





import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase_						   =    get_tests_dir("""fixtures/test_sentencepiece_no_bos.model""")



@require_sentencepiece
@require_tokenizers
class        _lowerCAmelCase    (			UpperCAmelCase_							,    unittest.TestCase							):


  '''simple docstring'''
  a_							:       List[str]      					=PegasusTokenizer
  a_							:       List[Any]      					=PegasusTokenizerFast
  a_							:       List[Any]      					=True
  a_							:       List[str]      					=True







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


    '''simple docstring'''





    super().setUp()

    # We have a SentencePiece fixture for testing
    _snake_case   :				str														=	PegasusTokenizer(UpperCamelCase							)
    tokenizer.save_pretrained(self.tmpdirname							)







  @cached_property
  def UpperCamelCase_ (      self      :					int							):


    '''simple docstring'''





    return PegasusTokenizer.from_pretrained('google/pegasus-large'							)







  def UpperCamelCase_ (      self      :					int      ,				**UpperCamelCase      :					int							):


    '''simple docstring'''





    return PegasusTokenizer.from_pretrained(self.tmpdirname      ,				**UpperCamelCase							)







  def UpperCamelCase_ (      self      :					Dict      ,				UpperCamelCase      :					List[Any]							):


    '''simple docstring'''





    return ("This is a test", "This is a test")







  def UpperCamelCase_ (      self      :					Optional[Any]							):


    '''simple docstring'''





    _snake_case   :				Optional[int]														=	'</s>'
    _snake_case   :				str														=	1

    self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase							)      ,				UpperCamelCase							)
    self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase							)      ,				UpperCamelCase							)







  def UpperCamelCase_ (      self      :					List[Any]							):


    '''simple docstring'''





    _snake_case   :				List[Any]														=	list(self.get_tokenizer().get_vocab().keys()							)

    self.assertEqual(vocab_keys[0]      ,				'<pad>'							)
    self.assertEqual(vocab_keys[1]      ,				'</s>'							)
    self.assertEqual(vocab_keys[-1]      ,				'v'							)
    self.assertEqual(len(UpperCamelCase							)      ,				11_03							)







  def UpperCamelCase_ (      self      :					Any							):


    '''simple docstring'''





    self.assertEqual(self.get_tokenizer().vocab_size      ,				11_03							)







  def UpperCamelCase_ (      self      :					Optional[int]							):


    '''simple docstring'''





    _snake_case   :				int														=	self.rust_tokenizer_class.from_pretrained(self.tmpdirname							)
    _snake_case   :				List[Any]														=	self.tokenizer_class.from_pretrained(self.tmpdirname							)
    _snake_case   :				List[str]														=	(
        'Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'
        ' </s> <pad> <pad> <pad>'
    )
    _snake_case   :				Dict														=	rust_tokenizer([raw_input_str]      ,				return_tensors=UpperCamelCase      ,				add_special_tokens=UpperCamelCase							).input_ids[0]
    _snake_case   :				str														=	py_tokenizer([raw_input_str]      ,				return_tensors=UpperCamelCase      ,				add_special_tokens=UpperCamelCase							).input_ids[0]
    self.assertListEqual(UpperCamelCase      ,				UpperCamelCase							)







  def UpperCamelCase_ (      self      :					Optional[Any]							):


    '''simple docstring'''





    _snake_case   :				str														=	self._large_tokenizer
    # <mask_1> masks whole sentence while <mask_2> masks single word
    _snake_case   :				List[str]														=	'<mask_1> To ensure a <mask_2> flow of bank resolutions.'
    _snake_case   :				Any														=	[2, 4_13, 6_15, 1_14, 3, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
    _snake_case   :				Optional[int]														=	tokenizer([raw_input_str]      ,				return_tensors=UpperCamelCase							).input_ids[0]
    self.assertListEqual(UpperCamelCase      ,				UpperCamelCase							)







  def UpperCamelCase_ (      self      :					Tuple							):


    '''simple docstring'''





    _snake_case   :				Dict														=	self._large_tokenizer
    # The tracebacks for the following asserts are **better** without messages or self.assertEqual
    assert tokenizer.vocab_size == 9_61_03
    assert tokenizer.pad_token_id == 0
    assert tokenizer.eos_token_id == 1
    assert tokenizer.offset == 1_03
    assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_05
    assert tokenizer.unk_token == "<unk>"
    assert tokenizer.model_max_length == 10_24
    _snake_case   :				int														=	'To ensure a smooth flow of bank resolutions.'
    _snake_case   :				str														=	[4_13, 6_15, 1_14, 22_91, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
    _snake_case   :				Optional[int]														=	tokenizer([raw_input_str]      ,				return_tensors=UpperCamelCase							).input_ids[0]
    self.assertListEqual(UpperCamelCase      ,				UpperCamelCase							)
    assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]							) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]







  @require_torch
  def UpperCamelCase_ (      self      :					List[str]							):


    '''simple docstring'''





    _snake_case   :				List[str]														=	['This is going to be way too long.' * 1_50, 'short example']
    _snake_case   :				Union[str, Any]														=	['not super long but more than 5 tokens', 'tiny']
    _snake_case   :				Dict														=	self._large_tokenizer(UpperCamelCase      ,				padding=UpperCamelCase      ,				truncation=UpperCamelCase      ,				return_tensors='pt'							)
    _snake_case   :				Dict														=	self._large_tokenizer(
        text_target=UpperCamelCase      ,				max_length=5      ,				padding=UpperCamelCase      ,				truncation=UpperCamelCase      ,				return_tensors='pt'							)

    assert batch.input_ids.shape == (2, 10_24)
    assert batch.attention_mask.shape == (2, 10_24)
    assert targets["input_ids"].shape == (2, 5)
    assert len(UpperCamelCase							) == 2  # input_ids, attention_mask.







  @slow
  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





    _snake_case   :				str														=	{'input_ids': [[3_89_79, 1_43, 1_84_85, 6_06, 1_30, 2_66_69, 8_76_86, 1_21, 5_41_89, 11_29, 1_11, 2_66_69, 8_76_86, 1_21, 91_14, 1_47_87, 1_21, 1_32_49, 1_58, 5_92, 9_56, 1_21, 1_46_21, 3_15_76, 1_43, 6_26_13, 1_08, 96_88, 9_30, 4_34_30, 1_15_62, 6_26_13, 3_04, 1_08, 1_14_43, 8_97, 1_08, 93_14, 1_74_15, 6_33_99, 1_08, 1_14_43, 76_14, 1_83_16, 1_18, 42_84, 71_48, 1_24_30, 1_43, 14_00, 2_57_03, 1_58, 1_11, 42_84, 71_48, 1_17_72, 1_43, 2_12_97, 10_64, 1_58, 1_22, 2_04, 35_06, 17_54, 11_33, 1_47_87, 15_81, 1_15, 3_32_24, 44_82, 1_11, 13_55, 1_10, 2_91_73, 3_17, 5_08_33, 1_08, 2_01_47, 9_46_65, 1_11, 7_71_98, 1_07, 1], [1_10, 6_26_13, 1_17, 6_38, 1_12, 11_33, 1_21, 2_00_98, 13_55, 7_90_50, 1_38_72, 1_35, 15_96, 5_35_41, 13_52, 1_41, 1_30_39, 55_42, 1_24, 3_02, 5_18, 1_11, 2_68, 29_56, 1_15, 1_49, 44_27, 1_07, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_39, 12_35, 27_99, 1_82_89, 1_77_80, 2_04, 1_09, 94_74, 12_96, 1_07, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
    # fmt: on

    self.tokenizer_integration_test_util(
        expected_encoding=UpperCamelCase      ,				model_name='google/bigbird-pegasus-large-arxiv'      ,				revision='ba85d0851d708441f91440d509690f1ab6353415'      ,				)




@require_sentencepiece
@require_tokenizers
class        _lowerCAmelCase    (			UpperCAmelCase_							,    unittest.TestCase							):


  '''simple docstring'''
  a_							:       List[Any]      					=PegasusTokenizer
  a_							:       Optional[Any]      					=PegasusTokenizerFast
  a_							:       Tuple      					=True
  a_							:       int      					=True







  def UpperCamelCase_ (      self      :					Optional[Any]							):


    '''simple docstring'''





    super().setUp()

    # We have a SentencePiece fixture for testing
    _snake_case   :				str														=	PegasusTokenizer(UpperCamelCase      ,				offset=0      ,				mask_token_sent=UpperCamelCase      ,				mask_token='[MASK]'							)
    tokenizer.save_pretrained(self.tmpdirname							)







  @cached_property
  def UpperCamelCase_ (      self      :					int							):


    '''simple docstring'''





    return PegasusTokenizer.from_pretrained('google/bigbird-pegasus-large-arxiv'							)







  def UpperCamelCase_ (      self      :					Dict      ,				**UpperCamelCase      :					List[str]							):


    '''simple docstring'''





    return PegasusTokenizer.from_pretrained(self.tmpdirname      ,				**UpperCamelCase							)







  def UpperCamelCase_ (      self      :					str      ,				UpperCamelCase      :					Any							):


    '''simple docstring'''





    return ("This is a test", "This is a test")







  def UpperCamelCase_ (      self      :					Any							):


    '''simple docstring'''





    _snake_case   :				Dict														=	self.rust_tokenizer_class.from_pretrained(self.tmpdirname							)
    _snake_case   :				List[Any]														=	self.tokenizer_class.from_pretrained(self.tmpdirname							)
    _snake_case   :				str														=	(
        'Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'
        ' <pad> <pad> <pad>'
    )
    _snake_case   :				Union[str, Any]														=	rust_tokenizer([raw_input_str]      ,				return_tensors=UpperCamelCase      ,				add_special_tokens=UpperCamelCase							).input_ids[0]
    _snake_case   :				List[Any]														=	py_tokenizer([raw_input_str]      ,				return_tensors=UpperCamelCase      ,				add_special_tokens=UpperCamelCase							).input_ids[0]
    self.assertListEqual(UpperCamelCase      ,				UpperCamelCase							)







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


    '''simple docstring'''





    _snake_case   :				Any														=	['This is going to be way too long.' * 10_00, 'short example']
    _snake_case   :				Any														=	['not super long but more than 5 tokens', 'tiny']
    _snake_case   :				Optional[Any]														=	self._large_tokenizer(UpperCamelCase      ,				padding=UpperCamelCase      ,				truncation=UpperCamelCase      ,				return_tensors='pt'							)
    _snake_case   :				Dict														=	self._large_tokenizer(
        text_target=UpperCamelCase      ,				max_length=5      ,				padding=UpperCamelCase      ,				truncation=UpperCamelCase      ,				return_tensors='pt'							)

    assert batch.input_ids.shape == (2, 40_96)
    assert batch.attention_mask.shape == (2, 40_96)
    assert targets["input_ids"].shape == (2, 5)
    assert len(UpperCamelCase							) == 2  # input_ids, attention_mask.







  def UpperCamelCase_ (      self      :					Tuple							):


    '''simple docstring'''





    _snake_case   :				List[Any]														=	(
        'This is an example string that is used to test the original TF implementation against the HF'
        ' implementation'
    )

    _snake_case   :				Optional[Any]														=	self._large_tokenizer(UpperCamelCase							).input_ids

    self.assertListEqual(
        UpperCamelCase      ,				[1_82, 1_17, 1_42, 5_87, 42_11, 1_20, 1_17, 2_63, 1_12, 8_04, 1_09, 8_56, 2_50_16, 31_37, 4_64, 1_09, 2_69_55, 31_37, 1]      ,				)











def     lowerCamelCase_	(      lowerCAmelCase:	int				)->		list:
  _snake_case   :				List[Any]														=	int(lowerCAmelCase				)
  if n_element < 1:
    _snake_case   :				int														=	ValueError('a should be a positive number'				)
    raise my_error

  _snake_case   :				Union[str, Any]														=	[1]
  _snake_case							,					_snake_case							,					_snake_case   :				Any														=	(0, 0, 0)
  _snake_case   :				str														=	1
  while index < n_element:
    while hamming_list[i] * 2 <= hamming_list[-1]:
      i += 1
    while hamming_list[j] * 3 <= hamming_list[-1]:
      j += 1
    while hamming_list[k] * 5 <= hamming_list[-1]:
      k += 1
    hamming_list.append(
        min(hamming_list[i] * 2    ,							hamming_list[j] * 3    ,							hamming_list[k] * 5				)				)
    index += 1
  return hamming_list


if __name__ == "__main__":
      lowerCAmelCase_						   =    input("""Enter the last number (nth term) of the Hamming Number Series: """)
      print("""Formula of Hamming Number Series => 2^i * 3^j * 5^k""")
      lowerCAmelCase_						   =    hamming(int(n))
      print("""-----------------------------------------------------""")
      print(F"""The list with nth numbers is: {hamming_numbers}""")
      print("""-----------------------------------------------------""")




