
"""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   __magic_name__	(      __snake_case				:       Optional[Any]  ) ->				Optional[Any]:
       lowercase    :				List[str]       		=							filter(lambda __snake_case  : p.requires_grad     , model.parameters()  )
       lowercase    :				List[str]       		=							sum([np.prod(p.size()  ) for p in model_parameters]  )
       return params


_A			:       List[str]   		=       logging.getLogger(__name__)


def   __magic_name__	(      __snake_case				:       Dict     , __snake_case				:       Optional[int]  ) ->				List[Any]:







       if metric == "rouge2":
              lowercase    :				Any       		=							'''{val_avg_rouge2:.4f}-{step_count}'''
       elif metric == "bleu":
              lowercase    :				List[Any]       		=							'''{val_avg_bleu:.4f}-{step_count}'''
       elif metric == "em":
              lowercase    :				Union[str, 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."  )

       lowercase    :				List[Any]       		=							ModelCheckpoint(
           dirpath=__snake_case     , filename=__snake_case     , monitor=f"""val_{metric}"""     , mode="max"     , save_top_k=3     , every_n_epochs=1     , )
       return checkpoint_callback


def   __magic_name__	(      __snake_case				:       Tuple     , __snake_case				:       List[Any]  ) ->				Optional[Any]:
       return EarlyStopping(
           monitor=f"""val_{metric}"""     , mode="min" if "loss" in metric else "max"     , patience=__snake_case     , verbose=__snake_case     , )

class 		a__						(      pl.Callback						):
   def 			__magic_name__			(			self	,    _a	,    _a				):
          lowercase    :				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(__lowercase				)
   @rank_zero_only
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a	,    _a=True				):
          logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""				)
          lowercase    :				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
          lowercase    :				Tuple       		=							Path(pl_module.hparams.output_dir				)
          if type_path == "test":
                 lowercase    :				str       		=							od / '''test_results.txt'''
                 lowercase    :				Optional[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.
                 lowercase    :				List[Any]       		=							od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
                 lowercase    :				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
                        lowercase    :				Union[str, Any]       		=							metrics[key]
                        if isinstance(__lowercase	,    torch.Tensor				):
                               lowercase    :				List[Any]       		=							val.item()
                        lowercase    :				Optional[Any]       		=							f"""{key}: {val:.6f}\n"""
                        writer.write(__lowercase				)

          if not save_generations:
                 return

          if "preds" in metrics:
                 lowercase    :				Union[str, Any]       		=							'''\n'''.join(metrics["preds"]				)
                 generations_file.open("w+"				).write(__lowercase				)
   @rank_zero_only
   def 			__magic_name__			(			self	,    _a	,    _a				):
          try:
                 lowercase    :				int       		=							pl_module.model.model.num_parameters()
          except AttributeError:
                 lowercase    :				Tuple       		=							pl_module.model.num_parameters()

          lowercase    :				Optional[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 			__magic_name__			(			self	,    _a	,    _a				):
          save_json(pl_module.metrics	,    pl_module.metrics_save_path				)
          return self._write_logs(__lowercase	,    __lowercase	,    "test"				)

   @rank_zero_only
   def 			__magic_name__			(			self	,    _a	,    _a				):
          save_json(pl_module.metrics	,    pl_module.metrics_save_path				)
          # Uncommenting this will save val generations
          # return self._write_logs(trainer, pl_module, "valid")



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Tuple			  =		[0 for i in range(len(SCREAMING_SNAKE_CASE	)	)]

    # initialize interval's left pointer and right pointer
    __UpperCamelCase  ,       __UpperCamelCase     :str			  =		0, 0

    for i in range(1  ,			len(SCREAMING_SNAKE_CASE	)	):
        # case when current index is inside the interval
        if i <= right_pointer:
            __UpperCamelCase     :Union[str, Any]			  =		min(right_pointer - i + 1  ,			z_result[i - left_pointer]	)
            __UpperCamelCase     :Tuple			  =		min_edge

        while go_next(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
            z_result[i] += 1

        # if new index's result gives us more right interval,
        # we've to update left_pointer and right_pointer
        if i + z_result[i] - 1 > right_pointer:
            __UpperCamelCase  ,       __UpperCamelCase     :Union[str, Any]			  =		i, i + z_result[i] - 1

    return z_result



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    return i + z_result[i] < len(SCREAMING_SNAKE_CASE	) and s[z_result[i]] == s[i + z_result[i]]



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :List[Any]			  =		0
    # concatenate 'pattern' and 'input_str' and call z_function
    # with concatenated string
    __UpperCamelCase     :Tuple			  =		z_function(pattern + input_str	)

    for val in z_result:
        # if value is greater then length of the pattern string
        # that means this index is starting position of substring
        # which is equal to pattern string
        if val >= len(SCREAMING_SNAKE_CASE	):
            answer += 1

    return answer


if __name__ == "__main__":
    import doctest

    doctest.testmod()





