





'''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    _a    (							lowerCAmelCase_	):





      """simple docstring"""



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


UpperCAmelCase					:		Dict		=							logging.getLogger(__name__)




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



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

      _snake_case		:	List[str]					     =     ModelCheckpoint(
          dirpath=lowerCAmelCase_			,      filename=lowerCAmelCase_			,      monitor=f'''val_{metric}'''			,      mode='''max'''			,      save_top_k=1			,      every_n_epochs=1			,      )

      return checkpoint_callback




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



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





class lowerCamelCase						(pl.Callback					):







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__		)		->						List[str]:






        """simple docstring"""





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







  @rank_zero_only
  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__=True		)		->						None:






        """simple docstring"""





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

        if not save_generations:
              return

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







  @rank_zero_only
  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__		)		->						List[Any]:






        """simple docstring"""





        try:
              _snake_case		:	Dict					     =     pl_module.model.model.num_parameters()
        except AttributeError:
              _snake_case		:	str					     =     pl_module.model.num_parameters()

        _snake_case		:	Any					     =     count_trainable_parameters(lowercase__		)
        # 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     ,     lowercase__     ,     lowercase__		)		->						Optional[int]:






        """simple docstring"""





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







  @rank_zero_only
  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__		)		->						List[str]:






        """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'''

from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

UpperCAmelCase					:		Any		=							TypeVar('T')
UpperCAmelCase					:		str		=							TypeVar('U')





class lowerCamelCase						(Generic[T, U]					):







  def __init__(		self     ,     lowercase__     ,     lowercase__		)		->						List[Any]:






        """simple docstring"""





        _snake_case		:	str					     =     key
        _snake_case		:	Optional[int]					     =     val
        _snake_case		:	DoubleLinkedListNode[T, U] | None					     =     None
        _snake_case		:	DoubleLinkedListNode[T, U] | None					     =     None







  def __repr__(		self		)		->						str:






        """simple docstring"""





        return (
            F'''Node: key: {self.key}, val: {self.val}, '''
            F'''has next: {bool(self.next		)}, has prev: {bool(self.prev		)}'''
        )





class lowerCamelCase						(Generic[T, U]					):







  def __init__(		self		)		->						None:






        """simple docstring"""





        _snake_case		:	DoubleLinkedListNode[T, U]					     =     DoubleLinkedListNode(lowercase__     ,     lowercase__		)
        _snake_case		:	DoubleLinkedListNode[T, U]					     =     DoubleLinkedListNode(lowercase__     ,     lowercase__		)
        _snake_case     ,							_snake_case		:	Union[str, Any]					     =     self.rear, self.head







  def __repr__(		self		)		->						str:






        """simple docstring"""





        _snake_case		:	List[Any]					     =     ['''DoubleLinkedList''']
        _snake_case		:	str					     =     self.head
        while node.next is not None:
              rep.append(str(lowercase__		)		)
              _snake_case		:	List[str]					     =     node.next
        rep.append(str(self.rear		)		)
        return ",\n    ".join(lowercase__		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						None:






        """simple docstring"""





        _snake_case		:	Tuple					     =     self.rear.prev

        # All nodes other than self.head are guaranteed to have non-None previous
        assert previous is not None

        _snake_case		:	Union[str, Any]					     =     node
        _snake_case		:	Optional[Any]					     =     previous
        _snake_case		:	int					     =     node
        _snake_case		:	Union[str, Any]					     =     self.rear







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						DoubleLinkedListNode[T, U] | None:






        """simple docstring"""





        if node.prev is None or node.next is None:
              return None

        _snake_case		:	Optional[int]					     =     node.next
        _snake_case		:	Any					     =     node.prev
        _snake_case		:	List[str]					     =     None
        _snake_case		:	Optional[int]					     =     None
        return node





class lowerCamelCase						(Generic[T, U]					):
  _lowercase  :	dict[Callable[[T], U], LRUCache[T, U]]       					=				{}







  def __init__(		self     ,     lowercase__		)		->						Union[str, Any]:






        """simple docstring"""





        _snake_case		:	DoubleLinkedList[T, U]					     =     DoubleLinkedList()
        _snake_case		:	Union[str, Any]					     =     capacity
        _snake_case		:	int					     =     0
        _snake_case		:	Dict					     =     0
        _snake_case		:	Union[str, Any]					     =     0
        _snake_case		:	dict[T, DoubleLinkedListNode[T, U]]					     =     {}







  def __repr__(		self		)		->						str:






        """simple docstring"""





        return (
            F'''CacheInfo(hits={self.hits}, misses={self.miss}, '''
            F'''capacity={self.capacity}, current size={self.num_keys})'''
        )







  def __contains__(		self     ,     lowercase__		)		->						bool:






        """simple docstring"""





        return key in self.cache







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						U | None:






        """simple docstring"""





        if key in self.cache:
              self.hits += 1
              _snake_case		:	DoubleLinkedListNode[T, U]					     =     self.cache[key]
              _snake_case		:	Tuple					     =     self.list.remove(self.cache[key]		)
              assert node == value_node

              # node is guaranteed not None because it is in self.cache
              assert node is not None
              self.list.add(lowercase__		)
              return node.val
        self.miss += 1
        return None







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__		)		->						None:






        """simple docstring"""





        if key not in self.cache:
              if self.num_keys >= self.capacity:
                    # delete first node (oldest) when over capacity
                    _snake_case		:	Dict					     =     self.list.head.next

                    # guaranteed to have a non-None first node when num_keys > 0
                    # explain to type checker via assertions
                    assert first_node is not None
                    assert first_node.key is not None
                    assert (
                        self.list.remove(lowercase__		) is not None
                    )  # node guaranteed to be in list assert node.key is not None

                    del self.cache[first_node.key]
                    self.num_keys -= 1
              _snake_case		:	Optional[int]					     =     DoubleLinkedListNode(lowercase__     ,     lowercase__		)
              self.list.add(self.cache[key]		)
              self.num_keys += 1

        else:
              # bump node to the end of the list, update value
              _snake_case		:	Optional[Any]					     =     self.list.remove(self.cache[key]		)
              assert node is not None  # node guaranteed to be in list
              _snake_case		:	Optional[Any]					     =     value
              self.list.add(lowercase__		)







  @classmethod
  def 				UpperCAmelCase_				(		cls     ,     lowercase__ = 128		)		->						Callable[[Callable[[T], U]], Callable[..., U]]:






        """simple docstring"""





        def cache_decorator_inner(lowercase__		) -> Callable[..., U]:
              def cache_decorator_wrapper(*lowercase__		) -> U:
                    if func not in cls.decorator_function_to_instance_map:
                          _snake_case		:	Optional[Any]					     =     LRUCache(lowercase__		)

                    _snake_case		:	Union[str, Any]					     =     cls.decorator_function_to_instance_map[func].get(args[0]		)
                    if result is None:
                          _snake_case		:	Tuple					     =     func(*lowercase__		)
                          cls.decorator_function_to_instance_map[func].put(args[0]     ,     lowercase__		)
                    return result

              def cache_info() -> LRUCache[T, U]:
                    return cls.decorator_function_to_instance_map[func]

              setattr(lowercase__     ,     '''cache_info'''     ,     lowercase__		)  # noqa: B010

              return cache_decorator_wrapper

        return cache_decorator_inner


if __name__ == "__main__":
   import doctest

   doctest.testmod()


