
'''simple docstring'''



import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


_lowercase          =						logging.get_logger(__name__)




class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						def __init__(   self       ,  _lowercase ):

												"""simple docstring"""



												super().__init__()
												_lowerCAmelCase    		=					nn.ModuleList(_lowercase )




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase = None       ,  _lowercase = None       ,  _lowercase = None       ,  _lowercase = None       ,  _lowercase = False       ,  _lowercase = True       ,  ):

												"""simple docstring"""



												for i, (image, scale, controlnet) in enumerate(zip(_lowercase       ,  _lowercase       ,  self.nets ) ):
																		_lowerCAmelCase		,       _lowerCAmelCase    		=					controlnet(
																		    _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  )

																		# merge samples
																		if i == 0:
																								_lowerCAmelCase		,       _lowerCAmelCase    		=					down_samples, mid_sample
																		else:
																								_lowerCAmelCase    		=					[
																								    samples_prev + samples_curr
																								    for samples_prev, samples_curr in zip(_lowercase       ,  _lowercase )
																								]
																								mid_block_res_sample += mid_sample

												return down_block_res_samples, mid_block_res_sample




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase = True       ,  _lowercase = None       ,  _lowercase = False       ,  _lowercase = None       ,  ):

												"""simple docstring"""



												_lowerCAmelCase    		=					0
												_lowerCAmelCase    		=					save_directory
												for controlnet in self.nets:
																		controlnet.save_pretrained(
																		    _lowercase       ,  is_main_process=_lowercase       ,  save_function=_lowercase       ,  safe_serialization=_lowercase       ,  variant=_lowercase       ,  )

																		idx += 1
																		_lowerCAmelCase    		=					model_path_to_save + F'_{idx}'




						@classmethod
						def    _lowercase      (   cls       ,  _lowercase       ,  **_lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					0
												_lowerCAmelCase    		=					[]

												# load controlnet and append to list until no controlnet directory exists anymore
												# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
												# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
												_lowerCAmelCase    		=					pretrained_model_path
												while os.path.isdir(_lowercase ):
																		_lowerCAmelCase    		=					ControlNetModel.from_pretrained(_lowercase       ,  **_lowercase )
																		controlnets.append(_lowercase )

																		idx += 1
																		_lowerCAmelCase    		=					pretrained_model_path + F'_{idx}'

												logger.info(F'{len(_lowercase )} controlnets loaded from {pretrained_model_path}.' )

												if len(_lowercase ) == 0:
																		raise ValueError(
																		    F'No ControlNets found under {os.path.dirname(_lowercase )}. Expected at least {pretrained_model_path + "_0"}.' )

												return cls(_lowercase )


'''simple docstring'''



from sklearn.metrics import mean_squared_error

import datasets


_lowercase          =						"""\
@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}
}
"""

_lowercase          =						"""\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
"""


_lowercase          =						"""
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.

    squared : bool, default=True
        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.

Returns:
    mse : mean squared error.
Examples:

    >>> mse_metric = datasets.load_metric(\"mse\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.375}
    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
    >>> print(rmse_result)
    {'mse': 0.6123724356957945}

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

    >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.7083333333333334}
    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {'mse': array([0.41666667, 1. ])}
"""




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






						'''simple docstring'''






						def    _lowercase      (   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_squared_error.html"""
												    ]       ,  )




						def    _lowercase      (   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    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase=None       ,  _lowercase="uniform_average"       ,  _lowercase=True ):

												"""simple docstring"""



												_lowerCAmelCase    		=					mean_squared_error(
												    _lowercase       ,  _lowercase       ,  sample_weight=_lowercase       ,  multioutput=_lowercase       ,  squared=_lowercase )

												return {"mse": mse}
