






'''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 ConditionalDetrImageProcessor





class 	SCREAMING_SNAKE_CASE					(					unittest.TestCase       ):






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






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

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

							return expected_height, expected_width





@require_torch
@require_vision
class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_	,   unittest.TestCase       ):
			snake_case__  =				ConditionalDetrImageProcessor if is_vision_available() else None






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]					)			->			Union[str, Any]:
							a_		:  Union[str, Any]											=      ConditionalDetrImageProcessingTester(self					)






			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]					)			->			Union[str, Any]:
							return self.image_processor_tester.prepare_image_processor_dict()






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]					)			->			List[str]:
							a_		:  Tuple											=      self.image_processing_class(**self.image_processor_dict					)
							self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,						'''image_mean'''					)					)
							self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,						'''image_std'''					)					)
							self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,						'''do_normalize'''					)					)
							self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,						'''do_resize'''					)					)
							self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,						'''size'''					)					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			int					)			->			Optional[int]:
							a_		:  Dict											=      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  ,						__SCREAMING_SNAKE_CASE					)

							a_		:  List[str]											=      self.image_processing_class.from_dict(
							    self.image_processor_dict  ,						size=42  ,						max_size=84  ,						pad_and_return_pixel_mask=__SCREAMING_SNAKE_CASE					)
							self.assertEqual(image_processor.size  ,						{'''shortest_edge''': 42, '''longest_edge''': 84}					)
							self.assertEqual(image_processor.do_pad  ,						__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Any					)			->			Tuple:
							pass






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]					)			->			int:
							# Initialize image_processing
							a_		:  int											=      self.image_processing_class(**self.image_processor_dict					)
							# create random PIL images
							a_		:  Dict											=      prepare_image_inputs(self.image_processor_tester  ,						equal_resolution=__SCREAMING_SNAKE_CASE					)
							for image in image_inputs:
											self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,						Image.Image					)

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

							a_				,			a_		:  Optional[int]											=      self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE					)

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

							# Test batched
							a_				,			a_		:  Any											=      self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE  ,						batched=__SCREAMING_SNAKE_CASE					)

							a_		:  Union[str, Any]											=      image_processing(__SCREAMING_SNAKE_CASE  ,						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 			SCREAMING_SNAKE_CASE      ( self      :			Any					)			->			List[str]:
							# Initialize image_processing
							a_		:  Optional[Any]											=      self.image_processing_class(**self.image_processor_dict					)
							# create random numpy tensors
							a_		:  int											=      prepare_image_inputs(self.image_processor_tester  ,						equal_resolution=__SCREAMING_SNAKE_CASE  ,						numpify=__SCREAMING_SNAKE_CASE					)
							for image in image_inputs:
											self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,						np.ndarray					)

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

							a_				,			a_		:  Union[str, Any]											=      self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE					)

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

							# Test batched
							a_		:  Union[str, Any]											=      image_processing(__SCREAMING_SNAKE_CASE  ,						return_tensors='''pt'''					).pixel_values

							a_				,			a_		:  int											=      self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE  ,						batched=__SCREAMING_SNAKE_CASE					)

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






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]					)			->			Dict:
							# Initialize image_processing
							a_		:  int											=      self.image_processing_class(**self.image_processor_dict					)
							# create random PyTorch tensors
							a_		:  Tuple											=      prepare_image_inputs(self.image_processor_tester  ,						equal_resolution=__SCREAMING_SNAKE_CASE  ,						torchify=__SCREAMING_SNAKE_CASE					)
							for image in image_inputs:
											self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,						torch.Tensor					)

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

							a_				,			a_		:  Dict											=      self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE					)

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

							# Test batched
							a_		:  List[str]											=      image_processing(__SCREAMING_SNAKE_CASE  ,						return_tensors='''pt'''					).pixel_values

							a_				,			a_		:  Union[str, Any]											=      self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE  ,						batched=__SCREAMING_SNAKE_CASE					)

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






			@slow
			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]					)			->			Union[str, Any]:
							# prepare image and target
							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_		:  List[str]											=      json.loads(f.read()					)

							a_		:  Optional[Any]											=      {'''image_id''': 3_9769, '''annotations''': target}

							# encode them
							a_		:  Dict											=      ConditionalDetrImageProcessor.from_pretrained('''microsoft/conditional-detr-resnet-50'''					)
							a_		:  Tuple											=      image_processing(images=__SCREAMING_SNAKE_CASE  ,						annotations=__SCREAMING_SNAKE_CASE  ,						return_tensors='''pt'''					)

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

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

							# verify area
							a_		:  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''']  ,						__SCREAMING_SNAKE_CASE					)					)
							# verify boxes
							a_		:  Optional[Any]											=      torch.Size([6, 4]					)
							self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape  ,						__SCREAMING_SNAKE_CASE					)
							a_		:  int											=      torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]					)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]  ,						__SCREAMING_SNAKE_CASE  ,						atol=1e-3					)					)
							# verify image_id
							a_		:  int											=      torch.tensor([3_9769]					)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']  ,						__SCREAMING_SNAKE_CASE					)					)
							# verify is_crowd
							a_		:  Any											=      torch.tensor([0, 0, 0, 0, 0, 0]					)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']  ,						__SCREAMING_SNAKE_CASE					)					)
							# verify class_labels
							a_		:  Union[str, Any]											=      torch.tensor([75, 75, 63, 65, 17, 17]					)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']  ,						__SCREAMING_SNAKE_CASE					)					)
							# verify orig_size
							a_		:  Optional[int]											=      torch.tensor([480, 640]					)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']  ,						__SCREAMING_SNAKE_CASE					)					)
							# verify size
							a_		:  List[Any]											=      torch.tensor([800, 1066]					)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']  ,						__SCREAMING_SNAKE_CASE					)					)







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

							a_		:  Tuple											=      {'''file_name''': '''000000039769.png''', '''image_id''': 3_9769, '''segments_info''': target}

							a_		:  int											=      pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''					)

							# encode them
							a_		:  str											=      ConditionalDetrImageProcessor(format='''coco_panoptic'''					)
							a_		:  List[str]											=      image_processing(images=__SCREAMING_SNAKE_CASE  ,						annotations=__SCREAMING_SNAKE_CASE  ,						masks_path=__SCREAMING_SNAKE_CASE  ,						return_tensors='''pt'''					)

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

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

							# verify area
							a_		:  Union[str, Any]											=      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''']  ,						__SCREAMING_SNAKE_CASE					)					)
							# verify boxes
							a_		:  int											=      torch.Size([6, 4]					)
							self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape  ,						__SCREAMING_SNAKE_CASE					)
							a_		:  Optional[Any]											=      torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]					)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]  ,						__SCREAMING_SNAKE_CASE  ,						atol=1e-3					)					)
							# verify image_id
							a_		:  Any											=      torch.tensor([3_9769]					)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']  ,						__SCREAMING_SNAKE_CASE					)					)
							# verify is_crowd
							a_		:  Any											=      torch.tensor([0, 0, 0, 0, 0, 0]					)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']  ,						__SCREAMING_SNAKE_CASE					)					)
							# verify class_labels
							a_		:  str											=      torch.tensor([17, 17, 63, 75, 75, 93]					)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']  ,						__SCREAMING_SNAKE_CASE					)					)
							# verify masks
							a_		:  Tuple											=      82_2873
							self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()  ,						__SCREAMING_SNAKE_CASE					)
							# verify orig_size
							a_		:  Optional[int]											=      torch.tensor([480, 640]					)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']  ,						__SCREAMING_SNAKE_CASE					)					)
							# verify size
							a_		:  List[Any]											=      torch.tensor([800, 1066]					)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']  ,						__SCREAMING_SNAKE_CASE					)					)












'''simple docstring'''







import torch

from transformers import AutoModel





class 	SCREAMING_SNAKE_CASE					(					torch.nn.Module       ):






			def __init__( self      :			Optional[int]  ,						__SCREAMING_SNAKE_CASE      :			int="sayef/fsner-bert-base-uncased"					)			->			str:
							super(__SCREAMING_SNAKE_CASE  ,						self					).__init__()

							a_		:  str											=      AutoModel.from_pretrained(__SCREAMING_SNAKE_CASE  ,						return_dict=__SCREAMING_SNAKE_CASE					)
							a_		:  Optional[int]											=      torch.nn.CosineSimilarity(3  ,						1e-08					)
							a_		:  Dict											=      torch.nn.Softmax(dim=1					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						**__SCREAMING_SNAKE_CASE      :			int					)			->			str:
							return self.bert(**__SCREAMING_SNAKE_CASE					).last_hidden_state






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			List[Any]					)			->			Optional[int]:
							return token_embeddings.sum(2  ,						keepdim=__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			int=1					)			->			Dict:
							return self.softmax(T * self.cos(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)					)







			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]					)			->			Any:
							a_		:  Dict											=      W_supports['''sizes'''].tolist()
							a_		:  Tuple											=      W_supports['''start_token_id'''].item()
							a_		:  List[Any]											=      W_supports['''end_token_id'''].item()

							del W_supports["sizes"]
							del W_supports["start_token_id"]
							del W_supports["end_token_id"]

							a_		:  int											=      self.BERT(**__SCREAMING_SNAKE_CASE					)
							a_		:  Any											=      self.BERT(**__SCREAMING_SNAKE_CASE					)

							a_		:  Optional[Any]											=      None
							a_		:  Tuple											=      None

							a_		:  List[str]											=      W_supports['''input_ids'''] == start_token_id
							a_		:  Dict											=      W_supports['''input_ids'''] == end_token_id

							for i, size in enumerate(__SCREAMING_SNAKE_CASE					):
											if i == 0:
															a_		:  str											=      0
											else:
															a_		:  str											=      support_sizes[i - 1]

											a_		:  Union[str, Any]											=      S[s : s + size][start_token_masks[s : s + size]]
											a_		:  Tuple											=      S[s : s + size][end_token_masks[s : s + size]]

											a_		:  Tuple											=      torch.matmul(q[i]  ,						s_start.T					).sum(1					).softmax(0					)
											a_		:  Optional[Any]											=      torch.matmul(q[i]  ,						s_end.T					).sum(1					).softmax(0					)

											if p_starts is not None:
															a_		:  Any											=      torch.vstack((p_starts, p_start)					)
															a_		:  Dict											=      torch.vstack((p_ends, p_end)					)
											else:
															a_		:  Optional[int]											=      p_start
															a_		:  List[Any]											=      p_end

							return p_starts, p_ends




