






import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast
@require_vision
class 							__snake_case(       unittest.TestCase   ):



 def     UpperCAmelCase     (						self : List[str]						):

    """simple docstring"""






    lowercase__    :						Any       =			tempfile.mkdtemp()

    lowercase__    :						int       =			CLIPImageProcessor()
    lowercase__    :						Tuple       =			BertTokenizer.from_pretrained(
        """hf-internal-testing/tiny-random-BertModel"""			,		model_input_names=["""input_ids""", """attention_mask"""]						)

    lowercase__    :						Tuple       =			GitProcessor(UpperCAmelCase__			,		UpperCAmelCase__						)

    processor.save_pretrained(self.tmpdirname						)



 def     UpperCAmelCase     (						self : Union[str, Any]			,		**UpperCAmelCase__ : Tuple						):

    """simple docstring"""






    return AutoProcessor.from_pretrained(self.tmpdirname			,		**UpperCAmelCase__						).tokenizer



 def     UpperCAmelCase     (						self : Dict			,		**UpperCAmelCase__ : int						):

    """simple docstring"""






    return AutoProcessor.from_pretrained(self.tmpdirname			,		**UpperCAmelCase__						).image_processor



 def     UpperCAmelCase     (						self : Optional[Any]						):

    """simple docstring"""






    shutil.rmtree(self.tmpdirname						)



 def     UpperCAmelCase     (						self : str						):

    """simple docstring"""






    lowercase__    :						List[str]       =			[np.random.randint(255			,		size=(3, 30, 400)			,		dtype=np.uinta						)]

    lowercase__    :						List[Any]       =			[Image.fromarray(np.moveaxis(UpperCAmelCase__			,		0			,		-1						)						) for x in image_inputs]

    return image_inputs



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

    """simple docstring"""






    lowercase__    :						List[Any]       =			GitProcessor(tokenizer=self.get_tokenizer()			,		image_processor=self.get_image_processor()						)
    processor.save_pretrained(self.tmpdirname						)

    lowercase__    :						Tuple       =			self.get_tokenizer(bos_token="""(BOS)"""			,		eos_token="""(EOS)"""						)
    lowercase__    :						Dict       =			self.get_image_processor(do_normalize=UpperCAmelCase__			,		padding_value=1.0						)

    lowercase__    :						Dict       =			GitProcessor.from_pretrained(
        self.tmpdirname			,		bos_token="""(BOS)"""			,		eos_token="""(EOS)"""			,		do_normalize=UpperCAmelCase__			,		padding_value=1.0						)

    self.assertEqual(processor.tokenizer.get_vocab()			,		tokenizer_add_kwargs.get_vocab()						)
    self.assertIsInstance(processor.tokenizer			,		UpperCAmelCase__						)

    self.assertEqual(processor.image_processor.to_json_string()			,		image_processor_add_kwargs.to_json_string()						)
    self.assertIsInstance(processor.image_processor			,		UpperCAmelCase__						)



 def     UpperCAmelCase     (						self : Optional[Any]						):

    """simple docstring"""






    lowercase__    :						Tuple       =			self.get_image_processor()
    lowercase__    :						Optional[int]       =			self.get_tokenizer()

    lowercase__    :						Dict       =			GitProcessor(tokenizer=UpperCAmelCase__			,		image_processor=UpperCAmelCase__						)

    lowercase__    :						Union[str, Any]       =			self.prepare_image_inputs()

    lowercase__    :						int       =			image_processor(UpperCAmelCase__			,		return_tensors="""np"""						)
    lowercase__    :						List[Any]       =			processor(images=UpperCAmelCase__			,		return_tensors="""np"""						)

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



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

    """simple docstring"""






    lowercase__    :						Dict       =			self.get_image_processor()
    lowercase__    :						int       =			self.get_tokenizer()

    lowercase__    :						Tuple       =			GitProcessor(tokenizer=UpperCAmelCase__			,		image_processor=UpperCAmelCase__						)

    lowercase__    :						List[Any]       =			"""lower newer"""

    lowercase__    :						Tuple       =			processor(text=UpperCAmelCase__						)

    lowercase__    :						List[Any]       =			tokenizer(UpperCAmelCase__			,		return_token_type_ids=UpperCAmelCase__						)

    for key in encoded_tok.keys():
       self.assertListEqual(encoded_tok[key]			,		encoded_processor[key]						)



 def     UpperCAmelCase     (						self : int						):

    """simple docstring"""






    lowercase__    :						Dict       =			self.get_image_processor()
    lowercase__    :						Dict       =			self.get_tokenizer()

    lowercase__    :						str       =			GitProcessor(tokenizer=UpperCAmelCase__			,		image_processor=UpperCAmelCase__						)

    lowercase__    :						int       =			"""lower newer"""
    lowercase__    :						Optional[int]       =			self.prepare_image_inputs()

    lowercase__    :						List[str]       =			processor(text=UpperCAmelCase__			,		images=UpperCAmelCase__						)

    self.assertListEqual(list(inputs.keys()						)			,		["""input_ids""", """attention_mask""", """pixel_values"""]						)

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



 def     UpperCAmelCase     (						self : List[str]						):

    """simple docstring"""






    lowercase__    :						Optional[Any]       =			self.get_image_processor()
    lowercase__    :						Tuple       =			self.get_tokenizer()

    lowercase__    :						Dict       =			GitProcessor(tokenizer=UpperCAmelCase__			,		image_processor=UpperCAmelCase__						)

    lowercase__    :						Union[str, Any]       =			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

    lowercase__    :						Optional[int]       =			processor.batch_decode(UpperCAmelCase__						)
    lowercase__    :						List[Any]       =			tokenizer.batch_decode(UpperCAmelCase__						)

    self.assertListEqual(UpperCAmelCase__			,		UpperCAmelCase__						)







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

    """simple docstring"""






    lowercase__    :						Dict       =			self.get_image_processor()
    lowercase__    :						int       =			self.get_tokenizer()

    lowercase__    :						List[Any]       =			GitProcessor(tokenizer=UpperCAmelCase__			,		image_processor=UpperCAmelCase__						)

    lowercase__    :						Union[str, Any]       =			"""lower newer"""
    lowercase__    :						Any       =			self.prepare_image_inputs()

    lowercase__    :						Optional[Any]       =			processor(text=UpperCAmelCase__			,		images=UpperCAmelCase__						)

    # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
    self.assertListEqual(list(inputs.keys()						)			,		["""input_ids""", """attention_mask""", """pixel_values"""]						)









def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    index
       _UpperCamelCase														=    2 * index + 1
       _UpperCamelCase														=    2 * index + 2
       if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
              _UpperCamelCase														=    left_index

       if right_index < heap_size and unsorted[right_index] > unsorted[largest]:
              _UpperCamelCase														=    right_index

       if largest != index:
              _UpperCamelCase							,		_UpperCamelCase														=    unsorted[index], unsorted[largest]
              heapify(lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       )


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase       ):
       _UpperCamelCase														=    len(lowerCAmelCase       )
       for i in range(n // 2 - 1     ,							-1     ,							-1       ):
              heapify(lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       )
       for i in range(n - 1     ,							0     ,							-1       ):
              _UpperCamelCase							,		_UpperCamelCase														=    unsorted[i], unsorted[0]
              heapify(lowerCAmelCase     ,							0     ,							lowerCAmelCase       )
       return unsorted


if __name__ == "__main__":
       lowercase     :			Union[str, Any]     						=	input("""Enter numbers separated by a comma:\n""").strip()
       lowercase     :			Dict     						=	[int(item) for item in user_input.split(""",""")]
       print(heap_sort(unsorted))






