'''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 DetaImageProcessor



class       UpperCAmelCase  (						unittest.TestCase ):





       '''simple docstring'''



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

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

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

        return expected_height, expected_width



@require_torch
@require_vision
class       UpperCAmelCase  (						a__   ,						unittest.TestCase ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   DetaImageProcessor if is_vision_available() else None

       def _lowerCAmelCase(						self			)  ->  Any:
        lowercase__    :   List[Any]						= DetaImageProcessingTester(self			)

       @property
       def _lowerCAmelCase(						self			)  ->  Optional[int]:
        return self.image_processor_tester.prepare_image_processor_dict()

       def _lowerCAmelCase(						self			)  ->  Tuple:
        lowercase__    :   Union[str, Any]						= 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 _lowerCAmelCase(						self			)  ->  Optional[Any]:
        lowercase__    :   Dict						= 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     ,       _a			)

       def _lowerCAmelCase(						self			)  ->  List[str]:
        pass

       def _lowerCAmelCase(						self			)  ->  Optional[Any]:
        # Initialize image_processing
        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=_a			)
        for image in image_inputs:
         self.assertIsInstance(_a     ,       Image.Image			)

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

        lowercase__    :   Any						= 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
        lowercase__    :   Any						= self.image_processor_tester.get_expected_values(_a     ,       batched=_a			)

        lowercase__    :   int						= 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 _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        # Initialize image_processing
        lowercase__    :   str						= self.image_processing_class(**self.image_processor_dict			)
        # create random numpy tensors
        lowercase__    :   Optional[Any]						= 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
        lowercase__    :   Optional[Any]						= image_processing(image_inputs[0]     ,       return_tensors='''pt'''			).pixel_values

        lowercase__    :   str						= 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
        lowercase__    :   Any						= image_processing(_a     ,       return_tensors='''pt'''			).pixel_values

        lowercase__    :   Union[str, Any]						= 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 _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        # Initialize image_processing
        lowercase__    :   List[str]						= self.image_processing_class(**self.image_processor_dict			)
        # create random PyTorch tensors
        lowercase__    :   List[str]						= 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
        lowercase__    :   Optional[int]						= image_processing(image_inputs[0]     ,       return_tensors='''pt'''			).pixel_values

        lowercase__    :   List[Any]						= 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
        lowercase__    :   List[str]						= image_processing(_a     ,       return_tensors='''pt'''			).pixel_values

        lowercase__    :   int						= 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 _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        # prepare image and target
        lowercase__    :   Any						= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
        with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''     ,       '''r'''			) as f:
         lowercase__    :   Tuple						= json.loads(f.read()			)

        lowercase__    :   Any						= {"image_id": 39769, "annotations": target}

        # encode them
        lowercase__    :   Union[str, Any]						= DetaImageProcessor()
        lowercase__    :   str						= image_processing(images=_a     ,       annotations=_a     ,       return_tensors='''pt'''			)

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

        lowercase__    :   Tuple						= 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
        lowercase__    :   int						= torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]			)
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']     ,       _a			)			)
        # verify boxes
        lowercase__    :   Optional[Any]						= torch.Size([6, 4]			)
        self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape     ,       _a			)
        lowercase__    :   List[Any]						= 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
        lowercase__    :   Any						= torch.tensor([39769]			)
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']     ,       _a			)			)
        # verify is_crowd
        lowercase__    :   Dict						= torch.tensor([0, 0, 0, 0, 0, 0]			)
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']     ,       _a			)			)
        # verify class_labels
        lowercase__    :   List[Any]						= torch.tensor([75, 75, 63, 65, 17, 17]			)
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']     ,       _a			)			)
        # verify orig_size
        lowercase__    :   List[Any]						= torch.tensor([480, 640]			)
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']     ,       _a			)			)
        # verify size
        lowercase__    :   Any						= torch.tensor([800, 1066]			)
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']     ,       _a			)			)


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

        lowercase__    :   Any						= {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}

        lowercase__    :   Dict						= pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''			)

        # encode them
        lowercase__    :   List[Any]						= DetaImageProcessor(format='''coco_panoptic'''			)
        lowercase__    :   Optional[int]						= image_processing(images=_a     ,       annotations=_a     ,       masks_path=_a     ,       return_tensors='''pt'''			)

        # verify pixel values
        lowercase__    :   Any						= torch.Size([1, 3, 800, 1066]			)
        self.assertEqual(encoding['''pixel_values'''].shape     ,       _a			)

        lowercase__    :   List[Any]						= 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
        lowercase__    :   Union[str, Any]						= torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]			)
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']     ,       _a			)			)
        # verify boxes
        lowercase__    :   Union[str, Any]						= torch.Size([6, 4]			)
        self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape     ,       _a			)
        lowercase__    :   int						= 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
        lowercase__    :   Optional[int]						= torch.tensor([39769]			)
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']     ,       _a			)			)
        # verify is_crowd
        lowercase__    :   Union[str, Any]						= torch.tensor([0, 0, 0, 0, 0, 0]			)
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']     ,       _a			)			)
        # verify class_labels
        lowercase__    :   Optional[Any]						= torch.tensor([17, 17, 63, 75, 75, 93]			)
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']     ,       _a			)			)
        # verify masks
        lowercase__    :   List[Any]						= 822873
        self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()     ,       _a			)
        # verify orig_size
        lowercase__    :   Optional[Any]						= torch.tensor([480, 640]			)
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']     ,       _a			)			)
        # verify size
        lowercase__    :   Optional[int]						= torch.tensor([800, 1066]			)
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']     ,       _a			)			)





'''simple docstring'''

def       __UpperCamelCase	(       UpperCAmelCase					):
 if not all(x.isalpha() for x in string					):
  raise ValueError('''String must only contain alphabetic characters.'''					)

 lowercase__    :   Tuple						= sorted(string.lower()					)
 return len(UpperCAmelCase					) == len(set(UpperCAmelCase					)					)


if __name__ == "__main__":
  __a:			Union[str, Any]	       =       input("""Enter a string """).strip()

  __a:			Tuple	       =       is_isogram(input_str)
  print(F'{input_str} is {"an" if isogram else "not an"} isogram.')

