
"""simple docstring"""


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


_lowercase							      = get_tests_dir('''fixtures/test_sentencepiece.model''')


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

_lowercase							      = 25_60_47
_lowercase							      = 25_61_45







@require_sentencepiece
@require_tokenizers
class  lowerCAmelCase_ (					_lowercase						,    unittest.TestCase			):



						'''simple docstring'''


						_lowerCamelCase:			Union[str, Any]					=			NllbTokenizer
						_lowerCamelCase:			Dict					=			NllbTokenizerFast
						_lowerCamelCase:			str					=			True
						_lowerCamelCase:			int					=			True
						_lowerCamelCase:			str					=			{}


						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[int]					)   ->      Any:
							super().setUp()

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


						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[Any]					)   ->      str:
							A        =      NllbTokenizer(A_     ,keep_accents=A_					)

							A        =      tokenizer.tokenize('This is a test'					)
							self.assertListEqual(A_     ,['▁This', '▁is', '▁a', '▁t', 'est']					)

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

							A        =      tokenizer.tokenize('I was born in 92000, and this is falsé.'					)
							self.assertListEqual(
							    A_     ,[
							        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',
							        '.',
							    ]     ,)
							A        =      tokenizer.convert_tokens_to_ids(A_					)
							self.assertListEqual(
							    A_     ,[
							        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]
							    ]     ,)

							A        =      tokenizer.convert_ids_to_tokens(A_					)
							self.assertListEqual(
							    A_     ,[
							        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   _SCREAMING_SNAKE_CASE					(       self   :					Tuple					)   ->      Tuple:
							A        =      (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})'					):
									A        =      self.rust_tokenizer_class.from_pretrained(A_     ,**A_					)
									A        =      self.tokenizer_class.from_pretrained(A_     ,**A_					)

									A        =      tempfile.mkdtemp()

									A        =      tokenizer_r.save_pretrained(A_					)
									A        =      tokenizer_p.save_pretrained(A_					)

									# 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					)					)
									A        =      tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f					)
									self.assertSequenceEqual(A_     ,A_					)

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

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

									shutil.rmtree(A_					)

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

									A        =      tokenizer_r.save_pretrained(A_     ,legacy_format=A_					)
									A        =      tokenizer_p.save_pretrained(A_					)

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

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

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

									shutil.rmtree(A_					)

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

									A        =      tokenizer_r.save_pretrained(A_     ,legacy_format=A_					)
									A        =      tokenizer_p.save_pretrained(A_					)

									# 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
									A        =      tokenizer_r.from_pretrained(A_					)
									A        =      tokenizer_p.from_pretrained(A_					)

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

									shutil.rmtree(A_					)


						@require_torch
						def   _SCREAMING_SNAKE_CASE					(       self   :					Dict					)   ->      Optional[int]:
							if not self.test_seqaseq:
								return

							A        =      self.get_tokenizers()
							for tokenizer in tokenizers:
								with self.subTest(F'{tokenizer.__class__.__name__}'					):
									# Longer text that will definitely require truncation.
									A        =      [
									    ' 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.',
									]
									A        =      [
									    '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:
										A        =      tokenizer.prepare_seqaseq_batch(
										    src_texts=A_     ,tgt_texts=A_     ,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
									A        =      tokenizer.prepare_seqaseq_batch(
									    A_     ,tgt_texts=A_     ,max_length=3     ,return_tensors='pt'					)
									self.assertEqual(batch.input_ids.shape[1]     ,3					)
									self.assertEqual(batch.labels.shape[1]     ,3					)

									A        =      tokenizer.prepare_seqaseq_batch(
									    src_texts=A_     ,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'     ,A_					)


						@unittest.skip('Unfortunately way too slow to build a BPE with SentencePiece.'					)
						def   _SCREAMING_SNAKE_CASE					(       self   :					List[Any]					)   ->      Optional[Any]:
							pass




						def   _SCREAMING_SNAKE_CASE					(       self   :					str					)   ->      Any:
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
								with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'					):
									A        =      [AddedToken('<special>'     ,lstrip=A_					)]

									A        =      self.rust_tokenizer_class.from_pretrained(
									    A_     ,additional_special_tokens=A_     ,**A_					)
									A        =      tokenizer_r.encode('Hey this is a <special> token'					)

									A        =      tokenizer_r.encode('<special>'     ,add_special_tokens=A_					)[0]

									self.assertTrue(special_token_id in r_output					)

									if self.test_slow_tokenizer:
										A        =      self.rust_tokenizer_class.from_pretrained(
										    A_     ,additional_special_tokens=A_     ,**A_     ,)
										A        =      self.tokenizer_class.from_pretrained(
										    A_     ,additional_special_tokens=A_     ,**A_					)

										A        =      tokenizer_p.encode('Hey this is a <special> token'					)

										A        =      tokenizer_cr.encode('Hey this is a <special> token'					)

										self.assertEqual(A_     ,A_					)
										self.assertEqual(A_     ,A_					)
										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			):



						'''simple docstring'''


						_lowerCamelCase:			Optional[Any]					=			'''facebook/nllb-200-distilled-600M'''
						_lowerCamelCase:			Any					=			[
						    ''' 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:			Dict					=			[
						    '''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.''',
						]
						_lowerCamelCase:			List[str]					=			[
						    256047,
						    16297,
						    134408,
						    8165,
						    248066,
						    14734,
						    950,
						    1135,
						    105721,
						    3573,
						    83,
						    27352,
						    108,
						    49486,
						    2,
						]


						@classmethod
						def   _SCREAMING_SNAKE_CASE					(       cls   :					int					)   ->      List[str]:
							A        =      NllbTokenizer.from_pretrained(
							    cls.checkpoint_name     ,src_lang='eng_Latn'     ,tgt_lang='ron_Latn'					)
							A        =      1
							return cls


						def   _SCREAMING_SNAKE_CASE					(       self   :					Union[str, Any]					)   ->      Optional[int]:
							self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Arab']     ,25_6001					)
							self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Latn']     ,25_6002					)
							self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['fra_Latn']     ,25_6057					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					List[str]					)   ->      Optional[Any]:
							A        =      self.tokenizer.batch_encode_plus(self.src_text					).input_ids[0]
							self.assertListEqual(self.expected_src_tokens     ,A_					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Tuple					)   ->      str:
							self.assertIn(A_     ,self.tokenizer.all_special_ids					)
							# fmt: off
							A        =      [RO_CODE, 4254, 9_8068, 11_2923, 3_9072, 3909, 713, 10_2767, 26, 1_7314, 3_5642, 1_4683, 3_3118, 2022, 6_6987, 2, 25_6047]
							# fmt: on

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


						def   _SCREAMING_SNAKE_CASE					(       self   :					Tuple					)   ->      Union[str, Any]:
							A        =      ['this is gunna be a long sentence ' * 20]
							assert isinstance(src_text[0]     ,A_					)
							A        =      10
							A        =      self.tokenizer(A_     ,max_length=A_     ,truncation=A_					).input_ids[0]
							self.assertEqual(ids[-1]     ,2					)
							self.assertEqual(ids[0]     ,A_					)
							self.assertEqual(len(A_					)     ,A_					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					str					)   ->      Tuple:
							self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']					)     ,[25_6203, 3]					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[Any]					)   ->      Optional[Any]:
							A        =      tempfile.mkdtemp()
							A        =      self.tokenizer.fairseq_tokens_to_ids
							self.tokenizer.save_pretrained(A_					)
							A        =      NllbTokenizer.from_pretrained(A_					)
							self.assertDictEqual(new_tok.fairseq_tokens_to_ids     ,A_					)


						@require_torch
						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[Any]					)   ->      str:
							A        =      self.tokenizer(
							    self.src_text     ,text_target=self.tgt_text     ,padding=A_     ,truncation=A_     ,max_length=len(self.expected_src_tokens					)     ,return_tensors='pt'     ,)
							A        =      shift_tokens_right(
							    batch['labels']     ,self.tokenizer.pad_token_id     ,self.tokenizer.lang_code_to_id['ron_Latn']					)

							self.assertIsInstance(A_     ,A_					)

							self.assertEqual((2, 15)     ,batch.input_ids.shape					)
							self.assertEqual((2, 15)     ,batch.attention_mask.shape					)
							A        =      batch.input_ids.tolist()[0]
							self.assertListEqual(self.expected_src_tokens     ,A_					)
							self.assertEqual(A_     ,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   _SCREAMING_SNAKE_CASE					(       self   :					Optional[int]					)   ->      Any:
							A        =      self.tokenizer(self.src_text     ,padding=A_     ,truncation=A_     ,max_length=3     ,return_tensors='pt'					)
							A        =      self.tokenizer(
							    text_target=self.tgt_text     ,padding=A_     ,truncation=A_     ,max_length=10     ,return_tensors='pt'					)
							A        =      targets['input_ids']
							A        =      shift_tokens_right(
							    A_     ,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   _SCREAMING_SNAKE_CASE					(       self   :					Dict					)   ->      Optional[Any]:
							A        =      self.tokenizer._build_translation_inputs(
							    'A test'     ,return_tensors='pt'     ,src_lang='eng_Latn'     ,tgt_lang='fra_Latn'					)

							self.assertEqual(
							    nested_simplify(A_					)     ,{
							        # A, test, EOS, en_XX
							        'input_ids': [[25_6047, 70, 7356, 2]],
							        'attention_mask': [[1, 1, 1, 1]],
							        # ar_AR
							        'forced_bos_token_id': 25_6057,
							    }     ,)




						@require_torch
						def   _SCREAMING_SNAKE_CASE					(       self   :					Union[str, Any]					)   ->      Dict:
							A        =      True
							A        =      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     ,[1_6297, 13_4408, 2_5653, 6370, 248, 254, 10_3929, 9_4995, 108, 4_9486, 2, 25_6047]					)

							A        =      False
							A        =      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     ,[25_6047, 1_6297, 13_4408, 2_5653, 6370, 248, 254, 10_3929, 9_4995, 108, 4_9486, 2]					)






def lowerCAmelCase__(__snake_case							)				->      str:







	'''simple docstring'''







	return "".join(chr(ord(__snake_case							) - 32							) if '''a''' <= char <= '''z''' else char for char in word							)


if __name__ == "__main__":
			from doctest import testmod

			testmod()



