


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

import numpy as np
import torch

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


snake_case__     							= logging.get_logger(__name__)  # pylint: disable=invalid-name



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class     snake_case__  (      _A  ):
				__lowerCAmelCase		=      42
				__lowerCAmelCase		=      None






def  __SCREAMING_SNAKE_CASE       (				lowercase__  :str      ,						lowercase__  :str=0.9_9_9      ,						lowercase__  :List[str]="cosine"      ,						)       ->  Optional[Any]:
						if alpha_transform_type == "cosine":

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

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

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



class     snake_case__  (      _A						,       _A  ):
				__lowerCAmelCase		=      [e.name for e in KarrasDiffusionSchedulers]
				__lowerCAmelCase		=      1
				@register_to_config
				def __init__(  self     ,      _UpperCAmelCase = 1000     ,      _UpperCAmelCase = 0.0001     ,      _UpperCAmelCase = 0.02     ,      _UpperCAmelCase = "linear"     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = "epsilon"     ,      _UpperCAmelCase = "linear"     ,      _UpperCAmelCase = False     ,      _UpperCAmelCase = "linspace"     ,      _UpperCAmelCase = 0     ,      ):




										"""simple docstring"""




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

										__magic_name__				:							List[Any]				=      1.0 - self.betas
										__magic_name__				:							str				=      torch.cumprod(self.alphas     ,      dim=0      )

										__magic_name__				:							List[Any]				=      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
										__magic_name__				:							List[str]				=      np.concatenate([sigmas[::-1], [0.0]]      ).astype(np.floataa      )
										__magic_name__				:							Optional[Any]				=      torch.from_numpy(_UpperCAmelCase      )

										# setable values
										__magic_name__				:							Optional[int]				=      None
										__magic_name__				:							str				=      np.linspace(0     ,      num_train_timesteps - 1     ,      _UpperCAmelCase     ,      dtype=_UpperCAmelCase      )[::-1].copy()
										__magic_name__				:							Any				=      torch.from_numpy(_UpperCAmelCase      )
										__magic_name__				:							Union[str, Any]				=      False
										__magic_name__				:							str				=      use_karras_sigmas
				@property
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




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

										return (self.sigmas.max() ** 2 + 1) ** 0.5
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										if isinstance(_UpperCAmelCase     ,      torch.Tensor      ):
																__magic_name__				:							Optional[Any]				=      timestep.to(self.timesteps.device      )
										__magic_name__				:							int				=      (self.timesteps == timestep).nonzero().item()
										__magic_name__				:							Union[str, Any]				=      self.sigmas[step_index]

										__magic_name__				:							Tuple				=      sample / ((sigma**2 + 1) ** 0.5)

										__magic_name__				:							List[Any]				=      True
										return sample
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase = None      ):




										"""simple docstring"""




										__magic_name__				:							Any				=      num_inference_steps

										# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
										if self.config.timestep_spacing == "linspace":
																__magic_name__				:							Dict				=      np.linspace(0     ,      self.config.num_train_timesteps - 1     ,      _UpperCAmelCase     ,      dtype=_UpperCAmelCase      )[
																    ::-1
																].copy()
										elif self.config.timestep_spacing == "leading":
																__magic_name__				:							Optional[Any]				=      self.config.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
																__magic_name__				:							List[Any]				=      (np.arange(0     ,      _UpperCAmelCase      ) * step_ratio).round()[::-1].copy().astype(_UpperCAmelCase      )
																timesteps += self.config.steps_offset
										elif self.config.timestep_spacing == "trailing":
																__magic_name__				:							Tuple				=      self.config.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
																__magic_name__				:							Union[str, Any]				=      (np.arange(self.config.num_train_timesteps     ,      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'."      )

										__magic_name__				:							Union[str, Any]				=      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
										__magic_name__				:							Union[str, Any]				=      np.log(_UpperCAmelCase      )

										if self.config.interpolation_type == "linear":
																__magic_name__				:							List[str]				=      np.interp(_UpperCAmelCase     ,      np.arange(0     ,      len(_UpperCAmelCase      )      )     ,      _UpperCAmelCase      )
										elif self.config.interpolation_type == "log_linear":
																__magic_name__				:							int				=      torch.linspace(np.log(sigmas[-1]      )     ,      np.log(sigmas[0]      )     ,      num_inference_steps + 1      ).exp()
										else:
																raise ValueError(
																    F"{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either"
																    ' \'linear\' or \'log_linear\''      )

										if self.use_karras_sigmas:
																__magic_name__				:							Optional[Any]				=      self._convert_to_karras(in_sigmas=_UpperCAmelCase     ,      num_inference_steps=self.num_inference_steps      )
																__magic_name__				:							Union[str, Any]				=      np.array([self._sigma_to_t(_UpperCAmelCase     ,      _UpperCAmelCase      ) for sigma in sigmas]      )

										__magic_name__				:							Any				=      np.concatenate([sigmas, [0.0]]      ).astype(np.floataa      )
										__magic_name__				:							Dict				=      torch.from_numpy(_UpperCAmelCase      ).to(device=_UpperCAmelCase      )
										if str(_UpperCAmelCase      ).startswith('mps'      ):
																# mps does not support float64
																__magic_name__				:							List[Any]				=      torch.from_numpy(_UpperCAmelCase      ).to(_UpperCAmelCase     ,      dtype=torch.floataa      )
										else:
																__magic_name__				:							List[Any]				=      torch.from_numpy(_UpperCAmelCase      ).to(device=_UpperCAmelCase      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							Optional[Any]				=      np.log(_UpperCAmelCase      )

										# get distribution
										__magic_name__				:							Union[str, Any]				=      log_sigma - log_sigmas[:, np.newaxis]

										# get sigmas range
										__magic_name__				:							List[str]				=      np.cumsum((dists >= 0)     ,      axis=0      ).argmax(axis=0      ).clip(max=log_sigmas.shape[0] - 2      )
										__magic_name__				:							int				=      low_idx + 1

										__magic_name__				:							int				=      log_sigmas[low_idx]
										__magic_name__				:							Tuple				=      log_sigmas[high_idx]

										# interpolate sigmas
										__magic_name__				:							Optional[int]				=      (low - log_sigma) / (low - high)
										__magic_name__				:							Any				=      np.clip(_UpperCAmelCase     ,      0     ,      1      )

										# transform interpolation to time range
										__magic_name__				:							Dict				=      (1 - w) * low_idx + w * high_idx
										__magic_name__				:							int				=      t.reshape(sigma.shape      )
										return t
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							float				=      in_sigmas[-1].item()
										__magic_name__				:							float				=      in_sigmas[0].item()

										__magic_name__				:							List[Any]				=      7.0  # 7.0 is the value used in the paper
										__magic_name__				:							Dict				=      np.linspace(0     ,      1     ,      _UpperCAmelCase      )
										__magic_name__				:							str				=      sigma_min ** (1 / rho)
										__magic_name__				:							Any				=      sigma_max ** (1 / rho)
										__magic_name__				:							List[str]				=      (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
										return sigmas
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase = 0.0     ,      _UpperCAmelCase = 0.0     ,      _UpperCAmelCase = float('inf'      )     ,      _UpperCAmelCase = 1.0     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = True     ,      ):




										"""simple docstring"""




										if (
										    isinstance(_UpperCAmelCase     ,      _UpperCAmelCase      )
										    or isinstance(_UpperCAmelCase     ,      torch.IntTensor      )
										    or isinstance(_UpperCAmelCase     ,      torch.LongTensor      )
										):
																raise ValueError(
																    (
																        'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
																        ' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'
																        ' one of the `scheduler.timesteps` as a timestep.'
																    )     ,      )

										if not self.is_scale_input_called:
																logger.warning(
																    'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
																    'See `StableDiffusionPipeline` for a usage example.'      )

										if isinstance(_UpperCAmelCase     ,      torch.Tensor      ):
																__magic_name__				:							Optional[Any]				=      timestep.to(self.timesteps.device      )

										__magic_name__				:							List[Any]				=      (self.timesteps == timestep).nonzero().item()
										__magic_name__				:							str				=      self.sigmas[step_index]

										__magic_name__				:							Dict				=      min(s_churn / (len(self.sigmas      ) - 1)     ,      2**0.5 - 1      ) if s_tmin <= sigma <= s_tmax else 0.0

										__magic_name__				:							Optional[Any]				=      randn_tensor(
										    model_output.shape     ,      dtype=model_output.dtype     ,      device=model_output.device     ,      generator=_UpperCAmelCase      )

										__magic_name__				:							Optional[Any]				=      noise * s_noise
										__magic_name__				:							int				=      sigma * (gamma + 1)

										if gamma > 0:
																__magic_name__				:							int				=      sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

										# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
										# NOTE: "original_sample" should not be an expected prediction_type but is left in for
										# backwards compatibility
										if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
																__magic_name__				:							str				=      model_output
										elif self.config.prediction_type == "epsilon":
																__magic_name__				:							Union[str, Any]				=      sample - sigma_hat * model_output
										elif self.config.prediction_type == "v_prediction":
																# * c_out + input * c_skip
																__magic_name__				:							Any				=      model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
										else:
																raise ValueError(
																    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"      )

										# 2. Convert to an ODE derivative
										__magic_name__				:							Tuple				=      (sample - pred_original_sample) / sigma_hat

										__magic_name__				:							Tuple				=      self.sigmas[step_index + 1] - sigma_hat

										__magic_name__				:							List[Any]				=      sample + derivative * dt

										if not return_dict:
																return (prev_sample,)

										return EulerDiscreteSchedulerOutput(prev_sample=_UpperCAmelCase     ,      pred_original_sample=_UpperCAmelCase      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      ):




										"""simple docstring"""




										__magic_name__				:							int				=      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
																__magic_name__				:							List[Any]				=      self.timesteps.to(original_samples.device     ,      dtype=torch.floataa      )
																__magic_name__				:							Union[str, Any]				=      timesteps.to(original_samples.device     ,      dtype=torch.floataa      )
										else:
																__magic_name__				:							Optional[int]				=      self.timesteps.to(original_samples.device      )
																__magic_name__				:							Union[str, Any]				=      timesteps.to(original_samples.device      )

										__magic_name__				:							Optional[int]				=      [(schedule_timesteps == t).nonzero().item() for t in timesteps]

										__magic_name__				:							str				=      sigmas[step_indices].flatten()
										while len(sigma.shape      ) < len(original_samples.shape      ):
																__magic_name__				:							List[Any]				=      sigma.unsqueeze(-1      )

										__magic_name__				:							Tuple				=      original_samples + noise * sigma
										return noisy_samples





				def __len__(  self      ):




										"""simple docstring"""




										return self.config.num_train_timesteps






from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
snake_case__     							= load_iris()
iris.keys()


print(F"""Target names: \n {iris.target_names} """)
print(F"""\n Features: \n {iris.feature_names}""")

# Train set e Test set
snake_case__							,     snake_case__							,     snake_case__							,     snake_case__     							= train_test_split(
    iris['data'], iris['target'], random_state=4
)

# KNN

snake_case__     							= KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
snake_case__     							= [[1, 2, 1, 4], [2, 3, 4, 5]]

snake_case__     							= knn.predict(X_new)

print(
    F"""\nNew array: \n {X_new}\n\nTarget Names Prediction: \n"""
    F""" {iris['target_names'][prediction]}"""
)


