
"""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  A__       (	unittest.TestCase):

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

		def 							__lowerCamelCase							(      self ):
			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 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE=False ):
			if not batched:
				__lowerCAmelCase			:						str									=      image_inputs[0]
				if isinstance(_SCREAMING_SNAKE_CASE       ,		Image.Image ):
					__lowerCAmelCase			,   __lowerCAmelCase			:						Optional[int]									=      image.size
				else:
					__lowerCAmelCase			,   __lowerCAmelCase			:						Union[str, Any]									=      image.shape[1], image.shape[2]
				if w < h:
					__lowerCAmelCase			:						str									=      int(self.size['shortest_edge'] * h / w )
					__lowerCAmelCase			:						Optional[int]									=      self.size['shortest_edge']
				elif w > h:
					__lowerCAmelCase			:						str									=      self.size['shortest_edge']
					__lowerCAmelCase			:						Union[str, Any]									=      int(self.size['shortest_edge'] * w / h )
				else:
					__lowerCAmelCase			:						str									=      self.size['shortest_edge']
					__lowerCAmelCase			:						Optional[Any]									=      self.size['shortest_edge']

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

			return expected_height, expected_width




@require_torch
@require_vision
class  A__       (	_lowerCamelCase       ,     unittest.TestCase):
		A_						:       List[str]				   =	DetrImageProcessor if is_vision_available() else None

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						List[Any]									=      DetrImageProcessingTester(self )

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

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Dict									=      self.image_processing_class(**self.image_processor_dict )
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,		'image_mean' ) )
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,		'image_std' ) )
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,		'do_normalize' ) )
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,		'do_rescale' ) )
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,		'rescale_factor' ) )
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,		'do_resize' ) )
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,		'size' ) )
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,		'do_pad' ) )

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Optional[Any]									=      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       ,		_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						List[Any]									=      self.image_processing_class.from_dict(
			    self.image_processor_dict       ,		size=42       ,		max_size=84       ,		pad_and_return_pixel_mask=_SCREAMING_SNAKE_CASE )
			self.assertEqual(image_processor.size       ,		{'shortest_edge': 42, 'longest_edge': 84} )
			self.assertEqual(image_processor.do_pad       ,		_SCREAMING_SNAKE_CASE )

		def 							__lowerCamelCase							(      self ):
			pass

		def 							__lowerCamelCase							(      self ):
			# Initialize image_processing
			__lowerCAmelCase			:						Tuple									=      self.image_processing_class(**self.image_processor_dict )
			# create random PIL images
			__lowerCAmelCase			:						str									=      prepare_image_inputs(self.image_processor_tester       ,		equal_resolution=_SCREAMING_SNAKE_CASE )
			for image in image_inputs:
				self.assertIsInstance(_SCREAMING_SNAKE_CASE       ,		Image.Image )

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

			__lowerCAmelCase			,   __lowerCAmelCase			:						List[Any]									=      self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE )

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

			# Test batched
			__lowerCAmelCase			,   __lowerCAmelCase			:						int									=      self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE       ,		batched=_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						Union[str, Any]									=      image_processing(_SCREAMING_SNAKE_CASE       ,		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 ):
			# Initialize image_processing
			__lowerCAmelCase			:						Tuple									=      self.image_processing_class(**self.image_processor_dict )
			# create random numpy tensors
			__lowerCAmelCase			:						str									=      prepare_image_inputs(self.image_processor_tester       ,		equal_resolution=_SCREAMING_SNAKE_CASE       ,		numpify=_SCREAMING_SNAKE_CASE )
			for image in image_inputs:
				self.assertIsInstance(_SCREAMING_SNAKE_CASE       ,		np.ndarray )

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

			__lowerCAmelCase			,   __lowerCAmelCase			:						Tuple									=      self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE )

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

			# Test batched
			__lowerCAmelCase			:						Union[str, Any]									=      image_processing(_SCREAMING_SNAKE_CASE       ,		return_tensors='pt' ).pixel_values

			__lowerCAmelCase			,   __lowerCAmelCase			:						List[Any]									=      self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE       ,		batched=_SCREAMING_SNAKE_CASE )

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

		def 							__lowerCamelCase							(      self ):
			# Initialize image_processing
			__lowerCAmelCase			:						Union[str, Any]									=      self.image_processing_class(**self.image_processor_dict )
			# create random PyTorch tensors
			__lowerCAmelCase			:						int									=      prepare_image_inputs(self.image_processor_tester       ,		equal_resolution=_SCREAMING_SNAKE_CASE       ,		torchify=_SCREAMING_SNAKE_CASE )
			for image in image_inputs:
				self.assertIsInstance(_SCREAMING_SNAKE_CASE       ,		torch.Tensor )

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

			__lowerCAmelCase			,   __lowerCAmelCase			:						Dict									=      self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE )

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

			# Test batched
			__lowerCAmelCase			:						Tuple									=      image_processing(_SCREAMING_SNAKE_CASE       ,		return_tensors='pt' ).pixel_values

			__lowerCAmelCase			,   __lowerCAmelCase			:						Any									=      self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE       ,		batched=_SCREAMING_SNAKE_CASE )

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

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

			__lowerCAmelCase			:						Tuple									=      {'image_id': 3_97_69, 'annotations': target}

			# encode them
			__lowerCAmelCase			:						Dict									=      DetrImageProcessor.from_pretrained('facebook/detr-resnet-50' )
			__lowerCAmelCase			:						int									=      image_processing(images=_SCREAMING_SNAKE_CASE       ,		annotations=_SCREAMING_SNAKE_CASE       ,		return_tensors='pt' )

			# verify pixel values
			__lowerCAmelCase			:						str									=      torch.Size([1, 3, 8_00, 10_66] )
			self.assertEqual(encoding['pixel_values'].shape       ,		_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						int									=      torch.tensor([0.2796, 0.3138, 0.3481] )
			self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]       ,		_SCREAMING_SNAKE_CASE       ,		atol=1E-4 ) )

			# verify area
			__lowerCAmelCase			:						List[str]									=      torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438] )
			self.assertTrue(torch.allclose(encoding['labels'][0]['area']       ,		_SCREAMING_SNAKE_CASE ) )
			# verify boxes
			__lowerCAmelCase			:						Tuple									=      torch.Size([6, 4] )
			self.assertEqual(encoding['labels'][0]['boxes'].shape       ,		_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						str									=      torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
			self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]       ,		_SCREAMING_SNAKE_CASE       ,		atol=1E-3 ) )
			# verify image_id
			__lowerCAmelCase			:						Dict									=      torch.tensor([3_97_69] )
			self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']       ,		_SCREAMING_SNAKE_CASE ) )
			# verify is_crowd
			__lowerCAmelCase			:						Optional[int]									=      torch.tensor([0, 0, 0, 0, 0, 0] )
			self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']       ,		_SCREAMING_SNAKE_CASE ) )
			# verify class_labels
			__lowerCAmelCase			:						Union[str, Any]									=      torch.tensor([75, 75, 63, 65, 17, 17] )
			self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']       ,		_SCREAMING_SNAKE_CASE ) )
			# verify orig_size
			__lowerCAmelCase			:						int									=      torch.tensor([4_80, 6_40] )
			self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']       ,		_SCREAMING_SNAKE_CASE ) )
			# verify size
			__lowerCAmelCase			:						List[Any]									=      torch.tensor([8_00, 10_66] )
			self.assertTrue(torch.allclose(encoding['labels'][0]['size']       ,		_SCREAMING_SNAKE_CASE ) )







		@slow
		def 							__lowerCamelCase							(      self ):
			# prepare image, target and masks_path
			__lowerCAmelCase			:						Optional[int]									=      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
			with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'       ,		'r' ) as f:
				__lowerCAmelCase			:						Optional[int]									=      json.loads(f.read() )

			__lowerCAmelCase			:						Optional[int]									=      {'file_name': '000000039769.png', 'image_id': 3_97_69, 'segments_info': target}

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

			# encode them
			__lowerCAmelCase			:						Optional[int]									=      DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic' )
			__lowerCAmelCase			:						Optional[Any]									=      image_processing(images=_SCREAMING_SNAKE_CASE       ,		annotations=_SCREAMING_SNAKE_CASE       ,		masks_path=_SCREAMING_SNAKE_CASE       ,		return_tensors='pt' )

			# verify pixel values
			__lowerCAmelCase			:						str									=      torch.Size([1, 3, 8_00, 10_66] )
			self.assertEqual(encoding['pixel_values'].shape       ,		_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						Dict									=      torch.tensor([0.2796, 0.3138, 0.3481] )
			self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]       ,		_SCREAMING_SNAKE_CASE       ,		atol=1E-4 ) )

			# verify area
			__lowerCAmelCase			:						int									=      torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147] )
			self.assertTrue(torch.allclose(encoding['labels'][0]['area']       ,		_SCREAMING_SNAKE_CASE ) )
			# verify boxes
			__lowerCAmelCase			:						Optional[int]									=      torch.Size([6, 4] )
			self.assertEqual(encoding['labels'][0]['boxes'].shape       ,		_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Dict									=      torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
			self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]       ,		_SCREAMING_SNAKE_CASE       ,		atol=1E-3 ) )
			# verify image_id
			__lowerCAmelCase			:						str									=      torch.tensor([3_97_69] )
			self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']       ,		_SCREAMING_SNAKE_CASE ) )
			# verify is_crowd
			__lowerCAmelCase			:						Optional[int]									=      torch.tensor([0, 0, 0, 0, 0, 0] )
			self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']       ,		_SCREAMING_SNAKE_CASE ) )
			# verify class_labels
			__lowerCAmelCase			:						str									=      torch.tensor([17, 17, 63, 75, 75, 93] )
			self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']       ,		_SCREAMING_SNAKE_CASE ) )
			# verify masks
			__lowerCAmelCase			:						Dict									=      82_28_73
			self.assertEqual(encoding['labels'][0]['masks'].sum().item()       ,		_SCREAMING_SNAKE_CASE )
			# verify orig_size
			__lowerCAmelCase			:						str									=      torch.tensor([4_80, 6_40] )
			self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']       ,		_SCREAMING_SNAKE_CASE ) )
			# verify size
			__lowerCAmelCase			:						List[Any]									=      torch.tensor([8_00, 10_66] )
			self.assertTrue(torch.allclose(encoding['labels'][0]['size']       ,		_SCREAMING_SNAKE_CASE ) )


"""simple docstring"""





from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

__UpperCamelCase  :      Tuple							      =		TypeVar('''T''')







class 				SCREAMING_SNAKE_CASE					(		Generic[T]       ):






					"""simple docstring"""



					lowercase__				 =       42  # Cache store of keys
					lowercase__				 =       42  # References of the keys in cache
					lowercase__				 =       10  # Maximum capacity of cache







					def __init__(       self			:				Dict   ,lowercase_			:				int		):
						lowerCAmelCase__	:						str  				=	deque()
						lowerCAmelCase__	:						Any  				=	set()
						if not n:
							lowerCAmelCase__	:						Optional[Any]  				=	sys.maxsize
						elif n < 0:
							raise ValueError('''n should be an integer greater than 0.'''		)
						else:
							lowerCAmelCase__	:						int  				=	n







					def        __lowerCAmelCase (       self			:				str   ,lowercase_			:				T		):
						if x not in self.key_reference:
							if len(self.dq_store		) == LRUCache._MAX_CAPACITY:
								lowerCAmelCase__	:						Any  				=	self.dq_store.pop()
								self.key_reference.remove(lowercase_		)
						else:
							self.dq_store.remove(lowercase_		)

						self.dq_store.appendleft(lowercase_		)
						self.key_reference.add(lowercase_		)







					def        __lowerCAmelCase (       self			:				int		):
						for k in self.dq_store:
							print(lowercase_		)







					def __repr__(       self			:				Tuple		):
						return F'LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store		)}'


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							__UpperCamelCase  :      LRUCache[str | int]							      =		LRUCache(4)
							lru_cache.refer('''A''')
							lru_cache.refer(2)
							lru_cache.refer(3)
							lru_cache.refer('''A''')
							lru_cache.refer(4)
							lru_cache.refer(5)
							lru_cache.display()

							print(lru_cache)
							assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"




