


'''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


__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 		_snake_case					(     _snake_case       ,   unittest.TestCase	):
					lowerCAmelCase      :List[str]   =					NllbTokenizer
					lowerCAmelCase      :Union[str, Any]   =					NllbTokenizerFast
					lowerCAmelCase      :Union[str, Any]   =					True
					lowerCAmelCase      :Dict   =					True
					lowerCAmelCase      :Optional[Any]   =					{}




					def 				snake_case__						(      self):
								super().setUp()

								# We have a SentencePiece fixture for testing
								UpperCAmelCase__  :					List[Any]        =     NllbTokenizer(_lowerCamelCase							,							keep_accents=_lowerCamelCase)
								tokenizer.save_pretrained(self.tmpdirname)




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					int        =     NllbTokenizer(_lowerCamelCase							,							keep_accents=_lowerCamelCase)

								UpperCAmelCase__  :					Optional[int]        =     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 [285, 46, 10, 170, 382]]							,							)

								UpperCAmelCase__  :					List[Any]        =     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""",
								        """.""",
								    ]							,							)
								UpperCAmelCase__  :					int        =     tokenizer.convert_tokens_to_ids(_lowerCamelCase)
								self.assertListEqual(
								    _lowerCamelCase							,							[
								        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]
								    ]							,							)

								UpperCAmelCase__  :					Optional[int]        =     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 				snake_case__						(      self):
								UpperCAmelCase__  :					Optional[Any]        =     (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})'''):
														UpperCAmelCase__  :					List[str]        =     self.rust_tokenizer_class.from_pretrained(_lowerCamelCase							,							**_lowerCamelCase)
														UpperCAmelCase__  :					int        =     self.tokenizer_class.from_pretrained(_lowerCamelCase							,							**_lowerCamelCase)

														UpperCAmelCase__  :					List[str]        =     tempfile.mkdtemp()

														UpperCAmelCase__  :					Optional[Any]        =     tokenizer_r.save_pretrained(_lowerCamelCase)
														UpperCAmelCase__  :					str        =     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))
														UpperCAmelCase__  :					Any        =     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
														UpperCAmelCase__  :					Union[str, Any]        =     tokenizer_r.from_pretrained(_lowerCamelCase)
														UpperCAmelCase__  :					List[str]        =     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
														UpperCAmelCase__  :					Dict        =     tempfile.mkdtemp()

														UpperCAmelCase__  :					Any        =     tokenizer_r.save_pretrained(_lowerCamelCase							,							legacy_format=_lowerCamelCase)
														UpperCAmelCase__  :					str        =     tokenizer_p.save_pretrained(_lowerCamelCase)

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

														# Checks everything loads correctly in the same way
														UpperCAmelCase__  :					int        =     tokenizer_r.from_pretrained(_lowerCamelCase)
														UpperCAmelCase__  :					Any        =     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
														UpperCAmelCase__  :					Optional[Any]        =     tempfile.mkdtemp()

														UpperCAmelCase__  :					List[str]        =     tokenizer_r.save_pretrained(_lowerCamelCase							,							legacy_format=_lowerCamelCase)
														UpperCAmelCase__  :					Optional[Any]        =     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
														UpperCAmelCase__  :					Tuple        =     tokenizer_r.from_pretrained(_lowerCamelCase)
														UpperCAmelCase__  :					List[str]        =     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 				snake_case__						(      self):

								if not self.test_seqaseq:
											return

								UpperCAmelCase__  :					Any        =     self.get_tokenizers()
								for tokenizer in tokenizers:
											with self.subTest(f'''{tokenizer.__class__.__name__}'''):
														# Longer text that will definitely require truncation.
														UpperCAmelCase__  :					int        =     [
														    """ 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[str]        =     [
														    """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:
																	UpperCAmelCase__  :					Dict        =     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
														UpperCAmelCase__  :					str        =     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)

														UpperCAmelCase__  :					Optional[int]        =     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 				snake_case__						(      self):
								pass




					def 				snake_case__						(      self):

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

														UpperCAmelCase__  :					Tuple        =     self.rust_tokenizer_class.from_pretrained(
														    _lowerCamelCase							,							additional_special_tokens=_lowerCamelCase							,							**_lowerCamelCase)
														UpperCAmelCase__  :					Optional[int]        =     tokenizer_r.encode("""Hey this is a <special> token""")

														UpperCAmelCase__  :					List[Any]        =     tokenizer_r.encode("""<special>"""							,							add_special_tokens=_lowerCamelCase)[0]

														self.assertTrue(special_token_id in r_output)

														if self.test_slow_tokenizer:
																	UpperCAmelCase__  :					List[str]        =     self.rust_tokenizer_class.from_pretrained(
																	    _lowerCamelCase							,							additional_special_tokens=_lowerCamelCase							,							**_lowerCamelCase							,							)
																	UpperCAmelCase__  :					Optional[int]        =     self.tokenizer_class.from_pretrained(
																	    _lowerCamelCase							,							additional_special_tokens=_lowerCamelCase							,							**_lowerCamelCase)

																	UpperCAmelCase__  :					Tuple        =     tokenizer_p.encode("""Hey this is a <special> token""")

																	UpperCAmelCase__  :					Optional[Any]        =     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 		_snake_case					(     unittest.TestCase	):
					lowerCAmelCase      :Tuple   =					'facebook/nllb-200-distilled-600M'
					lowerCAmelCase      :Union[str, 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      :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.',
					]
					lowerCAmelCase      :Dict   =					[
					    256_047,
					    16_297,
					    134_408,
					    8_165,
					    248_066,
					    14_734,
					    950,
					    1_135,
					    105_721,
					    3_573,
					    83,
					    27_352,
					    108,
					    49_486,
					    2,
					]




					@classmethod
					def 				snake_case__						(      cls):
								UpperCAmelCase__  :					int        =     NllbTokenizer.from_pretrained(
								    cls.checkpoint_name							,							src_lang="""eng_Latn"""							,							tgt_lang="""ron_Latn""")
								UpperCAmelCase__  :					Optional[int]        =     1
								return cls




					def 				snake_case__						(      self):
								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 				snake_case__						(      self):
								UpperCAmelCase__  :					List[Any]        =     self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
								self.assertListEqual(self.expected_src_tokens							,							_lowerCamelCase)




					def 				snake_case__						(      self):
								self.assertIn(_lowerCamelCase							,							self.tokenizer.all_special_ids)
								# fmt: off
								UpperCAmelCase__  :					Dict        =     [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

								UpperCAmelCase__  :					int        =     self.tokenizer.decode(_lowerCamelCase							,							skip_special_tokens=_lowerCamelCase)
								UpperCAmelCase__  :					Optional[int]        =     self.tokenizer.decode(generated_ids[1:]							,							skip_special_tokens=_lowerCamelCase)
								self.assertEqual(_lowerCamelCase							,							_lowerCamelCase)
								self.assertNotIn(self.tokenizer.eos_token							,							_lowerCamelCase)




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Optional[int]        =     ["""this is gunna be a long sentence """ * 20]
								assert isinstance(src_text[0]							,							_lowerCamelCase)
								UpperCAmelCase__  :					int        =     10
								UpperCAmelCase__  :					Tuple        =     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 				snake_case__						(      self):
								self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""])							,							[25_6203, 3])




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Tuple        =     tempfile.mkdtemp()
								UpperCAmelCase__  :					Optional[Any]        =     self.tokenizer.fairseq_tokens_to_ids
								self.tokenizer.save_pretrained(_lowerCamelCase)
								UpperCAmelCase__  :					Tuple        =     NllbTokenizer.from_pretrained(_lowerCamelCase)
								self.assertDictEqual(new_tok.fairseq_tokens_to_ids							,							_lowerCamelCase)




					@require_torch
					def 				snake_case__						(      self):
								UpperCAmelCase__  :					List[str]        =     self.tokenizer(
								    self.src_text							,							text_target=self.tgt_text							,							padding=_lowerCamelCase							,							truncation=_lowerCamelCase							,							max_length=len(self.expected_src_tokens)							,							return_tensors="""pt"""							,							)
								UpperCAmelCase__  :					Tuple        =     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)
								UpperCAmelCase__  :					Optional[Any]        =     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 				snake_case__						(      self):
								UpperCAmelCase__  :					Optional[Any]        =     self.tokenizer(self.src_text							,							padding=_lowerCamelCase							,							truncation=_lowerCamelCase							,							max_length=3							,							return_tensors="""pt""")
								UpperCAmelCase__  :					Dict        =     self.tokenizer(
								    text_target=self.tgt_text							,							padding=_lowerCamelCase							,							truncation=_lowerCamelCase							,							max_length=10							,							return_tensors="""pt""")
								UpperCAmelCase__  :					Tuple        =     targets["""input_ids"""]
								UpperCAmelCase__  :					List[str]        =     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 				snake_case__						(      self):
								UpperCAmelCase__  :					Dict        =     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_6047, 70, 7356, 2]],
								        """attention_mask""": [[1, 1, 1, 1]],
								        # ar_AR
								        """forced_bos_token_id""": 25_6057,
								    }							,							)




					@require_torch
					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Optional[Any]        =     True
								UpperCAmelCase__  :					Optional[int]        =     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])

								UpperCAmelCase__  :					Dict        =     False
								UpperCAmelCase__  :					Tuple        =     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])



'''simple docstring'''



def     _UpperCamelCase      (		UpperCamelCase__					):
			# if the collection is empty, returns empty
			if collection == []:
						return []

			# get some information about the collection
			UpperCAmelCase__  :					List[str]        =     len(UpperCamelCase__					)
			UpperCAmelCase__  :					Union[str, Any]        =     max(UpperCamelCase__					)
			UpperCAmelCase__  :					Optional[Any]        =     min(UpperCamelCase__					)

			# create the counting array
			UpperCAmelCase__  :					Dict        =     coll_max + 1 - coll_min
			UpperCAmelCase__  :					Optional[int]        =     [0] * counting_arr_length

			# count how much a number appears in the collection
			for number in collection:
						counting_arr[number - coll_min] += 1

			# sum each position with it's predecessors. now, counting_arr[i] tells
			# us how many elements <= i has in the collection
			for i in range(1 ,      UpperCamelCase__					):
						UpperCAmelCase__  :					List[str]        =     counting_arr[i] + counting_arr[i - 1]

			# create the output collection
			UpperCAmelCase__  :					Tuple        =     [0] * coll_len

			# place the elements in the output, respecting the original order (stable
			# sort) from end to begin, updating counting_arr
			for i in reversed(range(0 ,      UpperCamelCase__					)					):
						UpperCAmelCase__  :					Any        =     collection[i]
						counting_arr[collection[i] - coll_min] -= 1

			return ordered







def     _UpperCamelCase      (		UpperCamelCase__					):
			return "".join([chr(UpperCamelCase__					) for i in counting_sort([ord(UpperCamelCase__					) for c in string]					)]					)


if __name__ == "__main__":
		# Test string sort
		assert counting_sort_string('thisisthestring') == "eghhiiinrsssttt"

		__A	=input('Enter numbers separated by a comma:\n').strip()
		__A	=[int(item) for item in user_input.split(',')]
		print(counting_sort(unsorted))