import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput





def 		__lowerCamelCase							(UpperCAmelCase__   :							Optional[int]					,       UpperCAmelCase__   :							Any=0.999					,       UpperCAmelCase__   :							List[str]="cosine"					,       ):
					if alpha_transform_type == "cosine":

										def alpha_bar_fn(UpperCAmelCase__   :							int     ):
															return math.cos((t + 0.008) / 1.008 * math.pi / 2     ) ** 2

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(UpperCAmelCase__   :							Any     ):
															return math.exp(t * -12.0     )

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

					SCREAMING_SNAKE_CASE				=				[]
					for i in range(UpperCAmelCase__     ):
										SCREAMING_SNAKE_CASE				=				i / num_diffusion_timesteps
										SCREAMING_SNAKE_CASE				=				(i + 1) / num_diffusion_timesteps
										betas.append(min(1 - alpha_bar_fn(UpperCAmelCase__     ) / alpha_bar_fn(UpperCAmelCase__     )					,       UpperCAmelCase__     )     )
					return torch.tensor(UpperCAmelCase__					,       dtype=torch.floataa     )


class   lowercase			(   a ,				a   ):
							lowercase__  :				Any									=							[e.name for e in KarrasDiffusionSchedulers]
							lowercase__  :				Optional[Any]									=							2




							@register_to_config
							def __init__(  self      :    Dict		,							_UpperCamelCase      :    int = 1_000		,							_UpperCamelCase      :    float = 0.0_0_0_8_5		,							_UpperCamelCase      :    float = 0.0_1_2		,							_UpperCamelCase      :    str = "linear"		,							_UpperCamelCase      :    Optional[Union[np.ndarray, List[float]]] = None		,							_UpperCamelCase      :    str = "epsilon"		,							_UpperCamelCase      :    str = "linspace"		,							_UpperCamelCase      :    int = 0		,							)    -> Dict:



												'''simple docstring'''

												if trained_betas is not None:
																	SCREAMING_SNAKE_CASE				=				torch.tensor(_UpperCamelCase		,							dtype=torch.floataa					)
												elif beta_schedule == "linear":
																	SCREAMING_SNAKE_CASE				=				torch.linspace(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							dtype=torch.floataa					)
												elif beta_schedule == "scaled_linear":
																	# this schedule is very specific to the latent diffusion model.
																	SCREAMING_SNAKE_CASE				=				(
																	    torch.linspace(beta_start**0.5		,							beta_end**0.5		,							_UpperCamelCase		,							dtype=torch.floataa					) ** 2
																	)
												elif beta_schedule == "squaredcos_cap_v2":
																	# Glide cosine schedule
																	SCREAMING_SNAKE_CASE				=				betas_for_alpha_bar(_UpperCamelCase					)
												else:
																	raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"					)

												SCREAMING_SNAKE_CASE				=				1.0 - self.betas
												SCREAMING_SNAKE_CASE				=				torch.cumprod(self.alphas		,							dim=0					)

												#  set all values
												self.set_timesteps(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase					)




							def 		__snake_case(  self      :    str		,							_UpperCamelCase      :    Tuple		,							_UpperCamelCase      :    List[str]=None					)    -> List[Any]:



												'''simple docstring'''

												if schedule_timesteps is None:
																	SCREAMING_SNAKE_CASE				=				self.timesteps

												SCREAMING_SNAKE_CASE				=				(schedule_timesteps == timestep).nonzero()

												# The sigma index that is taken for the **very** first `step`
												# is always the second index (or the last index if there is only 1)
												# This way we can ensure we don't accidentally skip a sigma in
												# case we start in the middle of the denoising schedule (e.g. for image-to-image)
												if len(self._index_counter					) == 0:
																	SCREAMING_SNAKE_CASE				=				1 if len(_UpperCamelCase					) > 1 else 0
												else:
																	SCREAMING_SNAKE_CASE				=				timestep.cpu().item() if torch.is_tensor(_UpperCamelCase					) else timestep
																	SCREAMING_SNAKE_CASE				=				self._index_counter[timestep_int]

												return indices[pos].item()




							@property
							def 		__snake_case(  self      :    Optional[Any]					)    -> Tuple:



												'''simple docstring'''

												if self.config.timestep_spacing in ["linspace", "trailing"]:
																	return self.sigmas.max()

												return (self.sigmas.max() ** 2 + 1) ** 0.5




							def 		__snake_case(  self      :    Tuple		,							_UpperCamelCase      :    torch.FloatTensor		,							_UpperCamelCase      :    Union[float, torch.FloatTensor]		,							)    -> torch.FloatTensor:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				self.index_for_timestep(_UpperCamelCase					)

												if self.state_in_first_order:
																	SCREAMING_SNAKE_CASE				=				self.sigmas[step_index]
												else:
																	SCREAMING_SNAKE_CASE				=				self.sigmas_interpol[step_index - 1]

												SCREAMING_SNAKE_CASE				=				sample / ((sigma**2 + 1) ** 0.5)
												return sample




							def 		__snake_case(  self      :    List[Any]		,							_UpperCamelCase      :    int		,							_UpperCamelCase      :    Union[str, torch.device] = None		,							_UpperCamelCase      :    Optional[int] = None		,							)    -> int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				num_inference_steps

												SCREAMING_SNAKE_CASE				=				num_train_timesteps or self.config.num_train_timesteps

												# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
												if self.config.timestep_spacing == "linspace":
																	SCREAMING_SNAKE_CASE				=				np.linspace(0		,							num_train_timesteps - 1		,							_UpperCamelCase		,							dtype=_UpperCamelCase					)[::-1].copy()
												elif self.config.timestep_spacing == "leading":
																	SCREAMING_SNAKE_CASE				=				num_train_timesteps // self.num_inference_steps
																	# creates integer timesteps by multiplying by ratio
																	# casting to int to avoid issues when num_inference_step is power of 3
																	SCREAMING_SNAKE_CASE				=				(np.arange(0		,							_UpperCamelCase					) * step_ratio).round()[::-1].copy().astype(_UpperCamelCase					)
																	timesteps += self.config.steps_offset
												elif self.config.timestep_spacing == "trailing":
																	SCREAMING_SNAKE_CASE				=				num_train_timesteps / self.num_inference_steps
																	# creates integer timesteps by multiplying by ratio
																	# casting to int to avoid issues when num_inference_step is power of 3
																	SCREAMING_SNAKE_CASE				=				(np.arange(_UpperCamelCase		,							0		,							-step_ratio					)).round().copy().astype(_UpperCamelCase					)
																	timesteps -= 1
												else:
																	raise ValueError(
																	    F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."					)

												SCREAMING_SNAKE_CASE				=				np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
												SCREAMING_SNAKE_CASE				=				torch.from_numpy(np.log(_UpperCamelCase					)					).to(_UpperCamelCase					)

												SCREAMING_SNAKE_CASE				=				np.interp(_UpperCamelCase		,							np.arange(0		,							len(_UpperCamelCase					)					)		,							_UpperCamelCase					)
												SCREAMING_SNAKE_CASE				=				np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
												SCREAMING_SNAKE_CASE				=				torch.from_numpy(_UpperCamelCase					).to(device=_UpperCamelCase					)

												# compute up and down sigmas
												SCREAMING_SNAKE_CASE				=				sigmas.roll(-1					)
												SCREAMING_SNAKE_CASE				=				0.0
												SCREAMING_SNAKE_CASE				=				(sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
												SCREAMING_SNAKE_CASE				=				(sigmas_next**2 - sigmas_up**2) ** 0.5
												SCREAMING_SNAKE_CASE				=				0.0

												# compute interpolated sigmas
												SCREAMING_SNAKE_CASE				=				sigmas.log().lerp(sigmas_down.log()		,							0.5					).exp()
												SCREAMING_SNAKE_CASE				=				0.0

												# set sigmas
												SCREAMING_SNAKE_CASE				=				torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2					), sigmas[-1:]]					)
												SCREAMING_SNAKE_CASE				=				torch.cat(
												    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2					), sigmas_interpol[-1:]]					)
												SCREAMING_SNAKE_CASE				=				torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2					), sigmas_up[-1:]]					)
												SCREAMING_SNAKE_CASE				=				torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2					), sigmas_down[-1:]]					)

												if str(_UpperCamelCase					).startswith("mps"					):
																	# mps does not support float64
																	SCREAMING_SNAKE_CASE				=				torch.from_numpy(_UpperCamelCase					).to(_UpperCamelCase		,							dtype=torch.floataa					)
												else:
																	SCREAMING_SNAKE_CASE				=				torch.from_numpy(_UpperCamelCase					).to(_UpperCamelCase					)

												SCREAMING_SNAKE_CASE				=				self.sigma_to_t(_UpperCamelCase					).to(_UpperCamelCase		,							dtype=timesteps.dtype					)
												SCREAMING_SNAKE_CASE				=				torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])		,							dim=-1					).flatten()

												SCREAMING_SNAKE_CASE				=				torch.cat([timesteps[:1], interleaved_timesteps]					)

												SCREAMING_SNAKE_CASE				=				None

												# for exp beta schedules, such as the one for `pipeline_shap_e.py`
												# we need an index counter
												SCREAMING_SNAKE_CASE				=				defaultdict(_UpperCamelCase					)




							def 		__snake_case(  self      :    List[Any]		,							_UpperCamelCase      :    List[str]					)    -> List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				sigma.log()

												# get distribution
												SCREAMING_SNAKE_CASE				=				log_sigma - self.log_sigmas[:, None]

												# get sigmas range
												SCREAMING_SNAKE_CASE				=				dists.ge(0					).cumsum(dim=0					).argmax(dim=0					).clamp(max=self.log_sigmas.shape[0] - 2					)
												SCREAMING_SNAKE_CASE				=				low_idx + 1

												SCREAMING_SNAKE_CASE				=				self.log_sigmas[low_idx]
												SCREAMING_SNAKE_CASE				=				self.log_sigmas[high_idx]

												# interpolate sigmas
												SCREAMING_SNAKE_CASE				=				(low - log_sigma) / (low - high)
												SCREAMING_SNAKE_CASE				=				w.clamp(0		,							1					)

												# transform interpolation to time range
												SCREAMING_SNAKE_CASE				=				(1 - w) * low_idx + w * high_idx
												SCREAMING_SNAKE_CASE				=				t.view(sigma.shape					)
												return t




							@property
							def 		__snake_case(  self      :    Optional[Any]					)    -> Optional[int]:



												'''simple docstring'''

												return self.sample is None




							def 		__snake_case(  self      :    str		,							_UpperCamelCase      :    Union[torch.FloatTensor, np.ndarray]		,							_UpperCamelCase      :    Union[float, torch.FloatTensor]		,							_UpperCamelCase      :    Union[torch.FloatTensor, np.ndarray]		,							_UpperCamelCase      :    Optional[torch.Generator] = None		,							_UpperCamelCase      :    bool = True		,							)    -> Union[SchedulerOutput, Tuple]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				self.index_for_timestep(_UpperCamelCase					)

												# advance index counter by 1
												SCREAMING_SNAKE_CASE				=				timestep.cpu().item() if torch.is_tensor(_UpperCamelCase					) else timestep
												self._index_counter[timestep_int] += 1

												if self.state_in_first_order:
																	SCREAMING_SNAKE_CASE				=				self.sigmas[step_index]
																	SCREAMING_SNAKE_CASE				=				self.sigmas_interpol[step_index]
																	SCREAMING_SNAKE_CASE				=				self.sigmas_up[step_index]
																	SCREAMING_SNAKE_CASE				=				self.sigmas_down[step_index - 1]
												else:
																	# 2nd order / KPDM2's method
																	SCREAMING_SNAKE_CASE				=				self.sigmas[step_index - 1]
																	SCREAMING_SNAKE_CASE				=				self.sigmas_interpol[step_index - 1]
																	SCREAMING_SNAKE_CASE				=				self.sigmas_up[step_index - 1]
																	SCREAMING_SNAKE_CASE				=				self.sigmas_down[step_index - 1]

												# currently only gamma=0 is supported. This usually works best anyways.
												# We can support gamma in the future but then need to scale the timestep before
												# passing it to the model which requires a change in API
												SCREAMING_SNAKE_CASE				=				0
												SCREAMING_SNAKE_CASE				=				sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

												SCREAMING_SNAKE_CASE				=				model_output.device
												SCREAMING_SNAKE_CASE				=				randn_tensor(model_output.shape		,							dtype=model_output.dtype		,							device=_UpperCamelCase		,							generator=_UpperCamelCase					)

												# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
												if self.config.prediction_type == "epsilon":
																	SCREAMING_SNAKE_CASE				=				sigma_hat if self.state_in_first_order else sigma_interpol
																	SCREAMING_SNAKE_CASE				=				sample - sigma_input * model_output
												elif self.config.prediction_type == "v_prediction":
																	SCREAMING_SNAKE_CASE				=				sigma_hat if self.state_in_first_order else sigma_interpol
																	SCREAMING_SNAKE_CASE				=				model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
																	    sample / (sigma_input**2 + 1)
																	)
												elif self.config.prediction_type == "sample":
																	raise NotImplementedError("prediction_type not implemented yet: sample"					)
												else:
																	raise ValueError(
																	    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"					)

												if self.state_in_first_order:
																	# 2. Convert to an ODE derivative for 1st order
																	SCREAMING_SNAKE_CASE				=				(sample - pred_original_sample) / sigma_hat
																	# 3. delta timestep
																	SCREAMING_SNAKE_CASE				=				sigma_interpol - sigma_hat

																	# store for 2nd order step
																	SCREAMING_SNAKE_CASE				=				sample
																	SCREAMING_SNAKE_CASE				=				dt
																	SCREAMING_SNAKE_CASE				=				sample + derivative * dt
												else:
																	# DPM-Solver-2
																	# 2. Convert to an ODE derivative for 2nd order
																	SCREAMING_SNAKE_CASE				=				(sample - pred_original_sample) / sigma_interpol
																	# 3. delta timestep
																	SCREAMING_SNAKE_CASE				=				sigma_down - sigma_hat

																	SCREAMING_SNAKE_CASE				=				self.sample
																	SCREAMING_SNAKE_CASE				=				None

																	SCREAMING_SNAKE_CASE				=				sample + derivative * dt
																	SCREAMING_SNAKE_CASE				=				prev_sample + noise * sigma_up

												if not return_dict:
																	return (prev_sample,)

												return SchedulerOutput(prev_sample=_UpperCamelCase					)




							def 		__snake_case(  self      :    List[str]		,							_UpperCamelCase      :    torch.FloatTensor		,							_UpperCamelCase      :    torch.FloatTensor		,							_UpperCamelCase      :    torch.FloatTensor		,							)    -> torch.FloatTensor:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				self.sigmas.to(device=original_samples.device		,							dtype=original_samples.dtype					)
												if original_samples.device.type == "mps" and torch.is_floating_point(_UpperCamelCase					):
																	# mps does not support float64
																	SCREAMING_SNAKE_CASE				=				self.timesteps.to(original_samples.device		,							dtype=torch.floataa					)
																	SCREAMING_SNAKE_CASE				=				timesteps.to(original_samples.device		,							dtype=torch.floataa					)
												else:
																	SCREAMING_SNAKE_CASE				=				self.timesteps.to(original_samples.device					)
																	SCREAMING_SNAKE_CASE				=				timesteps.to(original_samples.device					)

												SCREAMING_SNAKE_CASE				=				[self.index_for_timestep(_UpperCamelCase		,							_UpperCamelCase					) for t in timesteps]

												SCREAMING_SNAKE_CASE				=				sigmas[step_indices].flatten()
												while len(sigma.shape					) < len(original_samples.shape					):
																	SCREAMING_SNAKE_CASE				=				sigma.unsqueeze(-1					)

												SCREAMING_SNAKE_CASE				=				original_samples + noise * sigma
												return noisy_samples





							def __len__(  self      :    Optional[int]					)    -> Any:



												'''simple docstring'''

												return self.config.num_train_timesteps





from sklearn.metrics import mean_absolute_error

import datasets


_lowerCamelCase    :      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}
}
'''

_lowerCamelCase    :      str	 =						'''\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
'''


_lowerCamelCase    :      List[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   lowercase			(   datasets.Metric   ):




							def 		__snake_case(  self      :    int					)    -> str:



												'''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 		__snake_case(  self      :    int					)    -> Tuple:



												'''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 		__snake_case(  self      :    Tuple		,							_UpperCamelCase      :    List[str]		,							_UpperCamelCase      :    List[Any]		,							_UpperCamelCase      :    str=None		,							_UpperCamelCase      :    Any="uniform_average"					)    -> int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				mean_absolute_error(_UpperCamelCase		,							_UpperCamelCase		,							sample_weight=_UpperCamelCase		,							multioutput=_UpperCamelCase					)

												return {"mae": mae_score}




