





'''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


__SCREAMING_SNAKE_CASE     :int       =				'''bert-base-cased'''
__SCREAMING_SNAKE_CASE     :Any       =				'''google/pegasus-xsum'''
__SCREAMING_SNAKE_CASE     :Optional[int]       =				[''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
__SCREAMING_SNAKE_CASE     :Union[str, Any]       =				['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
__SCREAMING_SNAKE_CASE     :str       =				'''patrickvonplaten/t5-tiny-random'''
__SCREAMING_SNAKE_CASE     :List[Any]       =				'''sshleifer/bart-tiny-random'''
__SCREAMING_SNAKE_CASE     :List[str]       =				'''sshleifer/tiny-mbart'''
__SCREAMING_SNAKE_CASE     :Dict       =				'''sshleifer/tiny-marian-en-de'''
def 	UpperCAmelCase_							(					__lowercase							:      Path			,     __lowercase							:      list       )		->       List[Any]:


     '''simple docstring'''



     _UpperCAmelCase     							=   "\n".join(__lowercase       )
     Path(__lowercase       ).open("w"       ).writelines(__lowercase       )
def 	UpperCAmelCase_							(					__lowercase							:      List[str]       )		->       Any:


     '''simple docstring'''



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







class   A_	(  lowerCAmelCase_			):




 @parameterized.expand(
     [
         MBART_TINY,
         MARIAN_TINY,
         T5_TINY,
         BART_TINY,
         PEGASUS_XSUM,
     ]	,			)
 @slow
 def      lowercase	(					self       :    str	,			snake_case_       :    str    ):
      _UpperCAmelCase     							=   AutoTokenizer.from_pretrained(snake_case_    )
      _UpperCAmelCase     							=   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )
      _UpperCAmelCase     							=   max(len(tokenizer.encode(snake_case_    )    ) for a in ARTICLES    )
      _UpperCAmelCase     							=   max(len(tokenizer.encode(snake_case_    )    ) for a in SUMMARIES    )
      _UpperCAmelCase     							=   4
      _UpperCAmelCase     							=   8
      assert max_len_target > max_src_len  # Will be truncated
      assert max_len_source > max_src_len  # Will be truncated
      _UpperCAmelCase							,  _UpperCAmelCase     							=   "ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
      _UpperCAmelCase     							=   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_	,			)
      _UpperCAmelCase     							=   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
           _UpperCAmelCase     							=   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      lowercase	(					self       :    Union[str, Any]	,			snake_case_       :    List[Any]    ):
      _UpperCAmelCase     							=   AutoTokenizer.from_pretrained(snake_case_    )
      _UpperCAmelCase     							=   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )
      _UpperCAmelCase     							=   max(len(tokenizer.encode(snake_case_    )    ) for a in ARTICLES    )
      _UpperCAmelCase     							=   max(len(tokenizer.encode(snake_case_    )    ) for a in SUMMARIES    )
      _UpperCAmelCase     							=   4
      _UpperCAmelCase     							=   LegacySeqaSeqDataset(
          snake_case_	,			data_dir=snake_case_	,			type_path="train"	,			max_source_length=2_0	,			max_target_length=snake_case_	,			)
      _UpperCAmelCase     							=   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      lowercase	(					self       :    Any    ):
      _UpperCAmelCase     							=   AutoTokenizer.from_pretrained("facebook/mbart-large-cc25"    )

      _UpperCAmelCase     							=   Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )    )
      _UpperCAmelCase     							=   tmp_dir.joinpath("train.source"    ).open().readlines()
      _UpperCAmelCase     							=   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_    )
      _UpperCAmelCase     							=   {x.name for x in tmp_dir.iterdir()}
      _UpperCAmelCase     							=   {x.name for x in save_dir.iterdir()}
      _UpperCAmelCase     							=   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      lowercase	(					self       :    Optional[int]    ):
      if not FAIRSEQ_AVAILABLE:
           return
      _UpperCAmelCase							,  _UpperCAmelCase							,  _UpperCAmelCase     							=   self._get_dataset(max_len=6_4    )
      _UpperCAmelCase     							=   6_4
      _UpperCAmelCase     							=   ds.make_dynamic_sampler(snake_case_	,			required_batch_size_multiple=snake_case_    )
      _UpperCAmelCase     							=   [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
      _UpperCAmelCase     							=   DataLoader(snake_case_	,			batch_sampler=snake_case_	,			collate_fn=ds.collate_fn	,			num_workers=2    )
      _UpperCAmelCase     							=   []
      _UpperCAmelCase     							=   []
      for batch in data_loader:
           _UpperCAmelCase     							=   batch["input_ids"].shape
           _UpperCAmelCase     							=   src_shape[0]
           assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
           _UpperCAmelCase     							=   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      lowercase	(					self       :    Optional[Any]    ):
      _UpperCAmelCase							,  _UpperCAmelCase							,  _UpperCAmelCase     							=   self._get_dataset(max_len=5_1_2    )
      _UpperCAmelCase     							=   2
      _UpperCAmelCase     							=   ds.make_sortish_sampler(snake_case_	,			shuffle=snake_case_    )

      _UpperCAmelCase     							=   DataLoader(snake_case_	,			batch_size=snake_case_	,			collate_fn=ds.collate_fn	,			num_workers=2    )
      _UpperCAmelCase     							=   DataLoader(snake_case_	,			batch_size=snake_case_	,			collate_fn=ds.collate_fn	,			num_workers=2	,			sampler=snake_case_    )

      _UpperCAmelCase     							=   tokenizer.pad_token_id

      def count_pad_tokens(snake_case_       :    List[Any]	,			snake_case_       :    Dict="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      lowercase	(					self       :    List[Any]	,			snake_case_       :    List[str]=1_0_0_0	,			snake_case_       :    List[Any]=1_2_8    ):
      if os.getenv("USE_REAL_DATA"	,			snake_case_    ):
           _UpperCAmelCase     							=   "examples/seq2seq/wmt_en_ro"
           _UpperCAmelCase     							=   max_len * 2 * 6_4
           if not Path(snake_case_    ).joinpath("train.len"    ).exists():
                save_len_file(snake_case_	,			snake_case_    )
      else:
           _UpperCAmelCase     							=   "examples/seq2seq/test_data/wmt_en_ro"
           _UpperCAmelCase     							=   max_len * 4
           save_len_file(snake_case_	,			snake_case_    )

      _UpperCAmelCase     							=   AutoTokenizer.from_pretrained(snake_case_    )
      _UpperCAmelCase     							=   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      lowercase	(					self       :    List[Any]    ):
      _UpperCAmelCase							,  _UpperCAmelCase							,  _UpperCAmelCase     							=   self._get_dataset()
      _UpperCAmelCase     							=   set(DistributedSortishSampler(snake_case_	,			2_5_6	,			num_replicas=2	,			rank=0	,			add_extra_examples=snake_case_    )    )
      _UpperCAmelCase     							=   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      lowercase	(					self       :    str	,			snake_case_       :    Union[str, Any]    ):
      _UpperCAmelCase     							=   AutoTokenizer.from_pretrained(snake_case_	,			use_fast=snake_case_    )
      if tok_name == MBART_TINY:
           _UpperCAmelCase     							=   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"	,			)
           _UpperCAmelCase     							=   train_dataset.dataset_kwargs
           assert "src_lang" in kwargs and "tgt_lang" in kwargs
      else:
           _UpperCAmelCase     							=   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	,			)
           _UpperCAmelCase     							=   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











'''simple docstring'''




def 	UpperCAmelCase_							(					__lowercase							:      int       )		->       int:


     '''simple docstring'''



     if not isinstance(__lowercase			,     __lowercase       ) or number < 0:
          raise ValueError("Input must be a non-negative integer"       )

     _UpperCAmelCase     							=   0
     while number:
          # This way we arrive at next set bit (next 1) instead of looping
          # through each bit and checking for 1s hence the
          # loop won't run 32 times it will only run the number of `1` times
          number &= number - 1
          count += 1
     return count


if __name__ == "__main__":
  import doctest

  doctest.testmod()




