





'''simple docstring'''



# 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				:







	'''simple docstring'''

	a			: CommonSchedulerState
	a			: jnp.ndarray

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


	@classmethod
	def   _UpperCAmelCase   (cls   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase   ,)							->							Dict:







				'''simple docstring'''





				return cls(
				    common=_UpperCamelCase   ,final_alpha_cumprod=_UpperCamelCase   ,init_noise_sigma=_UpperCamelCase   ,timesteps=_UpperCamelCase   ,)






@dataclass
class 							__lowercase				(					__lowercase  ):







	'''simple docstring'''

	a			: DDIMSchedulerState






class 							__lowercase				(					__lowercase       ,					__lowercase  ):







	'''simple docstring'''

	a			: Optional[int]      =				[e.name for e in FlaxKarrasDiffusionSchedulers]

	a			: jnp.dtype


	@property
	def   _UpperCAmelCase   (self       )							->							List[Any]:







				'''simple docstring'''





				return True


	@register_to_config
	def __init__(self   ,_lowerCamelCase = 1000   ,_lowerCamelCase = 0.0_0_0_1   ,_lowerCamelCase = 0.0_2   ,_lowerCamelCase = "linear"   ,_lowerCamelCase = None   ,_lowerCamelCase = True   ,_lowerCamelCase = 0   ,_lowerCamelCase = "epsilon"   ,_lowerCamelCase = jnp.floataa   ,)							->							str:







				'''simple docstring'''





				__lowercase   =							dtype


	def   _UpperCAmelCase   (self   ,_lowerCamelCase = None       )							->							List[Any]:







				'''simple docstring'''





				if common is None:
							__lowercase   =							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.
				__lowercase   =							(
				    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
				__lowercase   =							jnp.array(1.0   ,dtype=self.dtype       )

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

				return DDIMSchedulerState.create(
				    common=_UpperCamelCase   ,final_alpha_cumprod=_UpperCamelCase   ,init_noise_sigma=_UpperCamelCase   ,timesteps=_UpperCamelCase   ,)


	def   _UpperCAmelCase   (self   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase = None       )							->							Any:







				'''simple docstring'''





				return sample


	def   _UpperCAmelCase   (self   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase = ()       )							->							List[Any]:







				'''simple docstring'''





				__lowercase   =							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   =							(jnp.arange(0   ,_UpperCamelCase       ) * step_ratio).round()[::-1] + self.config.steps_offset

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


	def   _UpperCAmelCase   (self   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase       )							->							str:







				'''simple docstring'''





				__lowercase   =							state.common.alphas_cumprod[timestep]
				__lowercase   =							jnp.where(
				    prev_timestep >= 0   ,state.common.alphas_cumprod[prev_timestep]   ,state.final_alpha_cumprod       )
				__lowercase   =							1 - alpha_prod_t
				__lowercase   =							1 - alpha_prod_t_prev

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

				return variance


	def   _UpperCAmelCase   (self   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase = 0.0   ,_lowerCamelCase = True   ,)							->							str:







				'''simple docstring'''





				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)
				__lowercase   =							timestep - self.config.num_train_timesteps // state.num_inference_steps

				__lowercase   =							state.common.alphas_cumprod
				__lowercase   =							state.final_alpha_cumprod

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

				__lowercase   =							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":
							__lowercase   =							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
							__lowercase   =							model_output
				elif self.config.prediction_type == "sample":
							__lowercase   =							model_output
							__lowercase   =							(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
				elif self.config.prediction_type == "v_prediction":
							__lowercase   =							(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
							__lowercase   =							(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)
				__lowercase   =							self._get_variance(_UpperCamelCase   ,_UpperCamelCase   ,_UpperCamelCase       )
				__lowercase   =							eta * variance ** 0.5

				# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
				__lowercase   =							(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
				__lowercase   =							alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

				if not return_dict:
							return (prev_sample, state)

				return FlaxDDIMSchedulerOutput(prev_sample=_UpperCamelCase   ,state=_UpperCamelCase       )


	def   _UpperCAmelCase   (self   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase   ,)							->							Any:







				'''simple docstring'''





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


	def   _UpperCAmelCase   (self   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase   ,)							->							int:







				'''simple docstring'''





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


	def __len__(self       )							->							Optional[Any]:







				'''simple docstring'''





				return self.config.num_train_timesteps













import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only

from utils import save_json







def 		_UpperCamelCase      (			UpperCamelCase_				:    Tuple						)     ->      str:

						"""simple docstring"""



						lowerCAmelCase__			  =  filter(lambda UpperCamelCase_						: p.requires_grad			,				model.parameters()						)
						lowerCAmelCase__			  =  sum([np.prod(p.size()						) for p in model_parameters]						)
						return params


__snake_case			:			Dict			   =   logging.getLogger(__name__)


class 			__SCREAMING_SNAKE_CASE    (      pl.Callback):






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  {F"lr_group_{i}": param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups      )}
												pl_module.logger.log_metrics(_UpperCamelCase      )






						@rank_zero_only
						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase=True      ):






												"""simple docstring"""
												logger.info(F"***** {type_path} results at step {trainer.global_step:05d} *****"      )
												lowerCAmelCase__			  =  trainer.callback_metrics
												trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']}      )
												# Log results
												lowerCAmelCase__			  =  Path(pl_module.hparams.output_dir      )
												if type_path == "test":
																		lowerCAmelCase__			  =  od / 'test_results.txt'
																		lowerCAmelCase__			  =  od / 'test_generations.txt'
												else:
																		# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
																		# If people want this it will be easy enough to add back.
																		lowerCAmelCase__			  =  od / F"{type_path}_results/{trainer.global_step:05d}.txt"
																		lowerCAmelCase__			  =  od / F"{type_path}_generations/{trainer.global_step:05d}.txt"
																		results_file.parent.mkdir(exist_ok=_UpperCamelCase      )
																		generations_file.parent.mkdir(exist_ok=_UpperCamelCase      )
												with open(_UpperCamelCase   ,  'a+'      ) as writer:
																		for key in sorted(_UpperCamelCase      ):
																								if key in ["log", "progress_bar", "preds"]:
																														continue
																								lowerCAmelCase__			  =  metrics[key]
																								if isinstance(_UpperCamelCase   ,  torch.Tensor      ):
																														lowerCAmelCase__			  =  val.item()
																								lowerCAmelCase__			  =  F"{key}: {val:.6f}\n"
																								writer.write(_UpperCamelCase      )

												if not save_generations:
																		return

												if "preds" in metrics:
																		lowerCAmelCase__			  =  '\n'.join(metrics['preds']      )
																		generations_file.open('w+'      ).write(_UpperCamelCase      )






						@rank_zero_only
						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase      ):






												"""simple docstring"""
												try:
																		lowerCAmelCase__			  =  pl_module.model.model.num_parameters()
												except AttributeError:
																		lowerCAmelCase__			  =  pl_module.model.num_parameters()

												lowerCAmelCase__			  =  count_trainable_parameters(_UpperCamelCase      )
												# mp stands for million parameters
												trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1E6, 'grad_mp': n_trainable_pars / 1E6}      )






						@rank_zero_only
						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase      ):






												"""simple docstring"""
												save_json(pl_module.metrics   ,  pl_module.metrics_save_path      )
												return self._write_logs(_UpperCamelCase   ,  _UpperCamelCase   ,  'test'      )







						@rank_zero_only
						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase      ):






												"""simple docstring"""
												save_json(pl_module.metrics   ,  pl_module.metrics_save_path      )
												# Uncommenting this will save val generations
												# return self._write_logs(trainer, pl_module, "valid")







def 		_UpperCamelCase      (			UpperCamelCase_				:    Union[str, Any]			,				UpperCamelCase_				:    Union[str, Any]			,				UpperCamelCase_				:    List[Any]=1			,				UpperCamelCase_				:    List[str]=False						)     ->      Optional[Any]:

						"""simple docstring"""



						if metric == "rouge2":
												lowerCAmelCase__			  =  '{val_avg_rouge2:.4f}-{step_count}'
						elif metric == "bleu":
												lowerCAmelCase__			  =  '{val_avg_bleu:.4f}-{step_count}'
						elif metric == "loss":
												lowerCAmelCase__			  =  '{val_avg_loss:.4f}-{step_count}'
						else:
												raise NotImplementedError(
												    F"seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"
												    ' this function.'						)

						lowerCAmelCase__			  =  ModelCheckpoint(
						    dirpath=UpperCamelCase_			,				filename=UpperCamelCase_			,				monitor=F"val_{metric}"			,				mode='min' if 'loss' in metric else 'max'			,				save_top_k=UpperCamelCase_			,				)
						return checkpoint_callback







def 		_UpperCamelCase      (			UpperCamelCase_				:    Union[str, Any]			,				UpperCamelCase_				:    int						)     ->      Dict:

						"""simple docstring"""



						return EarlyStopping(
						    monitor=F"val_{metric}"			,				mode='min' if 'loss' in metric else 'max'			,				patience=UpperCamelCase_			,				verbose=UpperCamelCase_			,				)





