


"""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   _UpperCamelCase  (   unittest.TestCase		):
							'''simple docstring'''







							def __init__(     self    ,   __a    ,   __a=7    ,   __a=3    ,   __a=30    ,   __a=4_00    ,   __a=True    ,   __a=None    ,   __a=True    ,   __a=[0.5, 0.5, 0.5]    ,   __a=[0.5, 0.5, 0.5]    ,   __a=True    ,   __a=1 / 2_55    ,   __a=True    ,   ):
										# 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": 13_33}
										__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	):
										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	):
										if not batched:
													__lowerCAmelCase				    =			image_inputs[0]
													if isinstance(__a    ,   Image.Image	):
																__lowerCAmelCase    ,	__lowerCAmelCase				    =			image.size
													else:
																__lowerCAmelCase    ,	__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    ,	__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   _UpperCamelCase  (   snake_case_       ,unittest.TestCase		):
							'''simple docstring'''







							__UpperCAmelCase						:    Optional[int]				      =YolosImageProcessor if is_vision_available() else None

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			YolosImageProcessingTester(self	)

							@property
							def 			snake_case				(     self	):
										return self.image_processor_tester.prepare_image_processor_dict()

							def 			snake_case				(     self	):
										__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    ,   "size"	)	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.image_processing_class.from_dict(self.image_processor_dict	)
										self.assertEqual(image_processor.size    ,   {"shortest_edge": 18, "longest_edge": 13_33}	)
										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	):
										pass

							def 			snake_case				(     self	):
										# 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    ,	__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    ,	__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	):
										# 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    ,	__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    ,	__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	):
										# 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    ,	__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    ,	__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	):
										# Initialize image_processings
										__lowerCAmelCase				    =			self.image_processing_class(**self.image_processor_dict	)
										__lowerCAmelCase				    =			self.image_processing_class(do_resize=__a    ,   do_normalize=__a    ,   do_rescale=__a	)
										# 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 whether the method "pad" and calling the image processor return the same tensors
										__lowerCAmelCase				    =			image_processing_a.pad(__a    ,   return_tensors="pt"	)
										__lowerCAmelCase				    =			image_processing_a(__a    ,   return_tensors="pt"	)

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

							@slow
							def 			snake_case				(     self	):
										# 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_97_69, "annotations": target}

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

										# verify pixel values
										__lowerCAmelCase				    =			torch.Size([1, 3, 8_00, 10_66]	)
										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([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]	)
										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_97_69]	)
										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([4_80, 6_40]	)
										self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]    ,   __a	)	)
										# verify size
										__lowerCAmelCase				    =			torch.tensor([8_00, 10_66]	)
										self.assertTrue(torch.allclose(encoding["labels"][0]["size"]    ,   __a	)	)



							@slow
							def 			snake_case				(     self	):
										# 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_97_69, "segments_info": target}

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

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

										# verify pixel values
										__lowerCAmelCase				    =			torch.Size([1, 3, 8_00, 10_66]	)
										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_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]	)
										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_97_69]	)
										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_28_73
										self.assertEqual(encoding["labels"][0]["masks"].sum().item()    ,   __a	)
										# verify orig_size
										__lowerCAmelCase				    =			torch.tensor([4_80, 6_40]	)
										self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]    ,   __a	)	)
										# verify size
										__lowerCAmelCase				    =			torch.tensor([8_00, 10_66]	)
										self.assertTrue(torch.allclose(encoding["labels"][0]["size"]    ,   __a	)	)






from abc import ABC, abstractmethod
from argparse import ArgumentParser




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	@staticmethod
	@abstractmethod
	def     SCREAMING_SNAKE_CASE_(    lowercase )				->       int:
					raise NotImplementedError()



	@abstractmethod
	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					raise NotImplementedError()


