
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		,	lowerCamelCase		,	lowerCamelCase=7		,	lowerCamelCase=3		,	lowerCamelCase=18		,	lowerCamelCase=30		,	lowerCamelCase=4_00		,	lowerCamelCase=True		,	lowerCamelCase=None		,	lowerCamelCase=True		,	):
          snake_case__						=				size if size is not None else {"height": 18, "width": 18}
          snake_case__						=				parent
          snake_case__						=				batch_size
          snake_case__						=				num_channels
          snake_case__						=				image_size
          snake_case__						=				min_resolution
          snake_case__						=				max_resolution
          snake_case__						=				do_resize
          snake_case__						=				size
          snake_case__						=				apply_ocr



      def A_			(			self ):
          return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}





@require_torch
@require_pytesseract
class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase       ,					unittest.TestCase ):
      _A						:		Union[str, Any]    	=     LayoutLMvaImageProcessor if is_pytesseract_available() else None
      def A_			(			self ):
          snake_case__						=				LayoutLMvaImageProcessingTester(self )
      @property
      def A_			(			self ):
          return self.image_processor_tester.prepare_image_processor_dict()
      def A_			(			self ):
          snake_case__						=				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 A_			(			self ):
          snake_case__						=				self.image_processing_class.from_dict(self.image_processor_dict )
          self.assertEqual(image_processor.size		,	{"height": 18, "width": 18} )

          snake_case__						=				self.image_processing_class.from_dict(self.image_processor_dict		,	size=42 )
          self.assertEqual(image_processor.size		,	{"height": 42, "width": 42} )
      def A_			(			self ):
          pass
      def A_			(			self ):
          # Initialize image_processing
          snake_case__						=				self.image_processing_class(**self.image_processor_dict )
          # create random PIL images
          snake_case__						=				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__						=				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__						=				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 A_			(			self ):
          # Initialize image_processing
          snake_case__						=				self.image_processing_class(**self.image_processor_dict )
          # create random numpy tensors
          snake_case__						=				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__						=				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__						=				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 A_			(			self ):
          # Initialize image_processing
          snake_case__						=				self.image_processing_class(**self.image_processor_dict )
          # create random PyTorch tensors
          snake_case__						=				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__						=				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__						=				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 A_			(			self ):
          # with apply_OCR = True
          snake_case__						=				LayoutLMvaImageProcessor()

          from datasets import load_dataset

          snake_case__						=				load_dataset("hf-internal-testing/fixtures_docvqa"		,	split="test" )

          snake_case__						=				Image.open(ds[0]["file"] ).convert("RGB" )

          snake_case__						=				image_processing(lowerCamelCase		,	return_tensors="pt" )

          self.assertEqual(encoding.pixel_values.shape		,	(1, 3, 2_24, 2_24) )
          self.assertEqual(len(encoding.words )		,	len(encoding.boxes ) )

          # fmt: off
          # the words and boxes were obtained with Tesseract 4.1.1
          snake_case__						=				[["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__						=				[[[1_41, 57, 2_14, 69], [2_28, 58, 2_52, 69], [1_41, 75, 2_16, 88], [2_30, 79, 2_80, 88], [1_42, 2_60, 2_18, 2_73], [2_30, 2_61, 2_55, 2_73], [1_43, 2_79, 2_18, 2_90], [2_31, 2_82, 2_90, 2_91], [1_43, 3_42, 2_18, 3_54], [2_31, 3_45, 2_89, 3_55], [2_02, 3_62, 2_27, 3_73], [1_43, 3_79, 2_20, 3_92], [2_31, 3_82, 2_91, 3_94], [1_44, 7_14, 2_20, 7_26], [2_31, 7_15, 2_56, 7_26], [1_44, 7_32, 2_20, 7_45], [2_32, 7_36, 2_91, 7_47], [1_44, 7_69, 2_18, 7_82], [2_31, 7_70, 2_56, 7_82], [1_41, 7_88, 2_02, 8_01], [2_15, 7_91, 2_74, 8_04], [1_43, 8_26, 2_04, 8_38], [2_15, 8_26, 2_40, 8_38], [1_42, 8_44, 2_02, 8_57], [2_15, 8_47, 2_74, 8_59], [3_34, 57, 4_27, 69], [4_40, 57, 5_22, 69], [3_69, 75, 4_61, 88], [4_69, 75, 5_16, 88], [5_28, 76, 5_62, 88], [5_70, 76, 6_67, 88], [6_75, 75, 7_11, 87], [7_21, 79, 7_78, 88], [7_89, 75, 8_40, 88], [3_69, 97, 4_70, 1_07], [4_84, 94, 5_07, 1_06], [5_18, 94, 5_62, 1_07], [5_76, 94, 6_55, 1_10], [6_68, 94, 7_92, 1_09], [8_04, 95, 8_29, 1_07], [3_69, 1_13, 4_65, 1_25], [4_77, 1_16, 5_47, 1_25], [5_62, 1_13, 6_58, 1_25], [6_71, 1_16, 7_48, 1_25], [7_61, 1_13, 8_11, 1_25], [3_69, 1_31, 4_65, 1_43], [4_77, 1_33, 5_48, 1_43], [5_63, 1_30, 6_98, 1_45], [7_10, 1_30, 8_02, 1_46], [3_36, 1_71, 4_12, 1_83], [4_23, 1_71, 5_72, 1_83], [5_82, 1_70, 7_16, 1_84], [7_28, 1_71, 8_17, 1_87], [8_29, 1_71, 8_44, 1_86], [3_38, 1_97, 4_82, 2_12], [5_07, 1_96, 5_57, 2_09], [5_69, 1_96, 5_95, 2_08], [6_10, 1_96, 7_02, 2_09], [5_05, 2_14, 5_83, 2_26], [5_95, 2_14, 6_56, 2_27], [6_70, 2_15, 8_07, 2_27], [3_35, 2_59, 5_43, 2_74], [5_56, 2_59, 7_08, 2_72], [3_72, 2_79, 4_22, 2_91], [4_35, 2_79, 4_60, 2_91], [4_74, 2_79, 5_74, 2_92], [5_87, 2_78, 6_64, 2_91], [6_76, 2_78, 7_38, 2_91], [7_51, 2_79, 8_34, 2_91], [3_72, 2_98, 4_34, 3_10], [3_35, 3_41, 4_83, 3_54], [4_97, 3_41, 6_55, 3_54], [6_67, 3_41, 7_28, 3_54], [7_40, 3_41, 8_25, 3_54], [3_35, 3_60, 4_30, 3_72], [4_42, 3_60, 5_34, 3_72], [5_45, 3_59, 6_87, 3_72], [6_97, 3_60, 7_54, 3_72], [7_65, 3_60, 8_23, 3_73], [3_34, 3_78, 4_28, 3_91], [4_40, 3_78, 5_77, 3_94], [5_90, 3_78, 7_05, 3_91], [7_20, 3_78, 8_01, 3_91], [3_34, 3_97, 4_00, 4_09], [3_70, 4_16, 5_29, 4_29], [5_44, 4_16, 5_76, 4_32], [5_87, 4_16, 6_65, 4_28], [6_77, 4_16, 8_14, 4_29], [3_72, 4_35, 4_52, 4_50], [4_65, 4_34, 4_95, 4_47], [5_11, 4_34, 6_00, 4_47], [6_11, 4_36, 6_37, 4_47], [6_49, 4_36, 6_94, 4_51], [7_05, 4_38, 8_24, 4_47], [3_69, 4_53, 4_52, 4_66], [4_64, 4_54, 5_09, 4_66], [5_22, 4_53, 6_11, 4_69], [6_25, 4_53, 7_92, 4_69], [3_70, 4_72, 5_56, 4_88], [5_70, 4_72, 6_84, 4_87], [6_97, 4_72, 7_18, 4_85], [7_32, 4_72, 8_35, 4_88], [3_69, 4_90, 4_11, 5_03], [4_25, 4_90, 4_84, 5_03], [4_96, 4_90, 6_35, 5_06], [6_45, 4_90, 7_07, 5_03], [7_18, 4_91, 7_61, 5_03], [7_71, 4_90, 8_40, 5_03], [3_36, 5_10, 3_74, 5_21], [3_88, 5_10, 4_47, 5_22], [4_60, 5_10, 4_89, 5_21], [5_03, 5_10, 5_80, 5_22], [5_92, 5_09, 7_36, 5_25], [7_45, 5_09, 7_70, 5_22], [7_81, 5_09, 8_40, 5_22], [3_38, 5_28, 4_34, 5_41], [4_48, 5_28, 5_96, 5_41], [6_09, 5_27, 6_87, 5_40], [7_00, 5_28, 7_92, 5_41], [3_36, 5_46, 3_97, 5_59], [4_07, 5_46, 4_31, 5_59], [4_43, 5_46, 5_25, 5_60], [5_37, 5_46, 6_80, 5_62], [6_88, 5_46, 7_14, 5_59], [7_22, 5_46, 8_37, 5_62], [3_36, 5_65, 4_49, 5_81], [4_61, 5_65, 4_85, 5_77], [4_97, 5_65, 6_65, 5_81], [6_81, 5_65, 7_18, 5_77], [7_32, 5_65, 8_37, 5_80], [3_37, 5_84, 4_38, 5_97], [4_52, 5_83, 5_21, 5_96], [5_35, 5_84, 6_77, 5_99], [6_90, 5_83, 7_87, 5_96], [8_01, 5_83, 8_25, 5_96], [3_38, 6_02, 4_78, 6_15], [4_92, 6_02, 5_30, 6_14], [5_43, 6_02, 6_38, 6_15], [6_50, 6_02, 6_76, 6_14], [6_88, 6_02, 7_88, 6_15], [8_02, 6_02, 8_43, 6_14], [3_37, 6_21, 5_02, 6_33], [5_16, 6_21, 6_15, 6_37], [6_29, 6_21, 7_74, 6_36], [7_89, 6_21, 8_27, 6_33], [3_37, 6_39, 4_18, 6_52], [4_32, 6_40, 5_71, 6_53], [5_87, 6_39, 7_31, 6_55], [7_43, 6_39, 7_69, 6_52], [7_80, 6_39, 8_41, 6_52], [3_38, 6_58, 4_40, 6_73], [4_55, 6_58, 4_91, 6_70], [5_08, 6_58, 6_02, 6_71], [6_16, 6_58, 6_38, 6_70], [6_54, 6_58, 8_35, 6_74], [3_37, 6_77, 4_29, 6_89], [3_37, 7_14, 4_82, 7_26], [4_95, 7_14, 5_48, 7_26], [5_61, 7_14, 6_83, 7_26], [3_38, 7_70, 4_61, 7_82], [4_74, 7_69, 5_54, 7_85], [4_89, 7_88, 5_62, 8_03], [5_76, 7_88, 6_43, 8_01], [6_56, 7_87, 7_51, 8_04], [7_64, 7_88, 8_44, 8_01], [3_34, 8_25, 4_21, 8_38], [4_30, 8_24, 5_74, 8_38], [5_84, 8_24, 7_23, 8_41], [3_35, 8_44, 4_50, 8_57], [4_64, 8_43, 5_83, 8_60], [6_28, 8_62, 7_55, 8_75], [7_69, 8_61, 8_48, 8_78]]]  # noqa: E231
          # fmt: on

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

          # with apply_OCR = False
          snake_case__						=				LayoutLMvaImageProcessor(apply_ocr=lowerCamelCase )

          snake_case__						=				image_processing(lowerCamelCase		,	return_tensors="pt" )

          self.assertEqual(encoding.pixel_values.shape		,	(1, 3, 2_24, 2_24) )





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    return price * (1 + tax_rate)


if __name__ == "__main__":
     print(F'''{price_plus_tax(100, 0.2_5) = }''')
     print(F'''{price_plus_tax(1_2_5.5_0, 0.0_5) = }''')



