





"""simple docstring"""





import os
import re
import shutil
import sys
import tempfile
import unittest

import black


__A											=						os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
__A											=						"""    \"\"\"
    Output class for the scheduler's step function output.

    Args:
        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
            denoising loop.
        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
            The predicted denoised sample (x_{0}) based on the model output from the current timestep.
            `pred_original_sample` can be used to preview progress or for guidance.
    \"\"\"

    prev_sample: torch.FloatTensor
    pred_original_sample: Optional[torch.FloatTensor] = None
"""






class    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :List[str]											=				tempfile.mkdtemp()
         os.makedirs(os.path.join(self.diffusers_dir       ,					'schedulers/' ) )
         lowerCAmelCase__     :Optional[int]											=				self.diffusers_dir
         shutil.copy(
             os.path.join(__UpperCAmelCase       ,					'src/diffusers/schedulers/scheduling_ddpm.py' )       ,					os.path.join(self.diffusers_dir       ,					'schedulers/scheduling_ddpm.py' )       ,					)






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				'src/diffusers'
         shutil.rmtree(self.diffusers_dir )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase=None ):



         '''simple docstring'''

         lowerCAmelCase__     :Any											=				comment + F"\nclass {class_name}(nn.Module):\n" + class_code
         if overwrite_result is not None:
           lowerCAmelCase__     :str											=				comment + F"\nclass {class_name}(nn.Module):\n" + overwrite_result
         lowerCAmelCase__     :List[Any]											=				black.Mode(target_versions={black.TargetVersion.PYaa}       ,					line_length=1_1_9 )
         lowerCAmelCase__     :str											=				black.format_str(__UpperCAmelCase       ,					mode=__UpperCAmelCase )
         lowerCAmelCase__     :List[Any]											=				os.path.join(self.diffusers_dir       ,					'new_code.py' )
         with open(__UpperCAmelCase       ,					'w'       ,					newline='\n' ) as f:
           f.write(__UpperCAmelCase )
         if overwrite_result is None:
           self.assertTrue(len(check_copies.is_copy_consistent(__UpperCAmelCase ) ) == 0 )
         else:
           check_copies.is_copy_consistent(f.name       ,					overwrite=__UpperCAmelCase )
           with open(__UpperCAmelCase       ,					'r' ) as f:
             self.assertTrue(f.read()       ,					__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				check_copies.find_code_in_diffusers('schedulers.scheduling_ddpm.DDPMSchedulerOutput' )
         self.assertEqual(__UpperCAmelCase       ,					__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         self.check_copy_consistency(
             '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'       ,					'DDPMSchedulerOutput'       ,					REFERENCE_CODE + '\n'       ,					)

         # With no empty line at the end
         self.check_copy_consistency(
             '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'       ,					'DDPMSchedulerOutput'       ,					__UpperCAmelCase       ,					)

         # Copy consistency with rename
         self.check_copy_consistency(
             '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'       ,					'TestSchedulerOutput'       ,					re.sub('DDPM'       ,					'Test'       ,					__UpperCAmelCase )       ,					)

         # Copy consistency with a really long name
         lowerCAmelCase__     :List[str]											=				'TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'
         self.check_copy_consistency(
             F"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}"       ,					F"{long_class_name}SchedulerOutput"       ,					re.sub('Bert'       ,					__UpperCAmelCase       ,					__UpperCAmelCase )       ,					)

         # Copy consistency with overwrite
         self.check_copy_consistency(
             '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'       ,					'TestSchedulerOutput'       ,					__UpperCAmelCase       ,					overwrite_result=re.sub('DDPM'       ,					'Test'       ,					__UpperCAmelCase )       ,					)





'''simple docstring'''





from sklearn.metrics import mean_squared_error

import datasets


SCREAMING_SNAKE_CASE__          =  "\\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"

SCREAMING_SNAKE_CASE__          =  "\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n"


SCREAMING_SNAKE_CASE__          =  "\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\n    squared : bool, default=True\n        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n    mse : mean squared error.\nExamples:\n\n    >>> mse_metric = datasets.load_metric(\"mse\")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mse': 0.375}\n    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n    >>> print(rmse_result)\n    {'mse': 0.6123724356957945}\n\n    If you're using multi-dimensional lists, then set the config as follows :\n\n    >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mse': 0.7083333333333334}\n    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {'mse': array([0.41666667, 1. ])}\n"


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



       def 				_a   (					self   )	->				Optional[int]:
           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_squared_error.html"
               ]  ,)



       def 				_a   (					self   )	->				Tuple:
           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  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_=None  ,UpperCAmelCase_="uniform_average"  ,UpperCAmelCase_=True   )	->				Tuple:
           lowercase__									=    mean_squared_error(
               UpperCAmelCase_  ,UpperCAmelCase_  ,sample_weight=UpperCAmelCase_  ,multioutput=UpperCAmelCase_  ,squared=UpperCAmelCase_   )

           return {"mse": mse}




