




'''simple docstring'''


import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer







class      SCREAMING_SNAKE_CASE    (						snake_case_				):






      def 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

           a_     :							Dict     						=  tempfile.mkdtemp()
           a_     :							List[Any]     						=  5

           # Realm tok
           a_     :							List[Any]     						=  [
               """[UNK]""",
               """[CLS]""",
               """[SEP]""",
               """[PAD]""",
               """[MASK]""",
               """test""",
               """question""",
               """this""",
               """is""",
               """the""",
               """first""",
               """second""",
               """third""",
               """fourth""",
               """fifth""",
               """record""",
               """want""",
               """##want""",
               """##ed""",
               """wa""",
               """un""",
               """runn""",
               """##ing""",
               """,""",
               """low""",
               """lowest""",
           ]
           a_     :							Any     						=  os.path.join(self.tmpdirname						,						"""realm_tokenizer"""       )
           os.makedirs(lowercase__						,						exist_ok=lowercase__       )
           a_     :							Any     						=  os.path.join(lowercase__						,						VOCAB_FILES_NAMES["""vocab_file"""]       )
           with open(self.vocab_file						,						"""w"""						,						encoding="""utf-8"""       ) as vocab_writer:
                vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]       )       )

           a_     :							int     						=  os.path.join(self.tmpdirname						,						"""realm_block_records"""       )
           os.makedirs(lowercase__						,						exist_ok=lowercase__       )






      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname						,						"""realm_tokenizer"""       )       )






      def 							lowercase_						( self   :  Tuple       ):


           '''simple docstring'''

           shutil.rmtree(self.tmpdirname       )






      def 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

           a_     :							int     						=  RealmConfig(num_block_records=self.num_block_records       )
           return config






      def 							lowercase_						( self   :  List[str]       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  Dataset.from_dict(
               {
                   """id""": ["""0""", """1"""],
                   """question""": ["""foo""", """bar"""],
                   """answers""": [["""Foo""", """Bar"""], ["""Bar"""]],
               }       )
           return dataset






      def 							lowercase_						( self   :  int       ):


           '''simple docstring'''

           a_     :							Optional[Any]     						=  np.array(
               [
                   b"""This is the first record""",
                   b"""This is the second record""",
                   b"""This is the third record""",
                   b"""This is the fourth record""",
                   b"""This is the fifth record""",
                   b"""This is a longer longer longer record""",
               ]						,						dtype=lowercase__						,						)
           return block_records






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


           '''simple docstring'''

           a_     :							Dict     						=  RealmRetriever(
               block_records=self.get_dummy_block_records()						,						tokenizer=self.get_tokenizer()						,						)
           return retriever






      def 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

           a_     :							List[str]     						=  self.get_config()
           a_     :							Tuple     						=  self.get_dummy_retriever()
           a_     :							str     						=  retriever.tokenizer

           a_     :							int     						=  np.array([0, 3]						,						dtype="""long"""       )
           a_     :							Tuple     						=  tokenizer(["""Test question"""]       ).input_ids
           a_     :							int     						=  tokenizer(
               ["""the fourth"""]						,						add_special_tokens=lowercase__						,						return_token_type_ids=lowercase__						,						return_attention_mask=lowercase__						,						).input_ids
           a_     :							Optional[Any]     						=  config.reader_seq_len

           a_						,     a_						,     a_						,     a_     :							List[str]     						=  retriever(
               lowercase__						,						lowercase__						,						answer_ids=lowercase__						,						max_length=lowercase__						,						return_tensors="""np"""       )

           self.assertEqual(len(lowercase__       )						,						2       )
           self.assertEqual(len(lowercase__       )						,						2       )
           self.assertEqual(len(lowercase__       )						,						2       )
           self.assertEqual(concat_inputs.input_ids.shape						,						(2, 10)       )
           self.assertEqual(concat_inputs.attention_mask.shape						,						(2, 10)       )
           self.assertEqual(concat_inputs.token_type_ids.shape						,						(2, 10)       )
           self.assertEqual(concat_inputs.special_tokens_mask.shape						,						(2, 10)       )
           self.assertEqual(
               tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0]       )						,						["""[CLS]""", """test""", """question""", """[SEP]""", """this""", """is""", """the""", """first""", """record""", """[SEP]"""]						,						)
           self.assertEqual(
               tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1]       )						,						["""[CLS]""", """test""", """question""", """[SEP]""", """this""", """is""", """the""", """fourth""", """record""", """[SEP]"""]						,						)






      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           a_     :							str     						=  self.get_config()
           a_     :							Dict     						=  self.get_dummy_retriever()
           a_     :							List[str]     						=  retriever.tokenizer

           a_     :							Optional[int]     						=  np.array([0, 3, 5]						,						dtype="""long"""       )
           a_     :							Union[str, Any]     						=  tokenizer(["""Test question"""]       ).input_ids
           a_     :							str     						=  tokenizer(
               ["""the fourth""", """longer longer"""]						,						add_special_tokens=lowercase__						,						return_token_type_ids=lowercase__						,						return_attention_mask=lowercase__						,						).input_ids
           a_     :							Dict     						=  config.reader_seq_len

           a_						,     a_						,     a_						,     a_     :							Optional[Any]     						=  retriever(
               lowercase__						,						lowercase__						,						answer_ids=lowercase__						,						max_length=lowercase__						,						return_tensors="""np"""       )

           self.assertEqual([False, True, True]						,						lowercase__       )
           self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]]						,						lowercase__       )
           self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]]						,						lowercase__       )






      def 							lowercase_						( self   :  Tuple       ):


           '''simple docstring'''

           a_     :							List[Any]     						=  self.get_dummy_retriever()
           retriever.save_pretrained(os.path.join(self.tmpdirname						,						"""realm_block_records"""       )       )

           # Test local path
           a_     :							List[Any]     						=  retriever.from_pretrained(os.path.join(self.tmpdirname						,						"""realm_block_records"""       )       )
           self.assertEqual(retriever.block_records[0]						,						b"""This is the first record"""       )

           # Test mocked remote path
           with patch("""transformers.models.realm.retrieval_realm.hf_hub_download"""       ) as mock_hf_hub_download:
                a_     :							Dict     						=  os.path.join(
                    os.path.join(self.tmpdirname						,						"""realm_block_records"""       )						,						_REALM_BLOCK_RECORDS_FILENAME       )
                a_     :							Tuple     						=  RealmRetriever.from_pretrained("""google/realm-cc-news-pretrained-openqa"""       )

           self.assertEqual(retriever.block_records[0]						,						b"""This is the first record"""       )






'''simple docstring'''


def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					list[int] ,    UpperCamelCase__    :					str				):




     """simple docstring"""


     a_     :							Tuple     						=  int(UpperCamelCase__				)

     # Initialize Result
     a_     :							Tuple     						=  []

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

     return answer


# Driver Code
if __name__ == "__main__":
 lowerCAmelCase_      :  List[str]				     =  []
 lowerCAmelCase_      :  Union[str, Any]				     =  '0'

 if (
     input('Do you want to enter your denominations ? (yY/n): ').strip().lower()
     == "y"
 ):
  lowerCAmelCase_      :  str				     =  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()))
  lowerCAmelCase_      :  Union[str, Any]				     =  input('Enter the change you want to make in Indian Currency: ').strip()
 else:
  # All denominations of Indian Currency if user does not enter
  lowerCAmelCase_      :  List[Any]				     =  [1, 2, 5, 10, 20, 50, 100, 500, 2000]
  lowerCAmelCase_      :  str				     =  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}: ")
  lowerCAmelCase_      :  Optional[int]				     =  find_minimum_change(denominations, value)
  # Print result
  for i in range(len(answer)):
   print(answer[i], end=' ')
