


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							:			Tuple	,				SCREAMING_SNAKE_CASE__							:			Tuple	,				SCREAMING_SNAKE_CASE__							:			Optional[Any]=7	,				SCREAMING_SNAKE_CASE__							:			int=3	,				SCREAMING_SNAKE_CASE__							:			Dict=1_8	,				SCREAMING_SNAKE_CASE__							:			str=3_0	,				SCREAMING_SNAKE_CASE__							:			Dict=4_0_0	,				SCREAMING_SNAKE_CASE__							:			List[Any]=True	,				SCREAMING_SNAKE_CASE__							:			List[str]=None	,				SCREAMING_SNAKE_CASE__							:			List[str]=True	,				)						->     str:
             a_   :						Optional[int]				=    size if size is not None else {'height': 1_8, 'width': 1_8}
             a_   :						str				=    parent
             a_   :						Tuple				=    batch_size
             a_   :						List[Any]				=    num_channels
             a_   :						Optional[int]				=    image_size
             a_   :						Tuple				=    min_resolution
             a_   :						List[Any]				=    max_resolution
             a_   :						Optional[int]				=    do_resize
             a_   :						Tuple				=    size
             a_   :						Tuple				=    apply_ocr

      def 			SCREAMING_SNAKE_CASE				(			self							:			Any	)						->     Union[str, 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__							:	int	  =		LayoutLMvaImageProcessor if is_pytesseract_available() else None
      def 			SCREAMING_SNAKE_CASE				(			self							:			Optional[Any]	)						->     Dict:
             a_   :						Union[str, Any]				=    LayoutLMvaImageProcessingTester(self	)
      @property
      def 			SCREAMING_SNAKE_CASE				(			self							:			Any	)						->     Dict:
             return self.image_processor_tester.prepare_image_processor_dict()
      def 			SCREAMING_SNAKE_CASE				(			self							:			Tuple	)						->     List[str]:
             a_   :						Union[str, Any]				=    self.image_processing_class(**self.image_processor_dict	)
             self.assertTrue(hasattr(_UpperCamelCase	,				'do_resize'	)	)
             self.assertTrue(hasattr(_UpperCamelCase	,				'size'	)	)
             self.assertTrue(hasattr(_UpperCamelCase	,				'apply_ocr'	)	)
      def 			SCREAMING_SNAKE_CASE				(			self							:			Optional[int]	)						->     List[Any]:
             a_   :						str				=    self.image_processing_class.from_dict(self.image_processor_dict	)
             self.assertEqual(image_processor.size	,				{'height': 1_8, 'width': 1_8}	)

             a_   :						int				=    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]	)						->     int:
             pass
      def 			SCREAMING_SNAKE_CASE				(			self							:			Tuple	)						->     Any:
             # Initialize image_processing
             a_   :						int				=    self.image_processing_class(**self.image_processor_dict	)
             # create random PIL images
             a_   :						Optional[Any]				=    prepare_image_inputs(self.image_processor_tester	,				equal_resolution=_UpperCamelCase	)
             for image in image_inputs:
                    self.assertIsInstance(_UpperCamelCase	,				Image.Image	)

             # Test not batched input
             a_   :						List[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	,				_UpperCamelCase	)
             self.assertIsInstance(encoding.boxes	,				_UpperCamelCase	)

             # Test batched
             a_   :						List[Any]				=    image_processing(_UpperCamelCase	,				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							:			int	)						->     Optional[int]:
             # Initialize image_processing
             a_   :						List[Any]				=    self.image_processing_class(**self.image_processor_dict	)
             # create random numpy tensors
             a_   :						Optional[int]				=    prepare_image_inputs(self.image_processor_tester	,				equal_resolution=_UpperCamelCase	,				numpify=_UpperCamelCase	)
             for image in image_inputs:
                    self.assertIsInstance(_UpperCamelCase	,				np.ndarray	)

             # Test not batched input
             a_   :						List[str]				=    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_   :						List[Any]				=    image_processing(_UpperCamelCase	,				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							:			int	)						->     Any:
             # Initialize image_processing
             a_   :						Optional[Any]				=    self.image_processing_class(**self.image_processor_dict	)
             # create random PyTorch tensors
             a_   :						Any				=    prepare_image_inputs(self.image_processor_tester	,				equal_resolution=_UpperCamelCase	,				torchify=_UpperCamelCase	)
             for image in image_inputs:
                    self.assertIsInstance(_UpperCamelCase	,				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_   :						Optional[int]				=    image_processing(_UpperCamelCase	,				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							:			List[str]	)						->     Any:
             # with apply_OCR = True
             a_   :						List[Any]				=    LayoutLMvaImageProcessor()

             from datasets import load_dataset

             a_   :						Optional[int]				=    load_dataset('hf-internal-testing/fixtures_docvqa'	,				split='test'	)

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

             a_   :						Union[str, Any]				=    image_processing(_UpperCamelCase	,				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_   :						List[Any]				=    [[[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	,				_UpperCamelCase	)
             self.assertListEqual(encoding.boxes	,				_UpperCamelCase	)

             # with apply_OCR = False
             a_   :						Any				=    LayoutLMvaImageProcessor(apply_ocr=_UpperCamelCase	)

             a_   :						List[Any]				=    image_processing(_UpperCamelCase	,				return_tensors='pt'	)

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






'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import doctest
import sys
import warnings
from os.path import abspath, dirname, join

import _pytest

from transformers.testing_utils import HfDoctestModule, HfDocTestParser


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
lowerCAmelCase						=					abspath(join(dirname(__file__), """src"""))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="""ignore""", category=FutureWarning)

def 					lowerCamelCase_ ( __UpperCamelCase					:       Optional[Any]		)					->	str:







     """simple docstring"""
     config.addinivalue_line(
         'markers'							,    'is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested'		)
     config.addinivalue_line(
         'markers'							,    'is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested'		)
     config.addinivalue_line('markers'							,    'is_pipeline_test: mark test to run only when pipelines are tested'		)
     config.addinivalue_line('markers'							,    'is_staging_test: mark test to run only in the staging environment'		)
     config.addinivalue_line('markers'							,    'accelerate_tests: mark test that require accelerate'		)
     config.addinivalue_line('markers'							,    'tool_tests: mark the tool tests that are run on their specific schedule'		)

def 					lowerCamelCase_ ( __UpperCamelCase					:       Tuple		)					->	List[str]:







     """simple docstring"""
     from transformers.testing_utils import pytest_addoption_shared

     pytest_addoption_shared(__UpperCamelCase		)

def 					lowerCamelCase_ ( __UpperCamelCase					:       Union[str, Any]		)					->	str:







     """simple docstring"""
     from transformers.testing_utils import pytest_terminal_summary_main

     _A				=							terminalreporter.config.getoption('--make-reports'		)
     if make_reports:
          pytest_terminal_summary_main(__UpperCamelCase							,    id=__UpperCamelCase		)

def 					lowerCamelCase_ ( __UpperCamelCase					:       str							,    __UpperCamelCase					:       List[str]		)					->	Optional[int]:







     """simple docstring"""
     # If no tests are collected, pytest exists with code 5, which makes the CI fail.
     if exitstatus == 5:
          _A				=							0


# Doctest custom flag to ignore output.
lowerCAmelCase						=					doctest.register_optionflag("""IGNORE_RESULT""")

lowerCAmelCase						=					doctest.OutputChecker






class        lowerCAmelCase_  (     UpperCAmelCase	):






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase   )->      Tuple:
            if IGNORE_RESULT & optionflags:
                 return True
            return OutputChecker.check_output(self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase   )






lowerCAmelCase						=					CustomOutputChecker
lowerCAmelCase						=					HfDoctestModule
lowerCAmelCase						=					HfDocTestParser





