



'''simple docstring'''

# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)

@flax.struct.dataclass
class 	__magic_name__						:
							lowerCAmelCase       :    CommonSchedulerState

							# setable values
							lowerCAmelCase       :    jnp.ndarray
							lowerCAmelCase       :    jnp.ndarray
							lowerCAmelCase       :    Optional[int]   				=					None







							@classmethod
							def 							__lowercase			(							cls					:       Tuple	,_UpperCAmelCase					:       CommonSchedulerState	,_UpperCAmelCase					:       jnp.ndarray	,_UpperCAmelCase					:       jnp.ndarray		):
									return cls(common=_UpperCAmelCase	,init_noise_sigma=_UpperCAmelCase	,timesteps=_UpperCAmelCase		)

@dataclass
class 	__magic_name__						(    _UpperCamelCase   ):
							lowerCAmelCase       :    DDPMSchedulerState

class 	__magic_name__						(    _UpperCamelCase			,						_UpperCamelCase   ):
							lowerCAmelCase       :    Dict   				=					[e.name for e in FlaxKarrasDiffusionSchedulers]

							lowerCAmelCase       :    jnp.dtype







							@property
							def 							__lowercase			(							self					:       List[str]		):
									return True







							@register_to_config
							def __init__(							self					:       List[Any]	,_UpperCAmelCase					:       int = 1000	,_UpperCAmelCase					:       float = 0.00_01	,_UpperCAmelCase					:       float = 0.02	,_UpperCAmelCase					:       str = "linear"	,_UpperCAmelCase					:       Optional[jnp.ndarray] = None	,_UpperCAmelCase					:       str = "fixed_small"	,_UpperCAmelCase					:       bool = True	,_UpperCAmelCase					:       str = "epsilon"	,_UpperCAmelCase					:       jnp.dtype = jnp.floataa	,):
									_a     :      Any        =							dtype







							def 							__lowercase			(							self					:       Union[str, Any]	,_UpperCAmelCase					:       Optional[CommonSchedulerState] = None		):
									if common is None:
											_a     :      Optional[int]        =							CommonSchedulerState.create(self		)

									# standard deviation of the initial noise distribution
									_a     :      Union[str, Any]        =							jnp.array(1.0	,dtype=self.dtype		)

									_a     :      Dict        =							jnp.arange(0	,self.config.num_train_timesteps		).round()[::-1]

									return DDPMSchedulerState.create(
									    common=_UpperCAmelCase	,init_noise_sigma=_UpperCAmelCase	,timesteps=_UpperCAmelCase	,)







							def 							__lowercase			(							self					:       str	,_UpperCAmelCase					:       DDPMSchedulerState	,_UpperCAmelCase					:       jnp.ndarray	,_UpperCAmelCase					:       Optional[int] = None		):
									return sample







							def 							__lowercase			(							self					:       int	,_UpperCAmelCase					:       DDPMSchedulerState	,_UpperCAmelCase					:       int	,_UpperCAmelCase					:       Tuple = ()		):
									_a     :      Dict        =							self.config.num_train_timesteps // num_inference_steps
									# creates integer timesteps by multiplying by ratio
									# rounding to avoid issues when num_inference_step is power of 3
									_a     :      Optional[Any]        =							(jnp.arange(0	,_UpperCAmelCase		) * step_ratio).round()[::-1]

									return state.replace(
									    num_inference_steps=_UpperCAmelCase	,timesteps=_UpperCAmelCase	,)







							def 							__lowercase			(							self					:       Union[str, Any]	,_UpperCAmelCase					:       DDPMSchedulerState	,_UpperCAmelCase					:       Union[str, Any]	,_UpperCAmelCase					:       Tuple=None	,_UpperCAmelCase					:       Union[str, Any]=None		):
									_a     :      Union[str, Any]        =							state.common.alphas_cumprod[t]
									_a     :      Optional[int]        =							jnp.where(t > 0	,state.common.alphas_cumprod[t - 1]	,jnp.array(1.0	,dtype=self.dtype		)		)

									# 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
									_a     :      Optional[Any]        =							(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

									if variance_type is None:
											_a     :      Tuple        =							self.config.variance_type

									# hacks - were probably added for training stability
									if variance_type == "fixed_small":
											_a     :      Dict        =							jnp.clip(_UpperCAmelCase	,a_min=1E-20		)
									# for rl-diffuser https://arxiv.org/abs/2205.09991
									elif variance_type == "fixed_small_log":
											_a     :      int        =							jnp.log(jnp.clip(_UpperCAmelCase	,a_min=1E-20		)		)
									elif variance_type == "fixed_large":
											_a     :      Dict        =							state.common.betas[t]
									elif variance_type == "fixed_large_log":
											# Glide max_log
											_a     :      Tuple        =							jnp.log(state.common.betas[t]		)
									elif variance_type == "learned":
											return predicted_variance
									elif variance_type == "learned_range":
											_a     :      Optional[Any]        =							variance
											_a     :      Any        =							state.common.betas[t]
											_a     :      str        =							(predicted_variance + 1) / 2
											_a     :      Dict        =							frac * max_log + (1 - frac) * min_log

									return variance







							def 							__lowercase			(							self					:       Union[str, Any]	,_UpperCAmelCase					:       DDPMSchedulerState	,_UpperCAmelCase					:       jnp.ndarray	,_UpperCAmelCase					:       int	,_UpperCAmelCase					:       jnp.ndarray	,_UpperCAmelCase					:       Optional[jax.random.KeyArray] = None	,_UpperCAmelCase					:       bool = True	,):
									_a     :      Union[str, Any]        =							timestep

									if key is None:
											_a     :      Union[str, Any]        =							jax.random.PRNGKey(0		)

									if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
											_a       ,					_a     :      List[str]        =							jnp.split(_UpperCAmelCase	,sample.shape[1]	,axis=1		)
									else:
											_a     :      Any        =							None

									# 1. compute alphas, betas
									_a     :      List[Any]        =							state.common.alphas_cumprod[t]
									_a     :      int        =							jnp.where(t > 0	,state.common.alphas_cumprod[t - 1]	,jnp.array(1.0	,dtype=self.dtype		)		)
									_a     :      Tuple        =							1 - alpha_prod_t
									_a     :      List[Any]        =							1 - alpha_prod_t_prev

									# 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":
											_a     :      int        =							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
									elif self.config.prediction_type == "sample":
											_a     :      Any        =							model_output
									elif self.config.prediction_type == "v_prediction":
											_a     :      Union[str, Any]        =							(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
									else:
											raise ValueError(
											    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
											    ' for the FlaxDDPMScheduler.'		)

									# 3. Clip "predicted x_0"
									if self.config.clip_sample:
											_a     :      Union[str, Any]        =							jnp.clip(_UpperCAmelCase	,-1	,1		)

									# 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
									_a     :      Dict        =							(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
									_a     :      Any        =							state.common.alphas[t] ** 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
									_a     :      List[Any]        =							pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

									# 6. Add noise
									def random_variance():
											_a     :      List[Any]        =							jax.random.split(_UpperCAmelCase	,num=1		)
											_a     :      int        =							jax.random.normal(_UpperCAmelCase	,shape=model_output.shape	,dtype=self.dtype		)
											return (self._get_variance(_UpperCAmelCase	,_UpperCAmelCase	,predicted_variance=_UpperCAmelCase		) ** 0.5) * noise

									_a     :      List[str]        =							jnp.where(t > 0	,random_variance()	,jnp.zeros(model_output.shape	,dtype=self.dtype		)		)

									_a     :      Tuple        =							pred_prev_sample + variance

									if not return_dict:
											return (pred_prev_sample, state)

									return FlaxDDPMSchedulerOutput(prev_sample=_UpperCAmelCase	,state=_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Tuple	,_UpperCAmelCase					:       DDPMSchedulerState	,_UpperCAmelCase					:       jnp.ndarray	,_UpperCAmelCase					:       jnp.ndarray	,_UpperCAmelCase					:       jnp.ndarray	,):
									return add_noise_common(state.common	,_UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Any	,_UpperCAmelCase					:       DDPMSchedulerState	,_UpperCAmelCase					:       jnp.ndarray	,_UpperCAmelCase					:       jnp.ndarray	,_UpperCAmelCase					:       jnp.ndarray	,):
									return get_velocity_common(state.common	,_UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase		)







							def __len__(							self					:       List[Any]		):
									return self.config.num_train_timesteps









'''simple docstring'''

from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      np.ndarray:
		_a     :      Union[str, Any]        =							cva.getAffineTransform(lowerCAmelCase_						,			lowerCAmelCase_  )
		return cva.warpAffine(lowerCAmelCase_						,			lowerCAmelCase_						,			(rows, cols)  )


if __name__ == "__main__":
							# read original image
							__lowerCAmelCase									=   cva.imread(
							    str(Path(__file__).resolve().parent.parent / '''image_data''' / '''lena.jpg''')
							)
							# turn image in gray scale value
							__lowerCAmelCase									=   cva.cvtColor(image, cva.COLOR_BGR2GRAY)
							# get image shape
							__lowerCAmelCase			,					__lowerCAmelCase									=   gray_img.shape

							# set different points to rotate image
							__lowerCAmelCase									=   np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
							__lowerCAmelCase									=   np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
							__lowerCAmelCase									=   np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
							__lowerCAmelCase									=   np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

							# add all rotated images in a list
							__lowerCAmelCase									=   [
							    gray_img,
							    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
							    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
							    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
							]

							# plot different image rotations
							__lowerCAmelCase									=   plt.figure(1)
							__lowerCAmelCase									=   ['''Original''', '''Rotation 1''', '''Rotation 2''', '''Rotation 3''']
							for i, image in enumerate(images):
														plt.subplot(2, 2, i + 1), plt.imshow(image, '''gray''')
														plt.title(titles[i])
														plt.axis('''off''')
														plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
							plt.show()




