


# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
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_				:
				snake_case  						=42
				snake_case  						=42

				# setable values
				snake_case  						=42
				snake_case  						=42
				snake_case  						=None





				@classmethod
				def 	__UpperCamelCase						(							cls					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     )     ->   str:
											return cls(
											    common=lowercase_					,     final_alpha_cumprod=lowercase_					,     init_noise_sigma=lowercase_					,     timesteps=lowercase_					,     )







@dataclass
class 			lowercase_				(lowercase__      ):
				snake_case  						=42







class 			lowercase_				(lowercase__			,				lowercase__      ):
				snake_case  						=[e.name for e in FlaxKarrasDiffusionSchedulers]

				snake_case  						=42





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





				@register_to_config
				def __init__(							self					,     lowercase_ = 1000					,     lowercase_ = 0.00_01					,     lowercase_ = 0.02					,     lowercase_ = "linear"					,     lowercase_ = None					,     lowercase_ = True					,     lowercase_ = 0					,     lowercase_ = "epsilon"					,     lowercase_ = jnp.floataa					,     )     ->   Any:
											a__     						=dtype





				def 	__UpperCamelCase						(							self					,     lowercase_ = None)     ->   DDIMSchedulerState:
											if common is None:
																		a__     						=CommonSchedulerState.create(self)

											# At every step in ddim, we are looking into the previous alphas_cumprod
											# For the final step, there is no previous alphas_cumprod because we are already at 0
											# `set_alpha_to_one` decides whether we set this parameter simply to one or
											# whether we use the final alpha of the "non-previous" one.
											a__     						=(
											    jnp.array(1.0					,     dtype=self.dtype) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
											)

											# 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 DDIMSchedulerState.create(
											    common=lowercase_					,     final_alpha_cumprod=lowercase_					,     init_noise_sigma=lowercase_					,     timesteps=lowercase_					,     )





				def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_					,     lowercase_ = None)     ->   jnp.ndarray:
											return sample





				def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_					,     lowercase_ = ())     ->   DDIMSchedulerState:
											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					,     lowercase_) * step_ratio).round()[::-1] + self.config.steps_offset

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





				def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_					,     lowercase_)     ->   Union[str, Any]:
											a__     						=state.common.alphas_cumprod[timestep]
											a__     						=jnp.where(
											    prev_timestep >= 0					,     state.common.alphas_cumprod[prev_timestep]					,     state.final_alpha_cumprod)
											a__     						=1 - alpha_prod_t
											a__     						=1 - alpha_prod_t_prev

											a__     						=(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

											return variance





				def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_ = 0.0					,     lowercase_ = True					,     )     ->   Union[FlaxDDIMSchedulerOutput, Tuple]:
											if state.num_inference_steps is None:
																		raise ValueError(
																		    'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler')

											# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
											# Ideally, read DDIM paper in-detail understanding

											# Notation (<variable name> -> <name in paper>
											# - pred_noise_t -> e_theta(x_t, t)
											# - pred_original_sample -> f_theta(x_t, t) or x_0
											# - std_dev_t -> sigma_t
											# - eta -> η
											# - pred_sample_direction -> "direction pointing to x_t"
											# - pred_prev_sample -> "x_t-1"

											# 1. get previous step value (=t-1)
											a__     						=timestep - self.config.num_train_timesteps // state.num_inference_steps

											a__     						=state.common.alphas_cumprod
											a__     						=state.final_alpha_cumprod

											# 2. compute alphas, betas
											a__     						=alphas_cumprod[timestep]
											a__     						=jnp.where(prev_timestep >= 0					,     alphas_cumprod[prev_timestep]					,     lowercase_)

											a__     						=1 - alpha_prod_t

											# 3. compute predicted original sample from predicted noise also called
											# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											if self.config.prediction_type == "epsilon":
																		a__     						=(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
																		a__     						=model_output
											elif self.config.prediction_type == "sample":
																		a__     						=model_output
																		a__     						=(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
											elif self.config.prediction_type == "v_prediction":
																		a__     						=(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
																		a__     						=(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
											else:
																		raise ValueError(
																		    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
																		    ' `v_prediction`')

											# 4. compute variance: "sigma_t(η)" -> see formula (16)
											# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
											a__     						=self._get_variance(lowercase_					,     lowercase_					,     lowercase_)
											a__     						=eta * variance ** 0.5

											# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											a__     						=(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

											# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											a__     						=alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

											if not return_dict:
																		return (prev_sample, state)

											return FlaxDDIMSchedulerOutput(prev_sample=lowercase_					,     state=lowercase_)





				def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     )     ->   jnp.ndarray:
											return add_noise_common(state.common					,     lowercase_					,     lowercase_					,     lowercase_)





				def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     )     ->   jnp.ndarray:
											return get_velocity_common(state.common					,     lowercase_					,     lowercase_					,     lowercase_)





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





from typing import Dict, Optional, Tuple

import torch






def      _lowercase(				__a							:	torch.Tensor    ):
							a__     						=boundaries[1] - boundaries[0]
							a__     						=boundaries + step / 2
							a__     						=torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1    )]						,    dim=0    )
							return bin_centers






def      _lowercase(				__a							:	torch.Tensor						,    __a							:	torch.Tensor						,    ):
							a__     						=_calculate_bin_centers(__a    )
							return (
							    torch.sum(aligned_distance_error_probs * bin_centers						,    dim=-1    ),
							    bin_centers[-1],
							)






def      _lowercase(				__a							:	torch.Tensor						,    __a							:	int = 31						,    __a							:	int = 64						,    **__a							:	List[str]						,    ):
							a__     						=torch.linspace(0						,    __a						,    steps=(no_bins - 1)						,    device=logits.device    )

							a__     						=torch.nn.functional.softmax(__a						,    dim=-1    )
							a__						,    a__     						=_calculate_expected_aligned_error(
							    alignment_confidence_breaks=__a						,    aligned_distance_error_probs=__a						,    )

							return {
							    "aligned_confidence_probs": aligned_confidence_probs,
							    "predicted_aligned_error": predicted_aligned_error,
							    "max_predicted_aligned_error": max_predicted_aligned_error,
							}







def      _lowercase(				__a							:	torch.Tensor						,    __a							:	Optional[torch.Tensor] = None						,    __a							:	int = 31						,    __a							:	int = 64						,    __a							:	float = 1e-8						,    **__a							:	str						,    ):
							if residue_weights is None:
														a__     						=logits.new_ones(logits.shape[-2]    )

							a__     						=torch.linspace(0						,    __a						,    steps=(no_bins - 1)						,    device=logits.device    )

							a__     						=_calculate_bin_centers(__a    )
							torch.sum(__a    )
							a__     						=logits.shape[-2]
							a__     						=max(__a						,    19    )

							a__     						=1.24 * (clipped_n - 15) ** (1.0 / 3) - 1.8

							a__     						=torch.nn.functional.softmax(__a						,    dim=-1    )

							a__     						=1.0 / (1 + (bin_centers**2) / (da**2))
							a__     						=torch.sum(probs * tm_per_bin						,    dim=-1    )

							a__     						=residue_weights / (eps + residue_weights.sum())
							a__     						=torch.sum(predicted_tm_term * normed_residue_mask						,    dim=-1    )

							a__     						=per_alignment * residue_weights

							a__     						=(weighted == torch.max(__a    )).nonzero()[0]
							return per_alignment[tuple(__a    )]

