






# 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        __UpperCamelCase    :
	__snake_case	:CommonSchedulerState

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







	@classmethod
	def  _a   (  cls      :							Optional[Any]       ,				_lowerCAmelCase      :							Any       ,				_lowerCAmelCase      :							Tuple       ,				_lowerCAmelCase      :							Optional[int]			)		-> Any:
		"""simple docstring"""







		return cls(common=lowerCAmelCase_       ,				init_noise_sigma=lowerCAmelCase_       ,				timesteps=lowerCAmelCase_			)


@dataclass
class        __UpperCamelCase    (     __lowerCAmelCase   ):
	__snake_case	:DDPMSchedulerState


class        __UpperCamelCase    (     __lowerCAmelCase   ,			__lowerCAmelCase   ):
	__snake_case	:List[str]   =       [e.name for e in FlaxKarrasDiffusionSchedulers]

	__snake_case	:jnp.dtype







	@property
	def  _a   (  self      :							List[Any]			)		-> Any:
		"""simple docstring"""







		return True







	@register_to_config
	def __init__(  self      :							Optional[Any]       ,				_lowerCAmelCase      :							Union[str, Any] = 1000       ,				_lowerCAmelCase      :							List[Any] = 0.0_001       ,				_lowerCAmelCase      :							List[str] = 0.02       ,				_lowerCAmelCase      :							List[str] = "linear"       ,				_lowerCAmelCase      :							Optional[int] = None       ,				_lowerCAmelCase      :							List[str] = "fixed_small"       ,				_lowerCAmelCase      :							Optional[Any] = True       ,				_lowerCAmelCase      :							Any = "epsilon"       ,				_lowerCAmelCase      :							int = jnp.floataa       ,				)		-> Tuple:
		"""simple docstring"""







		__lowercase					       =  dtype







	def  _a   (  self      :							Dict       ,				_lowerCAmelCase      :							List[Any] = None			)		-> DDPMSchedulerState:
		"""simple docstring"""







		if common is None:
			__lowercase					       =  CommonSchedulerState.create(self			)

		# standard deviation of the initial noise distribution
		__lowercase					       =  jnp.array(1.0       ,				dtype=self.dtype			)

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

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







	def  _a   (  self      :							Dict       ,				_lowerCAmelCase      :							Dict       ,				_lowerCAmelCase      :							str       ,				_lowerCAmelCase      :							List[str] = None			)		-> jnp.ndarray:
		"""simple docstring"""







		return sample







	def  _a   (  self      :							List[str]       ,				_lowerCAmelCase      :							Any       ,				_lowerCAmelCase      :							Optional[Any]       ,				_lowerCAmelCase      :							str = ()			)		-> DDPMSchedulerState:
		"""simple docstring"""







		__lowercase					       =  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
		__lowercase					       =  (jnp.arange(0       ,				lowerCAmelCase_			) * step_ratio).round()[::-1]

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







	def  _a   (  self      :							Optional[Any]       ,				_lowerCAmelCase      :							Optional[Any]       ,				_lowerCAmelCase      :							Optional[int]       ,				_lowerCAmelCase      :							Any=None       ,				_lowerCAmelCase      :							str=None			)		-> Any:
		"""simple docstring"""







		__lowercase					       =  state.common.alphas_cumprod[t]
		__lowercase					       =  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
		__lowercase					       =  (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

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

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

		return variance







	def  _a   (  self      :							List[Any]       ,				_lowerCAmelCase      :							Tuple       ,				_lowerCAmelCase      :							int       ,				_lowerCAmelCase      :							Any       ,				_lowerCAmelCase      :							Any       ,				_lowerCAmelCase      :							Dict = None       ,				_lowerCAmelCase      :							List[Any] = True       ,				)		-> Union[FlaxDDPMSchedulerOutput, Tuple]:
		"""simple docstring"""







		__lowercase					       =  timestep

		if key is None:
			__lowercase					       =  jax.random.PRNGKey(0			)

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

		# 1. compute alphas, betas
		__lowercase					       =  state.common.alphas_cumprod[t]
		__lowercase					       =  jnp.where(t > 0       ,				state.common.alphas_cumprod[t - 1]       ,				jnp.array(1.0       ,				dtype=self.dtype			)			)
		__lowercase					       =  1 - alpha_prod_t
		__lowercase					       =  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":
			__lowercase					       =  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
		elif self.config.prediction_type == "sample":
			__lowercase					       =  model_output
		elif self.config.prediction_type == "v_prediction":
			__lowercase					       =  (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:
			__lowercase					       =  jnp.clip(lowerCAmelCase_       ,				-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
		__lowercase					       =  (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
		__lowercase					       =  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
		__lowercase					       =  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

		# 6. Add noise
		def random_variance():
			__lowercase					       =  jax.random.split(lowerCAmelCase_       ,				num=1			)
			__lowercase					       =  jax.random.normal(lowerCAmelCase_       ,				shape=model_output.shape       ,				dtype=self.dtype			)
			return (self._get_variance(lowerCAmelCase_       ,				lowerCAmelCase_       ,				predicted_variance=lowerCAmelCase_			) ** 0.5) * noise

		__lowercase					       =  jnp.where(t > 0       ,				random_variance()       ,				jnp.zeros(model_output.shape       ,				dtype=self.dtype			)			)

		__lowercase					       =  pred_prev_sample + variance

		if not return_dict:
			return (pred_prev_sample, state)

		return FlaxDDPMSchedulerOutput(prev_sample=lowerCAmelCase_       ,				state=lowerCAmelCase_			)







	def  _a   (  self      :							Tuple       ,				_lowerCAmelCase      :							int       ,				_lowerCAmelCase      :							Dict       ,				_lowerCAmelCase      :							Optional[int]       ,				_lowerCAmelCase      :							Any       ,				)		-> jnp.ndarray:
		"""simple docstring"""







		return add_noise_common(state.common       ,				lowerCAmelCase_       ,				lowerCAmelCase_       ,				lowerCAmelCase_			)







	def  _a   (  self      :							Any       ,				_lowerCAmelCase      :							List[str]       ,				_lowerCAmelCase      :							Optional[int]       ,				_lowerCAmelCase      :							Optional[Any]       ,				_lowerCAmelCase      :							Tuple       ,				)		-> jnp.ndarray:
		"""simple docstring"""







		return get_velocity_common(state.common       ,				lowerCAmelCase_       ,				lowerCAmelCase_       ,				lowerCAmelCase_			)







	def __len__(  self      :							Optional[Any]			)		-> Tuple:
		"""simple docstring"""







		return self.config.num_train_timesteps









import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	if (ksize % 2) == 0:
		__lowercase             =  ksize + 1
	__lowercase             =  np.zeros((ksize, ksize)				,  dtype=np.floataa					)

	# each value
	for y in range(lowerCamelCase					):
		for x in range(lowerCamelCase					):
			# distance from center
			__lowercase             =  x - ksize // 2
			__lowercase             =  y - ksize // 2

			# degree to radiant
			__lowercase             =  theta / 180 * np.pi
			__lowercase             =  np.cos(_theta					)
			__lowercase             =  np.sin(_theta					)

			# get kernel x
			__lowercase             =  cos_theta * px + sin_theta * py

			# get kernel y
			__lowercase             =  -sin_theta * px + cos_theta * py

			# fill kernel
			__lowercase             =  np.exp(
			    -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)					) * np.cos(2 * np.pi * _x / lambd + psi					)

	return gabor


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						# read original image
						__UpperCamelCase      :      List[Any]     						=						imread("""../image_data/lena.jpg""")
						# turn image in gray scale value
						__UpperCamelCase      :      Union[str, Any]     						=						cvtColor(img, COLOR_BGR2GRAY)

						# Apply multiple Kernel to detect edges
						__UpperCamelCase      :      Union[str, Any]     						=						np.zeros(gray.shape[:2])
						for theta in [0, 30, 60, 90, 120, 150]:
												__UpperCamelCase      :      Tuple     						=						gabor_filter_kernel(10, 8, theta, 10, 0, 0)
												out += filteraD(gray, CV_8UC3, kernel_aa)
						__UpperCamelCase      :      List[str]     						=						out / out.max() * 255
						__UpperCamelCase      :      List[str]     						=						out.astype(np.uinta)

						imshow("""Original""", gray)
						imshow("""Gabor filter with 20x20 mask and 6 directions""", out)

						waitKey(0)

