




'''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 YolosImageProcessor




class  _snake_case		(	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'''




										snake_case_						=      size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
										snake_case_						=      parent
										snake_case_						=      batch_size
										snake_case_						=      num_channels
										snake_case_						=      min_resolution
										snake_case_						=      max_resolution
										snake_case_						=      do_resize
										snake_case_						=      size
										snake_case_						=      do_normalize
										snake_case_						=      image_mean
										snake_case_						=      image_std
										snake_case_						=      do_rescale
										snake_case_						=      rescale_factor
										snake_case_						=      do_pad




			def    lowerCAmelCase__				(					self							)				->				List[str]:

										'''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				,							a__				,							a__=False							)				->				Tuple:

										'''simple docstring'''




										if not batched:
																	snake_case_						=      image_inputs[0]
																	if isinstance(__lowerCAmelCase				,							Image.Image							):
																								snake_case_					,      snake_case_						=      image.size
																	else:
																								snake_case_					,      snake_case_						=      image.shape[1], image.shape[2]
																	if w < h:
																								snake_case_						=      int(self.size["shortest_edge"] * h / w							)
																								snake_case_						=      self.size["shortest_edge"]
																	elif w > h:
																								snake_case_						=      self.size["shortest_edge"]
																								snake_case_						=      int(self.size["shortest_edge"] * w / h							)
																	else:
																								snake_case_						=      self.size["shortest_edge"]
																								snake_case_						=      self.size["shortest_edge"]

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

										return expected_height, expected_width




@require_torch
@require_vision
class  _snake_case		(	lowercase_						,						unittest.TestCase							):
			lowerCAmelCase_     :      List[str]						       =    YolosImageProcessor if is_vision_available() else None




			def    lowerCAmelCase__				(					self							)				->				Any:

										'''simple docstring'''




										snake_case_						=      YolosImageProcessingTester(self							)




			@property
			def    lowerCAmelCase__				(					self							)				->				Any:

										'''simple docstring'''




										return self.image_processor_tester.prepare_image_processor_dict()




			def    lowerCAmelCase__				(					self							)				->				Optional[Any]:

										'''simple docstring'''




										snake_case_						=      self.image_processing_class(**self.image_processor_dict							)
										self.assertTrue(hasattr(__lowerCAmelCase				,							"image_mean"							)							)
										self.assertTrue(hasattr(__lowerCAmelCase				,							"image_std"							)							)
										self.assertTrue(hasattr(__lowerCAmelCase				,							"do_normalize"							)							)
										self.assertTrue(hasattr(__lowerCAmelCase				,							"do_resize"							)							)
										self.assertTrue(hasattr(__lowerCAmelCase				,							"size"							)							)




			def    lowerCAmelCase__				(					self							)				->				Optional[int]:

										'''simple docstring'''




										snake_case_						=      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				,							__lowerCAmelCase							)

										snake_case_						=      self.image_processing_class.from_dict(
										    self.image_processor_dict				,							size=42				,							max_size=84				,							pad_and_return_pixel_mask=__lowerCAmelCase							)
										self.assertEqual(image_processor.size				,							{"shortest_edge": 42, "longest_edge": 84}							)
										self.assertEqual(image_processor.do_pad				,							__lowerCAmelCase							)




			def    lowerCAmelCase__				(					self							)				->				Union[str, Any]:

										'''simple docstring'''




										pass




			def    lowerCAmelCase__				(					self							)				->				int:

										'''simple docstring'''




										snake_case_						=      self.image_processing_class(**self.image_processor_dict							)
										# create random PIL images
										snake_case_						=      prepare_image_inputs(self.image_processor_tester				,							equal_resolution=__lowerCAmelCase							)
										for image in image_inputs:
																	self.assertIsInstance(__lowerCAmelCase				,							Image.Image							)

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

										snake_case_					,      snake_case_						=      self.image_processor_tester.get_expected_values(__lowerCAmelCase							)

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

										# Test batched
										snake_case_					,      snake_case_						=      self.image_processor_tester.get_expected_values(__lowerCAmelCase				,							batched=__lowerCAmelCase							)

										snake_case_						=      image_processing(__lowerCAmelCase				,							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							)				->				Any:

										'''simple docstring'''




										snake_case_						=      self.image_processing_class(**self.image_processor_dict							)
										# create random numpy tensors
										snake_case_						=      prepare_image_inputs(self.image_processor_tester				,							equal_resolution=__lowerCAmelCase				,							numpify=__lowerCAmelCase							)
										for image in image_inputs:
																	self.assertIsInstance(__lowerCAmelCase				,							np.ndarray							)

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

										snake_case_					,      snake_case_						=      self.image_processor_tester.get_expected_values(__lowerCAmelCase							)

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

										# Test batched
										snake_case_						=      image_processing(__lowerCAmelCase				,							return_tensors="pt"							).pixel_values

										snake_case_					,      snake_case_						=      self.image_processor_tester.get_expected_values(__lowerCAmelCase				,							batched=__lowerCAmelCase							)

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




			def    lowerCAmelCase__				(					self							)				->				Optional[Any]:

										'''simple docstring'''




										snake_case_						=      self.image_processing_class(**self.image_processor_dict							)
										# create random PyTorch tensors
										snake_case_						=      prepare_image_inputs(self.image_processor_tester				,							equal_resolution=__lowerCAmelCase				,							torchify=__lowerCAmelCase							)
										for image in image_inputs:
																	self.assertIsInstance(__lowerCAmelCase				,							torch.Tensor							)

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

										snake_case_					,      snake_case_						=      self.image_processor_tester.get_expected_values(__lowerCAmelCase							)

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

										# Test batched
										snake_case_						=      image_processing(__lowerCAmelCase				,							return_tensors="pt"							).pixel_values

										snake_case_					,      snake_case_						=      self.image_processor_tester.get_expected_values(__lowerCAmelCase				,							batched=__lowerCAmelCase							)

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




			def    lowerCAmelCase__				(					self							)				->				Union[str, Any]:

										'''simple docstring'''




										snake_case_						=      self.image_processing_class(**self.image_processor_dict							)
										snake_case_						=      self.image_processing_class(do_resize=__lowerCAmelCase				,							do_normalize=__lowerCAmelCase				,							do_rescale=__lowerCAmelCase							)
										# create random PyTorch tensors
										snake_case_						=      prepare_image_inputs(self.image_processor_tester				,							equal_resolution=__lowerCAmelCase				,							torchify=__lowerCAmelCase							)
										for image in image_inputs:
																	self.assertIsInstance(__lowerCAmelCase				,							torch.Tensor							)

										# Test whether the method "pad" and calling the image processor return the same tensors
										snake_case_						=      image_processing_a.pad(__lowerCAmelCase				,							return_tensors="pt"							)
										snake_case_						=      image_processing_a(__lowerCAmelCase				,							return_tensors="pt"							)

										self.assertTrue(
										    torch.allclose(encoded_images_with_method["pixel_values"]				,							encoded_images["pixel_values"]				,							atol=1e-4							)							)




			@slow
			def    lowerCAmelCase__				(					self							)				->				Tuple:

										'''simple docstring'''




										snake_case_						=      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
										with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"				,							"r"							) as f:
																	snake_case_						=      json.loads(f.read()							)

										snake_case_						=      {"image_id": 39_769, "annotations": target}

										# encode them
										snake_case_						=      YolosImageProcessor.from_pretrained("hustvl/yolos-small"							)
										snake_case_						=      image_processing(images=__lowerCAmelCase				,							annotations=__lowerCAmelCase				,							return_tensors="pt"							)

										# verify pixel values
										snake_case_						=      torch.Size([1, 3, 800, 1_066]							)
										self.assertEqual(encoding["pixel_values"].shape				,							__lowerCAmelCase							)

										snake_case_						=      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]				,							__lowerCAmelCase				,							atol=1e-4							)							)

										# verify area
										snake_case_						=      torch.tensor([5_8_8_7.9_6_0_0, 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"]				,							__lowerCAmelCase							)							)
										# verify boxes
										snake_case_						=      torch.Size([6, 4]							)
										self.assertEqual(encoding["labels"][0]["boxes"].shape				,							__lowerCAmelCase							)
										snake_case_						=      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]				,							__lowerCAmelCase				,							atol=1e-3							)							)
										# verify image_id
										snake_case_						=      torch.tensor([39_769]							)
										self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]				,							__lowerCAmelCase							)							)
										# verify is_crowd
										snake_case_						=      torch.tensor([0, 0, 0, 0, 0, 0]							)
										self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]				,							__lowerCAmelCase							)							)
										# verify class_labels
										snake_case_						=      torch.tensor([75, 75, 63, 65, 17, 17]							)
										self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]				,							__lowerCAmelCase							)							)
										# verify orig_size
										snake_case_						=      torch.tensor([480, 640]							)
										self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]				,							__lowerCAmelCase							)							)
										# verify size
										snake_case_						=      torch.tensor([800, 1_066]							)
										self.assertTrue(torch.allclose(encoding["labels"][0]["size"]				,							__lowerCAmelCase							)							)




			@slow
			def    lowerCAmelCase__				(					self							)				->				int:

										'''simple docstring'''




										snake_case_						=      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
										with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"				,							"r"							) as f:
																	snake_case_						=      json.loads(f.read()							)

										snake_case_						=      {"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}

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

										# encode them
										snake_case_						=      YolosImageProcessor(format="coco_panoptic"							)
										snake_case_						=      image_processing(images=__lowerCAmelCase				,							annotations=__lowerCAmelCase				,							masks_path=__lowerCAmelCase				,							return_tensors="pt"							)

										# verify pixel values
										snake_case_						=      torch.Size([1, 3, 800, 1_066]							)
										self.assertEqual(encoding["pixel_values"].shape				,							__lowerCAmelCase							)

										snake_case_						=      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]				,							__lowerCAmelCase				,							atol=1e-4							)							)

										# verify area
										snake_case_						=      torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]							)
										self.assertTrue(torch.allclose(encoding["labels"][0]["area"]				,							__lowerCAmelCase							)							)
										# verify boxes
										snake_case_						=      torch.Size([6, 4]							)
										self.assertEqual(encoding["labels"][0]["boxes"].shape				,							__lowerCAmelCase							)
										snake_case_						=      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]				,							__lowerCAmelCase				,							atol=1e-3							)							)
										# verify image_id
										snake_case_						=      torch.tensor([39_769]							)
										self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]				,							__lowerCAmelCase							)							)
										# verify is_crowd
										snake_case_						=      torch.tensor([0, 0, 0, 0, 0, 0]							)
										self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]				,							__lowerCAmelCase							)							)
										# verify class_labels
										snake_case_						=      torch.tensor([17, 17, 63, 75, 75, 93]							)
										self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]				,							__lowerCAmelCase							)							)
										# verify masks
										snake_case_						=      822_873
										self.assertEqual(encoding["labels"][0]["masks"].sum().item()				,							__lowerCAmelCase							)
										# verify orig_size
										snake_case_						=      torch.tensor([480, 640]							)
										self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]				,							__lowerCAmelCase							)							)
										# verify size
										snake_case_						=      torch.tensor([800, 1_066]							)
										self.assertTrue(torch.allclose(encoding["labels"][0]["size"]				,							__lowerCAmelCase							)							)











import numpy as np

from transformers import Pipeline





def lowerCAmelCase__(__snake_case							)				->      Tuple:







	'''simple docstring'''







	lowerCamelCase__								=		np.max(__snake_case       ,axis=-1       ,keepdims=__snake_case							)
	lowerCamelCase__								=		np.exp(outputs - maxes							)
	return shifted_exp / shifted_exp.sum(axis=-1       ,keepdims=__snake_case							)






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			def 		__lowerCamelCase				(    self       ,  **__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		{}
				if "second_text" in kwargs:
					lowerCamelCase__								=		kwargs['''second_text''']
				return preprocess_kwargs, {}, {}





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase=None							):



				'''simple docstring'''







				return self.tokenizer(__lowerCAmelCase       ,  text_pair=__lowerCAmelCase       ,  return_tensors=self.framework							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				return self.model(**__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		model_outputs.logits[0].numpy()
				lowerCamelCase__								=		softmax(__lowerCAmelCase							)

				lowerCamelCase__								=		np.argmax(__lowerCAmelCase							)
				lowerCamelCase__								=		self.model.config.idalabel[best_class]
				lowerCamelCase__								=		probabilities[best_class].item()
				lowerCamelCase__								=		logits.tolist()
				return {"label": label, "score": score, "logits": logits}



