



'''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


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


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

lowerCamelCase									=							25_6047
lowerCamelCase									=							25_6145





@require_sentencepiece
@require_tokenizers
class _UpperCamelCase		(  SCREAMING_SNAKE_CASE_   ,							unittest.TestCase		):





				'''simple docstring'''





				lowerCAmelCase__            =     NllbTokenizer
				lowerCAmelCase__            =     NllbTokenizerFast
				lowerCAmelCase__            =     True
				lowerCAmelCase__            =     True
				lowerCAmelCase__            =     {}

				def 					__lowerCamelCase  (				self	:	Dict):







											'''simple docstring'''

											super().setUp()

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

				def 					__lowerCamelCase  (				self	:	List[Any]):







											'''simple docstring'''

											__lowercase													=NllbTokenizer(lowercase_	,							keep_accents=lowercase_)

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

											self.assertListEqual(
											    tokenizer.convert_tokens_to_ids(lowercase_)	,							[value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]	,							)

											__lowercase													=tokenizer.tokenize('I was born in 92000, and this is falsé.')
											self.assertListEqual(
											    lowercase_	,							[
											        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(lowercase_)
											self.assertListEqual(
											    lowercase_	,							[
											        value + tokenizer.fairseq_offset
											        for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
											    ]	,							)

											__lowercase													=tokenizer.convert_ids_to_tokens(lowercase_)
											self.assertListEqual(
											    lowercase_	,							[
											        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 					__lowerCamelCase  (				self	:	str):







											'''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(lowercase_	,							**lowercase_)
																									__lowercase													=self.tokenizer_class.from_pretrained(lowercase_	,							**lowercase_)

																									__lowercase													=tempfile.mkdtemp()

																									__lowercase													=tokenizer_r.save_pretrained(lowercase_)
																									__lowercase													=tokenizer_p.save_pretrained(lowercase_)

																									# 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(lowercase_	,							lowercase_)

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

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

																									shutil.rmtree(lowercase_)

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

																									__lowercase													=tokenizer_r.save_pretrained(lowercase_	,							legacy_format=lowercase_)
																									__lowercase													=tokenizer_p.save_pretrained(lowercase_)

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

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

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

																									shutil.rmtree(lowercase_)

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

																									__lowercase													=tokenizer_r.save_pretrained(lowercase_	,							legacy_format=lowercase_)
																									__lowercase													=tokenizer_p.save_pretrained(lowercase_)

																									# 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(lowercase_)
																									__lowercase													=tokenizer_p.from_pretrained(lowercase_)

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

																									shutil.rmtree(lowercase_)

				@require_torch
				def 					__lowerCamelCase  (				self	:	Union[str, Any]):







											'''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=lowercase_	,							tgt_texts=lowercase_	,							max_length=3	,							max_target_length=1_0	,							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]	,							1_0)
																									# max_target_length will default to max_length if not specified
																									__lowercase													=tokenizer.prepare_seqaseq_batch(
																									    lowercase_	,							tgt_texts=lowercase_	,							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=lowercase_	,							max_length=3	,							max_target_length=1_0	,							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'	,							lowercase_)

				@unittest.skip('Unfortunately way too slow to build a BPE with SentencePiece.')
				def 					__lowerCamelCase  (				self	:	Union[str, Any]):







											'''simple docstring'''

											pass

				def 					__lowerCamelCase  (				self	:	str):







											'''simple docstring'''

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

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

																									__lowercase													=tokenizer_r.encode('<special>'	,							add_special_tokens=lowercase_)[0]

																									self.assertTrue(special_token_id in r_output)

																									if self.test_slow_tokenizer:
																																__lowercase													=self.rust_tokenizer_class.from_pretrained(
																																    lowercase_	,							additional_special_tokens=lowercase_	,							**lowercase_	,							)
																																__lowercase													=self.tokenizer_class.from_pretrained(
																																    lowercase_	,							additional_special_tokens=lowercase_	,							**lowercase_)

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

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

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





@require_torch
@require_sentencepiece
@require_tokenizers
class _UpperCamelCase		(  unittest.TestCase		):





				'''simple docstring'''





				lowerCAmelCase__            =     "facebook/nllb-200-distilled-600M"
				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.",
				]
				lowerCAmelCase__            =     [
				    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 					__lowerCamelCase  (				cls	:	Dict):







											'''simple docstring'''

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

				def 					__lowerCamelCase  (				self	:	Dict):







											'''simple docstring'''

											self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Arab']	,							2_5_6_0_0_1)
											self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Latn']	,							2_5_6_0_0_2)
											self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['fra_Latn']	,							2_5_6_0_5_7)

				def 					__lowerCamelCase  (				self	:	Tuple):







											'''simple docstring'''

											__lowercase													=self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
											self.assertListEqual(self.expected_src_tokens	,							lowercase_)

				def 					__lowerCamelCase  (				self	:	List[str]):







											'''simple docstring'''

											self.assertIn(lowercase_	,							self.tokenizer.all_special_ids)
											# fmt: off
											__lowercase													=[RO_CODE, 4_2_5_4, 9_8_0_6_8, 1_1_2_9_2_3, 3_9_0_7_2, 3_9_0_9, 7_1_3, 1_0_2_7_6_7, 2_6, 1_7_3_1_4, 3_5_6_4_2, 1_4_6_8_3, 3_3_1_1_8, 2_0_2_2, 6_6_9_8_7, 2, 2_5_6_0_4_7]
											# fmt: on

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

				def 					__lowerCamelCase  (				self	:	Union[str, Any]):







											'''simple docstring'''

											__lowercase													=['this is gunna be a long sentence ' * 2_0]
											assert isinstance(src_text[0]	,							lowercase_)
											__lowercase													=1_0
											__lowercase													=self.tokenizer(lowercase_	,							max_length=lowercase_	,							truncation=lowercase_).input_ids[0]
											self.assertEqual(ids[-1]	,							2)
											self.assertEqual(ids[0]	,							lowercase_)
											self.assertEqual(len(lowercase_)	,							lowercase_)

				def 					__lowerCamelCase  (				self	:	Union[str, Any]):







											'''simple docstring'''

											self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR'])	,							[2_5_6_2_0_3, 3])

				def 					__lowerCamelCase  (				self	:	Any):







											'''simple docstring'''

											__lowercase													=tempfile.mkdtemp()
											__lowercase													=self.tokenizer.fairseq_tokens_to_ids
											self.tokenizer.save_pretrained(lowercase_)
											__lowercase													=NllbTokenizer.from_pretrained(lowercase_)
											self.assertDictEqual(new_tok.fairseq_tokens_to_ids	,							lowercase_)

				@require_torch
				def 					__lowerCamelCase  (				self	:	Optional[int]):







											'''simple docstring'''

											__lowercase													=self.tokenizer(
											    self.src_text	,							text_target=self.tgt_text	,							padding=lowercase_	,							truncation=lowercase_	,							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(lowercase_	,							lowercase_)

											self.assertEqual((2, 1_5)	,							batch.input_ids.shape)
											self.assertEqual((2, 1_5)	,							batch.attention_mask.shape)
											__lowercase													=batch.input_ids.tolist()[0]
											self.assertListEqual(self.expected_src_tokens	,							lowercase_)
											self.assertEqual(lowercase_	,							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 					__lowerCamelCase  (				self	:	List[Any]):







											'''simple docstring'''

											__lowercase													=self.tokenizer(self.src_text	,							padding=lowercase_	,							truncation=lowercase_	,							max_length=3	,							return_tensors='pt')
											__lowercase													=self.tokenizer(
											    text_target=self.tgt_text	,							padding=lowercase_	,							truncation=lowercase_	,							max_length=1_0	,							return_tensors='pt')
											__lowercase													=targets['input_ids']
											__lowercase													=shift_tokens_right(
											    lowercase_	,							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]	,							1_0)

				@require_torch
				def 					__lowerCamelCase  (				self	:	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(lowercase_)	,							{
											        # A, test, EOS, en_XX
											        'input_ids': [[2_5_6_0_4_7, 7_0, 7_3_5_6, 2]],
											        'attention_mask': [[1, 1, 1, 1]],
											        # ar_AR
											        'forced_bos_token_id': 2_5_6_0_5_7,
											    }	,							)

				@require_torch
				def 					__lowerCamelCase  (				self	:	Union[str, 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_6_2_9_7, 1_3_4_4_0_8, 2_5_6_5_3, 6_3_7_0, 2_4_8, 2_5_4, 1_0_3_9_2_9, 9_4_9_9_5, 1_0_8, 4_9_4_8_6, 2, 2_5_6_0_4_7])

											__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	,							[2_5_6_0_4_7, 1_6_2_9_7, 1_3_4_4_0_8, 2_5_6_5_3, 6_3_7_0, 2_4_8, 2_5_4, 1_0_3_9_2_9, 9_4_9_9_5, 1_0_8, 4_9_4_8_6, 2])





"""simple docstring"""

from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

SCREAMING_SNAKE_CASE_            =    datasets.load_iris()

SCREAMING_SNAKE_CASE_            =    np.array(data['''data'''])
SCREAMING_SNAKE_CASE_            =    np.array(data['''target'''])
SCREAMING_SNAKE_CASE_            =    data['''target_names''']

SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_            =    train_test_split(X, y)




def      lowercase__    (       lowerCAmelCase					:       Optional[int]			,					lowerCAmelCase					:       Any			)			->   Optional[Any]:






    """simple docstring"""




    return np.linalg.norm(np.array(lowerCAmelCase			) - np.array(lowerCAmelCase			)			)




def      lowercase__    (       lowerCAmelCase					:       str			,					lowerCAmelCase					:       Any			,					lowerCAmelCase					:       List[Any]			,					lowerCAmelCase					:       Tuple			,					lowerCAmelCase					:       Dict=5			)			->   str:






    """simple docstring"""




    UpperCAmelCase          =       zip(lowerCAmelCase			,					lowerCAmelCase			)
    # List of distances of all points from the point to be classified
    UpperCAmelCase          =       []
    for data_point in data:
        UpperCAmelCase          =       euclidean_distance(data_point[0]			,					lowerCAmelCase			)
        distances.append((distance, data_point[1])			)
    # Choosing 'k' points with the least distances.
    UpperCAmelCase          =       [i[1] for i in sorted(lowerCAmelCase			)[:k]]
    # Most commonly occurring class among them
    # is the class into which the point is classified
    UpperCAmelCase          =       Counter(lowerCAmelCase			).most_common(1			)[0][0]
    return classes[result]


if __name__ == "__main__":
   print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))



