
'''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					):







		"""simple docstring"""

		def __init__(      self				:			Dict  ,	UpperCamelCase__				:			Optional[Any]  ,	UpperCamelCase__				:			int=7  ,	UpperCamelCase__				:			Optional[int]=3  ,	UpperCamelCase__				:			Tuple=30  ,	UpperCamelCase__				:			str=400  ,	UpperCamelCase__				:			List[Any]=True  ,	UpperCamelCase__				:			str=None  ,	UpperCamelCase__				:			Union[str, Any]=True  ,	UpperCamelCase__				:			List[Any]=[0.5, 0.5, 0.5]  ,	UpperCamelCase__				:			Union[str, Any]=[0.5, 0.5, 0.5]  ,	UpperCamelCase__				:			Union[str, Any]=True  ,	UpperCamelCase__				:			Optional[Any]=1 / 255  ,	UpperCamelCase__				:			Union[str, Any]=True  ,	)						->			Optional[Any]:






				"""simple docstring"""







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



		def       lowerCAmelCase				(      self				:			List[str]      )						->			Optional[Any]:






				"""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				:			Dict  ,	UpperCamelCase__				:			Any  ,	UpperCamelCase__				:			Union[str, Any]=False      )						->			Optional[int]:






				"""simple docstring"""







				if not batched:
						snake_case  :       Optional[int]     			=						image_inputs[0]
						if isinstance(UpperCamelCase__  ,	Image.Image      ):
								snake_case			,snake_case  :       Tuple     			=						image.size
						else:
								snake_case			,snake_case  :       Tuple     			=						image.shape[1], image.shape[2]
						if w < h:
								snake_case  :       int     			=						int(self.size['''shortest_edge'''] * h / w      )
								snake_case  :       int     			=						self.size['''shortest_edge''']
						elif w > h:
								snake_case  :       int     			=						self.size['''shortest_edge''']
								snake_case  :       List[str]     			=						int(self.size['''shortest_edge'''] * w / h      )
						else:
								snake_case  :       Dict     			=						self.size['''shortest_edge''']
								snake_case  :       Optional[int]     			=						self.size['''shortest_edge''']

				else:
						snake_case  :       int     			=						[]
						for image in image_inputs:
								snake_case			,snake_case  :       Optional[int]     			=						self.get_expected_values([image]      )
								expected_values.append((expected_height, expected_width)      )
						snake_case  :       Any     			=						max(UpperCamelCase__  ,	key=lambda UpperCamelCase__      : item[0]      )[0]
						snake_case  :       Tuple     			=						max(UpperCamelCase__  ,	key=lambda UpperCamelCase__      : item[1]      )[1]

				return expected_height, expected_width







@require_torch
@require_vision
class 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					unittest.TestCase					):







		"""simple docstring"""

		lowerCamelCase     							=						YolosImageProcessor if is_vision_available() else None



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






				"""simple docstring"""







				snake_case  :       List[Any]     			=						YolosImageProcessingTester(self      )



		@property
		def       lowerCAmelCase				(      self				:			Tuple      )						->			int:






				"""simple docstring"""







				return self.image_processor_tester.prepare_image_processor_dict()



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






				"""simple docstring"""







				snake_case  :       List[str]     			=						self.image_processing_class(**self.image_processor_dict      )
				self.assertTrue(hasattr(UpperCamelCase__  ,	'''image_mean'''      )      )
				self.assertTrue(hasattr(UpperCamelCase__  ,	'''image_std'''      )      )
				self.assertTrue(hasattr(UpperCamelCase__  ,	'''do_normalize'''      )      )
				self.assertTrue(hasattr(UpperCamelCase__  ,	'''do_resize'''      )      )
				self.assertTrue(hasattr(UpperCamelCase__  ,	'''size'''      )      )



		def       lowerCAmelCase				(      self				:			str      )						->			Optional[Any]:






				"""simple docstring"""







				snake_case  :       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  ,	UpperCamelCase__      )

				snake_case  :       Optional[int]     			=						self.image_processing_class.from_dict(
				    self.image_processor_dict  ,	size=42  ,	max_size=84  ,	pad_and_return_pixel_mask=UpperCamelCase__      )
				self.assertEqual(image_processor.size  ,	{'''shortest_edge''': 42, '''longest_edge''': 84}      )
				self.assertEqual(image_processor.do_pad  ,	UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			Dict      )						->			Optional[Any]:






				"""simple docstring"""







				pass



		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			str:






				"""simple docstring"""







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

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

				snake_case			,snake_case  :       str     			=						self.image_processor_tester.get_expected_values(UpperCamelCase__      )

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

				# Test batched
				snake_case			,snake_case  :       Any     			=						self.image_processor_tester.get_expected_values(UpperCamelCase__  ,	batched=UpperCamelCase__      )

				snake_case  :       int     			=						image_processing(UpperCamelCase__  ,	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				:			Union[str, Any]      )						->			Tuple:






				"""simple docstring"""







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

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

				snake_case			,snake_case  :       Union[str, Any]     			=						self.image_processor_tester.get_expected_values(UpperCamelCase__      )

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

				# Test batched
				snake_case  :       List[Any]     			=						image_processing(UpperCamelCase__  ,	return_tensors='''pt'''      ).pixel_values

				snake_case			,snake_case  :       Tuple     			=						self.image_processor_tester.get_expected_values(UpperCamelCase__  ,	batched=UpperCamelCase__      )

				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]      )						->			Tuple:






				"""simple docstring"""







				snake_case  :       Optional[int]     			=						self.image_processing_class(**self.image_processor_dict      )
				# create random PyTorch tensors
				snake_case  :       int     			=						prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=UpperCamelCase__  ,	torchify=UpperCamelCase__      )
				for image in image_inputs:
						self.assertIsInstance(UpperCamelCase__  ,	torch.Tensor      )

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

				snake_case			,snake_case  :       int     			=						self.image_processor_tester.get_expected_values(UpperCamelCase__      )

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

				# Test batched
				snake_case  :       int     			=						image_processing(UpperCamelCase__  ,	return_tensors='''pt'''      ).pixel_values

				snake_case			,snake_case  :       Dict     			=						self.image_processor_tester.get_expected_values(UpperCamelCase__  ,	batched=UpperCamelCase__      )

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



		def       lowerCAmelCase				(      self				:			Dict      )						->			List[Any]:






				"""simple docstring"""







				snake_case  :       Tuple     			=						self.image_processing_class(**self.image_processor_dict      )
				snake_case  :       List[str]     			=						self.image_processing_class(do_resize=UpperCamelCase__  ,	do_normalize=UpperCamelCase__  ,	do_rescale=UpperCamelCase__      )
				# create random PyTorch tensors
				snake_case  :       Tuple     			=						prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=UpperCamelCase__  ,	torchify=UpperCamelCase__      )
				for image in image_inputs:
						self.assertIsInstance(UpperCamelCase__  ,	torch.Tensor      )

				# Test whether the method "pad" and calling the image processor return the same tensors
				snake_case  :       int     			=						image_processing_a.pad(UpperCamelCase__  ,	return_tensors='''pt'''      )
				snake_case  :       Union[str, Any]     			=						image_processing_a(UpperCamelCase__  ,	return_tensors='''pt'''      )

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



		@slow
		def       lowerCAmelCase				(      self				:			str      )						->			Any:






				"""simple docstring"""







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

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

				# encode them
				snake_case  :       Any     			=						YolosImageProcessor.from_pretrained('''hustvl/yolos-small'''      )
				snake_case  :       List[str]     			=						image_processing(images=UpperCamelCase__  ,	annotations=UpperCamelCase__  ,	return_tensors='''pt'''      )

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

				snake_case  :       Tuple     			=						torch.tensor([0.2_796, 0.3_138, 0.3_481]      )
				self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]  ,	UpperCamelCase__  ,	atol=1e-4      )      )

				# verify area
				snake_case  :       Dict     			=						torch.tensor([5_887.9_600, 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''']  ,	UpperCamelCase__      )      )
				# verify boxes
				snake_case  :       Optional[int]     			=						torch.Size([6, 4]      )
				self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape  ,	UpperCamelCase__      )
				snake_case  :       Optional[Any]     			=						torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]      )
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]  ,	UpperCamelCase__  ,	atol=1e-3      )      )
				# verify image_id
				snake_case  :       List[str]     			=						torch.tensor([3_9769]      )
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']  ,	UpperCamelCase__      )      )
				# verify is_crowd
				snake_case  :       Optional[Any]     			=						torch.tensor([0, 0, 0, 0, 0, 0]      )
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']  ,	UpperCamelCase__      )      )
				# verify class_labels
				snake_case  :       str     			=						torch.tensor([75, 75, 63, 65, 17, 17]      )
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']  ,	UpperCamelCase__      )      )
				# verify orig_size
				snake_case  :       str     			=						torch.tensor([480, 640]      )
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']  ,	UpperCamelCase__      )      )
				# verify size
				snake_case  :       str     			=						torch.tensor([800, 1066]      )
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']  ,	UpperCamelCase__      )      )



		@slow
		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			Optional[int]:






				"""simple docstring"""







				snake_case  :       Tuple     			=						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  :       Union[str, Any]     			=						json.loads(f.read()      )

				snake_case  :       List[Any]     			=						{'''file_name''': '''000000039769.png''', '''image_id''': 3_9769, '''segments_info''': target}

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

				# encode them
				snake_case  :       Union[str, Any]     			=						YolosImageProcessor(format='''coco_panoptic'''      )
				snake_case  :       Tuple     			=						image_processing(images=UpperCamelCase__  ,	annotations=UpperCamelCase__  ,	masks_path=UpperCamelCase__  ,	return_tensors='''pt'''      )

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

				snake_case  :       Any     			=						torch.tensor([0.2_796, 0.3_138, 0.3_481]      )
				self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]  ,	UpperCamelCase__  ,	atol=1e-4      )      )

				# verify area
				snake_case  :       List[str]     			=						torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]      )
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']  ,	UpperCamelCase__      )      )
				# verify boxes
				snake_case  :       Optional[Any]     			=						torch.Size([6, 4]      )
				self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape  ,	UpperCamelCase__      )
				snake_case  :       List[Any]     			=						torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]      )
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]  ,	UpperCamelCase__  ,	atol=1e-3      )      )
				# verify image_id
				snake_case  :       Optional[int]     			=						torch.tensor([3_9769]      )
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']  ,	UpperCamelCase__      )      )
				# verify is_crowd
				snake_case  :       List[str]     			=						torch.tensor([0, 0, 0, 0, 0, 0]      )
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']  ,	UpperCamelCase__      )      )
				# verify class_labels
				snake_case  :       Tuple     			=						torch.tensor([17, 17, 63, 75, 75, 93]      )
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']  ,	UpperCamelCase__      )      )
				# verify masks
				snake_case  :       List[Any]     			=						82_2873
				self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()  ,	UpperCamelCase__      )
				# verify orig_size
				snake_case  :       Union[str, Any]     			=						torch.tensor([480, 640]      )
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']  ,	UpperCamelCase__      )      )
				# verify size
				snake_case  :       Optional[Any]     			=						torch.tensor([800, 1066]      )
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']  ,	UpperCamelCase__      )      )




'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase__						       =      {
    "configuration_poolformer": [
        "POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "PoolFormerConfig",
        "PoolFormerOnnxConfig",
    ]
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						       =      ["PoolFormerFeatureExtractor"]
						lowercase__						       =      ["PoolFormerImageProcessor"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						       =      [
						    "POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "PoolFormerForImageClassification",
						    "PoolFormerModel",
						    "PoolFormerPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_poolformer import (
						    POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    PoolFormerConfig,
						    PoolFormerOnnxConfig,
						)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_poolformer import PoolFormerFeatureExtractor
												from .image_processing_poolformer import PoolFormerImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_poolformer import (
												    POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    PoolFormerForImageClassification,
												    PoolFormerModel,
												    PoolFormerPreTrainedModel,
												)


else:
						import sys

						lowercase__						       =      _LazyModule(__name__, globals()["__file__"], _import_structure)


