





'''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


lowercase__		:     Tuple			=							get_tests_dir('fixtures/test_sentencepiece.model')


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

lowercase__		:     Dict			=							25_00_04
lowercase__		:     List[Any]			=							25_00_20
@require_sentencepiece
@require_tokenizers
class 							__lowerCAmelCase	(       __magic_name__		,		unittest.TestCase					):






       """simple docstring"""
       _snake_case      :	Dict									=       MBartTokenizer
       _snake_case      :	Tuple									=       MBartTokenizerFast
       _snake_case      :	Union[str, Any]									=       True
       _snake_case      :	Tuple									=       True





       def snake_case__		(				self  :						List[Any]	)       ->	Dict:

            '''simple docstring'''




            super().setUp()

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





       def snake_case__		(				self  :						Any	)       ->	Optional[Any]:

            '''simple docstring'''




            _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 snake_case__		(				self  :						Optional[int]	)       ->	Tuple:

            '''simple docstring'''




            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 							__lowerCAmelCase	(       unittest.TestCase					):






       """simple docstring"""
       _snake_case      :	Tuple									=       'facebook/mbart-large-en-ro'
       _snake_case      :	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.',
       ]
       _snake_case      :	Any									=       [
           '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.',
       ]
       _snake_case      :	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 snake_case__		(				cls  :						str	)       ->	int:

            '''simple docstring'''




            _UpperCamelCase							       =			MBartTokenizer.from_pretrained(
                cls.checkpoint_name			,					src_lang='''en_XX'''			,					tgt_lang='''ro_RO'''	)
            _UpperCamelCase							       =			1
            return cls





       def snake_case__		(				self  :						Any	)       ->	str:

            '''simple docstring'''




            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 snake_case__		(				self  :						Optional[Any]	)       ->	List[str]:

            '''simple docstring'''




            _UpperCamelCase							       =			self.tokenizer.batch_encode_plus(self.src_text	).input_ids[0]
            self.assertListEqual(self.expected_src_tokens			,					lowerCAmelCase__	)





       def snake_case__		(				self  :						Union[str, Any]	)       ->	int:

            '''simple docstring'''




            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 snake_case__		(				self  :						Dict	)       ->	Union[str, Any]:

            '''simple docstring'''




            _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 snake_case__		(				self  :						Dict	)       ->	List[Any]:

            '''simple docstring'''




            self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR''']	)			,					[250026, 250001]	)





       def snake_case__		(				self  :						int	)       ->	List[Any]:

            '''simple docstring'''




            _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 snake_case__		(				self  :						int	)       ->	Optional[Any]:

            '''simple docstring'''




            _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 snake_case__		(				self  :						Tuple	)       ->	Tuple:

            '''simple docstring'''




            _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 snake_case__		(				self  :						Dict	)       ->	Optional[Any]:

            '''simple docstring'''




            _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 snake_case__		(				self  :						str	)       ->	Tuple:

            '''simple docstring'''




            _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 argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader

from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
    ROUGE_KEYS,
    LegacySeqaSeqDataset,
    SeqaSeqDataset,
    assert_all_frozen,
    calculate_bleu,
    calculate_rouge,
    check_output_dir,
    flatten_list,
    freeze_embeds,
    freeze_params,
    get_git_info,
    label_smoothed_nll_loss,
    lmap,
    pickle_save,
    save_git_info,
    save_json,
    use_task_specific_params,
)


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa


_SCREAMING_SNAKE_CASE   =  logging.getLogger(__name__)






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		int					='''summarization'''
       __lowerCAmelCase    :		str					=['''loss''']
       __lowerCAmelCase    :		Dict					=ROUGE_KEYS
       __lowerCAmelCase    :		str					='''rouge2'''





       def __init__(				self	:Dict,      snake_case	:List[Any],      **snake_case	:Tuple):



          """simple docstring"""

          if hparams.sortish_sampler and hparams.gpus > 1:
             _lowercase              =False
          elif hparams.max_tokens_per_batch is not None:
             if hparams.gpus > 1:
                raise NotImplementedError('Dynamic Batch size does not work for multi-gpu training')
             if hparams.sortish_sampler:
                raise ValueError('--sortish_sampler and --max_tokens_per_batch may not be used simultaneously')

          super().__init__(snake_case,      num_labels=snake_case,      mode=self.mode,      **snake_case)
          use_task_specific_params(self.model,      'summarization')
          save_git_info(self.hparams.output_dir)
          _lowercase              =Path(self.output_dir) / 'metrics.json'
          _lowercase              =Path(self.output_dir) / 'hparams.pkl'
          pickle_save(self.hparams,      self.hparams_save_path)
          _lowercase              =0
          _lowercase              =defaultdict(snake_case)
          _lowercase              =self.config.model_type
          _lowercase              =self.config.tgt_vocab_size if self.model_type == 'fsmt' else self.config.vocab_size

          _lowercase              ={
              "data_dir": self.hparams.data_dir,
              "max_source_length": self.hparams.max_source_length,
              "prefix": self.model.config.prefix or "",
          }
          _lowercase              ={
              'train': self.hparams.n_train,
              'val': self.hparams.n_val,
              'test': self.hparams.n_test,
          }
          _lowercase              ={k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

          _lowercase              ={
              'train': self.hparams.max_target_length,
              'val': self.hparams.val_max_target_length,
              'test': self.hparams.test_max_target_length,
          }
          assert self.target_lens["train"] <= self.target_lens["val"], f'''target_lens: {self.target_lens}'''
          assert self.target_lens["train"] <= self.target_lens["test"], f'''target_lens: {self.target_lens}'''
          if self.hparams.freeze_embeds:
             freeze_embeds(self.model)
          if self.hparams.freeze_encoder:
             freeze_params(self.model.get_encoder())
             assert_all_frozen(self.model.get_encoder())

          _lowercase              =get_git_info()['repo_sha']
          _lowercase              =hparams.num_workers
          _lowercase              =None  # default to config
          if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer,      snake_case):
             _lowercase              =self.tokenizer.lang_code_to_id[hparams.tgt_lang]
             _lowercase              =self.decoder_start_token_id
          _lowercase              =(
              SeqaSeqDataset if hasattr(self.tokenizer,      'prepare_seq2seq_batch') else LegacySeqaSeqDataset
          )
          _lowercase              =False
          _lowercase              =self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
          if self.hparams.eval_max_gen_length is not None:
             _lowercase              =self.hparams.eval_max_gen_length
          else:
             _lowercase              =self.model.config.max_length
          _lowercase              =self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric





       def 				UpperCamelCase__						(				self	:str,      snake_case	:Dict[str, torch.Tensor]):



          """simple docstring"""

          _lowercase              ={
              k: self.tokenizer.batch_decode(v.tolist()) if 'mask' not in k else v.shape for k, v in batch.items()
          }
          save_json(snake_case,      Path(self.output_dir) / 'text_batch.json')
          save_json({k: v.tolist() for k, v in batch.items()},      Path(self.output_dir) / 'tok_batch.json')

          _lowercase              =True
          return readable_batch





       def 				UpperCamelCase__						(				self	:Dict,      snake_case	:List[str],      **snake_case	:List[Any]):



          """simple docstring"""

          return self.model(snake_case,      **snake_case)





       def 				UpperCamelCase__						(				self	:Any,      snake_case	:List[int]):



          """simple docstring"""

          _lowercase              =self.tokenizer.batch_decode(
              snake_case,      skip_special_tokens=snake_case,      clean_up_tokenization_spaces=snake_case)
          return lmap(str.strip,      snake_case)





       def 				UpperCamelCase__						(				self	:Union[str, Any],      snake_case	:dict):



          """simple docstring"""

          _lowercase              =self.tokenizer.pad_token_id
          _lowercase       ,       _lowercase              =batch['input_ids'], batch['attention_mask']
          _lowercase              =batch['labels']
          if isinstance(self.model,      snake_case):
             _lowercase              =self.model._shift_right(snake_case)
          else:
             _lowercase              =shift_tokens_right(snake_case,      snake_case)
          if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
             _lowercase              =decoder_input_ids
             self.save_readable_batch(snake_case)

          _lowercase              =self(snake_case,      attention_mask=snake_case,      decoder_input_ids=snake_case,      use_cache=snake_case)
          _lowercase              =outputs['logits']
          if self.hparams.label_smoothing == 0:
             # Same behavior as modeling_bart.py, besides ignoring pad_token_id
             _lowercase              =nn.CrossEntropyLoss(ignore_index=snake_case)

             assert lm_logits.shape[-1] == self.vocab_size
             _lowercase              =ce_loss_fct(lm_logits.view(-1,      lm_logits.shape[-1]),      tgt_ids.view(-1))
          else:
             _lowercase              =nn.functional.log_softmax(snake_case,      dim=-1)
             _lowercase       ,       _lowercase              =label_smoothed_nll_loss(
                 snake_case,      snake_case,      self.hparams.label_smoothing,      ignore_index=snake_case)
          return (loss,)





       @property
       def 				UpperCamelCase__						(				self	:Dict):



          """simple docstring"""

          return self.tokenizer.pad_token_id





       def 				UpperCamelCase__						(				self	:Tuple,      snake_case	:Dict,      snake_case	:str):



          """simple docstring"""

          _lowercase              =self._step(snake_case)

          _lowercase              =dict(zip(self.loss_names,      snake_case))
          # tokens per batch
          _lowercase              =batch['input_ids'].ne(self.pad).sum() + batch['labels'].ne(self.pad).sum()
          _lowercase              =batch['input_ids'].shape[0]
          _lowercase              =batch['input_ids'].eq(self.pad).sum()
          _lowercase              =batch['input_ids'].eq(self.pad).float().mean()
          # TODO(SS): make a wandb summary metric for this
          return {"loss": loss_tensors[0], "log": logs}





       def 				UpperCamelCase__						(				self	:List[Any],      snake_case	:Dict,      snake_case	:List[Any]):



          """simple docstring"""

          return self._generative_step(snake_case)





       def 				UpperCamelCase__						(				self	:List[Any],      snake_case	:List[str],      snake_case	:str="val"):



          """simple docstring"""

          self.step_count += 1
          _lowercase              ={k: torch.stack([x[k] for x in outputs]).mean() for k in self.loss_names}
          _lowercase              =losses['loss']
          _lowercase              ={
              k: np.array([x[k] for x in outputs]).mean() for k in self.metric_names + ['gen_time', 'gen_len']
          }
          _lowercase              =(
              generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
          )
          _lowercase              =torch.tensor(snake_case).type_as(snake_case)
          generative_metrics.update({k: v.item() for k, v in losses.items()})
          losses.update(snake_case)
          _lowercase              ={f'''{prefix}_avg_{k}''': x for k, x in losses.items()}
          _lowercase              =self.step_count
          self.metrics[prefix].append(snake_case)  # callback writes this to self.metrics_save_path
          _lowercase              =flatten_list([x['preds'] for x in outputs])
          return {
              "log": all_metrics,
              "preds": preds,
              f'''{prefix}_loss''': loss,
              f'''{prefix}_{self.val_metric}''': metric_tensor,
          }





       def 				UpperCamelCase__						(				self	:Any,      snake_case	:str,      snake_case	:List[str]):



          """simple docstring"""

          return calculate_rouge(snake_case,      snake_case)





       def 				UpperCamelCase__						(				self	:str,      snake_case	:dict):



          """simple docstring"""

          _lowercase              =time.time()

          # parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
          _lowercase              =self.model.generate(
              batch['input_ids'],      attention_mask=batch['attention_mask'],      use_cache=snake_case,      decoder_start_token_id=self.decoder_start_token_id,      num_beams=self.eval_beams,      max_length=self.eval_max_length,      )
          _lowercase              =(time.time() - ta) / batch['input_ids'].shape[0]
          _lowercase              =self.ids_to_clean_text(snake_case)
          _lowercase              =self.ids_to_clean_text(batch['labels'])
          _lowercase              =self._step(snake_case)
          _lowercase              =dict(zip(self.loss_names,      snake_case))
          _lowercase              =self.calc_generative_metrics(snake_case,      snake_case)
          _lowercase              =np.mean(lmap(snake_case,      snake_case))
          base_metrics.update(gen_time=snake_case,      gen_len=snake_case,      preds=snake_case,      target=snake_case,      **snake_case)
          return base_metrics





       def 				UpperCamelCase__						(				self	:Optional[Any],      snake_case	:str,      snake_case	:Optional[int]):



          """simple docstring"""

          return self._generative_step(snake_case)





       def 				UpperCamelCase__						(				self	:Union[str, Any],      snake_case	:List[str]):



          """simple docstring"""

          return self.validation_epoch_end(snake_case,      prefix='test')





       def 				UpperCamelCase__						(				self	:List[Any],      snake_case	:str):



          """simple docstring"""

          _lowercase              =self.n_obs[type_path]
          _lowercase              =self.target_lens[type_path]
          _lowercase              =self.dataset_class(
              self.tokenizer,      type_path=snake_case,      n_obs=snake_case,      max_target_length=snake_case,      **self.dataset_kwargs,      )
          return dataset





       def 				UpperCamelCase__						(				self	:str,      snake_case	:str,      snake_case	:int,      snake_case	:bool = False):



          """simple docstring"""

          _lowercase              =self.get_dataset(snake_case)

          if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
             _lowercase              =dataset.make_sortish_sampler(snake_case,      distributed=self.hparams.gpus > 1)
             return DataLoader(
                 snake_case,      batch_size=snake_case,      collate_fn=dataset.collate_fn,      shuffle=snake_case,      num_workers=self.num_workers,      sampler=snake_case,      )

          elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
             _lowercase              =dataset.make_dynamic_sampler(
                 self.hparams.max_tokens_per_batch,      distributed=self.hparams.gpus > 1)
             return DataLoader(
                 snake_case,      batch_sampler=snake_case,      collate_fn=dataset.collate_fn,      num_workers=self.num_workers,      )
          else:
             return DataLoader(
                 snake_case,      batch_size=snake_case,      collate_fn=dataset.collate_fn,      shuffle=snake_case,      num_workers=self.num_workers,      sampler=snake_case,      )





       def 				UpperCamelCase__						(				self	:Optional[Any]):



          """simple docstring"""

          _lowercase              =self.get_dataloader('train',      batch_size=self.hparams.train_batch_size,      shuffle=snake_case)
          return dataloader





       def 				UpperCamelCase__						(				self	:Dict):



          """simple docstring"""

          return self.get_dataloader('val',      batch_size=self.hparams.eval_batch_size)





       def 				UpperCamelCase__						(				self	:int):



          """simple docstring"""

          return self.get_dataloader('test',      batch_size=self.hparams.eval_batch_size)





       @staticmethod
       def 				UpperCamelCase__						(				snake_case	:Dict,      snake_case	:List[str]):



          """simple docstring"""

          BaseTransformer.add_model_specific_args(snake_case,      snake_case)
          add_generic_args(snake_case,      snake_case)
          parser.add_argument(
              '--max_source_length',      default=1024,      type=snake_case,      help=(
                  'The maximum total input sequence length after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              ),      )
          parser.add_argument(
              '--max_target_length',      default=56,      type=snake_case,      help=(
                  'The maximum total input sequence length after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              ),      )
          parser.add_argument(
              '--val_max_target_length',      default=142,      type=snake_case,      help=(
                  'The maximum total input sequence length after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              ),      )
          parser.add_argument(
              '--test_max_target_length',      default=142,      type=snake_case,      help=(
                  'The maximum total input sequence length after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              ),      )
          parser.add_argument('--freeze_encoder',      action='store_true')
          parser.add_argument('--freeze_embeds',      action='store_true')
          parser.add_argument('--sortish_sampler',      action='store_true',      default=snake_case)
          parser.add_argument('--overwrite_output_dir',      action='store_true',      default=snake_case)
          parser.add_argument('--max_tokens_per_batch',      type=snake_case,      default=snake_case)
          parser.add_argument('--logger_name',      type=snake_case,      choices=['default', 'wandb', 'wandb_shared'],      default='default')
          parser.add_argument('--n_train',      type=snake_case,      default=-1,      required=snake_case,      help='# examples. -1 means use all.')
          parser.add_argument('--n_val',      type=snake_case,      default=500,      required=snake_case,      help='# examples. -1 means use all.')
          parser.add_argument('--n_test',      type=snake_case,      default=-1,      required=snake_case,      help='# examples. -1 means use all.')
          parser.add_argument(
              '--task',      type=snake_case,      default='summarization',      required=snake_case,      help='# examples. -1 means use all.')
          parser.add_argument('--label_smoothing',      type=snake_case,      default=0.0,      required=snake_case)
          parser.add_argument('--src_lang',      type=snake_case,      default='',      required=snake_case)
          parser.add_argument('--tgt_lang',      type=snake_case,      default='',      required=snake_case)
          parser.add_argument('--eval_beams',      type=snake_case,      default=snake_case,      required=snake_case)
          parser.add_argument(
              '--val_metric',      type=snake_case,      default=snake_case,      required=snake_case,      choices=['bleu', 'rouge2', 'loss', None])
          parser.add_argument('--eval_max_gen_length',      type=snake_case,      default=snake_case,      help='never generate more than n tokens')
          parser.add_argument('--save_top_k',      type=snake_case,      default=1,      required=snake_case,      help='How many checkpoints to save')
          parser.add_argument(
              '--early_stopping_patience',      type=snake_case,      default=-1,      required=snake_case,      help=(
                  '-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So'
                  ' val_check_interval will effect it.'
              ),      )
          return parser






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		Union[str, Any]					='''translation'''
       __lowerCAmelCase    :		Any					=['''loss''']
       __lowerCAmelCase    :		Optional[int]					=['''bleu''']
       __lowerCAmelCase    :		Any					='''bleu'''





       def __init__(				self	:str,      snake_case	:Union[str, Any],      **snake_case	:Any):



          """simple docstring"""

          super().__init__(snake_case,      **snake_case)
          _lowercase              =hparams.src_lang
          _lowercase              =hparams.tgt_lang





       def 				UpperCamelCase__						(				self	:Dict,      snake_case	:Union[str, Any],      snake_case	:Any):



          """simple docstring"""

          return calculate_bleu(snake_case,      snake_case)





def        _snake_case   (_snake_case				:       Dict      ,    _snake_case				:       int=None)					->	SummarizationModule:
   Path(args.output_dir).mkdir(exist_ok=_snake_case)
   check_output_dir(_snake_case      ,    expected_items=3)

   if model is None:
      if "summarization" in args.task:
         _lowercase              =SummarizationModule(_snake_case)
      else:
         _lowercase              =TranslationModule(_snake_case)
   _lowercase              =Path(args.data_dir).name
   if (
       args.logger_name == "default"
       or args.fast_dev_run
       or str(args.output_dir).startswith('/tmp')
       or str(args.output_dir).startswith('/var')
   ):
      _lowercase              =True  # don't pollute wandb logs unnecessarily
   elif args.logger_name == "wandb":
      from pytorch_lightning.loggers import WandbLogger

      _lowercase              =os.environ.get('WANDB_PROJECT'      ,    _snake_case)
      _lowercase              =WandbLogger(name=model.output_dir.name      ,    project=_snake_case)

   elif args.logger_name == "wandb_shared":
      from pytorch_lightning.loggers import WandbLogger

      _lowercase              =WandbLogger(name=model.output_dir.name      ,    project=f'''hf_{dataset}''')

   if args.early_stopping_patience >= 0:
      _lowercase              =get_early_stopping_callback(model.val_metric      ,    args.early_stopping_patience)
   else:
      _lowercase              =False

   _lowercase              =args.val_metric == 'loss'
   _lowercase              =generic_train(
       _snake_case      ,    _snake_case      ,    logging_callback=SeqaSeqLoggingCallback()      ,    checkpoint_callback=get_checkpoint_callback(
           args.output_dir      ,    model.val_metric      ,    args.save_top_k      ,    _snake_case)      ,    early_stopping_callback=_snake_case      ,    logger=_snake_case      ,    )
   pickle_save(model.hparams      ,    model.output_dir / 'hparams.pkl')
   if not args.do_predict:
      return model

   _lowercase              =''
   _lowercase              =sorted(glob.glob(os.path.join(args.output_dir      ,    '*.ckpt')      ,    recursive=_snake_case))
   if checkpoints:
      _lowercase              =checkpoints[-1]
      _lowercase              =checkpoints[-1]
   trainer.logger.log_hyperparams(model.hparams)

   # test() without a model tests using the best checkpoint automatically
   trainer.test()
   return model


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE   =  argparse.ArgumentParser()
    _SCREAMING_SNAKE_CASE   =  pl.Trainer.add_argparse_args(parser)
    _SCREAMING_SNAKE_CASE   =  SummarizationModule.add_model_specific_args(parser, os.getcwd())

    _SCREAMING_SNAKE_CASE   =  parser.parse_args()

    main(args)


