




# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left


@flax.struct.dataclass
class        UpperCAmelCase__				:



     '''simple docstring'''
     # setable values
     _SCREAMING_SNAKE_CASE      :					Optional[jnp.ndarray]     					=    None
     _SCREAMING_SNAKE_CASE      :					Optional[jnp.ndarray]     					=    None
     _SCREAMING_SNAKE_CASE      :					Optional[jnp.ndarray]     					=    None
     @classmethod
     def        lowerCAmelCase__  (				cls	):
            return cls()



@dataclass
class        UpperCAmelCase__				(							UpperCAmelCase__				):



     '''simple docstring'''
     _SCREAMING_SNAKE_CASE      :					ScoreSdeVeSchedulerState
     _SCREAMING_SNAKE_CASE      :					jnp.ndarray
     _SCREAMING_SNAKE_CASE      :					Optional[jnp.ndarray]     					=    None



class        UpperCAmelCase__				(							UpperCAmelCase__      ,		UpperCAmelCase__				):



     '''simple docstring'''
     @property
     def        lowerCAmelCase__  (				self	):
            return True
     @register_to_config
     def __init__(				self ,    _lowerCAmelCase = 2_000 ,    _lowerCAmelCase = 0.15 ,    _lowerCAmelCase = 0.01 ,    _lowerCAmelCase = 13_48.0 ,    _lowerCAmelCase = 1E-5 ,    _lowerCAmelCase = 1 ,    ):
            pass
     def        lowerCAmelCase__  (				self	):
            a			   =ScoreSdeVeSchedulerState.create()
            return self.set_sigmas(
                _lowerCAmelCase ,    self.config.num_train_timesteps ,    self.config.sigma_min ,    self.config.sigma_max ,    self.config.sampling_eps ,    )
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase = () ,    _lowerCAmelCase = None	):
            a			   =sampling_eps if sampling_eps is not None else self.config.sampling_eps

            a			   =jnp.linspace(1 ,    _lowerCAmelCase ,    _lowerCAmelCase	)
            return state.replace(timesteps=_lowerCAmelCase	)
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase = None ,    _lowerCAmelCase = None ,    _lowerCAmelCase = None ,    ):
            a			   =sigma_min if sigma_min is not None else self.config.sigma_min
            a			   =sigma_max if sigma_max is not None else self.config.sigma_max
            a			   =sampling_eps if sampling_eps is not None else self.config.sampling_eps
            if state.timesteps is None:
                   a			   =self.set_timesteps(_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase	)

            a			   =jnp.exp(jnp.linspace(jnp.log(_lowerCAmelCase	) ,    jnp.log(_lowerCAmelCase	) ,    _lowerCAmelCase	)	)
            a			   =jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]	)

            return state.replace(discrete_sigmas=_lowerCAmelCase ,    sigmas=_lowerCAmelCase	)
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase	):
            return jnp.where(timesteps == 0 ,    jnp.zeros_like(_lowerCAmelCase	) ,    state.discrete_sigmas[timesteps - 1]	)
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase = True ,    ):
            if state.timesteps is None:
                   raise ValueError(
                       """`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""	)

            a			   =timestep * jnp.ones(
                sample.shape[0] ,    )
            a			   =(timestep * (len(state.timesteps	) - 1)).long()

            a			   =state.discrete_sigmas[timesteps]
            a			   =self.get_adjacent_sigma(_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase	)
            a			   =jnp.zeros_like(_lowerCAmelCase	)
            a			   =(sigma**2 - adjacent_sigma**2) ** 0.5

            # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
            # also equation 47 shows the analog from SDE models to ancestral sampling methods
            a			   =diffusion.flatten()
            a			   =broadcast_to_shape_from_left(_lowerCAmelCase ,    sample.shape	)
            a			   =drift - diffusion**2 * model_output

            #  equation 6: sample noise for the diffusion term of
            a			   =random.split(_lowerCAmelCase ,    num=1	)
            a			   =random.normal(key=_lowerCAmelCase ,    shape=sample.shape	)
            a			   =sample - drift  # subtract because `dt` is a small negative timestep
            # TODO is the variable diffusion the correct scaling term for the noise?
            a			   =prev_sample_mean + diffusion * noise  # add impact of diffusion field g

            if not return_dict:
                   return (prev_sample, prev_sample_mean, state)

            return FlaxSdeVeOutput(prev_sample=_lowerCAmelCase ,    prev_sample_mean=_lowerCAmelCase ,    state=_lowerCAmelCase	)
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase = True ,    ):
            if state.timesteps is None:
                   raise ValueError(
                       """`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""	)

            # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
            # sample noise for correction
            a			   =random.split(_lowerCAmelCase ,    num=1	)
            a			   =random.normal(key=_lowerCAmelCase ,    shape=sample.shape	)

            # compute step size from the model_output, the noise, and the snr
            a			   =jnp.linalg.norm(_lowerCAmelCase	)
            a			   =jnp.linalg.norm(_lowerCAmelCase	)
            a			   =(self.config.snr * noise_norm / grad_norm) ** 2 * 2
            a			   =step_size * jnp.ones(sample.shape[0]	)

            # compute corrected sample: model_output term and noise term
            a			   =step_size.flatten()
            a			   =broadcast_to_shape_from_left(_lowerCAmelCase ,    sample.shape	)
            a			   =sample + step_size * model_output
            a			   =prev_sample_mean + ((step_size * 2) ** 0.5) * noise

            if not return_dict:
                   return (prev_sample, state)

            return FlaxSdeVeOutput(prev_sample=_lowerCAmelCase ,    state=_lowerCAmelCase	)






     def __len__(				self	):
            return self.config.num_train_timesteps






import math
import os

import cva
import numpy as np

_lowerCamelCase							=      255.0





def 							lowerCamelCase    (							UpperCAmelCase_	:      float   ,       UpperCAmelCase_	:      float      )->			float:

       """simple docstring"""




       a			   =np.mean((original - contrast) ** 2      )
       if mse == 0:
              return 100

       return 20 * math.logaa(PIXEL_MAX / math.sqrt(UpperCAmelCase_      )      )





def 							lowerCamelCase    (							)->			None:

       """simple docstring"""




       a			   =os.path.dirname(os.path.realpath(UpperCAmelCase_      )      )
       # Loading images (original image and compressed image)
       a			   =cva.imread(os.path.join(UpperCAmelCase_   ,       """image_data/original_image.png"""      )      )
       a			   =cva.imread(os.path.join(UpperCAmelCase_   ,       """image_data/compressed_image.png"""      )   ,       1      )

       a			   =cva.imread(os.path.join(UpperCAmelCase_   ,       """image_data/PSNR-example-base.png"""      )      )
       a			   =cva.imread(
           os.path.join(UpperCAmelCase_   ,       """image_data/PSNR-example-comp-10.jpg"""      )   ,       1      )

       # Value expected: 29.73dB
       print("""-- First Test --"""      )
       print(F'''PSNR value is {peak_signal_to_noise_ratio(UpperCAmelCase_   ,       UpperCAmelCase_      )} dB'''      )

       # # Value expected: 31.53dB (Wikipedia Example)
       print("""\n-- Second Test --"""      )
       print(F'''PSNR value is {peak_signal_to_noise_ratio(UpperCAmelCase_   ,       UpperCAmelCase_      )} dB'''      )


if __name__ == "__main__":
    main()
