



"""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  ):




      """simple docstring"""




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



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]       ):
        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      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      List[Any]=False       ):
        if not batched:
          lowerCAmelCase_   :				Any								= image_inputs[0]
          if isinstance(SCREAMING_SNAKE_CASE_    ,			Image.Image       ):
            lowerCAmelCase_						,lowerCAmelCase_   :				Optional[int]								= image.size
          else:
            lowerCAmelCase_						,lowerCAmelCase_   :				int								= image.shape[1], image.shape[2]
          if w < h:
            lowerCAmelCase_   :				Any								= int(self.size['shortest_edge'] * h / w       )
            lowerCAmelCase_   :				List[Any]								= self.size['shortest_edge']
          elif w > h:
            lowerCAmelCase_   :				Optional[int]								= self.size['shortest_edge']
            lowerCAmelCase_   :				int								= int(self.size['shortest_edge'] * w / h       )
          else:
            lowerCAmelCase_   :				str								= self.size['shortest_edge']
            lowerCAmelCase_   :				List[str]								= self.size['shortest_edge']

        else:
          lowerCAmelCase_   :				Union[str, Any]								= []
          for image in image_inputs:
            lowerCAmelCase_						,lowerCAmelCase_   :				Tuple								= self.get_expected_values([image]       )
            expected_values.append((expected_height, expected_width)       )
          lowerCAmelCase_   :				int								= max(SCREAMING_SNAKE_CASE_    ,			key=lambda SCREAMING_SNAKE_CASE_       : item[0]       )[0]
          lowerCAmelCase_   :				Optional[int]								= max(SCREAMING_SNAKE_CASE_    ,			key=lambda SCREAMING_SNAKE_CASE_       : item[1]       )[1]

        return expected_height, expected_width







@require_torch
@require_vision
class       UpperCamelCase__   (	lowercase_,					unittest.TestCase  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     ConditionalDetrImageProcessor if is_vision_available() else None



      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]       ):
        lowerCAmelCase_   :				str								= ConditionalDetrImageProcessingTester(self       )



      @property
      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]       ):
        return self.image_processor_tester.prepare_image_processor_dict()



      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]       ):
        lowerCAmelCase_   :				List[str]								= self.image_processing_class(**self.image_processor_dict       )
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,			'image_mean'       )       )
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,			'image_std'       )       )
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,			'do_normalize'       )       )
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,			'do_resize'       )       )
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,			'size'       )       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]       ):
        lowerCAmelCase_   :				Optional[Any]								= 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    ,			SCREAMING_SNAKE_CASE_       )

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



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]       ):
        pass



      def      SCREAMING_SNAKE_CASE__ (      self				:      Dict       ):
        # Initialize image_processing
        lowerCAmelCase_   :				Union[str, Any]								= self.image_processing_class(**self.image_processor_dict       )
        # create random PIL images
        lowerCAmelCase_   :				Union[str, Any]								= prepare_image_inputs(self.image_processor_tester    ,			equal_resolution=SCREAMING_SNAKE_CASE_       )
        for image in image_inputs:
          self.assertIsInstance(SCREAMING_SNAKE_CASE_    ,			Image.Image       )

        # Test not batched input
        lowerCAmelCase_   :				Optional[int]								= image_processing(image_inputs[0]    ,			return_tensors='pt'       ).pixel_values

        lowerCAmelCase_						,lowerCAmelCase_   :				Optional[Any]								= self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_       )

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

        # Test batched
        lowerCAmelCase_						,lowerCAmelCase_   :				Any								= self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_    ,			batched=SCREAMING_SNAKE_CASE_       )

        lowerCAmelCase_   :				Dict								= image_processing(SCREAMING_SNAKE_CASE_    ,			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      SCREAMING_SNAKE_CASE__ (      self				:      Dict       ):
        # Initialize image_processing
        lowerCAmelCase_   :				Tuple								= self.image_processing_class(**self.image_processor_dict       )
        # create random numpy tensors
        lowerCAmelCase_   :				Dict								= prepare_image_inputs(self.image_processor_tester    ,			equal_resolution=SCREAMING_SNAKE_CASE_    ,			numpify=SCREAMING_SNAKE_CASE_       )
        for image in image_inputs:
          self.assertIsInstance(SCREAMING_SNAKE_CASE_    ,			np.ndarray       )

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

        lowerCAmelCase_						,lowerCAmelCase_   :				str								= self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_       )

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

        # Test batched
        lowerCAmelCase_   :				Union[str, Any]								= image_processing(SCREAMING_SNAKE_CASE_    ,			return_tensors='pt'       ).pixel_values

        lowerCAmelCase_						,lowerCAmelCase_   :				Tuple								= self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_    ,			batched=SCREAMING_SNAKE_CASE_       )

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



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        # Initialize image_processing
        lowerCAmelCase_   :				List[Any]								= self.image_processing_class(**self.image_processor_dict       )
        # create random PyTorch tensors
        lowerCAmelCase_   :				List[Any]								= prepare_image_inputs(self.image_processor_tester    ,			equal_resolution=SCREAMING_SNAKE_CASE_    ,			torchify=SCREAMING_SNAKE_CASE_       )
        for image in image_inputs:
          self.assertIsInstance(SCREAMING_SNAKE_CASE_    ,			torch.Tensor       )

        # Test not batched input
        lowerCAmelCase_   :				List[Any]								= image_processing(image_inputs[0]    ,			return_tensors='pt'       ).pixel_values

        lowerCAmelCase_						,lowerCAmelCase_   :				Dict								= self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_       )

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

        # Test batched
        lowerCAmelCase_   :				Dict								= image_processing(SCREAMING_SNAKE_CASE_    ,			return_tensors='pt'       ).pixel_values

        lowerCAmelCase_						,lowerCAmelCase_   :				Optional[int]								= self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_    ,			batched=SCREAMING_SNAKE_CASE_       )

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



      @slow
      def      SCREAMING_SNAKE_CASE__ (      self				:      Tuple       ):
        # prepare image and target
        lowerCAmelCase_   :				Optional[int]								= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
        with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'    ,			'r'       ) as f:
          lowerCAmelCase_   :				int								= json.loads(f.read()       )

        lowerCAmelCase_   :				Dict								= {'image_id': 3_9_7_6_9, 'annotations': target}

        # encode them
        lowerCAmelCase_   :				Optional[int]								= ConditionalDetrImageProcessor.from_pretrained('microsoft/conditional-detr-resnet-50'       )
        lowerCAmelCase_   :				Union[str, Any]								= image_processing(images=SCREAMING_SNAKE_CASE_    ,			annotations=SCREAMING_SNAKE_CASE_    ,			return_tensors='pt'       )

        # verify pixel values
        lowerCAmelCase_   :				List[str]								= torch.Size([1, 3, 8_0_0, 1_0_6_6]       )
        self.assertEqual(encoding['pixel_values'].shape    ,			SCREAMING_SNAKE_CASE_       )

        lowerCAmelCase_   :				Dict								= torch.tensor([0.27_96, 0.31_38, 0.34_81]       )
        self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]    ,			SCREAMING_SNAKE_CASE_    ,			atol=1E-4       )       )

        # verify area
        lowerCAmelCase_   :				Any								= 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']    ,			SCREAMING_SNAKE_CASE_       )       )
        # verify boxes
        lowerCAmelCase_   :				Union[str, Any]								= torch.Size([6, 4]       )
        self.assertEqual(encoding['labels'][0]['boxes'].shape    ,			SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				Tuple								= torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]       )
        self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    ,			SCREAMING_SNAKE_CASE_    ,			atol=1E-3       )       )
        # verify image_id
        lowerCAmelCase_   :				Dict								= torch.tensor([3_9_7_6_9]       )
        self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,			SCREAMING_SNAKE_CASE_       )       )
        # verify is_crowd
        lowerCAmelCase_   :				List[str]								= torch.tensor([0, 0, 0, 0, 0, 0]       )
        self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,			SCREAMING_SNAKE_CASE_       )       )
        # verify class_labels
        lowerCAmelCase_   :				Tuple								= torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]       )
        self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,			SCREAMING_SNAKE_CASE_       )       )
        # verify orig_size
        lowerCAmelCase_   :				Union[str, Any]								= torch.tensor([4_8_0, 6_4_0]       )
        self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,			SCREAMING_SNAKE_CASE_       )       )
        # verify size
        lowerCAmelCase_   :				Union[str, Any]								= torch.tensor([8_0_0, 1_0_6_6]       )
        self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,			SCREAMING_SNAKE_CASE_       )       )





      @slow
      def      SCREAMING_SNAKE_CASE__ (      self				:      Dict       ):
        # prepare image, target and masks_path
        lowerCAmelCase_   :				Union[str, Any]								= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
        with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'    ,			'r'       ) as f:
          lowerCAmelCase_   :				Any								= json.loads(f.read()       )

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

        lowerCAmelCase_   :				List[str]								= pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'       )

        # encode them
        lowerCAmelCase_   :				Optional[Any]								= ConditionalDetrImageProcessor(format='coco_panoptic'       )
        lowerCAmelCase_   :				Any								= image_processing(images=SCREAMING_SNAKE_CASE_    ,			annotations=SCREAMING_SNAKE_CASE_    ,			masks_path=SCREAMING_SNAKE_CASE_    ,			return_tensors='pt'       )

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

        lowerCAmelCase_   :				Optional[Any]								= torch.tensor([0.27_96, 0.31_38, 0.34_81]       )
        self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]    ,			SCREAMING_SNAKE_CASE_    ,			atol=1E-4       )       )

        # verify area
        lowerCAmelCase_   :				List[str]								= 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']    ,			SCREAMING_SNAKE_CASE_       )       )
        # verify boxes
        lowerCAmelCase_   :				List[str]								= torch.Size([6, 4]       )
        self.assertEqual(encoding['labels'][0]['boxes'].shape    ,			SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				Tuple								= torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]       )
        self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    ,			SCREAMING_SNAKE_CASE_    ,			atol=1E-3       )       )
        # verify image_id
        lowerCAmelCase_   :				str								= torch.tensor([3_9_7_6_9]       )
        self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,			SCREAMING_SNAKE_CASE_       )       )
        # verify is_crowd
        lowerCAmelCase_   :				Any								= torch.tensor([0, 0, 0, 0, 0, 0]       )
        self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,			SCREAMING_SNAKE_CASE_       )       )
        # verify class_labels
        lowerCAmelCase_   :				int								= torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]       )
        self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,			SCREAMING_SNAKE_CASE_       )       )
        # verify masks
        lowerCAmelCase_   :				int								= 8_2_2_8_7_3
        self.assertEqual(encoding['labels'][0]['masks'].sum().item()    ,			SCREAMING_SNAKE_CASE_       )
        # verify orig_size
        lowerCAmelCase_   :				Tuple								= torch.tensor([4_8_0, 6_4_0]       )
        self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,			SCREAMING_SNAKE_CASE_       )       )
        # verify size
        lowerCAmelCase_   :				Optional[int]								= torch.tensor([8_0_0, 1_0_6_6]       )
        self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,			SCREAMING_SNAKE_CASE_       )       )










"""simple docstring"""







from __future__ import annotations

def 				UpperCamelCase_    (    lowerCAmelCase__      :				list[int]    ,   lowerCAmelCase__      :				int						)						->   bool:

  """simple docstring"""




  if len(lowerCAmelCase__						) == 0:
    return False
  lowerCAmelCase_   :				Union[str, Any]								= len(lowerCAmelCase__						) // 2
  if a_list[midpoint] == item:
    return True
  if item < a_list[midpoint]:
    return binary_search(a_list[:midpoint]    ,   lowerCAmelCase__						)
  else:
    return binary_search(a_list[midpoint + 1 :]    ,   lowerCAmelCase__						)


if __name__ == "__main__":
     lowercase__		:				str          =	input("""Enter numbers separated by comma:\n""").strip()
     lowercase__		:				Optional[int]          =	[int(item.strip()) for item in user_input.split(""",""")]
     lowercase__		:				Optional[Any]          =	int(input("""Enter the number to be found in the list:\n""").strip())
     lowercase__		:				int          =	"""""" if binary_search(sequence, target) else """not """
     print(f'{target} was {not_str}found in {sequence}')





