



"""simple docstring"""






from sklearn.metrics import mean_absolute_error

import datasets


lowerCamelCase				:     Tuple								=  """\\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"""

lowerCamelCase				:     Optional[Any]								=  """\\nMean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual\nvalues.\n"""


lowerCamelCase				:     List[str]								=  """\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    __snake_case(					datasets.Metric       ):




    def   A   (   self			):





      '''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   A   (   self			):





      '''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   A   (   self    ,							A_    ,							A_    ,							A_=None    ,							A_="uniform_average"			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			mean_absolute_error(_UpperCAmelCase    ,							_UpperCAmelCase    ,							sample_weight=_UpperCAmelCase    ,							multioutput=_UpperCAmelCase			)

      return {"mae": mae_score}




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	):
				__lowerCAmelCase		=   '''hf-internal-testing/tiny-stable-diffusion-pipe'''
				__lowerCAmelCase		=   1
				__lowerCAmelCase		=   77
				__lowerCAmelCase		=   32
				__lowerCAmelCase		=   4
				__lowerCAmelCase		=   __lowerCAmelCase		=   64
				__lowerCAmelCase		=   torch.manual_seed(0	)
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase=0.9999   ):




										"""simple docstring"""




										__magic_name__					:				List[Any]				=   UNetaDConditionModel.from_pretrained(self.model_id  ,   subfolder='unet'   )
										__magic_name__					:				int				=   unet.to(_UpperCAmelCase   )
										__magic_name__					:				Optional[int]				=   EMAModel(unet.parameters()  ,   decay=_UpperCAmelCase  ,   model_cls=_UpperCAmelCase  ,   model_config=unet.config   )
										return unet, ema_unet
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				List[str]				=   torch.randn(
										    self.batch_size  ,   self.num_in_channels  ,   self.latent_height  ,   self.latent_width  ,   generator=self.generator   ).to(_UpperCAmelCase   )
										__magic_name__					:				List[str]				=   torch.randint(0  ,   1000  ,   size=(self.batch_size,)  ,   generator=self.generator   ).to(_UpperCAmelCase   )
										__magic_name__					:				Any				=   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   ):




										"""simple docstring"""




										__magic_name__					:				Any				=   {}
										for k, param in unet.state_dict().items():
																__magic_name__					:				str				=   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   ):




										"""simple docstring"""




										__magic_name__		,			__magic_name__					:				int				=   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   ):




										"""simple docstring"""




										__magic_name__		,			__magic_name__					:				Union[str, Any]				=   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()   )
										__magic_name__					:				Any				=   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   ):




										"""simple docstring"""




										__magic_name__		,			__magic_name__					:				Optional[Any]				=   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.
										__magic_name__					:				Any				=   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.
										__magic_name__					:				Any				=   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   ):




										"""simple docstring"""




										__magic_name__		,			__magic_name__					:				List[str]				=   self.get_models()

										# First backprop + EMA
										__magic_name__					:				int				=   self.simulate_backprop(_UpperCAmelCase   )
										ema_unet.step(unet_step_one.parameters()   )
										__magic_name__					:				Any				=   ema_unet.shadow_params

										# Second backprop + EMA
										__magic_name__					:				Any				=   self.simulate_backprop(_UpperCAmelCase   )
										ema_unet.step(unet_step_two.parameters()   )
										__magic_name__					:				Union[str, Any]				=   ema_unet.shadow_params

										for step_one, step_two in zip(_UpperCAmelCase  ,   _UpperCAmelCase   ):
																assert ~torch.allclose(_UpperCAmelCase  ,   _UpperCAmelCase   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__		,			__magic_name__					:				Optional[Any]				=   self.get_models(decay=0.0   )
										__magic_name__					:				str				=   self.simulate_backprop(_UpperCAmelCase   )
										ema_unet.step(unet_step_one.parameters()   )
										__magic_name__					:				List[Any]				=   ema_unet.shadow_params

										__magic_name__					:				Optional[Any]				=   self.simulate_backprop(_UpperCAmelCase   )
										ema_unet.step(unet_step_two.parameters()   )
										__magic_name__					:				Optional[Any]				=   ema_unet.shadow_params

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





				@skip_mps
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__		,			__magic_name__					:				Optional[int]				=   self.get_models()
										__magic_name__		,			__magic_name__		,			__magic_name__					:				str				=   self.get_dummy_inputs()

										with tempfile.TemporaryDirectory() as tmpdir:
																ema_unet.save_pretrained(_UpperCAmelCase   )
																__magic_name__					:				Optional[int]				=   UNetaDConditionModel.from_pretrained(_UpperCAmelCase  ,   model_cls=_UpperCAmelCase   )
																__magic_name__					:				str				=   loaded_unet.to(unet.device   )

										# Since no EMA step has been performed the outputs should match.
										__magic_name__					:				Union[str, Any]				=   unet(_UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase   ).sample
										__magic_name__					:				List[Any]				=   loaded_unet(_UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase   ).sample

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


