

# 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 					a	:
						_lowercase         =      4_2

						# setable values
						_lowercase         =      4_2
						_lowercase         =      4_2
						_lowercase         =      None


						@classmethod
						def 							_UpperCAmelCase					(							cls    ,       A_    ,       A_    ,       A_						):







										'''simple docstring'''


										return cls(common=A_    ,       init_noise_sigma=A_    ,       timesteps=A_						)







@dataclass
class 					a	(						__A					):
						_lowercase         =      4_2







class 					a	(						__A				,			__A					):
						_lowercase         =      [e.name for e in FlaxKarrasDiffusionSchedulers]

						_lowercase         =      4_2


						@property
						def 							_UpperCAmelCase					(							self						):







										'''simple docstring'''


										return True


						@register_to_config
						def __init__(							self    ,       A_ = 1000    ,       A_ = 0.00_01    ,       A_ = 0.02    ,       A_ = "linear"    ,       A_ = None    ,       A_ = "fixed_small"    ,       A_ = True    ,       A_ = "epsilon"    ,       A_ = jnp.floataa    ,       ):







										'''simple docstring'''


										_UpperCAmelCase							:		Tuple					=     dtype


						def 							_UpperCAmelCase					(							self    ,       A_ = None						):







										'''simple docstring'''


										if common is None:
														_UpperCAmelCase							:		Optional[int]					=     CommonSchedulerState.create(self						)

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

										_UpperCAmelCase							:		List[Any]					=     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						):







										'''simple docstring'''


										return sample


						def 							_UpperCAmelCase					(							self    ,       A_    ,       A_    ,       A_ = ()						):







										'''simple docstring'''


										_UpperCAmelCase							:		Optional[int]					=     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
										_UpperCAmelCase							:		Dict					=     (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						):







										'''simple docstring'''


										_UpperCAmelCase							:		Union[str, Any]					=     state.common.alphas_cumprod[t]
										_UpperCAmelCase							:		Union[str, 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
										_UpperCAmelCase							:		List[str]					=     (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

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

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

										return variance


						def 							_UpperCAmelCase					(							self    ,       A_    ,       A_    ,       A_    ,       A_    ,       A_ = None    ,       A_ = True    ,       ):







										'''simple docstring'''


										_UpperCAmelCase							:		Optional[Any]					=     timestep

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

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

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

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

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

										_UpperCAmelCase							:		List[str]					=     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_    ,       ):







										'''simple docstring'''


										return add_noise_common(state.common    ,       A_    ,       A_    ,       A_						)


						def 							_UpperCAmelCase					(							self    ,       A_    ,       A_    ,       A_    ,       A_    ,       ):







										'''simple docstring'''


										return get_velocity_common(state.common    ,       A_    ,       A_    ,       A_						)





						def __len__(							self						):







										'''simple docstring'''


										return self.config.num_train_timesteps





from __future__ import annotations

def 			lowerCamelCase__			(       _a):
	if len(_a) == 0:
		return []
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Tuple					  =							min(_a), max(_a)
	SCREAMING_SNAKE_CASE :   Dict					  =							int(max_value - min_value) + 1
	SCREAMING_SNAKE_CASE :   list[list]					  =							[[] for _ in range(_a)]

	for i in my_list:
		buckets[int(i - min_value)].append(_a)

	return [v for bucket in buckets for v in sorted(_a)]


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

			testmod()
			assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
			assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]