





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     _UpperCAmelCase   (   unittest.TestCase   ):

		"""simple docstring"""


		def __init__(    self			:		Optional[int] ,  lowerCAmelCase_			:		Dict ,  lowerCAmelCase_			:		Any=7 ,  lowerCAmelCase_			:		Tuple=3 ,  lowerCAmelCase_			:		int=3_0 ,  lowerCAmelCase_			:		int=4_0_0 ,  lowerCAmelCase_			:		Optional[Any]=True ,  lowerCAmelCase_			:		List[Any]=None ,  lowerCAmelCase_			:		Optional[int]=True ,  lowerCAmelCase_			:		Any=1 / 2_5_5 ,  lowerCAmelCase_			:		Dict=True ,  lowerCAmelCase_			:		Dict=[0.5, 0.5, 0.5] ,  lowerCAmelCase_			:		Tuple=[0.5, 0.5, 0.5] ,  lowerCAmelCase_			:		List[Any]=True ,  )							->						List[str]:
					# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
					__lowerCAmelCase							     =     size if size is not None else {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}
					__lowerCAmelCase							     =     parent
					__lowerCAmelCase							     =     batch_size
					__lowerCAmelCase							     =     num_channels
					__lowerCAmelCase							     =     min_resolution
					__lowerCAmelCase							     =     max_resolution
					__lowerCAmelCase							     =     do_resize
					__lowerCAmelCase							     =     size
					__lowerCAmelCase							     =     do_rescale
					__lowerCAmelCase							     =     rescale_factor
					__lowerCAmelCase							     =     do_normalize
					__lowerCAmelCase							     =     image_mean
					__lowerCAmelCase							     =     image_std
					__lowerCAmelCase							     =     do_pad






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

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

					return expected_height, expected_width





@require_torch
@require_vision
class     _UpperCAmelCase   (   SCREAMING_SNAKE_CASE__		,   unittest.TestCase   ):

		"""simple docstring"""


		a_      =			DetrImageProcessor if is_vision_available() else None






		def     lowercase		(    self			:		int					)							->						str:
					__lowerCAmelCase							     =     DetrImageProcessingTester(self					)






		@property
		def     lowercase		(    self			:		List[Any]					)							->						List[Any]:
					return self.image_processor_tester.prepare_image_processor_dict()






		def     lowercase		(    self			:		Tuple					)							->						List[Any]:
					__lowerCAmelCase							     =     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_rescale'					)					)
					self.assertTrue(hasattr(A__ ,  'rescale_factor'					)					)
					self.assertTrue(hasattr(A__ ,  'do_resize'					)					)
					self.assertTrue(hasattr(A__ ,  'size'					)					)
					self.assertTrue(hasattr(A__ ,  'do_pad'					)					)






		def     lowercase		(    self			:		str					)							->						Optional[int]:
					__lowerCAmelCase							     =     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 ,  A__					)

					__lowerCAmelCase							     =     self.image_processing_class.from_dict(
					    self.image_processor_dict ,  size=4_2 ,  max_size=8_4 ,  pad_and_return_pixel_mask=A__					)
					self.assertEqual(image_processor.size ,  {'shortest_edge': 4_2, 'longest_edge': 8_4}					)
					self.assertEqual(image_processor.do_pad ,  A__					)






		def     lowercase		(    self			:		Dict					)							->						int:
					pass






		def     lowercase		(    self			:		Dict					)							->						str:
					# Initialize image_processing
					__lowerCAmelCase							     =     self.image_processing_class(**self.image_processor_dict					)
					# create random PIL images
					__lowerCAmelCase							     =     prepare_image_inputs(self.image_processor_tester ,  equal_resolution=A__					)
					for image in image_inputs:
								self.assertIsInstance(A__ ,  Image.Image					)

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

					__lowerCAmelCase     ,     __lowerCAmelCase							     =     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
					__lowerCAmelCase     ,     __lowerCAmelCase							     =     self.image_processor_tester.get_expected_values(A__ ,  batched=A__					)

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

					__lowerCAmelCase     ,     __lowerCAmelCase							     =     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
					__lowerCAmelCase							     =     image_processing(A__ ,  return_tensors='pt'					).pixel_values

					__lowerCAmelCase     ,     __lowerCAmelCase							     =     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     lowercase		(    self			:		Tuple					)							->						str:
					# Initialize image_processing
					__lowerCAmelCase							     =     self.image_processing_class(**self.image_processor_dict					)
					# create random PyTorch tensors
					__lowerCAmelCase							     =     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
					__lowerCAmelCase							     =     image_processing(image_inputs[0] ,  return_tensors='pt'					).pixel_values

					__lowerCAmelCase     ,     __lowerCAmelCase							     =     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
					__lowerCAmelCase							     =     image_processing(A__ ,  return_tensors='pt'					).pixel_values

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

					__lowerCAmelCase							     =     {'image_id': 3_9_7_6_9, 'annotations': target}

					# encode them
					__lowerCAmelCase							     =     DetrImageProcessor.from_pretrained('facebook/detr-resnet-50'					)
					__lowerCAmelCase							     =     image_processing(images=A__ ,  annotations=A__ ,  return_tensors='pt'					)

					# verify pixel values
					__lowerCAmelCase							     =     torch.Size([1, 3, 8_0_0, 1_0_6_6]					)
					self.assertEqual(encoding['pixel_values'].shape ,  A__					)

					__lowerCAmelCase							     =     torch.tensor([0.27_96, 0.31_38, 0.34_81]					)
					self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] ,  A__ ,  atol=1e-4					)					)

					# verify area
					__lowerCAmelCase							     =     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'] ,  A__					)					)
					# verify boxes
					__lowerCAmelCase							     =     torch.Size([6, 4]					)
					self.assertEqual(encoding['labels'][0]['boxes'].shape ,  A__					)
					__lowerCAmelCase							     =     torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]					)
					self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] ,  A__ ,  atol=1e-3					)					)
					# verify image_id
					__lowerCAmelCase							     =     torch.tensor([3_9_7_6_9]					)
					self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] ,  A__					)					)
					# verify is_crowd
					__lowerCAmelCase							     =     torch.tensor([0, 0, 0, 0, 0, 0]					)
					self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] ,  A__					)					)
					# verify class_labels
					__lowerCAmelCase							     =     torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]					)
					self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] ,  A__					)					)
					# verify orig_size
					__lowerCAmelCase							     =     torch.tensor([4_8_0, 6_4_0]					)
					self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] ,  A__					)					)
					# verify size
					__lowerCAmelCase							     =     torch.tensor([8_0_0, 1_0_6_6]					)
					self.assertTrue(torch.allclose(encoding['labels'][0]['size'] ,  A__					)					)






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

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

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

					# encode them
					__lowerCAmelCase							     =     DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic'					)
					__lowerCAmelCase							     =     image_processing(images=A__ ,  annotations=A__ ,  masks_path=A__ ,  return_tensors='pt'					)

					# verify pixel values
					__lowerCAmelCase							     =     torch.Size([1, 3, 8_0_0, 1_0_6_6]					)
					self.assertEqual(encoding['pixel_values'].shape ,  A__					)

					__lowerCAmelCase							     =     torch.tensor([0.27_96, 0.31_38, 0.34_81]					)
					self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] ,  A__ ,  atol=1e-4					)					)

					# verify area
					__lowerCAmelCase							     =     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'] ,  A__					)					)
					# verify boxes
					__lowerCAmelCase							     =     torch.Size([6, 4]					)
					self.assertEqual(encoding['labels'][0]['boxes'].shape ,  A__					)
					__lowerCAmelCase							     =     torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]					)
					self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] ,  A__ ,  atol=1e-3					)					)
					# verify image_id
					__lowerCAmelCase							     =     torch.tensor([3_9_7_6_9]					)
					self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] ,  A__					)					)
					# verify is_crowd
					__lowerCAmelCase							     =     torch.tensor([0, 0, 0, 0, 0, 0]					)
					self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] ,  A__					)					)
					# verify class_labels
					__lowerCAmelCase							     =     torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]					)
					self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] ,  A__					)					)
					# verify masks
					__lowerCAmelCase							     =     8_2_2_8_7_3
					self.assertEqual(encoding['labels'][0]['masks'].sum().item() ,  A__					)
					# verify orig_size
					__lowerCAmelCase							     =     torch.tensor([4_8_0, 6_4_0]					)
					self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] ,  A__					)					)
					# verify size
					__lowerCAmelCase							     =     torch.tensor([8_0_0, 1_0_6_6]					)
					self.assertTrue(torch.allclose(encoding['labels'][0]['size'] ,  A__					)					)








from functools import lru_cache

@lru_cache
def   a_   (			lowerCAmelCase_				:      int     ):
   if num < 0:
      raise ValueError('Number should not be negative.'     )

   return 1 if num in (0, 1) else num * factorial(num - 1     )


if __name__ == "__main__":
     import doctest

     doctest.testmod()

