






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







def       lowercase__	(					A_:						Path  ,      A_:						list		)	->				Optional[int]:







       """simple docstring"""






       __UpperCAmelCase     				="""\n""".join(A_		)
       Path(A_		).open("""w"""		).writelines(A_		)







def       lowercase__	(					A_:						Dict		)	->				Tuple:







       """simple docstring"""






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





class 				_A			(     UpperCamelCase				):
    """simple docstring"""

    @parameterized.expand(
        [
            MBART_TINY,
            MARIAN_TINY,
            T5_TINY,
            BART_TINY,
            PEGASUS_XSUM,
        ] ,     )
    @slow
    def _a							(				self      :     Union[str, Any] ,     __SCREAMING_SNAKE_CASE      :     int      )      ->							List[str]:
           __UpperCAmelCase     				=AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
           __UpperCAmelCase     				=max(len(tokenizer.encode(__SCREAMING_SNAKE_CASE      )      ) for a in ARTICLES      )
           __UpperCAmelCase     				=max(len(tokenizer.encode(__SCREAMING_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(
               __SCREAMING_SNAKE_CASE ,     data_dir=__SCREAMING_SNAKE_CASE ,     type_path="""train""" ,     max_source_length=__SCREAMING_SNAKE_CASE ,     max_target_length=__SCREAMING_SNAKE_CASE ,     src_lang=__SCREAMING_SNAKE_CASE ,     tgt_lang=__SCREAMING_SNAKE_CASE ,     )
           __UpperCAmelCase     				=DataLoader(__SCREAMING_SNAKE_CASE ,     batch_size=2 ,     collate_fn=train_dataset.collate_fn      )
           for batch in dataloader:
                  assert isinstance(__SCREAMING_SNAKE_CASE ,     __SCREAMING_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 _a							(				self      :     Dict ,     __SCREAMING_SNAKE_CASE      :     List[Any]      )      ->							Any:
           __UpperCAmelCase     				=AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
           __UpperCAmelCase     				=max(len(tokenizer.encode(__SCREAMING_SNAKE_CASE      )      ) for a in ARTICLES      )
           __UpperCAmelCase     				=max(len(tokenizer.encode(__SCREAMING_SNAKE_CASE      )      ) for a in SUMMARIES      )
           __UpperCAmelCase     				=4
           __UpperCAmelCase     				=LegacySeqaSeqDataset(
               __SCREAMING_SNAKE_CASE ,     data_dir=__SCREAMING_SNAKE_CASE ,     type_path="""train""" ,     max_source_length=20 ,     max_target_length=__SCREAMING_SNAKE_CASE ,     )
           __UpperCAmelCase     				=DataLoader(__SCREAMING_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 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      :     int      )      ->							str:
           __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(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     128 ,     __SCREAMING_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(__SCREAMING_SNAKE_CASE      ) < len(__SCREAMING_SNAKE_CASE      )
           assert len(__SCREAMING_SNAKE_CASE      ) == 1
           assert len(packed_examples[0]      ) == sum(len(__SCREAMING_SNAKE_CASE      ) for x in orig_examples      )
           assert orig_paths == new_paths



    @pytest.mark.skipif(not FAIRSEQ_AVAILABLE ,     reason="""This test requires fairseq"""      )
    def _a							(				self      :     str      )      ->							str:
           if not FAIRSEQ_AVAILABLE:
                  return
           __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase     				=self._get_dataset(max_len=64      )
           __UpperCAmelCase     				=64
           __UpperCAmelCase     				=ds.make_dynamic_sampler(__SCREAMING_SNAKE_CASE ,     required_batch_size_multiple=__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=[len(__SCREAMING_SNAKE_CASE      ) for x in batch_sampler]
           assert len(set(__SCREAMING_SNAKE_CASE      )      ) > 1  # it's not dynamic batch size if every batch is the same length
           assert sum(__SCREAMING_SNAKE_CASE      ) == len(__SCREAMING_SNAKE_CASE      )  # no dropped or added examples
           __UpperCAmelCase     				=DataLoader(__SCREAMING_SNAKE_CASE ,     batch_sampler=__SCREAMING_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(__SCREAMING_SNAKE_CASE      )
                  if num_src_tokens > (max_tokens * 1.1):
                         failures.append(__SCREAMING_SNAKE_CASE      )
           assert num_src_per_batch[0] == max(__SCREAMING_SNAKE_CASE      )
           if failures:
                  raise AssertionError(f'''too many tokens in {len(__SCREAMING_SNAKE_CASE      )} batches'''      )



    def _a							(				self      :     List[Any]      )      ->							int:
           __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase     				=self._get_dataset(max_len=512      )
           __UpperCAmelCase     				=2
           __UpperCAmelCase     				=ds.make_sortish_sampler(__SCREAMING_SNAKE_CASE ,     shuffle=__SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=DataLoader(__SCREAMING_SNAKE_CASE ,     batch_size=__SCREAMING_SNAKE_CASE ,     collate_fn=ds.collate_fn ,     num_workers=2      )
           __UpperCAmelCase     				=DataLoader(__SCREAMING_SNAKE_CASE ,     batch_size=__SCREAMING_SNAKE_CASE ,     collate_fn=ds.collate_fn ,     num_workers=2 ,     sampler=__SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=tokenizer.pad_token_id

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

           assert sum(count_pad_tokens(__SCREAMING_SNAKE_CASE ,     k="""labels"""      )      ) < sum(count_pad_tokens(__SCREAMING_SNAKE_CASE ,     k="""labels"""      )      )
           assert sum(count_pad_tokens(__SCREAMING_SNAKE_CASE      )      ) < sum(count_pad_tokens(__SCREAMING_SNAKE_CASE      )      )
           assert len(__SCREAMING_SNAKE_CASE      ) == len(__SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     Any ,     __SCREAMING_SNAKE_CASE      :     Any=1000 ,     __SCREAMING_SNAKE_CASE      :     int=128      )      ->							List[str]:
           if os.getenv("""USE_REAL_DATA""" ,     __SCREAMING_SNAKE_CASE      ):
                  __UpperCAmelCase     				="""examples/seq2seq/wmt_en_ro"""
                  __UpperCAmelCase     				=max_len * 2 * 64
                  if not Path(__SCREAMING_SNAKE_CASE      ).joinpath("""train.len"""      ).exists():
                         save_len_file(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )
           else:
                  __UpperCAmelCase     				="""examples/seq2seq/test_data/wmt_en_ro"""
                  __UpperCAmelCase     				=max_len * 4
                  save_len_file(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=SeqaSeqDataset(
               __SCREAMING_SNAKE_CASE ,     data_dir=__SCREAMING_SNAKE_CASE ,     type_path="""train""" ,     max_source_length=__SCREAMING_SNAKE_CASE ,     max_target_length=__SCREAMING_SNAKE_CASE ,     n_obs=__SCREAMING_SNAKE_CASE ,     )
           return ds, max_tokens, tokenizer



    def _a							(				self      :     Optional[Any]      )      ->							str:
           __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase     				=self._get_dataset()
           __UpperCAmelCase     				=set(DistributedSortishSampler(__SCREAMING_SNAKE_CASE ,     256 ,     num_replicas=2 ,     rank=0 ,     add_extra_examples=__SCREAMING_SNAKE_CASE      )      )
           __UpperCAmelCase     				=set(DistributedSortishSampler(__SCREAMING_SNAKE_CASE ,     256 ,     num_replicas=2 ,     rank=1 ,     add_extra_examples=__SCREAMING_SNAKE_CASE      )      )
           assert idsa.intersection(__SCREAMING_SNAKE_CASE      ) == set()



    @parameterized.expand(
        [
            MBART_TINY,
            MARIAN_TINY,
            T5_TINY,
            BART_TINY,
            PEGASUS_XSUM,
        ] ,     )
    def _a							(				self      :     List[Any] ,     __SCREAMING_SNAKE_CASE      :     List[Any]      )      ->							Optional[Any]:
           __UpperCAmelCase     				=AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE ,     use_fast=__SCREAMING_SNAKE_CASE      )
           if tok_name == MBART_TINY:
                  __UpperCAmelCase     				=SeqaSeqDataset(
                      __SCREAMING_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(
                      __SCREAMING_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(__SCREAMING_SNAKE_CASE      ) == 1 if tok_name == BART_TINY else len(__SCREAMING_SNAKE_CASE      ) == 0











def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> float:




    """simple docstring"""




    return price * (1 + tax_rate)


if __name__ == "__main__":
 print(f"{price_plus_tax(100, 0.2_5) = }")
 print(f"{price_plus_tax(1_2_5.5_0, 0.0_5) = }")





