



'''simple docstring'''





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


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






@require_sentencepiece
@require_tokenizers
class __A			(  A   ,   unittest.TestCase						):



 '''simple docstring'''







 __lowerCamelCase    :       Optional[Any]          =       PegasusTokenizer
 __lowerCamelCase    :       List[Any]          =       PegasusTokenizerFast
 __lowerCamelCase    :       Any          =       True
 __lowerCamelCase    :       Tuple          =       True



 def 					a__  (self       )    ->     str:


      """simple docstring"""



      super().setUp()

      # We have a SentencePiece fixture for testing
      _a      =  PegasusTokenizer(A       )
      tokenizer.save_pretrained(self.tmpdirname       )



 @cached_property
 def 					a__  (self       )    ->     Union[str, Any]:


      """simple docstring"""



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



 def 					a__  (self ,   **A       )    ->     PegasusTokenizer:


      """simple docstring"""



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



 def 					a__  (self ,   A       )    ->     Optional[int]:


      """simple docstring"""



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



 def 					a__  (self       )    ->     List[str]:


      """simple docstring"""



      _a      =  '''</s>'''
      _a      =  1

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



 def 					a__  (self       )    ->     Any:


      """simple docstring"""



      _a      =  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(A       ) ,   1_103       )



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


      """simple docstring"""



      self.assertEqual(self.get_tokenizer().vocab_size ,   1_103       )



 def 					a__  (self       )    ->     Optional[int]:


      """simple docstring"""



      _a      =  self.rust_tokenizer_class.from_pretrained(self.tmpdirname       )
      _a      =  self.tokenizer_class.from_pretrained(self.tmpdirname       )
      _a      =  (
          '''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>'''
      )
      _a      =  rust_tokenizer([raw_input_str] ,   return_tensors=A ,   add_special_tokens=A       ).input_ids[0]
      _a      =  py_tokenizer([raw_input_str] ,   return_tensors=A ,   add_special_tokens=A       ).input_ids[0]
      self.assertListEqual(A ,   A       )



 def 					a__  (self       )    ->     List[Any]:


      """simple docstring"""



      _a      =  self._large_tokenizer
      # <mask_1> masks whole sentence while <mask_2> masks single word
      _a      =  '''<mask_1> To ensure a <mask_2> flow of bank resolutions.'''
      _a      =  [2, 413, 615, 114, 3, 1_971, 113, 1_679, 10_710, 107, 1]
      _a      =  tokenizer([raw_input_str] ,   return_tensors=A       ).input_ids[0]
      self.assertListEqual(A ,   A       )



 def 					a__  (self       )    ->     int:


      """simple docstring"""



      _a      =  self._large_tokenizer
      # The tracebacks for the following asserts are **better** without messages or self.assertEqual
      assert tokenizer.vocab_size == 96_103
      assert tokenizer.pad_token_id == 0
      assert tokenizer.eos_token_id == 1
      assert tokenizer.offset == 103
      assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
      assert tokenizer.unk_token == "<unk>"
      assert tokenizer.model_max_length == 1_024
      _a      =  '''To ensure a smooth flow of bank resolutions.'''
      _a      =  [413, 615, 114, 2_291, 1_971, 113, 1_679, 10_710, 107, 1]
      _a      =  tokenizer([raw_input_str] ,   return_tensors=A       ).input_ids[0]
      self.assertListEqual(A ,   A       )
      assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]       ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]



 @require_torch
 def 					a__  (self       )    ->     Dict:


      """simple docstring"""



      _a      =  ['''This is going to be way too long.''' * 150, '''short example''']
      _a      =  ['''not super long but more than 5 tokens''', '''tiny''']
      _a      =  self._large_tokenizer(A ,   padding=A ,   truncation=A ,   return_tensors='''pt'''       )
      _a      =  self._large_tokenizer(
          text_target=A ,   max_length=5 ,   padding=A ,   truncation=A ,   return_tensors='''pt'''       )

      assert batch.input_ids.shape == (2, 1_024)
      assert batch.attention_mask.shape == (2, 1_024)
      assert targets["input_ids"].shape == (2, 5)
      assert len(A       ) == 2  # input_ids, attention_mask.



 @slow
 def 					a__  (self       )    ->     str:


      """simple docstring"""



      _a      =  {'''input_ids''': [[38_979, 143, 18_485, 606, 130, 26_669, 87_686, 121, 54_189, 1_129, 111, 26_669, 87_686, 121, 9_114, 14_787, 121, 13_249, 158, 592, 956, 121, 14_621, 31_576, 143, 62_613, 108, 9_688, 930, 43_430, 11_562, 62_613, 304, 108, 11_443, 897, 108, 9_314, 17_415, 63_399, 108, 11_443, 7_614, 18_316, 118, 4_284, 7_148, 12_430, 143, 1_400, 25_703, 158, 111, 4_284, 7_148, 11_772, 143, 21_297, 1_064, 158, 122, 204, 3_506, 1_754, 1_133, 14_787, 1_581, 115, 33_224, 4_482, 111, 1_355, 110, 29_173, 317, 50_833, 108, 20_147, 94_665, 111, 77_198, 107, 1], [110, 62_613, 117, 638, 112, 1_133, 121, 20_098, 1_355, 79_050, 13_872, 135, 1_596, 53_541, 1_352, 141, 13_039, 5_542, 124, 302, 518, 111, 268, 2_956, 115, 149, 4_427, 107, 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], [139, 1_235, 2_799, 18_289, 17_780, 204, 109, 9_474, 1_296, 107, 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=A ,   model_name='''google/bigbird-pegasus-large-arxiv''' ,   revision='''ba85d0851d708441f91440d509690f1ab6353415''' ,   )






@require_sentencepiece
@require_tokenizers
class __A			(  A   ,   unittest.TestCase						):



 '''simple docstring'''







 __lowerCamelCase    :       Any          =       PegasusTokenizer
 __lowerCamelCase    :       Optional[int]          =       PegasusTokenizerFast
 __lowerCamelCase    :       Dict          =       True
 __lowerCamelCase    :       Optional[Any]          =       True



 def 					a__  (self       )    ->     Dict:


      """simple docstring"""



      super().setUp()

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



 @cached_property
 def 					a__  (self       )    ->     Union[str, Any]:


      """simple docstring"""



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



 def 					a__  (self ,   **A       )    ->     PegasusTokenizer:


      """simple docstring"""



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



 def 					a__  (self ,   A       )    ->     Tuple:


      """simple docstring"""



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



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


      """simple docstring"""



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



 @require_torch
 def 					a__  (self       )    ->     str:


      """simple docstring"""



      _a      =  ['''This is going to be way too long.''' * 1_000, '''short example''']
      _a      =  ['''not super long but more than 5 tokens''', '''tiny''']
      _a      =  self._large_tokenizer(A ,   padding=A ,   truncation=A ,   return_tensors='''pt'''       )
      _a      =  self._large_tokenizer(
          text_target=A ,   max_length=5 ,   padding=A ,   truncation=A ,   return_tensors='''pt'''       )

      assert batch.input_ids.shape == (2, 4_096)
      assert batch.attention_mask.shape == (2, 4_096)
      assert targets["input_ids"].shape == (2, 5)
      assert len(A       ) == 2  # input_ids, attention_mask.



 def 					a__  (self       )    ->     List[Any]:


      """simple docstring"""



      _a      =  (
          '''This is an example string that is used to test the original TF implementation against the HF'''
          ''' implementation'''
      )

      _a      =  self._large_tokenizer(A       ).input_ids

      self.assertListEqual(
          A ,   [182, 117, 142, 587, 4_211, 120, 117, 263, 112, 804, 109, 856, 25_016, 3_137, 464, 109, 26_955, 3_137, 1] ,   )








'''simple docstring'''





def lowerCAmelCase  (__A  ,	__A):



     """simple docstring"""



     _a      =  int(__A)

     # Initialize Result
     _a      =  []

     # Traverse through all denomination
     for denomination in reversed(__A):
          # Find denominations
          while int(__A) >= int(__A):
               total_value -= int(__A)
               answer.append(__A)  # Append the "answers" array

     return answer


# Driver Code
if __name__ == "__main__":
  lowercase_		       =  []
  lowercase_		       =  "0"

  if (
      input("Do you want to enter your denominations ? (yY/n): ").strip().lower()
      == "y"
  ):
    lowercase_		       =  int(input("Enter the number of denominations you want to add: ").strip())

    for i in range(0, n):
      denominations.append(int(input(F"""Denomination {i}: """).strip()))
    lowercase_		       =  input("Enter the change you want to make in Indian Currency: ").strip()
  else:
    # All denominations of Indian Currency if user does not enter
    lowercase_		       =  [1, 2, 5, 10, 20, 50, 100, 500, 2_000]
    lowercase_		       =  input("Enter the change you want to make: ").strip()

  if int(value) == 0 or int(value) < 0:
    print("The total value cannot be zero or negative.")

  else:
    print(F"""Following is minimal change for {value}: """)
    lowercase_		       =  find_minimum_change(denominations, value)
    # Print result
    for i in range(len(answer)):
      print(answer[i], end=" ")



