






'''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 						A__			(				__lowerCAmelCase		:			str						):
						lowerCamelCase__         =  filter(lambda __lowerCAmelCase						: p.requires_grad   ,    model.parameters()						)
						lowerCamelCase__         =  sum([np.prod(p.size()						) for p in model_parameters]						)
						return params


UpperCamelCase    :      int  			=		logging.getLogger(__name__)
def 						A__			(				__lowerCAmelCase		:			List[Any]   ,    __lowerCAmelCase		:			Union[str, Any]						):
						if metric == "rouge2":
												lowerCamelCase__         =  """{val_avg_rouge2:.4f}-{step_count}"""
						elif metric == "bleu":
												lowerCamelCase__         =  """{val_avg_bleu:.4f}-{step_count}"""
						elif metric == "em":
												lowerCamelCase__         =  """{val_avg_em:.4f}-{step_count}"""
						elif metric == "loss":
												lowerCamelCase__         =  """{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."""						)

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

						return checkpoint_callback
def 						A__			(				__lowerCAmelCase		:			Optional[int]   ,    __lowerCAmelCase		:			Tuple						):
						return EarlyStopping(
						    monitor=F'''val_{metric}'''   ,    mode="""min""" if """loss""" in metric else """max"""   ,    patience=__lowerCAmelCase   ,    verbose=__lowerCAmelCase   ,    )





class 		UpperCamelCase__    (pl.Callback			):




		'''simple docstring'''





		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  {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   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase=True       ):
								logger.info(F'''***** {type_path} results at step {trainer.global_step:05d} *****'''       )
								lowerCamelCase__         =  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
								lowerCamelCase__         =  Path(pl_module.hparams.output_dir       )
								if type_path == "test":
														lowerCamelCase__         =  od / """test_results.txt"""
														lowerCamelCase__         =  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.
														lowerCamelCase__         =  od / F'''{type_path}_results/{trainer.global_step:05d}.txt'''
														lowerCamelCase__         =  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
																				lowerCamelCase__         =  metrics[key]
																				if isinstance(_lowerCAmelCase   ,torch.Tensor       ):
																										lowerCamelCase__         =  val.item()
																				lowerCamelCase__         =  F'''{key}: {val:.6f}\n'''
																				writer.write(_lowerCAmelCase       )

								if not save_generations:
														return

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






		@rank_zero_only
		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
								try:
														lowerCamelCase__         =  pl_module.model.model.num_parameters()
								except AttributeError:
														lowerCamelCase__         =  pl_module.model.num_parameters()

								lowerCamelCase__         =  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   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
								save_json(pl_module.metrics   ,pl_module.metrics_save_path       )
								return self._write_logs(_lowerCAmelCase   ,_lowerCAmelCase   ,"""test"""       )






		@rank_zero_only
		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
								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'''







import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing




class 		UpperCamelCase__    (a			):




		'''simple docstring'''





		def __init__(      self   ,_lowerCAmelCase = "▁"   ,_lowerCAmelCase = True   ,_lowerCAmelCase = "<unk>"   ,_lowerCAmelCase = "</s>"   ,_lowerCAmelCase = "<pad>"   ,):
								lowerCamelCase__         =  {
								    """pad""": {"""id""": 0, """token""": pad_token},
								    """eos""": {"""id""": 1, """token""": eos_token},
								    """unk""": {"""id""": 2, """token""": unk_token},
								}

								lowerCamelCase__         =  [None] * len(self.special_tokens       )
								for token_dict in self.special_tokens.values():
														lowerCamelCase__         =  token_dict["""token"""]

								lowerCamelCase__         =  Tokenizer(Unigram()       )

								lowerCamelCase__         =  normalizers.Sequence(
								    [
								        normalizers.Nmt(),
								        normalizers.NFKC(),
								        normalizers.Replace(Regex(""" {2,}"""       )   ,""" """       ),
								        normalizers.Lowercase(),
								    ]       )
								lowerCamelCase__         =  pre_tokenizers.Sequence(
								    [
								        pre_tokenizers.Metaspace(replacement=_lowerCAmelCase   ,add_prefix_space=_lowerCAmelCase       ),
								        pre_tokenizers.Digits(individual_digits=_lowerCAmelCase       ),
								        pre_tokenizers.Punctuation(),
								    ]       )
								lowerCamelCase__         =  decoders.Metaspace(replacement=_lowerCAmelCase   ,add_prefix_space=_lowerCAmelCase       )

								lowerCamelCase__         =  TemplateProcessing(
								    single=F'''$A {self.special_tokens["eos"]["token"]}'''   ,special_tokens=[(self.special_tokens["""eos"""]["""token"""], self.special_tokens["""eos"""]["""id"""])]   ,)

								lowerCamelCase__         =  {
								    """model""": """SentencePieceUnigram""",
								    """replacement""": replacement,
								    """add_prefix_space""": add_prefix_space,
								}

								super().__init__(_lowerCAmelCase   ,_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase = 80_00   ,_lowerCAmelCase = True   ,):
								lowerCamelCase__         =  trainers.UnigramTrainer(
								    vocab_size=_lowerCAmelCase   ,special_tokens=self.special_tokens_list   ,show_progress=_lowerCAmelCase   ,)

								if isinstance(_lowerCAmelCase   ,_lowerCAmelCase       ):
														lowerCamelCase__         =  [files]
								self._tokenizer.train(_lowerCAmelCase   ,trainer=_lowerCAmelCase       )

								self.add_unk_id()






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase = 80_00   ,_lowerCAmelCase = True   ,):
								lowerCamelCase__         =  trainers.UnigramTrainer(
								    vocab_size=_lowerCAmelCase   ,special_tokens=self.special_tokens_list   ,show_progress=_lowerCAmelCase   ,)

								self._tokenizer.train_from_iterator(_lowerCAmelCase   ,trainer=_lowerCAmelCase       )

								self.add_unk_id()






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  json.loads(self._tokenizer.to_str()       )

								lowerCamelCase__         =  self.special_tokens["""unk"""]["""id"""]

								lowerCamelCase__         =  Tokenizer.from_str(json.dumps(_lowerCAmelCase       )       )

