
import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    is_torch_available,
)
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


_A   =     get_tests_dir("fixtures/test_sentencepiece.model")


if is_torch_available():
			from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

_A   =     25_60_47
_A   =     25_61_45





@require_sentencepiece
@require_tokenizers
class 	_lowerCAmelCase					(	__a					,      unittest.TestCase       ):
				_lowercase					=NllbTokenizer
				_lowercase					=NllbTokenizerFast
				_lowercase					=True
				_lowercase					=True
				_lowercase					={}




				def      __a						(					self     )				->      List[Any]:
							super().setUp()

							# We have a SentencePiece fixture for testing
							lowerCAmelCase_						=	NllbTokenizer(_UpperCamelCase				,				keep_accents=_UpperCamelCase     )
							tokenizer.save_pretrained(self.tmpdirname     )




				def      __a						(					self     )				->      Union[str, Any]:
							lowerCAmelCase_						=	NllbTokenizer(_UpperCamelCase				,				keep_accents=_UpperCamelCase     )

							lowerCAmelCase_						=	tokenizer.tokenize("This is a test"     )
							self.assertListEqual(_UpperCamelCase				,				["▁This", "▁is", "▁a", "▁t", "est"]     )

							self.assertListEqual(
							    tokenizer.convert_tokens_to_ids(_UpperCamelCase     )				,				[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]				,				)

							lowerCAmelCase_						=	tokenizer.tokenize("I was born in 92000, and this is falsé."     )
							self.assertListEqual(
							    _UpperCamelCase				,				[
							        SPIECE_UNDERLINE + "I",
							        SPIECE_UNDERLINE + "was",
							        SPIECE_UNDERLINE + "b",
							        "or",
							        "n",
							        SPIECE_UNDERLINE + "in",
							        SPIECE_UNDERLINE + "",
							        "9",
							        "2",
							        "0",
							        "0",
							        "0",
							        ",",
							        SPIECE_UNDERLINE + "and",
							        SPIECE_UNDERLINE + "this",
							        SPIECE_UNDERLINE + "is",
							        SPIECE_UNDERLINE + "f",
							        "al",
							        "s",
							        "é",
							        ".",
							    ]				,				)
							lowerCAmelCase_						=	tokenizer.convert_tokens_to_ids(_UpperCamelCase     )
							self.assertListEqual(
							    _UpperCamelCase				,				[
							        value + tokenizer.fairseq_offset
							        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
							    ]				,				)

							lowerCAmelCase_						=	tokenizer.convert_ids_to_tokens(_UpperCamelCase     )
							self.assertListEqual(
							    _UpperCamelCase				,				[
							        SPIECE_UNDERLINE + "I",
							        SPIECE_UNDERLINE + "was",
							        SPIECE_UNDERLINE + "b",
							        "or",
							        "n",
							        SPIECE_UNDERLINE + "in",
							        SPIECE_UNDERLINE + "",
							        "<unk>",
							        "2",
							        "0",
							        "0",
							        "0",
							        ",",
							        SPIECE_UNDERLINE + "and",
							        SPIECE_UNDERLINE + "this",
							        SPIECE_UNDERLINE + "is",
							        SPIECE_UNDERLINE + "f",
							        "al",
							        "s",
							        "<unk>",
							        ".",
							    ]				,				)




				def      __a						(					self     )				->      int:
							lowerCAmelCase_						=	(self.rust_tokenizer_class, "hf-internal-testing/tiny-random-nllb", {})
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
													lowerCAmelCase_						=	self.rust_tokenizer_class.from_pretrained(_UpperCamelCase				,				**_UpperCamelCase     )
													lowerCAmelCase_						=	self.tokenizer_class.from_pretrained(_UpperCamelCase				,				**_UpperCamelCase     )

													lowerCAmelCase_						=	tempfile.mkdtemp()

													lowerCAmelCase_						=	tokenizer_r.save_pretrained(_UpperCamelCase     )
													lowerCAmelCase_						=	tokenizer_p.save_pretrained(_UpperCamelCase     )

													# Checks it save with the same files + the tokenizer.json file for the fast one
													self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files     )     )
													lowerCAmelCase_						=	tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f     )
													self.assertSequenceEqual(_UpperCamelCase				,				_UpperCamelCase     )

													# Checks everything loads correctly in the same way
													lowerCAmelCase_						=	tokenizer_r.from_pretrained(_UpperCamelCase     )
													lowerCAmelCase_						=	tokenizer_p.from_pretrained(_UpperCamelCase     )

													# Check special tokens are set accordingly on Rust and Python
													for key in tokenizer_pp.special_tokens_map:
																self.assertTrue(hasattr(_UpperCamelCase				,				_UpperCamelCase     )     )

													shutil.rmtree(_UpperCamelCase     )

													# Save tokenizer rust, legacy_format=True
													lowerCAmelCase_						=	tempfile.mkdtemp()

													lowerCAmelCase_						=	tokenizer_r.save_pretrained(_UpperCamelCase				,				legacy_format=_UpperCamelCase     )
													lowerCAmelCase_						=	tokenizer_p.save_pretrained(_UpperCamelCase     )

													# Checks it save with the same files
													self.assertSequenceEqual(_UpperCamelCase				,				_UpperCamelCase     )

													# Checks everything loads correctly in the same way
													lowerCAmelCase_						=	tokenizer_r.from_pretrained(_UpperCamelCase     )
													lowerCAmelCase_						=	tokenizer_p.from_pretrained(_UpperCamelCase     )

													# Check special tokens are set accordingly on Rust and Python
													for key in tokenizer_pp.special_tokens_map:
																self.assertTrue(hasattr(_UpperCamelCase				,				_UpperCamelCase     )     )

													shutil.rmtree(_UpperCamelCase     )

													# Save tokenizer rust, legacy_format=False
													lowerCAmelCase_						=	tempfile.mkdtemp()

													lowerCAmelCase_						=	tokenizer_r.save_pretrained(_UpperCamelCase				,				legacy_format=_UpperCamelCase     )
													lowerCAmelCase_						=	tokenizer_p.save_pretrained(_UpperCamelCase     )

													# Checks it saved the tokenizer.json file
													self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files     )     )

													# Checks everything loads correctly in the same way
													lowerCAmelCase_						=	tokenizer_r.from_pretrained(_UpperCamelCase     )
													lowerCAmelCase_						=	tokenizer_p.from_pretrained(_UpperCamelCase     )

													# Check special tokens are set accordingly on Rust and Python
													for key in tokenizer_pp.special_tokens_map:
																self.assertTrue(hasattr(_UpperCamelCase				,				_UpperCamelCase     )     )

													shutil.rmtree(_UpperCamelCase     )




				@require_torch
				def      __a						(					self     )				->      Any:
							if not self.test_seqaseq:
										return

							lowerCAmelCase_						=	self.get_tokenizers()
							for tokenizer in tokenizers:
										with self.subTest(f"""{tokenizer.__class__.__name__}"""     ):
													# Longer text that will definitely require truncation.
													lowerCAmelCase_						=	[
													    " UN Chief Says There Is No Military Solution in Syria",
													    " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for"
													    " Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons"
													    " will only worsen the violence and misery for millions of people.",
													]
													lowerCAmelCase_						=	[
													    "Şeful ONU declară că nu există o soluţie militară în Siria",
													    "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al"
													    " Rusiei pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi"
													    " că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
													]
													try:
																lowerCAmelCase_						=	tokenizer.prepare_seqaseq_batch(
																    src_texts=_UpperCamelCase				,				tgt_texts=_UpperCamelCase				,				max_length=3				,				max_target_length=10				,				return_tensors="pt"				,				src_lang="eng_Latn"				,				tgt_lang="ron_Latn"				,				)
													except NotImplementedError:
																return
													self.assertEqual(batch.input_ids.shape[1]				,				3     )
													self.assertEqual(batch.labels.shape[1]				,				10     )
													# max_target_length will default to max_length if not specified
													lowerCAmelCase_						=	tokenizer.prepare_seqaseq_batch(
													    _UpperCamelCase				,				tgt_texts=_UpperCamelCase				,				max_length=3				,				return_tensors="pt"     )
													self.assertEqual(batch.input_ids.shape[1]				,				3     )
													self.assertEqual(batch.labels.shape[1]				,				3     )

													lowerCAmelCase_						=	tokenizer.prepare_seqaseq_batch(
													    src_texts=_UpperCamelCase				,				max_length=3				,				max_target_length=10				,				return_tensors="pt"     )
													self.assertEqual(batch_encoder_only.input_ids.shape[1]				,				3     )
													self.assertEqual(batch_encoder_only.attention_mask.shape[1]				,				3     )
													self.assertNotIn("decoder_input_ids"				,				_UpperCamelCase     )




				@unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece."     )
				def      __a						(					self     )				->      str:
							pass




				def      __a						(					self     )				->      List[str]:
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
													lowerCAmelCase_						=	[AddedToken("<special>"				,				lstrip=_UpperCamelCase     )]

													lowerCAmelCase_						=	self.rust_tokenizer_class.from_pretrained(
													    _UpperCamelCase				,				additional_special_tokens=_UpperCamelCase				,				**_UpperCamelCase     )
													lowerCAmelCase_						=	tokenizer_r.encode("Hey this is a <special> token"     )

													lowerCAmelCase_						=	tokenizer_r.encode("<special>"				,				add_special_tokens=_UpperCamelCase     )[0]

													self.assertTrue(special_token_id in r_output     )

													if self.test_slow_tokenizer:
																lowerCAmelCase_						=	self.rust_tokenizer_class.from_pretrained(
																    _UpperCamelCase				,				additional_special_tokens=_UpperCamelCase				,				**_UpperCamelCase				,				)
																lowerCAmelCase_						=	self.tokenizer_class.from_pretrained(
																    _UpperCamelCase				,				additional_special_tokens=_UpperCamelCase				,				**_UpperCamelCase     )

																lowerCAmelCase_						=	tokenizer_p.encode("Hey this is a <special> token"     )

																lowerCAmelCase_						=	tokenizer_cr.encode("Hey this is a <special> token"     )

																self.assertEqual(_UpperCamelCase				,				_UpperCamelCase     )
																self.assertEqual(_UpperCamelCase				,				_UpperCamelCase     )
																self.assertTrue(special_token_id in p_output     )
																self.assertTrue(special_token_id in cr_output     )





@require_torch
@require_sentencepiece
@require_tokenizers
class 	_lowerCAmelCase					(	unittest.TestCase       ):
				_lowercase					='''facebook/nllb-200-distilled-600M'''
				_lowercase					=[
				    ''' UN Chief Says There Is No Military Solution in Syria''',
				    ''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.''',
				]
				_lowercase					=[
				    '''Şeful ONU declară că nu există o soluţie militară în Siria''',
				    '''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'''
				    ''' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'''
				    ''' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
				]
				_lowercase					=[
				    25_60_47,
				    1_62_97,
				    13_44_08,
				    81_65,
				    24_80_66,
				    1_47_34,
				    9_50,
				    11_35,
				    10_57_21,
				    35_73,
				    83,
				    2_73_52,
				    1_08,
				    4_94_86,
				    2,
				]




				@classmethod
				def      __a						(					cls     )				->      Dict:
							lowerCAmelCase_						=	NllbTokenizer.from_pretrained(
							    cls.checkpoint_name				,				src_lang="eng_Latn"				,				tgt_lang="ron_Latn"     )
							lowerCAmelCase_						=	1
							return cls




				def      __a						(					self     )				->      int:
							self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Arab"]				,				256_001     )
							self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Latn"]				,				256_002     )
							self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["fra_Latn"]				,				256_057     )




				def      __a						(					self     )				->      List[str]:
							lowerCAmelCase_						=	self.tokenizer.batch_encode_plus(self.src_text     ).input_ids[0]
							self.assertListEqual(self.expected_src_tokens				,				_UpperCamelCase     )




				def      __a						(					self     )				->      List[str]:
							self.assertIn(_UpperCamelCase				,				self.tokenizer.all_special_ids     )
							# fmt: off
							lowerCAmelCase_						=	[RO_CODE, 4_254, 98_068, 112_923, 39_072, 3_909, 713, 102_767, 26, 17_314, 35_642, 14_683, 33_118, 2_022, 66_987, 2, 256_047]
							# fmt: on

							lowerCAmelCase_						=	self.tokenizer.decode(_UpperCamelCase				,				skip_special_tokens=_UpperCamelCase     )
							lowerCAmelCase_						=	self.tokenizer.decode(generated_ids[1:]				,				skip_special_tokens=_UpperCamelCase     )
							self.assertEqual(_UpperCamelCase				,				_UpperCamelCase     )
							self.assertNotIn(self.tokenizer.eos_token				,				_UpperCamelCase     )




				def      __a						(					self     )				->      List[str]:
							lowerCAmelCase_						=	["this is gunna be a long sentence " * 20]
							assert isinstance(src_text[0]				,				_UpperCamelCase     )
							lowerCAmelCase_						=	10
							lowerCAmelCase_						=	self.tokenizer(_UpperCamelCase				,				max_length=_UpperCamelCase				,				truncation=_UpperCamelCase     ).input_ids[0]
							self.assertEqual(ids[-1]				,				2     )
							self.assertEqual(ids[0]				,				_UpperCamelCase     )
							self.assertEqual(len(_UpperCamelCase     )				,				_UpperCamelCase     )




				def      __a						(					self     )				->      Dict:
							self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]     )				,				[256_203, 3]     )




				def      __a						(					self     )				->      List[str]:
							lowerCAmelCase_						=	tempfile.mkdtemp()
							lowerCAmelCase_						=	self.tokenizer.fairseq_tokens_to_ids
							self.tokenizer.save_pretrained(_UpperCamelCase     )
							lowerCAmelCase_						=	NllbTokenizer.from_pretrained(_UpperCamelCase     )
							self.assertDictEqual(new_tok.fairseq_tokens_to_ids				,				_UpperCamelCase     )




				@require_torch
				def      __a						(					self     )				->      int:
							lowerCAmelCase_						=	self.tokenizer(
							    self.src_text				,				text_target=self.tgt_text				,				padding=_UpperCamelCase				,				truncation=_UpperCamelCase				,				max_length=len(self.expected_src_tokens     )				,				return_tensors="pt"				,				)
							lowerCAmelCase_						=	shift_tokens_right(
							    batch["labels"]				,				self.tokenizer.pad_token_id				,				self.tokenizer.lang_code_to_id["ron_Latn"]     )

							self.assertIsInstance(_UpperCamelCase				,				_UpperCamelCase     )

							self.assertEqual((2, 15)				,				batch.input_ids.shape     )
							self.assertEqual((2, 15)				,				batch.attention_mask.shape     )
							lowerCAmelCase_						=	batch.input_ids.tolist()[0]
							self.assertListEqual(self.expected_src_tokens				,				_UpperCamelCase     )
							self.assertEqual(_UpperCamelCase				,				batch.decoder_input_ids[0, 0]     )  # EOS
							# Test that special tokens are reset
							self.assertEqual(self.tokenizer.prefix_tokens				,				[EN_CODE]     )
							self.assertEqual(self.tokenizer.suffix_tokens				,				[self.tokenizer.eos_token_id]     )




				def      __a						(					self     )				->      Any:
							lowerCAmelCase_						=	self.tokenizer(self.src_text				,				padding=_UpperCamelCase				,				truncation=_UpperCamelCase				,				max_length=3				,				return_tensors="pt"     )
							lowerCAmelCase_						=	self.tokenizer(
							    text_target=self.tgt_text				,				padding=_UpperCamelCase				,				truncation=_UpperCamelCase				,				max_length=10				,				return_tensors="pt"     )
							lowerCAmelCase_						=	targets["input_ids"]
							lowerCAmelCase_						=	shift_tokens_right(
							    _UpperCamelCase				,				self.tokenizer.pad_token_id				,				decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang]				,				)

							self.assertEqual(batch.input_ids.shape[1]				,				3     )
							self.assertEqual(batch.decoder_input_ids.shape[1]				,				10     )




				@require_torch
				def      __a						(					self     )				->      str:
							lowerCAmelCase_						=	self.tokenizer._build_translation_inputs(
							    "A test"				,				return_tensors="pt"				,				src_lang="eng_Latn"				,				tgt_lang="fra_Latn"     )

							self.assertEqual(
							    nested_simplify(_UpperCamelCase     )				,				{
							        # A, test, EOS, en_XX
							        "input_ids": [[256_047, 70, 7_356, 2]],
							        "attention_mask": [[1, 1, 1, 1]],
							        # ar_AR
							        "forced_bos_token_id": 256_057,
							    }				,				)




				@require_torch
				def      __a						(					self     )				->      Any:
							lowerCAmelCase_						=	True
							lowerCAmelCase_						=	self.tokenizer(
							    "UN Chief says there is no military solution in Syria"				,				src_lang="eng_Latn"				,				tgt_lang="fra_Latn"     )
							self.assertEqual(
							    inputs.input_ids				,				[16_297, 134_408, 25_653, 6_370, 248, 254, 103_929, 94_995, 108, 49_486, 2, 256_047]     )

							lowerCAmelCase_						=	False
							lowerCAmelCase_						=	self.tokenizer(
							    "UN Chief says there is no military solution in Syria"				,				src_lang="eng_Latn"				,				tgt_lang="fra_Latn"     )
							self.assertEqual(
							    inputs.input_ids				,				[256_047, 16_297, 134_408, 25_653, 6_370, 248, 254, 103_929, 94_995, 108, 49_486, 2]     )





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>")



