

"""simple docstring"""




import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
	from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
	__A     :    Union[str, Any]    =		False

if is_vision_available():
	from PIL import Image

	from transformers import (
	    AutoProcessor,
	    PixaStructImageProcessor,
	    PixaStructProcessor,
	    PreTrainedTokenizerFast,
	    TaTokenizer,
	)





@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11     ,		reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."     ,		)
@require_vision
@require_torch
class 			__UpperCamelCase (   unittest.TestCase						):


		def 					SCREAMING_SNAKE_CASE__   (self :		Tuple):
							A			=					tempfile.mkdtemp()

							A			=					PixaStructImageProcessor()
							A			=					TaTokenizer.from_pretrained("t5-small")

							A			=					PixaStructProcessor(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)

							processor.save_pretrained(self.tmpdirname)


		def 					SCREAMING_SNAKE_CASE__   (self :		int     ,     **__SCREAMING_SNAKE_CASE :		List[Any]):
							return AutoProcessor.from_pretrained(self.tmpdirname     ,     **__SCREAMING_SNAKE_CASE).tokenizer


		def 					SCREAMING_SNAKE_CASE__   (self :		Tuple     ,     **__SCREAMING_SNAKE_CASE :		List[Any]):
							return AutoProcessor.from_pretrained(self.tmpdirname     ,     **__SCREAMING_SNAKE_CASE).image_processor


		def 					SCREAMING_SNAKE_CASE__   (self :		int):
							shutil.rmtree(self.tmpdirname)


		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[Any]):
							A			=					[np.random.randint(2_5_5     ,     size=(3, 3_0, 4_0_0)     ,     dtype=np.uinta)]

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

							return image_inputs


		def 					SCREAMING_SNAKE_CASE__   (self :		List[str]):
							A			=					PixaStructProcessor(tokenizer=self.get_tokenizer()     ,     image_processor=self.get_image_processor())
							processor.save_pretrained(self.tmpdirname)

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

							A			=					PixaStructProcessor.from_pretrained(
							    self.tmpdirname     ,     bos_token="(BOS)"     ,     eos_token="(EOS)"     ,     do_normalize=__SCREAMING_SNAKE_CASE     ,     padding_value=1.0)

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

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


		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[int]):
							A			=					self.get_image_processor()
							A			=					self.get_tokenizer()

							A			=					PixaStructProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,     image_processor=__SCREAMING_SNAKE_CASE)

							A			=					self.prepare_image_inputs()

							A			=					image_processor(__SCREAMING_SNAKE_CASE     ,     return_tensors="np")
							A			=					processor(images=__SCREAMING_SNAKE_CASE     ,     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 					SCREAMING_SNAKE_CASE__   (self :		Optional[int]):
							A			=					self.get_image_processor()
							A			=					self.get_tokenizer()

							A			=					PixaStructProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,     image_processor=__SCREAMING_SNAKE_CASE)

							A			=					"lower newer"

							A			=					processor(text=__SCREAMING_SNAKE_CASE)

							A			=					tokenizer(__SCREAMING_SNAKE_CASE     ,     return_token_type_ids=__SCREAMING_SNAKE_CASE     ,     add_special_tokens=__SCREAMING_SNAKE_CASE)

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


		def 					SCREAMING_SNAKE_CASE__   (self :		List[str]):
							A			=					self.get_image_processor()
							A			=					self.get_tokenizer()

							A			=					PixaStructProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,     image_processor=__SCREAMING_SNAKE_CASE)

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

							A			=					processor(text=__SCREAMING_SNAKE_CASE     ,     images=__SCREAMING_SNAKE_CASE)

							self.assertListEqual(
							    list(inputs.keys())     ,     ["flattened_patches", "attention_mask", "decoder_attention_mask", "decoder_input_ids"])

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


		def 					SCREAMING_SNAKE_CASE__   (self :		Union[str, Any]):
							A			=					self.get_image_processor()
							A			=					self.get_tokenizer()

							A			=					PixaStructProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,     image_processor=__SCREAMING_SNAKE_CASE)

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

							A			=					processor(text=__SCREAMING_SNAKE_CASE     ,     images=__SCREAMING_SNAKE_CASE)

							A			=					[5_1_2, 1_0_2_4, 2_0_4_8, 4_0_9_6]
							A			=					[7_7_0, 7_7_0, 7_7_0, 7_7_0]
							# with text
							for i, max_patch in enumerate(__SCREAMING_SNAKE_CASE):
												A			=					processor(text=__SCREAMING_SNAKE_CASE     ,     images=__SCREAMING_SNAKE_CASE     ,     max_patches=__SCREAMING_SNAKE_CASE)
												self.assertEqual(inputs["flattened_patches"][0].shape[0]     ,     __SCREAMING_SNAKE_CASE)
												self.assertEqual(inputs["flattened_patches"][0].shape[1]     ,     expected_hidden_size[i])

							# without text input
							for i, max_patch in enumerate(__SCREAMING_SNAKE_CASE):
												A			=					processor(images=__SCREAMING_SNAKE_CASE     ,     max_patches=__SCREAMING_SNAKE_CASE)
												self.assertEqual(inputs["flattened_patches"][0].shape[0]     ,     __SCREAMING_SNAKE_CASE)
												self.assertEqual(inputs["flattened_patches"][0].shape[1]     ,     expected_hidden_size[i])


		def 					SCREAMING_SNAKE_CASE__   (self :		int):
							A			=					self.get_image_processor()
							A			=					self.get_tokenizer()

							A			=					PixaStructProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,     image_processor=__SCREAMING_SNAKE_CASE)

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

							A			=					processor.batch_decode(__SCREAMING_SNAKE_CASE)
							A			=					tokenizer.batch_decode(__SCREAMING_SNAKE_CASE)

							self.assertListEqual(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		List[Any]):
							A			=					self.get_image_processor()
							A			=					self.get_tokenizer()

							A			=					PixaStructProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,     image_processor=__SCREAMING_SNAKE_CASE)

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

							A			=					processor(text=__SCREAMING_SNAKE_CASE     ,     images=__SCREAMING_SNAKE_CASE)

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

							A			=					processor(text=__SCREAMING_SNAKE_CASE)

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








"""simple docstring"""




def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			=					index
					A			=					2 * index + 1
					A			=					2 * index + 2
					if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
										A			=					left_index

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

					if largest != index:
										A			,    A			=					unsorted[index], unsorted[largest]
										heapify(lowercase__			,							lowercase__			,							lowercase__      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					A			=					len(lowercase__      )
					for i in range(n // 2 - 1			,							-1			,							-1      ):
										heapify(lowercase__			,							lowercase__			,							lowercase__      )
					for i in range(n - 1			,							0			,							-1      ):
										A			,    A			=					unsorted[i], unsorted[0]
										heapify(lowercase__			,							0			,							lowercase__      )
					return unsorted


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





