





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

def      UpperCAmelCase_    (    _UpperCAmelCase							:Path  ,						_UpperCAmelCase							:list				)	->				Union[str, Any]:
     '''simple docstring'''


     A_          =       '''\n'''.join(_UpperCAmelCase				)
     Path(_UpperCAmelCase				).open('''w'''				).writelines(_UpperCAmelCase				)

def      UpperCAmelCase_    (    _UpperCAmelCase							:Union[str, Any]				)	->				List[str]:
     '''simple docstring'''


     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 UpperCAmelCase_				( _SCREAMING_SNAKE_CASE ):


  @parameterized.expand(
      [
          MBART_TINY,
          MARIAN_TINY,
          T5_TINY,
          BART_TINY,
          PEGASUS_XSUM,
      ] ,)
  @slow
  def 						__UpperCAmelCase     (							self ,__snake_case ):



       """simple docstring"""





       A_          =       AutoTokenizer.from_pretrained(__snake_case )
       A_          =       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
       A_          =       max(len(tokenizer.encode(__snake_case ) ) for a in ARTICLES )
       A_          =       max(len(tokenizer.encode(__snake_case ) ) 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(
           __snake_case ,data_dir=__snake_case ,type_path='''train''' ,max_source_length=__snake_case ,max_target_length=__snake_case ,src_lang=__snake_case ,tgt_lang=__snake_case ,)
       A_          =       DataLoader(__snake_case ,batch_size=2 ,collate_fn=train_dataset.collate_fn )
       for batch in dataloader:
            assert isinstance(__snake_case ,__snake_case )
            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 ,__snake_case ):



       """simple docstring"""





       A_          =       AutoTokenizer.from_pretrained(__snake_case )
       A_          =       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
       A_          =       max(len(tokenizer.encode(__snake_case ) ) for a in ARTICLES )
       A_          =       max(len(tokenizer.encode(__snake_case ) ) for a in SUMMARIES )
       A_          =       4
       A_          =       LegacySeqaSeqDataset(
           __snake_case ,data_dir=__snake_case ,type_path='''train''' ,max_source_length=2_0 ,max_target_length=__snake_case ,)
       A_          =       DataLoader(__snake_case ,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 2_0 >= 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 ):



       """simple docstring"""





       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(__snake_case ,__snake_case ,1_2_8 ,__snake_case )
       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(__snake_case ) < len(__snake_case )
       assert len(__snake_case ) == 1
       assert len(packed_examples[0] ) == sum(len(__snake_case ) for x in orig_examples )
       assert orig_paths == new_paths


  @pytest.mark.skipif(not FAIRSEQ_AVAILABLE ,reason='''This test requires fairseq''' )
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       if not FAIRSEQ_AVAILABLE:
            return
       A_   ,    A_   ,    A_          =       self._get_dataset(max_len=6_4 )
       A_          =       6_4
       A_          =       ds.make_dynamic_sampler(__snake_case ,required_batch_size_multiple=__snake_case )
       A_          =       [len(__snake_case ) for x in batch_sampler]
       assert len(set(__snake_case ) ) > 1  # it's not dynamic batch size if every batch is the same length
       assert sum(__snake_case ) == len(__snake_case )  # no dropped or added examples
       A_          =       DataLoader(__snake_case ,batch_sampler=__snake_case ,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(__snake_case )
            if num_src_tokens > (max_tokens * 1.1):
                 failures.append(__snake_case )
       assert num_src_per_batch[0] == max(__snake_case )
       if failures:
            raise AssertionError(f'too many tokens in {len(__snake_case )} batches' )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_   ,    A_   ,    A_          =       self._get_dataset(max_len=5_1_2 )
       A_          =       2
       A_          =       ds.make_sortish_sampler(__snake_case ,shuffle=__snake_case )

       A_          =       DataLoader(__snake_case ,batch_size=__snake_case ,collate_fn=ds.collate_fn ,num_workers=2 )
       A_          =       DataLoader(__snake_case ,batch_size=__snake_case ,collate_fn=ds.collate_fn ,num_workers=2 ,sampler=__snake_case )

       A_          =       tokenizer.pad_token_id

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

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


  def 						__UpperCAmelCase     (							self ,__snake_case=1_0_0_0 ,__snake_case=1_2_8 ):



       """simple docstring"""





       if os.getenv('''USE_REAL_DATA''' ,__snake_case ):
            A_          =       '''examples/seq2seq/wmt_en_ro'''
            A_          =       max_len * 2 * 6_4
            if not Path(__snake_case ).joinpath('''train.len''' ).exists():
                 save_len_file(__snake_case ,__snake_case )
       else:
            A_          =       '''examples/seq2seq/test_data/wmt_en_ro'''
            A_          =       max_len * 4
            save_len_file(__snake_case ,__snake_case )

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


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_   ,    A_   ,    A_          =       self._get_dataset()
       A_          =       set(DistributedSortishSampler(__snake_case ,2_5_6 ,num_replicas=2 ,rank=0 ,add_extra_examples=__snake_case ) )
       A_          =       set(DistributedSortishSampler(__snake_case ,2_5_6 ,num_replicas=2 ,rank=1 ,add_extra_examples=__snake_case ) )
       assert idsa.intersection(__snake_case ) == set()




  @parameterized.expand(
      [
          MBART_TINY,
          MARIAN_TINY,
          T5_TINY,
          BART_TINY,
          PEGASUS_XSUM,
      ] ,)
  def 						__UpperCAmelCase     (							self ,__snake_case ):



       """simple docstring"""





       A_          =       AutoTokenizer.from_pretrained(__snake_case ,use_fast=__snake_case )
       if tok_name == MBART_TINY:
            A_          =       SeqaSeqDataset(
                __snake_case ,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(
                __snake_case ,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(__snake_case ) == 1 if tok_name == BART_TINY else len(__snake_case ) == 0










from __future__ import annotations

def      UpperCAmelCase_    (    _UpperCAmelCase							:list[float]  ,						_UpperCAmelCase							:list[float]				)	->				float:
     '''simple docstring'''


     A_          =       sorted(numsa + numsa				)
     A_   ,    A_          =       divmod(len(_UpperCAmelCase				)  ,						2				)
     if mod == 1:
          return all_numbers[div]
     else:
          return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      a__      :     Optional[Any]         =		[float(x) for x in input('Enter the elements of first array: ').split()]
      a__      :     int         =		[float(x) for x in input('Enter the elements of second array: ').split()]
      print(f'''The median of two arrays is: {median_of_two_arrays(array_a, array_a)}''')



