





'''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 				__SCREAMING_SNAKE_CASE  (unittest.TestCase      ):






				"""simple docstring"""







				def __init__(  self     :					List[str]					,				__a     :					Dict					,				__a     :					List[str]=7					,				__a     :					int=3					,				__a     :					Optional[int]=30					,				__a     :					Optional[Any]=4_00					,				__a     :					List[str]=True					,				__a     :					Tuple=None					,				__a     :					Optional[int]=True					,				__a     :					Dict=1 / 2_55					,				__a     :					Any=True					,				__a     :					Optional[int]=[0.5, 0.5, 0.5]					,				__a     :					Any=[0.5, 0.5, 0.5]					,				__a     :					Any=True					,				):
								# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
								_a								= size if size is not None else {"shortest_edge": 18, "longest_edge": 13_33}
								_a								= parent
								_a								= batch_size
								_a								= num_channels
								_a								= min_resolution
								_a								= max_resolution
								_a								= do_resize
								_a								= size
								_a								= do_rescale
								_a								= rescale_factor
								_a								= do_normalize
								_a								= image_mean
								_a								= image_std
								_a								= do_pad




				def   UpperCamelCase__   (  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   UpperCamelCase__   (  self     :					List[str]					,				__a     :					List[Any]					,				__a     :					List[str]=False					):
								if not batched:
												_a								= image_inputs[0]
												if isinstance(__a					,				Image.Image					):
																_a	,					_a								= image.size
												else:
																_a	,					_a								= image.shape[1], image.shape[2]
												if w < h:
																_a								= int(self.size["shortest_edge"] * h / w					)
																_a								= self.size["shortest_edge"]
												elif w > h:
																_a								= self.size["shortest_edge"]
																_a								= int(self.size["shortest_edge"] * w / h					)
												else:
																_a								= self.size["shortest_edge"]
																_a								= self.size["shortest_edge"]

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

								return expected_height, expected_width


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






				"""simple docstring"""







				__a            =DetrImageProcessor if is_vision_available() else None




				def   UpperCamelCase__   (  self     :					Optional[Any]					):
								_a								= DetrImageProcessingTester(self					)




				@property
				def   UpperCamelCase__   (  self     :					Optional[Any]					):
								return self.image_processor_tester.prepare_image_processor_dict()




				def   UpperCamelCase__   (  self     :					Dict					):
								_a								= 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_rescale"					)					)
								self.assertTrue(hasattr(__a					,				"rescale_factor"					)					)
								self.assertTrue(hasattr(__a					,				"do_resize"					)					)
								self.assertTrue(hasattr(__a					,				"size"					)					)
								self.assertTrue(hasattr(__a					,				"do_pad"					)					)




				def   UpperCamelCase__   (  self     :					Tuple					):
								_a								= 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					)

								_a								= 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   UpperCamelCase__   (  self     :					Dict					):
								pass




				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								# Initialize image_processing
								_a								= self.image_processing_class(**self.image_processor_dict					)
								# create random PIL images
								_a								= prepare_image_inputs(self.image_processor_tester					,				equal_resolution=__a					)
								for image in image_inputs:
												self.assertIsInstance(__a					,				Image.Image					)

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

								_a	,					_a								= 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
								_a	,					_a								= self.image_processor_tester.get_expected_values(__a					,				batched=__a					)

								_a								= 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   UpperCamelCase__   (  self     :					int					):
								# Initialize image_processing
								_a								= self.image_processing_class(**self.image_processor_dict					)
								# create random numpy tensors
								_a								= 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
								_a								= image_processing(image_inputs[0]					,				return_tensors="pt"					).pixel_values

								_a	,					_a								= 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
								_a								= image_processing(__a					,				return_tensors="pt"					).pixel_values

								_a	,					_a								= 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   UpperCamelCase__   (  self     :					List[str]					):
								# Initialize image_processing
								_a								= self.image_processing_class(**self.image_processor_dict					)
								# create random PyTorch tensors
								_a								= 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
								_a								= image_processing(image_inputs[0]					,				return_tensors="pt"					).pixel_values

								_a	,					_a								= 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
								_a								= image_processing(__a					,				return_tensors="pt"					).pixel_values

								_a	,					_a								= 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,
								    )					,				)




				@slow
				def   UpperCamelCase__   (  self     :					List[Any]					):
								# prepare image and target
								_a								= Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
								with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"					,				"r"					) as f:
												_a								= json.loads(f.read()					)

								_a								= {"image_id": 3_97_69, "annotations": target}

								# encode them
								_a								= DetrImageProcessor.from_pretrained("facebook/detr-resnet-50"					)
								_a								= image_processing(images=__a					,				annotations=__a					,				return_tensors="pt"					)

								# verify pixel values
								_a								= torch.Size([1, 3, 8_00, 10_66]					)
								self.assertEqual(encoding["pixel_values"].shape					,				__a					)

								_a								= torch.tensor([0.2796, 0.3138, 0.3481]					)
								self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]					,				__a					,				atol=1e-4					)					)

								# verify area
								_a								= torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]					)
								self.assertTrue(torch.allclose(encoding["labels"][0]["area"]					,				__a					)					)
								# verify boxes
								_a								= torch.Size([6, 4]					)
								self.assertEqual(encoding["labels"][0]["boxes"].shape					,				__a					)
								_a								= torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]					)
								self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]					,				__a					,				atol=1e-3					)					)
								# verify image_id
								_a								= torch.tensor([3_97_69]					)
								self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]					,				__a					)					)
								# verify is_crowd
								_a								= torch.tensor([0, 0, 0, 0, 0, 0]					)
								self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]					,				__a					)					)
								# verify class_labels
								_a								= torch.tensor([75, 75, 63, 65, 17, 17]					)
								self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]					,				__a					)					)
								# verify orig_size
								_a								= torch.tensor([4_80, 6_40]					)
								self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]					,				__a					)					)
								# verify size
								_a								= torch.tensor([8_00, 10_66]					)
								self.assertTrue(torch.allclose(encoding["labels"][0]["size"]					,				__a					)					)




				@slow
				def   UpperCamelCase__   (  self     :					List[str]					):
								# prepare image, target and masks_path
								_a								= Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
								with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"					,				"r"					) as f:
												_a								= json.loads(f.read()					)

								_a								= {"file_name": "000000039769.png", "image_id": 3_97_69, "segments_info": target}

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

								# encode them
								_a								= DetrImageProcessor.from_pretrained("facebook/detr-resnet-50-panoptic"					)
								_a								= image_processing(images=__a					,				annotations=__a					,				masks_path=__a					,				return_tensors="pt"					)

								# verify pixel values
								_a								= torch.Size([1, 3, 8_00, 10_66]					)
								self.assertEqual(encoding["pixel_values"].shape					,				__a					)

								_a								= torch.tensor([0.2796, 0.3138, 0.3481]					)
								self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]					,				__a					,				atol=1e-4					)					)

								# verify area
								_a								= torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]					)
								self.assertTrue(torch.allclose(encoding["labels"][0]["area"]					,				__a					)					)
								# verify boxes
								_a								= torch.Size([6, 4]					)
								self.assertEqual(encoding["labels"][0]["boxes"].shape					,				__a					)
								_a								= torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]					)
								self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]					,				__a					,				atol=1e-3					)					)
								# verify image_id
								_a								= torch.tensor([3_97_69]					)
								self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]					,				__a					)					)
								# verify is_crowd
								_a								= torch.tensor([0, 0, 0, 0, 0, 0]					)
								self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]					,				__a					)					)
								# verify class_labels
								_a								= torch.tensor([17, 17, 63, 75, 75, 93]					)
								self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]					,				__a					)					)
								# verify masks
								_a								= 82_28_73
								self.assertEqual(encoding["labels"][0]["masks"].sum().item()					,				__a					)
								# verify orig_size
								_a								= torch.tensor([4_80, 6_40]					)
								self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]					,				__a					)					)
								# verify size
								_a								= torch.tensor([8_00, 10_66]					)
								self.assertTrue(torch.allclose(encoding["labels"][0]["size"]					,				__a					)					)







'''simple docstring'''




import re

from filelock import FileLock


try:
  import nltk

  __SCREAMING_SNAKE_CASE     :Optional[int]       =				True
except (ImportError, ModuleNotFoundError):
  __SCREAMING_SNAKE_CASE     :str       =				False

if NLTK_AVAILABLE:
  with FileLock('''.lock''') as lock:
    nltk.download('''punkt''', quiet=True)
def 	UpperCAmelCase_							(					__lowercase							:      str       )		->       str:


     '''simple docstring'''



     re.sub("<n>"			,     ""			,     __lowercase       )  # remove pegasus newline char
     assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
     return "\n".join(nltk.sent_tokenize(__lowercase       )       )




