





"""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 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						Any:






				"""simple docstring"""


				snake_case_						:   Optional[Any]           =  '''\n'''.join(_UpperCamelCase			)
				Path(_UpperCamelCase			).open('''w'''			).writelines(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Dict:






				"""simple docstring"""


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





class  __lowerCAmelCase	(      __lowerCAmelCase					):






			@parameterized.expand(
			    [
			        MBART_TINY,
			        MARIAN_TINY,
			        T5_TINY,
			        BART_TINY,
			        PEGASUS_XSUM,
			    ] ,							)
			@slow
			def       lowerCamelCase  (self ,							__magic_name__   )					->       Optional[Any]:


							'''simple docstring'''



							snake_case_						:   Dict           =  AutoTokenizer.from_pretrained(lowerCamelCase__   )
							snake_case_						:   Optional[int]           =  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )
							snake_case_						:   Tuple           =  max(len(tokenizer.encode(lowerCamelCase__   )   ) for a in ARTICLES   )
							snake_case_						:   List[str]           =  max(len(tokenizer.encode(lowerCamelCase__   )   ) for a in SUMMARIES   )
							snake_case_						:   int           =  4
							snake_case_						:   Union[str, Any]           =  8
							assert max_len_target > max_src_len  # Will be truncated
							assert max_len_source > max_src_len  # Will be truncated
							snake_case_						:   Union[str, Any]           =  '''ro_RO''', '''de_DE'''  # ignored for all but mbart, but never causes error.
							snake_case_						:   List[str]           =  SeqaSeqDataset(
							    lowerCamelCase__ ,							data_dir=lowerCamelCase__ ,							type_path='''train''' ,							max_source_length=lowerCamelCase__ ,							max_target_length=lowerCamelCase__ ,							src_lang=lowerCamelCase__ ,							tgt_lang=lowerCamelCase__ ,							)
							snake_case_						:   Dict           =  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
											snake_case_						:   Dict           =  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 ,							__magic_name__   )					->       List[Any]:


							'''simple docstring'''



							snake_case_						:   Dict           =  AutoTokenizer.from_pretrained(lowerCamelCase__   )
							snake_case_						:   str           =  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )
							snake_case_						:   Union[str, Any]           =  max(len(tokenizer.encode(lowerCamelCase__   )   ) for a in ARTICLES   )
							snake_case_						:   str           =  max(len(tokenizer.encode(lowerCamelCase__   )   ) for a in SUMMARIES   )
							snake_case_						:   Union[str, Any]           =  4
							snake_case_						:   Optional[int]           =  LegacySeqaSeqDataset(
							    lowerCamelCase__ ,							data_dir=lowerCamelCase__ ,							type_path='''train''' ,							max_source_length=20 ,							max_target_length=lowerCamelCase__ ,							)
							snake_case_						:   List[str]           =  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 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       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   Dict           =  AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25'''   )

							snake_case_						:   List[str]           =  Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )   )
							snake_case_						:   int           =  tmp_dir.joinpath('''train.source'''   ).open().readlines()
							snake_case_						:   Tuple           =  Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )   )
							pack_data_dir(lowerCamelCase__ ,							lowerCamelCase__ ,							128 ,							lowerCamelCase__   )
							snake_case_						:   str           =  {x.name for x in tmp_dir.iterdir()}
							snake_case_						:   Optional[int]           =  {x.name for x in save_dir.iterdir()}
							snake_case_						:   Dict           =  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       lowerCamelCase  (self   )					->       Optional[Any]:


							'''simple docstring'''



							if not FAIRSEQ_AVAILABLE:
											return
							snake_case_						:   Tuple           =  self._get_dataset(max_len=64   )
							snake_case_						:   List[str]           =  64
							snake_case_						:   Optional[int]           =  ds.make_dynamic_sampler(lowerCamelCase__ ,							required_batch_size_multiple=lowerCamelCase__   )
							snake_case_						:   Union[str, Any]           =  [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
							snake_case_						:   Any           =  DataLoader(lowerCamelCase__ ,							batch_sampler=lowerCamelCase__ ,							collate_fn=ds.collate_fn ,							num_workers=2   )
							snake_case_						:   int           =  []
							snake_case_						:   Tuple           =  []
							for batch in data_loader:
											snake_case_						:   int           =  batch['''input_ids'''].shape
											snake_case_						:   Any           =  src_shape[0]
											assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
											snake_case_						:   Tuple           =  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       lowerCamelCase  (self   )					->       Optional[Any]:


							'''simple docstring'''



							snake_case_						:   Tuple           =  self._get_dataset(max_len=512   )
							snake_case_						:   Union[str, Any]           =  2
							snake_case_						:   Optional[int]           =  ds.make_sortish_sampler(lowerCamelCase__ ,							shuffle=lowerCamelCase__   )

							snake_case_						:   List[Any]           =  DataLoader(lowerCamelCase__ ,							batch_size=lowerCamelCase__ ,							collate_fn=ds.collate_fn ,							num_workers=2   )
							snake_case_						:   str           =  DataLoader(lowerCamelCase__ ,							batch_size=lowerCamelCase__ ,							collate_fn=ds.collate_fn ,							num_workers=2 ,							sampler=lowerCamelCase__   )

							snake_case_						:   Optional[Any]           =  tokenizer.pad_token_id

							def count_pad_tokens(__magic_name__ ,							__magic_name__="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       lowerCamelCase  (self ,							__magic_name__=1000 ,							__magic_name__=128   )					->       str:


							'''simple docstring'''



							if os.getenv('''USE_REAL_DATA''' ,							lowerCamelCase__   ):
											snake_case_						:   List[str]           =  '''examples/seq2seq/wmt_en_ro'''
											snake_case_						:   int           =  max_len * 2 * 64
											if not Path(lowerCamelCase__   ).joinpath('''train.len'''   ).exists():
															save_len_file(lowerCamelCase__ ,							lowerCamelCase__   )
							else:
											snake_case_						:   Optional[Any]           =  '''examples/seq2seq/test_data/wmt_en_ro'''
											snake_case_						:   Optional[Any]           =  max_len * 4
											save_len_file(lowerCamelCase__ ,							lowerCamelCase__   )

							snake_case_						:   List[str]           =  AutoTokenizer.from_pretrained(lowerCamelCase__   )
							snake_case_						:   Any           =  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       lowerCamelCase  (self   )					->       str:


							'''simple docstring'''



							snake_case_						:   List[Any]           =  self._get_dataset()
							snake_case_						:   Any           =  set(DistributedSortishSampler(lowerCamelCase__ ,							256 ,							num_replicas=2 ,							rank=0 ,							add_extra_examples=lowerCamelCase__   )   )
							snake_case_						:   str           =  set(DistributedSortishSampler(lowerCamelCase__ ,							256 ,							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       lowerCamelCase  (self ,							__magic_name__   )					->       str:


							'''simple docstring'''



							snake_case_						:   Union[str, Any]           =  AutoTokenizer.from_pretrained(lowerCamelCase__ ,							use_fast=lowerCamelCase__   )
							if tok_name == MBART_TINY:
											snake_case_						:   Dict           =  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''' ,							)
											snake_case_						:   List[Any]           =  train_dataset.dataset_kwargs
											assert "src_lang" in kwargs and "tgt_lang" in kwargs
							else:
											snake_case_						:   List[Any]           =  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 ,							)
											snake_case_						:   Optional[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(lowerCamelCase__   ) == 1 if tok_name == BART_TINY else len(lowerCamelCase__   ) == 0







def 					lowerCamelCase_						(    _UpperCamelCase			)		->						int:






				"""simple docstring"""


				if not isinstance(_UpperCamelCase			,    _UpperCamelCase			):
								raise ValueError('''multiplicative_persistence() only accepts integral values'''			)
				if num < 0:
								raise ValueError('''multiplicative_persistence() does not accept negative values'''			)

				snake_case_						:   List[Any]           =  0
				snake_case_						:   Tuple           =  str(_UpperCamelCase			)

				while len(_UpperCamelCase			) != 1:
								snake_case_						:   Tuple           =  [int(_UpperCamelCase			) for i in num_string]

								snake_case_						:   Dict           =  1
								for i in range(0			,    len(_UpperCamelCase			)			):
												total *= numbers[i]

								snake_case_						:   str           =  str(_UpperCamelCase			)

								steps += 1
				return steps






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						int:






				"""simple docstring"""


				if not isinstance(_UpperCamelCase			,    _UpperCamelCase			):
								raise ValueError('''additive_persistence() only accepts integral values'''			)
				if num < 0:
								raise ValueError('''additive_persistence() does not accept negative values'''			)

				snake_case_						:   Any           =  0
				snake_case_						:   Tuple           =  str(_UpperCamelCase			)

				while len(_UpperCamelCase			) != 1:
								snake_case_						:   List[str]           =  [int(_UpperCamelCase			) for i in num_string]

								snake_case_						:   Optional[int]           =  0
								for i in range(0			,    len(_UpperCamelCase			)			):
												total += numbers[i]

								snake_case_						:   Tuple           =  str(_UpperCamelCase			)

								steps += 1
				return steps


if __name__ == "__main__":
	import doctest

	doctest.testmod()
