


"""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 _lowerCAmelCase				(      unittest.TestCase	):







   def  lowerCamelCase		(	self						)	->		Dict:

       '''simple docstring'''




       snake_case    :      str      					=    "ZinengTang/tvlt-base"
       snake_case    :      List[str]      					=    tempfile.mkdtemp()







   def  lowerCamelCase		(	self						,	**UpperCamelCase__						)	->		Union[str, Any]:

       '''simple docstring'''




       return TvltImageProcessor.from_pretrained(self.checkpoint						,	**UpperCamelCase__						)







   def  lowerCamelCase		(	self						,	**UpperCamelCase__						)	->		Tuple:

       '''simple docstring'''




       return TvltFeatureExtractor.from_pretrained(self.checkpoint						,	**UpperCamelCase__						)







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

       '''simple docstring'''




       shutil.rmtree(self.tmpdirname						)







   def  lowerCamelCase		(	self						)	->		Any:

       '''simple docstring'''




       snake_case    :      Tuple      					=    self.get_image_processor()
       snake_case    :      Optional[Any]      					=    self.get_feature_extractor()

       snake_case    :      Any      					=    TvltProcessor(image_processor=UpperCamelCase__						,	feature_extractor=UpperCamelCase__						)
       processor.save_pretrained(self.tmpdirname						)
       snake_case    :      List[Any]      					=    TvltProcessor.from_pretrained(self.tmpdirname						)

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







   def  lowerCamelCase		(	self						)	->		Dict:

       '''simple docstring'''




       snake_case    :      List[str]      					=    self.get_image_processor()
       snake_case    :      Optional[Any]      					=    self.get_feature_extractor()

       snake_case    :      Union[str, Any]      					=    TvltProcessor(image_processor=UpperCamelCase__						,	feature_extractor=UpperCamelCase__						)

       snake_case    :      Dict      					=    np.ones([1_2000]						)

       snake_case    :      Optional[Any]      					=    feature_extractor(UpperCamelCase__						,	return_tensors="np"						)
       snake_case    :      Optional[Any]      					=    processor(audio=UpperCamelCase__						,	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						)	->		str:

       '''simple docstring'''




       snake_case    :      Tuple      					=    self.get_image_processor()
       snake_case    :      Tuple      					=    self.get_feature_extractor()

       snake_case    :      List[Any]      					=    TvltProcessor(image_processor=UpperCamelCase__						,	feature_extractor=UpperCamelCase__						)

       snake_case    :      Union[str, Any]      					=    np.ones([3, 224, 224]						)

       snake_case    :      List[str]      					=    image_processor(UpperCamelCase__						,	return_tensors="np"						)
       snake_case    :      Tuple      					=    processor(images=UpperCamelCase__						,	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						)	->		Dict:

       '''simple docstring'''




       snake_case    :      Dict      					=    self.get_image_processor()
       snake_case    :      Union[str, Any]      					=    self.get_feature_extractor()

       snake_case    :      List[str]      					=    TvltProcessor(image_processor=UpperCamelCase__						,	feature_extractor=UpperCamelCase__						)

       snake_case    :      Any      					=    np.ones([1_2000]						)
       snake_case    :      Dict      					=    np.ones([3, 224, 224]						)

       snake_case    :      int      					=    processor(audio=UpperCamelCase__						,	images=UpperCamelCase__						)

       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(UpperCamelCase__						):
           processor()







   def  lowerCamelCase		(	self						)	->		Tuple:

       '''simple docstring'''




       snake_case    :      Any      					=    self.get_image_processor()
       snake_case    :      List[Any]      					=    self.get_feature_extractor()

       snake_case    :      List[str]      					=    TvltProcessor(image_processor=UpperCamelCase__						,	feature_extractor=UpperCamelCase__						)

       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   __lowerCAmelCase						(						lowercase  :  Union[str, Any] )	->						List[str]:


    """simple docstring"""





    snake_case    :      List[str]      					=    len(lowercase )
    for i in range(length - 1 ):
        snake_case    :      List[str]      					=    i
        for k in range(i + 1						,							lowercase ):
            if collection[k] < collection[least]:
                snake_case    :      List[str]      					=    k
        if least != i:
            snake_case					,snake_case    :      Union[str, Any]      					=    (collection[i], collection[least])
    return collection


if __name__ == "__main__":
  __snake_case   			=    input("""Enter numbers separated by a comma:\n""").strip()
  __snake_case   			=    [int(item) for item in user_input.split(""",""")]
  print(selection_sort(unsorted))





