



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 DetaImageProcessor

class 	lowerCamelCase_    ( unittest.TestCase						):






						'''simple docstring'''







						def __init__(	self      :			Any	,	__lowerCamelCase      :			Optional[Any]	,	__lowerCamelCase      :			Optional[int]=7	,	__lowerCamelCase      :			Dict=3	,	__lowerCamelCase      :			List[Any]=30	,	__lowerCamelCase      :			Union[str, Any]=4_00	,	__lowerCamelCase      :			str=True	,	__lowerCamelCase      :			str=None	,	__lowerCamelCase      :			Dict=True	,	__lowerCamelCase      :			List[Any]=[0.5, 0.5, 0.5]	,	__lowerCamelCase      :			int=[0.5, 0.5, 0.5]	,	__lowerCamelCase      :			Dict=True	,	__lowerCamelCase      :			Any=1 / 2_55	,	__lowerCamelCase      :			Optional[int]=True	,	)      ->				List[str]:
									# 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": 13_33}
									A							:					Any              =						parent
									A							:					Optional[int]              =						batch_size
									A							:					List[Any]              =						num_channels
									A							:					str              =						min_resolution
									A							:					Optional[int]              =						max_resolution
									A							:					Optional[int]              =						do_resize
									A							:					List[Any]              =						size
									A							:					Union[str, Any]              =						do_normalize
									A							:					str              =						image_mean
									A							:					Optional[Any]              =						image_std
									A							:					Any              =						do_rescale
									A							:					Union[str, Any]              =						rescale_factor
									A							:					Dict              =						do_pad







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Tuple:
									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 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	__lowerCamelCase      :			Optional[int]	,	__lowerCamelCase      :			Union[str, Any]=False )      ->				Optional[int]:
									if not batched:
												A							:					List[str]              =						image_inputs[0]
												if isinstance(__lowerCamelCase	,	Image.Image ):
															A       , A							:					Tuple              =						image.size
												else:
															A       , A							:					int              =						image.shape[1], image.shape[2]
												if w < h:
															A							:					Dict              =						int(self.size["shortest_edge"] * h / w )
															A							:					List[Any]              =						self.size["shortest_edge"]
												elif w > h:
															A							:					Optional[int]              =						self.size["shortest_edge"]
															A							:					Optional[int]              =						int(self.size["shortest_edge"] * w / h )
												else:
															A							:					Union[str, Any]              =						self.size["shortest_edge"]
															A							:					List[Any]              =						self.size["shortest_edge"]

									else:
												A							:					Dict              =						[]
												for image in image_inputs:
															A       , A							:					Union[str, Any]              =						self.get_expected_values([image] )
															expected_values.append((expected_height, expected_width) )
												A							:					Optional[int]              =						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 	lowerCamelCase_    ( _A						,unittest.TestCase						):






						'''simple docstring'''
						a__   =  DetaImageProcessor if is_vision_available() else None







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				Optional[int]:
									A							:					Optional[Any]              =						DetaImageProcessingTester(self )







						@property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				int:
									return self.image_processor_tester.prepare_image_processor_dict()







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Tuple:
									A							:					str              =						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	,	"do_rescale" ) )
									self.assertTrue(hasattr(__lowerCamelCase	,	"do_pad" ) )
									self.assertTrue(hasattr(__lowerCamelCase	,	"size" ) )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				Union[str, Any]:
									A							:					Dict              =						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	,	__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any] )      ->				Optional[Any]:
									pass







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Optional[int]:
									# Initialize image_processing
									A							:					List[str]              =						self.image_processing_class(**self.image_processor_dict )
									# create random PIL images
									A							:					str              =						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							:					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       , A							:					Any              =						self.image_processor_tester.get_expected_values(__lowerCamelCase	,	batched=__lowerCamelCase )

									A							:					List[str]              =						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 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				str:
									# Initialize image_processing
									A							:					int              =						self.image_processing_class(**self.image_processor_dict )
									# create random numpy tensors
									A							:					int              =						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							:					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							:					List[Any]              =						image_processing(__lowerCamelCase	,	return_tensors="pt" ).pixel_values

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

									A       , A							:					List[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							:					Optional[int]              =						image_processing(__lowerCamelCase	,	return_tensors="pt" ).pixel_values

									A       , A							:					Union[str, 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,
									    )	,	)







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

									A							:					Tuple              =						{"image_id": 3_97_69, "annotations": target}

									# encode them
									A							:					int              =						DetaImageProcessor()
									A							:					Any              =						image_processing(images=__lowerCamelCase	,	annotations=__lowerCamelCase	,	return_tensors="pt" )

									# verify pixel values
									A							:					Optional[Any]              =						torch.Size([1, 3, 8_00, 10_66] )
									self.assertEqual(encoding["pixel_values"].shape	,	__lowerCamelCase )

									A							:					Union[str, Any]              =						torch.tensor([0.2796, 0.3138, 0.3481] )
									self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]	,	__lowerCamelCase	,	atol=1e-4 ) )

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







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

									A							:					Optional[Any]              =						{"file_name": "000000039769.png", "image_id": 3_97_69, "segments_info": target}

									A							:					List[Any]              =						pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic" )

									# encode them
									A							:					List[Any]              =						DetaImageProcessor(format="coco_panoptic" )
									A							:					Dict              =						image_processing(images=__lowerCamelCase	,	annotations=__lowerCamelCase	,	masks_path=__lowerCamelCase	,	return_tensors="pt" )

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

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

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




from collections import deque

from .hash_table import HashTable

class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''







						def __init__(	self      :			Union[str, Any]	,	*__lowerCamelCase      :			Dict	,	**__lowerCamelCase      :			int )      ->				Optional[int]:
									super().__init__(*__lowerCamelCase	,	**__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	__lowerCamelCase      :			List[str]	,	__lowerCamelCase      :			Union[str, Any] )      ->				Union[str, Any]:
									A							:					Optional[Any]              =						deque([] ) if self.values[key] is None else self.values[key]
									self.values[key].appendleft(__lowerCamelCase )
									A							:					Dict              =						self.values[key]







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				str:
									return (
									    sum(self.charge_factor - len(__lowerCamelCase ) for slot in self.values )
									    / self.size_table
									    * self.charge_factor
									)







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any]	,	__lowerCamelCase      :			Any	,	__lowerCamelCase      :			List[Any]=None )      ->				Optional[int]:
									if not (
									    len(self.values[key] ) == self.charge_factor and self.values.count(__lowerCamelCase ) == 0
									):
												return key
									return super()._collision_resolution(__lowerCamelCase	,	__lowerCamelCase )