





"""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 DetrImageProcessor


class 			__UpperCamelCase							(		unittest.TestCase		):






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






							def        __a  (  self )      ->					Optional[int]:
								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_rescale": self.do_rescale,
								    "rescale_factor": self.rescale_factor,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_pad": self.do_pad,
								}






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__=False )      ->					List[Any]:
								if not batched:
									a :						Dict      =							image_inputs[0]
									if isinstance(lowerCAmelCase__ ,   Image.Image ):
										a,			a :						Optional[int]      =							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 :						Any      =							self.size["shortest_edge"]
									elif w > h:
										a :						List[str]      =							self.size["shortest_edge"]
										a :						Union[str, Any]      =							int(self.size["shortest_edge"] * w / h )
									else:
										a :						List[str]      =							self.size["shortest_edge"]
										a :						Union[str, Any]      =							self.size["shortest_edge"]

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

								return expected_height, expected_width


@require_torch
@require_vision
class 			__UpperCamelCase							(		a__						,			unittest.TestCase		):
							lowerCamelCase  :   Any    =DetrImageProcessor if is_vision_available() else None






							def        __a  (  self )      ->					List[str]:
								a :						Any      =							DetrImageProcessingTester(self )






							@property
							def        __a  (  self )      ->					Dict:
								return self.image_processor_tester.prepare_image_processor_dict()






							def        __a  (  self )      ->					Any:
								a :						Dict      =							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_rescale" ) )
								self.assertTrue(hasattr(lowerCAmelCase__ ,   "rescale_factor" ) )
								self.assertTrue(hasattr(lowerCAmelCase__ ,   "do_resize" ) )
								self.assertTrue(hasattr(lowerCAmelCase__ ,   "size" ) )
								self.assertTrue(hasattr(lowerCAmelCase__ ,   "do_pad" ) )






							def        __a  (  self )      ->					str:
								a :						Optional[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 ,   lowerCAmelCase__ )

								a :						Dict      =							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        __a  (  self )      ->					Union[str, Any]:
								pass






							def        __a  (  self )      ->					List[str]:
								# Initialize image_processing
								a :						Union[str, Any]      =							self.image_processing_class(**self.image_processor_dict )
								# create random PIL images
								a :						Optional[int]      =							prepare_image_inputs(self.image_processor_tester ,   equal_resolution=lowerCAmelCase__ )
								for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase__ ,   Image.Image )

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

								a,			a :						Optional[int]      =							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
								a,			a :						List[Any]      =							self.image_processor_tester.get_expected_values(lowerCAmelCase__ ,   batched=lowerCAmelCase__ )

								a :						Optional[Any]      =							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        __a  (  self )      ->					List[Any]:
								# Initialize image_processing
								a :						str      =							self.image_processing_class(**self.image_processor_dict )
								# create random numpy tensors
								a :						Tuple      =							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
								a :						List[str]      =							image_processing(image_inputs[0] ,   return_tensors="pt" ).pixel_values

								a,			a :						Optional[Any]      =							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
								a :						Dict      =							image_processing(lowerCAmelCase__ ,   return_tensors="pt" ).pixel_values

								a,			a :						Any      =							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        __a  (  self )      ->					int:
								# Initialize image_processing
								a :						str      =							self.image_processing_class(**self.image_processor_dict )
								# create random PyTorch tensors
								a :						List[Any]      =							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
								a :						List[str]      =							image_processing(image_inputs[0] ,   return_tensors="pt" ).pixel_values

								a,			a :						Dict      =							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
								a :						Dict      =							image_processing(lowerCAmelCase__ ,   return_tensors="pt" ).pixel_values

								a,			a :						str      =							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,
								    ) ,   )






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

								a :						Union[str, Any]      =							{"image_id": 3_9769, "annotations": target}

								# encode them
								a :						Optional[int]      =							DetrImageProcessor.from_pretrained("facebook/detr-resnet-50" )
								a :						Optional[Any]      =							image_processing(images=lowerCAmelCase__ ,   annotations=lowerCAmelCase__ ,   return_tensors="pt" )

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

								a :						Optional[int]      =							torch.tensor([0.2_796, 0.3_138, 0.3_481] )
								self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] ,   lowerCAmelCase__ ,   atol=1E-4 ) )

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






							@slow
							def        __a  (  self )      ->					Tuple:
								# prepare image, target and masks_path
								a :						Dict      =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
								with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt" ,   "r" ) as f:
									a :						str      =							json.loads(f.read() )

								a :						Optional[int]      =							{"file_name": "000000039769.png", "image_id": 3_9769, "segments_info": target}

								a :						Dict      =							pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic" )

								# encode them
								a :						Tuple      =							DetrImageProcessor.from_pretrained("facebook/detr-resnet-50-panoptic" )
								a :						List[Any]      =							image_processing(images=lowerCAmelCase__ ,   annotations=lowerCAmelCase__ ,   masks_path=lowerCAmelCase__ ,   return_tensors="pt" )

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

								a :						Union[str, Any]      =							torch.tensor([0.2_796, 0.3_138, 0.3_481] )
								self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] ,   lowerCAmelCase__ ,   atol=1E-4 ) )

								# verify area
								a :						Dict      =							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"] ,   lowerCAmelCase__ ) )
								# verify boxes
								a :						List[str]      =							torch.Size([6, 4] )
								self.assertEqual(encoding["labels"][0]["boxes"].shape ,   lowerCAmelCase__ )
								a :						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] ,   lowerCAmelCase__ ,   atol=1E-3 ) )
								# verify image_id
								a :						Optional[int]      =							torch.tensor([3_9769] )
								self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] ,   lowerCAmelCase__ ) )
								# verify is_crowd
								a :						int      =							torch.tensor([0, 0, 0, 0, 0, 0] )
								self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] ,   lowerCAmelCase__ ) )
								# verify class_labels
								a :						List[str]      =							torch.tensor([17, 17, 63, 75, 75, 93] )
								self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] ,   lowerCAmelCase__ ) )
								# verify masks
								a :						Dict      =							82_2873
								self.assertEqual(encoding["labels"][0]["masks"].sum().item() ,   lowerCAmelCase__ )
								# verify orig_size
								a :						Any      =							torch.tensor([480, 640] )
								self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] ,   lowerCAmelCase__ ) )
								# verify size
								a :						str      =							torch.tensor([800, 1066] )
								self.assertTrue(torch.allclose(encoding["labels"][0]["size"] ,   lowerCAmelCase__ ) )









"""simple docstring"""




def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Tuple   )					->Optional[Any]:






	'''simple docstring'''






	a :						Any      =							[]
	a :						List[str]      =							set({"(", "[", "{"}   )
	a :						int      =							set({")", "]", "}"}   )
	a :						int      =							{"{": "}", "[": "]", "(": ")"}

	for i in range(len(_lowercase   )   ):
		if s[i] in open_brackets:
			stack.append(s[i]   )

		elif s[i] in closed_brackets and (
		    len(_lowercase   ) == 0 or (len(_lowercase   ) > 0 and open_to_closed[stack.pop()] != s[i])
		):
			return False

	return len(_lowercase   ) == 0






def  _SCREAMING_SNAKE_CASE						(      )					->Tuple:






	'''simple docstring'''






	a :						Any      =							input("Enter sequence of brackets: "   )
	if is_balanced(_lowercase   ):
		print(_lowercase       ,			"is balanced"   )
	else:
		print(_lowercase       ,			"is not balanced"   )


if __name__ == "__main__":
		main()


