


'''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 DetrImageProcessor
class _lowerCamelCase	(						unittest.TestCase							):



		'''simple docstring'''







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







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

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

			return expected_height, expected_width







@require_torch
@require_vision
class _lowerCamelCase	(						lowercase__		,					unittest.TestCase							):



		'''simple docstring'''







		A_     :      Union[str, Any]				   =    DetrImageProcessor if is_vision_available() else None







		def        __lowerCAmelCase		( self		:						Optional[int]      )  ->			int:
			__magic_name__	:   Optional[Any]					     =					DetrImageProcessingTester(self      )







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







		def        __lowerCAmelCase		( self		:						Dict      )  ->			Tuple:
			__magic_name__	:   Tuple					     =					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        __lowerCAmelCase		( self		:						int      )  ->			List[str]:
			__magic_name__	:   List[str]					     =					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      )

			__magic_name__	:   List[str]					     =					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        __lowerCAmelCase		( self		:						str      )  ->			Dict:
			pass







		def        __lowerCAmelCase		( self		:						List[str]      )  ->			List[str]:
			# Initialize image_processing
			__magic_name__	:   int					     =					self.image_processing_class(**self.image_processor_dict      )
			# create random PIL images
			__magic_name__	:   Optional[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
			__magic_name__	:   Tuple					     =					image_processing(image_inputs[0]  ,					return_tensors='pt'      ).pixel_values

			__magic_name__     ,    __magic_name__	:   int					     =					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
			__magic_name__     ,    __magic_name__	:   Optional[Any]					     =					self.image_processor_tester.get_expected_values(_A  ,					batched=_A      )

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

			__magic_name__     ,    __magic_name__	:   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
			__magic_name__	:   Any					     =					image_processing(_A  ,					return_tensors='pt'      ).pixel_values

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

			__magic_name__     ,    __magic_name__	:   int					     =					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
			__magic_name__	:   Any					     =					image_processing(_A  ,					return_tensors='pt'      ).pixel_values

			__magic_name__     ,    __magic_name__	:   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,
			    )  ,					)







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

			__magic_name__	:   Tuple					     =					{'image_id': 39769, 'annotations': target}

			# encode them
			__magic_name__	:   List[Any]					     =					DetrImageProcessor.from_pretrained('facebook/detr-resnet-50'      )
			__magic_name__	:   List[str]					     =					image_processing(images=_A  ,					annotations=_A  ,					return_tensors='pt'      )

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

			__magic_name__	:   List[Any]					     =					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
			__magic_name__	:   Union[str, Any]					     =					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']  ,					_A      )      )
			# verify boxes
			__magic_name__	:   Optional[int]					     =					torch.Size([6, 4]      )
			self.assertEqual(encoding['labels'][0]['boxes'].shape  ,					_A      )
			__magic_name__	:   str					     =					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
			__magic_name__	:   Optional[int]					     =					torch.tensor([39769]      )
			self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']  ,					_A      )      )
			# verify is_crowd
			__magic_name__	:   Union[str, Any]					     =					torch.tensor([0, 0, 0, 0, 0, 0]      )
			self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']  ,					_A      )      )
			# verify class_labels
			__magic_name__	:   List[str]					     =					torch.tensor([75, 75, 63, 65, 17, 17]      )
			self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']  ,					_A      )      )
			# verify orig_size
			__magic_name__	:   Optional[int]					     =					torch.tensor([480, 640]      )
			self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']  ,					_A      )      )
			# verify size
			__magic_name__	:   str					     =					torch.tensor([800, 1066]      )
			self.assertTrue(torch.allclose(encoding['labels'][0]['size']  ,					_A      )      )







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

			__magic_name__	:   List[Any]					     =					{'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}

			__magic_name__	:   str					     =					pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'      )

			# encode them
			__magic_name__	:   Tuple					     =					DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic'      )
			__magic_name__	:   List[Any]					     =					image_processing(images=_A  ,					annotations=_A  ,					masks_path=_A  ,					return_tensors='pt'      )

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

			__magic_name__	:   Optional[int]					     =					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
			__magic_name__	:   Tuple					     =					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']  ,					_A      )      )
			# verify boxes
			__magic_name__	:   Any					     =					torch.Size([6, 4]      )
			self.assertEqual(encoding['labels'][0]['boxes'].shape  ,					_A      )
			__magic_name__	:   List[Any]					     =					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
			__magic_name__	:   Optional[int]					     =					torch.tensor([39769]      )
			self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']  ,					_A      )      )
			# verify is_crowd
			__magic_name__	:   Optional[Any]					     =					torch.tensor([0, 0, 0, 0, 0, 0]      )
			self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']  ,					_A      )      )
			# verify class_labels
			__magic_name__	:   Dict					     =					torch.tensor([17, 17, 63, 75, 75, 93]      )
			self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']  ,					_A      )      )
			# verify masks
			__magic_name__	:   List[Any]					     =					822873
			self.assertEqual(encoding['labels'][0]['masks'].sum().item()  ,					_A      )
			# verify orig_size
			__magic_name__	:   Tuple					     =					torch.tensor([480, 640]      )
			self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']  ,					_A      )      )
			# verify size
			__magic_name__	:   Tuple					     =					torch.tensor([800, 1066]      )
			self.assertTrue(torch.allclose(encoding['labels'][0]['size']  ,					_A      )      )



'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase				:str        =      {
    '''configuration_vision_encoder_decoder''': ['''VisionEncoderDecoderConfig''', '''VisionEncoderDecoderOnnxConfig''']
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase				:List[str]        =      ['''VisionEncoderDecoderModel''']

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase				:List[str]        =      ['''TFVisionEncoderDecoderModel''']

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase				:Optional[Any]        =      ['''FlaxVisionEncoderDecoderModel''']

if TYPE_CHECKING:
							from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
							import sys

							lowerCAmelCase				:Tuple        =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)