import math






def  snake_case  (       snake_case__			:list   ,	snake_case__			:int = 0   ,	snake_case__			:int = 0)						->   list:
       _A        =  end or len(snake_case__)
       for i in range(snake_case__   ,	snake_case__):
              _A        =  i
              _A        =  array[i]
              while temp_index != start and temp_index_value < array[temp_index - 1]:
                     _A        =  array[temp_index - 1]
                     temp_index -= 1
              _A        =  temp_index_value
       return array






def  snake_case  (       snake_case__			:list   ,	snake_case__			:int   ,	snake_case__			:int)						->   None:  # Max Heap
       _A        =  index
       _A        =  2 * index + 1  # Left Node
       _A        =  2 * index + 2  # Right Node

       if left_index < heap_size and array[largest] < array[left_index]:
              _A        =  left_index

       if right_index < heap_size and array[largest] < array[right_index]:
              _A        =  right_index

       if largest != index:
              _A   ,  _A        =  array[largest], array[index]
              heapify(snake_case__   ,	snake_case__   ,	snake_case__)






def  snake_case  (       snake_case__			:list)						->   list:
       _A        =  len(snake_case__)

       for i in range(n // 2   ,	-1   ,	-1):
              heapify(snake_case__   ,	snake_case__   ,	snake_case__)

       for i in range(n - 1   ,	0   ,	-1):
              _A   ,  _A        =  array[0], array[i]
              heapify(snake_case__   ,	0   ,	snake_case__)

       return array






def  snake_case  (       snake_case__			:list   ,	snake_case__			:int   ,	snake_case__			:int   ,	snake_case__			:int)						->   int:
       if (array[first_index] > array[middle_index]) != (
           array[first_index] > array[last_index]
       ):
              return array[first_index]
       elif (array[middle_index] > array[first_index]) != (
           array[middle_index] > array[last_index]
       ):
              return array[middle_index]
       else:
              return array[last_index]






def  snake_case  (       snake_case__			:list   ,	snake_case__			:int   ,	snake_case__			:int   ,	snake_case__			:int)						->   int:
       _A        =  low
       _A        =  high
       while True:
              while array[i] < pivot:
                     i += 1
              j -= 1
              while pivot < array[j]:
                     j -= 1
              if i >= j:
                     return i
              _A   ,  _A        =  array[j], array[i]
              i += 1






def  snake_case  (       snake_case__			:list)						->   list:
       if len(snake_case__) == 0:
              return array
       _A        =  2 * math.ceil(math.loga(len(snake_case__)))
       _A        =  16
       return intro_sort(snake_case__   ,	0   ,	len(snake_case__)   ,	snake_case__   ,	snake_case__)






def  snake_case  (       snake_case__			:list   ,	snake_case__			:int   ,	snake_case__			:int   ,	snake_case__			:int   ,	snake_case__			:int)						->   list:
       while end - start > size_threshold:
              if max_depth == 0:
                     return heap_sort(snake_case__)
              max_depth -= 1
              _A        =  median_of_a(snake_case__   ,	snake_case__   ,	start + ((end - start) // 2) + 1   ,	end - 1)
              _A        =  partition(snake_case__   ,	snake_case__   ,	snake_case__   ,	snake_case__)
              intro_sort(snake_case__   ,	snake_case__   ,	snake_case__   ,	snake_case__   ,	snake_case__)
              _A        =  p
       return insertion_sort(snake_case__   ,	snake_case__   ,	snake_case__)


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    _SCREAMING_SNAKE_CASE										=						input('Enter numbers separated by a comma : ').strip()
    _SCREAMING_SNAKE_CASE										=						[float(item) for item in user_input.split(',')]
    print(sort(unsorted))





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,
        BertTokenizerFast,
        BlipImageProcessor,
        GPTaTokenizer,
        InstructBlipProcessor,
        PreTrainedTokenizerFast,
    )



@require_vision
class 				a							(			unittest.TestCase       ):







 """simple docstring"""






 def      UpperCAmelCase							(					self							)  ->					List[Any]:
        _A        =  tempfile.mkdtemp()

        _A        =  BlipImageProcessor()
        _A        =  GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""							)
        _A        =  BertTokenizerFast.from_pretrained("""hf-internal-testing/tiny-random-bert"""							)

        _A        =  InstructBlipProcessor(lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)

        processor.save_pretrained(self.tmpdirname							)



 def      UpperCAmelCase							(					self		,				**lowerCAmelCase_							)  ->					Any:
        return AutoProcessor.from_pretrained(self.tmpdirname		,				**lowerCAmelCase_							).tokenizer



 def      UpperCAmelCase							(					self		,				**lowerCAmelCase_							)  ->					Dict:
        return AutoProcessor.from_pretrained(self.tmpdirname		,				**lowerCAmelCase_							).image_processor



 def      UpperCAmelCase							(					self		,				**lowerCAmelCase_							)  ->					Union[str, Any]:
        return AutoProcessor.from_pretrained(self.tmpdirname		,				**lowerCAmelCase_							).qformer_tokenizer



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



 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _A        =  [np.random.randint(2_55		,				size=(3, 30, 4_00)		,				dtype=np.uinta							)]

        _A        =  [Image.fromarray(np.moveaxis(lowerCAmelCase_		,				0		,				-1							)							) for x in image_inputs]

        return image_inputs



 def      UpperCAmelCase							(					self							)  ->					str:
        _A        =  InstructBlipProcessor(
            tokenizer=self.get_tokenizer()		,				image_processor=self.get_image_processor()		,				qformer_tokenizer=self.get_qformer_tokenizer()		,				)
        processor.save_pretrained(self.tmpdirname							)

        _A        =  self.get_tokenizer(bos_token="""(BOS)"""		,				eos_token="""(EOS)"""							)
        _A        =  self.get_image_processor(do_normalize=lowerCAmelCase_		,				padding_value=1.0							)

        _A        =  InstructBlipProcessor.from_pretrained(
            self.tmpdirname		,				bos_token="""(BOS)"""		,				eos_token="""(EOS)"""		,				do_normalize=lowerCAmelCase_		,				padding_value=1.0							)

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

        self.assertEqual(processor.image_processor.to_json_string()		,				image_processor_add_kwargs.to_json_string()							)
        self.assertIsInstance(processor.image_processor		,				lowerCAmelCase_							)
        self.assertIsInstance(processor.qformer_tokenizer		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					int:
        _A        =  self.get_image_processor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_qformer_tokenizer()

        _A        =  InstructBlipProcessor(
            tokenizer=lowerCAmelCase_		,				image_processor=lowerCAmelCase_		,				qformer_tokenizer=lowerCAmelCase_							)

        _A        =  self.prepare_image_inputs()

        _A        =  image_processor(lowerCAmelCase_		,				return_tensors="""np"""							)
        _A        =  processor(images=lowerCAmelCase_		,				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							)  ->					Dict:
        _A        =  self.get_image_processor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_qformer_tokenizer()

        _A        =  InstructBlipProcessor(
            tokenizer=lowerCAmelCase_		,				image_processor=lowerCAmelCase_		,				qformer_tokenizer=lowerCAmelCase_							)

        _A        =  """lower newer"""

        _A        =  processor(text=lowerCAmelCase_							)

        _A        =  tokenizer(lowerCAmelCase_		,				return_token_type_ids=lowerCAmelCase_							)
        _A        =  qformer_tokenizer(lowerCAmelCase_		,				return_token_type_ids=lowerCAmelCase_							)

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

        for key in encoded_tokens_qformer.keys():
               self.assertListEqual(encoded_tokens_qformer[key]		,				encoded_processor["""qformer_""" + key]							)



 def      UpperCAmelCase							(					self							)  ->					List[str]:
        _A        =  self.get_image_processor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_qformer_tokenizer()

        _A        =  InstructBlipProcessor(
            tokenizer=lowerCAmelCase_		,				image_processor=lowerCAmelCase_		,				qformer_tokenizer=lowerCAmelCase_							)

        _A        =  """lower newer"""
        _A        =  self.prepare_image_inputs()

        _A        =  processor(text=lowerCAmelCase_		,				images=lowerCAmelCase_							)

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

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



 def      UpperCAmelCase							(					self							)  ->					int:
        _A        =  self.get_image_processor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_qformer_tokenizer()

        _A        =  InstructBlipProcessor(
            tokenizer=lowerCAmelCase_		,				image_processor=lowerCAmelCase_		,				qformer_tokenizer=lowerCAmelCase_							)

        _A        =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        _A        =  processor.batch_decode(lowerCAmelCase_							)
        _A        =  tokenizer.batch_decode(lowerCAmelCase_							)

        self.assertListEqual(lowerCAmelCase_		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        _A        =  self.get_image_processor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_qformer_tokenizer()

        _A        =  InstructBlipProcessor(
            tokenizer=lowerCAmelCase_		,				image_processor=lowerCAmelCase_		,				qformer_tokenizer=lowerCAmelCase_							)

        _A        =  """lower newer"""
        _A        =  self.prepare_image_inputs()

        _A        =  processor(text=lowerCAmelCase_		,				images=lowerCAmelCase_							)

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




