

"""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


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





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      Optional[Any]:
 __magic_name__     =      '''\n'''.join(__UpperCamelCase						)
 Path(__UpperCamelCase						).open('''w'''						).writelines(__UpperCamelCase						)





def 	lowercase      (   __UpperCamelCase						) ->      Optional[Any]:
 for split in ["train", "val", "test"]:
  _dump_articles(os.path.join(__UpperCamelCase      ,   f'''{split}.source'''						)      ,   __UpperCamelCase						)
  _dump_articles(os.path.join(__UpperCamelCase      ,   f'''{split}.target'''						)      ,   __UpperCamelCase						)
 return tmp_dir
class        _lowercase					(     __UpperCAmelCase     ):







     @parameterized.expand(
         [
             MBART_TINY,
             MARIAN_TINY,
             T5_TINY,
             BART_TINY,
             PEGASUS_XSUM,
         ] ,						)
     @slow
     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_       ):
      __magic_name__     =      AutoTokenizer.from_pretrained(UpperCamelCase_       )
      __magic_name__     =      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
      __magic_name__     =      max(len(tokenizer.encode(UpperCamelCase_       )       ) for a in ARTICLES       )
      __magic_name__     =      max(len(tokenizer.encode(UpperCamelCase_       )       ) for a in SUMMARIES       )
      __magic_name__     =      4
      __magic_name__     =      8
      assert max_len_target > max_src_len  # Will be truncated
      assert max_len_source > max_src_len  # Will be truncated
      __magic_name__   ,						__magic_name__     =      '''ro_RO''', '''de_DE'''  # ignored for all but mbart, but never causes error.
      __magic_name__     =      SeqaSeqDataset(
          UpperCamelCase_ ,						data_dir=UpperCamelCase_ ,						type_path='''train''' ,						max_source_length=UpperCamelCase_ ,						max_target_length=UpperCamelCase_ ,						src_lang=UpperCamelCase_ ,						tgt_lang=UpperCamelCase_ ,						)
      __magic_name__     =      DataLoader(UpperCamelCase_ ,						batch_size=2 ,						collate_fn=train_dataset.collate_fn       )
      for batch in dataloader:
       assert isinstance(UpperCamelCase_ ,						UpperCamelCase_       )
       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
       __magic_name__     =      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 	lowerCAmelCase__    (      self ,						UpperCamelCase_       ):
      __magic_name__     =      AutoTokenizer.from_pretrained(UpperCamelCase_       )
      __magic_name__     =      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
      __magic_name__     =      max(len(tokenizer.encode(UpperCamelCase_       )       ) for a in ARTICLES       )
      __magic_name__     =      max(len(tokenizer.encode(UpperCamelCase_       )       ) for a in SUMMARIES       )
      __magic_name__     =      4
      __magic_name__     =      LegacySeqaSeqDataset(
          UpperCamelCase_ ,						data_dir=UpperCamelCase_ ,						type_path='''train''' ,						max_source_length=20 ,						max_target_length=UpperCamelCase_ ,						)
      __magic_name__     =      DataLoader(UpperCamelCase_ ,						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 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25'''       )

      __magic_name__     =      Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
      __magic_name__     =      tmp_dir.joinpath('''train.source'''       ).open().readlines()
      __magic_name__     =      Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
      pack_data_dir(UpperCamelCase_ ,						UpperCamelCase_ ,						128 ,						UpperCamelCase_       )
      __magic_name__     =      {x.name for x in tmp_dir.iterdir()}
      __magic_name__     =      {x.name for x in save_dir.iterdir()}
      __magic_name__     =      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(UpperCamelCase_       ) < len(UpperCamelCase_       )
      assert len(UpperCamelCase_       ) == 1
      assert len(packed_examples[0]       ) == sum(len(UpperCamelCase_       ) for x in orig_examples       )
      assert orig_paths == new_paths







     @pytest.mark.skipif(not FAIRSEQ_AVAILABLE ,						reason='''This test requires fairseq'''       )
     def 	lowerCAmelCase__    (      self       ):
      if not FAIRSEQ_AVAILABLE:
       return
      __magic_name__   ,						__magic_name__   ,						__magic_name__     =      self._get_dataset(max_len=64       )
      __magic_name__     =      64
      __magic_name__     =      ds.make_dynamic_sampler(UpperCamelCase_ ,						required_batch_size_multiple=UpperCamelCase_       )
      __magic_name__     =      [len(UpperCamelCase_       ) for x in batch_sampler]
      assert len(set(UpperCamelCase_       )       ) > 1  # it's not dynamic batch size if every batch is the same length
      assert sum(UpperCamelCase_       ) == len(UpperCamelCase_       )  # no dropped or added examples
      __magic_name__     =      DataLoader(UpperCamelCase_ ,						batch_sampler=UpperCamelCase_ ,						collate_fn=ds.collate_fn ,						num_workers=2       )
      __magic_name__     =      []
      __magic_name__     =      []
      for batch in data_loader:
       __magic_name__     =      batch['''input_ids'''].shape
       __magic_name__     =      src_shape[0]
       assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
       __magic_name__     =      np.product(batch['''input_ids'''].shape       )
       num_src_per_batch.append(UpperCamelCase_       )
       if num_src_tokens > (max_tokens * 1.1):
        failures.append(UpperCamelCase_       )
      assert num_src_per_batch[0] == max(UpperCamelCase_       )
      if failures:
       raise AssertionError(f'''too many tokens in {len(UpperCamelCase_       )} batches'''       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__   ,						__magic_name__   ,						__magic_name__     =      self._get_dataset(max_len=512       )
      __magic_name__     =      2
      __magic_name__     =      ds.make_sortish_sampler(UpperCamelCase_ ,						shuffle=UpperCamelCase_       )

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

      __magic_name__     =      tokenizer.pad_token_id

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

      assert sum(count_pad_tokens(UpperCamelCase_ ,						k='''labels'''       )       ) < sum(count_pad_tokens(UpperCamelCase_ ,						k='''labels'''       )       )
      assert sum(count_pad_tokens(UpperCamelCase_       )       ) < sum(count_pad_tokens(UpperCamelCase_       )       )
      assert len(UpperCamelCase_       ) == len(UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_=1000 ,						UpperCamelCase_=128       ):
      if os.getenv('''USE_REAL_DATA''' ,						UpperCamelCase_       ):
       __magic_name__     =      '''examples/seq2seq/wmt_en_ro'''
       __magic_name__     =      max_len * 2 * 64
       if not Path(UpperCamelCase_       ).joinpath('''train.len'''       ).exists():
        save_len_file(UpperCamelCase_ ,						UpperCamelCase_       )
      else:
       __magic_name__     =      '''examples/seq2seq/test_data/wmt_en_ro'''
       __magic_name__     =      max_len * 4
       save_len_file(UpperCamelCase_ ,						UpperCamelCase_       )

      __magic_name__     =      AutoTokenizer.from_pretrained(UpperCamelCase_       )
      __magic_name__     =      SeqaSeqDataset(
          UpperCamelCase_ ,						data_dir=UpperCamelCase_ ,						type_path='''train''' ,						max_source_length=UpperCamelCase_ ,						max_target_length=UpperCamelCase_ ,						n_obs=UpperCamelCase_ ,						)
      return ds, max_tokens, tokenizer







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__   ,						__magic_name__   ,						__magic_name__     =      self._get_dataset()
      __magic_name__     =      set(DistributedSortishSampler(UpperCamelCase_ ,						256 ,						num_replicas=2 ,						rank=0 ,						add_extra_examples=UpperCamelCase_       )       )
      __magic_name__     =      set(DistributedSortishSampler(UpperCamelCase_ ,						256 ,						num_replicas=2 ,						rank=1 ,						add_extra_examples=UpperCamelCase_       )       )
      assert idsa.intersection(UpperCamelCase_       ) == set()







     @parameterized.expand(
         [
             MBART_TINY,
             MARIAN_TINY,
             T5_TINY,
             BART_TINY,
             PEGASUS_XSUM,
         ] ,						)
     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_       ):
      __magic_name__     =      AutoTokenizer.from_pretrained(UpperCamelCase_ ,						use_fast=UpperCamelCase_       )
      if tok_name == MBART_TINY:
       __magic_name__     =      SeqaSeqDataset(
           UpperCamelCase_ ,						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''' ,						)
       __magic_name__     =      train_dataset.dataset_kwargs
       assert "src_lang" in kwargs and "tgt_lang" in kwargs
      else:
       __magic_name__     =      SeqaSeqDataset(
           UpperCamelCase_ ,						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 ,						)
       __magic_name__     =      train_dataset.dataset_kwargs
       assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
       assert len(UpperCamelCase_       ) == 1 if tok_name == BART_TINY else len(UpperCamelCase_       ) == 0




"""simple docstring"""






def 	lowercase      (   ) ->      int:
 return 1





def 	lowercase      (   __UpperCamelCase						) ->      int:
 return 0 if x < 0 else two_pence(x - 2						) + one_pence()





def 	lowercase      (   __UpperCamelCase						) ->      int:
 return 0 if x < 0 else five_pence(x - 5						) + two_pence(__UpperCamelCase						)





def 	lowercase      (   __UpperCamelCase						) ->      int:
 return 0 if x < 0 else ten_pence(x - 10						) + five_pence(__UpperCamelCase						)





def 	lowercase      (   __UpperCamelCase						) ->      int:
 return 0 if x < 0 else twenty_pence(x - 20						) + ten_pence(__UpperCamelCase						)





def 	lowercase      (   __UpperCamelCase						) ->      int:
 return 0 if x < 0 else fifty_pence(x - 50						) + twenty_pence(__UpperCamelCase						)





def 	lowercase      (   __UpperCamelCase						) ->      int:
 return 0 if x < 0 else one_pound(x - 100						) + fifty_pence(__UpperCamelCase						)





def 	lowercase      (   __UpperCamelCase						) ->      int:
 return 0 if x < 0 else two_pound(x - 200						) + one_pound(__UpperCamelCase						)





def 	lowercase      (   __UpperCamelCase = 200						) ->      int:
 return two_pound(__UpperCamelCase						)


if __name__ == "__main__":
 print(solution(int(input().strip())))

