




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 	lowerCamelCase_							(				unittest.TestCase ):


 '''simple docstring'''







 def __init__(							self  :		Tuple    ,    _lowerCAmelCase  :		Optional[Any]    ,    _lowerCAmelCase  :		Dict=7    ,    _lowerCAmelCase  :		Optional[int]=3    ,    _lowerCAmelCase  :		Optional[Any]=30    ,    _lowerCAmelCase  :		List[Any]=400    ,    _lowerCAmelCase  :		int=True    ,    _lowerCAmelCase  :		Union[str, Any]=None    ,    _lowerCAmelCase  :		Dict=True    ,    _lowerCAmelCase  :		List[str]=[0.5, 0.5, 0.5]    ,    _lowerCAmelCase  :		Union[str, Any]=[0.5, 0.5, 0.5]    ,    _lowerCAmelCase  :		int=True    ,    _lowerCAmelCase  :		Union[str, Any]=1 / 255    ,    _lowerCAmelCase  :		List[Any]=True    ,    ):
       # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
       SCREAMING_SNAKE_CASE_     						=      size if size is not None else {'shortest_edge': 18, 'longest_edge': 1_333}
       SCREAMING_SNAKE_CASE_     						=      parent
       SCREAMING_SNAKE_CASE_     						=      batch_size
       SCREAMING_SNAKE_CASE_     						=      num_channels
       SCREAMING_SNAKE_CASE_     						=      min_resolution
       SCREAMING_SNAKE_CASE_     						=      max_resolution
       SCREAMING_SNAKE_CASE_     						=      do_resize
       SCREAMING_SNAKE_CASE_     						=      size
       SCREAMING_SNAKE_CASE_     						=      do_normalize
       SCREAMING_SNAKE_CASE_     						=      image_mean
       SCREAMING_SNAKE_CASE_     						=      image_std
       SCREAMING_SNAKE_CASE_     						=      do_rescale
       SCREAMING_SNAKE_CASE_     						=      rescale_factor
       SCREAMING_SNAKE_CASE_     						=      do_pad



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

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

       return expected_height, expected_width





@require_torch
@require_vision
class 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE	,       unittest.TestCase ):


 '''simple docstring'''







 lowercase_       =   DetaImageProcessor if is_vision_available() else None



 def 				lowerCAmelCase_  (							self  :		Optional[int]						):
       SCREAMING_SNAKE_CASE_     						=      DetaImageProcessingTester(self						)



 @property
 def 				lowerCAmelCase_  (							self  :		Optional[int]						):
       return self.image_processor_tester.prepare_image_processor_dict()



 def 				lowerCAmelCase_  (							self  :		Optional[Any]						):
       SCREAMING_SNAKE_CASE_     						=      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_resize'						)						)
       self.assertTrue(hasattr(_lowerCAmelCase    ,    'do_rescale'						)						)
       self.assertTrue(hasattr(_lowerCAmelCase    ,    'do_pad'						)						)
       self.assertTrue(hasattr(_lowerCAmelCase    ,    'size'						)						)



 def 				lowerCAmelCase_  (							self  :		List[Any]						):
       SCREAMING_SNAKE_CASE_     						=      self.image_processing_class.from_dict(self.image_processor_dict						)
       self.assertEqual(image_processor.size    ,    {'shortest_edge': 18, 'longest_edge': 1_333}						)
       self.assertEqual(image_processor.do_pad    ,    _lowerCAmelCase						)



 def 				lowerCAmelCase_  (							self  :		str						):
       pass



 def 				lowerCAmelCase_  (							self  :		int						):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE_     						=      self.image_processing_class(**self.image_processor_dict						)
       # create random PIL images
       SCREAMING_SNAKE_CASE_     						=      prepare_image_inputs(self.image_processor_tester    ,    equal_resolution=_lowerCAmelCase						)
       for image in image_inputs:
             self.assertIsInstance(_lowerCAmelCase    ,    Image.Image						)

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

       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      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
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self.image_processor_tester.get_expected_values(_lowerCAmelCase    ,    batched=_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_     						=      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 				lowerCAmelCase_  (							self  :		Optional[int]						):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE_     						=      self.image_processing_class(**self.image_processor_dict						)
       # create random numpy tensors
       SCREAMING_SNAKE_CASE_     						=      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
       SCREAMING_SNAKE_CASE_     						=      image_processing(image_inputs[0]    ,    return_tensors='pt'						).pixel_values

       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      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
       SCREAMING_SNAKE_CASE_     						=      image_processing(_lowerCAmelCase    ,    return_tensors='pt'						).pixel_values

       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      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 				lowerCAmelCase_  (							self  :		Optional[Any]						):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE_     						=      self.image_processing_class(**self.image_processor_dict						)
       # create random PyTorch tensors
       SCREAMING_SNAKE_CASE_     						=      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
       SCREAMING_SNAKE_CASE_     						=      image_processing(image_inputs[0]    ,    return_tensors='pt'						).pixel_values

       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      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
       SCREAMING_SNAKE_CASE_     						=      image_processing(_lowerCAmelCase    ,    return_tensors='pt'						).pixel_values

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

       SCREAMING_SNAKE_CASE_     						=      {'image_id': 39_769, 'annotations': target}

       # encode them
       SCREAMING_SNAKE_CASE_     						=      DetaImageProcessor()
       SCREAMING_SNAKE_CASE_     						=      image_processing(images=_lowerCAmelCase    ,    annotations=_lowerCAmelCase    ,    return_tensors='pt'						)

       # verify pixel values
       SCREAMING_SNAKE_CASE_     						=      torch.Size([1, 3, 800, 1_066]						)
       self.assertEqual(encoding['pixel_values'].shape    ,    _lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_     						=      torch.tensor([0.2796, 0.3138, 0.3481]						)
       self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]    ,    _lowerCAmelCase    ,    atol=1E-4						)						)

       # verify area
       SCREAMING_SNAKE_CASE_     						=      torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]						)
       self.assertTrue(torch.allclose(encoding['labels'][0]['area']    ,    _lowerCAmelCase						)						)
       # verify boxes
       SCREAMING_SNAKE_CASE_     						=      torch.Size([6, 4]						)
       self.assertEqual(encoding['labels'][0]['boxes'].shape    ,    _lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]						)
       self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    ,    _lowerCAmelCase    ,    atol=1E-3						)						)
       # verify image_id
       SCREAMING_SNAKE_CASE_     						=      torch.tensor([39_769]						)
       self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,    _lowerCAmelCase						)						)
       # verify is_crowd
       SCREAMING_SNAKE_CASE_     						=      torch.tensor([0, 0, 0, 0, 0, 0]						)
       self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,    _lowerCAmelCase						)						)
       # verify class_labels
       SCREAMING_SNAKE_CASE_     						=      torch.tensor([75, 75, 63, 65, 17, 17]						)
       self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,    _lowerCAmelCase						)						)
       # verify orig_size
       SCREAMING_SNAKE_CASE_     						=      torch.tensor([480, 640]						)
       self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,    _lowerCAmelCase						)						)
       # verify size
       SCREAMING_SNAKE_CASE_     						=      torch.tensor([800, 1_066]						)
       self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,    _lowerCAmelCase						)						)




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

       SCREAMING_SNAKE_CASE_     						=      {'file_name': '000000039769.png', 'image_id': 39_769, 'segments_info': target}

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

       # encode them
       SCREAMING_SNAKE_CASE_     						=      DetaImageProcessor(format='coco_panoptic'						)
       SCREAMING_SNAKE_CASE_     						=      image_processing(images=_lowerCAmelCase    ,    annotations=_lowerCAmelCase    ,    masks_path=_lowerCAmelCase    ,    return_tensors='pt'						)

       # verify pixel values
       SCREAMING_SNAKE_CASE_     						=      torch.Size([1, 3, 800, 1_066]						)
       self.assertEqual(encoding['pixel_values'].shape    ,    _lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_     						=      torch.tensor([0.2796, 0.3138, 0.3481]						)
       self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]    ,    _lowerCAmelCase    ,    atol=1E-4						)						)

       # verify area
       SCREAMING_SNAKE_CASE_     						=      torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]						)
       self.assertTrue(torch.allclose(encoding['labels'][0]['area']    ,    _lowerCAmelCase						)						)
       # verify boxes
       SCREAMING_SNAKE_CASE_     						=      torch.Size([6, 4]						)
       self.assertEqual(encoding['labels'][0]['boxes'].shape    ,    _lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]						)
       self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    ,    _lowerCAmelCase    ,    atol=1E-3						)						)
       # verify image_id
       SCREAMING_SNAKE_CASE_     						=      torch.tensor([39_769]						)
       self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,    _lowerCAmelCase						)						)
       # verify is_crowd
       SCREAMING_SNAKE_CASE_     						=      torch.tensor([0, 0, 0, 0, 0, 0]						)
       self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,    _lowerCAmelCase						)						)
       # verify class_labels
       SCREAMING_SNAKE_CASE_     						=      torch.tensor([17, 17, 63, 75, 75, 93]						)
       self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,    _lowerCAmelCase						)						)
       # verify masks
       SCREAMING_SNAKE_CASE_     						=      822_873
       self.assertEqual(encoding['labels'][0]['masks'].sum().item()    ,    _lowerCAmelCase						)
       # verify orig_size
       SCREAMING_SNAKE_CASE_     						=      torch.tensor([480, 640]						)
       self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,    _lowerCAmelCase						)						)
       # verify size
       SCREAMING_SNAKE_CASE_     						=      torch.tensor([800, 1_066]						)
       self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,    _lowerCAmelCase						)						)





from abc import ABC, abstractmethod
from argparse import ArgumentParser





class 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE ):


 '''simple docstring'''







 @staticmethod
 @abstractmethod
 def 				lowerCAmelCase_  (							_lowerCAmelCase  :		ArgumentParser						):
       raise NotImplementedError()




 @abstractmethod
 def 				lowerCAmelCase_  (							self  :		Dict						):
       raise NotImplementedError()