


# 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 lowerCAmelCase_		:

 '''simple docstring'''

 __snake_case				=						42

 # setable values
 __snake_case				=						42
 __snake_case				=						42
 __snake_case				=						None



 @classmethod
 def 		UpperCamelCase__  (			cls	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       return cls(common=_UpperCAmelCase	,   init_noise_sigma=_UpperCAmelCase	,   timesteps=_UpperCAmelCase      )







@dataclass
class lowerCAmelCase_		(				lowerCamelCase__ ):

 '''simple docstring'''

 __snake_case				=						42







class lowerCAmelCase_		(				lowerCamelCase__      ,		lowerCamelCase__ ):

 '''simple docstring'''

 __snake_case				=						[e.name for e in FlaxKarrasDiffusionSchedulers]

 __snake_case				=						42



 @property
 def 		UpperCamelCase__  (			self      ):
       return True



 @register_to_config
 def __init__(			self	,   _UpperCAmelCase = 10_00	,   _UpperCAmelCase = 0.0_001	,   _UpperCAmelCase = 0.02	,   _UpperCAmelCase = "linear"	,   _UpperCAmelCase = None	,   _UpperCAmelCase = "fixed_small"	,   _UpperCAmelCase = True	,   _UpperCAmelCase = "epsilon"	,   _UpperCAmelCase = jnp.floataa	,   ):
       snake_case_ =   dtype



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase = None      ):
       if common is None:
             snake_case_ =   CommonSchedulerState.create(self      )

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

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

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



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase = None      ):
       return sample



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase = ()      ):
       snake_case_ =   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
       snake_case_ =   (jnp.arange(0	,   _UpperCAmelCase      ) * step_ratio).round()[::-1]

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



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase=None	,   _UpperCAmelCase=None      ):
       snake_case_ =   state.common.alphas_cumprod[t]
       snake_case_ =   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
       snake_case_ =   (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

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

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

       return variance



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase = None	,   _UpperCAmelCase = True	,   ):
       snake_case_ =   timestep

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

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

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

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

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

       snake_case_ =   pred_prev_sample + variance

       if not return_dict:
             return (pred_prev_sample, state)

       return FlaxDDPMSchedulerOutput(prev_sample=_UpperCAmelCase	,   state=_UpperCAmelCase      )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   ):
       return add_noise_common(state.common	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase      )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   ):
       return get_velocity_common(state.common	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase      )





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



def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						int:




      """simple docstring"""

      assert column_title.isupper()
      snake_case_ =   0
      snake_case_ =   len(SCREAMING_SNAKE_CASE      ) - 1
      snake_case_ =   0

      while index >= 0:
            snake_case_ =   (ord(column_title[index]      ) - 64) * pow(26      ,      SCREAMING_SNAKE_CASE      )
            answer += value
            power += 1
            index -= 1

      return answer


if __name__ == "__main__":
   from doctest import testmod

   testmod()