






'''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


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





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       Optional[Any]:
							lowerCamelCase__					:	Tuple         =       """\n""".join(UpperCamelCase      )
							Path(UpperCamelCase      ).open("""w"""      ).writelines(UpperCamelCase      )





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase      )      ->       Any:
							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 _lowercase	(				_lowercase		):




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

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

														lowerCamelCase__					:	Any         =       DataLoader(UpperCamelCase__			,							batch_size=UpperCamelCase__			,							collate_fn=ds.collate_fn			,							num_workers=2     )
														lowerCamelCase__					:	Dict         =       DataLoader(UpperCamelCase__			,							batch_size=UpperCamelCase__			,							collate_fn=ds.collate_fn			,							num_workers=2			,							sampler=UpperCamelCase__     )

														lowerCamelCase__					:	List[str]         =       tokenizer.pad_token_id

														def count_pad_tokens(UpperCamelCase__:				Tuple			,							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 		lowerCamelCase_				(							self:				int			,							UpperCamelCase__:				Tuple=1_000			,							UpperCamelCase__:				Dict=128     ):
														if os.getenv("""USE_REAL_DATA"""			,							UpperCamelCase__     ):
																					lowerCamelCase__					:	Tuple         =       """examples/seq2seq/wmt_en_ro"""
																					lowerCamelCase__					:	List[str]         =       max_len * 2 * 64
																					if not Path(UpperCamelCase__     ).joinpath("""train.len"""     ).exists():
																												save_len_file(UpperCamelCase__			,							UpperCamelCase__     )
														else:
																					lowerCamelCase__					:	Optional[int]         =       """examples/seq2seq/test_data/wmt_en_ro"""
																					lowerCamelCase__					:	str         =       max_len * 4
																					save_len_file(UpperCamelCase__			,							UpperCamelCase__     )

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



import requests

_A   =     "https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="






def    lowerCamelCase__				(				__lowerCAmelCase  :  str				):




			"""simple docstring"""


			lowerCAmelCase_						=	requests.get(_NEWS_API + bbc_news_api_key				).json()
			# each article in the list is a dict
			for i, article in enumerate(bbc_news_page["articles"]    ,							1				):
						print(F"""{i}.) {article['title']}"""				)


if __name__ == "__main__":
			fetch_bbc_news(bbc_news_api_key="<Your BBC News API key goes here>")



