


"""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


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


def      _snake_case						(						_snake_case				:			Path ,   _snake_case				:			list			):
      lowerCAmelCase					:	Any       =   '''\n'''.join(_snake_case			)
      Path(_snake_case			).open('''w'''			).writelines(_snake_case			)


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






class 		snake_case_(       a__			):






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

           lowerCAmelCase					:	Dict       =   Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)				)
           lowerCAmelCase					:	List[str]       =   tmp_dir.joinpath('''train.source'''				).open().readlines()
           lowerCAmelCase					:	Union[str, Any]       =   Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)				)
           pack_data_dir(UpperCamelCase_					,				UpperCamelCase_					,				1_2_8					,				UpperCamelCase_				)
           lowerCAmelCase					:	Any       =   {x.name for x in tmp_dir.iterdir()}
           lowerCAmelCase					:	Optional[int]       =   {x.name for x in save_dir.iterdir()}
           lowerCAmelCase					:	Tuple       =   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  :					Tuple				):
           if not FAIRSEQ_AVAILABLE:
                 return
           lowerCAmelCase,      lowerCAmelCase,      lowerCAmelCase					:	List[str]       =   self._get_dataset(max_len=6_4				)
           lowerCAmelCase					:	Any       =   6_4
           lowerCAmelCase					:	Dict       =   ds.make_dynamic_sampler(UpperCamelCase_					,				required_batch_size_multiple=UpperCamelCase_				)
           lowerCAmelCase					:	List[Any]       =   [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
           lowerCAmelCase					:	Any       =   DataLoader(UpperCamelCase_					,				batch_sampler=UpperCamelCase_					,				collate_fn=ds.collate_fn					,				num_workers=2				)
           lowerCAmelCase					:	Optional[int]       =   []
           lowerCAmelCase					:	List[str]       =   []
           for batch in data_loader:
                 lowerCAmelCase					:	Union[str, Any]       =   batch['''input_ids'''].shape
                 lowerCAmelCase					:	Optional[Any]       =   src_shape[0]
                 assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
                 lowerCAmelCase					:	Optional[int]       =   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  :					Optional[Any]				):
           lowerCAmelCase,      lowerCAmelCase,      lowerCAmelCase					:	Any       =   self._get_dataset(max_len=5_1_2				)
           lowerCAmelCase					:	Tuple       =   2
           lowerCAmelCase					:	List[str]       =   ds.make_sortish_sampler(UpperCamelCase_					,				shuffle=UpperCamelCase_				)

           lowerCAmelCase					:	List[Any]       =   DataLoader(UpperCamelCase_					,				batch_size=UpperCamelCase_					,				collate_fn=ds.collate_fn					,				num_workers=2				)
           lowerCAmelCase					:	Tuple       =   DataLoader(UpperCamelCase_					,				batch_size=UpperCamelCase_					,				collate_fn=ds.collate_fn					,				num_workers=2					,				sampler=UpperCamelCase_				)

           lowerCAmelCase					:	Optional[int]       =   tokenizer.pad_token_id

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

           lowerCAmelCase					:	str       =   AutoTokenizer.from_pretrained(UpperCamelCase_				)
           lowerCAmelCase					:	str       =   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  :					List[Any]				):
           lowerCAmelCase,      lowerCAmelCase,      lowerCAmelCase					:	Dict       =   self._get_dataset()
           lowerCAmelCase					:	List[Any]       =   set(DistributedSortishSampler(UpperCamelCase_					,				2_5_6					,				num_replicas=2					,				rank=0					,				add_extra_examples=UpperCamelCase_				)				)
           lowerCAmelCase					:	int       =   set(DistributedSortishSampler(UpperCamelCase_					,				2_5_6					,				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  :					Optional[int]					,				UpperCamelCase_  :					Tuple				):
           lowerCAmelCase					:	List[Any]       =   AutoTokenizer.from_pretrained(UpperCamelCase_					,				use_fast=UpperCamelCase_				)
           if tok_name == MBART_TINY:
                 lowerCAmelCase					:	Any       =   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'''					,				)
                 lowerCAmelCase					:	Any       =   train_dataset.dataset_kwargs
                 assert "src_lang" in kwargs and "tgt_lang" in kwargs
           else:
                 lowerCAmelCase					:	Tuple       =   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					,				)
                 lowerCAmelCase					:	Tuple       =   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"""





import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


snake_case__		:     Optional[Any]    				=				namedtuple(
    '''_TestCommandArgs''',
    [
        '''dataset''',
        '''name''',
        '''cache_dir''',
        '''data_dir''',
        '''all_configs''',
        '''save_infos''',
        '''ignore_verifications''',
        '''force_redownload''',
        '''clear_cache''',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)


def      _snake_case						(						_snake_case				:			List[Any] ,   _snake_case				:			List[str]			):
      return (abs(source - target			) / target) < 0.01






@pytest.mark.integration
def      _snake_case						(						_snake_case				:			Any			):
      lowerCAmelCase					:	Union[str, Any]       =   _TestCommandArgs(dataset=_snake_case ,   all_configs=_snake_case ,   save_infos=_snake_case			)
      lowerCAmelCase					:	str       =   TestCommand(*_snake_case			)
      test_command.run()
      lowerCAmelCase					:	str       =   os.path.join(_snake_case ,   '''README.md'''			)
      assert os.path.exists(_snake_case			)
      lowerCAmelCase					:	Tuple       =   DatasetInfosDict.from_directory(_snake_case			)
      lowerCAmelCase					:	List[str]       =   DatasetInfosDict(
          {
              '''default''': DatasetInfo(
                  features=Features(
                      {
                          '''tokens''': Sequence(Value('''string'''			)			),
                          '''ner_tags''': Sequence(
                              ClassLabel(names=['''O''', '''B-PER''', '''I-PER''', '''B-ORG''', '''I-ORG''', '''B-LOC''', '''I-LOC''']			)			),
                          '''langs''': Sequence(Value('''string'''			)			),
                          '''spans''': Sequence(Value('''string'''			)			),
                      }			) ,   splits=[
                      {
                          '''name''': '''train''',
                          '''num_bytes''': 2351563,
                          '''num_examples''': 10000,
                      },
                      {
                          '''name''': '''validation''',
                          '''num_bytes''': 238418,
                          '''num_examples''': 1000,
                      },
                  ] ,   download_size=3940680 ,   dataset_size=2589981 ,   )
          }			)
      assert dataset_infos.keys() == expected_dataset_infos.keys()
      for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
            lowerCAmelCase,      lowerCAmelCase					:	Union[str, Any]       =   getattr(dataset_infos['''default'''] ,   _snake_case			), getattr(expected_dataset_infos['''default'''] ,   _snake_case			)
            if key == "num_bytes":
                  assert is_apercent_close(_snake_case ,   _snake_case			)
            elif key == "splits":
                  assert list(_snake_case			) == list(_snake_case			)
                  for split in result:
                        assert result[split].name == expected[split].name
                        assert result[split].num_examples == expected[split].num_examples
                        assert is_apercent_close(result[split].num_bytes ,   expected[split].num_bytes			)
            else:
                  result == expected


