"""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      UpperCAmelCase_	:
      lowerCamelCase__		:    CommonSchedulerState

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


      @classmethod
      def 		_UpperCAmelCase       (   cls		,			a		,			a		,			a							)       ->					Optional[int]:
             return cls(common=a		,			init_noise_sigma=a		,			timesteps=a							)





@dataclass
class      UpperCAmelCase_	(    _a):
      lowerCamelCase__		:    DDPMSchedulerState





class      UpperCAmelCase_	(    _a ,							_a):
      lowerCamelCase__		:    Tuple   =  [e.name for e in FlaxKarrasDiffusionSchedulers]

      lowerCamelCase__		:    jnp.dtype


      @property
      def 		_UpperCAmelCase       (   self							)       ->					str:
             return True


      @register_to_config
      def __init__(   self		,			a = 1_0_0_0		,			a = 0.0_001		,			a = 0.02		,			a = "linear"		,			a = None		,			a = "fixed_small"		,			a = True		,			a = "epsilon"		,			a = jnp.floataa		,			)       ->					str:
             lowercase__			:		List[Any]       =	dtype


      def 		_UpperCAmelCase       (   self		,			a = None							)       ->					DDPMSchedulerState:
             if common is None:
                    lowercase__			:		str       =	CommonSchedulerState.create(self							)

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

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

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


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a = None							)       ->					jnp.ndarray:
             return sample


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a = ()							)       ->					DDPMSchedulerState:
             lowercase__			:		Tuple       =	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__			:		Optional[int]       =	(jnp.arange(0		,			a							) * step_ratio).round()[::-1]

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


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a=None		,			a=None							)       ->					Optional[int]:
             lowercase__			:		Any       =	state.common.alphas_cumprod[t]
             lowercase__			:		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
             lowercase__			:		List[str]       =	(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

             if variance_type is None:
                    lowercase__			:		Optional[int]       =	self.config.variance_type

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

             return variance


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a		,			a		,			a = None		,			a = True		,			)       ->					Union[FlaxDDPMSchedulerOutput, Tuple]:
             lowercase__			:		Dict       =	timestep

             if key is None:
                    lowercase__			:		Optional[int]       =	jax.random.PRNGKey(0							)

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

             # 1. compute alphas, betas
             lowercase__			:		int       =	state.common.alphas_cumprod[t]
             lowercase__			:		List[Any]       =	jnp.where(t > 0		,			state.common.alphas_cumprod[t - 1]		,			jnp.array(1.0		,			dtype=self.dtype							)							)
             lowercase__			:		Tuple       =	1 - alpha_prod_t
             lowercase__			:		Optional[int]       =	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__			:		Dict       =	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
             elif self.config.prediction_type == "sample":
                    lowercase__			:		List[str]       =	model_output
             elif self.config.prediction_type == "v_prediction":
                    lowercase__			:		Tuple       =	(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__			:		Any       =	jnp.clip(a		,			-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__			:		List[str]       =	(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
             lowercase__			:		Optional[int]       =	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__			:		List[Any]       =	pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

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

             lowercase__			:		Optional[Any]       =	pred_prev_sample + variance

             if not return_dict:
                    return (pred_prev_sample, state)

             return FlaxDDPMSchedulerOutput(prev_sample=a		,			state=a							)


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a		,			a		,			)       ->					jnp.ndarray:
             return add_noise_common(state.common		,			a		,			a		,			a							)


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a		,			a		,			)       ->					jnp.ndarray:
             return get_velocity_common(state.common		,			a		,			a		,			a							)


      def __len__(   self							)       ->					Optional[Any]:
             return self.config.num_train_timesteps




'''simple docstring'''
import copy
import re







class 							A__    :
  A__		 =				'hp'
  A__		 =				{}
  A__		 =				None





  @classmethod
  def 						A  (		cls      :	Optional[Any]							,					_a      :	Optional[Any]							,					_a      :	Any )	->    Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =prefix
       _SCREAMING_SNAKE_CASE							    =defaults
       cls.build_naming_info()





  @staticmethod
  def 						A  (		_a      :	Optional[Any]							,					_a      :	List[Any] )	->    Any:





       '''simple docstring'''






       if len(_a ) == 0:
            return ""
       _SCREAMING_SNAKE_CASE							    =None
       if any(char.isdigit() for char in word ):
            raise Exception(f"Parameters should not contain numbers: '{word}' contains a number" )
       if word in info["short_word"]:
            return info["short_word"][word]
       for prefix_len in range(1							,					len(_a ) + 1 ):
            _SCREAMING_SNAKE_CASE							    =word[:prefix_len]
            if prefix in info["reverse_short_word"]:
                 continue
            else:
                 _SCREAMING_SNAKE_CASE							    =prefix
                 break

       if short_word is None:
            # Paranoid fallback
            def int_to_alphabetic(_a      :	str ):
                 _SCREAMING_SNAKE_CASE							    =''
                 while integer != 0:
                      _SCREAMING_SNAKE_CASE							    =chr(ord('A' ) + integer % 10 ) + s
                      integer //= 10
                 return s

            _SCREAMING_SNAKE_CASE							    =0
            while True:
                 _SCREAMING_SNAKE_CASE							    =word + '#' + int_to_alphabetic(_a )
                 if sword in info["reverse_short_word"]:
                      continue
                 else:
                      _SCREAMING_SNAKE_CASE							    =sword
                      break

       _SCREAMING_SNAKE_CASE							    =short_word
       _SCREAMING_SNAKE_CASE							    =word
       return short_word





  @staticmethod
  def 						A  (		_a      :	Optional[Any]							,					_a      :	int )	->    Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =param_name.split('_' )

       _SCREAMING_SNAKE_CASE							    =[TrialShortNamer.shortname_for_word(_a							,					_a ) for word in words]

       # We try to create a separatorless short name, but if there is a collision we have to fallback
       # to a separated short name
       _SCREAMING_SNAKE_CASE							    =['', '_']

       for separator in separators:
            _SCREAMING_SNAKE_CASE							    =separator.join(_a )
            if shortname not in info["reverse_short_param"]:
                 _SCREAMING_SNAKE_CASE							    =shortname
                 _SCREAMING_SNAKE_CASE							    =param_name
                 return shortname

       return param_name





  @staticmethod
  def 						A  (		_a      :	Dict							,					_a      :	int )	->    Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =TrialShortNamer.shortname_for_key(_a							,					_a )
       _SCREAMING_SNAKE_CASE							    =short_name
       _SCREAMING_SNAKE_CASE							    =param_name





  @classmethod
  def 						A  (		cls      :	Optional[int] )	->    Tuple:





       '''simple docstring'''






       if cls.NAMING_INFO is not None:
            return

       _SCREAMING_SNAKE_CASE							    ={
           'short_word': {},
           'reverse_short_word': {},
           'short_param': {},
           'reverse_short_param': {},
       }

       _SCREAMING_SNAKE_CASE							    =list(cls.DEFAULTS.keys() )

       for k in field_keys:
            cls.add_new_param_name(_a							,					_a )

       _SCREAMING_SNAKE_CASE							    =info





  @classmethod
  def 						A  (		cls      :	List[Any]							,					_a      :	int )	->    int:





       '''simple docstring'''






       cls.build_naming_info()
       assert cls.PREFIX is not None
       _SCREAMING_SNAKE_CASE							    =[copy.copy(cls.PREFIX )]

       for k, v in params.items():
            if k not in cls.DEFAULTS:
                 raise Exception(f"You should provide a default value for the param name {k} with value {v}" )
            if v == cls.DEFAULTS[k]:
                 # The default value is not added to the name
                 continue

            _SCREAMING_SNAKE_CASE							    =cls.NAMING_INFO['short_param'][k]

            if isinstance(_a							,					_a ):
                 _SCREAMING_SNAKE_CASE							    =1 if v else 0

            _SCREAMING_SNAKE_CASE							    ='' if isinstance(_a							,					(int, float) ) else '-'
            _SCREAMING_SNAKE_CASE							    =f"{key}{sep}{v}"
            name.append(_a )

       return "_".join(_a )





  @classmethod
  def 						A  (		cls      :	Optional[Any]							,					_a      :	List[Any] )	->    Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =repr[len(cls.PREFIX ) + 1 :]
       if repr == "":
            _SCREAMING_SNAKE_CASE							    =[]
       else:
            _SCREAMING_SNAKE_CASE							    =repr.split('_' )

       _SCREAMING_SNAKE_CASE							    ={}

       for value in values:
            if "-" in value:
                 _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =value.split('-' )
            else:
                 _SCREAMING_SNAKE_CASE							    =re.sub('[0-9.]'							,					''							,					_a )
                 _SCREAMING_SNAKE_CASE							    =float(re.sub('[^0-9.]'							,					''							,					_a ) )

            _SCREAMING_SNAKE_CASE							    =cls.NAMING_INFO['reverse_short_param'][p_k]

            _SCREAMING_SNAKE_CASE							    =p_v

       for k in cls.DEFAULTS:
            if k not in parameters:
                 _SCREAMING_SNAKE_CASE							    =cls.DEFAULTS[k]

       return parameters
