





'''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 lowerCamelCase						(     __lowerCamelCase :    Optional[int]    )						->Union[str, Any]:
    _SCREAMING_SNAKE_CASE									=			filter(lambda __lowerCamelCase    : p.requires_grad					,       model.parameters()    )
    _SCREAMING_SNAKE_CASE									=			sum([np.prod(p.size()    ) for p in model_parameters]    )
    return params


lowercase_					= logging.getLogger(__name__)





def lowerCamelCase						(     __lowerCamelCase :    Optional[int]					,       __lowerCamelCase :    str    )						->Optional[Any]:
    if metric == "rouge2":
        _SCREAMING_SNAKE_CASE									=			"""{val_avg_rouge2:.4f}-{step_count}"""
    elif metric == "bleu":
        _SCREAMING_SNAKE_CASE									=			"""{val_avg_bleu:.4f}-{step_count}"""
    elif metric == "em":
        _SCREAMING_SNAKE_CASE									=			"""{val_avg_em:.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."""    )

    _SCREAMING_SNAKE_CASE									=			ModelCheckpoint(
        dirpath=__lowerCamelCase					,       filename=__lowerCamelCase					,       monitor=F'val_{metric}'					,       mode="""max"""					,       save_top_k=3					,       every_n_epochs=1					,       )
    return checkpoint_callback





def lowerCamelCase						(     __lowerCamelCase :    Tuple					,       __lowerCamelCase :    List[Any]    )						->str:
    return EarlyStopping(
        monitor=F'val_{metric}'					,       mode="""min""" if """loss""" in metric else """max"""					,       patience=__lowerCamelCase					,       verbose=__lowerCamelCase					,       )






class 					a_     (				pl.Callback       ):

    '''simple docstring'''


    def 			snake_case_(		self ,		A ,		A       )		->   int:
        _SCREAMING_SNAKE_CASE									=			{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 			snake_case_(		self ,		A ,		A ,		A ,		A=True       )		->   None:
        logger.info(f'***** {type_path} results at step {trainer.global_step:05d} *****'       )
        _SCREAMING_SNAKE_CASE									=			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
        _SCREAMING_SNAKE_CASE									=			Path(pl_module.hparams.output_dir       )
        if type_path == "test":
            _SCREAMING_SNAKE_CASE									=			od / """test_results.txt"""
            _SCREAMING_SNAKE_CASE									=			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.
            _SCREAMING_SNAKE_CASE									=			od / f'{type_path}_results/{trainer.global_step:05d}.txt'
            _SCREAMING_SNAKE_CASE									=			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
                _SCREAMING_SNAKE_CASE									=			metrics[key]
                if isinstance(A ,		torch.Tensor       ):
                    _SCREAMING_SNAKE_CASE									=			val.item()
                _SCREAMING_SNAKE_CASE									=			f'{key}: {val:.6f}\n'
                writer.write(A       )

        if not save_generations:
            return

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


    @rank_zero_only
    def 			snake_case_(		self ,		A ,		A       )		->   int:
        try:
            _SCREAMING_SNAKE_CASE									=			pl_module.model.model.num_parameters()
        except AttributeError:
            _SCREAMING_SNAKE_CASE									=			pl_module.model.num_parameters()

        _SCREAMING_SNAKE_CASE									=			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 			snake_case_(		self ,		A ,		A       )		->   Optional[Any]:
        save_json(pl_module.metrics ,		pl_module.metrics_save_path       )
        return self._write_logs(A ,		A ,		"""test"""       )





    @rank_zero_only
    def 			snake_case_(		self ,		A ,		A       )		->   int:
        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 copy import deepcopy

class  _UpperCAmelCase  :



      '''simple docstring'''

      def __init__(     self					,      snake_case_ = None					,      snake_case_ = None  ):

        """simple docstring"""

        if arr is None and size is not None:
          A_  :       Union[str, Any]										=  size
          A_  :       List[str]										=  [0] * size
        elif arr is not None:
          self.init(snake_case_  )
        else:
          raise ValueError('Either arr or size must be specified'  )

      def 					lowerCamelCase_			(     self					,      snake_case_  ):

        """simple docstring"""

        A_  :       Union[str, Any]										=  len(snake_case_  )
        A_  :       Optional[int]										=  deepcopy(snake_case_  )
        for i in range(1					,      self.size  ):
          A_  :       Optional[Any]										=  self.next_(snake_case_  )
          if j < self.size:
            self.tree[j] += self.tree[i]

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       int										=  self.tree[:]
        for i in range(self.size - 1					,      0					,      -1  ):
          A_  :       Optional[int]										=  self.next_(snake_case_  )
          if j < self.size:
            arr[j] -= arr[i]
        return arr

      @staticmethod
      def 					lowerCamelCase_			(     snake_case_  ):

        """simple docstring"""

        return index + (index & (-index))

      @staticmethod
      def 					lowerCamelCase_			(     snake_case_  ):

        """simple docstring"""

        return index - (index & (-index))

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        if index == 0:
          self.tree[0] += value
          return
        while index < self.size:
          self.tree[index] += value
          A_  :       List[str]										=  self.next_(snake_case_  )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        self.add(snake_case_					,      value - self.get(snake_case_  )  )

      def 					lowerCamelCase_			(     self					,      snake_case_  ):

        """simple docstring"""

        if right == 0:
          return 0
        A_  :       Any										=  self.tree[0]
        right -= 1  # make right inclusive
        while right > 0:
          result += self.tree[right]
          A_  :       Tuple										=  self.prev(snake_case_  )
        return result

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        return self.prefix(snake_case_  ) - self.prefix(snake_case_  )

      def 					lowerCamelCase_			(     self					,      snake_case_  ):

        """simple docstring"""

        return self.query(snake_case_					,      index + 1  )

      def 					lowerCamelCase_			(     self					,      snake_case_  ):

        """simple docstring"""

        value -= self.tree[0]
        if value < 0:
          return -1

        A_  :       List[Any]										=  1  # Largest power of 2 <= size
        while j * 2 < self.size:
          j *= 2

        A_  :       Tuple										=  0

        while j > 0:
          if i + j < self.size and self.tree[i + j] <= value:
            value -= self.tree[i + j]
            i += j
          j //= 2
        return i


if __name__ == "__main__":
   import doctest

   doctest.testmod()