





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        _a(    unittest.TestCase    ):







		def __init__(    self ,						__snake_case ,						__snake_case=7 ,						__snake_case=3 ,						__snake_case=1_8 ,						__snake_case=3_0 ,						__snake_case=4_0_0 ,						__snake_case=True ,						__snake_case=None ,						__snake_case=True ,						)      ->      Union[str, Any]:


							'''simple docstring'''

							_snake_case       :					Optional[int]				       =   size if size is not None else {"height": 1_8, "width": 1_8}
							_snake_case       :					List[str]				       =   parent
							_snake_case       :					Optional[int]				       =   batch_size
							_snake_case       :					Any				       =   num_channels
							_snake_case       :					str				       =   image_size
							_snake_case       :					Union[str, Any]				       =   min_resolution
							_snake_case       :					Any				       =   max_resolution
							_snake_case       :					List[str]				       =   do_resize
							_snake_case       :					Dict				       =   size
							_snake_case       :					Optional[Any]				       =   apply_ocr







		def        lowercase       (    self				)      ->      Union[str, Any]:


							'''simple docstring'''

							return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}






@require_torch
@require_pytesseract
class        _a(    __UpperCAmelCase	,   unittest.TestCase    ):
		lowerCamelCase__     :List[Any]   				=		LayoutLMvaImageProcessor if is_pytesseract_available() else None







		def        lowercase       (    self				)      ->      int:


							'''simple docstring'''

							_snake_case       :					str				       =   LayoutLMvaImageProcessingTester(self				)







		@property
		def        lowercase       (    self				)      ->      Optional[int]:


							'''simple docstring'''

							return self.image_processor_tester.prepare_image_processor_dict()







		def        lowercase       (    self				)      ->      int:


							'''simple docstring'''

							_snake_case       :					Optional[Any]				       =   self.image_processing_class(**self.image_processor_dict				)
							self.assertTrue(hasattr(_lowerCamelCase ,						"do_resize"				)				)
							self.assertTrue(hasattr(_lowerCamelCase ,						"size"				)				)
							self.assertTrue(hasattr(_lowerCamelCase ,						"apply_ocr"				)				)







		def        lowercase       (    self				)      ->      int:


							'''simple docstring'''

							_snake_case       :					str				       =   self.image_processing_class.from_dict(self.image_processor_dict				)
							self.assertEqual(image_processor.size ,						{"height": 1_8, "width": 1_8}				)

							_snake_case       :					Optional[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        lowercase       (    self				)      ->      Optional[Any]:


							'''simple docstring'''

							pass







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


							'''simple docstring'''

							_snake_case       :					Any				       =   self.image_processing_class(**self.image_processor_dict				)
							# create random PIL images
							_snake_case       :					List[Any]				       =   prepare_image_inputs(self.image_processor_tester ,						equal_resolution=_lowerCamelCase				)
							for image in image_inputs:
												self.assertIsInstance(_lowerCamelCase ,						Image.Image				)

							# Test not batched input
							_snake_case       :					Optional[int]				       =   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 ,						_lowerCamelCase				)
							self.assertIsInstance(encoding.boxes ,						_lowerCamelCase				)

							# Test batched
							_snake_case       :					Optional[Any]				       =   image_processing(_lowerCamelCase ,						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        lowercase       (    self				)      ->      Dict:


							'''simple docstring'''

							_snake_case       :					int				       =   self.image_processing_class(**self.image_processor_dict				)
							# create random numpy tensors
							_snake_case       :					int				       =   prepare_image_inputs(self.image_processor_tester ,						equal_resolution=_lowerCamelCase ,						numpify=_lowerCamelCase				)
							for image in image_inputs:
												self.assertIsInstance(_lowerCamelCase ,						np.ndarray				)

							# Test not batched input
							_snake_case       :					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
							_snake_case       :					Any				       =   image_processing(_lowerCamelCase ,						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        lowercase       (    self				)      ->      Dict:


							'''simple docstring'''

							_snake_case       :					Dict				       =   self.image_processing_class(**self.image_processor_dict				)
							# create random PyTorch tensors
							_snake_case       :					Optional[int]				       =   prepare_image_inputs(self.image_processor_tester ,						equal_resolution=_lowerCamelCase ,						torchify=_lowerCamelCase				)
							for image in image_inputs:
												self.assertIsInstance(_lowerCamelCase ,						torch.Tensor				)

							# Test not batched input
							_snake_case       :					List[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
							_snake_case       :					str				       =   image_processing(_lowerCamelCase ,						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        lowercase       (    self				)      ->      List[str]:


							'''simple docstring'''

							_snake_case       :					str				       =   LayoutLMvaImageProcessor()

							from datasets import load_dataset

							_snake_case       :					Any				       =   load_dataset("hf-internal-testing/fixtures_docvqa" ,						split="test"				)

							_snake_case       :					Dict				       =   Image.open(ds[0]["file"]				).convert("RGB"				)

							_snake_case       :					Optional[int]				       =   image_processing(_lowerCamelCase ,						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
							_snake_case       :					Dict				       =   [["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
							_snake_case       :					Optional[int]				       =   [[[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 ,						_lowerCamelCase				)
							self.assertListEqual(encoding.boxes ,						_lowerCamelCase				)

							# with apply_OCR = False
							_snake_case       :					List[str]				       =   LayoutLMvaImageProcessor(apply_ocr=_lowerCamelCase				)

							_snake_case       :					List[str]				       =   image_processing(_lowerCamelCase ,						return_tensors="pt"				)

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






import sys
import turtle







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  ):
					my_pen.up()
					my_pen.goto(vertexa[0]  ,  vertexa[1]				)
					my_pen.down()
					my_pen.goto(vertexa[0]  ,  vertexa[1]				)
					my_pen.goto(vertexa[0]  ,  vertexa[1]				)
					my_pen.goto(vertexa[0]  ,  vertexa[1]				)

					if depth == 0:
										return

					triangle(UpperCAmelCase  ,  get_mid(UpperCAmelCase  ,  UpperCAmelCase				)  ,  get_mid(UpperCAmelCase  ,  UpperCAmelCase				)  ,  depth - 1				)
					triangle(UpperCAmelCase  ,  get_mid(UpperCAmelCase  ,  UpperCAmelCase				)  ,  get_mid(UpperCAmelCase  ,  UpperCAmelCase				)  ,  depth - 1				)
					triangle(UpperCAmelCase  ,  get_mid(UpperCAmelCase  ,  UpperCAmelCase				)  ,  get_mid(UpperCAmelCase  ,  UpperCAmelCase				)  ,  depth - 1				)


if __name__ == "__main__":
							if len(sys.argv) != 2:
														raise ValueError(
														    'Correct format for using this script: '
														    'python fractals.py <int:depth_for_fractal>'
														)
							__lowerCAmelCase :Any     =    turtle.Turtle()
							my_pen.ht()
							my_pen.speed(5)
							my_pen.pencolor('red')

							__lowerCAmelCase :Optional[int]     =    [(-1_75, -1_25), (0, 1_75), (1_75, -1_25)]  # vertices of triangle
							triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))