



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
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		SCREAMING_SNAKE_CASE__								=	42
		SCREAMING_SNAKE_CASE__								=	None







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__=0.999    ,			snake_case__="cosine"    ,			)	->     Union[str, Any]:



				"""simple docstring"""





				if alpha_transform_type == "cosine":

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

				elif alpha_transform_type == "exp":

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

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

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


class 	__snake_case   ( SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE ):
		@register_to_config
		def __init__(							self    ,a_ = 1000    ,a_ = "fixed_small_log"    ,a_ = True    ,a_ = 1.0    ,a_ = "epsilon"    ,a_ = "squaredcos_cap_v2"    ,):







						"""simple docstring"""
						if beta_schedule != "squaredcos_cap_v2":
										raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''						)

						lowerCAmelCase__		  =  betas_for_alpha_bar(a_						)

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

						# standard deviation of the initial noise distribution
						lowerCAmelCase__		  =  1.0

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

						lowerCAmelCase__		  =  variance_type
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_ = None						):







						"""simple docstring"""
						return sample
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_ = None						):







						"""simple docstring"""
						lowerCAmelCase__		  =  num_inference_steps
						lowerCAmelCase__		  =  (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
						lowerCAmelCase__		  =  (np.arange(0    ,a_						) * step_ratio).round()[::-1].copy().astype(np.intaa						)
						lowerCAmelCase__		  =  torch.from_numpy(a_						).to(a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_=None    ,a_=None    ,a_=None						):







						"""simple docstring"""
						if prev_timestep is None:
										lowerCAmelCase__		  =  t - 1

						lowerCAmelCase__		  =  self.alphas_cumprod[t]
						lowerCAmelCase__		  =  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
						lowerCAmelCase__		  =  1 - alpha_prod_t
						lowerCAmelCase__		  =  1 - alpha_prod_t_prev

						if prev_timestep == t - 1:
										lowerCAmelCase__		  =  self.betas[t]
						else:
										lowerCAmelCase__		  =  1 - alpha_prod_t / 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
						lowerCAmelCase__		  =  beta_prod_t_prev / beta_prod_t * beta

						if variance_type is None:
										lowerCAmelCase__		  =  self.config.variance_type

						# hacks - were probably added for training stability
						if variance_type == "fixed_small_log":
										lowerCAmelCase__		  =  torch.log(torch.clamp(a_    ,min=1e-2_0						)						)
										lowerCAmelCase__		  =  torch.exp(0.5 * variance						)
						elif variance_type == "learned_range":
										# NOTE difference with DDPM scheduler
										lowerCAmelCase__		  =  variance.log()
										lowerCAmelCase__		  =  beta.log()

										lowerCAmelCase__		  =  (predicted_variance + 1) / 2
										lowerCAmelCase__		  =  frac * max_log + (1 - frac) * min_log

						return variance
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_    ,a_    ,a_ = None    ,a_=None    ,a_ = True    ,):







						"""simple docstring"""
						lowerCAmelCase__		  =  timestep

						if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
										lowerCAmelCase__  ,   lowerCAmelCase__		  =  torch.split(a_    ,sample.shape[1]    ,dim=1						)
						else:
										lowerCAmelCase__		  =  None

						# 1. compute alphas, betas
						if prev_timestep is None:
										lowerCAmelCase__		  =  t - 1

						lowerCAmelCase__		  =  self.alphas_cumprod[t]
						lowerCAmelCase__		  =  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
						lowerCAmelCase__		  =  1 - alpha_prod_t
						lowerCAmelCase__		  =  1 - alpha_prod_t_prev

						if prev_timestep == t - 1:
										lowerCAmelCase__		  =  self.betas[t]
										lowerCAmelCase__		  =  self.alphas[t]
						else:
										lowerCAmelCase__		  =  1 - alpha_prod_t / alpha_prod_t_prev
										lowerCAmelCase__		  =  1 - beta

						# 2. compute predicted original sample from predicted noise also called
						# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
						if self.config.prediction_type == "epsilon":
										lowerCAmelCase__		  =  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
						elif self.config.prediction_type == "sample":
										lowerCAmelCase__		  =  model_output
						else:
										raise ValueError(
										    f'prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'
										    ' for the UnCLIPScheduler.'						)

						# 3. Clip "predicted x_0"
						if self.config.clip_sample:
										lowerCAmelCase__		  =  torch.clamp(
										    a_    ,-self.config.clip_sample_range    ,self.config.clip_sample_range						)

						# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
						# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
						lowerCAmelCase__		  =  (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
						lowerCAmelCase__		  =  alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

						# 5. Compute predicted previous sample µ_t
						# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
						lowerCAmelCase__		  =  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

						# 6. Add noise
						lowerCAmelCase__		  =  0
						if t > 0:
										lowerCAmelCase__		  =  randn_tensor(
										    model_output.shape    ,dtype=model_output.dtype    ,generator=a_    ,device=model_output.device						)

										lowerCAmelCase__		  =  self._get_variance(
										    a_    ,predicted_variance=a_    ,prev_timestep=a_    ,)

										if self.variance_type == "fixed_small_log":
														lowerCAmelCase__		  =  variance
										elif self.variance_type == "learned_range":
														lowerCAmelCase__		  =  (0.5 * variance).exp()
										else:
														raise ValueError(
														    f'variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'
														    ' for the UnCLIPScheduler.'						)

										lowerCAmelCase__		  =  variance * variance_noise

						lowerCAmelCase__		  =  pred_prev_sample + variance

						if not return_dict:
										return (pred_prev_sample,)

						return UnCLIPSchedulerOutput(prev_sample=a_    ,pred_original_sample=a_						)



		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_    ,a_    ,):







						"""simple docstring"""
						# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
						lowerCAmelCase__		  =  self.alphas_cumprod.to(device=original_samples.device    ,dtype=original_samples.dtype						)
						lowerCAmelCase__		  =  timesteps.to(original_samples.device						)

						lowerCAmelCase__		  =  alphas_cumprod[timesteps] ** 0.5
						lowerCAmelCase__		  =  sqrt_alpha_prod.flatten()
						while len(sqrt_alpha_prod.shape						) < len(original_samples.shape						):
										lowerCAmelCase__		  =  sqrt_alpha_prod.unsqueeze(-1						)

						lowerCAmelCase__		  =  (1 - alphas_cumprod[timesteps]) ** 0.5
						lowerCAmelCase__		  =  sqrt_one_minus_alpha_prod.flatten()
						while len(sqrt_one_minus_alpha_prod.shape						) < len(original_samples.shape						):
										lowerCAmelCase__		  =  sqrt_one_minus_alpha_prod.unsqueeze(-1						)

						lowerCAmelCase__		  =  sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
						return noisy_samples





from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

_lowerCAmelCase	:   int    =   datasets.load_iris()

_lowerCAmelCase	:   Tuple    =   np.array(data["data"])
_lowerCAmelCase	:   Any    =   np.array(data["target"])
_lowerCAmelCase	:   Union[str, Any]    =   data["target_names"]

_lowerCAmelCase,		_lowerCAmelCase,		_lowerCAmelCase,		_lowerCAmelCase	:   Optional[Any]    =   train_test_split(X, y)







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Tuple:



				"""simple docstring"""





				return np.linalg.norm(np.array(snake_case__					) - np.array(snake_case__					)					)







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__=5					)	->     Dict:



				"""simple docstring"""





				lowerCAmelCase__		  =  zip(snake_case__    ,			snake_case__					)
				# List of distances of all points from the point to be classified
				lowerCAmelCase__		  =  []
				for data_point in data:
								lowerCAmelCase__		  =  euclidean_distance(data_point[0]    ,			snake_case__					)
								distances.append((distance, data_point[1])					)
				# Choosing 'k' points with the least distances.
				lowerCAmelCase__		  =  [i[1] for i in sorted(snake_case__					)[:k]]
				# Most commonly occurring class among them
				# is the class into which the point is classified
				lowerCAmelCase__		  =  Counter(snake_case__					).most_common(1					)[0][0]
				return classes[result]


if __name__ == "__main__":
	print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))
