


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


UpperCAmelCase_ :     Dict								=      logging.getLogger(__name__)


def 			_lowercase       (				UpperCamelCase__       :					Dict,				UpperCamelCase__       :					Any			):
 if metric == "rouge2":
  __A  :		Any       =			'{val_avg_rouge2:.4f}-{step_count}'
 elif metric == "bleu":
  __A  :		Dict       =			'{val_avg_bleu:.4f}-{step_count}'
 elif metric == "em":
  __A  :		Optional[int]       =			'{val_avg_em:.4f}-{step_count}'
 elif metric == "loss":
  __A  :		Tuple       =			'{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.'			)

 __A  :		List[Any]       =			ModelCheckpoint(
     dirpath=UpperCamelCase__,				filename=UpperCamelCase__,				monitor=f"""val_{metric}""",				mode='max',				save_top_k=1,				every_n_epochs=1,				)

 return checkpoint_callback


def 			_lowercase       (				UpperCamelCase__       :					Tuple,				UpperCamelCase__       :					Union[str, Any]			):
 return EarlyStopping(
     monitor=f"""val_{metric}""",				mode='min' if 'loss' in metric else 'max',				patience=UpperCamelCase__,				verbose=UpperCamelCase__,				)


class 						_lowerCamelCase					(    pl.Callback				):




 '''simple docstring'''


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  __A  :		Union[str, Any]       =			{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  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase=True						):

  """simple docstring"""







  logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""						)
  __A  :		List[Any]       =			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  :		Tuple       =			Path(pl_module.hparams.output_dir						)
  if type_path == "test":
   __A  :		int       =			od / 'test_results.txt'
   __A  :		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.
   __A  :		List[str]       =			od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
   __A  :		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
    __A  :		Optional[Any]       =			metrics[key]
    if isinstance(__lowercase      ,      torch.Tensor						):
     __A  :		Optional[int]       =			val.item()
    __A  :		str       =			F"""{key}: {val:.6f}\n"""
    writer.write(__lowercase						)

  if not save_generations:
   return

  if "preds" in metrics:
   __A  :		List[Any]       =			'\n'.join(metrics['preds']						)
   generations_file.open('w+'						).write(__lowercase						)


 @rank_zero_only
 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  try:
   __A  :		Any       =			pl_module.model.model.num_parameters()
  except AttributeError:
   __A  :		List[Any]       =			pl_module.model.num_parameters()

  __A  :		Optional[int]       =			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  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







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







 @rank_zero_only
 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """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 heapq import heappop, heappush

import numpy as np


def 			_lowercase       (				UpperCamelCase__       :					np.ndarray,				UpperCamelCase__       :					tuple[int, int],				UpperCamelCase__       :					tuple[int, int],				UpperCamelCase__       :					bool,				):
 __A     ,__A  :		Optional[Any]       =			grid.shape
 __A  :		List[Any]       =			[-1, 1, 0, 0]
 __A  :		Optional[int]       =			[0, 0, -1, 1]
 if allow_diagonal:
  dx += [-1, -1, 1, 1]
  dy += [-1, 1, -1, 1]

 __A     ,__A  :		int       =			[(0, source)], set()
 __A  :		Any       =			np.full((rows, cols),				np.inf			)
 __A  :		int       =			0
 __A  :		Any       =			np.empty((rows, cols),				dtype=UpperCamelCase__			)
 __A  :		List[Any]       =			None

 while queue:
  ((__A)     ,(__A))  :		List[Any]       =			heappop(UpperCamelCase__			)
  if (x, y) in visited:
   continue
  visited.add((x, y)			)

  if (x, y) == destination:
   __A  :		Tuple       =			[]
   while (x, y) != source:
    path.append((x, y)			)
    __A     ,__A  :		int       =			predecessors[x, y]
   path.append(UpperCamelCase__			)  # add the source manually
   path.reverse()
   return matrix[destination], path

  for i in range(len(UpperCamelCase__			)			):
   __A     ,__A  :		List[Any]       =			x + dx[i], y + dy[i]
   if 0 <= nx < rows and 0 <= ny < cols:
    __A  :		str       =			grid[nx][ny]
    if next_node == 1 and matrix[nx, ny] > dist + 1:
     heappush(UpperCamelCase__,				(dist + 1, (nx, ny))			)
     __A  :		int       =			dist + 1
     __A  :		List[str]       =			(x, y)

 return np.inf, []


if __name__ == "__main__":
 import doctest

 doctest.testmod()






