




# 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_       :
				__a     :       CommonSchedulerState

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






				@classmethod
				def 		snake_case	(			cls      ,snake_case__      ,snake_case__      ,snake_case__ ):
							return cls(common=snake_case__      ,init_noise_sigma=snake_case__      ,timesteps=snake_case__ )







@dataclass
class 				lowerCAmelCase_       (					lowerCamelCase_							):
				__a     :       DDPMSchedulerState







class 				lowerCAmelCase_       (					lowerCamelCase_						,     lowerCamelCase_							):
				__a     :       Tuple						=     [e.name for e in FlaxKarrasDiffusionSchedulers]

				__a     :       jnp.dtype






				@property
				def 		snake_case	(			self ):
							return True






				@register_to_config
				def __init__(			self      ,snake_case__ = 1000      ,snake_case__ = 0.0001      ,snake_case__ = 0.02      ,snake_case__ = "linear"      ,snake_case__ = None      ,snake_case__ = "fixed_small"      ,snake_case__ = True      ,snake_case__ = "epsilon"      ,snake_case__ = jnp.floataa      ,):
							SCREAMING_SNAKE_CASE_   : Any					  =      dtype






				def 		snake_case	(			self      ,snake_case__ = None ):
							if common is None:
										SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      CommonSchedulerState.create(self )

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

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

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






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__ = None ):
							return sample






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__ = () ):
							SCREAMING_SNAKE_CASE_   : 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
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      (jnp.arange(0      ,snake_case__ ) * step_ratio).round()[::-1]

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






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__=None      ,snake_case__=None ):
							SCREAMING_SNAKE_CASE_   : List[str]					  =      state.common.alphas_cumprod[t]
							SCREAMING_SNAKE_CASE_   : int					  =      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
							SCREAMING_SNAKE_CASE_   : Dict					  =      (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

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

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

							return variance






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__ = None      ,snake_case__ = True      ,):
							SCREAMING_SNAKE_CASE_   : int					  =      timestep

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

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

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

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

							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      jnp.where(t > 0      ,random_variance()      ,jnp.zeros(model_output.shape      ,dtype=self.dtype ) )

							SCREAMING_SNAKE_CASE_   : str					  =      pred_prev_sample + variance

							if not return_dict:
										return (pred_prev_sample, state)

							return FlaxDDPMSchedulerOutput(prev_sample=snake_case__      ,state=snake_case__ )






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,):
							return add_noise_common(state.common      ,snake_case__      ,snake_case__      ,snake_case__ )






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,):
							return get_velocity_common(state.common      ,snake_case__      ,snake_case__      ,snake_case__ )






				def __len__(			self ):
							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_rag import save_json


def A    (							__UpperCamelCase       )    ->   Optional[int]:
			A__	  =     filter(lambda __UpperCamelCase       : p.requires_grad			,						model.parameters()       )
			A__	  =     sum([np.prod(p.size()       ) for p in model_parameters]       )
			return params


SCREAMING_SNAKE_CASE__   			=   logging.getLogger(__name__)


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

			A__	  =     ModelCheckpoint(
			    dirpath=__UpperCamelCase			,						filename=__UpperCamelCase			,						monitor=f'''val_{metric}'''			,						mode='max'			,						save_top_k=1			,						every_n_epochs=1			,						)

			return checkpoint_callback


def A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   Any:
			return EarlyStopping(
			    monitor=f'''val_{metric}'''			,						mode='min' if 'loss' in metric else 'max'			,						patience=__UpperCamelCase			,						verbose=__UpperCamelCase			,						)






class   __lowerCAmelCase  (		pl.Callback   ):
					"""simple docstring"""






					def      _a				(  self       :							Dict					,       _snake_case       :							Union[str, Any]					,       _snake_case       :							str       ):






								"""simple docstring"""


								A__	  =     {F'''lr_group_{i}''': param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups       )}
								pl_module.logger.log_metrics(_snake_case       )



					@rank_zero_only
					def      _a				(  self       :							Union[str, Any]					,       _snake_case       :							pl.Trainer					,       _snake_case       :							pl.LightningModule					,       _snake_case       :							str					,       _snake_case       :							Optional[Any]=True       ):






								"""simple docstring"""


								logger.info(F'''***** {type_path} results at step {trainer.global_step:05d} *****'''       )
								A__	  =     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
								A__	  =     Path(pl_module.hparams.output_dir       )
								if type_path == "test":
											A__	  =     od / 'test_results.txt'
											A__	  =     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.
											A__	  =     od / F'''{type_path}_results/{trainer.global_step:05d}.txt'''
											A__	  =     od / F'''{type_path}_generations/{trainer.global_step:05d}.txt'''
											results_file.parent.mkdir(exist_ok=_snake_case       )
											generations_file.parent.mkdir(exist_ok=_snake_case       )
								with open(_snake_case					,       'a+'       ) as writer:
											for key in sorted(_snake_case       ):
														if key in ["log", "progress_bar", "preds"]:
																	continue
														A__	  =     metrics[key]
														if isinstance(_snake_case					,       torch.Tensor       ):
																	A__	  =     val.item()
														A__	  =     F'''{key}: {val:.6f}\n'''
														writer.write(_snake_case       )

								if not save_generations:
											return

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



					@rank_zero_only
					def      _a				(  self       :							Dict					,       _snake_case       :							List[str]					,       _snake_case       :							List[Any]       ):






								"""simple docstring"""


								try:
											A__	  =     pl_module.model.model.num_parameters()
								except AttributeError:
											A__	  =     pl_module.model.num_parameters()

								A__	  =     count_trainable_parameters(_snake_case       )
								# 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      _a				(  self       :							int					,       _snake_case       :							pl.Trainer					,       _snake_case       :							pl.LightningModule       ):






								"""simple docstring"""


								save_json(pl_module.metrics					,       pl_module.metrics_save_path       )
								return self._write_logs(_snake_case					,       _snake_case					,       'test'       )



					@rank_zero_only
					def      _a				(  self       :							Optional[Any]					,       _snake_case       :							pl.Trainer					,       _snake_case       :							List[Any]       ):






								"""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")




