


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        _lowerCamelCase(	lowercase__   , lowercase__	)						->	Optional[int]:

		'''simple docstring'''







		__lowercase=			'\n'.join(lowercase__	)
		Path(lowercase__	).open('w'	).writelines(lowercase__	)







def        _lowerCamelCase(	lowercase__	)						->	Tuple:

		'''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    (   A_ ):


							@parameterized.expand(
							    [
							        MBART_TINY,
							        MARIAN_TINY,
							        T5_TINY,
							        BART_TINY,
							        PEGASUS_XSUM,
							    ]      ,				)
							@slow
							def 			_A       (self      ,				lowerCAmelCase					):
									__lowercase=			AutoTokenizer.from_pretrained(lowerCAmelCase					)
									__lowercase=			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
									__lowercase=			max(len(tokenizer.encode(lowerCAmelCase					)					) for a in ARTICLES					)
									__lowercase=			max(len(tokenizer.encode(lowerCAmelCase					)					) for a in SUMMARIES					)
									__lowercase=			4
									__lowercase=			8
									assert max_len_target > max_src_len  # Will be truncated
									assert max_len_source > max_src_len  # Will be truncated
									__lowercase,							__lowercase=			'ro_RO', 'de_DE'  # ignored for all but mbart, but never causes error.
									__lowercase=			SeqaSeqDataset(
									    lowerCAmelCase      ,				data_dir=lowerCAmelCase      ,				type_path='train'      ,				max_source_length=lowerCAmelCase      ,				max_target_length=lowerCAmelCase      ,				src_lang=lowerCAmelCase      ,				tgt_lang=lowerCAmelCase      ,				)
									__lowercase=			DataLoader(lowerCAmelCase      ,				batch_size=2      ,				collate_fn=train_dataset.collate_fn					)
									for batch in dataloader:
											assert isinstance(lowerCAmelCase      ,				lowerCAmelCase					)
											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
											__lowercase=			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      ,				lowerCAmelCase					):
									__lowercase=			AutoTokenizer.from_pretrained(lowerCAmelCase					)
									__lowercase=			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
									__lowercase=			max(len(tokenizer.encode(lowerCAmelCase					)					) for a in ARTICLES					)
									__lowercase=			max(len(tokenizer.encode(lowerCAmelCase					)					) for a in SUMMARIES					)
									__lowercase=			4
									__lowercase=			LegacySeqaSeqDataset(
									    lowerCAmelCase      ,				data_dir=lowerCAmelCase      ,				type_path='train'      ,				max_source_length=2_0      ,				max_target_length=lowerCAmelCase      ,				)
									__lowercase=			DataLoader(lowerCAmelCase      ,				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 			_A       (self					):
									__lowercase=			AutoTokenizer.from_pretrained('facebook/mbart-large-cc25'					)

									__lowercase=			Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)					)
									__lowercase=			tmp_dir.joinpath('train.source'					).open().readlines()
									__lowercase=			Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)					)
									pack_data_dir(lowerCAmelCase      ,				lowerCAmelCase      ,				1_2_8      ,				lowerCAmelCase					)
									__lowercase=			{x.name for x in tmp_dir.iterdir()}
									__lowercase=			{x.name for x in save_dir.iterdir()}
									__lowercase=			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(lowerCAmelCase					) < len(lowerCAmelCase					)
									assert len(lowerCAmelCase					) == 1
									assert len(packed_examples[0]					) == sum(len(lowerCAmelCase					) for x in orig_examples					)
									assert orig_paths == new_paths


							@pytest.mark.skipif(not FAIRSEQ_AVAILABLE      ,				reason='This test requires fairseq'					)
							def 			_A       (self					):
									if not FAIRSEQ_AVAILABLE:
											return
									__lowercase,							__lowercase,							__lowercase=			self._get_dataset(max_len=6_4					)
									__lowercase=			6_4
									__lowercase=			ds.make_dynamic_sampler(lowerCAmelCase      ,				required_batch_size_multiple=lowerCAmelCase					)
									__lowercase=			[len(lowerCAmelCase					) for x in batch_sampler]
									assert len(set(lowerCAmelCase					)					) > 1  # it's not dynamic batch size if every batch is the same length
									assert sum(lowerCAmelCase					) == len(lowerCAmelCase					)  # no dropped or added examples
									__lowercase=			DataLoader(lowerCAmelCase      ,				batch_sampler=lowerCAmelCase      ,				collate_fn=ds.collate_fn      ,				num_workers=2					)
									__lowercase=			[]
									__lowercase=			[]
									for batch in data_loader:
											__lowercase=			batch['input_ids'].shape
											__lowercase=			src_shape[0]
											assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
											__lowercase=			np.product(batch['input_ids'].shape					)
											num_src_per_batch.append(lowerCAmelCase					)
											if num_src_tokens > (max_tokens * 1.1):
													failures.append(lowerCAmelCase					)
									assert num_src_per_batch[0] == max(lowerCAmelCase					)
									if failures:
											raise AssertionError(f'too many tokens in {len(lowerCAmelCase					)} batches'					)


							def 			_A       (self					):
									__lowercase,							__lowercase,							__lowercase=			self._get_dataset(max_len=5_1_2					)
									__lowercase=			2
									__lowercase=			ds.make_sortish_sampler(lowerCAmelCase      ,				shuffle=lowerCAmelCase					)

									__lowercase=			DataLoader(lowerCAmelCase      ,				batch_size=lowerCAmelCase      ,				collate_fn=ds.collate_fn      ,				num_workers=2					)
									__lowercase=			DataLoader(lowerCAmelCase      ,				batch_size=lowerCAmelCase      ,				collate_fn=ds.collate_fn      ,				num_workers=2      ,				sampler=lowerCAmelCase					)

									__lowercase=			tokenizer.pad_token_id

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

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


							def 			_A       (self      ,				lowerCAmelCase=1_0_0_0      ,				lowerCAmelCase=1_2_8					):
									if os.getenv('USE_REAL_DATA'      ,				lowerCAmelCase					):
											__lowercase=			'examples/seq2seq/wmt_en_ro'
											__lowercase=			max_len * 2 * 6_4
											if not Path(lowerCAmelCase					).joinpath('train.len'					).exists():
													save_len_file(lowerCAmelCase      ,				lowerCAmelCase					)
									else:
											__lowercase=			'examples/seq2seq/test_data/wmt_en_ro'
											__lowercase=			max_len * 4
											save_len_file(lowerCAmelCase      ,				lowerCAmelCase					)

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


							def 			_A       (self					):
									__lowercase,							__lowercase,							__lowercase=			self._get_dataset()
									__lowercase=			set(DistributedSortishSampler(lowerCAmelCase      ,				2_5_6      ,				num_replicas=2      ,				rank=0      ,				add_extra_examples=lowerCAmelCase					)					)
									__lowercase=			set(DistributedSortishSampler(lowerCAmelCase      ,				2_5_6      ,				num_replicas=2      ,				rank=1      ,				add_extra_examples=lowerCAmelCase					)					)
									assert idsa.intersection(lowerCAmelCase					) == set()







							@parameterized.expand(
							    [
							        MBART_TINY,
							        MARIAN_TINY,
							        T5_TINY,
							        BART_TINY,
							        PEGASUS_XSUM,
							    ]      ,				)
							def 			_A       (self      ,				lowerCAmelCase					):
									__lowercase=			AutoTokenizer.from_pretrained(lowerCAmelCase      ,				use_fast=lowerCAmelCase					)
									if tok_name == MBART_TINY:
											__lowercase=			SeqaSeqDataset(
											    lowerCAmelCase      ,				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'      ,				)
											__lowercase=			train_dataset.dataset_kwargs
											assert "src_lang" in kwargs and "tgt_lang" in kwargs
									else:
											__lowercase=			SeqaSeqDataset(
											    lowerCAmelCase      ,				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      ,				)
											__lowercase=			train_dataset.dataset_kwargs
											assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
											assert len(lowerCAmelCase					) == 1 if tok_name == BART_TINY else len(lowerCAmelCase					) == 0










def        _lowerCamelCase(	lowercase__   , lowercase__ = " "	)						->	list:

		'''simple docstring'''







		__lowercase=			[]

		__lowercase=			0
		for index, char in enumerate(lowercase__	):
				if char == separator:
						split_words.append(string[last_index:index]	)
						__lowercase=			index + 1
				elif index + 1 == len(lowercase__	):
						split_words.append(string[last_index : index + 1]	)
		return split_words


if __name__ == "__main__":
	from doctest import testmod

	testmod()






