'''simple docstring'''

import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
  from transformers import TvltImageProcessor

if is_speech_available():
  from transformers import TvltFeatureExtractor

from transformers import TvltProcessor



@require_torch
class       UpperCAmelCase  (						unittest.TestCase ):





       '''simple docstring'''



       def _lowerCAmelCase(						self			)  ->  Dict:
        lowercase__    :   Tuple						= '''ZinengTang/tvlt-base'''
        lowercase__    :   str						= tempfile.mkdtemp()

       def _lowerCAmelCase(						self     ,       **__lowerCAmelCase			)  ->  List[Any]:
        return TvltImageProcessor.from_pretrained(self.checkpoint     ,       **__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       **__lowerCAmelCase			)  ->  Tuple:
        return TvltFeatureExtractor.from_pretrained(self.checkpoint     ,       **__lowerCAmelCase			)

       def _lowerCAmelCase(						self			)  ->  int:
        shutil.rmtree(self.tmpdirname			)

       def _lowerCAmelCase(						self			)  ->  Any:
        lowercase__    :   int						= self.get_image_processor()
        lowercase__    :   Optional[Any]						= self.get_feature_extractor()

        lowercase__    :   Any						= TvltProcessor(image_processor=__lowerCAmelCase     ,       feature_extractor=__lowerCAmelCase			)
        processor.save_pretrained(self.tmpdirname			)
        lowercase__    :   Tuple						= TvltProcessor.from_pretrained(self.tmpdirname			)

        self.assertIsInstance(processor.feature_extractor     ,       __lowerCAmelCase			)
        self.assertIsInstance(processor.image_processor     ,       __lowerCAmelCase			)

       def _lowerCAmelCase(						self			)  ->  Optional[Any]:
        lowercase__    :   int						= self.get_image_processor()
        lowercase__    :   str						= self.get_feature_extractor()

        lowercase__    :   Dict						= TvltProcessor(image_processor=__lowerCAmelCase     ,       feature_extractor=__lowerCAmelCase			)

        lowercase__    :   Any						= np.ones([12000]			)

        lowercase__    :   int						= feature_extractor(__lowerCAmelCase     ,       return_tensors='''np'''			)
        lowercase__    :   Tuple						= processor(audio=__lowerCAmelCase     ,       return_tensors='''np'''			)

        for key in audio_dict.keys():
         self.assertAlmostEqual(audio_dict[key].sum()     ,       input_processor[key].sum()     ,       delta=1E-2			)

       def _lowerCAmelCase(						self			)  ->  List[str]:
        lowercase__    :   Union[str, Any]						= self.get_image_processor()
        lowercase__    :   Tuple						= self.get_feature_extractor()

        lowercase__    :   List[str]						= TvltProcessor(image_processor=__lowerCAmelCase     ,       feature_extractor=__lowerCAmelCase			)

        lowercase__    :   Any						= np.ones([3, 224, 224]			)

        lowercase__    :   Any						= image_processor(__lowerCAmelCase     ,       return_tensors='''np'''			)
        lowercase__    :   str						= processor(images=__lowerCAmelCase     ,       return_tensors='''np'''			)

        for key in image_dict.keys():
         self.assertAlmostEqual(image_dict[key].sum()     ,       input_processor[key].sum()     ,       delta=1E-2			)

       def _lowerCAmelCase(						self			)  ->  List[str]:
        lowercase__    :   List[str]						= self.get_image_processor()
        lowercase__    :   List[str]						= self.get_feature_extractor()

        lowercase__    :   Union[str, Any]						= TvltProcessor(image_processor=__lowerCAmelCase     ,       feature_extractor=__lowerCAmelCase			)

        lowercase__    :   List[str]						= np.ones([12000]			)
        lowercase__    :   Any						= np.ones([3, 224, 224]			)

        lowercase__    :   Optional[Any]						= processor(audio=__lowerCAmelCase     ,       images=__lowerCAmelCase			)

        self.assertListEqual(list(inputs.keys()			)     ,       ['''audio_values''', '''audio_mask''', '''pixel_values''', '''pixel_mask''']			)

        # test if it raises when no input is passed
        with pytest.raises(__lowerCAmelCase			):
         processor()


       def _lowerCAmelCase(						self			)  ->  List[Any]:
        lowercase__    :   str						= self.get_image_processor()
        lowercase__    :   Union[str, Any]						= self.get_feature_extractor()

        lowercase__    :   Optional[int]						= TvltProcessor(image_processor=__lowerCAmelCase     ,       feature_extractor=__lowerCAmelCase			)

        self.assertListEqual(
            processor.model_input_names     ,       image_processor.model_input_names + feature_extractor.model_input_names     ,       msg='''`processor` and `image_processor`+`feature_extractor` model input names do not match'''     ,       )


'''simple docstring'''

def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   Dict						= len(UpperCAmelCase					)
 print('''The following activities are selected:'''					)

 # The first activity is always selected
 lowercase__    :   str						= 0
 print(UpperCAmelCase		,					end=''','''					)

 # Consider rest of the activities
 for j in range(UpperCAmelCase					):
  # If this activity has start time greater than
  # or equal to the finish time of previously
  # selected activity, then select it
  if start[j] >= finish[i]:
   print(UpperCAmelCase		,					end=''','''					)
   lowercase__    :   str						= j


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  __a:			str	       =       [1, 3, 0, 5, 8, 5]
  __a:			Optional[Any]	       =       [2, 4, 6, 7, 9, 9]
  print_max_activities(start, finish)

