



"""simple docstring"""


from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

__A   :							List[str]										=   TypeVar("T")
class 			_a (					Generic[T]):
							"""simple docstring"""



							def __init__(		self     :    int				,   __UpperCamelCase     :    bool = True   )->None:
											_UpperCAmelCase					=    {}  # dictionary of lists
											_UpperCAmelCase					=    directed



							def  lowercase__						(		self     :    Optional[Any]				,   __UpperCamelCase     :    T				,   __UpperCamelCase     :    T   )->GraphAdjacencyList[T]:

											if not self.directed:  # For undirected graphs
															# if both source vertex and destination vertex are both present in the
															# adjacency list, add destination vertex to source vertex list of adjacent
															# vertices and add source vertex to destination vertex list of adjacent
															# vertices.
															if source_vertex in self.adj_list and destination_vertex in self.adj_list:
																			self.adj_list[source_vertex].append(__UpperCamelCase   )
																			self.adj_list[destination_vertex].append(__UpperCamelCase   )
															# if only source vertex is present in adjacency list, add destination vertex
															# to source vertex list of adjacent vertices, then create a new vertex with
															# destination vertex as key and assign a list containing the source vertex
															# as it's first adjacent vertex.
															elif source_vertex in self.adj_list:
																			self.adj_list[source_vertex].append(__UpperCamelCase   )
																			_UpperCAmelCase					=    [source_vertex]
															# if only destination vertex is present in adjacency list, add source vertex
															# to destination vertex list of adjacent vertices, then create a new vertex
															# with source vertex as key and assign a list containing the source vertex
															# as it's first adjacent vertex.
															elif destination_vertex in self.adj_list:
																			self.adj_list[destination_vertex].append(__UpperCamelCase   )
																			_UpperCAmelCase					=    [destination_vertex]
															# if both source vertex and destination vertex are not present in adjacency
															# list, create a new vertex with source vertex as key and assign a list
															# containing the destination vertex as it's first adjacent vertex also
															# create a new vertex with destination vertex as key and assign a list
															# containing the source vertex as it's first adjacent vertex.
															else:
																			_UpperCAmelCase					=    [destination_vertex]
																			_UpperCAmelCase					=    [source_vertex]
											else:  # For directed graphs
															# if both source vertex and destination vertex are present in adjacency
															# list, add destination vertex to source vertex list of adjacent vertices.
															if source_vertex in self.adj_list and destination_vertex in self.adj_list:
																			self.adj_list[source_vertex].append(__UpperCamelCase   )
															# if only source vertex is present in adjacency list, add destination
															# vertex to source vertex list of adjacent vertices and create a new vertex
															# with destination vertex as key, which has no adjacent vertex
															elif source_vertex in self.adj_list:
																			self.adj_list[source_vertex].append(__UpperCamelCase   )
																			_UpperCAmelCase					=    []
															# if only destination vertex is present in adjacency list, create a new
															# vertex with source vertex as key and assign a list containing destination
															# vertex as first adjacent vertex
															elif destination_vertex in self.adj_list:
																			_UpperCAmelCase					=    [destination_vertex]
															# if both source vertex and destination vertex are not present in adjacency
															# list, create a new vertex with source vertex as key and a list containing
															# destination vertex as it's first adjacent vertex. Then create a new vertex
															# with destination vertex as key, which has no adjacent vertex
															else:
																			_UpperCAmelCase					=    [destination_vertex]
																			_UpperCAmelCase					=    []

											return self



							def __repr__(		self     :    List[str]   )->str:
											return pformat(self.adj_list   )








"""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	(      _SCREAMING_SNAKE_CASE   :						Optional[Any]				):






				'''simple docstring'''



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


__A   :							Dict										=   logging.getLogger(__name__)







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Optional[Any]					,     _SCREAMING_SNAKE_CASE   :						Any				):






				'''simple docstring'''



				if metric == "rouge2":
								_UpperCAmelCase					=    '''{val_avg_rouge2:.4f}-{step_count}'''
				elif metric == "bleu":
								_UpperCAmelCase					=    '''{val_avg_bleu:.4f}-{step_count}'''
				elif metric == "em":
								_UpperCAmelCase					=    '''{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.'''				)

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







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						List[str]				):






				'''simple docstring'''



				return EarlyStopping(
				    monitor=f'val_{metric}'					,     mode='''min''' if '''loss''' in metric else '''max'''					,     patience=_SCREAMING_SNAKE_CASE					,     verbose=_SCREAMING_SNAKE_CASE					,     )
class 			_a (					pl.Callback):
			"""simple docstring"""



			def  lowercase__						(		self     :    List[str]				,   __UpperCamelCase     :    str				,   __UpperCamelCase     :    str   )->Tuple:
							_UpperCAmelCase					=    {F'lr_group_{i}': param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups   )}
							pl_module.logger.log_metrics(__UpperCamelCase   )



			@rank_zero_only
			def  lowercase__						(		self     :    Union[str, Any]				,   __UpperCamelCase     :    pl.Trainer				,   __UpperCamelCase     :    pl.LightningModule				,   __UpperCamelCase     :    str				,   __UpperCamelCase     :    Tuple=True   )->None:
							logger.info(F'***** {type_path} results at step {trainer.global_step:05d} *****'   )
							_UpperCAmelCase					=    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
							_UpperCAmelCase					=    Path(pl_module.hparams.output_dir   )
							if type_path == "test":
											_UpperCAmelCase					=    od / '''test_results.txt'''
											_UpperCAmelCase					=    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.
											_UpperCAmelCase					=    od / F'{type_path}_results/{trainer.global_step:05d}.txt'
											_UpperCAmelCase					=    od / F'{type_path}_generations/{trainer.global_step:05d}.txt'
											results_file.parent.mkdir(exist_ok=__UpperCamelCase   )
											generations_file.parent.mkdir(exist_ok=__UpperCamelCase   )
							with open(__UpperCamelCase				,   '''a+'''   ) as writer:
											for key in sorted(__UpperCamelCase   ):
															if key in ["log", "progress_bar", "preds"]:
																			continue
															_UpperCAmelCase					=    metrics[key]
															if isinstance(__UpperCamelCase				,   torch.Tensor   ):
																			_UpperCAmelCase					=    val.item()
															_UpperCAmelCase					=    F'{key}: {val:.6f}\n'
															writer.write(__UpperCamelCase   )

							if not save_generations:
											return

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



			@rank_zero_only
			def  lowercase__						(		self     :    Optional[int]				,   __UpperCamelCase     :    int				,   __UpperCamelCase     :    Dict   )->Union[str, Any]:
							try:
											_UpperCAmelCase					=    pl_module.model.model.num_parameters()
							except AttributeError:
											_UpperCAmelCase					=    pl_module.model.num_parameters()

							_UpperCAmelCase					=    count_trainable_parameters(__UpperCamelCase   )
							# 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  lowercase__						(		self     :    str				,   __UpperCamelCase     :    pl.Trainer				,   __UpperCamelCase     :    pl.LightningModule   )->List[Any]:
							save_json(pl_module.metrics				,   pl_module.metrics_save_path   )
							return self._write_logs(__UpperCamelCase				,   __UpperCamelCase				,   '''test'''   )



			@rank_zero_only
			def  lowercase__						(		self     :    Optional[Any]				,   __UpperCamelCase     :    pl.Trainer				,   __UpperCamelCase     :    List[str]   )->Optional[Any]:
							save_json(pl_module.metrics				,   pl_module.metrics_save_path   )
							# Uncommenting this will save val generations
							# return self._write_logs(trainer, pl_module, "valid")



