


"""simple docstring"""




import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


_A			=						get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
   from transformers.models.mbart.modeling_mbart import shift_tokens_right

_A			=						25_0004
_A			=						25_0020






@require_sentencepiece
@require_tokenizers
class    UpperCAmelCase__	( A_       ,  unittest.TestCase		):



    """simple docstring"""






    UpperCAmelCase__						:		int              =  MBartTokenizer
    UpperCAmelCase__						:		Optional[int]              =  MBartTokenizerFast
    UpperCAmelCase__						:		List[str]              =  True
    UpperCAmelCase__						:		Optional[int]              =  True






    def _a (      self   )  ->	str:
         super().setUp()

         # We have a SentencePiece fixture for testing
         __UpperCamelCase   							=MBartTokenizer(_lowerCAmelCase ,       keep_accents=_lowerCAmelCase   )
         tokenizer.save_pretrained(self.tmpdirname   )






    def _a (      self   )  ->	Optional[Any]:
         __UpperCamelCase   							=MBartTokenizer(_lowerCAmelCase ,       keep_accents=_lowerCAmelCase   )

         __UpperCamelCase   							=tokenizer.tokenize('This is a test'   )
         self.assertListEqual(_lowerCAmelCase ,       ['▁This', '▁is', '▁a', '▁t', 'est']   )

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(_lowerCAmelCase   ) ,       [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] ,       )

         __UpperCamelCase   							=tokenizer.tokenize('I was born in 92000, and this is falsé.'   )
         self.assertListEqual(
             _lowerCAmelCase ,       [
                 SPIECE_UNDERLINE + 'I',
                 SPIECE_UNDERLINE + 'was',
                 SPIECE_UNDERLINE + 'b',
                 'or',
                 'n',
                 SPIECE_UNDERLINE + 'in',
                 SPIECE_UNDERLINE + '',
                 '9',
                 '2',
                 '0',
                 '0',
                 '0',
                 ',',
                 SPIECE_UNDERLINE + 'and',
                 SPIECE_UNDERLINE + 'this',
                 SPIECE_UNDERLINE + 'is',
                 SPIECE_UNDERLINE + 'f',
                 'al',
                 's',
                 'é',
                 '.',
             ] ,       )
         __UpperCamelCase   							=tokenizer.convert_tokens_to_ids(_lowerCAmelCase   )
         self.assertListEqual(
             _lowerCAmelCase ,       [
                 value + tokenizer.fairseq_offset
                 for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
                 #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
             ] ,       )

         __UpperCamelCase   							=tokenizer.convert_ids_to_tokens(_lowerCAmelCase   )
         self.assertListEqual(
             _lowerCAmelCase ,       [
                 SPIECE_UNDERLINE + 'I',
                 SPIECE_UNDERLINE + 'was',
                 SPIECE_UNDERLINE + 'b',
                 'or',
                 'n',
                 SPIECE_UNDERLINE + 'in',
                 SPIECE_UNDERLINE + '',
                 '<unk>',
                 '2',
                 '0',
                 '0',
                 '0',
                 ',',
                 SPIECE_UNDERLINE + 'and',
                 SPIECE_UNDERLINE + 'this',
                 SPIECE_UNDERLINE + 'is',
                 SPIECE_UNDERLINE + 'f',
                 'al',
                 's',
                 '<unk>',
                 '.',
             ] ,       )






    def _a (      self   )  ->	str:

         if not self.test_slow_tokenizer:
              # as we don't have a slow version, we can't compare the outputs between slow and fast versions
              return

         __UpperCamelCase   							=(self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-mbart', {})
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'   ):
                   __UpperCamelCase   							=self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase ,       **_lowerCAmelCase   )
                   __UpperCamelCase   							=self.tokenizer_class.from_pretrained(_lowerCAmelCase ,       **_lowerCAmelCase   )

                   __UpperCamelCase   							=tempfile.mkdtemp()

                   __UpperCamelCase   							=tokenizer_r.save_pretrained(_lowerCAmelCase   )
                   __UpperCamelCase   							=tokenizer_p.save_pretrained(_lowerCAmelCase   )

                   # Checks it save with the same files + the tokenizer.json file for the fast one
                   self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files   )   )
                   __UpperCamelCase   							=tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f   )
                   self.assertSequenceEqual(_lowerCAmelCase ,       _lowerCAmelCase   )

                   # Checks everything loads correctly in the same way
                   __UpperCamelCase   							=tokenizer_r.from_pretrained(_lowerCAmelCase   )
                   __UpperCamelCase   							=tokenizer_p.from_pretrained(_lowerCAmelCase   )

                   # Check special tokens are set accordingly on Rust and Python
                   for key in tokenizer_pp.special_tokens_map:
                        self.assertTrue(hasattr(_lowerCAmelCase ,       _lowerCAmelCase   )   )
                        # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                        # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

                   shutil.rmtree(_lowerCAmelCase   )

                   # Save tokenizer rust, legacy_format=True
                   __UpperCamelCase   							=tempfile.mkdtemp()

                   __UpperCamelCase   							=tokenizer_r.save_pretrained(_lowerCAmelCase ,       legacy_format=_lowerCAmelCase   )
                   __UpperCamelCase   							=tokenizer_p.save_pretrained(_lowerCAmelCase   )

                   # Checks it save with the same files
                   self.assertSequenceEqual(_lowerCAmelCase ,       _lowerCAmelCase   )

                   # Checks everything loads correctly in the same way
                   __UpperCamelCase   							=tokenizer_r.from_pretrained(_lowerCAmelCase   )
                   __UpperCamelCase   							=tokenizer_p.from_pretrained(_lowerCAmelCase   )

                   # Check special tokens are set accordingly on Rust and Python
                   for key in tokenizer_pp.special_tokens_map:
                        self.assertTrue(hasattr(_lowerCAmelCase ,       _lowerCAmelCase   )   )

                   shutil.rmtree(_lowerCAmelCase   )

                   # Save tokenizer rust, legacy_format=False
                   __UpperCamelCase   							=tempfile.mkdtemp()

                   __UpperCamelCase   							=tokenizer_r.save_pretrained(_lowerCAmelCase ,       legacy_format=_lowerCAmelCase   )
                   __UpperCamelCase   							=tokenizer_p.save_pretrained(_lowerCAmelCase   )

                   # Checks it saved the tokenizer.json file
                   self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files   )   )

                   # Checks everything loads correctly in the same way
                   __UpperCamelCase   							=tokenizer_r.from_pretrained(_lowerCAmelCase   )
                   __UpperCamelCase   							=tokenizer_p.from_pretrained(_lowerCAmelCase   )

                   # Check special tokens are set accordingly on Rust and Python
                   for key in tokenizer_pp.special_tokens_map:
                        self.assertTrue(hasattr(_lowerCAmelCase ,       _lowerCAmelCase   )   )

                   shutil.rmtree(_lowerCAmelCase   )






@require_torch
@require_sentencepiece
@require_tokenizers
class    UpperCAmelCase__	( unittest.TestCase		):



    """simple docstring"""






    UpperCAmelCase__						:		List[Any]              =  "facebook/mbart-large-en-ro"
    UpperCAmelCase__						:		List[Any]              =  [
        " UN Chief Says There Is No Military Solution in Syria",
        " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.",
    ]
    UpperCAmelCase__						:		Dict              =  [
        "Şeful ONU declară că nu există o soluţie militară în Siria",
        "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
        " pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"
        " face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
    ]
    UpperCAmelCase__						:		List[Any]              =  [8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2, EN_CODE]






    @classmethod
    def _a (      cls   )  ->	Tuple:
         __UpperCamelCase   							=MBartTokenizer.from_pretrained(
             cls.checkpoint_name ,       src_lang='en_XX' ,       tgt_lang='ro_RO'   )
         __UpperCamelCase   							=1
         return cls






    def _a (      self   )  ->	int:
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR'] ,       250001   )
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN'] ,       250004   )
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO'] ,       250020   )






    def _a (      self   )  ->	List[Any]:
         __UpperCamelCase   							=self.tokenizer.batch_encode_plus(self.src_text   ).input_ids[0]
         self.assertListEqual(self.expected_src_tokens ,       _lowerCAmelCase   )






    def _a (      self   )  ->	str:
         self.assertIn(_lowerCAmelCase ,       self.tokenizer.all_special_ids   )
         __UpperCamelCase   							=[RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
         __UpperCamelCase   							=self.tokenizer.decode(_lowerCAmelCase ,       skip_special_tokens=_lowerCAmelCase   )
         __UpperCamelCase   							=self.tokenizer.decode(generated_ids[1:] ,       skip_special_tokens=_lowerCAmelCase   )
         self.assertEqual(_lowerCAmelCase ,       _lowerCAmelCase   )
         self.assertNotIn(self.tokenizer.eos_token ,       _lowerCAmelCase   )






    def _a (      self   )  ->	int:
         __UpperCamelCase   							=['this is gunna be a long sentence ' * 20]
         assert isinstance(src_text[0] ,       _lowerCAmelCase   )
         __UpperCamelCase   							=10
         __UpperCamelCase   							=self.tokenizer(_lowerCAmelCase ,       max_length=_lowerCAmelCase ,       truncation=_lowerCAmelCase   ).input_ids[0]
         self.assertEqual(ids[-2] ,       2   )
         self.assertEqual(ids[-1] ,       _lowerCAmelCase   )
         self.assertEqual(len(_lowerCAmelCase   ) ,       _lowerCAmelCase   )






    def _a (      self   )  ->	Any:
         self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']   ) ,       [250026, 250001]   )






    def _a (      self   )  ->	Dict:
         __UpperCamelCase   							=tempfile.mkdtemp()
         __UpperCamelCase   							=self.tokenizer.fairseq_tokens_to_ids
         self.tokenizer.save_pretrained(_lowerCAmelCase   )
         __UpperCamelCase   							=MBartTokenizer.from_pretrained(_lowerCAmelCase   )
         self.assertDictEqual(new_tok.fairseq_tokens_to_ids ,       _lowerCAmelCase   )






    @require_torch
    def _a (      self   )  ->	Tuple:
         __UpperCamelCase   							=self.tokenizer(self.src_text ,       text_target=self.tgt_text ,       padding=_lowerCAmelCase ,       return_tensors='pt'   )
         __UpperCamelCase   							=shift_tokens_right(batch['labels'] ,       self.tokenizer.pad_token_id   )

         # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
         assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
         assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
         assert batch.decoder_input_ids[1][-1] == 2
         assert batch.labels[1][-2:].tolist() == [2, RO_CODE]






    @require_torch
    def _a (      self   )  ->	Any:
         __UpperCamelCase   							=self.tokenizer(
             self.src_text ,       text_target=self.tgt_text ,       padding=_lowerCAmelCase ,       truncation=_lowerCAmelCase ,       max_length=len(self.expected_src_tokens   ) ,       return_tensors='pt' ,       )

         __UpperCamelCase   							=shift_tokens_right(batch['labels'] ,       self.tokenizer.pad_token_id   )

         self.assertIsInstance(_lowerCAmelCase ,       _lowerCAmelCase   )

         self.assertEqual((2, 14) ,       batch.input_ids.shape   )
         self.assertEqual((2, 14) ,       batch.attention_mask.shape   )
         __UpperCamelCase   							=batch.input_ids.tolist()[0]
         self.assertListEqual(self.expected_src_tokens ,       _lowerCAmelCase   )
         self.assertEqual(2 ,       batch.decoder_input_ids[0, -1]   )  # EOS
         # Test that special tokens are reset
         self.assertEqual(self.tokenizer.prefix_tokens ,       []   )
         self.assertEqual(self.tokenizer.suffix_tokens ,       [self.tokenizer.eos_token_id, EN_CODE]   )






    def _a (      self   )  ->	Union[str, Any]:
         __UpperCamelCase   							=self.tokenizer(self.src_text ,       padding=_lowerCAmelCase ,       truncation=_lowerCAmelCase ,       max_length=3 ,       return_tensors='pt'   )
         __UpperCamelCase   							=self.tokenizer(
             text_target=self.tgt_text ,       padding=_lowerCAmelCase ,       truncation=_lowerCAmelCase ,       max_length=10 ,       return_tensors='pt'   )
         __UpperCamelCase   							=targets['input_ids']
         __UpperCamelCase   							=shift_tokens_right(_lowerCAmelCase ,       self.tokenizer.pad_token_id   )

         self.assertEqual(batch.input_ids.shape[1] ,       3   )
         self.assertEqual(batch.decoder_input_ids.shape[1] ,       10   )






    @require_torch
    def _a (      self   )  ->	str:
         __UpperCamelCase   							=self.tokenizer._build_translation_inputs(
             'A test' ,       return_tensors='pt' ,       src_lang='en_XX' ,       tgt_lang='ar_AR'   )

         self.assertEqual(
             nested_simplify(_lowerCAmelCase   ) ,       {
                 # A, test, EOS, en_XX
                 'input_ids': [[62, 3034, 2, 250004]],
                 'attention_mask': [[1, 1, 1, 1]],
                 # ar_AR
                 'forced_bos_token_id': 250001,
             } ,       )









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    (      SCREAMING_SNAKE_CASE__      : Tuple    ):
     __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			=						logging.getLogger(__name__)

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Tuple      ,					SCREAMING_SNAKE_CASE__      : Optional[Any]    ):
     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=SCREAMING_SNAKE_CASE__      ,					filename=SCREAMING_SNAKE_CASE__      ,					monitor=F'val_{metric}'      ,					mode='max'      ,					save_top_k=1      ,					every_n_epochs=1      ,					)

     return checkpoint_callback

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : Union[str, Any]    ):
     return EarlyStopping(
         monitor=F'val_{metric}'      ,					mode='min' if 'loss' in metric else 'max'      ,					patience=SCREAMING_SNAKE_CASE__      ,					verbose=SCREAMING_SNAKE_CASE__      ,					)






class    UpperCAmelCase__	( pl.Callback		):



    """simple docstring"""






    def _a (      self ,       A_ ,       A_   )  ->	int:
         __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 _a (      self ,       A_ ,       A_ ,       A_ ,       A_=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=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 _a (      self ,       A_ ,       A_   )  ->	Optional[int]:
         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 _a (      self ,       A_ ,       A_   )  ->	List[str]:
         save_json(pl_module.metrics ,       pl_module.metrics_save_path   )
         return self._write_logs(A_ ,       A_ ,       'test'   )






    @rank_zero_only
    def _a (      self ,       A_ ,       A_   )  ->	List[str]:
         save_json(pl_module.metrics ,       pl_module.metrics_save_path   )
         # Uncommenting this will save val generations
         # return self._write_logs(trainer, pl_module, "valid")





