






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 import save_json





def 						A_					(						lowercase_			)				->List[str]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		filter(lambda lowercase_			: p.requires_grad	,  model.parameters()			)
 SCREAMING_SNAKE_CASE				    =		sum([np.prod(p.size()			) for p in model_parameters]			)
 return params


__UpperCAmelCase      =				logging.getLogger(__name__)
class 	a_(		pl.Callback	):



     """simple docstring"""



     def     __UpperCamelCase						(		self					:	Any  ,  lowerCAmelCase__					:	int  ,  lowerCAmelCase__					:	List[Any])							->	Tuple:

      """simple docstring"""
      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(lowerCAmelCase__)





     @rank_zero_only
     def     __UpperCamelCase						(		self					:	List[str]  ,  lowerCAmelCase__					:	pl.Trainer  ,  lowerCAmelCase__					:	pl.LightningModule  ,  lowerCAmelCase__					:	str  ,  lowerCAmelCase__					:	Optional[Any]=True)							->	None:

      """simple docstring"""
      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=lowerCAmelCase__)
       generations_file.parent.mkdir(exist_ok=lowerCAmelCase__)
      with open(lowerCAmelCase__  ,  'a+') as writer:
       for key in sorted(lowerCAmelCase__):
        if key in ["log", "progress_bar", "preds"]:
         continue
        SCREAMING_SNAKE_CASE				    =		metrics[key]
        if isinstance(lowerCAmelCase__  ,  torch.Tensor):
         SCREAMING_SNAKE_CASE				    =		val.item()
        SCREAMING_SNAKE_CASE				    =		f'''{key}: {val:.6f}\n'''
        writer.write(lowerCAmelCase__)

      if not save_generations:
       return

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





     @rank_zero_only
     def     __UpperCamelCase						(		self					:	List[str]  ,  lowerCAmelCase__					:	int  ,  lowerCAmelCase__					:	str)							->	Optional[int]:

      """simple docstring"""
      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(lowerCAmelCase__)
      # 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					:	List[str]  ,  lowerCAmelCase__					:	pl.Trainer  ,  lowerCAmelCase__					:	pl.LightningModule)							->	int:

      """simple docstring"""
      save_json(pl_module.metrics  ,  pl_module.metrics_save_path)
      return self._write_logs(lowerCAmelCase__  ,  lowerCAmelCase__  ,  'test')





     @rank_zero_only
     def     __UpperCamelCase						(		self					:	str  ,  lowerCAmelCase__					:	pl.Trainer  ,  lowerCAmelCase__					:	Dict)							->	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")





def 						A_					(						lowercase_	,  lowercase_	,  lowercase_=1	,  lowercase_=False			)				->Optional[Any]:







 """simple docstring"""

 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 == "loss":
  SCREAMING_SNAKE_CASE				    =		'{val_avg_loss:.4f}-{step_count}'
 else:
  raise NotImplementedError(
      f'''seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to'''
      ' this function.'			)

 SCREAMING_SNAKE_CASE				    =		ModelCheckpoint(
     dirpath=lowercase_	,  filename=lowercase_	,  monitor=f'''val_{metric}'''	,  mode='min' if 'loss' in metric else 'max'	,  save_top_k=lowercase_	,  )
 return checkpoint_callback





def 						A_					(						lowercase_	,  lowercase_			)				->Tuple:







 """simple docstring"""

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











"""simple docstring"""
from __future__ import annotations

from queue import Queue






def    A__							(     UpperCamelCase   ,    UpperCamelCase			):
							a ^= b
							b ^= a
							a ^= b
							return a, b






def    A__							(     UpperCamelCase   ,    UpperCamelCase			):
							A						=    1
							while (1 << j) < max_node:
														for i in range(1   ,    max_node + 1			):
																					A						=    parent[j - 1][parent[j - 1][i]]
														j += 1
							return parent






def    A__							(     UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase			):
							# u must be deeper in the tree than v
							if level[u] < level[v]:
														A,      A						=    swap(UpperCamelCase   ,    UpperCamelCase			)
							# making depth of u same as depth of v
							for i in range(18   ,    -1   ,    -1			):
														if level[u] - (1 << i) >= level[v]:
																					A						=    parent[i][u]
    # at the same depth if u==v that mean lca is found
							if u == v:
														return u
							# moving both nodes upwards till lca in found
							for i in range(18   ,    -1   ,    -1			):
														if parent[i][u] not in [0, parent[i][v]]:
																					A,      A						=    parent[i][u], parent[i][v]
    # returning longest common ancestor of u,v
							return parent[0][u]






def    A__							(     UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase = 1   ,    ):
							A						=    0
							A						=    Queue(maxsize=UpperCamelCase			)
							q.put(UpperCamelCase			)
							while q.qsize() != 0:
														A						=    q.get()
														for v in graph[u]:
																					if level[v] == -1:
																												A						=    level[u] + 1
																												q.put(UpperCamelCase			)
																												A						=    u
							return level, parent






def    A__							(     ):
							A						=    13
							# initializing with 0
							A						=    [[0 for _ in range(max_node + 10			)] for _ in range(20			)]
							# initializing with -1 which means every node is unvisited
							A						=    [-1 for _ in range(max_node + 10			)]
							A						=    {
							    1: [2, 3, 4],
							    2: [5],
							    3: [6, 7],
							    4: [8],
							    5: [9, 10],
							    6: [11],
							    7: [],
							    8: [12, 13],
							    9: [],
							    10: [],
							    11: [],
							    12: [],
							    13: [],
							}
							A,      A						=    breadth_first_search(UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase   ,    1			)
							A						=    create_sparse(UpperCamelCase   ,    UpperCamelCase			)
							print("LCA of node 1 and 3 is: "   ,    lowest_common_ancestor(1   ,    3   ,    UpperCamelCase   ,    UpperCamelCase			)			)
							print("LCA of node 5 and 6 is: "   ,    lowest_common_ancestor(5   ,    6   ,    UpperCamelCase   ,    UpperCamelCase			)			)
							print("LCA of node 7 and 11 is: "   ,    lowest_common_ancestor(7   ,    11   ,    UpperCamelCase   ,    UpperCamelCase			)			)
							print("LCA of node 6 and 7 is: "   ,    lowest_common_ancestor(6   ,    7   ,    UpperCamelCase   ,    UpperCamelCase			)			)
							print("LCA of node 4 and 12 is: "   ,    lowest_common_ancestor(4   ,    12   ,    UpperCamelCase   ,    UpperCamelCase			)			)
							print("LCA of node 8 and 8 is: "   ,    lowest_common_ancestor(8   ,    8   ,    UpperCamelCase   ,    UpperCamelCase			)			)


if __name__ == "__main__":
		main()


