      )					->   str:

			'''simple docstring'''




			__lowercase	,			__lowercase					=    self.get_models()
			__lowercase	,			__lowercase	,			__lowercase					=    self.get_dummy_inputs()

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

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

			assert torch.allclose(__lowerCamelCase				,			__lowerCamelCase				,			atol=1E-4      )











import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, slow, torch_device

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin

class 		snake_case_      (       UpperCAmelCase_    ,    UpperCAmelCase_    ,    unittest.TestCase	):







	'''simple docstring'''
	__UpperCamelCase        =		UNetaDModel
	__UpperCamelCase        =		'''sample'''




	@property
	def       UpperCAmelCase (	self       :    str      )					->   List[str]:

			'''simple docstring'''




			__lowercase					=    4
			__lowercase					=    14
			__lowercase					=    16

			__lowercase					=    floats_tensor((batch_size, num_features, seq_len)      ).to(__lowerCamelCase      )
			__lowercase					=    torch.tensor([10] * batch_size      ).to(__lowerCamelCase      )

			return {"sample": noise, "timestep": time_step}




	@property
	def       UpperCAmelCase (	self       :    List[str]      )					->   Union[str, Any]:

			'''simple docstring'''




			return (4, 14, 16)




	@property
	def       UpperCAmelCase (	self       :    Dict      )					->   List[Any]:

			'''simple docstring'''




			return (4, 14, 16)




	def       UpperCAmelCase (	self       :    Optional[int]      )					->   Dict:

			'''simple docstring'''




			pass




	def       UpperCAmelCase (	self       :    Union[str, Any]      )					->   Dict:

			'''simple docstring'''




			pass




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    List[Any]      )					->   Optional[Any]:

			'''simple docstring'''




			super().test_determinism()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    List[str]      )					->   Dict:

			'''simple docstring'''




			super().test_outputs_equivalence()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Optional[Any]      )					->   List[Any]:

			'''simple docstring'''




			super().test_from_save_pretrained()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Union[str, Any]      )					->   Any:

			'''simple docstring'''




			super().test_from_save_pretrained_variant()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Optional[Any]      )					->   int:

			'''simple docstring'''




			super().test_model_from_pretrained()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Optional[int]      )					->   Tuple:

			'''simple docstring'''




			super().test_output()




	def       UpperCAmelCase (	self       :    List[Any]      )					->   List[str]:

			'''simple docstring'''




			__lowercase					=    {
			    'block_out_channels': (32, 64, 128, 256),
			    'in_channels': 14,
			    'out_channels': 14,
			    'time_embedding_type': 'positional',
			    'use_timestep_embedding': True,
			    'flip_sin_to_cos': False,
			    'freq_shift': 1.0,
			    'out_block_type': 'OutConv1DBlock',
			    'mid_block_type': 'MidResTemporalBlock1D',
			    'down_block_types': ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'),
			    'up_block_types': ('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D'),
			    'act_fn': 'mish',
			}
			__lowercase					=    self.dummy_input
			return init_dict, inputs_dict




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Optional[int]      )					->   Any:

			'''simple docstring'''




			__lowercase	,			__lowercase					=    UNetaDModel.from_pretrained(
			    'bglick13/hopper-medium-v2-value-function-hor32'				,			output_loading_info=__lowerCamelCase				,			subfolder='unet'      )
			self.assertIsNotNone(__lowerCamelCase      )
			self.assertEqual(len(loading_info['missing_keys']      )				,			0      )

			model.to(__lowerCamelCase      )
			__lowercase					=    model(**self.dummy_input      )

			assert image is not None, "Make sure output is not None"




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    List[str]      )					->   Dict:

			'''simple docstring'''




			__lowercase					=    UNetaDModel.from_pretrained('bglick13/hopper-medium-v2-value-function-hor32'				,			subfolder='unet'      )
			torch.manual_seed(0      )
			if torch.cuda.is_available():
					torch.cuda.manual_seed_all(0      )

			__lowercase					=    model.config.in_channels
			__lowercase					=    16
			__lowercase					=    torch.randn((1, seq_len, num_features)      ).permute(
			    0				,			2				,			1      )  # match original, we can update values and remove
			__lowercase					=    torch.full((num_features,)				,			0      )

			with torch.no_grad():
					__lowercase					=    model(__lowerCamelCase				,			__lowerCamelCase      ).sample.permute(0				,			2				,			1      )

			__lowercase					=    output[0, -3:, -3:].flatten()
			# fmt: off
			__lowercase					=    torch.tensor([-2.137172, 1.1426016, 0.3688687, -0.766922, 0.7303146, 0.11038864, -0.4760633, 0.13270172, 0.02591348]      )
			# fmt: on
			self.assertTrue(torch.allclose(__lowerCamelCase				,			__lowerCamelCase				,			rtol=1E-3      )      )




	def       UpperCAmelCase (	self       :    List[Any]      )					->   int:

			'''simple docstring'''




			pass






	@slow
	def       UpperCAmelCase (	self       :    List[str]      )					->   Dict:

			'''simple docstring'''




			__lowercase					=    'harmonai/maestro-150k'
			__lowercase					=    UNetaDModel.from_pretrained(__lowerCamelCase				,			subfolder='unet'      )
			model.to(__lowerCamelCase      )

			__lowercase					=    65_536
			__lowercase					=    torch.sin(torch.arange(__lowerCamelCase      )[None, None, :].repeat(1				,			2				,			1      )      ).to(__lowerCamelCase      )
			__lowercase					=    torch.tensor([1]      ).to(__lowerCamelCase      )

			with torch.no_grad():
					__lowercase					=    model(__lowerCamelCase				,			__lowerCamelCase      ).sample

			__lowercase					=    output.abs().sum()
			__lowercase					=    output.abs().max()

			assert (output_sum - 224.0896).abs() < 0.5
			assert (output_max - 0.0607).abs() < 4E-4





class 		snake_case_      (       UpperCAmelCase_    ,    UpperCAmelCase_    ,    unittest.TestCase	):







	'''simple docstring'''
	__UpperCamelCase        =		UNetaDModel
	__UpperCamelCase        =		'''sample'''




	@property
	def       UpperCAmelCase (	self       :    int      )					->   Union[str, Any]:

			'''simple docstring'''




			__lowercase					=    4
			__lowercase					=    14
			__lowercase					=    16

			__lowercase					=    floats_tensor((batch_size, num_features, seq_len)      ).to(__lowerCamelCase      )
			__lowercase					=    torch.tensor([10] * batch_size      ).to(__lowerCamelCase      )

			return {"sample": noise, "timestep": time_step}




	@property
	def       UpperCAmelCase (	self       :    List[str]      )					->   Union[str, Any]:

			'''simple docstring'''




			return (4, 14, 16)




	@property
	def       UpperCAmelCase (	self       :    Tuple      )					->   Union[str, Any]:

			'''simple docstring'''




			return (4, 14, 1)




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Dict      )					->   str:

			'''simple docstring'''




			super().test_determinism()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    str      )					->   int:

			'''simple docstring'''




			super().test_outputs_equivalence()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    str      )					->   List[Any]:

			'''simple docstring'''




			super().test_from_save_pretrained()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    List[Any]      )					->   int:

			'''simple docstring'''




			super().test_from_save_pretrained_variant()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    List[str]      )					->   Dict:

			'''simple docstring'''




			super().test_model_from_pretrained()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    List[str]      )					->   Tuple:

			'''simple docstring'''




			__lowercase	,			__lowercase					=    self.prepare_init_args_and_inputs_for_common()
			__lowercase					=    self.model_class(**__lowerCamelCase      )
			model.to(__lowerCamelCase      )
			model.eval()

			with torch.no_grad():
					__lowercase					=    model(**__lowerCamelCase      )

					if isinstance(__lowerCamelCase				,			__lowerCamelCase      ):
							__lowercase					=    output.sample

			self.assertIsNotNone(__lowerCamelCase      )
			__lowercase					=    torch.Size((inputs_dict['sample'].shape[0], 1)      )
			self.assertEqual(output.shape				,			__lowerCamelCase				,			'Input and output shapes do not match'      )




	def       UpperCAmelCase (	self       :    Optional[int]      )					->   str:

			'''simple docstring'''




			pass




	def       UpperCAmelCase (	self       :    Optional[int]      )					->   Optional[int]:

			'''simple docstring'''




			pass




	def       UpperCAmelCase (	self       :    Optional[Any]      )					->   Optional[Any]:

			'''simple docstring'''




			__lowercase					=    {
			    'in_channels': 14,
			    'out_channels': 14,
			    'down_block_types': ['DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'],
			    'up_block_types': [],
			    'out_block_type': 'ValueFunction',
			    'mid_block_type': 'ValueFunctionMidBlock1D',
			    'block_out_channels': [32, 64, 128, 256],
			    'layers_per_block': 1,
			    'downsample_each_block': True,
			    'use_timestep_embedding': True,
			    'freq_shift': 1.0,
			    'flip_sin_to_cos': False,
			    'time_embedding_type': 'positional',
			    'act_fn': 'mish',
			}
			__lowercase					=    self.dummy_input
			return init_dict, inputs_dict




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Any      )					->   Union[str, Any]:

			'''simple docstring'''




			__lowercase	,			__lowercase					=    UNetaDModel.from_pretrained(
			    'bglick13/hopper-medium-v2-value-function-hor32'				,			output_loading_info=__lowerCamelCase				,			subfolder='value_function'      )
			self.assertIsNotNone(__lowerCamelCase      )
			self.assertEqual(len(vf_loading_info['missing_keys']      )				,			0      )

			value_function.to(__lowerCamelCase      )
			__lowercase					=    value_function(**self.dummy_input      )

			assert image is not None, "Make sure output is not None"




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Union[str, Any]      )					->   Optional[Any]:

			'''simple docstring'''




			__lowercase	,			__lowercase					=    UNetaDModel.from_pretrained(
			    'bglick13/hopper-medium-v2-value-function-hor32'				,			output_loading_info=__lowerCamelCase				,			subfolder='value_function'      )
			torch.manual_seed(0      )
			if torch.cuda.is_available():
					torch.cuda.manual_seed_all(0      )

			__lowercase					=    value_function.config.in_channels
			__lowercase					=    14
			__lowercase					=    torch.randn((1, seq_len, num_features)      ).permute(
			    0				,			2				,			1      )  # match original, we can update values and remove
			__lowercase					=    torch.full((num_features,)				,			0      )

			with torch.no_grad():
					__lowercase					=    value_function(__lowerCamelCase				,			__lowerCamelCase      ).sample

			# fmt: off
			__lowercase					=    torch.tensor([165.25] * seq_len      )
			# fmt: on
			self.assertTrue(torch.allclose(__lowerCamelCase				,			__lowerCamelCase				,			rtol=1E-3      )      )






	def       UpperCAmelCase (	self       :    Dict      )					->   str:

			'''simple docstring'''




			pass




