





"""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 DetrImageProcessor
class lowerCamelCase__	(     unittest.TestCase					):



   def __init__(							self   , snake_case   , snake_case=7   , snake_case=3   , snake_case=3_0   , snake_case=4_0_0   , snake_case=True   , snake_case=None   , snake_case=True   , snake_case=1 / 2_5_5   , snake_case=True   , snake_case=[0.5, 0.5, 0.5]   , snake_case=[0.5, 0.5, 0.5]   , snake_case=True   , )			->     List[str]:







      """simple docstring"""
      lowercase     :      Any	=							size if size is not None else {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}
      lowercase     :      List[Any]	=							parent
      lowercase     :      int	=							batch_size
      lowercase     :      Any	=							num_channels
      lowercase     :      str	=							min_resolution
      lowercase     :      Any	=							max_resolution
      lowercase     :      List[Any]	=							do_resize
      lowercase     :      Tuple	=							size
      lowercase     :      int	=							do_rescale
      lowercase     :      Tuple	=							rescale_factor
      lowercase     :      Optional[int]	=							do_normalize
      lowercase     :      Optional[Any]	=							image_mean
      lowercase     :      Union[str, Any]	=							image_std
      lowercase     :      List[str]	=							do_pad



   def  _UpperCAmelCase						(							self						)			->     Union[str, Any]:







      """simple docstring"""
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_rescale": self.do_rescale,
          "rescale_factor": self.rescale_factor,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_pad": self.do_pad,
      }





   def  _UpperCAmelCase						(							self   , snake_case   , snake_case=False						)			->     Optional[Any]:







      """simple docstring"""




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

      else:
         lowercase     :      Any	=							[]
         for image in image_inputs:
            lowercase     :      Any	=							self.get_expected_values([image]						)
            expected_values.append((expected_height, expected_width)						)
         lowercase     :      List[Any]	=							max(lowercase_   , key=lambda snake_case						: item[0]						)[0]
         lowercase     :      Union[str, Any]	=							max(lowercase_   , key=lambda snake_case						: item[1]						)[1]

      return expected_height, expected_width




@require_torch
@require_vision
class lowerCamelCase__	(     UpperCamelCase_	,					unittest.TestCase					):
   __UpperCAmelCase										=       DetrImageProcessor if is_vision_available() else None



   def  _UpperCAmelCase						(							self						)			->     List[Any]:







      """simple docstring"""
      lowercase     :      Any	=							DetrImageProcessingTester(self						)



   @property
   def  _UpperCAmelCase						(							self						)			->     Optional[int]:







      """simple docstring"""
      return self.image_processor_tester.prepare_image_processor_dict()



   def  _UpperCAmelCase						(							self						)			->     Any:







      """simple docstring"""
      lowercase     :      Dict	=							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_rescale"""						)						)
      self.assertTrue(hasattr(lowercase_   , """rescale_factor"""						)						)
      self.assertTrue(hasattr(lowercase_   , """do_resize"""						)						)
      self.assertTrue(hasattr(lowercase_   , """size"""						)						)
      self.assertTrue(hasattr(lowercase_   , """do_pad"""						)						)



   def  _UpperCAmelCase						(							self						)			->     List[str]:







      """simple docstring"""
      lowercase     :      str	=							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_						)

      lowercase     :      List[str]	=							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  _UpperCAmelCase						(							self						)			->     Union[str, Any]:







      """simple docstring"""
      pass



   def  _UpperCAmelCase						(							self						)			->     int:







      """simple docstring"""
      lowercase     :      str	=							self.image_processing_class(**self.image_processor_dict						)
      # create random PIL images
      lowercase     :      Any	=							prepare_image_inputs(self.image_processor_tester   , equal_resolution=lowercase_						)
      for image in image_inputs:
         self.assertIsInstance(lowercase_   , Image.Image						)

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

      lowercase     :      Optional[Any]	=							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
      lowercase     :      str	=							self.image_processor_tester.get_expected_values(lowercase_   , batched=lowercase_						)

      lowercase     :      Optional[Any]	=							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  _UpperCAmelCase						(							self						)			->     Any:







      """simple docstring"""
      lowercase     :      Optional[Any]	=							self.image_processing_class(**self.image_processor_dict						)
      # create random numpy tensors
      lowercase     :      List[str]	=							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
      lowercase     :      Optional[int]	=							image_processing(image_inputs[0]   , return_tensors="""pt"""						).pixel_values

      lowercase     :      Tuple	=							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
      lowercase     :      Dict	=							image_processing(lowercase_   , return_tensors="""pt"""						).pixel_values

      lowercase     :      Dict	=							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  _UpperCAmelCase						(							self						)			->     int:







      """simple docstring"""
      lowercase     :      Tuple	=							self.image_processing_class(**self.image_processor_dict						)
      # create random PyTorch tensors
      lowercase     :      int	=							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
      lowercase     :      Optional[Any]	=							image_processing(image_inputs[0]   , return_tensors="""pt"""						).pixel_values

      lowercase     :      List[str]	=							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
      lowercase     :      str	=							image_processing(lowercase_   , return_tensors="""pt"""						).pixel_values

      lowercase     :      List[str]	=							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  _UpperCAmelCase						(							self						)			->     Tuple:







      """simple docstring"""
      lowercase     :      Tuple	=							Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
      with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""   , """r"""						) as f:
         lowercase     :      List[str]	=							json.loads(f.read()						)

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

      # encode them
      lowercase     :      Optional[int]	=							DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50"""						)
      lowercase     :      Dict	=							image_processing(images=lowercase_   , annotations=lowercase_   , return_tensors="""pt"""						)

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

      lowercase     :      Optional[int]	=							torch.tensor([0.27_96, 0.31_38, 0.34_81]						)
      self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]   , lowercase_   , atol=1E-4						)						)

      # verify area
      lowercase     :      str	=							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"""]   , lowercase_						)						)
      # verify boxes
      lowercase     :      Optional[Any]	=							torch.Size([6, 4]						)
      self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape   , lowercase_						)
      lowercase     :      Any	=							torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]						)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]   , lowercase_   , atol=1E-3						)						)
      # verify image_id
      lowercase     :      str	=							torch.tensor([3_9_7_6_9]						)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]   , lowercase_						)						)
      # verify is_crowd
      lowercase     :      Any	=							torch.tensor([0, 0, 0, 0, 0, 0]						)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]   , lowercase_						)						)
      # verify class_labels
      lowercase     :      Union[str, Any]	=							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
      lowercase     :      int	=							torch.tensor([4_8_0, 6_4_0]						)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]   , lowercase_						)						)
      # verify size
      lowercase     :      Tuple	=							torch.tensor([8_0_0, 1_0_6_6]						)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]   , lowercase_						)						)





   @slow
   def  _UpperCAmelCase						(							self						)			->     Tuple:







      """simple docstring"""
      lowercase     :      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:
         lowercase     :      Optional[int]	=							json.loads(f.read()						)

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

      lowercase     :      str	=							pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""						)

      # encode them
      lowercase     :      str	=							DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50-panoptic"""						)
      lowercase     :      int	=							image_processing(images=lowercase_   , annotations=lowercase_   , masks_path=lowercase_   , return_tensors="""pt"""						)

      # verify pixel values
      lowercase     :      Optional[Any]	=							torch.Size([1, 3, 8_0_0, 1_0_6_6]						)
      self.assertEqual(encoding["""pixel_values"""].shape   , lowercase_						)

      lowercase     :      Union[str, Any]	=							torch.tensor([0.27_96, 0.31_38, 0.34_81]						)
      self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]   , lowercase_   , atol=1E-4						)						)

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






from timeit import timeit

lowerCamelCase		   =			{
    'MALAYALAM': True,
    'String': False,
    'rotor': True,
    'level': True,
    'A': True,
    'BB': True,
    'ABC': False,
    'amanaplanacanalpanama': True,  # "a man a plan a canal panama"
}
# Ensure our test data is valid
assert all((key == key[::-1]) is value for key, value in test_data.items())




def       a_ (       SCREAMING_SNAKE_CASE__   : str	):
       '''simple docstring'''





       _lowerCamelCase			:						Optional[int]            =0
       _lowerCamelCase			:						Union[str, Any]            =len(SCREAMING_SNAKE_CASE__	) - 1
       while start_i < end_i:
              if s[start_i] == s[end_i]:
                     start_i += 1
                     end_i -= 1
              else:
                     return False
       return True




def       a_ (       SCREAMING_SNAKE_CASE__   : str	):
       '''simple docstring'''





       _lowerCamelCase			:						List[str]            =len(SCREAMING_SNAKE_CASE__	) // 2
       _lowerCamelCase			:						Optional[Any]            =len(SCREAMING_SNAKE_CASE__	)

       # We need to traverse till half of the length of string
       # as we can get access of the i'th last element from
       # i'th index.
       # eg: [0,1,2,3,4,5] => 4th index can be accessed
       # with the help of 1st index (i==n-i-1)
       # where n is length of string
       return all(s[i] == s[n - i - 1] for i in range(SCREAMING_SNAKE_CASE__	)	)




def       a_ (       SCREAMING_SNAKE_CASE__   : str	):
       '''simple docstring'''





       if len(SCREAMING_SNAKE_CASE__	) <= 2:
              return True
       if s[0] == s[len(SCREAMING_SNAKE_CASE__	) - 1]:
              return is_palindrome_recursive(s[1:-1]	)
       else:
              return False




def       a_ (       SCREAMING_SNAKE_CASE__   : str	):
       '''simple docstring'''





       return s == s[::-1]




def       a_ (       SCREAMING_SNAKE_CASE__   : str	):
       '''simple docstring'''





       _lowerCamelCase			:						Optional[int]            =F'''all({name}(key) is value for key, value in test_data.items())'''
       _lowerCamelCase			:						List[Any]            =F'''from __main__ import test_data, {name}'''
       _lowerCamelCase			:						Any            =500_000
       _lowerCamelCase			:						Dict            =timeit(stmt=SCREAMING_SNAKE_CASE__						,     setup=SCREAMING_SNAKE_CASE__						,     number=SCREAMING_SNAKE_CASE__	)
       print(F'''{name:<35} finished {number:,} runs in {result:.5f} seconds'''	)


if __name__ == "__main__":
 for key, value in test_data.items():
  assert is_palindrome(key) is is_palindrome_recursive(key)
  assert is_palindrome(key) is is_palindrome_slice(key)
  print(F"""{key:21} {value}""")
 print('a man a plan a canal panama')

 # finished 500,000 runs in 0.46793 seconds
 benchmark_function('is_palindrome_slice')
 # finished 500,000 runs in 0.85234 seconds
 benchmark_function('is_palindrome')
 # finished 500,000 runs in 1.32028 seconds
 benchmark_function('is_palindrome_recursive')
 # finished 500,000 runs in 2.08679 seconds
 benchmark_function('is_palindrome_traversal')


