
"""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 DeformableDetrImageProcessor


class     lowercase (		unittest.TestCase       ):



		def __init__(			self    ,				A_    ,				A_=7    ,				A_=3    ,				A_=30    ,				A_=400    ,				A_=True    ,				A_=None    ,				A_=True    ,				A_=[0.5, 0.5, 0.5]    ,				A_=[0.5, 0.5, 0.5]    ,				A_=True    ,				A_=1 / 255    ,				A_=True    ,				)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					size if size is not None else {'shortest_edge': 18, 'longest_edge': 1_333}
				UpperCamelCase          =					parent
				UpperCamelCase          =					batch_size
				UpperCamelCase          =					num_channels
				UpperCamelCase          =					min_resolution
				UpperCamelCase          =					max_resolution
				UpperCamelCase          =					do_resize
				UpperCamelCase          =					size
				UpperCamelCase          =					do_normalize
				UpperCamelCase          =					image_mean
				UpperCamelCase          =					image_std
				UpperCamelCase          =					do_rescale
				UpperCamelCase          =					rescale_factor
				UpperCamelCase          =					do_pad



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""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    ,				A_    ,				A_=False	)  -> int:
				"""simple docstring"""
				if not batched:
						UpperCamelCase          =					image_inputs[0]
						if isinstance(A_    ,				Image.Image	):
								UpperCamelCase     ,  UpperCamelCase          =					image.size
						else:
								UpperCamelCase     ,  UpperCamelCase          =					image.shape[1], image.shape[2]
						if w < h:
								UpperCamelCase          =					int(self.size['shortest_edge'] * h / w	)
								UpperCamelCase          =					self.size['shortest_edge']
						elif w > h:
								UpperCamelCase          =					self.size['shortest_edge']
								UpperCamelCase          =					int(self.size['shortest_edge'] * w / h	)
						else:
								UpperCamelCase          =					self.size['shortest_edge']
								UpperCamelCase          =					self.size['shortest_edge']

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

				return expected_height, expected_width






@require_torch
@require_vision
class     lowercase (		_SCREAMING_SNAKE_CASE  ,       unittest.TestCase       ):
		__lowercase	:	str     					=    DeformableDetrImageProcessor if is_vision_available() else None



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					DeformableDetrImageProcessingTester(self	)



		@property
		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				return self.image_processor_tester.prepare_image_processor_dict()



		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					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 __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					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    ,				A_	)

				UpperCamelCase          =					self.image_processing_class.from_dict(
				    self.image_processor_dict    ,				size=42    ,				max_size=84    ,				pad_and_return_pixel_mask=A_	)
				self.assertEqual(image_processor.size    ,				{'shortest_edge': 42, 'longest_edge': 84}	)
				self.assertEqual(image_processor.do_pad    ,				A_	)



		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				pass



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					self.image_processing_class(**self.image_processor_dict	)
				# create random PIL images
				UpperCamelCase          =					prepare_image_inputs(self.image_processor_tester    ,				equal_resolution=A_	)
				for image in image_inputs:
						self.assertIsInstance(A_    ,				Image.Image	)

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

				UpperCamelCase     ,  UpperCamelCase          =					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
				UpperCamelCase     ,  UpperCamelCase          =					self.image_processor_tester.get_expected_values(A_    ,				batched=A_	)

				UpperCamelCase          =					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 __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					self.image_processing_class(**self.image_processor_dict	)
				# create random numpy tensors
				UpperCamelCase          =					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
				UpperCamelCase          =					image_processing(image_inputs[0]    ,				return_tensors='pt'	).pixel_values

				UpperCamelCase     ,  UpperCamelCase          =					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
				UpperCamelCase          =					image_processing(A_    ,				return_tensors='pt'	).pixel_values

				UpperCamelCase     ,  UpperCamelCase          =					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 __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					self.image_processing_class(**self.image_processor_dict	)
				# create random PyTorch tensors
				UpperCamelCase          =					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
				UpperCamelCase          =					image_processing(image_inputs[0]    ,				return_tensors='pt'	).pixel_values

				UpperCamelCase     ,  UpperCamelCase          =					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
				UpperCamelCase          =					image_processing(A_    ,				return_tensors='pt'	).pixel_values

				UpperCamelCase     ,  UpperCamelCase          =					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 __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
				with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'    ,				'r'	) as f:
						UpperCamelCase          =					json.loads(f.read()	)

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

				# encode them
				UpperCamelCase          =					DeformableDetrImageProcessor()
				UpperCamelCase          =					image_processing(images=A_    ,				annotations=A_    ,				return_tensors='pt'	)

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

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

				# verify area
				UpperCamelCase          =					torch.tensor([5887.9600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]	)
				self.assertTrue(torch.allclose(encoding['labels'][0]['area']    ,				A_	)	)
				# verify boxes
				UpperCamelCase          =					torch.Size([6, 4]	)
				self.assertEqual(encoding['labels'][0]['boxes'].shape    ,				A_	)
				UpperCamelCase          =					torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]	)
				self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    ,				A_    ,				atol=1e-3	)	)
				# verify image_id
				UpperCamelCase          =					torch.tensor([39_769]	)
				self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,				A_	)	)
				# verify is_crowd
				UpperCamelCase          =					torch.tensor([0, 0, 0, 0, 0, 0]	)
				self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,				A_	)	)
				# verify class_labels
				UpperCamelCase          =					torch.tensor([75, 75, 63, 65, 17, 17]	)
				self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,				A_	)	)
				# verify orig_size
				UpperCamelCase          =					torch.tensor([480, 640]	)
				self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,				A_	)	)
				# verify size
				UpperCamelCase          =					torch.tensor([800, 1_066]	)
				self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,				A_	)	)



		@slow
		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
				with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'    ,				'r'	) as f:
						UpperCamelCase          =					json.loads(f.read()	)

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

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

				# encode them
				UpperCamelCase          =					DeformableDetrImageProcessor(format='coco_panoptic'	)
				UpperCamelCase          =					image_processing(images=A_    ,				annotations=A_    ,				masks_path=A_    ,				return_tensors='pt'	)

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

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

				# verify area
				UpperCamelCase          =					torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5879.6562, 7634.1147]	)
				self.assertTrue(torch.allclose(encoding['labels'][0]['area']    ,				A_	)	)
				# verify boxes
				UpperCamelCase          =					torch.Size([6, 4]	)
				self.assertEqual(encoding['labels'][0]['boxes'].shape    ,				A_	)
				UpperCamelCase          =					torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]	)
				self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    ,				A_    ,				atol=1e-3	)	)
				# verify image_id
				UpperCamelCase          =					torch.tensor([39_769]	)
				self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,				A_	)	)
				# verify is_crowd
				UpperCamelCase          =					torch.tensor([0, 0, 0, 0, 0, 0]	)
				self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,				A_	)	)
				# verify class_labels
				UpperCamelCase          =					torch.tensor([17, 17, 63, 75, 75, 93]	)
				self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,				A_	)	)
				# verify masks
				UpperCamelCase          =					822_873
				self.assertEqual(encoding['labels'][0]['masks'].sum().item()    ,				A_	)
				# verify orig_size
				UpperCamelCase          =					torch.tensor([480, 640]	)
				self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,				A_	)	)
				# verify size
				UpperCamelCase          =					torch.tensor([800, 1_066]	)
				self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,				A_	)	)


from typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin


class     lowercase (		_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE       ):



		@register_to_config
		def __init__(			self    ,				A_ = 768    ,				)  -> List[Any]:
				"""simple docstring"""
				super().__init__()

				UpperCamelCase          =					nn.Parameter(torch.zeros(1    ,				A_	)	)
				UpperCamelCase          =					nn.Parameter(torch.ones(1    ,				A_	)	)



		def __UpperCamelCase			(			self    ,				A_ = None    ,				A_ = None    ,				)  -> Any:
				"""simple docstring"""
				UpperCamelCase          =					nn.Parameter(self.mean.to(A_	).to(A_	)	)
				UpperCamelCase          =					nn.Parameter(self.std.to(A_	).to(A_	)	)
				return self



		def __UpperCamelCase			(			self    ,				A_	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					(embeds - self.mean) * 1.0 / self.std
				return embeds



		def __UpperCamelCase			(			self    ,				A_	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					(embeds * self.std) + self.mean
				return embeds
