


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 DeformableDetrImageProcessor






class _lowerCamelCase(						unittest.TestCase   ):
       def __init__(					self,      lowerCamelCase,      lowerCamelCase=7,      lowerCamelCase=3,      lowerCamelCase=30,      lowerCamelCase=4_00,      lowerCamelCase=True,      lowerCamelCase=None,      lowerCamelCase=True,      lowerCamelCase=[0.5, 0.5, 0.5],      lowerCamelCase=[0.5, 0.5, 0.5],      lowerCamelCase=True,      lowerCamelCase=1 / 2_55,      lowerCamelCase=True,      )       ->   Tuple:




           """simple docstring"""

           _lowercase       :							List[str]      		=		size if size is not None else {'shortest_edge': 18, 'longest_edge': 13_33}
           _lowercase       :							str      		=		parent
           _lowercase       :							List[Any]      		=		batch_size
           _lowercase       :							Union[str, Any]      		=		num_channels
           _lowercase       :							str      		=		min_resolution
           _lowercase       :							str      		=		max_resolution
           _lowercase       :							Union[str, Any]      		=		do_resize
           _lowercase       :							List[Any]      		=		size
           _lowercase       :							Union[str, Any]      		=		do_normalize
           _lowercase       :							int      		=		image_mean
           _lowercase       :							str      		=		image_std
           _lowercase       :							str      		=		do_rescale
           _lowercase       :							List[str]      		=		rescale_factor
           _lowercase       :							Optional[int]      		=		do_pad
       def 			UpperCamelCase     (					self)       ->   List[Any]:




           """simple docstring"""

           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 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase=False)       ->   int:




           """simple docstring"""

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

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

           return expected_height, expected_width






@require_torch
@require_vision
class _lowerCamelCase(						_a, unittest.TestCase   ):
       lowercase_ :	str     =			DeformableDetrImageProcessor if is_vision_available() else None
       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		DeformableDetrImageProcessingTester(self)
       @property
       def 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

           return self.image_processor_tester.prepare_image_processor_dict()
       def 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

           _lowercase       :							Optional[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_resize'))
           self.assertTrue(hasattr(lowerCamelCase,      'do_rescale'))
           self.assertTrue(hasattr(lowerCamelCase,      'do_pad'))
           self.assertTrue(hasattr(lowerCamelCase,      'size'))
       def 			UpperCamelCase     (					self)       ->   List[Any]:




           """simple docstring"""

           _lowercase       :							List[str]      		=		self.image_processing_class.from_dict(self.image_processor_dict)
           self.assertEqual(image_processor.size,      {'shortest_edge': 18, 'longest_edge': 13_33})
           self.assertEqual(image_processor.do_pad,      lowerCamelCase)

           _lowercase       :							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 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           pass
       def 			UpperCamelCase     (					self)       ->   Any:




           """simple docstring"""

           _lowercase       :							List[str]      		=		self.image_processing_class(**self.image_processor_dict)
           # create random PIL images
           _lowercase       :							str      		=		prepare_image_inputs(self.image_processor_tester,      equal_resolution=lowerCamelCase)
           for image in image_inputs:
               self.assertIsInstance(lowerCamelCase,      Image.Image)

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

           _lowercase		,				_lowercase       :							List[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
           _lowercase		,				_lowercase       :							Optional[int]      		=		self.image_processor_tester.get_expected_values(lowerCamelCase,      batched=lowerCamelCase)

           _lowercase       :							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 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

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

           _lowercase		,				_lowercase       :							Tuple      		=		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
           _lowercase       :							Any      		=		image_processing(lowerCamelCase,      return_tensors='pt').pixel_values

           _lowercase		,				_lowercase       :							Optional[Any]      		=		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 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """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=lowerCamelCase,      torchify=lowerCamelCase)
           for image in image_inputs:
               self.assertIsInstance(lowerCamelCase,      torch.Tensor)

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

           _lowercase		,				_lowercase       :							Tuple      		=		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
           _lowercase       :							Optional[int]      		=		image_processing(lowerCamelCase,      return_tensors='pt').pixel_values

           _lowercase		,				_lowercase       :							Optional[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 			UpperCamelCase     (					self)       ->   Dict:




           """simple docstring"""

           _lowercase       :							Optional[int]      		=		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
           with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt',      'r') as f:
               _lowercase       :							Optional[Any]      		=		json.loads(f.read())

           _lowercase       :							Optional[Any]      		=		{'image_id': 3_97_69, 'annotations': target}

           # encode them
           _lowercase       :							Optional[Any]      		=		DeformableDetrImageProcessor()
           _lowercase       :							Optional[Any]      		=		image_processing(images=lowerCamelCase,      annotations=lowerCamelCase,      return_tensors='pt')

           # verify pixel values
           _lowercase       :							Tuple      		=		torch.Size([1, 3, 8_00, 10_66])
           self.assertEqual(encoding['pixel_values'].shape,      lowerCamelCase)

           _lowercase       :							Optional[int]      		=		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],      lowerCamelCase,      atol=1E-4))

           # verify area
           _lowercase       :							Optional[Any]      		=		torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8])
           self.assertTrue(torch.allclose(encoding['labels'][0]['area'],      lowerCamelCase))
           # verify boxes
           _lowercase       :							List[str]      		=		torch.Size([6, 4])
           self.assertEqual(encoding['labels'][0]['boxes'].shape,      lowerCamelCase)
           _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],      lowerCamelCase,      atol=1E-3))
           # verify image_id
           _lowercase       :							int      		=		torch.tensor([3_97_69])
           self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'],      lowerCamelCase))
           # verify is_crowd
           _lowercase       :							Tuple      		=		torch.tensor([0, 0, 0, 0, 0, 0])
           self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'],      lowerCamelCase))
           # verify class_labels
           _lowercase       :							Union[str, Any]      		=		torch.tensor([75, 75, 63, 65, 17, 17])
           self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'],      lowerCamelCase))
           # verify orig_size
           _lowercase       :							str      		=		torch.tensor([4_80, 6_40])
           self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'],      lowerCamelCase))
           # verify size
           _lowercase       :							Optional[int]      		=		torch.tensor([8_00, 10_66])
           self.assertTrue(torch.allclose(encoding['labels'][0]['size'],      lowerCamelCase))




       @slow
       def 			UpperCamelCase     (					self)       ->   List[Any]:




           """simple docstring"""

           _lowercase       :							int      		=		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[Any]      		=		json.loads(f.read())

           _lowercase       :							int      		=		{'file_name': '000000039769.png', 'image_id': 3_97_69, 'segments_info': target}

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

           # encode them
           _lowercase       :							str      		=		DeformableDetrImageProcessor(format='coco_panoptic')
           _lowercase       :							Dict      		=		image_processing(images=lowerCamelCase,      annotations=lowerCamelCase,      masks_path=lowerCamelCase,      return_tensors='pt')

           # verify pixel values
           _lowercase       :							List[Any]      		=		torch.Size([1, 3, 8_00, 10_66])
           self.assertEqual(encoding['pixel_values'].shape,      lowerCamelCase)

           _lowercase       :							str      		=		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],      lowerCamelCase,      atol=1E-4))

           # verify area
           _lowercase       :							str      		=		torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7])
           self.assertTrue(torch.allclose(encoding['labels'][0]['area'],      lowerCamelCase))
           # verify boxes
           _lowercase       :							str      		=		torch.Size([6, 4])
           self.assertEqual(encoding['labels'][0]['boxes'].shape,      lowerCamelCase)
           _lowercase       :							Union[str, Any]      		=		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],      lowerCamelCase,      atol=1E-3))
           # verify image_id
           _lowercase       :							Union[str, Any]      		=		torch.tensor([3_97_69])
           self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'],      lowerCamelCase))
           # verify is_crowd
           _lowercase       :							Union[str, Any]      		=		torch.tensor([0, 0, 0, 0, 0, 0])
           self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'],      lowerCamelCase))
           # verify class_labels
           _lowercase       :							Union[str, Any]      		=		torch.tensor([17, 17, 63, 75, 75, 93])
           self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'],      lowerCamelCase))
           # verify masks
           _lowercase       :							Optional[int]      		=		82_28_73
           self.assertEqual(encoding['labels'][0]['masks'].sum().item(),      lowerCamelCase)
           # verify orig_size
           _lowercase       :							str      		=		torch.tensor([4_80, 6_40])
           self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'],      lowerCamelCase))
           # verify size
           _lowercase       :							str      		=		torch.tensor([8_00, 10_66])
           self.assertTrue(torch.allclose(encoding['labels'][0]['size'],      lowerCamelCase))






import random
from typing import Any



def 				UpperCamelCase_(      lowerCamelCase_ )    ->						list[Any]:
    for _ in range(len(lowerCamelCase_ ) ):
        _lowercase       :							Optional[int]      		=		random.randint(0							, len(lowerCamelCase_ ) - 1 )
        _lowercase       :							str      		=		random.randint(0							, len(lowerCamelCase_ ) - 1 )
        _lowercase		,				_lowercase       :							Optional[int]      		=		data[b], data[a]
    return data


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE       :      str												= [0, 1, 2, 3, 4, 5, 6, 7]
 SCREAMING_SNAKE_CASE       :      int												= ["python", "says", "hello", "!"]
 print("Fisher-Yates Shuffle:")
 print("List", integers, strings)
 print("FY Shuffle", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))


