






"""simple docstring"""







from sklearn.metrics import mean_absolute_error

import datasets


__a							:						Optional[int]   							=			'\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n'

__a							:						int   							=			'\\nMean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual\nvalues.\n'


__a							:						Tuple   							=			'\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n                 "raw_values" : Returns a full set of errors in case of multioutput input.\n\n                 "uniform_average" : Errors of all outputs are averaged with uniform weight.\n\nReturns:\n    mae : mean absolute error.\n        If multioutput is "raw_values", then mean absolute error is returned for each output separately. If multioutput is "uniform_average" or an ndarray of weights, then the weighted average of all output errors is returned.\n        MAE output is non-negative floating point. The best value is 0.0.\nExamples:\n\n    >>> mae_metric = datasets.load_metric("mae")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mae\': 0.5}\n\n    If you\'re using multi-dimensional lists, then set the config as follows :\n\n    >>> mae_metric = datasets.load_metric("mae", "multilist")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mae\': 0.75}\n    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n    >>> print(results)\n    {\'mae\': array([0.5, 1. ])}\n'







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,			_KWARGS_DESCRIPTION				)
class 							_SCREAMING_SNAKE_CASE    (	datasets.Metric				):





							"""simple docstring"""





							def 			lowercase		( self:  str     ):

												'''simple docstring'''

												return datasets.MetricInfo(
												    description=_DESCRIPTION					,		citation=_CITATION					,		inputs_description=_KWARGS_DESCRIPTION					,		features=datasets.Features(self._get_feature_types()     )					,		reference_urls=[
												        '''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html'''
												    ]					,		)




							def 			lowercase		( self:  Optional[Any]     ):

												'''simple docstring'''

												if self.config_name == "multilist":
																	return {
																	    "predictions": datasets.Sequence(datasets.Value('''float'''     )     ),
																	    "references": datasets.Sequence(datasets.Value('''float'''     )     ),
																	}
												else:
																	return {
																	    "predictions": datasets.Value('''float'''     ),
																	    "references": datasets.Value('''float'''     ),
																	}





							def 			lowercase		( self:  str					,		__A:  Union[str, Any]					,		__A:  Tuple					,		__A:  str=None					,		__A:  Union[str, Any]="uniform_average"     ):

												'''simple docstring'''

												a__ 							=	mean_absolute_error(__A					,		__A					,		sample_weight=__A					,		multioutput=__A     )

												return {"mae": mae_score}










"""simple docstring"""







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 							_SCREAMING_SNAKE_CASE    (	__snake_case							,			__snake_case							,			unittest.TestCase				):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE     =UNetaDModel
							_SCREAMING_SNAKE_CASE     ='sample'




							@property
							def 			lowercase		( self:  Any     ):

												'''simple docstring'''

												a__ 							=	4
												a__ 							=	14
												a__ 							=	16

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

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




							@property
							def 			lowercase		( self:  List[Any]     ):

												'''simple docstring'''

												return (4, 14, 16)




							@property
							def 			lowercase		( self:  List[Any]     ):

												'''simple docstring'''

												return (4, 14, 16)




							def 			lowercase		( self:  Optional[Any]     ):

												'''simple docstring'''

												pass




							def 			lowercase		( self:  Any     ):

												'''simple docstring'''

												pass




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

												'''simple docstring'''

												super().test_determinism()




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

												'''simple docstring'''

												super().test_outputs_equivalence()




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  Tuple     ):

												'''simple docstring'''

												super().test_from_save_pretrained()




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

												'''simple docstring'''

												super().test_from_save_pretrained_variant()




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

												'''simple docstring'''

												super().test_model_from_pretrained()




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  Tuple     ):

												'''simple docstring'''

												super().test_output()




							def 			lowercase		( self:  Optional[Any]     ):

												'''simple docstring'''

												a__ 							=	{
												    '''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''',
												}
												a__ 							=	self.dummy_input
												return init_dict, inputs_dict




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

												'''simple docstring'''

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

												model.to(__A     )
												a__ 							=	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 			lowercase		( self:  str     ):

												'''simple docstring'''

												a__ 							=	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     )

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

												with torch.no_grad():
																	a__ 							=	model(__A					,		__A     ).sample.permute(0					,		2					,		1     )

												a__ 							=	output[0, -3:, -3:].flatten()
												# fmt: off
												a__ 							=	torch.tensor([-2.1_3_7_1_7_2, 1.1_4_2_6_0_1_6, 0.3_6_8_8_6_8_7, -0.7_6_6_9_2_2, 0.7_3_0_3_1_4_6, 0.1_1_0_3_8_8_6_4, -0.4_7_6_0_6_3_3, 0.1_3_2_7_0_1_7_2, 0.0_2_5_9_1_3_4_8]     )
												# fmt: on
												self.assertTrue(torch.allclose(__A					,		__A					,		rtol=1e-3     )     )




							def 			lowercase		( self:  int     ):

												'''simple docstring'''

												pass





							@slow
							def 			lowercase		( self:  Any     ):

												'''simple docstring'''

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

												a__ 							=	65536
												a__ 							=	torch.sin(torch.arange(__A     )[None, None, :].repeat(1					,		2					,		1     )     ).to(__A     )
												a__ 							=	torch.tensor([1]     ).to(__A     )

												with torch.no_grad():
																	a__ 							=	model(__A					,		__A     ).sample

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

												assert (output_sum - 2_2_4.0_8_9_6).abs() < 0.5
												assert (output_max - 0.0_6_0_7).abs() < 4e-4







class 							_SCREAMING_SNAKE_CASE    (	__snake_case							,			__snake_case							,			unittest.TestCase				):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE     =UNetaDModel
							_SCREAMING_SNAKE_CASE     ='sample'




							@property
							def 			lowercase		( self:  List[str]     ):

												'''simple docstring'''

												a__ 							=	4
												a__ 							=	14
												a__ 							=	16

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

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




							@property
							def 			lowercase		( self:  Dict     ):

												'''simple docstring'''

												return (4, 14, 16)




							@property
							def 			lowercase		( self:  int     ):

												'''simple docstring'''

												return (4, 14, 1)




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

												'''simple docstring'''

												super().test_determinism()




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

												'''simple docstring'''

												super().test_outputs_equivalence()




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

												'''simple docstring'''

												super().test_from_save_pretrained()




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

												'''simple docstring'''

												super().test_from_save_pretrained_variant()




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

												'''simple docstring'''

												super().test_model_from_pretrained()




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

												'''simple docstring'''

												a__							,a__ 							=	self.prepare_init_args_and_inputs_for_common()
												a__ 							=	self.model_class(**__A     )
												model.to(__A     )
												model.eval()

												with torch.no_grad():
																	a__ 							=	model(**__A     )

																	if isinstance(__A					,		__A     ):
																						a__ 							=	output.sample

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




							def 			lowercase		( self:  Dict     ):

												'''simple docstring'''

												pass




							def 			lowercase		( self:  Dict     ):

												'''simple docstring'''

												pass




							def 			lowercase		( self:  Any     ):

												'''simple docstring'''

												a__ 							=	{
												    '''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''',
												}
												a__ 							=	self.dummy_input
												return init_dict, inputs_dict




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

												'''simple docstring'''

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

												value_function.to(__A     )
												a__ 							=	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 			lowercase		( self:  Union[str, Any]     ):

												'''simple docstring'''

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

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

												with torch.no_grad():
																	a__ 							=	value_function(__A					,		__A     ).sample

												# fmt: off
												a__ 							=	torch.tensor([1_6_5.2_5] * seq_len     )
												# fmt: on
												self.assertTrue(torch.allclose(__A					,		__A					,		rtol=1e-3     )     )





							def 			lowercase		( self:  List[str]     ):

												'''simple docstring'''

												pass


