


import unittest

import numpy as np

from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_pytesseract_available():
   from PIL import Image

   from transformers import LayoutLMvaImageProcessor
class 				SCREAMING_SNAKE_CASE__						(       unittest.TestCase    ):
      def __init__( self       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=7  ,							SCREAMING_SNAKE_CASE__       : Any=3  ,							SCREAMING_SNAKE_CASE__       : Dict=1_8  ,							SCREAMING_SNAKE_CASE__       : List[Any]=3_0  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=4_0_0  ,							SCREAMING_SNAKE_CASE__       : Tuple=True  ,							SCREAMING_SNAKE_CASE__       : Dict=None  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=True  ,							)	->    Optional[int]:
             a_      :	str							=						size if size is not None else {'height': 1_8, 'width': 1_8}
             a_      :	str							=						parent
             a_      :	List[Any]							=						batch_size
             a_      :	Optional[Any]							=						num_channels
             a_      :	Any							=						image_size
             a_      :	Dict							=						min_resolution
             a_      :	Tuple							=						max_resolution
             a_      :	List[str]							=						do_resize
             a_      :	Optional[int]							=						size
             a_      :	List[str]							=						apply_ocr

      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    Optional[Any]:
             return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}
@require_torch
@require_pytesseract
class 				SCREAMING_SNAKE_CASE__						(       lowercase__		,					unittest.TestCase    ):
      snake_case__       :  Any       =		LayoutLMvaImageProcessor if is_pytesseract_available() else None
      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    Optional[int]:
             a_      :	List[Any]							=						LayoutLMvaImageProcessingTester(self  )
      @property
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    List[str]:
             return self.image_processor_tester.prepare_image_processor_dict()
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    List[str]:
             a_      :	int							=						self.image_processing_class(**self.image_processor_dict  )
             self.assertTrue(hasattr(__A  ,							'do_resize'  )  )
             self.assertTrue(hasattr(__A  ,							'size'  )  )
             self.assertTrue(hasattr(__A  ,							'apply_ocr'  )  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    Optional[int]:
             a_      :	Optional[Any]							=						self.image_processing_class.from_dict(self.image_processor_dict  )
             self.assertEqual(image_processor.size  ,							{'height': 1_8, 'width': 1_8}  )

             a_      :	Any							=						self.image_processing_class.from_dict(self.image_processor_dict  ,							size=4_2  )
             self.assertEqual(image_processor.size  ,							{'height': 4_2, 'width': 4_2}  )
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  )	->    Union[str, Any]:
             pass
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    Tuple:
             # Initialize image_processing
             a_      :	Optional[Any]							=						self.image_processing_class(**self.image_processor_dict  )
             # create random PIL images
             a_      :	Optional[Any]							=						prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=__A  )
             for image in image_inputs:
                    self.assertIsInstance(__A  ,							Image.Image  )

             # Test not batched input
             a_      :	str							=						image_processing(image_inputs[0]  ,							return_tensors='pt'  )
             self.assertEqual(
                 encoding.pixel_values.shape  ,							(
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.size['height'],
                     self.image_processor_tester.size['width'],
                 )  ,							)

             self.assertIsInstance(encoding.words  ,							__A  )
             self.assertIsInstance(encoding.boxes  ,							__A  )

             # Test batched
             a_      :	Any							=						image_processing(__A  ,							return_tensors='pt'  ).pixel_values
             self.assertEqual(
                 encoded_images.shape  ,							(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.size['height'],
                     self.image_processor_tester.size['width'],
                 )  ,							)
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    str:
             # Initialize image_processing
             a_      :	Tuple							=						self.image_processing_class(**self.image_processor_dict  )
             # create random numpy tensors
             a_      :	Optional[int]							=						prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=__A  ,							numpify=__A  )
             for image in image_inputs:
                    self.assertIsInstance(__A  ,							np.ndarray  )

             # Test not batched input
             a_      :	Union[str, Any]							=						image_processing(image_inputs[0]  ,							return_tensors='pt'  ).pixel_values
             self.assertEqual(
                 encoded_images.shape  ,							(
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.size['height'],
                     self.image_processor_tester.size['width'],
                 )  ,							)

             # Test batched
             a_      :	Union[str, Any]							=						image_processing(__A  ,							return_tensors='pt'  ).pixel_values
             self.assertEqual(
                 encoded_images.shape  ,							(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.size['height'],
                     self.image_processor_tester.size['width'],
                 )  ,							)
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    int:
             # Initialize image_processing
             a_      :	List[str]							=						self.image_processing_class(**self.image_processor_dict  )
             # create random PyTorch tensors
             a_      :	Dict							=						prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=__A  ,							torchify=__A  )
             for image in image_inputs:
                    self.assertIsInstance(__A  ,							torch.Tensor  )

             # Test not batched input
             a_      :	int							=						image_processing(image_inputs[0]  ,							return_tensors='pt'  ).pixel_values
             self.assertEqual(
                 encoded_images.shape  ,							(
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.size['height'],
                     self.image_processor_tester.size['width'],
                 )  ,							)

             # Test batched
             a_      :	Dict							=						image_processing(__A  ,							return_tensors='pt'  ).pixel_values
             self.assertEqual(
                 encoded_images.shape  ,							(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.size['height'],
                     self.image_processor_tester.size['width'],
                 )  ,							)

      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    Optional[Any]:
             # with apply_OCR = True
             a_      :	int							=						LayoutLMvaImageProcessor()

             from datasets import load_dataset

             a_      :	str							=						load_dataset('hf-internal-testing/fixtures_docvqa'  ,							split='test'  )

             a_      :	Union[str, Any]							=						Image.open(ds[0]['file']  ).convert('RGB'  )

             a_      :	int							=						image_processing(__A  ,							return_tensors='pt'  )

             self.assertEqual(encoding.pixel_values.shape  ,							(1, 3, 2_2_4, 2_2_4)  )
             self.assertEqual(len(encoding.words  )  ,							len(encoding.boxes  )  )

             # fmt: off
             # the words and boxes were obtained with Tesseract 4.1.1
             a_      :	Union[str, Any]							=						[['11:14', 'to', '11:39', 'a.m', '11:39', 'to', '11:44', 'a.m.', '11:44', 'a.m.', 'to', '12:25', 'p.m.', '12:25', 'to', '12:58', 'p.m.', '12:58', 'to', '4:00', 'p.m.', '2:00', 'to', '5:00', 'p.m.', 'Coffee', 'Break', 'Coffee', 'will', 'be', 'served', 'for', 'men', 'and', 'women', 'in', 'the', 'lobby', 'adjacent', 'to', 'exhibit', 'area.', 'Please', 'move', 'into', 'exhibit', 'area.', '(Exhibits', 'Open)', 'TRRF', 'GENERAL', 'SESSION', '(PART', '|)', 'Presiding:', 'Lee', 'A.', 'Waller', 'TRRF', 'Vice', 'President', '“Introductory', 'Remarks”', 'Lee', 'A.', 'Waller,', 'TRRF', 'Vice', 'Presi-', 'dent', 'Individual', 'Interviews', 'with', 'TRRF', 'Public', 'Board', 'Members', 'and', 'Sci-', 'entific', 'Advisory', 'Council', 'Mem-', 'bers', 'Conducted', 'by', 'TRRF', 'Treasurer', 'Philip', 'G.', 'Kuehn', 'to', 'get', 'answers', 'which', 'the', 'public', 'refrigerated', 'warehousing', 'industry', 'is', 'looking', 'for.', 'Plus', 'questions', 'from', 'the', 'floor.', 'Dr.', 'Emil', 'M.', 'Mrak,', 'University', 'of', 'Cal-', 'ifornia,', 'Chairman,', 'TRRF', 'Board;', 'Sam', 'R.', 'Cecil,', 'University', 'of', 'Georgia', 'College', 'of', 'Agriculture;', 'Dr.', 'Stanley', 'Charm,', 'Tufts', 'University', 'School', 'of', 'Medicine;', 'Dr.', 'Robert', 'H.', 'Cotton,', 'ITT', 'Continental', 'Baking', 'Company;', 'Dr.', 'Owen', 'Fennema,', 'University', 'of', 'Wis-', 'consin;', 'Dr.', 'Robert', 'E.', 'Hardenburg,', 'USDA.', 'Questions', 'and', 'Answers', 'Exhibits', 'Open', 'Capt.', 'Jack', 'Stoney', 'Room', 'TRRF', 'Scientific', 'Advisory', 'Council', 'Meeting', 'Ballroom', 'Foyer']]  # noqa: E231
             a_      :	Tuple							=						[[[1_4_1, 5_7, 2_1_4, 6_9], [2_2_8, 5_8, 2_5_2, 6_9], [1_4_1, 7_5, 2_1_6, 8_8], [2_3_0, 7_9, 2_8_0, 8_8], [1_4_2, 2_6_0, 2_1_8, 2_7_3], [2_3_0, 2_6_1, 2_5_5, 2_7_3], [1_4_3, 2_7_9, 2_1_8, 2_9_0], [2_3_1, 2_8_2, 2_9_0, 2_9_1], [1_4_3, 3_4_2, 2_1_8, 3_5_4], [2_3_1, 3_4_5, 2_8_9, 3_5_5], [2_0_2, 3_6_2, 2_2_7, 3_7_3], [1_4_3, 3_7_9, 2_2_0, 3_9_2], [2_3_1, 3_8_2, 2_9_1, 3_9_4], [1_4_4, 7_1_4, 2_2_0, 7_2_6], [2_3_1, 7_1_5, 2_5_6, 7_2_6], [1_4_4, 7_3_2, 2_2_0, 7_4_5], [2_3_2, 7_3_6, 2_9_1, 7_4_7], [1_4_4, 7_6_9, 2_1_8, 7_8_2], [2_3_1, 7_7_0, 2_5_6, 7_8_2], [1_4_1, 7_8_8, 2_0_2, 8_0_1], [2_1_5, 7_9_1, 2_7_4, 8_0_4], [1_4_3, 8_2_6, 2_0_4, 8_3_8], [2_1_5, 8_2_6, 2_4_0, 8_3_8], [1_4_2, 8_4_4, 2_0_2, 8_5_7], [2_1_5, 8_4_7, 2_7_4, 8_5_9], [3_3_4, 5_7, 4_2_7, 6_9], [4_4_0, 5_7, 5_2_2, 6_9], [3_6_9, 7_5, 4_6_1, 8_8], [4_6_9, 7_5, 5_1_6, 8_8], [5_2_8, 7_6, 5_6_2, 8_8], [5_7_0, 7_6, 6_6_7, 8_8], [6_7_5, 7_5, 7_1_1, 8_7], [7_2_1, 7_9, 7_7_8, 8_8], [7_8_9, 7_5, 8_4_0, 8_8], [3_6_9, 9_7, 4_7_0, 1_0_7], [4_8_4, 9_4, 5_0_7, 1_0_6], [5_1_8, 9_4, 5_6_2, 1_0_7], [5_7_6, 9_4, 6_5_5, 1_1_0], [6_6_8, 9_4, 7_9_2, 1_0_9], [8_0_4, 9_5, 8_2_9, 1_0_7], [3_6_9, 1_1_3, 4_6_5, 1_2_5], [4_7_7, 1_1_6, 5_4_7, 1_2_5], [5_6_2, 1_1_3, 6_5_8, 1_2_5], [6_7_1, 1_1_6, 7_4_8, 1_2_5], [7_6_1, 1_1_3, 8_1_1, 1_2_5], [3_6_9, 1_3_1, 4_6_5, 1_4_3], [4_7_7, 1_3_3, 5_4_8, 1_4_3], [5_6_3, 1_3_0, 6_9_8, 1_4_5], [7_1_0, 1_3_0, 8_0_2, 1_4_6], [3_3_6, 1_7_1, 4_1_2, 1_8_3], [4_2_3, 1_7_1, 5_7_2, 1_8_3], [5_8_2, 1_7_0, 7_1_6, 1_8_4], [7_2_8, 1_7_1, 8_1_7, 1_8_7], [8_2_9, 1_7_1, 8_4_4, 1_8_6], [3_3_8, 1_9_7, 4_8_2, 2_1_2], [5_0_7, 1_9_6, 5_5_7, 2_0_9], [5_6_9, 1_9_6, 5_9_5, 2_0_8], [6_1_0, 1_9_6, 7_0_2, 2_0_9], [5_0_5, 2_1_4, 5_8_3, 2_2_6], [5_9_5, 2_1_4, 6_5_6, 2_2_7], [6_7_0, 2_1_5, 8_0_7, 2_2_7], [3_3_5, 2_5_9, 5_4_3, 2_7_4], [5_5_6, 2_5_9, 7_0_8, 2_7_2], [3_7_2, 2_7_9, 4_2_2, 2_9_1], [4_3_5, 2_7_9, 4_6_0, 2_9_1], [4_7_4, 2_7_9, 5_7_4, 2_9_2], [5_8_7, 2_7_8, 6_6_4, 2_9_1], [6_7_6, 2_7_8, 7_3_8, 2_9_1], [7_5_1, 2_7_9, 8_3_4, 2_9_1], [3_7_2, 2_9_8, 4_3_4, 3_1_0], [3_3_5, 3_4_1, 4_8_3, 3_5_4], [4_9_7, 3_4_1, 6_5_5, 3_5_4], [6_6_7, 3_4_1, 7_2_8, 3_5_4], [7_4_0, 3_4_1, 8_2_5, 3_5_4], [3_3_5, 3_6_0, 4_3_0, 3_7_2], [4_4_2, 3_6_0, 5_3_4, 3_7_2], [5_4_5, 3_5_9, 6_8_7, 3_7_2], [6_9_7, 3_6_0, 7_5_4, 3_7_2], [7_6_5, 3_6_0, 8_2_3, 3_7_3], [3_3_4, 3_7_8, 4_2_8, 3_9_1], [4_4_0, 3_7_8, 5_7_7, 3_9_4], [5_9_0, 3_7_8, 7_0_5, 3_9_1], [7_2_0, 3_7_8, 8_0_1, 3_9_1], [3_3_4, 3_9_7, 4_0_0, 4_0_9], [3_7_0, 4_1_6, 5_2_9, 4_2_9], [5_4_4, 4_1_6, 5_7_6, 4_3_2], [5_8_7, 4_1_6, 6_6_5, 4_2_8], [6_7_7, 4_1_6, 8_1_4, 4_2_9], [3_7_2, 4_3_5, 4_5_2, 4_5_0], [4_6_5, 4_3_4, 4_9_5, 4_4_7], [5_1_1, 4_3_4, 6_0_0, 4_4_7], [6_1_1, 4_3_6, 6_3_7, 4_4_7], [6_4_9, 4_3_6, 6_9_4, 4_5_1], [7_0_5, 4_3_8, 8_2_4, 4_4_7], [3_6_9, 4_5_3, 4_5_2, 4_6_6], [4_6_4, 4_5_4, 5_0_9, 4_6_6], [5_2_2, 4_5_3, 6_1_1, 4_6_9], [6_2_5, 4_5_3, 7_9_2, 4_6_9], [3_7_0, 4_7_2, 5_5_6, 4_8_8], [5_7_0, 4_7_2, 6_8_4, 4_8_7], [6_9_7, 4_7_2, 7_1_8, 4_8_5], [7_3_2, 4_7_2, 8_3_5, 4_8_8], [3_6_9, 4_9_0, 4_1_1, 5_0_3], [4_2_5, 4_9_0, 4_8_4, 5_0_3], [4_9_6, 4_9_0, 6_3_5, 5_0_6], [6_4_5, 4_9_0, 7_0_7, 5_0_3], [7_1_8, 4_9_1, 7_6_1, 5_0_3], [7_7_1, 4_9_0, 8_4_0, 5_0_3], [3_3_6, 5_1_0, 3_7_4, 5_2_1], [3_8_8, 5_1_0, 4_4_7, 5_2_2], [4_6_0, 5_1_0, 4_8_9, 5_2_1], [5_0_3, 5_1_0, 5_8_0, 5_2_2], [5_9_2, 5_0_9, 7_3_6, 5_2_5], [7_4_5, 5_0_9, 7_7_0, 5_2_2], [7_8_1, 5_0_9, 8_4_0, 5_2_2], [3_3_8, 5_2_8, 4_3_4, 5_4_1], [4_4_8, 5_2_8, 5_9_6, 5_4_1], [6_0_9, 5_2_7, 6_8_7, 5_4_0], [7_0_0, 5_2_8, 7_9_2, 5_4_1], [3_3_6, 5_4_6, 3_9_7, 5_5_9], [4_0_7, 5_4_6, 4_3_1, 5_5_9], [4_4_3, 5_4_6, 5_2_5, 5_6_0], [5_3_7, 5_4_6, 6_8_0, 5_6_2], [6_8_8, 5_4_6, 7_1_4, 5_5_9], [7_2_2, 5_4_6, 8_3_7, 5_6_2], [3_3_6, 5_6_5, 4_4_9, 5_8_1], [4_6_1, 5_6_5, 4_8_5, 5_7_7], [4_9_7, 5_6_5, 6_6_5, 5_8_1], [6_8_1, 5_6_5, 7_1_8, 5_7_7], [7_3_2, 5_6_5, 8_3_7, 5_8_0], [3_3_7, 5_8_4, 4_3_8, 5_9_7], [4_5_2, 5_8_3, 5_2_1, 5_9_6], [5_3_5, 5_8_4, 6_7_7, 5_9_9], [6_9_0, 5_8_3, 7_8_7, 5_9_6], [8_0_1, 5_8_3, 8_2_5, 5_9_6], [3_3_8, 6_0_2, 4_7_8, 6_1_5], [4_9_2, 6_0_2, 5_3_0, 6_1_4], [5_4_3, 6_0_2, 6_3_8, 6_1_5], [6_5_0, 6_0_2, 6_7_6, 6_1_4], [6_8_8, 6_0_2, 7_8_8, 6_1_5], [8_0_2, 6_0_2, 8_4_3, 6_1_4], [3_3_7, 6_2_1, 5_0_2, 6_3_3], [5_1_6, 6_2_1, 6_1_5, 6_3_7], [6_2_9, 6_2_1, 7_7_4, 6_3_6], [7_8_9, 6_2_1, 8_2_7, 6_3_3], [3_3_7, 6_3_9, 4_1_8, 6_5_2], [4_3_2, 6_4_0, 5_7_1, 6_5_3], [5_8_7, 6_3_9, 7_3_1, 6_5_5], [7_4_3, 6_3_9, 7_6_9, 6_5_2], [7_8_0, 6_3_9, 8_4_1, 6_5_2], [3_3_8, 6_5_8, 4_4_0, 6_7_3], [4_5_5, 6_5_8, 4_9_1, 6_7_0], [5_0_8, 6_5_8, 6_0_2, 6_7_1], [6_1_6, 6_5_8, 6_3_8, 6_7_0], [6_5_4, 6_5_8, 8_3_5, 6_7_4], [3_3_7, 6_7_7, 4_2_9, 6_8_9], [3_3_7, 7_1_4, 4_8_2, 7_2_6], [4_9_5, 7_1_4, 5_4_8, 7_2_6], [5_6_1, 7_1_4, 6_8_3, 7_2_6], [3_3_8, 7_7_0, 4_6_1, 7_8_2], [4_7_4, 7_6_9, 5_5_4, 7_8_5], [4_8_9, 7_8_8, 5_6_2, 8_0_3], [5_7_6, 7_8_8, 6_4_3, 8_0_1], [6_5_6, 7_8_7, 7_5_1, 8_0_4], [7_6_4, 7_8_8, 8_4_4, 8_0_1], [3_3_4, 8_2_5, 4_2_1, 8_3_8], [4_3_0, 8_2_4, 5_7_4, 8_3_8], [5_8_4, 8_2_4, 7_2_3, 8_4_1], [3_3_5, 8_4_4, 4_5_0, 8_5_7], [4_6_4, 8_4_3, 5_8_3, 8_6_0], [6_2_8, 8_6_2, 7_5_5, 8_7_5], [7_6_9, 8_6_1, 8_4_8, 8_7_8]]]  # noqa: E231
             # fmt: on

             self.assertListEqual(encoding.words  ,							__A  )
             self.assertListEqual(encoding.boxes  ,							__A  )

             # with apply_OCR = False
             a_      :	str							=						LayoutLMvaImageProcessor(apply_ocr=__A  )

             a_      :	Optional[Any]							=						image_processing(__A  ,							return_tensors='pt'  )

             self.assertEqual(encoding.pixel_values.shape  ,							(1, 3, 2_2_4, 2_2_4)  )






# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py

import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile







def  SCREAMING_SNAKE_CASE_		(    __A				:    List[Any]						,					__A				:    List[Any]						,					__A				:    List[Any]						,					__A				:    Optional[int]       )			->	Dict:

       """simple docstring"""

       a_      :	Optional[Any]							=						multiprocessing.Manager()
       a_      :	Tuple							=						manager.list()

       a_      :	Optional[int]							=						multiprocessing.Process(target=__A						,					args=(check_program, result, timeout)       )
       p.start()
       p.join(timeout=timeout + 1       )
       if p.is_alive():
              p.kill()

       if not result:
              result.append('timed out'       )

       return {
           "task_id": task_id,
           "passed": result[0] == "passed",
           "result": result[0],
           "completion_id": completion_id,
       }







def  SCREAMING_SNAKE_CASE_		(    __A				:    Tuple						,					__A				:    Optional[Any]						,					__A				:    Optional[Any]       )			->	Dict:

       """simple docstring"""

       with create_tempdir():
              # These system calls are needed when cleaning up tempdir.
              import os
              import shutil

              a_      :	Any							=						shutil.rmtree
              a_      :	List[str]							=						os.rmdir
              a_      :	List[Any]							=						os.chdir

              # Disable functionalities that can make destructive changes to the test.
              reliability_guard()

              # Run program.
              try:
                     a_      :	Any							=						{}
                     with swallow_io():
                            with time_limit(__A       ):
                                   exec(__A						,					__A       )
                     result.append('passed'       )
              except TimeoutException:
                     result.append('timed out'       )
              except BaseException as e:
                     result.append(F"""failed: {e}"""       )

              # Needed for cleaning up.
              a_      :	Tuple							=						rmtree
              a_      :	Any							=						rmdir
              a_      :	Optional[int]							=						chdir







@contextlib.contextmanager
def  SCREAMING_SNAKE_CASE_		(    __A				:    List[str]       )			->	str:

       """simple docstring"""

       def signal_handler(__A				:    List[str]						,					__A				:    Dict       ):
              raise TimeoutException('Timed out!'       )

       signal.setitimer(signal.ITIMER_REAL						,					__A       )
       signal.signal(signal.SIGALRM						,					__A       )
       try:
              yield
       finally:
              signal.setitimer(signal.ITIMER_REAL						,					0       )







@contextlib.contextmanager
def  SCREAMING_SNAKE_CASE_		(    )			->	Dict:

       """simple docstring"""

       a_      :	int							=						WriteOnlyStringIO()
       with contextlib.redirect_stdout(__A       ):
              with contextlib.redirect_stderr(__A       ):
                     with redirect_stdin(__A       ):
                            yield







@contextlib.contextmanager
def  SCREAMING_SNAKE_CASE_		(    )			->	Optional[int]:

       """simple docstring"""

       with tempfile.TemporaryDirectory() as dirname:
              with chdir(__A       ):
                     yield dirname
class 				SCREAMING_SNAKE_CASE__						(       lowercase__    ):
      pass
class 				SCREAMING_SNAKE_CASE__						(       io.StringIO    ):
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  ,							*SCREAMING_SNAKE_CASE__       : List[str]  ,							**SCREAMING_SNAKE_CASE__       : List[Any]  )	->    int:
             raise OSError
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  ,							*SCREAMING_SNAKE_CASE__       : Tuple  ,							**SCREAMING_SNAKE_CASE__       : Dict  )	->    List[str]:
             raise OSError
      def  SCREAMING_SNAKE_CASE							( self       : int  ,							*SCREAMING_SNAKE_CASE__       : Dict  ,							**SCREAMING_SNAKE_CASE__       : List[Any]  )	->    Optional[Any]:
             raise OSError

      def  SCREAMING_SNAKE_CASE							( self       : Any  ,							*SCREAMING_SNAKE_CASE__       : Union[str, Any]  ,							**SCREAMING_SNAKE_CASE__       : List[Any]  )	->    List[Any]:
             return False
class 				SCREAMING_SNAKE_CASE__						(       contextlib._RedirectStream    ):  # type: ignore
      snake_case__       :  Optional[int]       =		'''stdin'''







@contextlib.contextmanager
def  SCREAMING_SNAKE_CASE_		(    __A				:    Tuple       )			->	Union[str, Any]:

       """simple docstring"""

       if root == ".":
              yield
              return
       a_      :	str							=						os.getcwd()
       os.chdir(__A       )
       try:
              yield
       except BaseException as exc:
              raise exc
       finally:
              os.chdir(__A       )







def  SCREAMING_SNAKE_CASE_		(    __A				:    Dict=None       )			->	Dict:

       """simple docstring"""

       if maximum_memory_bytes is not None:
              import resource

              resource.setrlimit(resource.RLIMIT_AS						,					(maximum_memory_bytes, maximum_memory_bytes)       )
              resource.setrlimit(resource.RLIMIT_DATA						,					(maximum_memory_bytes, maximum_memory_bytes)       )
              if not platform.uname().system == "Darwin":
                     resource.setrlimit(resource.RLIMIT_STACK						,					(maximum_memory_bytes, maximum_memory_bytes)       )

       faulthandler.disable()

       import builtins

       a_      :	str							=						None
       a_      :	str							=						None

       import os

       a_      :	Optional[int]							=						'1'

       a_      :	Any							=						None
       a_      :	int							=						None
       a_      :	int							=						None
       a_      :	Tuple							=						None
       a_      :	Dict							=						None
       a_      :	Any							=						None
       a_      :	Dict							=						None
       a_      :	Dict							=						None
       a_      :	List[str]							=						None
       a_      :	Optional[Any]							=						None
       a_      :	Tuple							=						None
       a_      :	Union[str, Any]							=						None
       a_      :	Dict							=						None
       a_      :	Dict							=						None
       a_      :	int							=						None
       a_      :	str							=						None
       a_      :	Union[str, Any]							=						None
       a_      :	str							=						None
       a_      :	Optional[int]							=						None
       a_      :	List[Any]							=						None
       a_      :	Union[str, Any]							=						None
       a_      :	Dict							=						None
       a_      :	Tuple							=						None
       a_      :	List[str]							=						None
       a_      :	str							=						None
       a_      :	str							=						None
       a_      :	str							=						None

       import shutil

       a_      :	Dict							=						None
       a_      :	Tuple							=						None
       a_      :	str							=						None

       import subprocess

       a_      :	str							=						None  # type: ignore

       a_      :	List[Any]							=						None

       import sys

       a_      :	Optional[int]							=						None
       a_      :	int							=						None
       a_      :	Any							=						None
       a_      :	str							=						None
       a_      :	str							=						None


