


'''simple docstring'''






import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


a_    :			Any      				=			"bert-base-cased"
a_    :			Dict      				=			"google/pegasus-xsum"
a_    :			Any      				=			[" Sam ate lunch today.", "Sams lunch ingredients."]
a_    :			Optional[int]      				=			["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
a_    :			Any      				=			"patrickvonplaten/t5-tiny-random"
a_    :			Tuple      				=			"sshleifer/bart-tiny-random"
a_    :			str      				=			"sshleifer/tiny-mbart"
a_    :			List[str]      				=			"sshleifer/tiny-marian-en-de"






def 		_A (lowerCAmelCase__     :Tuple ,  lowerCAmelCase__     :int						)      ->     Optional[int]:




     '''simple docstring'''



     _a						=							'\n'.join(lowerCamelCase__						)
     Path(lowerCamelCase__						).open('w'						).writelines(lowerCamelCase__						)






def 		_A (lowerCAmelCase__     :Any						)      ->     Optional[int]:




     '''simple docstring'''



     for split in ["train", "val", "test"]:
          _dump_articles(os.path.join(lowerCamelCase__ ,  f'{split}.source'						) ,  lowerCamelCase__						)
          _dump_articles(os.path.join(lowerCamelCase__ ,  f'{split}.target'						) ,  lowerCamelCase__						)
     return tmp_dir






class        a   (   snake_case_				):
 @parameterized.expand(
     [
         MBART_TINY,
         MARIAN_TINY,
         T5_TINY,
         BART_TINY,
         PEGASUS_XSUM,
     ]					, )
 @slow
 def 							__UpperCAmelCase						(       self					, __magic_name__  )    ->							List[Any]:
      _a						=							AutoTokenizer.from_pretrained(__magic_name__  )
      _a						=							make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )
      _a						=							max(len(tokenizer.encode(__magic_name__  )  ) for a in ARTICLES  )
      _a						=							max(len(tokenizer.encode(__magic_name__  )  ) for a in SUMMARIES  )
      _a						=							4
      _a						=							8
      assert max_len_target > max_src_len  # Will be truncated
      assert max_len_source > max_src_len  # Will be truncated
      _a					,      _a						=							'ro_RO', 'de_DE'  # ignored for all but mbart, but never causes error.
      _a						=							SeqaSeqDataset(
          __magic_name__					, data_dir=__magic_name__					, type_path='train'					, max_source_length=__magic_name__					, max_target_length=__magic_name__					, src_lang=__magic_name__					, tgt_lang=__magic_name__					, )
      _a						=							DataLoader(__magic_name__					, batch_size=2					, collate_fn=train_dataset.collate_fn  )
      for batch in dataloader:
           assert isinstance(__magic_name__					, __magic_name__  )
           assert batch["attention_mask"].shape == batch["input_ids"].shape
           # show that articles were trimmed.
           assert batch["input_ids"].shape[1] == max_src_len
           # show that targets are the same len
           assert batch["labels"].shape[1] == max_tgt_len
           if tok_name != MBART_TINY:
                continue
           # check language codes in correct place
           _a						=							shift_tokens_right(batch['labels']					, tokenizer.pad_token_id  )
           assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
           assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
           assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
           assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

           break  # No need to test every batch
 @parameterized.expand([BART_TINY, BERT_BASE_CASED]  )
 def 							__UpperCAmelCase						(       self					, __magic_name__  )    ->							str:
      _a						=							AutoTokenizer.from_pretrained(__magic_name__  )
      _a						=							make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )
      _a						=							max(len(tokenizer.encode(__magic_name__  )  ) for a in ARTICLES  )
      _a						=							max(len(tokenizer.encode(__magic_name__  )  ) for a in SUMMARIES  )
      _a						=							4
      _a						=							LegacySeqaSeqDataset(
          __magic_name__					, data_dir=__magic_name__					, type_path='train'					, max_source_length=20					, max_target_length=__magic_name__					, )
      _a						=							DataLoader(__magic_name__					, batch_size=2					, collate_fn=train_dataset.collate_fn  )
      for batch in dataloader:
           assert batch["attention_mask"].shape == batch["input_ids"].shape
           # show that articles were trimmed.
           assert batch["input_ids"].shape[1] == max_len_source
           assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
           # show that targets were truncated
           assert batch["labels"].shape[1] == trunc_target  # Truncated
           assert max_len_target > trunc_target  # Truncated
           break  # No need to test every batch
 def 							__UpperCAmelCase						(       self  )    ->							str:
      _a						=							AutoTokenizer.from_pretrained('facebook/mbart-large-cc25'  )

      _a						=							Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )  )
      _a						=							tmp_dir.joinpath('train.source'  ).open().readlines()
      _a						=							Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )  )
      pack_data_dir(__magic_name__					, __magic_name__					, 1_28					, __magic_name__  )
      _a						=							{x.name for x in tmp_dir.iterdir()}
      _a						=							{x.name for x in save_dir.iterdir()}
      _a						=							save_dir.joinpath('train.source'  ).open().readlines()
      # orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
      # desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
      assert len(__magic_name__  ) < len(__magic_name__  )
      assert len(__magic_name__  ) == 1
      assert len(packed_examples[0]  ) == sum(len(__magic_name__  ) for x in orig_examples  )
      assert orig_paths == new_paths
 @pytest.mark.skipif(not FAIRSEQ_AVAILABLE					, reason='This test requires fairseq'  )
 def 							__UpperCAmelCase						(       self  )    ->							Optional[Any]:
      if not FAIRSEQ_AVAILABLE:
           return
      _a					,      _a					,      _a						=							self._get_dataset(max_len=64  )
      _a						=							64
      _a						=							ds.make_dynamic_sampler(__magic_name__					, required_batch_size_multiple=__magic_name__  )
      _a						=							[len(__magic_name__  ) for x in batch_sampler]
      assert len(set(__magic_name__  )  ) > 1  # it's not dynamic batch size if every batch is the same length
      assert sum(__magic_name__  ) == len(__magic_name__  )  # no dropped or added examples
      _a						=							DataLoader(__magic_name__					, batch_sampler=__magic_name__					, collate_fn=ds.collate_fn					, num_workers=2  )
      _a						=							[]
      _a						=							[]
      for batch in data_loader:
           _a						=							batch['input_ids'].shape
           _a						=							src_shape[0]
           assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
           _a						=							np.product(batch['input_ids'].shape  )
           num_src_per_batch.append(__magic_name__  )
           if num_src_tokens > (max_tokens * 1.1):
                failures.append(__magic_name__  )
      assert num_src_per_batch[0] == max(__magic_name__  )
      if failures:
           raise AssertionError(f'too many tokens in {len(__magic_name__  )} batches'  )
 def 							__UpperCAmelCase						(       self  )    ->							Tuple:
      _a					,      _a					,      _a						=							self._get_dataset(max_len=5_12  )
      _a						=							2
      _a						=							ds.make_sortish_sampler(__magic_name__					, shuffle=__magic_name__  )

      _a						=							DataLoader(__magic_name__					, batch_size=__magic_name__					, collate_fn=ds.collate_fn					, num_workers=2  )
      _a						=							DataLoader(__magic_name__					, batch_size=__magic_name__					, collate_fn=ds.collate_fn					, num_workers=2					, sampler=__magic_name__  )

      _a						=							tokenizer.pad_token_id

      def count_pad_tokens(__magic_name__					, __magic_name__="input_ids"  ):
           return [batch[k].eq(__magic_name__  ).sum().item() for batch in data_loader]

      assert sum(count_pad_tokens(__magic_name__					, k='labels'  )  ) < sum(count_pad_tokens(__magic_name__					, k='labels'  )  )
      assert sum(count_pad_tokens(__magic_name__  )  ) < sum(count_pad_tokens(__magic_name__  )  )
      assert len(__magic_name__  ) == len(__magic_name__  )
 def 							__UpperCAmelCase						(       self					, __magic_name__=10_00					, __magic_name__=1_28  )    ->							str:
      if os.getenv('USE_REAL_DATA'					, __magic_name__  ):
           _a						=							'examples/seq2seq/wmt_en_ro'
           _a						=							max_len * 2 * 64
           if not Path(__magic_name__  ).joinpath('train.len'  ).exists():
                save_len_file(__magic_name__					, __magic_name__  )
      else:
           _a						=							'examples/seq2seq/test_data/wmt_en_ro'
           _a						=							max_len * 4
           save_len_file(__magic_name__					, __magic_name__  )

      _a						=							AutoTokenizer.from_pretrained(__magic_name__  )
      _a						=							SeqaSeqDataset(
          __magic_name__					, data_dir=__magic_name__					, type_path='train'					, max_source_length=__magic_name__					, max_target_length=__magic_name__					, n_obs=__magic_name__					, )
      return ds, max_tokens, tokenizer
 def 							__UpperCAmelCase						(       self  )    ->							int:
      _a					,      _a					,      _a						=							self._get_dataset()
      _a						=							set(DistributedSortishSampler(__magic_name__					, 2_56					, num_replicas=2					, rank=0					, add_extra_examples=__magic_name__  )  )
      _a						=							set(DistributedSortishSampler(__magic_name__					, 2_56					, num_replicas=2					, rank=1					, add_extra_examples=__magic_name__  )  )
      assert idsa.intersection(__magic_name__  ) == set()




 @parameterized.expand(
     [
         MBART_TINY,
         MARIAN_TINY,
         T5_TINY,
         BART_TINY,
         PEGASUS_XSUM,
     ]					, )
 def 							__UpperCAmelCase						(       self					, __magic_name__  )    ->							Union[str, Any]:
      _a						=							AutoTokenizer.from_pretrained(__magic_name__					, use_fast=__magic_name__  )
      if tok_name == MBART_TINY:
           _a						=							SeqaSeqDataset(
               __magic_name__					, data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )					, type_path='train'					, max_source_length=4					, max_target_length=8					, src_lang='EN'					, tgt_lang='FR'					, )
           _a						=							train_dataset.dataset_kwargs
           assert "src_lang" in kwargs and "tgt_lang" in kwargs
      else:
           _a						=							SeqaSeqDataset(
               __magic_name__					, data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )					, type_path='train'					, max_source_length=4					, max_target_length=8					, )
           _a						=							train_dataset.dataset_kwargs
           assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
           assert len(__magic_name__  ) == 1 if tok_name == BART_TINY else len(__magic_name__  ) == 0



__A				={str(digit): digit**5 for digit in range(1_0)}





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				return sum(DIGITS_FIFTH_POWER[digit] for digit in str(lowerCamelCase__    )    )





def 			lowerCamelCase_ (				):
				return sum(
				    number
				    for number in range(1_0_0_0	, 1_0_0_0_0_0_0    )
				    if number == digits_fifth_powers_sum(lowerCamelCase__    )    )


if __name__ == "__main__":
			print(solution())


