






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 							__magic_name__     (	_a				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      List[str]        =  4_2
	SCREAMING_SNAKE_CASE_					:      int        =  None






def 	_UpperCAmelCase    (  a		:	str						,			a		:	Optional[Any]=0.9_99						,			a		:	Union[str, Any]="cosine"						,			)			->			int:





						"""simple docstring"""






						if alpha_transform_type == "cosine":

												def alpha_bar_fn(a		:	List[str]							):
																		return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2							) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(a		:	Optional[Any]							):
																		return math.exp(t * -12.0							)

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

						lowercase_      :   Any        =			[]
						for i in range(UpperCAmelCase__							):
												lowercase_      :   Dict        =			i / num_diffusion_timesteps
												lowercase_      :   Optional[int]        =			(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 							__magic_name__     (	_a,						_a				):







	"""simple docstring"""



	@register_to_config
	def __init__(							self  ,      _lowercase = 1000  ,      _lowercase = "fixed_small_log"  ,      _lowercase = True  ,      _lowercase = 1.0  ,      _lowercase = "epsilon"  ,      _lowercase = "squaredcos_cap_v2"  ,      )						->  Dict:
							if beta_schedule != "squaredcos_cap_v2":
													raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''       )

							lowercase_      :   Dict        =			betas_for_alpha_bar(_A       )

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

							# standard deviation of the initial noise distribution
							lowercase_      :   Tuple        =			1.0

							# setable values
							lowercase_      :   str        =			None
							lowercase_      :   Optional[Any]        =			torch.from_numpy(np.arange(0  ,      _A       )[::-1].copy()       )

							lowercase_      :   Dict        =			variance_type
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase = None       )						->  Dict:
							return sample
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase = None       )						->  Any:
							lowercase_      :   Any        =			num_inference_steps
							lowercase_      :   str        =			(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
							lowercase_      :   Optional[int]        =			(np.arange(0  ,      _A       ) * step_ratio).round()[::-1].copy().astype(np.intaa       )
							lowercase_      :   Tuple        =			torch.from_numpy(_A       ).to(_A       )
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None       )						->  Any:
							if prev_timestep is None:
													lowercase_      :   Optional[Any]        =			t - 1

							lowercase_      :   str        =			self.alphas_cumprod[t]
							lowercase_      :   str        =			self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
							lowercase_      :   Dict        =			1 - alpha_prod_t
							lowercase_      :   Optional[Any]        =			1 - alpha_prod_t_prev

							if prev_timestep == t - 1:
													lowercase_      :   int        =			self.betas[t]
							else:
													lowercase_      :   Tuple        =			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
							lowercase_      :   str        =			beta_prod_t_prev / beta_prod_t * beta

							if variance_type is None:
													lowercase_      :   List[str]        =			self.config.variance_type

							# hacks - were probably added for training stability
							if variance_type == "fixed_small_log":
													lowercase_      :   List[str]        =			torch.log(torch.clamp(_A  ,      min=1E-2_0       )       )
													lowercase_      :   Optional[int]        =			torch.exp(0.5 * variance       )
							elif variance_type == "learned_range":
													# NOTE difference with DDPM scheduler
													lowercase_      :   Optional[int]        =			variance.log()
													lowercase_      :   Optional[int]        =			beta.log()

													lowercase_      :   Optional[Any]        =			(predicted_variance + 1) / 2
													lowercase_      :   Union[str, Any]        =			frac * max_log + (1 - frac) * min_log

							return variance
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase = None  ,      _lowercase=None  ,      _lowercase = True  ,      )						->  Dict:
							lowercase_      :   Union[str, Any]        =			timestep

							if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
													lowercase_  ,      lowercase_      :   int        =			torch.split(_A  ,      sample.shape[1]  ,      dim=1       )
							else:
													lowercase_      :   List[Any]        =			None

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

							lowercase_      :   Dict        =			self.alphas_cumprod[t]
							lowercase_      :   int        =			self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
							lowercase_      :   int        =			1 - alpha_prod_t
							lowercase_      :   List[str]        =			1 - alpha_prod_t_prev

							if prev_timestep == t - 1:
													lowercase_      :   List[str]        =			self.betas[t]
													lowercase_      :   int        =			self.alphas[t]
							else:
													lowercase_      :   List[Any]        =			1 - alpha_prod_t / alpha_prod_t_prev
													lowercase_      :   str        =			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":
													lowercase_      :   Dict        =			(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
							elif self.config.prediction_type == "sample":
													lowercase_      :   Tuple        =			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:
													lowercase_      :   int        =			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
							lowercase_      :   Union[str, Any]        =			(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
							lowercase_      :   int        =			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
							lowercase_      :   List[str]        =			pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

							# 6. Add noise
							lowercase_      :   Optional[Any]        =			0
							if t > 0:
													lowercase_      :   str        =			randn_tensor(
													    model_output.shape  ,      dtype=model_output.dtype  ,      generator=_A  ,      device=model_output.device       )

													lowercase_      :   Optional[int]        =			self._get_variance(
													    _A  ,      predicted_variance=_A  ,      prev_timestep=_A  ,      )

													if self.variance_type == "fixed_small_log":
																			lowercase_      :   List[str]        =			variance
													elif self.variance_type == "learned_range":
																			lowercase_      :   Tuple        =			(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.'       )

													lowercase_      :   int        =			variance * variance_noise

							lowercase_      :   Optional[int]        =			pred_prev_sample + variance

							if not return_dict:
													return (pred_prev_sample,)

							return UnCLIPSchedulerOutput(prev_sample=_A  ,      pred_original_sample=_A       )







	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      )						->  int:
							# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
							lowercase_      :   int        =			self.alphas_cumprod.to(device=original_samples.device  ,      dtype=original_samples.dtype       )
							lowercase_      :   Union[str, Any]        =			timesteps.to(original_samples.device       )

							lowercase_      :   str        =			alphas_cumprod[timesteps] ** 0.5
							lowercase_      :   Optional[Any]        =			sqrt_alpha_prod.flatten()
							while len(sqrt_alpha_prod.shape       ) < len(original_samples.shape       ):
													lowercase_      :   Optional[int]        =			sqrt_alpha_prod.unsqueeze(-1       )

							lowercase_      :   int        =			(1 - alphas_cumprod[timesteps]) ** 0.5
							lowercase_      :   Tuple        =			sqrt_one_minus_alpha_prod.flatten()
							while len(sqrt_one_minus_alpha_prod.shape       ) < len(original_samples.shape       ):
													lowercase_      :   List[Any]        =			sqrt_one_minus_alpha_prod.unsqueeze(-1       )

							lowercase_      :   Dict        =			sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
							return noisy_samples








'''simple docstring'''

from __future__ import annotations






def 	_UpperCAmelCase    (  a		:	int = 4							)			->			list[list[int]]:





						"""simple docstring"""
						lowercase_      :   Tuple        =			abs(a							) or 4
						return [[1 + x + y * row_size for x in range(a							)] for y in range(a							)]






def 	_UpperCAmelCase    (  a		:	list[list[int]]							)			->			list[list[int]]:





						"""simple docstring"""
						return reverse_row(transpose(a							)							)
						# OR.. transpose(reverse_column(matrix))






def 	_UpperCAmelCase    (  a		:	list[list[int]]							)			->			list[list[int]]:





						"""simple docstring"""
						return reverse_row(reverse_column(a							)							)
						# OR.. reverse_column(reverse_row(matrix))






def 	_UpperCAmelCase    (  a		:	list[list[int]]							)			->			list[list[int]]:





						"""simple docstring"""
						return reverse_column(transpose(a							)							)
						# OR.. transpose(reverse_row(matrix))






def 	_UpperCAmelCase    (  a		:	list[list[int]]							)			->			list[list[int]]:





						"""simple docstring"""
						lowercase_      :   Any        =			[list(a							) for x in zip(*a							)]
						return matrix






def 	_UpperCAmelCase    (  a		:	list[list[int]]							)			->			list[list[int]]:





						"""simple docstring"""
						lowercase_      :   List[str]        =			matrix[::-1]
						return matrix






def 	_UpperCAmelCase    (  a		:	list[list[int]]							)			->			list[list[int]]:





						"""simple docstring"""
						lowercase_      :   str        =			[x[::-1] for x in matrix]
						return matrix






def 	_UpperCAmelCase    (  a		:	list[list[int]]							)			->			None:





						"""simple docstring"""
						for i in matrix:
												print(*a							)


if __name__ == "__main__":
						A:      Dict  				=				make_matrix()
						print("\norigin:\n")
						print_matrix(matrix)
						print("\nrotate 90 counterclockwise:\n")
						print_matrix(rotate_aa(matrix))

						A:      List[Any]  				=				make_matrix()
						print("\norigin:\n")
						print_matrix(matrix)
						print("\nrotate 180:\n")
						print_matrix(rotate_aaa(matrix))

						A:      List[str]  				=				make_matrix()
						print("\norigin:\n")
						print_matrix(matrix)
						print("\nrotate 270 counterclockwise:\n")
						print_matrix(rotate_aaa(matrix))
