'''simple docstring'''

def       __UpperCamelCase	(       ):
 lowercase__    :   Any						= [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]

 lowercase__    :   Any						= 6
 lowercase__    :   Optional[Any]						= 1
 lowercase__    :   int						= 1901

 lowercase__    :   List[str]						= 0

 while year < 2001:
  day += 7

  if (year % 4 == 0 and year % 100 != 0) or (year % 400 == 0):
   if day > days_per_month[month - 1] and month != 2:
    month += 1
    lowercase__    :   List[Any]						= day - days_per_month[month - 2]
   elif day > 29 and month == 2:
    month += 1
    lowercase__    :   Any						= day - 29
  else:
   if day > days_per_month[month - 1]:
    month += 1
    lowercase__    :   List[Any]						= day - days_per_month[month - 2]

  if month > 12:
   year += 1
   lowercase__    :   Dict						= 1

  if year < 2001 and day == 1:
   sundays += 1
 return sundays


if __name__ == "__main__":
  print(solution())


'''simple docstring'''

import unittest

from datasets import load_dataset

from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow



@is_pipeline_test
@require_torch
class       UpperCAmelCase  (						unittest.TestCase ):





       '''simple docstring'''



       @require_torch
       def _lowerCAmelCase(						self			)  ->  int:
        lowercase__    :   str						= pipeline(
            task='''zero-shot-audio-classification'''     ,       model='''hf-internal-testing/tiny-clap-htsat-unfused'''			)
        lowercase__    :   Optional[Any]						= load_dataset('''ashraq/esc50'''			)
        lowercase__    :   Tuple						= dataset['''train''']['''audio'''][-1]['''array''']
        lowercase__    :   Optional[Any]						= audio_classifier(__lowerCAmelCase     ,       candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']			)
        self.assertEqual(
            nested_simplify(__lowerCAmelCase			)     ,       [{'''score''': 0.5_0_1, '''label''': '''Sound of a dog'''}, {'''score''': 0.4_9_9, '''label''': '''Sound of vaccum cleaner'''}]     ,       )

       @unittest.skip('''No models are available in TF'''			)
       def _lowerCAmelCase(						self			)  ->  str:
        pass

       @slow
       @require_torch
       def _lowerCAmelCase(						self			)  ->  Tuple:
        lowercase__    :   List[str]						= pipeline(
            task='''zero-shot-audio-classification'''     ,       model='''laion/clap-htsat-unfused'''     ,       )
        # This is an audio of a dog
        lowercase__    :   int						= load_dataset('''ashraq/esc50'''			)
        lowercase__    :   str						= dataset['''train''']['''audio'''][-1]['''array''']
        lowercase__    :   Any						= audio_classifier(__lowerCAmelCase     ,       candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']			)

        self.assertEqual(
            nested_simplify(__lowerCAmelCase			)     ,       [
                {'''score''': 0.9_9_9, '''label''': '''Sound of a dog'''},
                {'''score''': 0.0_0_1, '''label''': '''Sound of vaccum cleaner'''},
            ]     ,       )

        lowercase__    :   Dict						= audio_classifier([audio] * 5     ,       candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']			)
        self.assertEqual(
            nested_simplify(__lowerCAmelCase			)     ,       [
                [
                    {'''score''': 0.9_9_9, '''label''': '''Sound of a dog'''},
                    {'''score''': 0.0_0_1, '''label''': '''Sound of vaccum cleaner'''},
                ],
            ]
            * 5     ,       )
        lowercase__    :   Any						= audio_classifier(
            [audio] * 5     ,       candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']     ,       batch_size=5			)
        self.assertEqual(
            nested_simplify(__lowerCAmelCase			)     ,       [
                [
                    {'''score''': 0.9_9_9, '''label''': '''Sound of a dog'''},
                    {'''score''': 0.0_0_1, '''label''': '''Sound of vaccum cleaner'''},
                ],
            ]
            * 5     ,       )


       @unittest.skip('''No models are available in TF'''			)
       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        pass

