
from sklearn.metrics import mean_absolute_error

import datasets


UpperCamelCase_  =	"\\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"

UpperCamelCase_  =	"\\nMean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual\nvalues.\n"


UpperCamelCase_  =	"\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							):





				def        _UpperCAmelCase						(		self :		Optional[Any]							):


								"""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        _UpperCAmelCase						(		self :		str							):


								"""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        _UpperCAmelCase						(		self :		int					,      snake_case_ :		Optional[Any]					,      snake_case_ :		Dict					,      snake_case_ :		Optional[int]=None					,      snake_case_ :		Optional[int]="uniform_average"							):


								"""simple docstring"""

								A     :  Optional[int]   =				mean_absolute_error(snake_case_					,      snake_case_					,      sample_weight=snake_case_					,      multioutput=snake_case_							)

								return {"mae": mae_score}

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin

@dataclass
class     _SCREAMING_SNAKE_CASE							(		snake_case							):
				lowerCamelCase_       =     42
				lowerCamelCase_       =     42





def 							_lowerCamelCase			(					lowerCamelCase_:  str				,	lowerCamelCase_:  int=0.9_9_9				,	lowerCamelCase_:  Optional[Any]="cosine"				,	):



				'''simple docstring'''







				if alpha_transform_type == "cosine":

								def alpha_bar_fn(lowerCamelCase_:  Optional[Any]		):
												return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2		) ** 2

				elif alpha_transform_type == "exp":

								def alpha_bar_fn(lowerCamelCase_:  Optional[Any]		):
												return math.exp(t * -1_2.0		)

				else:
								raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""		)

				A     :  Dict   =				[]
				for i in range(lowerCamelCase_		):
								A     :  Dict   =				i / num_diffusion_timesteps
								A     :  List[str]   =				(i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar_fn(lowerCamelCase_		) / alpha_bar_fn(lowerCamelCase_		)				,	lowerCamelCase_		)		)
				return torch.tensor(lowerCamelCase_				,	dtype=torch.floataa		)






class     _SCREAMING_SNAKE_CASE							(		snake_case,    snake_case							):
				lowerCamelCase_       =     1





				@register_to_config
				def __init__(		self :		Optional[Any]					,      snake_case_ :		int = 1000					,      snake_case_ :		float = 0.00_01					,      snake_case_ :		float = 0.02					,      snake_case_ :		str = "linear"					,      snake_case_ :		float = 0.0					,      snake_case_ :		Optional[np.ndarray] = None					,      snake_case_ :		bool = True					,      ):


								"""simple docstring"""

								if trained_betas is not None:
												A     :  Any   =				torch.from_numpy(snake_case_							)
								elif beta_schedule == "linear":
												A     :  Tuple   =				torch.linspace(snake_case_					,      snake_case_					,      snake_case_					,      dtype=torch.floataa							)
								elif beta_schedule == "scaled_linear":
												# this schedule is very specific to the latent diffusion model.
												A     :  str   =				(
												    torch.linspace(beta_start**0.5					,      beta_end**0.5					,      snake_case_					,      dtype=torch.floataa							) ** 2
												)
								elif beta_schedule == "squaredcos_cap_v2":
												# Glide cosine schedule
												A     :  int   =				betas_for_alpha_bar(snake_case_							)
								elif beta_schedule == "sigmoid":
												# GeoDiff sigmoid schedule
												A     :  Optional[Any]   =				torch.linspace(-6					,      6					,      snake_case_							)
												A     :  str   =				torch.sigmoid(snake_case_							) * (beta_end - beta_start) + beta_start
								else:
												raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""							)

								A     :  Any   =				1.0 - self.betas
								A     :  int   =				torch.cumprod(self.alphas					,      dim=0							)
								A     :  Any   =				torch.tensor(1.0							)

								A     :  Dict   =				torch.tensor(1.0							)

								# standard deviation of the initial noise distribution
								A     :  Optional[Any]   =				1.0

								# setable values
								A     :  Dict   =				None
								A     :  Dict   =				torch.from_numpy(np.arange(0					,      snake_case_							)[::-1].copy()							)

								A     :  Any   =				eta





				def        _UpperCAmelCase						(		self :		List[Any]					,      snake_case_ :		torch.FloatTensor					,      snake_case_ :		Optional[int] = None							):


								"""simple docstring"""

								return sample





				def        _UpperCAmelCase						(		self :		List[Any]					,      snake_case_ :		int					,      snake_case_ :		int = 10					,      snake_case_ :		int = 10					,      snake_case_ :		Union[str, torch.device] = None					,      ):


								"""simple docstring"""

								A     :  List[Any]   =				min(self.config.num_train_timesteps					,      snake_case_							)
								A     :  int   =				num_inference_steps

								A     :  List[Any]   =				[]

								A     :  Optional[Any]   =				{}
								for j in range(0					,      num_inference_steps - jump_length					,      snake_case_							):
												A     :  List[Any]   =				jump_n_sample - 1

								A     :  Optional[int]   =				num_inference_steps
								while t >= 1:
												A     :  Optional[Any]   =				t - 1
												timesteps.append(snake_case_							)

												if jumps.get(snake_case_					,      0							) > 0:
																A     :  Optional[int]   =				jumps[t] - 1
																for _ in range(snake_case_							):
																				A     :  Dict   =				t + 1
																				timesteps.append(snake_case_							)

								A     :  Optional[Any]   =				np.array(snake_case_							) * (self.config.num_train_timesteps // self.num_inference_steps)
								A     :  Optional[Any]   =				torch.from_numpy(snake_case_							).to(snake_case_							)





				def        _UpperCAmelCase						(		self :		Optional[int]					,      snake_case_ :		Optional[int]							):


								"""simple docstring"""

								A     :  str   =				t - self.config.num_train_timesteps // self.num_inference_steps

								A     :  str   =				self.alphas_cumprod[t]
								A     :  List[Any]   =				self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
								A     :  Dict   =				1 - alpha_prod_t
								A     :  Any   =				1 - alpha_prod_t_prev

								# For t > 0, compute predicted variance βt (see formula (6) and (7) from
								# https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
								# previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
								# variance to pred_sample
								# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
								# without eta.
								# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
								A     :  int   =				(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

								return variance





				def        _UpperCAmelCase						(		self :		List[str]					,      snake_case_ :		torch.FloatTensor					,      snake_case_ :		int					,      snake_case_ :		torch.FloatTensor					,      snake_case_ :		torch.FloatTensor					,      snake_case_ :		torch.FloatTensor					,      snake_case_ :		Optional[torch.Generator] = None					,      snake_case_ :		bool = True					,      ):


								"""simple docstring"""

								A     :  Any   =				timestep
								A     :  List[str]   =				timestep - self.config.num_train_timesteps // self.num_inference_steps

								# 1. compute alphas, betas
								A     :  List[Any]   =				self.alphas_cumprod[t]
								A     :  Optional[int]   =				self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
								A     :  Optional[Any]   =				1 - alpha_prod_t

								# 2. compute predicted original sample from predicted noise also called
								# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
								A     :  Optional[Any]   =				(sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

								# 3. Clip "predicted x_0"
								if self.config.clip_sample:
												A     :  Optional[Any]   =				torch.clamp(snake_case_					,      -1					,      1							)

								# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
								# substitute formula (7) in the algorithm coming from DDPM paper
								# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
								# DDIM schedule gives the same results as DDPM with eta = 1.0
								# Noise is being reused in 7. and 8., but no impact on quality has
								# been observed.

								# 5. Add noise
								A     :  Dict   =				model_output.device
								A     :  str   =				randn_tensor(model_output.shape					,      generator=snake_case_					,      device=snake_case_					,      dtype=model_output.dtype							)
								A     :  Tuple   =				self.eta * self._get_variance(snake_case_							) ** 0.5

								A     :  List[str]   =				0
								if t > 0 and self.eta > 0:
												A     :  Optional[int]   =				std_dev_t * noise

								# 6. compute "direction pointing to x_t" of formula (12)
								# from https://arxiv.org/pdf/2010.02502.pdf
								A     :  List[Any]   =				(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

								# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
								A     :  Dict   =				alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

								# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
								A     :  Any   =				(alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

								# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
								A     :  Optional[int]   =				mask * prev_known_part + (1.0 - mask) * prev_unknown_part

								if not return_dict:
												return (
												    pred_prev_sample,
												    pred_original_sample,
												)

								return RePaintSchedulerOutput(prev_sample=snake_case_					,      pred_original_sample=snake_case_							)





				def        _UpperCAmelCase						(		self :		Tuple					,      snake_case_ :		Any					,      snake_case_ :		Tuple					,      snake_case_ :		List[str]=None							):


								"""simple docstring"""

								A     :  List[str]   =				self.config.num_train_timesteps // self.num_inference_steps

								for i in range(snake_case_							):
												A     :  Dict   =				self.betas[timestep + i]
												if sample.device.type == "mps":
																# randn does not work reproducibly on mps
																A     :  Any   =				randn_tensor(sample.shape					,      dtype=sample.dtype					,      generator=snake_case_							)
																A     :  Any   =				noise.to(sample.device							)
												else:
																A     :  Union[str, Any]   =				randn_tensor(sample.shape					,      generator=snake_case_					,      device=sample.device					,      dtype=sample.dtype							)

												# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
												A     :  str   =				(1 - beta) ** 0.5 * sample + beta**0.5 * noise

								return sample





				def        _UpperCAmelCase						(		self :		List[str]					,      snake_case_ :		torch.FloatTensor					,      snake_case_ :		torch.FloatTensor					,      snake_case_ :		torch.IntTensor					,      ):


								"""simple docstring"""

								raise NotImplementedError('''Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.'''							)






				def __len__(		self :		Optional[Any]							):


								"""simple docstring"""

								return self.config.num_train_timesteps