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_      =      256047
A_      =      256145
@require_sentencepiece
@require_tokenizers
class      __lowercase					(    _A       ,     unittest.TestCase    ):
						lowercase					 =     NllbTokenizer
						lowercase					 =     NllbTokenizerFast
						lowercase					 =     True
						lowercase					 =     True
						lowercase					 =     {}

						def        __a		(	self				:      Optional[Any]   )							->		Optional[Any]:


									'''simple docstring'''

									super().setUp()

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

						def        __a		(	self				:      Union[str, Any]   )							->		List[str]:


									'''simple docstring'''

									lowercase							=		NllbTokenizer(__lowerCamelCase							,							keep_accents=__lowerCamelCase   )

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

									self.assertListEqual(
									    tokenizer.convert_tokens_to_ids(__lowerCamelCase   )							,							[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]							,							)

									lowercase							=		tokenizer.tokenize('''I was born in 92000, and this is falsé.'''   )
									self.assertListEqual(
									    __lowerCamelCase							,							[
									        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''',
									        '''.''',
									    ]							,							)
									lowercase							=		tokenizer.convert_tokens_to_ids(__lowerCamelCase   )
									self.assertListEqual(
									    __lowerCamelCase							,							[
									        value + tokenizer.fairseq_offset
									        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
									    ]							,							)

									lowercase							=		tokenizer.convert_ids_to_tokens(__lowerCamelCase   )
									self.assertListEqual(
									    __lowerCamelCase							,							[
									        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   )							->		int:


									'''simple docstring'''

									lowercase							=		(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})'   ):
															lowercase							=		self.rust_tokenizer_class.from_pretrained(__lowerCamelCase							,							**__lowerCamelCase   )
															lowercase							=		self.tokenizer_class.from_pretrained(__lowerCamelCase							,							**__lowerCamelCase   )

															lowercase							=		tempfile.mkdtemp()

															lowercase							=		tokenizer_r.save_pretrained(__lowerCamelCase   )
															lowercase							=		tokenizer_p.save_pretrained(__lowerCamelCase   )

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

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

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

															shutil.rmtree(__lowerCamelCase   )

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

															lowercase							=		tokenizer_r.save_pretrained(__lowerCamelCase							,							legacy_format=__lowerCamelCase   )
															lowercase							=		tokenizer_p.save_pretrained(__lowerCamelCase   )

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

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

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

															shutil.rmtree(__lowerCamelCase   )

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

															lowercase							=		tokenizer_r.save_pretrained(__lowerCamelCase							,							legacy_format=__lowerCamelCase   )
															lowercase							=		tokenizer_p.save_pretrained(__lowerCamelCase   )

															# 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
															lowercase							=		tokenizer_r.from_pretrained(__lowerCamelCase   )
															lowercase							=		tokenizer_p.from_pretrained(__lowerCamelCase   )

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

															shutil.rmtree(__lowerCamelCase   )

						@require_torch
						def        __a		(	self				:      Any   )							->		Tuple:


									'''simple docstring'''

									if not self.test_seqaseq:
												return

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

															lowercase							=		tokenizer.prepare_seqaseq_batch(
															    src_texts=__lowerCamelCase							,							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'''							,							__lowerCamelCase   )

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


									'''simple docstring'''

									pass



						def        __a		(	self				:      Optional[int]   )							->		Optional[Any]:


									'''simple docstring'''

									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
												with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'   ):
															lowercase							=		[AddedToken('''<special>'''							,							lstrip=__lowerCamelCase   )]

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

															lowercase							=		tokenizer_r.encode('''<special>'''							,							add_special_tokens=__lowerCamelCase   )[0]

															self.assertTrue(special_token_id in r_output   )

															if self.test_slow_tokenizer:
																		lowercase							=		self.rust_tokenizer_class.from_pretrained(
																		    __lowerCamelCase							,							additional_special_tokens=__lowerCamelCase							,							**__lowerCamelCase							,							)
																		lowercase							=		self.tokenizer_class.from_pretrained(
																		    __lowerCamelCase							,							additional_special_tokens=__lowerCamelCase							,							**__lowerCamelCase   )

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

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

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





@require_torch
@require_sentencepiece
@require_tokenizers
class      __lowercase					(    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					 =     [
						    256047,
						    16297,
						    134408,
						    8165,
						    248066,
						    14734,
						    950,
						    1135,
						    105721,
						    3573,
						    83,
						    27352,
						    108,
						    49486,
						    2,
						]

						@classmethod
						def        __a		(	cls				:      Dict   )							->		Tuple:


									'''simple docstring'''

									lowercase							=		NllbTokenizer.from_pretrained(
									    cls.checkpoint_name							,							src_lang='''eng_Latn'''							,							tgt_lang='''ron_Latn'''   )
									lowercase							=		1
									return cls

						def        __a		(	self				:      Any   )							->		Optional[Any]:


									'''simple docstring'''

									self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ace_Arab''']							,							25_60_01   )
									self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ace_Latn''']							,							25_60_02   )
									self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''fra_Latn''']							,							25_60_57   )

						def        __a		(	self				:      Tuple   )							->		Dict:


									'''simple docstring'''

									lowercase							=		self.tokenizer.batch_encode_plus(self.src_text   ).input_ids[0]
									self.assertListEqual(self.expected_src_tokens							,							__lowerCamelCase   )

						def        __a		(	self				:      str   )							->		Any:


									'''simple docstring'''

									self.assertIn(__lowerCamelCase							,							self.tokenizer.all_special_ids   )
									# fmt: off
									lowercase							=		[RO_CODE, 42_54, 9_80_68, 11_29_23, 3_90_72, 39_09, 7_13, 10_27_67, 26, 1_73_14, 3_56_42, 1_46_83, 3_31_18, 20_22, 6_69_87, 2, 25_60_47]
									# fmt: on

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

						def        __a		(	self				:      Optional[Any]   )							->		List[str]:


									'''simple docstring'''

									lowercase							=		['''this is gunna be a long sentence ''' * 20]
									assert isinstance(src_text[0]							,							__lowerCamelCase   )
									lowercase							=		10
									lowercase							=		self.tokenizer(__lowerCamelCase							,							max_length=__lowerCamelCase							,							truncation=__lowerCamelCase   ).input_ids[0]
									self.assertEqual(ids[-1]							,							2   )
									self.assertEqual(ids[0]							,							__lowerCamelCase   )
									self.assertEqual(len(__lowerCamelCase   )							,							__lowerCamelCase   )

						def        __a		(	self				:      List[str]   )							->		Dict:


									'''simple docstring'''

									self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR''']   )							,							[25_62_03, 3]   )

						def        __a		(	self				:      Dict   )							->		List[Any]:


									'''simple docstring'''

									lowercase							=		tempfile.mkdtemp()
									lowercase							=		self.tokenizer.fairseq_tokens_to_ids
									self.tokenizer.save_pretrained(__lowerCamelCase   )
									lowercase							=		NllbTokenizer.from_pretrained(__lowerCamelCase   )
									self.assertDictEqual(new_tok.fairseq_tokens_to_ids							,							__lowerCamelCase   )

						@require_torch
						def        __a		(	self				:      List[str]   )							->		Any:


									'''simple docstring'''

									lowercase							=		self.tokenizer(
									    self.src_text							,							text_target=self.tgt_text							,							padding=__lowerCamelCase							,							truncation=__lowerCamelCase							,							max_length=len(self.expected_src_tokens   )							,							return_tensors='''pt'''							,							)
									lowercase							=		shift_tokens_right(
									    batch['''labels''']							,							self.tokenizer.pad_token_id							,							self.tokenizer.lang_code_to_id['''ron_Latn''']   )

									self.assertIsInstance(__lowerCamelCase							,							__lowerCamelCase   )

									self.assertEqual((2, 15)							,							batch.input_ids.shape   )
									self.assertEqual((2, 15)							,							batch.attention_mask.shape   )
									lowercase							=		batch.input_ids.tolist()[0]
									self.assertListEqual(self.expected_src_tokens							,							__lowerCamelCase   )
									self.assertEqual(__lowerCamelCase							,							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				:      str   )							->		Any:


									'''simple docstring'''

									lowercase							=		self.tokenizer(self.src_text							,							padding=__lowerCamelCase							,							truncation=__lowerCamelCase							,							max_length=3							,							return_tensors='''pt'''   )
									lowercase							=		self.tokenizer(
									    text_target=self.tgt_text							,							padding=__lowerCamelCase							,							truncation=__lowerCamelCase							,							max_length=10							,							return_tensors='''pt'''   )
									lowercase							=		targets['''input_ids''']
									lowercase							=		shift_tokens_right(
									    __lowerCamelCase							,							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				:      int   )							->		List[str]:


									'''simple docstring'''

									lowercase							=		self.tokenizer._build_translation_inputs(
									    '''A test'''							,							return_tensors='''pt'''							,							src_lang='''eng_Latn'''							,							tgt_lang='''fra_Latn'''   )

									self.assertEqual(
									    nested_simplify(__lowerCamelCase   )							,							{
									        # A, test, EOS, en_XX
									        '''input_ids''': [[25_60_47, 70, 73_56, 2]],
									        '''attention_mask''': [[1, 1, 1, 1]],
									        # ar_AR
									        '''forced_bos_token_id''': 25_60_57,
									    }							,							)



						@require_torch
						def        __a		(	self				:      Union[str, Any]   )							->		List[Any]:


									'''simple docstring'''

									lowercase							=		True
									lowercase							=		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_62_97, 13_44_08, 2_56_53, 63_70, 2_48, 2_54, 10_39_29, 9_49_95, 1_08, 4_94_86, 2, 25_60_47]   )

									lowercase							=		False
									lowercase							=		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_60_47, 1_62_97, 13_44_08, 2_56_53, 63_70, 2_48, 2_54, 10_39_29, 9_49_95, 1_08, 4_94_86, 2]   )






from __future__ import annotations

from fractions import Fraction
from math import gcd, sqrt






def        __UpperCAmelCase    (       UpperCAmelCase       )->       bool:



			"""simple docstring"""







			lowercase							=		int(number**0.5       )
			return number == sq * sq






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       tuple[int, int]:



			"""simple docstring"""







			lowercase							=		x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
			lowercase							=		x_den * y_den * z_den
			lowercase							=		gcd(UpperCAmelCase,		UpperCAmelCase       )
			top //= hcf
			bottom //= hcf
			return top, bottom






def        __UpperCAmelCase    (       UpperCAmelCase = 35       )->       int:



			"""simple docstring"""







			lowercase							=		set()
			lowercase							=		42
			lowercase							=		Fraction(0       )
			lowercase							=		42

			for x_num in range(1,		order + 1       ):
						for x_den in range(x_num + 1,		order + 1       ):
									for y_num in range(1,		order + 1       ):
												for y_den in range(y_num + 1,		order + 1       ):
															# n=1
															lowercase							=		x_num * y_den + x_den * y_num
															lowercase							=		x_den * y_den
															lowercase							=		gcd(UpperCAmelCase,		UpperCAmelCase       )
															z_num //= hcf
															z_den //= hcf
															if 0 < z_num < z_den <= order:
																		lowercase							=		add_three(
																		    UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
																		unique_s.add(UpperCAmelCase       )

															# n=2
															lowercase							=		(
															    x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
															)
															lowercase							=		x_den * x_den * y_den * y_den
															if is_sq(UpperCAmelCase       ) and is_sq(UpperCAmelCase       ):
																		lowercase							=		int(sqrt(UpperCAmelCase       )       )
																		lowercase							=		int(sqrt(UpperCAmelCase       )       )
																		lowercase							=		gcd(UpperCAmelCase,		UpperCAmelCase       )
																		z_num //= hcf
																		z_den //= hcf
																		if 0 < z_num < z_den <= order:
																					lowercase							=		add_three(
																					    UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
																					unique_s.add(UpperCAmelCase       )

                    # n=-1
															lowercase							=		x_num * y_num
															lowercase							=		x_den * y_num + x_num * y_den
															lowercase							=		gcd(UpperCAmelCase,		UpperCAmelCase       )
															z_num //= hcf
															z_den //= hcf
															if 0 < z_num < z_den <= order:
																		lowercase							=		add_three(
																		    UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
																		unique_s.add(UpperCAmelCase       )

															# n=2
															lowercase							=		x_num * x_num * y_num * y_num
															lowercase							=		(
															    x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
															)
															if is_sq(UpperCAmelCase       ) and is_sq(UpperCAmelCase       ):
																		lowercase							=		int(sqrt(UpperCAmelCase       )       )
																		lowercase							=		int(sqrt(UpperCAmelCase       )       )
																		lowercase							=		gcd(UpperCAmelCase,		UpperCAmelCase       )
																		z_num //= hcf
																		z_den //= hcf
																		if 0 < z_num < z_den <= order:
																					lowercase							=		add_three(
																					    UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
																					unique_s.add(UpperCAmelCase       )

			for num, den in unique_s:
						total += Fraction(UpperCAmelCase,		UpperCAmelCase       )

			return total.denominator + total.numerator


if __name__ == "__main__":
						print(F"{solution() = }")





