
"""simple docstring"""

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 ConditionalDetrImageProcessor
class      _UpperCAmelCase							(		unittest.TestCase							):




      def __init__(							self   ,       lowercase_   ,       lowercase_=7   ,       lowercase_=3   ,       lowercase_=3_0   ,       lowercase_=4_0_0   ,       lowercase_=True   ,       lowercase_=None   ,       lowercase_=True   ,       lowercase_=[0.5, 0.5, 0.5]   ,       lowercase_=[0.5, 0.5, 0.5]   ,       lowercase_=True   ,       lowercase_=1 / 2_5_5   ,       lowercase_=True   ,       )						->					int:
          # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
          UpperCAmelCase									=					size if size is not None else {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}
          UpperCAmelCase									=					parent
          UpperCAmelCase									=					batch_size
          UpperCAmelCase									=					num_channels
          UpperCAmelCase									=					min_resolution
          UpperCAmelCase									=					max_resolution
          UpperCAmelCase									=					do_resize
          UpperCAmelCase									=					size
          UpperCAmelCase									=					do_normalize
          UpperCAmelCase									=					image_mean
          UpperCAmelCase									=					image_std
          UpperCAmelCase									=					do_rescale
          UpperCAmelCase									=					rescale_factor
          UpperCAmelCase									=					do_pad




      def     a_      (							self							)						->					Optional[int]:
          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   ,       lowercase_   ,       lowercase_=False							)						->					Dict:
          if not batched:
              UpperCAmelCase									=					image_inputs[0]
              if isinstance(lowercase_   ,       Image.Image							):
                  UpperCAmelCase			,							UpperCAmelCase									=					image.size
              else:
                  UpperCAmelCase			,							UpperCAmelCase									=					image.shape[1], image.shape[2]
              if w < h:
                  UpperCAmelCase									=					int(self.size['shortest_edge'] * h / w							)
                  UpperCAmelCase									=					self.size['shortest_edge']
              elif w > h:
                  UpperCAmelCase									=					self.size['shortest_edge']
                  UpperCAmelCase									=					int(self.size['shortest_edge'] * w / h							)
              else:
                  UpperCAmelCase									=					self.size['shortest_edge']
                  UpperCAmelCase									=					self.size['shortest_edge']

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

          return expected_height, expected_width

@require_torch
@require_vision
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_     ,			unittest.TestCase							):
      __SCREAMING_SNAKE_CASE   :							Optional[int]							      =	ConditionalDetrImageProcessor if is_vision_available() else None




      def     a_      (							self							)						->					Dict:
          UpperCAmelCase									=					ConditionalDetrImageProcessingTester(self							)




      @property
      def     a_      (							self							)						->					Dict:
          return self.image_processor_tester.prepare_image_processor_dict()




      def     a_      (							self							)						->					Union[str, Any]:
          UpperCAmelCase									=					self.image_processing_class(**self.image_processor_dict							)
          self.assertTrue(hasattr(lowercase_   ,       'image_mean'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'image_std'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'do_normalize'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'do_resize'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'size'							)							)




      def     a_      (							self							)						->					Union[str, Any]:
          UpperCAmelCase									=					self.image_processing_class.from_dict(self.image_processor_dict							)
          self.assertEqual(image_processor.size   ,       {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}							)
          self.assertEqual(image_processor.do_pad   ,       lowercase_							)

          UpperCAmelCase									=					self.image_processing_class.from_dict(
              self.image_processor_dict   ,       size=4_2   ,       max_size=8_4   ,       pad_and_return_pixel_mask=lowercase_							)
          self.assertEqual(image_processor.size   ,       {'shortest_edge': 4_2, 'longest_edge': 8_4}							)
          self.assertEqual(image_processor.do_pad   ,       lowercase_							)




      def     a_      (							self							)						->					Any:
          pass




      def     a_      (							self							)						->					Any:
          # Initialize image_processing
          UpperCAmelCase									=					self.image_processing_class(**self.image_processor_dict							)
          # create random PIL images
          UpperCAmelCase									=					prepare_image_inputs(self.image_processor_tester   ,       equal_resolution=lowercase_							)
          for image in image_inputs:
              self.assertIsInstance(lowercase_   ,       Image.Image							)

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

          UpperCAmelCase			,							UpperCAmelCase									=					self.image_processor_tester.get_expected_values(lowercase_							)

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

          # Test batched
          UpperCAmelCase			,							UpperCAmelCase									=					self.image_processor_tester.get_expected_values(lowercase_   ,       batched=lowercase_							)

          UpperCAmelCase									=					image_processing(lowercase_   ,       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							)						->					List[str]:
          # Initialize image_processing
          UpperCAmelCase									=					self.image_processing_class(**self.image_processor_dict							)
          # create random numpy tensors
          UpperCAmelCase									=					prepare_image_inputs(self.image_processor_tester   ,       equal_resolution=lowercase_   ,       numpify=lowercase_							)
          for image in image_inputs:
              self.assertIsInstance(lowercase_   ,       np.ndarray							)

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

          UpperCAmelCase			,							UpperCAmelCase									=					self.image_processor_tester.get_expected_values(lowercase_							)

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

          # Test batched
          UpperCAmelCase									=					image_processing(lowercase_   ,       return_tensors='pt'							).pixel_values

          UpperCAmelCase			,							UpperCAmelCase									=					self.image_processor_tester.get_expected_values(lowercase_   ,       batched=lowercase_							)

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




      def     a_      (							self							)						->					int:
          # Initialize image_processing
          UpperCAmelCase									=					self.image_processing_class(**self.image_processor_dict							)
          # create random PyTorch tensors
          UpperCAmelCase									=					prepare_image_inputs(self.image_processor_tester   ,       equal_resolution=lowercase_   ,       torchify=lowercase_							)
          for image in image_inputs:
              self.assertIsInstance(lowercase_   ,       torch.Tensor							)

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

          UpperCAmelCase			,							UpperCAmelCase									=					self.image_processor_tester.get_expected_values(lowercase_							)

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

          # Test batched
          UpperCAmelCase									=					image_processing(lowercase_   ,       return_tensors='pt'							).pixel_values

          UpperCAmelCase			,							UpperCAmelCase									=					self.image_processor_tester.get_expected_values(lowercase_   ,       batched=lowercase_							)

          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							)						->					List[Any]:
          # prepare image and target
          UpperCAmelCase									=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
          with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'   ,       'r'							) as f:
              UpperCAmelCase									=					json.loads(f.read()							)

          UpperCAmelCase									=					{'image_id': 3_9_7_6_9, 'annotations': target}

          # encode them
          UpperCAmelCase									=					ConditionalDetrImageProcessor.from_pretrained('microsoft/conditional-detr-resnet-50'							)
          UpperCAmelCase									=					image_processing(images=lowercase_   ,       annotations=lowercase_   ,       return_tensors='pt'							)

          # verify pixel values
          UpperCAmelCase									=					torch.Size([1, 3, 8_0_0, 1_0_6_6]							)
          self.assertEqual(encoding['pixel_values'].shape   ,       lowercase_							)

          UpperCAmelCase									=					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]   ,       lowercase_   ,       atol=1E-4							)							)

          # verify area
          UpperCAmelCase									=					torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]							)
          self.assertTrue(torch.allclose(encoding['labels'][0]['area']   ,       lowercase_							)							)
          # verify boxes
          UpperCAmelCase									=					torch.Size([6, 4]							)
          self.assertEqual(encoding['labels'][0]['boxes'].shape   ,       lowercase_							)
          UpperCAmelCase									=					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]   ,       lowercase_   ,       atol=1E-3							)							)
          # verify image_id
          UpperCAmelCase									=					torch.tensor([3_9_7_6_9]							)
          self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']   ,       lowercase_							)							)
          # verify is_crowd
          UpperCAmelCase									=					torch.tensor([0, 0, 0, 0, 0, 0]							)
          self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']   ,       lowercase_							)							)
          # verify class_labels
          UpperCAmelCase									=					torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]							)
          self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']   ,       lowercase_							)							)
          # verify orig_size
          UpperCAmelCase									=					torch.tensor([4_8_0, 6_4_0]							)
          self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']   ,       lowercase_							)							)
          # verify size
          UpperCAmelCase									=					torch.tensor([8_0_0, 1_0_6_6]							)
          self.assertTrue(torch.allclose(encoding['labels'][0]['size']   ,       lowercase_							)							)




      @slow
      def     a_      (							self							)						->					List[str]:
          # prepare image, target and masks_path
          UpperCAmelCase									=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
          with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'   ,       'r'							) as f:
              UpperCAmelCase									=					json.loads(f.read()							)

          UpperCAmelCase									=					{'file_name': '000000039769.png', 'image_id': 3_9_7_6_9, 'segments_info': target}

          UpperCAmelCase									=					pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'							)

          # encode them
          UpperCAmelCase									=					ConditionalDetrImageProcessor(format='coco_panoptic'							)
          UpperCAmelCase									=					image_processing(images=lowercase_   ,       annotations=lowercase_   ,       masks_path=lowercase_   ,       return_tensors='pt'							)

          # verify pixel values
          UpperCAmelCase									=					torch.Size([1, 3, 8_0_0, 1_0_6_6]							)
          self.assertEqual(encoding['pixel_values'].shape   ,       lowercase_							)

          UpperCAmelCase									=					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]   ,       lowercase_   ,       atol=1E-4							)							)

          # verify area
          UpperCAmelCase									=					torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]							)
          self.assertTrue(torch.allclose(encoding['labels'][0]['area']   ,       lowercase_							)							)
          # verify boxes
          UpperCAmelCase									=					torch.Size([6, 4]							)
          self.assertEqual(encoding['labels'][0]['boxes'].shape   ,       lowercase_							)
          UpperCAmelCase									=					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]   ,       lowercase_   ,       atol=1E-3							)							)
          # verify image_id
          UpperCAmelCase									=					torch.tensor([3_9_7_6_9]							)
          self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']   ,       lowercase_							)							)
          # verify is_crowd
          UpperCAmelCase									=					torch.tensor([0, 0, 0, 0, 0, 0]							)
          self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']   ,       lowercase_							)							)
          # verify class_labels
          UpperCAmelCase									=					torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]							)
          self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']   ,       lowercase_							)							)
          # verify masks
          UpperCAmelCase									=					8_2_2_8_7_3
          self.assertEqual(encoding['labels'][0]['masks'].sum().item()   ,       lowercase_							)
          # verify orig_size
          UpperCAmelCase									=					torch.tensor([4_8_0, 6_4_0]							)
          self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']   ,       lowercase_							)							)
          # verify size
          UpperCAmelCase									=					torch.tensor([8_0_0, 1_0_6_6]							)
          self.assertTrue(torch.allclose(encoding['labels'][0]['size']   ,       lowercase_							)							)





"""simple docstring"""

import sys
import turtle




def     lowercase__						(					lowerCAmelCase				:					tuple[float, float] ,				lowerCAmelCase				:					tuple[float, float] ) ->      tuple[float, float]:






    """simple docstring"""




    return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2




def     lowercase__						(					lowerCAmelCase				:					tuple[float, float] ,				lowerCAmelCase				:					tuple[float, float] ,				lowerCAmelCase				:					tuple[float, float] ,				lowerCAmelCase				:					int ,				) ->      None:






    """simple docstring"""




    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(lowerCAmelCase ,				get_mid(lowerCAmelCase ,				lowerCAmelCase ) ,				get_mid(lowerCAmelCase ,				lowerCAmelCase ) ,				depth - 1 )
    triangle(lowerCAmelCase ,				get_mid(lowerCAmelCase ,				lowerCAmelCase ) ,				get_mid(lowerCAmelCase ,				lowerCAmelCase ) ,				depth - 1 )
    triangle(lowerCAmelCase ,				get_mid(lowerCAmelCase ,				lowerCAmelCase ) ,				get_mid(lowerCAmelCase ,				lowerCAmelCase ) ,				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>'''
      )
   SCREAMING_SNAKE_CASE_    					=						turtle.Turtle()
   my_pen.ht()
   my_pen.speed(5)
   my_pen.pencolor('''red''')

   SCREAMING_SNAKE_CASE_    					=						[(-175, -125), (0, 175), (175, -125)]  # vertices of triangle
   triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))



