

"""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 	UpperCAmelCase__   (				A__   )      ->      List[str]:




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


SCREAMING_SNAKE_CASE_     :			Any	  =			logging.getLogger(__name__)



def 	UpperCAmelCase__   (				A__	,    A__   )      ->      Tuple:




   """simple docstring"""
   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=A__	,    filename=A__	,    monitor=f'val_{metric}'	,    mode="max"	,    save_top_k=1	,    every_n_epochs=1	,    )

   return checkpoint_callback



def 	UpperCAmelCase__   (				A__	,    A__   )      ->      List[Any]:




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


class 		_A       (			pl.Callback					):





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					Dict:
         lowerCamelCase__						=			{f'lr_group_{i}': param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups		)}
         pl_module.logger.log_metrics(SCREAMING_SNAKE_CASE__		)





      @rank_zero_only
      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=True		)   ->					None:
         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=SCREAMING_SNAKE_CASE__		)
            generations_file.parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE__		)
         with open(SCREAMING_SNAKE_CASE__    ,						"a+"		) as writer:
            for key in sorted(SCREAMING_SNAKE_CASE__		):
               if key in ["log", "progress_bar", "preds"]:
                  continue
               lowerCamelCase__						=			metrics[key]
               if isinstance(SCREAMING_SNAKE_CASE__    ,						torch.Tensor		):
                  lowerCamelCase__						=			val.item()
               lowerCamelCase__						=			f'{key}: {val:.6f}\n'
               writer.write(SCREAMING_SNAKE_CASE__		)

         if not save_generations:
            return

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





      @rank_zero_only
      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					Tuple:
         try:
            lowerCamelCase__						=			pl_module.model.model.num_parameters()
         except AttributeError:
            lowerCamelCase__						=			pl_module.model.num_parameters()

         lowerCamelCase__						=			count_trainable_parameters(SCREAMING_SNAKE_CASE__		)
         # 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    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					int:
         save_json(pl_module.metrics    ,						pl_module.metrics_save_path		)
         return self._write_logs(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						"test"		)





      @rank_zero_only
      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					Tuple:
         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 os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin


class 		_A       (			__a				,       unittest.TestCase					):
      __a								=	PhobertTokenizer
      __a								=	False





      def 					_lowerCamelCase  (  self		)   ->					Any:
         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         lowerCamelCase__						=			["T@@", "i", "I", "R@@", "r", "e@@"]
         lowerCamelCase__						=			dict(zip(SCREAMING_SNAKE_CASE__    ,						range(len(SCREAMING_SNAKE_CASE__		)		)		)		)
         lowerCamelCase__						=			["#version: 0.2", "l à</w>"]
         lowerCamelCase__						=			{"unk_token": "<unk>"}

         lowerCamelCase__						=			os.path.join(self.tmpdirname    ,						VOCAB_FILES_NAMES["vocab_file"]		)
         lowerCamelCase__						=			os.path.join(self.tmpdirname    ,						VOCAB_FILES_NAMES["merges_file"]		)

         with open(self.vocab_file    ,						"w"    ,						encoding="utf-8"		) as fp:
            for token in vocab_tokens:
               fp.write(f'{token} {vocab_tokens[token]}\n'		)
         with open(self.merges_file    ,						"w"    ,						encoding="utf-8"		) as fp:
            fp.write("\n".join(SCREAMING_SNAKE_CASE__		)		)





      def 					_lowerCamelCase  (  self    ,						**SCREAMING_SNAKE_CASE__		)   ->					Tuple:
         kwargs.update(self.special_tokens_map		)
         return PhobertTokenizer.from_pretrained(self.tmpdirname    ,						**SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					Tuple:
         lowerCamelCase__						=			"Tôi là VinAI Research"
         lowerCamelCase__						=			"T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
         return input_text, output_text





      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         lowerCamelCase__						=			PhobertTokenizer(self.vocab_file    ,						self.merges_file    ,						**self.special_tokens_map		)
         lowerCamelCase__						=			"Tôi là VinAI Research"
         lowerCamelCase__						=			"T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
         lowerCamelCase__						=			tokenizer.tokenize(SCREAMING_SNAKE_CASE__		)
         print(SCREAMING_SNAKE_CASE__		)
         self.assertListEqual(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			tokens + [tokenizer.unk_token]

         lowerCamelCase__						=			[4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__		)    ,						SCREAMING_SNAKE_CASE__		)




