



'''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					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				Any:
		_a					:				Optional[Any]				=							'\n'.join(lowerCAmelCase_  )
		Path(lowerCAmelCase_  ).open('w'  ).writelines(lowerCAmelCase_  )



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				Union[str, Any]:
		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      __magic_name__						(   _UpperCamelCase       ):







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

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

									_a					:				Dict				=							DataLoader(_UpperCAmelCase     ,batch_size=_UpperCAmelCase     ,collate_fn=ds.collate_fn     ,num_workers=2		)
									_a					:				int				=							DataLoader(_UpperCAmelCase     ,batch_size=_UpperCAmelCase     ,collate_fn=ds.collate_fn     ,num_workers=2     ,sampler=_UpperCAmelCase		)

									_a					:				Optional[int]				=							tokenizer.pad_token_id

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

									_a					:				Optional[Any]				=							AutoTokenizer.from_pretrained(_UpperCAmelCase		)
									_a					:				int				=							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 							__lowercase      (							self :	Optional[Any]		):
									_a	, _a	, _a					:				List[Any]				=							self._get_dataset()
									_a					:				Tuple				=							set(DistributedSortishSampler(_UpperCAmelCase     ,256     ,num_replicas=2     ,rank=0     ,add_extra_examples=_UpperCAmelCase		)		)
									_a					:				List[str]				=							set(DistributedSortishSampler(_UpperCAmelCase     ,256     ,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 							__lowercase      (							self :	Any     ,_UpperCAmelCase :	Dict		):
									_a					:				List[str]				=							AutoTokenizer.from_pretrained(_UpperCAmelCase     ,use_fast=_UpperCAmelCase		)
									if tok_name == MBART_TINY:
											_a					:				int				=							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'     ,)
											_a					:				Tuple				=							train_dataset.dataset_kwargs
											assert "src_lang" in kwargs and "tgt_lang" in kwargs
									else:
											_a					:				Tuple				=							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     ,)
											_a					:				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(_UpperCAmelCase		) == 1 if tok_name == BART_TINY else len(_UpperCAmelCase		) == 0









'''simple docstring'''

import heapq



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				set[int]:
		_a					:				list[list]				=							[]

		# for each node and his adjacency list add them and the rank of the node to queue
		# using heapq module the queue will be filled like a Priority Queue
		# heapq works with a min priority queue, so I used -1*len(v) to build it
		for key, value in graph.items():
				# O(log(n))
				heapq.heappush(lowerCAmelCase_						,      [-1 * len(lowerCAmelCase_  ), (key, value)]  )

		# chosen_vertices = set of chosen vertices
		_a					:				str				=							set()

		# while queue isn't empty and there are still edges
		#   (queue[0][0] is the rank of the node with max rank)
		while queue and queue[0][0] != 0:
				# extract vertex with max rank from queue and add it to chosen_vertices
				_a					:				Optional[int]				=							heapq.heappop(lowerCAmelCase_  )[1][0]
				chosen_vertices.add(lowerCAmelCase_  )

				# Remove all arcs adjacent to argmax
				for elem in queue:
						# if v haven't adjacent node, skip
						if elem[0] == 0:
								continue
						# if argmax is reachable from elem
						# remove argmax from elem's adjacent list and update his rank
						if argmax in elem[1][1]:
								_a					:				Dict				=							elem[1][1].index(lowerCAmelCase_  )
								del elem[1][1][index]
								elem[0] += 1
        # re-order the queue
				heapq.heapify(lowerCAmelCase_  )
		return chosen_vertices


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							__lowerCAmelCase						      =       {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
							print(f"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")




