


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 							a						(unittest.TestCase ):




							"""simple docstring"""


							def __init__(				self			:    Tuple       ,      lowerCamelCase			:    Optional[Any]       ,      lowerCamelCase			:    List[str]=7       ,      lowerCamelCase			:    Union[str, Any]=3       ,      lowerCamelCase			:    Dict=30       ,      lowerCamelCase			:    Any=400       ,      lowerCamelCase			:    List[str]=True       ,      lowerCamelCase			:    Union[str, Any]=None       ,      lowerCamelCase			:    Tuple=True       ,      lowerCamelCase			:    int=1 / 255       ,      lowerCamelCase			:    Union[str, Any]=True       ,      lowerCamelCase			:    Optional[int]=[0.5, 0.5, 0.5]       ,      lowerCamelCase			:    int=[0.5, 0.5, 0.5]       ,      lowerCamelCase			:    Union[str, Any]=True       ,      )      ->       Optional[int]:
														# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
														__snake_case						:     str									=  size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
														__snake_case						:     Union[str, Any]									=  parent
														__snake_case						:     Optional[Any]									=  batch_size
														__snake_case						:     Union[str, Any]									=  num_channels
														__snake_case						:     Dict									=  min_resolution
														__snake_case						:     Optional[Any]									=  max_resolution
														__snake_case						:     Tuple									=  do_resize
														__snake_case						:     Tuple									=  size
														__snake_case						:     Optional[int]									=  do_rescale
														__snake_case						:     Optional[Any]									=  rescale_factor
														__snake_case						:     Optional[Any]									=  do_normalize
														__snake_case						:     Union[str, Any]									=  image_mean
														__snake_case						:     Any									=  image_std
														__snake_case						:     str									=  do_pad

							def        __snake_case   (				self			:    Any							)      ->       Union[str, Any]:
														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        __snake_case   (				self			:    List[str]       ,      lowerCamelCase			:    Optional[Any]       ,      lowerCamelCase			:    List[str]=False							)      ->       Any:
														if not batched:
																					__snake_case						:     List[Any]									=  image_inputs[0]
																					if isinstance(lowerCamelCase       ,      Image.Image							):
																												__snake_case					,     __snake_case						:     Union[str, Any]									=  image.size
																					else:
																												__snake_case					,     __snake_case						:     Dict									=  image.shape[1], image.shape[2]
																					if w < h:
																												__snake_case						:     Optional[int]									=  int(self.size["shortest_edge"] * h / w							)
																												__snake_case						:     Optional[int]									=  self.size["shortest_edge"]
																					elif w > h:
																												__snake_case						:     List[Any]									=  self.size["shortest_edge"]
																												__snake_case						:     str									=  int(self.size["shortest_edge"] * w / h							)
																					else:
																												__snake_case						:     Any									=  self.size["shortest_edge"]
																												__snake_case						:     Any									=  self.size["shortest_edge"]

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

														return expected_height, expected_width

@require_torch
@require_vision
class 							a						(_lowerCAmelCase	,							unittest.TestCase ):




							"""simple docstring"""


							__UpperCAmelCase       :				Union[str, Any]	    =    DetrImageProcessor if is_vision_available() else None

							def        __snake_case   (				self			:    Optional[int]							)      ->       Tuple:
														__snake_case						:     int									=  DetrImageProcessingTester(self							)

							@property
							def        __snake_case   (				self			:    Union[str, Any]							)      ->       Optional[int]:
														return self.image_processor_tester.prepare_image_processor_dict()

							def        __snake_case   (				self			:    Optional[Any]							)      ->       List[str]:
														__snake_case						:     Union[str, Any]									=  self.image_processing_class(**self.image_processor_dict							)
														self.assertTrue(hasattr(lowerCamelCase       ,      "image_mean"							)							)
														self.assertTrue(hasattr(lowerCamelCase       ,      "image_std"							)							)
														self.assertTrue(hasattr(lowerCamelCase       ,      "do_normalize"							)							)
														self.assertTrue(hasattr(lowerCamelCase       ,      "do_rescale"							)							)
														self.assertTrue(hasattr(lowerCamelCase       ,      "rescale_factor"							)							)
														self.assertTrue(hasattr(lowerCamelCase       ,      "do_resize"							)							)
														self.assertTrue(hasattr(lowerCamelCase       ,      "size"							)							)
														self.assertTrue(hasattr(lowerCamelCase       ,      "do_pad"							)							)

							def        __snake_case   (				self			:    List[str]							)      ->       Optional[int]:
														__snake_case						:     Tuple									=  self.image_processing_class.from_dict(self.image_processor_dict							)
														self.assertEqual(image_processor.size       ,      {"shortest_edge": 18, "longest_edge": 1333}							)
														self.assertEqual(image_processor.do_pad       ,      lowerCamelCase							)

														__snake_case						:     Optional[Any]									=  self.image_processing_class.from_dict(
														    self.image_processor_dict       ,      size=42       ,      max_size=84       ,      pad_and_return_pixel_mask=lowerCamelCase							)
														self.assertEqual(image_processor.size       ,      {"shortest_edge": 42, "longest_edge": 84}							)
														self.assertEqual(image_processor.do_pad       ,      lowerCamelCase							)

							def        __snake_case   (				self			:    Any							)      ->       Optional[Any]:
														pass

							def        __snake_case   (				self			:    str							)      ->       int:
														# Initialize image_processing
														__snake_case						:     List[str]									=  self.image_processing_class(**self.image_processor_dict							)
														# create random PIL images
														__snake_case						:     Any									=  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						:     Optional[Any]									=  image_processing(image_inputs[0]       ,      return_tensors="pt"							).pixel_values

														__snake_case					,     __snake_case						:     Any									=  self.image_processor_tester.get_expected_values(lowerCamelCase							)

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

														# Test batched
														__snake_case					,     __snake_case						:     Any									=  self.image_processor_tester.get_expected_values(lowerCamelCase       ,      batched=lowerCamelCase							)

														__snake_case						:     List[Any]									=  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,
														        expected_height,
														        expected_width,
														    )       ,      )

							def        __snake_case   (				self			:    List[Any]							)      ->       int:
														# Initialize image_processing
														__snake_case						:     List[Any]									=  self.image_processing_class(**self.image_processor_dict							)
														# create random numpy tensors
														__snake_case						:     List[Any]									=  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						:     Tuple									=  image_processing(image_inputs[0]       ,      return_tensors="pt"							).pixel_values

														__snake_case					,     __snake_case						:     Dict									=  self.image_processor_tester.get_expected_values(lowerCamelCase							)

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

														# Test batched
														__snake_case						:     Tuple									=  image_processing(lowerCamelCase       ,      return_tensors="pt"							).pixel_values

														__snake_case					,     __snake_case						:     Dict									=  self.image_processor_tester.get_expected_values(lowerCamelCase       ,      batched=lowerCamelCase							)

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

							def        __snake_case   (				self			:    Tuple							)      ->       str:
														# Initialize image_processing
														__snake_case						:     List[str]									=  self.image_processing_class(**self.image_processor_dict							)
														# create random PyTorch tensors
														__snake_case						:     List[Any]									=  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						:     Dict									=  image_processing(image_inputs[0]       ,      return_tensors="pt"							).pixel_values

														__snake_case					,     __snake_case						:     str									=  self.image_processor_tester.get_expected_values(lowerCamelCase							)

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

														# Test batched
														__snake_case						:     Tuple									=  image_processing(lowerCamelCase       ,      return_tensors="pt"							).pixel_values

														__snake_case					,     __snake_case						:     int									=  self.image_processor_tester.get_expected_values(lowerCamelCase       ,      batched=lowerCamelCase							)

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

							@slow
							def        __snake_case   (				self			:    Dict							)      ->       str:
														# prepare image and target
														__snake_case						:     Optional[Any]									=  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
														with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"       ,      "r"							) as f:
																					__snake_case						:     List[str]									=  json.loads(f.read()							)

														__snake_case						:     Union[str, Any]									=  {"image_id": 39769, "annotations": target}

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

														# verify pixel values
														__snake_case						:     int									=  torch.Size([1, 3, 800, 1066]							)
														self.assertEqual(encoding["pixel_values"].shape       ,      lowerCamelCase							)

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

														# verify area
														__snake_case						:     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"]       ,      lowerCamelCase							)							)
														# verify boxes
														__snake_case						:     Optional[Any]									=  torch.Size([6, 4]							)
														self.assertEqual(encoding["labels"][0]["boxes"].shape       ,      lowerCamelCase							)
														__snake_case						:     Optional[Any]									=  torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]							)
														self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]       ,      lowerCamelCase       ,      atol=1E-3							)							)
														# verify image_id
														__snake_case						:     str									=  torch.tensor([39769]							)
														self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       ,      lowerCamelCase							)							)
														# verify is_crowd
														__snake_case						:     Union[str, Any]									=  torch.tensor([0, 0, 0, 0, 0, 0]							)
														self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       ,      lowerCamelCase							)							)
														# verify class_labels
														__snake_case						:     List[Any]									=  torch.tensor([75, 75, 63, 65, 17, 17]							)
														self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       ,      lowerCamelCase							)							)
														# verify orig_size
														__snake_case						:     str									=  torch.tensor([480, 640]							)
														self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       ,      lowerCamelCase							)							)
														# verify size
														__snake_case						:     List[str]									=  torch.tensor([800, 1066]							)
														self.assertTrue(torch.allclose(encoding["labels"][0]["size"]       ,      lowerCamelCase							)							)






							@slow
							def        __snake_case   (				self			:    Tuple							)      ->       Union[str, Any]:
														# prepare image, target and masks_path
														__snake_case						:     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:
																					__snake_case						:     List[str]									=  json.loads(f.read()							)

														__snake_case						:     Union[str, Any]									=  {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}

														__snake_case						:     Tuple									=  pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"							)

														# encode them
														__snake_case						:     Dict									=  DetrImageProcessor.from_pretrained("facebook/detr-resnet-50-panoptic"							)
														__snake_case						:     List[Any]									=  image_processing(images=lowerCamelCase       ,      annotations=lowerCamelCase       ,      masks_path=lowerCamelCase       ,      return_tensors="pt"							)

														# verify pixel values
														__snake_case						:     List[Any]									=  torch.Size([1, 3, 800, 1066]							)
														self.assertEqual(encoding["pixel_values"].shape       ,      lowerCamelCase							)

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

														# verify area
														__snake_case						:     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"]       ,      lowerCamelCase							)							)
														# verify boxes
														__snake_case						:     Any									=  torch.Size([6, 4]							)
														self.assertEqual(encoding["labels"][0]["boxes"].shape       ,      lowerCamelCase							)
														__snake_case						:     str									=  torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]							)
														self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]       ,      lowerCamelCase       ,      atol=1E-3							)							)
														# verify image_id
														__snake_case						:     Any									=  torch.tensor([39769]							)
														self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       ,      lowerCamelCase							)							)
														# verify is_crowd
														__snake_case						:     Tuple									=  torch.tensor([0, 0, 0, 0, 0, 0]							)
														self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       ,      lowerCamelCase							)							)
														# verify class_labels
														__snake_case						:     Any									=  torch.tensor([17, 17, 63, 75, 75, 93]							)
														self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       ,      lowerCamelCase							)							)
														# verify masks
														__snake_case						:     str									=  822873
														self.assertEqual(encoding["labels"][0]["masks"].sum().item()       ,      lowerCamelCase							)
														# verify orig_size
														__snake_case						:     Tuple									=  torch.tensor([480, 640]							)
														self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       ,      lowerCamelCase							)							)
														# verify size
														__snake_case						:     Any									=  torch.tensor([800, 1066]							)
														self.assertTrue(torch.allclose(encoding["labels"][0]["size"]       ,      lowerCamelCase							)							)









'''simple docstring'''



from timeit import timeit

__UpperCamelCase   :							int         =   {
    """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  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      bool:

   """simple docstring"""
   __a					  =     0
   __a					  =     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  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      bool:

   """simple docstring"""
   __a					  =     len(SCREAMING_SNAKE_CASE__     ) // 2
   __a					  =     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  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      bool:

   """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  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      bool:

   """simple docstring"""
   return s == s[::-1]



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      None:

   """simple docstring"""
   __a					  =     f"""all({name}(key) is value for key, value in test_data.items())"""
   __a					  =     f"""from __main__ import test_data, {name}"""
   __a					  =     500000
   __a					  =     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""")