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    _UpperCamelCase      (						lowerCAmelCase__ ):


			"""simple docstring"""






			__a  : torch.FloatTensor
			__a  : torch.FloatTensor
			__a  : Optional[torch.FloatTensor]			 =						None




class    _UpperCamelCase      (						lowerCAmelCase__						,lowerCAmelCase__ ):


			"""simple docstring"""






			__a  : Tuple			 =						2





			@register_to_config
			def __init__(   self      ,   lowerCAmelCase__ = 0.02      ,   lowerCAmelCase__ = 1_00      ,   lowerCAmelCase__ = 1.007      ,   lowerCAmelCase__ = 80      ,   lowerCAmelCase__ = 0.05      ,   lowerCAmelCase__ = 50      ,   )     ->					str:

							'''simple docstring'''


							__lowercase 			=							sigma_max

							# setable values
							__lowercase 			=							None
							__lowercase 			=							None
							__lowercase 			=							None  # sigma(t_i)





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__ = None						)     ->					Optional[Any]:

							'''simple docstring'''


							return sample





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__ = None						)     ->					List[str]:

							'''simple docstring'''


							__lowercase 			=							num_inference_steps
							__lowercase 			=							np.arange(0      ,   self.num_inference_steps						)[::-1].copy()
							__lowercase 			=							torch.from_numpy(__a						).to(__a						)
							__lowercase 			=							[
							    (
							        self.config.sigma_max**2
							        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
							    )
							    for i in self.timesteps
							]
							__lowercase 			=							torch.tensor(__a      ,   dtype=torch.floataa      ,   device=__a						)





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__ = None						)     ->					Dict:

							'''simple docstring'''


							if self.config.s_min <= sigma <= self.config.s_max:
											__lowercase 			=							min(self.config.s_churn / self.num_inference_steps      ,   2**0.5 - 1						)
							else:
											__lowercase 			=							0

							# sample eps ~ N(0, S_noise^2 * I)
							__lowercase 			=							self.config.s_noise * randn_tensor(sample.shape      ,   generator=__a						).to(sample.device						)
							__lowercase 			=							sigma + gamma * sigma
							__lowercase 			=							sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

							return sample_hat, sigma_hat





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__ = True      ,   )     ->					Union[str, Any]:

							'''simple docstring'''


							__lowercase 			=							sample_hat + sigma_hat * model_output
							__lowercase 			=							(sample_hat - pred_original_sample) / sigma_hat
							__lowercase 			=							sample_hat + (sigma_prev - sigma_hat) * derivative

							if not return_dict:
											return (sample_prev, derivative)

							return KarrasVeOutput(
							    prev_sample=__a      ,   derivative=__a      ,   pred_original_sample=__a						)





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__ = True      ,   )     ->					List[str]:

							'''simple docstring'''


							__lowercase 			=							sample_prev + sigma_prev * model_output
							__lowercase 			=							(sample_prev - pred_original_sample) / sigma_prev
							__lowercase 			=							sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

							if not return_dict:
											return (sample_prev, derivative)

							return KarrasVeOutput(
							    prev_sample=__a      ,   derivative=__a      ,   pred_original_sample=__a						)





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)     ->					List[Any]:

							'''simple docstring'''


							raise NotImplementedError()



"""simple docstring"""


from __future__ import annotations


def _lowerCamelCase	(		_UpperCamelCase = 4      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			abs(_UpperCamelCase      ) or 4
			return [[1 + x + y * row_size for x in range(_UpperCamelCase      )] for y in range(_UpperCamelCase      )]


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			return reverse_row(transpose(_UpperCamelCase      )      )
			# OR.. transpose(reverse_column(matrix))


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			return reverse_row(reverse_column(_UpperCamelCase      )      )
			# OR.. reverse_column(reverse_row(matrix))


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			return reverse_column(transpose(_UpperCamelCase      )      )
			# OR.. transpose(reverse_row(matrix))


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			[list(_UpperCamelCase      ) for x in zip(*_UpperCamelCase      )]
			return matrix


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			matrix[::-1]
			return matrix


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			[x[::-1] for x in matrix]
			return matrix


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			for i in matrix:
						print(*_UpperCamelCase      )


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

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




