



import copy
import random

from transformers import CLIPTokenizer







class     __lowerCAmelCase					(    _UpperCAmelCase		):



			def __init__(		self     :						Optional[int]				,	*A     :						Union[str, Any]				,	**A     :						str)     ->					Optional[int]:



						"""simple docstring"""






						super().__init__(*lowerCamelCase_				,	**lowerCamelCase_)
						_UpperCAmelCase	 =     {}



			def 					_lowerCamelCase (		self     :						Dict				,	A     :						List[Any]				,	*A     :						int				,	**A     :						Union[str, Any])     ->					Dict:



						"""simple docstring"""






						_UpperCAmelCase	 =     super().add_tokens(lowerCamelCase_				,	*lowerCamelCase_				,	**lowerCamelCase_)
						if num_added_tokens == 0:
									raise ValueError(
									    F"The tokenizer already contains the token {placeholder_token}. Please pass a different"
									    ' `placeholder_token` that is not already in the tokenizer.')



			def 					_lowerCamelCase (		self     :						Dict				,	A     :						List[Any]				,	*A     :						Optional[Any]				,	A     :						str=1				,	**A     :						Tuple)     ->					Dict:



						"""simple docstring"""






						_UpperCAmelCase	 =     []
						if num_vec_per_token == 1:
									self.try_adding_tokens(lowerCamelCase_				,	*lowerCamelCase_				,	**lowerCamelCase_)
									output.append(lowerCamelCase_)
						else:
									_UpperCAmelCase	 =     []
									for i in range(lowerCamelCase_):
												_UpperCAmelCase	 =     placeholder_token + F"_{i}"
												self.try_adding_tokens(lowerCamelCase_				,	*lowerCamelCase_				,	**lowerCamelCase_)
												output.append(lowerCamelCase_)
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
						for token in self.token_map:
									if token in placeholder_token:
												raise ValueError(
												    F"The tokenizer already has placeholder token {token} that can get confused with"
												    F" {placeholder_token}keep placeholder tokens independent")
						_UpperCAmelCase	 =     output



			def 					_lowerCamelCase (		self     :						Tuple				,	A     :						Union[str, Any]				,	A     :						Tuple=False				,	A     :						Optional[int]=1.0)     ->					Any:



						"""simple docstring"""






						if isinstance(lowerCamelCase_				,	lowerCamelCase_):
									_UpperCAmelCase	 =     []
									for i in range(len(lowerCamelCase_)):
												output.append(self.replace_placeholder_tokens_in_text(text[i]				,	vector_shuffle=lowerCamelCase_))
									return output
						for placeholder_token in self.token_map:
									if placeholder_token in text:
												_UpperCAmelCase	 =     self.token_map[placeholder_token]
												_UpperCAmelCase	 =     tokens[: 1 + int(len(lowerCamelCase_) * prop_tokens_to_load)]
												if vector_shuffle:
															_UpperCAmelCase	 =     copy.copy(lowerCamelCase_)
															random.shuffle(lowerCamelCase_)
												_UpperCAmelCase	 =     text.replace(lowerCamelCase_				,	' '.join(lowerCamelCase_))
						return text



			def __call__(		self     :						Any				,	A     :						Union[str, Any]				,	*A     :						str				,	A     :						Optional[int]=False				,	A     :						Tuple=1.0				,	**A     :						Tuple)     ->					Any:



						"""simple docstring"""






						return super().__call__(
						    self.replace_placeholder_tokens_in_text(
						        lowerCamelCase_				,	vector_shuffle=lowerCamelCase_				,	prop_tokens_to_load=lowerCamelCase_)				,	*lowerCamelCase_				,	**lowerCamelCase_				,	)



			def 					_lowerCamelCase (		self     :						Tuple				,	A     :						str				,	*A     :						Union[str, Any]				,	A     :						Optional[Any]=False				,	A     :						List[str]=1.0				,	**A     :						Union[str, Any])     ->					Optional[int]:



						"""simple docstring"""






						return super().encode(
						    self.replace_placeholder_tokens_in_text(
						        lowerCamelCase_				,	vector_shuffle=lowerCamelCase_				,	prop_tokens_to_load=lowerCamelCase_)				,	*lowerCamelCase_				,	**lowerCamelCase_				,	)





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 							A			(  _UpperCAmelCase       :     List[Any]					)       ->				int:







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


UpperCAmelCase__     = logging.getLogger(__name__)





def 							A			(  _UpperCAmelCase       :     List[Any]							,     _UpperCAmelCase       :     Union[str, Any]					)       ->				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}'
			elif metric == "loss":
						_UpperCAmelCase	 =     '{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.'					)

			_UpperCAmelCase	 =     ModelCheckpoint(
			    dirpath=_UpperCAmelCase							,     filename=_UpperCAmelCase							,     monitor=F"val_{metric}"							,     mode='max'							,     save_top_k=1							,     every_n_epochs=1							,     )

			return checkpoint_callback





def 							A			(  _UpperCAmelCase       :     Any							,     _UpperCAmelCase       :     int					)       ->				str:







			'''simple docstring'''
			return EarlyStopping(
			    monitor=F"val_{metric}"							,     mode='min' if 'loss' in metric else 'max'							,     patience=_UpperCAmelCase							,     verbose=_UpperCAmelCase							,     )







class     __lowerCAmelCase					(    pl.Callback		):



			def 					_lowerCamelCase (		self     :						Optional[int]				,	A     :						List[Any]				,	A     :						int)     ->					Dict:



						"""simple docstring"""






						_UpperCAmelCase	 =     {F"lr_group_{i}": param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups)}
						pl_module.logger.log_metrics(A)



			@rank_zero_only
			def 					_lowerCamelCase (		self     :						Optional[Any]				,	A     :						pl.Trainer				,	A     :						pl.LightningModule				,	A     :						str				,	A     :						int=True)     ->					None:



						"""simple docstring"""






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

						if not save_generations:
									return

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



			@rank_zero_only
			def 					_lowerCamelCase (		self     :						str				,	A     :						Optional[int]				,	A     :						List[str])     ->					Optional[Any]:



						"""simple docstring"""






						try:
									_UpperCAmelCase	 =     pl_module.model.model.num_parameters()
						except AttributeError:
									_UpperCAmelCase	 =     pl_module.model.num_parameters()

						_UpperCAmelCase	 =     count_trainable_parameters(A)
						# 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 					_lowerCamelCase (		self     :						Dict				,	A     :						pl.Trainer				,	A     :						pl.LightningModule)     ->					int:



						"""simple docstring"""






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



			@rank_zero_only
			def 					_lowerCamelCase (		self     :						Tuple				,	A     :						pl.Trainer				,	A     :						str)     ->					Dict:



						"""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")
