# 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        lowercase_   :
							_lowerCamelCase						=		42

							# setable values
							_lowerCamelCase						=		42
							_lowerCamelCase						=		42
							_lowerCamelCase						=		None



							@classmethod
							def 	UpperCamelCase	(						cls				,    lowercase_				,    lowercase_				,    lowercase_    ):
													return cls(common=lowercase_				,    init_noise_sigma=lowercase_				,    timesteps=lowercase_    )




@dataclass
class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		42




class        lowercase_   (					__snake_case						,			__snake_case		):
							_lowerCamelCase						=		[e.name for e in FlaxKarrasDiffusionSchedulers]

							_lowerCamelCase						=		42



							@property
							def 	UpperCamelCase	(						self    ):
													return True



							@register_to_config
							def __init__(						self				,    lowercase_ = 1_000				,    lowercase_ = 0.0_001				,    lowercase_ = 0.02				,    lowercase_ = "linear"				,    lowercase_ = None				,    lowercase_ = "fixed_small"				,    lowercase_ = True				,    lowercase_ = "epsilon"				,    lowercase_ = jnp.floataa				,    ):
													_snake_case						:					str    =   dtype



							def 	UpperCamelCase	(						self				,    lowercase_ = None    ):
													if common is None:
																			_snake_case						:					List[Any]    =   CommonSchedulerState.create(self    )

													# standard deviation of the initial noise distribution
													_snake_case						:					List[Any]    =   jnp.array(1.0				,    dtype=self.dtype    )

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

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



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_ = None    ):
													return sample



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_ = ()    ):
													_snake_case						:					Optional[Any]    =   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						:					Union[str, Any]    =   (jnp.arange(0				,    lowercase_    ) * step_ratio).round()[::-1]

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



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_=None				,    lowercase_=None    ):
													_snake_case						:					List[Any]    =   state.common.alphas_cumprod[t]
													_snake_case						:					List[Any]    =   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						:					List[str]    =   (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

													if variance_type is None:
																			_snake_case						:					int    =   self.config.variance_type

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

													return variance



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_ = None				,    lowercase_ = True				,    ):
													_snake_case						:					Any    =   timestep

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

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

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

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

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

													_snake_case						:					Tuple    =   pred_prev_sample + variance

													if not return_dict:
																			return (pred_prev_sample, state)

													return FlaxDDPMSchedulerOutput(prev_sample=lowercase_				,    state=lowercase_    )



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    ):
													return add_noise_common(state.common				,    lowercase_				,    lowercase_				,    lowercase_    )



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    ):
													return get_velocity_common(state.common				,    lowercase_				,    lowercase_				,    lowercase_    )



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




def  snake_case						(__lowercase						)	->	bool:

						'''simple docstring'''



						_snake_case						:					Dict    =   0
						for ch in input_str:
												_snake_case						:					int    =   ord(__lowercase						)
												_snake_case						:					List[Any]    =   pow(2 ,      __lowercase						)

												# If we already turned on bit for current character's unicode
												if bitmap >> ch_unicode & 1 == 1:
																		return False
												bitmap |= ch_bit_index_on
						return True


if __name__ == "__main__":
				import doctest

				doctest.testmod()