


'''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	:   List[str]):
      A_    :	str							 =					filter(lambda lowerCamelCase: p.requires_grad				,    model.parameters())
      A_    :	Tuple							 =					sum([np.prod(p.size()) for p in model_parameters])
      return params


__magic_name__								=   logging.getLogger(__name__)




def   lowerCamelCase       (   lowerCamelCase	:   str				,    lowerCamelCase	:   List[str]):





      if metric == "rouge2":
            A_    :	str							 =					"""{val_avg_rouge2:.4f}-{step_count}"""
      elif metric == "bleu":
            A_    :	Optional[int]							 =					"""{val_avg_bleu:.4f}-{step_count}"""
      elif metric == "em":
            A_    :	Optional[Any]							 =					"""{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.""")

      A_    :	Any							 =					ModelCheckpoint(
          dirpath=a__				,    filename=a__				,    monitor=F'val_{metric}'				,    mode="""max"""				,    save_top_k=3				,    every_n_epochs=1				,    )
      return checkpoint_callback




def   lowerCamelCase       (   lowerCamelCase	:   str				,    lowerCamelCase	:   Any):
      return EarlyStopping(
          monitor=F'val_{metric}'				,    mode="""min""" if """loss""" in metric else """max"""				,    patience=a__				,    verbose=a__				,    )






class 						__lowerCAmelCase		(				pl.Callback     ):


  '''simple docstring'''







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

        '''simple docstring'''




        A_    :	List[str]							 =					{f'lr_group_{i}': param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups			)}
        pl_module.logger.log_metrics(_SCREAMING_SNAKE_CASE			)






  @rank_zero_only
  def 							_a				(				self			:							Union[str, Any]						,_a			:							int						,_a			:							List[str]						,_a			:							int						,_a			:							int=True			):

        '''simple docstring'''




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

        if not save_generations:
              return

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






  @rank_zero_only
  def 							_a				(				self			:							Dict						,_a			:							str						,_a			:							Optional[int]			):

        '''simple docstring'''




        try:
              A_    :	List[str]							 =					pl_module.model.model.num_parameters()
        except AttributeError:
              A_    :	int							 =					pl_module.model.num_parameters()

        A_    :	Any							 =					count_trainable_parameters(_SCREAMING_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			:							Dict						,_a			:							Dict						,_a			:							int			):

        '''simple docstring'''




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






  @rank_zero_only
  def 							_a				(				self			:							Union[str, Any]						,_a			:							Optional[Any]						,_a			:							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")




from __future__ import annotations

def 			lowerCAmelCase__					(     a__:						str     ,    a__:						str			)    ->						bool:


							'''simple docstring'''





							_UpperCAmelCase											=     get_failure_array(a__			)

							# 2) Step through text searching for pattern
							_UpperCAmelCase						,			_UpperCAmelCase											=     0, 0  # index into text, pattern
							while i < len(a__			):
														if pattern[j] == text[i]:
																					if j == (len(a__			) - 1):
																												return True
																					j += 1

														# if this is a prefix in our pattern
														# just go back far enough to continue
														elif j > 0:
																					_UpperCAmelCase											=     failure[j - 1]
																					continue
														i += 1
							return False

def 			lowerCAmelCase__					(     a__:						str			)    ->						list[int]:


							'''simple docstring'''





							_UpperCAmelCase											=     [0]
							_UpperCAmelCase											=     0
							_UpperCAmelCase											=     1
							while j < len(a__			):
														if pattern[i] == pattern[j]:
																					i += 1
														elif i > 0:
																					_UpperCAmelCase											=     failure[i - 1]
																					continue
														j += 1
														failure.append(a__			)
							return failure


if __name__ == "__main__":
		# Test 1)
		lowerCAmelCase__    :List[Any]       =				'''abc1abc12'''
		lowerCAmelCase__    :Optional[int]       =				'''alskfjaldsabc1abc1abc12k23adsfabcabc'''
		lowerCAmelCase__    :int       =				'''alskfjaldsk23adsfabcabc'''
		assert kmp(pattern, texta) and not kmp(pattern, texta)

		# Test 2)
		lowerCAmelCase__    :List[str]       =				'''ABABX'''
		lowerCAmelCase__    :int       =				'''ABABZABABYABABX'''
		assert kmp(pattern, text)

		# Test 3)
		lowerCAmelCase__    :int       =				'''AAAB'''
		lowerCAmelCase__    :Any       =				'''ABAAAAAB'''
		assert kmp(pattern, text)

		# Test 4)
		lowerCAmelCase__    :Union[str, Any]       =				'''abcdabcy'''
		lowerCAmelCase__    :Any       =				'''abcxabcdabxabcdabcdabcy'''
		assert kmp(pattern, text)

		# Test 5)
		lowerCAmelCase__    :List[Any]       =				'''aabaabaaa'''
		assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]
