






'''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


__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 		_UpperCAmelCase   (							__A							:   Path						,		__A							:   list      ):
				a_		:  Optional[int]											=      '''\n'''.join(__A      )
				Path(__A      ).open('''w'''      ).writelines(__A      )





def 		_UpperCAmelCase   (							__A							:   str      ):
				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 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):






			@parameterized.expand(
			    [
			        MBART_TINY,
			        MARIAN_TINY,
			        T5_TINY,
			        BART_TINY,
			        PEGASUS_XSUM,
			    ]  ,						)
			@slow
			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple  ,						__SCREAMING_SNAKE_CASE      :			List[str]					)			->			Optional[int]:
							a_		:  Any											=      AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE					)
							a_		:  Dict											=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
							a_		:  Tuple											=      max(len(tokenizer.encode(__SCREAMING_SNAKE_CASE					)					) for a in ARTICLES					)
							a_		:  str											=      max(len(tokenizer.encode(__SCREAMING_SNAKE_CASE					)					) for a in SUMMARIES					)
							a_		:  List[Any]											=      4
							a_		:  List[str]											=      8
							assert max_len_target > max_src_len  # Will be truncated
							assert max_len_source > max_src_len  # Will be truncated
							a_				,			a_		:  List[str]											=      '''ro_RO''', '''de_DE'''  # ignored for all but mbart, but never causes error.
							a_		:  List[str]											=      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  ,						)
							a_		:  List[str]											=      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
											a_		:  List[Any]											=      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 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			Dict					)			->			Any:
							a_		:  Tuple											=      AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE					)
							a_		:  List[str]											=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
							a_		:  Any											=      max(len(tokenizer.encode(__SCREAMING_SNAKE_CASE					)					) for a in ARTICLES					)
							a_		:  Union[str, Any]											=      max(len(tokenizer.encode(__SCREAMING_SNAKE_CASE					)					) for a in SUMMARIES					)
							a_		:  int											=      4
							a_		:  Optional[int]											=      LegacySeqaSeqDataset(
							    __SCREAMING_SNAKE_CASE  ,						data_dir=__SCREAMING_SNAKE_CASE  ,						type_path='''train'''  ,						max_source_length=20  ,						max_target_length=__SCREAMING_SNAKE_CASE  ,						)
							a_		:  Optional[int]											=      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 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]					)			->			Dict:
							a_		:  Optional[int]											=      AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25'''					)

							a_		:  Union[str, Any]											=      Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)					)
							a_		:  int											=      tmp_dir.joinpath('''train.source'''					).open().readlines()
							a_		:  Any											=      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					)
							a_		:  Union[str, Any]											=      {x.name for x in tmp_dir.iterdir()}
							a_		:  List[Any]											=      {x.name for x in save_dir.iterdir()}
							a_		:  List[Any]											=      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 			SCREAMING_SNAKE_CASE      ( self      :			Dict					)			->			Dict:
							if not FAIRSEQ_AVAILABLE:
											return
							a_				,			a_				,			a_		:  Optional[Any]											=      self._get_dataset(max_len=64					)
							a_		:  Union[str, Any]											=      64
							a_		:  int											=      ds.make_dynamic_sampler(__SCREAMING_SNAKE_CASE  ,						required_batch_size_multiple=__SCREAMING_SNAKE_CASE					)
							a_		:  Dict											=      [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
							a_		:  List[str]											=      DataLoader(__SCREAMING_SNAKE_CASE  ,						batch_sampler=__SCREAMING_SNAKE_CASE  ,						collate_fn=ds.collate_fn  ,						num_workers=2					)
							a_		:  Tuple											=      []
							a_		:  List[str]											=      []
							for batch in data_loader:
											a_		:  Union[str, Any]											=      batch['''input_ids'''].shape
											a_		:  Dict											=      src_shape[0]
											assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
											a_		:  List[str]											=      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 			SCREAMING_SNAKE_CASE      ( self      :			List[str]					)			->			List[str]:
							a_				,			a_				,			a_		:  Tuple											=      self._get_dataset(max_len=512					)
							a_		:  Union[str, Any]											=      2
							a_		:  List[Any]											=      ds.make_sortish_sampler(__SCREAMING_SNAKE_CASE  ,						shuffle=__SCREAMING_SNAKE_CASE					)

							a_		:  List[Any]											=      DataLoader(__SCREAMING_SNAKE_CASE  ,						batch_size=__SCREAMING_SNAKE_CASE  ,						collate_fn=ds.collate_fn  ,						num_workers=2					)
							a_		:  Optional[Any]											=      DataLoader(__SCREAMING_SNAKE_CASE  ,						batch_size=__SCREAMING_SNAKE_CASE  ,						collate_fn=ds.collate_fn  ,						num_workers=2  ,						sampler=__SCREAMING_SNAKE_CASE					)

							a_		:  Optional[int]											=      tokenizer.pad_token_id

							def count_pad_tokens(__SCREAMING_SNAKE_CASE      :			Tuple  ,						__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 			SCREAMING_SNAKE_CASE      ( self      :			int  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]=1000  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]=128					)			->			Optional[int]:
							if os.getenv('''USE_REAL_DATA'''  ,						__SCREAMING_SNAKE_CASE					):
											a_		:  int											=      '''examples/seq2seq/wmt_en_ro'''
											a_		:  Tuple											=      max_len * 2 * 64
											if not Path(__SCREAMING_SNAKE_CASE					).joinpath('''train.len'''					).exists():
															save_len_file(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)
							else:
											a_		:  Optional[int]											=      '''examples/seq2seq/test_data/wmt_en_ro'''
											a_		:  Tuple											=      max_len * 4
											save_len_file(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)

							a_		:  Optional[Any]											=      AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE					)
							a_		:  int											=      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 			SCREAMING_SNAKE_CASE      ( self      :			List[str]					)			->			Tuple:
							a_				,			a_				,			a_		:  Union[str, Any]											=      self._get_dataset()
							a_		:  Optional[Any]											=      set(DistributedSortishSampler(__SCREAMING_SNAKE_CASE  ,						256  ,						num_replicas=2  ,						rank=0  ,						add_extra_examples=__SCREAMING_SNAKE_CASE					)					)
							a_		:  int											=      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 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]					)			->			Union[str, Any]:
							a_		:  Tuple											=      AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE  ,						use_fast=__SCREAMING_SNAKE_CASE					)
							if tok_name == MBART_TINY:
											a_		:  Union[str, Any]											=      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'''  ,						)
											a_		:  Optional[int]											=      train_dataset.dataset_kwargs
											assert "src_lang" in kwargs and "tgt_lang" in kwargs
							else:
											a_		:  int											=      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  ,						)
											a_		:  List[Any]											=      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












'''simple docstring'''







def 		_UpperCAmelCase   (							__A							:   str						,		__A							:   str      ):

				def get_matched_characters(__A							:   str						,		__A							:   str      ) -> str:
								a_		:  Union[str, Any]											=      []
								a_		:  int											=      min(len(_stra      )						,		len(_stra      )      ) // 2
								for i, l in enumerate(_stra      ):
												a_		:  Any											=      int(max(0						,		i - limit      )      )
												a_		:  Union[str, Any]											=      int(min(i + limit + 1						,		len(_stra      )      )      )
												if l in _stra[left:right]:
																matched.append(__A      )
																a_		:  Any											=      f'{_stra[0:_stra.index(__A      )]} {_stra[_stra.index(__A      ) + 1:]}'

								return "".join(__A      )

				# matching characters
				a_		:  Optional[Any]											=      get_matched_characters(__A						,		__A      )
				a_		:  int											=      get_matched_characters(__A						,		__A      )
				a_		:  Any											=      len(__A      )

				# transposition
				a_		:  List[Any]											=      (
				    len([(ca, ca) for ca, ca in zip(__A						,		__A      ) if ca != ca]      ) // 2
				)

				if not match_count:
								a_		:  Dict											=      0.0
				else:
								a_		:  Optional[int]											=      (
								    1
								    / 3
								    * (
								        match_count / len(__A      )
								        + match_count / len(__A      )
								        + (match_count - transpositions) / match_count
								    )
								)

				# common prefix up to 4 characters
				a_		:  List[str]											=      0
				for ca, ca in zip(stra[:4]						,		stra[:4]      ):
								if ca == ca:
												prefix_len += 1
								else:
												break

				return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(jaro_winkler('hello', 'world'))




