
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       :				Optional[int]       =			get_tests_dir('''fixtures/test_sentencepiece.model''')


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

_lowerCamelCase       :				List[str]       =			256_047
_lowerCamelCase       :				Union[str, Any]       =			256_145




@require_sentencepiece
@require_tokenizers
class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase							,unittest.TestCase					):





	'''simple docstring'''



	_UpperCAmelCase       :		Optional[int]       =					NllbTokenizer
	_UpperCAmelCase       :		List[Any]       =					NllbTokenizerFast
	_UpperCAmelCase       :		Dict       =					True
	_UpperCAmelCase       :		Optional[Any]       =					True
	_UpperCAmelCase       :		Dict       =					{}



	def  A							(  self	:    Optional[int]	):


				'''simple docstring'''


				super().setUp()

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



	def  A							(  self	:    Dict	):


				'''simple docstring'''


				_snake_case		=			NllbTokenizer(lowercase			,  keep_accents=lowercase	)

				_snake_case		=			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 [285, 46, 10, 170, 382]]			,  )

				_snake_case		=			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',
				        '.',
				    ]			,  )
				_snake_case		=			tokenizer.convert_tokens_to_ids(lowercase	)
				self.assertListEqual(
				    lowercase			,  [
				        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]
				    ]			,  )

				_snake_case		=			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  A							(  self	:    int	):


				'''simple docstring'''


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

										_snake_case		=			tempfile.mkdtemp()

										_snake_case		=			tokenizer_r.save_pretrained(lowercase	)
										_snake_case		=			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	)	)
										_snake_case		=			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
										_snake_case		=			tokenizer_r.from_pretrained(lowercase	)
										_snake_case		=			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
										_snake_case		=			tempfile.mkdtemp()

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

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

										# Checks everything loads correctly in the same way
										_snake_case		=			tokenizer_r.from_pretrained(lowercase	)
										_snake_case		=			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
										_snake_case		=			tempfile.mkdtemp()

										_snake_case		=			tokenizer_r.save_pretrained(lowercase			,  legacy_format=lowercase	)
										_snake_case		=			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
										_snake_case		=			tokenizer_r.from_pretrained(lowercase	)
										_snake_case		=			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  A							(  self	:    List[str]	):


				'''simple docstring'''


				if not self.test_seqaseq:
							return

				_snake_case		=			self.get_tokenizers()
				for tokenizer in tokenizers:
							with self.subTest(f'''{tokenizer.__class__.__name__}'''	):
										# Longer text that will definitely require truncation.
										_snake_case		=			[
										    ' 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.',
										]
										_snake_case		=			[
										    '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:
													_snake_case		=			tokenizer.prepare_seqaseq_batch(
													    src_texts=lowercase			,  tgt_texts=lowercase			,  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
										_snake_case		=			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	)

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



	@unittest.skip('Unfortunately way too slow to build a BPE with SentencePiece.'	)
	def  A							(  self	:    List[str]	):


				'''simple docstring'''


				pass





	def  A							(  self	:    int	):


				'''simple docstring'''


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

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

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

										self.assertTrue(special_token_id in r_output	)

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

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

													_snake_case		=			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    SCREAMING_SNAKE_CASE__     (	unittest.TestCase					):





	'''simple docstring'''



	_UpperCAmelCase       :		Dict       =					"facebook/nllb-200-distilled-600M"
	_UpperCAmelCase       :		Tuple       =					[
	    " 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.",
	]
	_UpperCAmelCase       :		List[Any]       =					[
	    "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.",
	]
	_UpperCAmelCase       :		Optional[Any]       =					[
	    2_5_6_0_4_7,
	    1_6_2_9_7,
	    1_3_4_4_0_8,
	    8_1_6_5,
	    2_4_8_0_6_6,
	    1_4_7_3_4,
	    9_5_0,
	    1_1_3_5,
	    1_0_5_7_2_1,
	    3_5_7_3,
	    8_3,
	    2_7_3_5_2,
	    1_0_8,
	    4_9_4_8_6,
	    2,
	]



	@classmethod
	def  A							(  cls	:    str	):


				'''simple docstring'''


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



	def  A							(  self	:    Optional[int]	):


				'''simple docstring'''


				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	:    str	):


				'''simple docstring'''


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



	def  A							(  self	:    List[str]	):


				'''simple docstring'''


				self.assertIn(lowercase			,  self.tokenizer.all_special_ids	)
				# fmt: off
				_snake_case		=			[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

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



	def  A							(  self	:    int	):


				'''simple docstring'''


				_snake_case		=			['this is gunna be a long sentence ' * 20]
				assert isinstance(src_text[0]			,  lowercase	)
				_snake_case		=			10
				_snake_case		=			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  A							(  self	:    str	):


				'''simple docstring'''


				self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']	)			,  [256_203, 3]	)



	def  A							(  self	:    int	):


				'''simple docstring'''


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



	@require_torch
	def  A							(  self	:    Optional[Any]	):


				'''simple docstring'''


				_snake_case		=			self.tokenizer(
				    self.src_text			,  text_target=self.tgt_text			,  padding=lowercase			,  truncation=lowercase			,  max_length=len(self.expected_src_tokens	)			,  return_tensors='pt'			,  )
				_snake_case		=			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, 15)			,  batch.input_ids.shape	)
				self.assertEqual((2, 15)			,  batch.attention_mask.shape	)
				_snake_case		=			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  A							(  self	:    List[Any]	):


				'''simple docstring'''


				_snake_case		=			self.tokenizer(self.src_text			,  padding=lowercase			,  truncation=lowercase			,  max_length=3			,  return_tensors='pt'	)
				_snake_case		=			self.tokenizer(
				    text_target=self.tgt_text			,  padding=lowercase			,  truncation=lowercase			,  max_length=10			,  return_tensors='pt'	)
				_snake_case		=			targets['input_ids']
				_snake_case		=			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]			,  10	)



	@require_torch
	def  A							(  self	:    Dict	):


				'''simple docstring'''


				_snake_case		=			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': [[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	:    List[Any]	):


				'''simple docstring'''


				_snake_case		=			True
				_snake_case		=			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]	)

				_snake_case		=			False
				_snake_case		=			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]	)

class    SCREAMING_SNAKE_CASE__     :





	'''simple docstring'''



	def __init__(  self	:    List[str]			,  lowercase	:    list[int]	):


				'''simple docstring'''


				_snake_case		=			len(lowercase	)
				_snake_case		=			[0] * len_array

				if len_array > 0:
							_snake_case		=			array[0]

				for i in range(1			,  lowercase	):
							_snake_case		=			self.prefix_sum[i - 1] + array[i]



	def  A							(  self	:    Optional[Any]			,  lowercase	:    int			,  lowercase	:    int	):


				'''simple docstring'''


				if start == 0:
							return self.prefix_sum[end]

				return self.prefix_sum[end] - self.prefix_sum[start - 1]





	def  A							(  self	:    Union[str, Any]			,  lowercase	:    int	):


				'''simple docstring'''


				_snake_case		=			{0}
				for sum_item in self.prefix_sum:
							if sum_item - target_sum in sums:
										return True

							sums.add(lowercase	)

				return False


if __name__ == "__main__":
			import doctest

			doctest.testmod()