
import json
import pathlib
import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import DeformableDetrImageProcessor




class 					_SCREAMING_SNAKE_CASE  (	unittest.TestCase ):
      def __init__(			self		,	lowerCamelCase		,	lowerCamelCase=7		,	lowerCamelCase=3		,	lowerCamelCase=30		,	lowerCamelCase=4_00		,	lowerCamelCase=True		,	lowerCamelCase=None		,	lowerCamelCase=True		,	lowerCamelCase=[0.5, 0.5, 0.5]		,	lowerCamelCase=[0.5, 0.5, 0.5]		,	lowerCamelCase=True		,	lowerCamelCase=1 / 2_55		,	lowerCamelCase=True		,	):
          # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
          snake_case__						=				size if size is not None else {"shortest_edge": 18, "longest_edge": 13_33}
          snake_case__						=				parent
          snake_case__						=				batch_size
          snake_case__						=				num_channels
          snake_case__						=				min_resolution
          snake_case__						=				max_resolution
          snake_case__						=				do_resize
          snake_case__						=				size
          snake_case__						=				do_normalize
          snake_case__						=				image_mean
          snake_case__						=				image_std
          snake_case__						=				do_rescale
          snake_case__						=				rescale_factor
          snake_case__						=				do_pad
      def A_			(			self ):
          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_normalize": self.do_normalize,
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_rescale": self.do_rescale,
              "rescale_factor": self.rescale_factor,
              "do_pad": self.do_pad,
          }



      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase=False ):
          if not batched:
              snake_case__						=				image_inputs[0]
              if isinstance(__A		,	Image.Image ):
                  snake_case__						=				image.size
              else:
                  snake_case__						=				image.shape[1], image.shape[2]
              if w < h:
                  snake_case__						=				int(self.size["shortest_edge"] * h / w )
                  snake_case__						=				self.size["shortest_edge"]
              elif w > h:
                  snake_case__						=				self.size["shortest_edge"]
                  snake_case__						=				int(self.size["shortest_edge"] * w / h )
              else:
                  snake_case__						=				self.size["shortest_edge"]
                  snake_case__						=				self.size["shortest_edge"]

          else:
              snake_case__						=				[]
              for image in image_inputs:
                  snake_case__						=				self.get_expected_values([image] )
                  expected_values.append((expected_height, expected_width) )
              snake_case__						=				max(__A		,	key=lambda lowerCamelCase : item[0] )[0]
              snake_case__						=				max(__A		,	key=lambda lowerCamelCase : item[1] )[1]

          return expected_height, expected_width





@require_torch
@require_vision
class 					_SCREAMING_SNAKE_CASE  (	UpperCamelCase_       ,					unittest.TestCase ):
      _A						:		Tuple    	=     DeformableDetrImageProcessor if is_vision_available() else None
      def A_			(			self ):
          snake_case__						=				DeformableDetrImageProcessingTester(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(__A		,	"image_mean" ) )
          self.assertTrue(hasattr(__A		,	"image_std" ) )
          self.assertTrue(hasattr(__A		,	"do_normalize" ) )
          self.assertTrue(hasattr(__A		,	"do_resize" ) )
          self.assertTrue(hasattr(__A		,	"do_rescale" ) )
          self.assertTrue(hasattr(__A		,	"do_pad" ) )
          self.assertTrue(hasattr(__A		,	"size" ) )
      def A_			(			self ):
          snake_case__						=				self.image_processing_class.from_dict(self.image_processor_dict )
          self.assertEqual(image_processor.size		,	{"shortest_edge": 18, "longest_edge": 13_33} )
          self.assertEqual(image_processor.do_pad		,	__A )

          snake_case__						=				self.image_processing_class.from_dict(
              self.image_processor_dict		,	size=42		,	max_size=84		,	pad_and_return_pixel_mask=__A )
          self.assertEqual(image_processor.size		,	{"shortest_edge": 42, "longest_edge": 84} )
          self.assertEqual(image_processor.do_pad		,	__A )
      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=__A )
          for image in image_inputs:
              self.assertIsInstance(__A		,	Image.Image )

          # Test not batched input
          snake_case__						=				image_processing(image_inputs[0]		,	return_tensors="pt" ).pixel_values

          snake_case__						=				self.image_processor_tester.get_expected_values(__A )

          self.assertEqual(
              encoded_images.shape		,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)		,	)

          # Test batched
          snake_case__						=				self.image_processor_tester.get_expected_values(__A		,	batched=__A )

          snake_case__						=				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,
                  expected_height,
                  expected_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=__A		,	numpify=__A )
          for image in image_inputs:
              self.assertIsInstance(__A		,	np.ndarray )

          # Test not batched input
          snake_case__						=				image_processing(image_inputs[0]		,	return_tensors="pt" ).pixel_values

          snake_case__						=				self.image_processor_tester.get_expected_values(__A )

          self.assertEqual(
              encoded_images.shape		,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)		,	)

          # Test batched
          snake_case__						=				image_processing(__A		,	return_tensors="pt" ).pixel_values

          snake_case__						=				self.image_processor_tester.get_expected_values(__A		,	batched=__A )

          self.assertEqual(
              encoded_images.shape		,	(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_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=__A		,	torchify=__A )
          for image in image_inputs:
              self.assertIsInstance(__A		,	torch.Tensor )

          # Test not batched input
          snake_case__						=				image_processing(image_inputs[0]		,	return_tensors="pt" ).pixel_values

          snake_case__						=				self.image_processor_tester.get_expected_values(__A )

          self.assertEqual(
              encoded_images.shape		,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)		,	)

          # Test batched
          snake_case__						=				image_processing(__A		,	return_tensors="pt" ).pixel_values

          snake_case__						=				self.image_processor_tester.get_expected_values(__A		,	batched=__A )

          self.assertEqual(
              encoded_images.shape		,	(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )		,	)
      @slow
      def A_			(			self ):
          # prepare image and target
          snake_case__						=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
          with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"		,	"r" ) as f:
              snake_case__						=				json.loads(f.read() )

          snake_case__						=				{"image_id": 3_97_69, "annotations": target}

          # encode them
          snake_case__						=				DeformableDetrImageProcessor()
          snake_case__						=				image_processing(images=__A		,	annotations=__A		,	return_tensors="pt" )

          # verify pixel values
          snake_case__						=				torch.Size([1, 3, 8_00, 10_66] )
          self.assertEqual(encoding["pixel_values"].shape		,	__A )

          snake_case__						=				torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
          self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]		,	__A		,	atol=1e-4 ) )

          # verify area
          snake_case__						=				torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38] )
          self.assertTrue(torch.allclose(encoding["labels"][0]["area"]		,	__A ) )
          # verify boxes
          snake_case__						=				torch.Size([6, 4] )
          self.assertEqual(encoding["labels"][0]["boxes"].shape		,	__A )
          snake_case__						=				torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
          self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]		,	__A		,	atol=1e-3 ) )
          # verify image_id
          snake_case__						=				torch.tensor([3_97_69] )
          self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]		,	__A ) )
          # verify is_crowd
          snake_case__						=				torch.tensor([0, 0, 0, 0, 0, 0] )
          self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]		,	__A ) )
          # verify class_labels
          snake_case__						=				torch.tensor([75, 75, 63, 65, 17, 17] )
          self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]		,	__A ) )
          # verify orig_size
          snake_case__						=				torch.tensor([4_80, 6_40] )
          self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]		,	__A ) )
          # verify size
          snake_case__						=				torch.tensor([8_00, 10_66] )
          self.assertTrue(torch.allclose(encoding["labels"][0]["size"]		,	__A ) )



      @slow
      def A_			(			self ):
          # prepare image, target and masks_path
          snake_case__						=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
          with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"		,	"r" ) as f:
              snake_case__						=				json.loads(f.read() )

          snake_case__						=				{"file_name": "000000039769.png", "image_id": 3_97_69, "segments_info": target}

          snake_case__						=				pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic" )

          # encode them
          snake_case__						=				DeformableDetrImageProcessor(format="coco_panoptic" )
          snake_case__						=				image_processing(images=__A		,	annotations=__A		,	masks_path=__A		,	return_tensors="pt" )

          # verify pixel values
          snake_case__						=				torch.Size([1, 3, 8_00, 10_66] )
          self.assertEqual(encoding["pixel_values"].shape		,	__A )

          snake_case__						=				torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
          self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]		,	__A		,	atol=1e-4 ) )

          # verify area
          snake_case__						=				torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47] )
          self.assertTrue(torch.allclose(encoding["labels"][0]["area"]		,	__A ) )
          # verify boxes
          snake_case__						=				torch.Size([6, 4] )
          self.assertEqual(encoding["labels"][0]["boxes"].shape		,	__A )
          snake_case__						=				torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
          self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]		,	__A		,	atol=1e-3 ) )
          # verify image_id
          snake_case__						=				torch.tensor([3_97_69] )
          self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]		,	__A ) )
          # verify is_crowd
          snake_case__						=				torch.tensor([0, 0, 0, 0, 0, 0] )
          self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]		,	__A ) )
          # verify class_labels
          snake_case__						=				torch.tensor([17, 17, 63, 75, 75, 93] )
          self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]		,	__A ) )
          # verify masks
          snake_case__						=				82_28_73
          self.assertEqual(encoding["labels"][0]["masks"].sum().item()		,	__A )
          # verify orig_size
          snake_case__						=				torch.tensor([4_80, 6_40] )
          self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]		,	__A ) )
          # verify size
          snake_case__						=				torch.tensor([8_00, 10_66] )
          self.assertTrue(torch.allclose(encoding["labels"][0]["size"]		,	__A ) )





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) = }''')



