




from __future__ import annotations






def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__   )			->							list:





     '''simple docstring'''


     lowercase :       Optional[int]       =   []
     lowercase      ,	lowercase :       Dict       =   input_list[low:mid], input_list[mid : high + 1]
     while left and right:
          result.append((left if left[0] <= right[0] else right).pop(0   )   )
     lowercase :       List[str]       =   result + left + right
     return input_list






def  snake_case(    __magic_name__   )			->							list:





     '''simple docstring'''


     if len(lowerCAmelCase__   ) <= 1:
          return input_list
     lowercase :       Optional[Any]       =   list(lowerCAmelCase__   )

     # iteration for two-way merging
     lowercase :       List[str]       =   2
     while p <= len(lowerCAmelCase__   ):
          # getting low, high and middle value for merge-sort of single list
          for i in range(0			,		len(lowerCAmelCase__   )			,		lowerCAmelCase__   ):
               lowercase :       Optional[Any]       =   i
               lowercase :       List[str]       =   i + p - 1
               lowercase :       Dict       =   (low + high + 1) // 2
               lowercase :       str       =   merge(lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__   )
          # final merge of last two parts
          if p * 2 >= len(lowerCAmelCase__   ):
               lowercase :       Union[str, Any]       =   i
               lowercase :       Any       =   merge(lowerCAmelCase__			,		0			,		lowerCAmelCase__			,		len(lowerCAmelCase__   ) - 1   )
               break
          p *= 2

     return input_list


if __name__ == "__main__":
 lowerCAmelCase_						=			input('Enter numbers separated by a comma:\n').strip()
 if user_input == "":
  lowerCAmelCase_						=			[]
 else:
  lowerCAmelCase_						=			[int(item.strip()) for item in user_input.split(',')]
 print(iter_merge_sort(unsorted))





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  snake_case(    __magic_name__			,		__magic_name__   )			->							Optional[Any]:





     '''simple docstring'''


     lowercase :       List[str]       =			'''\n'''.join(__magic_name__   )
     Path(__magic_name__   ).open('''w'''   ).writelines(__magic_name__   )






def  snake_case(    __magic_name__   )			->							Optional[int]:





     '''simple docstring'''


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



class 						_A (  _lowerCamelCase				):






  @parameterized.expand(
      [
          MBART_TINY,
          MARIAN_TINY,
          T5_TINY,
          BART_TINY,
          PEGASUS_XSUM,
      ]				,						)
  @slow
  def  __a      (  self		:     List[str]				,						_A		:     Optional[Any]				)		->   Dict:






       """simple docstring"""







       lowercase :       int       =			AutoTokenizer.from_pretrained(_A				)
       lowercase :       Optional[int]       =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
       lowercase :       List[str]       =			max(len(tokenizer.encode(_A				)				) for a in ARTICLES				)
       lowercase :       Optional[int]       =			max(len(tokenizer.encode(_A				)				) for a in SUMMARIES				)
       lowercase :       str       =			4
       lowercase :       List[Any]       =			8
       assert max_len_target > max_src_len  # Will be truncated
       assert max_len_source > max_src_len  # Will be truncated
       lowercase      ,	lowercase :       Optional[int]       =			'''ro_RO''', '''de_DE'''  # ignored for all but mbart, but never causes error.
       lowercase :       int       =			SeqaSeqDataset(
           _A				,						data_dir=_A				,						type_path='''train'''				,						max_source_length=_A				,						max_target_length=_A				,						src_lang=_A				,						tgt_lang=_A				,						)
       lowercase :       Optional[int]       =			DataLoader(_A				,						batch_size=2				,						collate_fn=train_dataset.collate_fn				)
       for batch in dataloader:
            assert isinstance(_A				,						_A				)
            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
            lowercase :       int       =			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  __a      (  self		:     int				,						_A		:     Tuple				)		->   List[str]:






       """simple docstring"""







       lowercase :       int       =			AutoTokenizer.from_pretrained(_A				)
       lowercase :       int       =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
       lowercase :       Optional[int]       =			max(len(tokenizer.encode(_A				)				) for a in ARTICLES				)
       lowercase :       List[Any]       =			max(len(tokenizer.encode(_A				)				) for a in SUMMARIES				)
       lowercase :       List[Any]       =			4
       lowercase :       Any       =			LegacySeqaSeqDataset(
           _A				,						data_dir=_A				,						type_path='''train'''				,						max_source_length=20				,						max_target_length=_A				,						)
       lowercase :       Optional[Any]       =			DataLoader(_A				,						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  __a      (  self		:     List[str]				)		->   int:






       """simple docstring"""







       lowercase :       Tuple       =			AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25'''				)

       lowercase :       List[str]       =			Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)				)
       lowercase :       Optional[int]       =			tmp_dir.joinpath('''train.source'''				).open().readlines()
       lowercase :       List[Any]       =			Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)				)
       pack_data_dir(_A				,						_A				,						128				,						_A				)
       lowercase :       Dict       =			{x.name for x in tmp_dir.iterdir()}
       lowercase :       Optional[Any]       =			{x.name for x in save_dir.iterdir()}
       lowercase :       int       =			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(_A				) < len(_A				)
       assert len(_A				) == 1
       assert len(packed_examples[0]				) == sum(len(_A				) for x in orig_examples				)
       assert orig_paths == new_paths






  @pytest.mark.skipif(not FAIRSEQ_AVAILABLE				,						reason='''This test requires fairseq'''				)
  def  __a      (  self		:     Any				)		->   Dict:






       """simple docstring"""







       if not FAIRSEQ_AVAILABLE:
            return
       lowercase      ,	lowercase      ,	lowercase :       Any       =			self._get_dataset(max_len=64				)
       lowercase :       List[Any]       =			64
       lowercase :       Any       =			ds.make_dynamic_sampler(_A				,						required_batch_size_multiple=_A				)
       lowercase :       Tuple       =			[len(_A				) for x in batch_sampler]
       assert len(set(_A				)				) > 1  # it's not dynamic batch size if every batch is the same length
       assert sum(_A				) == len(_A				)  # no dropped or added examples
       lowercase :       str       =			DataLoader(_A				,						batch_sampler=_A				,						collate_fn=ds.collate_fn				,						num_workers=2				)
       lowercase :       Optional[int]       =			[]
       lowercase :       str       =			[]
       for batch in data_loader:
            lowercase :       Tuple       =			batch['''input_ids'''].shape
            lowercase :       List[Any]       =			src_shape[0]
            assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
            lowercase :       Dict       =			np.product(batch['''input_ids'''].shape				)
            num_src_per_batch.append(_A				)
            if num_src_tokens > (max_tokens * 1.1):
                 failures.append(_A				)
       assert num_src_per_batch[0] == max(_A				)
       if failures:
            raise AssertionError(f"""too many tokens in {len(_A				)} batches"""				)






  def  __a      (  self		:     int				)		->   Any:






       """simple docstring"""







       lowercase      ,	lowercase      ,	lowercase :       Tuple       =			self._get_dataset(max_len=512				)
       lowercase :       Tuple       =			2
       lowercase :       Union[str, Any]       =			ds.make_sortish_sampler(_A				,						shuffle=_A				)

       lowercase :       List[Any]       =			DataLoader(_A				,						batch_size=_A				,						collate_fn=ds.collate_fn				,						num_workers=2				)
       lowercase :       List[Any]       =			DataLoader(_A				,						batch_size=_A				,						collate_fn=ds.collate_fn				,						num_workers=2				,						sampler=_A				)

       lowercase :       int       =			tokenizer.pad_token_id

       def count_pad_tokens(_A		:     List[Any]				,						_A		:     Union[str, Any]="input_ids"				):
            return [batch[k].eq(_A				).sum().item() for batch in data_loader]

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






  def  __a      (  self		:     Any				,						_A		:     Union[str, Any]=1_000				,						_A		:     str=128				)		->   List[Any]:






       """simple docstring"""







       if os.getenv('''USE_REAL_DATA'''				,						_A				):
            lowercase :       Optional[Any]       =			'''examples/seq2seq/wmt_en_ro'''
            lowercase :       Optional[int]       =			max_len * 2 * 64
            if not Path(_A				).joinpath('''train.len'''				).exists():
                 save_len_file(_A				,						_A				)
       else:
            lowercase :       Tuple       =			'''examples/seq2seq/test_data/wmt_en_ro'''
            lowercase :       Optional[Any]       =			max_len * 4
            save_len_file(_A				,						_A				)

       lowercase :       Optional[Any]       =			AutoTokenizer.from_pretrained(_A				)
       lowercase :       Union[str, Any]       =			SeqaSeqDataset(
           _A				,						data_dir=_A				,						type_path='''train'''				,						max_source_length=_A				,						max_target_length=_A				,						n_obs=_A				,						)
       return ds, max_tokens, tokenizer






  def  __a      (  self		:     List[str]				)		->   List[str]:






       """simple docstring"""







       lowercase      ,	lowercase      ,	lowercase :       Union[str, Any]       =			self._get_dataset()
       lowercase :       int       =			set(DistributedSortishSampler(_A				,						256				,						num_replicas=2				,						rank=0				,						add_extra_examples=_A				)				)
       lowercase :       Dict       =			set(DistributedSortishSampler(_A				,						256				,						num_replicas=2				,						rank=1				,						add_extra_examples=_A				)				)
       assert idsa.intersection(_A				) == set()






  @parameterized.expand(
      [
          MBART_TINY,
          MARIAN_TINY,
          T5_TINY,
          BART_TINY,
          PEGASUS_XSUM,
      ]				,						)
  def  __a      (  self		:     Union[str, Any]				,						_A		:     Tuple				)		->   Optional[int]:






       """simple docstring"""







       lowercase :       Union[str, Any]       =			AutoTokenizer.from_pretrained(_A				,						use_fast=_A				)
       if tok_name == MBART_TINY:
            lowercase :       Tuple       =			SeqaSeqDataset(
                _A				,						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'''				,						)
            lowercase :       Union[str, Any]       =			train_dataset.dataset_kwargs
            assert "src_lang" in kwargs and "tgt_lang" in kwargs
       else:
            lowercase :       List[Any]       =			SeqaSeqDataset(
                _A				,						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				,						)
            lowercase :       Dict       =			train_dataset.dataset_kwargs
            assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
            assert len(_A				) == 1 if tok_name == BART_TINY else len(_A				) == 0