
"""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 					lowerCAmelCase_					:



						'''simple docstring'''


						_lowerCamelCase:     CommonSchedulerState

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


						@classmethod
						def     _SCREAMING_SNAKE_CASE   (			cls  :   str	,A_  :   CommonSchedulerState	,A_  :   jnp.ndarray	,A_  :   jnp.ndarray   )  -> Union[str, Any]:
							return cls(common=A_	,init_noise_sigma=A_	,timesteps=A_   )







@dataclass
class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     DDPMSchedulerState







class 					lowerCAmelCase_					(   _lowercase		,       _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     Tuple   =     [e.name for e in FlaxKarrasDiffusionSchedulers]

						_lowerCamelCase:     jnp.dtype


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple   )  -> Union[str, Any]:
							return True


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


						def     _SCREAMING_SNAKE_CASE   (			self  :   int	,A_  :   Optional[CommonSchedulerState] = None   )  -> DDPMSchedulerState:
							if common is None:
								A     = CommonSchedulerState.create(self   )

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

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

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


						def     _SCREAMING_SNAKE_CASE   (			self  :   int	,A_  :   DDPMSchedulerState	,A_  :   jnp.ndarray	,A_  :   Optional[int] = None   )  -> jnp.ndarray:
							return sample


						def     _SCREAMING_SNAKE_CASE   (			self  :   int	,A_  :   DDPMSchedulerState	,A_  :   int	,A_  :   Tuple = ()   )  -> DDPMSchedulerState:
							A     = 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     = (jnp.arange(0	,A_   ) * step_ratio).round()[::-1]

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


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]	,A_  :   DDPMSchedulerState	,A_  :   Optional[Any]	,A_  :   str=None	,A_  :   Optional[Any]=None   )  -> Optional[int]:
							A     = state.common.alphas_cumprod[t]
							A     = 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     = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

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

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

							return variance


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]	,A_  :   DDPMSchedulerState	,A_  :   jnp.ndarray	,A_  :   int	,A_  :   jnp.ndarray	,A_  :   Optional[jax.random.KeyArray] = None	,A_  :   bool = True	,)  -> Union[FlaxDDPMSchedulerOutput, Tuple]:
							A     = timestep

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

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

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

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

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

							A     = pred_prev_sample + variance

							if not return_dict:
								return (pred_prev_sample, state)

							return FlaxDDPMSchedulerOutput(prev_sample=A_	,state=A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Any	,A_  :   DDPMSchedulerState	,A_  :   jnp.ndarray	,A_  :   jnp.ndarray	,A_  :   jnp.ndarray	,)  -> jnp.ndarray:
							return add_noise_common(state.common	,A_	,A_	,A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]	,A_  :   DDPMSchedulerState	,A_  :   jnp.ndarray	,A_  :   jnp.ndarray	,A_  :   jnp.ndarray	,)  -> jnp.ndarray:
							return get_velocity_common(state.common	,A_	,A_	,A_   )




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

"""simple docstring"""


import copy
import re







class 					lowerCAmelCase_					:



						'''simple docstring'''


						_lowerCamelCase:     str   =     '''hp'''
						_lowerCamelCase:     List[Any]   =     {}
						_lowerCamelCase:     List[Any]   =     None


						@classmethod
						def     _SCREAMING_SNAKE_CASE   (			cls  :   List[Any]	,A_  :   List[str]	,A_  :   Optional[Any]   )  -> Tuple:
							A     = prefix
							A     = defaults
							cls.build_naming_info()


						@staticmethod
						def     _SCREAMING_SNAKE_CASE   (			A_  :   Any	,A_  :   List[Any]   )  -> int:
							if len(A_   ) == 0:
								return ""
							A     = 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   ):
								A     = word[:prefix_len]
								if prefix in info["reverse_short_word"]:
									continue
								else:
									A     = prefix
									break

							if short_word is None:
								# Paranoid fallback
								def int_to_alphabetic(A_  :   Optional[Any]   ):
									A     = ''
									while integer != 0:
										A     = chr(ord('A'   ) + integer % 10   ) + s
										integer //= 10
									return s

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

							A     = short_word
							A     = word
							return short_word


						@staticmethod
						def     _SCREAMING_SNAKE_CASE   (			A_  :   List[Any]	,A_  :   Union[str, Any]   )  -> Union[str, Any]:
							A     = param_name.split('_'   )

							A     = [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
							A     = ['', '_']

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

							return param_name


						@staticmethod
						def     _SCREAMING_SNAKE_CASE   (			A_  :   List[Any]	,A_  :   Any   )  -> Tuple:
							A     = TrialShortNamer.shortname_for_key(A_	,A_   )
							A     = short_name
							A     = param_name


						@classmethod
						def     _SCREAMING_SNAKE_CASE   (			cls  :   Dict   )  -> List[Any]:
							if cls.NAMING_INFO is not None:
								return

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

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

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

							A     = info


						@classmethod
						def     _SCREAMING_SNAKE_CASE   (			cls  :   List[Any]	,A_  :   Union[str, Any]   )  -> Union[str, Any]:
							cls.build_naming_info()
							assert cls.PREFIX is not None
							A     = [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

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

								if isinstance(A_	,A_   ):
									A     = 1 if v else 0

								A     = '' if isinstance(A_	,(int, float)   ) else '-'
								A     = F'{key}{sep}{v}'
								name.append(A_   )

							return "_".join(A_   )




						@classmethod
						def     _SCREAMING_SNAKE_CASE   (			cls  :   List[str]	,A_  :   Any   )  -> int:
							A     = repr[len(cls.PREFIX   ) + 1 :]
							if repr == "":
								A     = []
							else:
								A     = repr.split('_'   )

							A     = {}

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

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

								A     = p_v

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

							return parameters