

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   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	'''\n'''.join(SCREAMING_SNAKE_CASE__       )
						Path(SCREAMING_SNAKE_CASE__       ).open('''w'''       ).writelines(SCREAMING_SNAKE_CASE__       )


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





class 	snake_case_   (		__A					):







			'''simple docstring'''

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

									snake_case_   =	Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)							)
									snake_case_   =	tmp_dir.joinpath('''train.source'''							).open().readlines()
									snake_case_   =	Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)							)
									pack_data_dir(_UpperCamelCase							,   _UpperCamelCase							,   1_2_8							,   _UpperCamelCase							)
									snake_case_   =	{x.name for x in tmp_dir.iterdir()}
									snake_case_   =	{x.name for x in save_dir.iterdir()}
									snake_case_   =	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(_UpperCamelCase							) < len(_UpperCamelCase							)
									assert len(_UpperCamelCase							) == 1
									assert len(packed_examples[0]							) == sum(len(_UpperCamelCase							) 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 :				Union[str, Any]							)      ->Optional[Any]:
									if not FAIRSEQ_AVAILABLE:
															return
									snake_case_,    snake_case_,    snake_case_   =	self._get_dataset(max_len=6_4							)
									snake_case_   =	6_4
									snake_case_   =	ds.make_dynamic_sampler(_UpperCamelCase							,   required_batch_size_multiple=_UpperCamelCase							)
									snake_case_   =	[len(_UpperCamelCase							) for x in batch_sampler]
									assert len(set(_UpperCamelCase							)							) > 1  # it's not dynamic batch size if every batch is the same length
									assert sum(_UpperCamelCase							) == len(_UpperCamelCase							)  # no dropped or added examples
									snake_case_   =	DataLoader(_UpperCamelCase							,   batch_sampler=_UpperCamelCase							,   collate_fn=ds.collate_fn							,   num_workers=2							)
									snake_case_   =	[]
									snake_case_   =	[]
									for batch in data_loader:
															snake_case_   =	batch['''input_ids'''].shape
															snake_case_   =	src_shape[0]
															assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
															snake_case_   =	np.product(batch['''input_ids'''].shape							)
															num_src_per_batch.append(_UpperCamelCase							)
															if num_src_tokens > (max_tokens * 1.1):
																					failures.append(_UpperCamelCase							)
									assert num_src_per_batch[0] == max(_UpperCamelCase							)
									if failures:
															raise AssertionError(f'''too many tokens in {len(_UpperCamelCase							)} batches'''							)







			def 				snake_case__(     self :				List[str]							)      ->int:
									snake_case_,    snake_case_,    snake_case_   =	self._get_dataset(max_len=5_1_2							)
									snake_case_   =	2
									snake_case_   =	ds.make_sortish_sampler(_UpperCamelCase							,   shuffle=_UpperCamelCase							)

									snake_case_   =	DataLoader(_UpperCamelCase							,   batch_size=_UpperCamelCase							,   collate_fn=ds.collate_fn							,   num_workers=2							)
									snake_case_   =	DataLoader(_UpperCamelCase							,   batch_size=_UpperCamelCase							,   collate_fn=ds.collate_fn							,   num_workers=2							,   sampler=_UpperCamelCase							)

									snake_case_   =	tokenizer.pad_token_id

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

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







			def 				snake_case__(     self :				Optional[Any]							,   _UpperCamelCase :				Optional[Any]=1_0_0_0							,   _UpperCamelCase :				List[Any]=1_2_8							)      ->List[Any]:
									if os.getenv('''USE_REAL_DATA'''							,   _UpperCamelCase							):
															snake_case_   =	'''examples/seq2seq/wmt_en_ro'''
															snake_case_   =	max_len * 2 * 6_4
															if not Path(_UpperCamelCase							).joinpath('''train.len'''							).exists():
																					save_len_file(_UpperCamelCase							,   _UpperCamelCase							)
									else:
															snake_case_   =	'''examples/seq2seq/test_data/wmt_en_ro'''
															snake_case_   =	max_len * 4
															save_len_file(_UpperCamelCase							,   _UpperCamelCase							)

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







			def 				snake_case__(     self :				Union[str, Any]							)      ->Optional[int]:
									snake_case_,    snake_case_,    snake_case_   =	self._get_dataset()
									snake_case_   =	set(DistributedSortishSampler(_UpperCamelCase							,   2_5_6							,   num_replicas=2							,   rank=0							,   add_extra_examples=_UpperCamelCase							)							)
									snake_case_   =	set(DistributedSortishSampler(_UpperCamelCase							,   2_5_6							,   num_replicas=2							,   rank=1							,   add_extra_examples=_UpperCamelCase							)							)
									assert idsa.intersection(_UpperCamelCase							) == set()







			@parameterized.expand(
			    [
			        MBART_TINY,
			        MARIAN_TINY,
			        T5_TINY,
			        BART_TINY,
			        PEGASUS_XSUM,
			    ]							,   )
			def 				snake_case__(     self :				List[str]							,   _UpperCamelCase :				Tuple							)      ->Optional[Any]:
									snake_case_   =	AutoTokenizer.from_pretrained(_UpperCamelCase							,   use_fast=_UpperCamelCase							)
									if tok_name == MBART_TINY:
															snake_case_   =	SeqaSeqDataset(
															    _UpperCamelCase							,   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_   =	train_dataset.dataset_kwargs
															assert "src_lang" in kwargs and "tgt_lang" in kwargs
									else:
															snake_case_   =	SeqaSeqDataset(
															    _UpperCamelCase							,   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_   =	train_dataset.dataset_kwargs
															assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
															assert len(_UpperCamelCase							) == 1 if tok_name == BART_TINY else len(_UpperCamelCase							) == 0


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	[]
						if len(SCREAMING_SNAKE_CASE__       ) == 1:
												return [nums.copy()]
						for _ in range(len(SCREAMING_SNAKE_CASE__       )       ):
												snake_case_   =	nums.pop(0       )
												snake_case_   =	permute(SCREAMING_SNAKE_CASE__       )
												for perm in permutations:
																		perm.append(SCREAMING_SNAKE_CASE__       )
												result.extend(SCREAMING_SNAKE_CASE__       )
												nums.append(SCREAMING_SNAKE_CASE__       )
						return result


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):

						def backtrack(SCREAMING_SNAKE_CASE__       ):
												if start == len(SCREAMING_SNAKE_CASE__       ) - 1:
																		output.append(nums[:]       )
												else:
																		for i in range(SCREAMING_SNAKE_CASE__  ,		len(SCREAMING_SNAKE_CASE__       )       ):
																								snake_case_,    snake_case_   =	nums[i], nums[start]
																								backtrack(start + 1       )
																								snake_case_,    snake_case_   =	nums[i], nums[start]  # backtrack

						snake_case_   =	[]
						backtrack(0       )
						return output


if __name__ == "__main__":
					import doctest

					# use res to print the data in permute2 function
					lowerCAmelCase_       	=	permutea([1, 2, 3])
					print(res)
					doctest.testmod()