



"""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


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






def      snake_case__   (		__lowerCamelCase				:    Path							,		__lowerCamelCase				:    list	):
			"""simple docstring"""
			lowerCamelCase__		:		str			='''\n'''.join(__lowerCamelCase	)
			Path(__lowerCamelCase	).open('''w'''	).writelines(__lowerCamelCase	)






def      snake_case__   (		__lowerCamelCase				:    Optional[Any]	):
			"""simple docstring"""
			for split in ["train", "val", "test"]:
						_dump_articles(os.path.join(__lowerCamelCase							,		f'''{split}.source'''	)							,		__lowerCamelCase	)
						_dump_articles(os.path.join(__lowerCamelCase							,		f'''{split}.target'''	)							,		__lowerCamelCase	)
			return tmp_dir

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





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

								lowerCamelCase__		:		str			=Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )     )
								lowerCamelCase__		:		Any			=tmp_dir.joinpath('''train.source'''     ).open().readlines()
								lowerCamelCase__		:		str			=Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )     )
								pack_data_dir(lowerCamelCase,  lowerCamelCase,  128,  lowerCamelCase     )
								lowerCamelCase__		:		str			={x.name for x in tmp_dir.iterdir()}
								lowerCamelCase__		:		Union[str, Any]			={x.name for x in save_dir.iterdir()}
								lowerCamelCase__		:		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 		snake_case       (       self							:  Dict     )->					Union[str, Any]:
								if not FAIRSEQ_AVAILABLE:
											return
								lowerCamelCase__	,   lowerCamelCase__	,   lowerCamelCase__		:		Tuple			=self._get_dataset(max_len=64     )
								lowerCamelCase__		:		Dict			=64
								lowerCamelCase__		:		Union[str, Any]			=ds.make_dynamic_sampler(lowerCamelCase,  required_batch_size_multiple=lowerCamelCase     )
								lowerCamelCase__		:		Optional[int]			=[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
								lowerCamelCase__		:		int			=DataLoader(lowerCamelCase,  batch_sampler=lowerCamelCase,  collate_fn=ds.collate_fn,  num_workers=2     )
								lowerCamelCase__		:		Any			=[]
								lowerCamelCase__		:		Any			=[]
								for batch in data_loader:
											lowerCamelCase__		:		Optional[int]			=batch['''input_ids'''].shape
											lowerCamelCase__		:		str			=src_shape[0]
											assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
											lowerCamelCase__		:		int			=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 		snake_case       (       self							:  str     )->					Optional[Any]:
								lowerCamelCase__	,   lowerCamelCase__	,   lowerCamelCase__		:		Optional[int]			=self._get_dataset(max_len=512     )
								lowerCamelCase__		:		Tuple			=2
								lowerCamelCase__		:		Dict			=ds.make_sortish_sampler(lowerCamelCase,  shuffle=lowerCamelCase     )

								lowerCamelCase__		:		int			=DataLoader(lowerCamelCase,  batch_size=lowerCamelCase,  collate_fn=ds.collate_fn,  num_workers=2     )
								lowerCamelCase__		:		int			=DataLoader(lowerCamelCase,  batch_size=lowerCamelCase,  collate_fn=ds.collate_fn,  num_workers=2,  sampler=lowerCamelCase     )

								lowerCamelCase__		:		int			=tokenizer.pad_token_id

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

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









"""simple docstring"""






from functools import lru_cache






def      snake_case__   (		__lowerCamelCase				:    int	):
			"""simple docstring"""
			lowerCamelCase__		:		Optional[Any]			=2
			lowerCamelCase__		:		Optional[int]			=set()
			while i * i <= n:
						if n % i:
									i += 1
						else:
									n //= i
									factors.add(__lowerCamelCase	)
			if n > 1:
						factors.add(__lowerCamelCase	)
			return factors






@lru_cache
def      snake_case__   (		__lowerCamelCase				:    int	):
			"""simple docstring"""
			return len(unique_prime_factors(__lowerCamelCase	)	)






def      snake_case__   (		__lowerCamelCase				:    list	):
			"""simple docstring"""
			return len(set(__lowerCamelCase	)	) in (0, 1)






def      snake_case__   (		__lowerCamelCase				:    int	):
			"""simple docstring"""
			lowerCamelCase__		:		Tuple			=2

			while True:
						# Increment each value of a generated range
						lowerCamelCase__		:		Tuple			=[base + i for i in range(__lowerCamelCase	)]

						# Run elements through out unique_prime_factors function
						# Append our target number to the end.
						lowerCamelCase__		:		Optional[Any]			=[upf_len(__lowerCamelCase	) for x in group]
						checker.append(__lowerCamelCase	)

						# If all numbers in the list are equal, return the group variable.
						if equality(__lowerCamelCase	):
									return group

						# Increment our base variable by 1
						base += 1






def      snake_case__   (		__lowerCamelCase				:    int = 4	):
			"""simple docstring"""
			lowerCamelCase__		:		List[Any]			=run(__lowerCamelCase	)
			return results[0] if len(__lowerCamelCase	) else None


if __name__ == "__main__":
		print(solution())




