



"""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 DetaImageProcessor




class SCREAMING_SNAKE_CASE__					(		unittest.TestCase      ):



						def __init__(self						,     _lowercase						,     _lowercase=7						,     _lowercase=3						,     _lowercase=30						,     _lowercase=400						,     _lowercase=True						,     _lowercase=None						,     _lowercase=True						,     _lowercase=[0.5, 0.5, 0.5]						,     _lowercase=[0.5, 0.5, 0.5]						,     _lowercase=True						,     _lowercase=1 / 255						,     _lowercase=True						,     ):





													'''simple docstring'''

													__a		:		Optional[Any]									=       size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
													__a		:		Union[str, Any]									=       parent
													__a		:		int									=       batch_size
													__a		:		Optional[int]									=       num_channels
													__a		:		Any									=       min_resolution
													__a		:		str									=       max_resolution
													__a		:		Union[str, Any]									=       do_resize
													__a		:		List[str]									=       size
													__a		:		Any									=       do_normalize
													__a		:		Tuple									=       image_mean
													__a		:		Tuple									=       image_std
													__a		:		List[str]									=       do_rescale
													__a		:		List[str]									=       rescale_factor
													__a		:		int									=       do_pad



						def     lowerCAmelCase__(self							):





													'''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     lowerCAmelCase__(self						,     _lowercase						,     _lowercase=False							):





													'''simple docstring'''

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

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

													return expected_height, expected_width







@require_torch
@require_vision
class SCREAMING_SNAKE_CASE__					(		__UpperCAmelCase     ,					unittest.TestCase      ):
						_lowerCAmelCase							=					DetaImageProcessor if is_vision_available() else None



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		str									=       DetaImageProcessingTester(self							)



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return self.image_processor_tester.prepare_image_processor_dict()



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Optional[Any]									=       self.image_processing_class(**self.image_processor_dict							)
													self.assertTrue(hasattr(_lowercase						,     """image_mean"""							)							)
													self.assertTrue(hasattr(_lowercase						,     """image_std"""							)							)
													self.assertTrue(hasattr(_lowercase						,     """do_normalize"""							)							)
													self.assertTrue(hasattr(_lowercase						,     """do_resize"""							)							)
													self.assertTrue(hasattr(_lowercase						,     """do_rescale"""							)							)
													self.assertTrue(hasattr(_lowercase						,     """do_pad"""							)							)
													self.assertTrue(hasattr(_lowercase						,     """size"""							)							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Any									=       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						,     _lowercase							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													pass



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

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

													# Test not batched input
													__a		:		Tuple									=       image_processing(image_inputs[0]						,     return_tensors="""pt"""							).pixel_values

													__a							,     __a		:		List[str]									=       self.image_processor_tester.get_expected_values(_lowercase							)

													self.assertEqual(
													    encoded_images.shape						,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)						,     )

													# Test batched
													__a							,     __a		:		Dict									=       self.image_processor_tester.get_expected_values(_lowercase						,     batched=_lowercase							)

													__a		:		int									=       image_processing(_lowercase						,     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							):





													'''simple docstring'''

													__a		:		Tuple									=       self.image_processing_class(**self.image_processor_dict							)
													# create random numpy tensors
													__a		:		Optional[Any]									=       prepare_image_inputs(self.image_processor_tester						,     equal_resolution=_lowercase						,     numpify=_lowercase							)
													for image in image_inputs:
																				self.assertIsInstance(_lowercase						,     np.ndarray							)

													# Test not batched input
													__a		:		Any									=       image_processing(image_inputs[0]						,     return_tensors="""pt"""							).pixel_values

													__a							,     __a		:		List[Any]									=       self.image_processor_tester.get_expected_values(_lowercase							)

													self.assertEqual(
													    encoded_images.shape						,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)						,     )

													# Test batched
													__a		:		Optional[int]									=       image_processing(_lowercase						,     return_tensors="""pt"""							).pixel_values

													__a							,     __a		:		Union[str, Any]									=       self.image_processor_tester.get_expected_values(_lowercase						,     batched=_lowercase							)

													self.assertEqual(
													    encoded_images.shape						,     (
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        expected_height,
													        expected_width,
													    )						,     )



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Tuple									=       self.image_processing_class(**self.image_processor_dict							)
													# create random PyTorch tensors
													__a		:		Union[str, Any]									=       prepare_image_inputs(self.image_processor_tester						,     equal_resolution=_lowercase						,     torchify=_lowercase							)
													for image in image_inputs:
																				self.assertIsInstance(_lowercase						,     torch.Tensor							)

													# Test not batched input
													__a		:		List[Any]									=       image_processing(image_inputs[0]						,     return_tensors="""pt"""							).pixel_values

													__a							,     __a		:		Tuple									=       self.image_processor_tester.get_expected_values(_lowercase							)

													self.assertEqual(
													    encoded_images.shape						,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)						,     )

													# Test batched
													__a		:		Union[str, Any]									=       image_processing(_lowercase						,     return_tensors="""pt"""							).pixel_values

													__a							,     __a		:		Union[str, Any]									=       self.image_processor_tester.get_expected_values(_lowercase						,     batched=_lowercase							)

													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							):





													'''simple docstring'''

													__a		:		List[Any]									=       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
													with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""						,     """r"""							) as f:
																				__a		:		Union[str, Any]									=       json.loads(f.read()							)

													__a		:		Optional[int]									=       {"""image_id""": 39769, """annotations""": target}

													# encode them
													__a		:		Union[str, Any]									=       DetaImageProcessor()
													__a		:		str									=       image_processing(images=_lowercase						,     annotations=_lowercase						,     return_tensors="""pt"""							)

													# verify pixel values
													__a		:		str									=       torch.Size([1, 3, 800, 1066]							)
													self.assertEqual(encoding["""pixel_values"""].shape						,     _lowercase							)

													__a		:		Optional[int]									=       torch.tensor([0.2796, 0.3138, 0.3481]							)
													self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]						,     _lowercase						,     atol=1e-4							)							)

													# verify area
													__a		:		int									=       torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]							)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]						,     _lowercase							)							)
													# verify boxes
													__a		:		List[str]									=       torch.Size([6, 4]							)
													self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape						,     _lowercase							)
													__a		:		Tuple									=       torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]							)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]						,     _lowercase						,     atol=1e-3							)							)
													# verify image_id
													__a		:		List[Any]									=       torch.tensor([39769]							)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]						,     _lowercase							)							)
													# verify is_crowd
													__a		:		Tuple									=       torch.tensor([0, 0, 0, 0, 0, 0]							)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]						,     _lowercase							)							)
													# verify class_labels
													__a		:		int									=       torch.tensor([75, 75, 63, 65, 17, 17]							)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]						,     _lowercase							)							)
													# verify orig_size
													__a		:		Dict									=       torch.tensor([480, 640]							)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]						,     _lowercase							)							)
													# verify size
													__a		:		Dict									=       torch.tensor([800, 1066]							)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]						,     _lowercase							)							)





						@slow
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Tuple									=       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
													with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""						,     """r"""							) as f:
																				__a		:		List[Any]									=       json.loads(f.read()							)

													__a		:		Dict									=       {"""file_name""": """000000039769.png""", """image_id""": 39769, """segments_info""": target}

													__a		:		Union[str, Any]									=       pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""							)

													# encode them
													__a		:		Union[str, Any]									=       DetaImageProcessor(format="""coco_panoptic"""							)
													__a		:		List[Any]									=       image_processing(images=_lowercase						,     annotations=_lowercase						,     masks_path=_lowercase						,     return_tensors="""pt"""							)

													# verify pixel values
													__a		:		Optional[int]									=       torch.Size([1, 3, 800, 1066]							)
													self.assertEqual(encoding["""pixel_values"""].shape						,     _lowercase							)

													__a		:		Optional[Any]									=       torch.tensor([0.2796, 0.3138, 0.3481]							)
													self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]						,     _lowercase						,     atol=1e-4							)							)

													# verify area
													__a		:		Optional[Any]									=       torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]							)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]						,     _lowercase							)							)
													# verify boxes
													__a		:		Tuple									=       torch.Size([6, 4]							)
													self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape						,     _lowercase							)
													__a		:		str									=       torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]							)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]						,     _lowercase						,     atol=1e-3							)							)
													# verify image_id
													__a		:		Optional[Any]									=       torch.tensor([39769]							)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]						,     _lowercase							)							)
													# verify is_crowd
													__a		:		str									=       torch.tensor([0, 0, 0, 0, 0, 0]							)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]						,     _lowercase							)							)
													# verify class_labels
													__a		:		Optional[int]									=       torch.tensor([17, 17, 63, 75, 75, 93]							)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]						,     _lowercase							)							)
													# verify masks
													__a		:		List[str]									=       822873
													self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()						,     _lowercase							)
													# verify orig_size
													__a		:		List[str]									=       torch.tensor([480, 640]							)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]						,     _lowercase							)							)
													# verify size
													__a		:		Optional[Any]									=       torch.tensor([800, 1066]							)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]						,     _lowercase							)							)






"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__			   =  logging.get_logger(__name__)

lowercase__			   =  {
    "sayakpaul/vit-msn-base": "https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json",
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}




class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
						_lowerCAmelCase							=					"vit_msn"



						def __init__(self						,     _lowercase=768						,     _lowercase=12						,     _lowercase=12						,     _lowercase=3072						,     _lowercase="gelu"						,     _lowercase=0.0						,     _lowercase=0.0						,     _lowercase=0.02						,     _lowercase=1e-06						,     _lowercase=224						,     _lowercase=16						,     _lowercase=3						,     _lowercase=True						,     **_lowercase						,     ):





													'''simple docstring'''

													super().__init__(**_lowercase							)

													__a		:		int									=       hidden_size
													__a		:		str									=       num_hidden_layers
													__a		:		str									=       num_attention_heads
													__a		:		Optional[Any]									=       intermediate_size
													__a		:		Union[str, Any]									=       hidden_act
													__a		:		Tuple									=       hidden_dropout_prob
													__a		:		Any									=       attention_probs_dropout_prob
													__a		:		List[Any]									=       initializer_range
													__a		:		Any									=       layer_norm_eps
													__a		:		Dict									=       image_size
													__a		:		List[Any]									=       patch_size
													__a		:		Dict									=       num_channels
													__a		:		Optional[Any]									=       qkv_bias

