



'''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 __snake_case(				unittest.TestCase ):


						'''simple docstring'''



						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:
							# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
							lowerCAmelCase   =     size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
							lowerCAmelCase   =     parent
							lowerCAmelCase   =     batch_size
							lowerCAmelCase   =     num_channels
							lowerCAmelCase   =     min_resolution
							lowerCAmelCase   =     max_resolution
							lowerCAmelCase   =     do_resize
							lowerCAmelCase   =     size
							lowerCAmelCase   =     do_normalize
							lowerCAmelCase   =     image_mean
							lowerCAmelCase   =     image_std
							lowerCAmelCase   =     do_rescale
							lowerCAmelCase   =     rescale_factor
							lowerCAmelCase   =     do_pad


						def     __snake_case   ( self				)     ->     Tuple:
							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     __snake_case   ( self     ,	A_     ,	A_=False				)     ->     str:
							if not batched:
								lowerCAmelCase   =     image_inputs[0]
								if isinstance(_A     ,	Image.Image				):
									lowerCAmelCase   =     image.size
								else:
									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   =     self.get_expected_values([image]				)
									expected_values.append((expected_height, expected_width)				)
								lowerCAmelCase   =     max(_A     ,	key=lambda A_				: item[0]				)[0]
								lowerCAmelCase   =     max(_A     ,	key=lambda A_				: item[1]				)[1]

							return expected_height, expected_width







@require_torch
@require_vision
class __snake_case(				_lowerCAmelCase		,							unittest.TestCase ):


						'''simple docstring'''



						UpperCAmelCase							:   Union[str, Any]									=		DeformableDetrImageProcessor if is_vision_available() else None


						def     __snake_case   ( self				)     ->     int:
							lowerCAmelCase   =     DeformableDetrImageProcessingTester(self				)


						@property
						def     __snake_case   ( self				)     ->     Dict:
							return self.image_processor_tester.prepare_image_processor_dict()


						def     __snake_case   ( self				)     ->     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_resize"""				)				)
							self.assertTrue(hasattr(_A     ,	"""do_rescale"""				)				)
							self.assertTrue(hasattr(_A     ,	"""do_pad"""				)				)
							self.assertTrue(hasattr(_A     ,	"""size"""				)				)


						def     __snake_case   ( self				)     ->     List[str]:
							lowerCAmelCase   =     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				)

							lowerCAmelCase   =     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     __snake_case   ( self				)     ->     List[Any]:
							pass


						def     __snake_case   ( self				)     ->     Optional[int]:
							# 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   =     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   =     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     __snake_case   ( self				)     ->     Optional[int]:
							# 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   =     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   =     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     __snake_case   ( self				)     ->     Union[str, Any]:
							# 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   =     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   =     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     __snake_case   ( self				)     ->     Dict:
							# 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_9769, 'annotations': target}

							# encode them
							lowerCAmelCase   =     DeformableDetrImageProcessor()
							lowerCAmelCase   =     image_processing(images=_A     ,	annotations=_A     ,	return_tensors="""pt"""				)

							# verify pixel values
							lowerCAmelCase   =     torch.Size([1, 3, 800, 1066]				)
							self.assertEqual(encoding["""pixel_values"""].shape     ,	_A				)

							lowerCAmelCase   =     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]     ,	_A     ,	atol=1e-4				)				)

							# verify area
							lowerCAmelCase   =     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
							lowerCAmelCase   =     torch.Size([6, 4]				)
							self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape     ,	_A				)
							lowerCAmelCase   =     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]     ,	_A     ,	atol=1e-3				)				)
							# verify image_id
							lowerCAmelCase   =     torch.tensor([3_9769]				)
							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([75, 75, 63, 65, 17, 17]				)
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]     ,	_A				)				)
							# verify orig_size
							lowerCAmelCase   =     torch.tensor([480, 640]				)
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]     ,	_A				)				)
							# verify size
							lowerCAmelCase   =     torch.tensor([800, 1066]				)
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]     ,	_A				)				)




						@slow
						def     __snake_case   ( self				)     ->     Union[str, Any]:
							# 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_9769, 'segments_info': target}

							lowerCAmelCase   =     pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""				)

							# encode them
							lowerCAmelCase   =     DeformableDetrImageProcessor(format="""coco_panoptic"""				)
							lowerCAmelCase   =     image_processing(images=_A     ,	annotations=_A     ,	masks_path=_A     ,	return_tensors="""pt"""				)

							# verify pixel values
							lowerCAmelCase   =     torch.Size([1, 3, 800, 1066]				)
							self.assertEqual(encoding["""pixel_values"""].shape     ,	_A				)

							lowerCAmelCase   =     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]     ,	_A     ,	atol=1e-4				)				)

							# verify area
							lowerCAmelCase   =     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
							lowerCAmelCase   =     torch.Size([6, 4]				)
							self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape     ,	_A				)
							lowerCAmelCase   =     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]     ,	_A     ,	atol=1e-3				)				)
							# verify image_id
							lowerCAmelCase   =     torch.tensor([3_9769]				)
							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([17, 17, 63, 75, 75, 93]				)
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]     ,	_A				)				)
							# verify masks
							lowerCAmelCase   =     82_2873
							self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()     ,	_A				)
							# verify orig_size
							lowerCAmelCase   =     torch.tensor([480, 640]				)
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]     ,	_A				)				)
							# verify size
							lowerCAmelCase   =     torch.tensor([800, 1066]				)
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]     ,	_A				)				)




'''simple docstring'''

from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						UpperCAmelCase							:   Union[str, Any]									=		["image_processor", "tokenizer"]
						UpperCAmelCase							:   Tuple									=		"BlipImageProcessor"
						UpperCAmelCase							:   Union[str, Any]									=		("BertTokenizer", "BertTokenizerFast")


						def __init__( self     ,	A_     ,	A_				)     ->     Dict:
							lowerCAmelCase   =     False
							super().__init__(A_     ,	A_				)
							lowerCAmelCase   =     self.image_processor


						def __call__( self     ,	A_ = None     ,	A_ = None     ,	A_ = True     ,	A_ = False     ,	A_ = None     ,	A_ = None     ,	A_ = 0     ,	A_ = None     ,	A_ = None     ,	A_ = False     ,	A_ = False     ,	A_ = False     ,	A_ = False     ,	A_ = False     ,	A_ = True     ,	A_ = None     ,	**A_     ,	)     ->     BatchEncoding:
							if images is None and text is None:
								raise ValueError("""You have to specify either images or text."""				)

							# Get only text
							if images is None:
								lowerCAmelCase   =     self.tokenizer
								lowerCAmelCase   =     self.tokenizer(
								    text=A_     ,	add_special_tokens=A_     ,	padding=A_     ,	truncation=A_     ,	max_length=A_     ,	stride=A_     ,	pad_to_multiple_of=A_     ,	return_attention_mask=A_     ,	return_overflowing_tokens=A_     ,	return_special_tokens_mask=A_     ,	return_offsets_mapping=A_     ,	return_token_type_ids=A_     ,	return_length=A_     ,	verbose=A_     ,	return_tensors=A_     ,	**A_     ,	)
								return text_encoding

							# add pixel_values
							lowerCAmelCase   =     self.image_processor(A_     ,	return_tensors=A_				)

							if text is not None:
								lowerCAmelCase   =     self.tokenizer(
								    text=A_     ,	add_special_tokens=A_     ,	padding=A_     ,	truncation=A_     ,	max_length=A_     ,	stride=A_     ,	pad_to_multiple_of=A_     ,	return_attention_mask=A_     ,	return_overflowing_tokens=A_     ,	return_special_tokens_mask=A_     ,	return_offsets_mapping=A_     ,	return_token_type_ids=A_     ,	return_length=A_     ,	verbose=A_     ,	return_tensors=A_     ,	**A_     ,	)
							else:
								lowerCAmelCase   =     None

							if text_encoding is not None:
								encoding_image_processor.update(A_				)

							return encoding_image_processor


						def     __snake_case   ( self     ,	*A_     ,	**A_				)     ->     Union[str, Any]:
							return self.tokenizer.batch_decode(*A_     ,	**A_				)


						def     __snake_case   ( self     ,	*A_     ,	**A_				)     ->     Tuple:
							return self.tokenizer.decode(*A_     ,	**A_				)




						@property
						def     __snake_case   ( self				)     ->     str:
							lowerCAmelCase   =     self.tokenizer.model_input_names
							lowerCAmelCase   =     self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)