






'''simple docstring'''
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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Optional[Any]           =     filter(lambda _UpperCamelCase							: p.requires_grad						,		model.parameters()							)
   lowercase_	:    List[str]           =     sum([np.prod(p.size()							) for p in model_parameters]							)
   return params


UpperCamelCase__ =			logging.getLogger(__name__)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   if metric == "rouge2":
      lowercase_	:    Optional[int]           =     "{val_avg_rouge2:.4f}-{step_count}"
   elif metric == "bleu":
      lowercase_	:    Union[str, Any]           =     "{val_avg_bleu:.4f}-{step_count}"
   elif metric == "em":
      lowercase_	:    Tuple           =     "{val_avg_em:.4f}-{step_count}"
   elif metric == "loss":
      lowercase_	:    Any           =     "{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."							)

   lowercase_	:    List[str]           =     ModelCheckpoint(
       dirpath=_UpperCamelCase						,		filename=_UpperCamelCase						,		monitor=F"""val_{metric}"""						,		mode="max"						,		save_top_k=1						,		every_n_epochs=1						,		)

   return checkpoint_callback




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




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





class        _UpperCAmelCase						(		pl.Callback			):




    def 				lowerCAmelCase__				(			self  :       Union[str, Any]  ,     a  :       List[str]  ,     a  :       int			):






       '''simple docstring'''






       lowercase_	:    Dict           =     {f"""lr_group_{i}""": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups			)}
       pl_module.logger.log_metrics(a			)




    @rank_zero_only
    def 				lowerCAmelCase__				(			self  :       List[str]  ,     a  :       pl.Trainer  ,     a  :       pl.LightningModule  ,     a  :       str  ,     a  :       Optional[Any]=True			):






       '''simple docstring'''






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

       if not save_generations:
          return

       if "preds" in metrics:
          lowercase_	:    str           =     "\n".join(metrics["preds"]			)
          generations_file.open("w+"			).write(a			)




    @rank_zero_only
    def 				lowerCAmelCase__				(			self  :       str  ,     a  :       Optional[Any]  ,     a  :       Any			):






       '''simple docstring'''






       try:
          lowercase_	:    Optional[int]           =     pl_module.model.model.num_parameters()
       except AttributeError:
          lowercase_	:    List[Any]           =     pl_module.model.num_parameters()

       lowercase_	:    Union[str, Any]           =     count_trainable_parameters(a			)
       # 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 				lowerCAmelCase__				(			self  :       Union[str, Any]  ,     a  :       pl.Trainer  ,     a  :       pl.LightningModule			):






       '''simple docstring'''






       save_json(pl_module.metrics  ,     pl_module.metrics_save_path			)
       return self._write_logs(a  ,     a  ,     "test"			)





    @rank_zero_only
    def 				lowerCAmelCase__				(			self  :       Any  ,     a  :       pl.Trainer  ,     a  :       Optional[int]			):






       '''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")












'''simple docstring'''
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


UpperCamelCase__ =			'scheduler_config.json'


class        _UpperCAmelCase						(		snake_case			):
    __lowerCamelCase:   int		       =    1
    __lowerCamelCase:   List[Any]		       =    2
    __lowerCamelCase:   Optional[Any]		       =    3
    __lowerCamelCase:   int		       =    4
    __lowerCamelCase:   Optional[int]		       =    5


@dataclass
class        _UpperCAmelCase						(		snake_case			):
    __lowerCamelCase:   jnp.ndarray


class        _UpperCAmelCase						:
    __lowerCamelCase:   List[str]		       =    SCHEDULER_CONFIG_NAME
    __lowerCamelCase:   Optional[int]		       =    ['dtype']
    __lowerCamelCase:   int		       =    []
    __lowerCamelCase:   Dict		       =    True




    @classmethod
    def 				lowerCAmelCase__				(			cls  :       Tuple  ,     a  :       Dict[str, Any] = None  ,     a  :       Optional[str] = None  ,     a  :       Union[str, Any]=False  ,     **a  :       Union[str, Any]  ,     ):






       '''simple docstring'''






       lowercase_    ,		lowercase_	:    Any           =     cls.load_config(
           pretrained_model_name_or_path=a  ,     subfolder=a  ,     return_unused_kwargs=a  ,     **a  ,     )
       lowercase_    ,		lowercase_	:    Union[str, Any]           =     cls.from_config(a  ,     return_unused_kwargs=a  ,     **a			)

       if hasattr(a  ,     "create_state"			) and getattr(a  ,     "has_state"  ,     a			):
          lowercase_	:    Tuple           =     scheduler.create_state()

       if return_unused_kwargs:
          return scheduler, state, unused_kwargs

       return scheduler, state




    def 				lowerCAmelCase__				(			self  :       int  ,     a  :       Union[str, os.PathLike]  ,     a  :       bool = False  ,     **a  :       int			):






       '''simple docstring'''






       self.save_config(save_directory=a  ,     push_to_hub=a  ,     **a			)




    @property
    def 				lowerCAmelCase__				(			self  :       Tuple			):






       '''simple docstring'''






       return self._get_compatibles()





    @classmethod
    def 				lowerCAmelCase__				(			cls  :       Dict			):






       '''simple docstring'''






       lowercase_	:    str           =     list(set([cls.__name__] + cls._compatibles			)			)
       lowercase_	:    str           =     importlib.import_module(__name__.split("."			)[0]			)
       lowercase_	:    Optional[Any]           =     [
           getattr(a  ,     a			) for c in compatible_classes_str if hasattr(a  ,     a			)
       ]
       return compatible_classes




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   assert len(_UpperCamelCase							) >= x.ndim
   return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(_UpperCamelCase							) - x.ndim)							)						,		_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase=0.999						,		_UpperCamelCase=jnp.floataa							):


   """simple docstring"""




   def alpha_bar(_UpperCamelCase							):
      return math.cos((time_step + 0.008) / 1.008 * math.pi / 2							) ** 2

   lowercase_	:    int           =     []
   for i in range(_UpperCamelCase							):
      lowercase_	:    Union[str, Any]           =     i / num_diffusion_timesteps
      lowercase_	:    Dict           =     (i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar(_UpperCamelCase							) / alpha_bar(_UpperCamelCase							)						,		_UpperCamelCase							)							)
   return jnp.array(_UpperCamelCase						,		dtype=_UpperCamelCase							)


@flax.struct.dataclass
class        _UpperCAmelCase						:
    __lowerCamelCase:   jnp.ndarray
    __lowerCamelCase:   jnp.ndarray
    __lowerCamelCase:   jnp.ndarray




    @classmethod
    def 				lowerCAmelCase__				(			cls  :       Tuple  ,     a  :       Optional[int]			):






       '''simple docstring'''






       lowercase_	:    Any           =     scheduler.config

       if config.trained_betas is not None:
          lowercase_	:    Union[str, Any]           =     jnp.asarray(config.trained_betas  ,     dtype=scheduler.dtype			)
       elif config.beta_schedule == "linear":
          lowercase_	:    List[Any]           =     jnp.linspace(config.beta_start  ,     config.beta_end  ,     config.num_train_timesteps  ,     dtype=scheduler.dtype			)
       elif config.beta_schedule == "scaled_linear":
          # this schedule is very specific to the latent diffusion model.
          lowercase_	:    Tuple           =     (
              jnp.linspace(
                  config.beta_start**0.5  ,     config.beta_end**0.5  ,     config.num_train_timesteps  ,     dtype=scheduler.dtype			)
              ** 2
          )
       elif config.beta_schedule == "squaredcos_cap_v2":
          # Glide cosine schedule
          lowercase_	:    Union[str, Any]           =     betas_for_alpha_bar(config.num_train_timesteps  ,     dtype=scheduler.dtype			)
       else:
          raise NotImplementedError(
              f"""beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}"""			)

       lowercase_	:    str           =     1.0 - betas

       lowercase_	:    Dict           =     jnp.cumprod(a  ,     axis=0			)

       return cls(
           alphas=a  ,     betas=a  ,     alphas_cumprod=a  ,     )




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    List[str]           =     state.alphas_cumprod

   lowercase_	:    Optional[Any]           =     alphas_cumprod[timesteps] ** 0.5
   lowercase_	:    int           =     sqrt_alpha_prod.flatten()
   lowercase_	:    Union[str, Any]           =     broadcast_to_shape_from_left(_UpperCamelCase						,		original_samples.shape							)

   lowercase_	:    Optional[int]           =     (1 - alphas_cumprod[timesteps]) ** 0.5
   lowercase_	:    Union[str, Any]           =     sqrt_one_minus_alpha_prod.flatten()
   lowercase_	:    Any           =     broadcast_to_shape_from_left(_UpperCamelCase						,		original_samples.shape							)

   return sqrt_alpha_prod, sqrt_one_minus_alpha_prod




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_    ,		lowercase_	:    int           =     get_sqrt_alpha_prod(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)
   lowercase_	:    str           =     sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
   return noisy_samples





def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_    ,		lowercase_	:    int           =     get_sqrt_alpha_prod(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)
   lowercase_	:    Any           =     sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
   return velocity




