

import tempfile
import unittest

from diffusers import (
    DDIMScheduler,
    DDPMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    PNDMScheduler,
    logging,
)
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils.testing_utils import CaptureLogger



class    A__(__lowerCamelCase    ):
						"""simple docstring"""






						_A  :							List[Any]   			=	"config.json"





						@register_to_config
						def __init__(  self       ,     _lowercase=2       ,     _lowercase=5       ,     _lowercase=(2, 5)       ,     _lowercase="for diffusion"       ,     _lowercase=[1, 3]       ,     )      ->				Any:
									pass







class    A__(__lowerCamelCase    ):
						"""simple docstring"""






						_A  :							List[str]   			=	"config.json"





						@register_to_config
						def __init__(  self       ,     _lowercase=2       ,     _lowercase=5       ,     _lowercase=(2, 5)       ,     _lowercase="for diffusion"       ,     _lowercase=[1, 3]       ,     )      ->				List[str]:
									pass







class    A__(__lowerCamelCase    ):
						"""simple docstring"""






						_A  :							Optional[Any]   			=	"config.json"





						@register_to_config
						def __init__(  self       ,     _lowercase=2       ,     _lowercase=5       ,     _lowercase=(2, 5)       ,     _lowercase="for diffusion"       ,     _lowercase=[1, 3]       ,     _lowercase=[1, 3]       ,     )      ->				Any:
									pass







class    A__(__lowerCamelCase    ):
						"""simple docstring"""






						_A  :							List[Any]   			=	"config.json"





						@register_to_config
						def __init__(  self       ,     _lowercase=2       ,     _lowercase=5       ,     _lowercase=(2, 5)       ,     _lowercase="for diffusion"       ,     _lowercase=[1, 5]       ,     _lowercase=[5, 4]       ,     )      ->				List[Any]:
									pass







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






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





									with self.assertRaises(_UpperCamelCase  ):
												ConfigMixin.load_config("""dummy_path"""  )





						def 	UpperCamelCase__			(  self  )      ->				List[str]:
									a_		:		Any				    =				SampleObject()
									a_		:		Optional[int]				    =				obj.config
									assert config["a"] == 2
									assert config["b"] == 5
									assert config["c"] == (2, 5)
									assert config["d"] == "for diffusion"
									assert config["e"] == [1, 3]

									# init ignore private arguments
									a_		:		Dict				    =				SampleObject(_name_or_path="""lalala"""  )
									a_		:		List[Any]				    =				obj.config
									assert config["a"] == 2
									assert config["b"] == 5
									assert config["c"] == (2, 5)
									assert config["d"] == "for diffusion"
									assert config["e"] == [1, 3]

									# can override default
									a_		:		Optional[int]				    =				SampleObject(c=6  )
									a_		:		List[Any]				    =				obj.config
									assert config["a"] == 2
									assert config["b"] == 5
									assert config["c"] == 6
									assert config["d"] == "for diffusion"
									assert config["e"] == [1, 3]

									# can use positional arguments.
									a_		:		Optional[int]				    =				SampleObject(1       ,     c=6  )
									a_		:		int				    =				obj.config
									assert config["a"] == 1
									assert config["b"] == 5
									assert config["c"] == 6
									assert config["d"] == "for diffusion"
									assert config["e"] == [1, 3]





						def 	UpperCamelCase__			(  self  )      ->				Tuple:
									a_		:		Any				    =				SampleObject()
									a_		:		str				    =				obj.config

									assert config["a"] == 2
									assert config["b"] == 5
									assert config["c"] == (2, 5)
									assert config["d"] == "for diffusion"
									assert config["e"] == [1, 3]

									with tempfile.TemporaryDirectory() as tmpdirname:
												obj.save_config(_UpperCamelCase  )
												a_		:		Optional[int]				    =				SampleObject.from_config(SampleObject.load_config(_UpperCamelCase  )  )
												a_		:		Optional[Any]				    =				new_obj.config

									# unfreeze configs
									a_		:		Optional[int]				    =				dict(_UpperCamelCase  )
									a_		:		Optional[int]				    =				dict(_UpperCamelCase  )

									assert config.pop("""c"""  ) == (2, 5)  # instantiated as tuple
									assert new_config.pop("""c"""  ) == [2, 5]  # saved & loaded as list because of json
									config.pop("""_use_default_values"""  )
									assert config == new_config





						def 	UpperCamelCase__			(  self  )      ->				Union[str, Any]:
									a_		:		Optional[Any]				    =				logging.get_logger("""diffusers.configuration_utils"""  )
									# 30 for warning
									logger.setLevel(30  )

									with CaptureLogger(_UpperCamelCase  ) as cap_logger:
												a_		:		Optional[Any]				    =				DDIMScheduler.from_pretrained(
												    """hf-internal-testing/tiny-stable-diffusion-torch"""       ,     subfolder="""scheduler"""  )

									assert ddim.__class__ == DDIMScheduler
									# no warning should be thrown
									assert cap_logger.out == ""





						def 	UpperCamelCase__			(  self  )      ->				Optional[Any]:
									a_		:		Optional[Any]				    =				logging.get_logger("""diffusers.configuration_utils"""  )
									# 30 for warning
									logger.setLevel(30  )

									with CaptureLogger(_UpperCamelCase  ) as cap_logger:
												a_		:		Any				    =				EulerDiscreteScheduler.from_pretrained(
												    """hf-internal-testing/tiny-stable-diffusion-torch"""       ,     subfolder="""scheduler"""  )

									assert euler.__class__ == EulerDiscreteScheduler
									# no warning should be thrown
									assert cap_logger.out == ""





						def 	UpperCamelCase__			(  self  )      ->				int:
									a_		:		Optional[Any]				    =				logging.get_logger("""diffusers.configuration_utils"""  )
									# 30 for warning
									logger.setLevel(30  )

									with CaptureLogger(_UpperCamelCase  ) as cap_logger:
												a_		:		Any				    =				EulerAncestralDiscreteScheduler.from_pretrained(
												    """hf-internal-testing/tiny-stable-diffusion-torch"""       ,     subfolder="""scheduler"""  )

									assert euler.__class__ == EulerAncestralDiscreteScheduler
									# no warning should be thrown
									assert cap_logger.out == ""





						def 	UpperCamelCase__			(  self  )      ->				Optional[int]:
									a_		:		Union[str, Any]				    =				logging.get_logger("""diffusers.configuration_utils"""  )
									# 30 for warning
									logger.setLevel(30  )

									with CaptureLogger(_UpperCamelCase  ) as cap_logger:
												a_		:		Optional[Any]				    =				PNDMScheduler.from_pretrained(
												    """hf-internal-testing/tiny-stable-diffusion-torch"""       ,     subfolder="""scheduler"""  )

									assert pndm.__class__ == PNDMScheduler
									# no warning should be thrown
									assert cap_logger.out == ""





						def 	UpperCamelCase__			(  self  )      ->				List[str]:
									a_		:		Optional[Any]				    =				logging.get_logger("""diffusers.configuration_utils"""  )
									# 30 for warning
									logger.setLevel(30  )

									with CaptureLogger(_UpperCamelCase  ) as cap_logger:
												a_		:		int				    =				DDPMScheduler.from_pretrained(
												    """hf-internal-testing/tiny-stable-diffusion-torch"""       ,     subfolder="""scheduler"""       ,     prediction_type="""sample"""       ,     beta_end=8       ,     )

									with CaptureLogger(_UpperCamelCase  ) as cap_logger_a:
												a_		:		List[str]				    =				DDPMScheduler.from_pretrained("""google/ddpm-celebahq-256"""       ,     beta_start=88  )

									assert ddpm.__class__ == DDPMScheduler
									assert ddpm.config.prediction_type == "sample"
									assert ddpm.config.beta_end == 8
									assert ddpm_a.config.beta_start == 88

									# no warning should be thrown
									assert cap_logger.out == ""
									assert cap_logger_a.out == ""





						def 	UpperCamelCase__			(  self  )      ->				Optional[int]:
									a_		:		Union[str, Any]				    =				logging.get_logger("""diffusers.configuration_utils"""  )
									# 30 for warning
									logger.setLevel(30  )

									with CaptureLogger(_UpperCamelCase  ) as cap_logger:
												a_		:		Optional[int]				    =				DPMSolverMultistepScheduler.from_pretrained(
												    """hf-internal-testing/tiny-stable-diffusion-torch"""       ,     subfolder="""scheduler"""  )

									assert dpm.__class__ == DPMSolverMultistepScheduler
									# no warning should be thrown
									assert cap_logger.out == ""





						def 	UpperCamelCase__			(  self  )      ->				str:
									a_		:		Tuple				    =				SampleObject()

									a_		:		Union[str, Any]				    =				{k: v for k, v in config.config.items() if not k.startswith("""_"""  )}

									# make sure that default config has all keys in `_use_default_values`
									assert set(config_dict.keys()  ) == set(config.config._use_default_values  )

									with tempfile.TemporaryDirectory() as tmpdirname:
												config.save_config(_UpperCamelCase  )

												# now loading it with SampleObject2 should put f into `_use_default_values`
												a_		:		Optional[Any]				    =				SampleObjecta.from_config(_UpperCamelCase  )

												assert "f" in config._use_default_values
												assert config.f == [1, 3]

									# now loading the config, should **NOT** use [1, 3] for `f`, but the default [1, 4] value
									# **BECAUSE** it is part of `config._use_default_values`
									a_		:		Tuple				    =				SampleObjecta.from_config(config.config  )
									assert new_config.f == [5, 4]

									config.config._use_default_values.pop()
									a_		:		Tuple				    =				SampleObjecta.from_config(config.config  )
									assert new_config_a.f == [1, 3]

									# Nevertheless "e" should still be correctly loaded to [1, 3] from SampleObject2 instead of defaulting to [1, 5]
									assert new_config_a.e == [1, 3]








from sklearn.metrics import mean_absolute_error

import datasets


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

__snake_case							:     List[Any] 						=	"""\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
"""


__snake_case							:     str 						=	"""
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

                 \"raw_values\" : Returns a full set of errors in case of multioutput input.

                 \"uniform_average\" : Errors of all outputs are averaged with uniform weight.

Returns:
    mae : mean absolute error.
        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.
        MAE output is non-negative floating point. The best value is 0.0.
Examples:

    >>> mae_metric = datasets.load_metric(\"mae\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.5}

    If you're using multi-dimensional lists, then set the config as follows :

    >>> mae_metric = datasets.load_metric(\"mae\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.75}
    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results)
    {'mae': array([0.5, 1. ])}
"""



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,       _KWARGS_DESCRIPTION    )
class    A__(datasets.Metric    ):
						"""simple docstring"""






						def 	UpperCamelCase__			(  self  )      ->				List[str]:
									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 	UpperCamelCase__			(  self  )      ->				Union[str, Any]:
									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 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase       ,     _lowercase=None       ,     _lowercase="uniform_average"  )      ->				Union[str, Any]:
									a_		:		Any				    =				mean_absolute_error(_lowercase       ,     _lowercase       ,     sample_weight=_lowercase       ,     multioutput=_lowercase  )

									return {"mae": mae_score}


