


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 							lowercase_					(			unittest.TestCase):






							"""simple docstring"""







							def __init__( self      ,     _UpperCAmelCase      ,     _UpperCAmelCase=7      ,     _UpperCAmelCase=3      ,     _UpperCAmelCase=30      ,     _UpperCAmelCase=400      ,     _UpperCAmelCase=True      ,     _UpperCAmelCase=None      ,     _UpperCAmelCase=True      ,     _UpperCAmelCase=[0.5, 0.5, 0.5]      ,     _UpperCAmelCase=[0.5, 0.5, 0.5]      ,     _UpperCAmelCase=True      ,     _UpperCAmelCase=1 / 255      ,     _UpperCAmelCase=True      ,     ):







													"""simple docstring"""


													a_		      =		size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
													a_		      =		parent
													a_		      =		batch_size
													a_		      =		num_channels
													a_		      =		min_resolution
													a_		      =		max_resolution
													a_		      =		do_resize
													a_		      =		size
													a_		      =		do_normalize
													a_		      =		image_mean
													a_		      =		image_std
													a_		      =		do_rescale
													a_		      =		rescale_factor
													a_		      =		do_pad


							def     lowercase__  ( self		):







													"""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     lowercase__  ( self      ,     _UpperCAmelCase      ,     _UpperCAmelCase=False		):







													"""simple docstring"""


													if not batched:
																			a_		      =		image_inputs[0]
																			if isinstance(lowerCamelCase__      ,     Image.Image		):
																									a_		      =		image.size
																			else:
																									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_		      =		self.get_expected_values([image]		)
																									expected_values.append((expected_height, expected_width)		)
																			a_		      =		max(lowerCamelCase__      ,     key=lambda _UpperCAmelCase		: item[0]		)[0]
																			a_		      =		max(lowerCamelCase__      ,     key=lambda _UpperCAmelCase		: item[1]		)[1]

													return expected_height, expected_width





@require_torch
@require_vision
class 							lowercase_					(			UpperCAmelCase__      ,unittest.TestCase):






							"""simple docstring"""







							snake_case_										=		YolosImageProcessor if is_vision_available() else None


							def     lowercase__  ( self		):







													"""simple docstring"""


													a_		      =		YolosImageProcessingTester(self		)


							@property
							def     lowercase__  ( self		):







													"""simple docstring"""


													return self.image_processor_tester.prepare_image_processor_dict()


							def     lowercase__  ( self		):







													"""simple docstring"""


													a_		      =		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__      ,     """size"""		)		)


							def     lowercase__  ( self		):







													"""simple docstring"""


													a_		      =		self.image_processing_class.from_dict(self.image_processor_dict		)
													self.assertEqual(image_processor.size      ,     {"""shortest_edge""": 18, """longest_edge""": 1_333}		)
													self.assertEqual(image_processor.do_pad      ,     lowerCamelCase__		)

													a_		      =		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     lowercase__  ( self		):







													"""simple docstring"""


													pass


							def     lowercase__  ( self		):







													"""simple docstring"""


													a_		      =		self.image_processing_class(**self.image_processor_dict		)
													# create random PIL images
													a_		      =		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_		      =		image_processing(image_inputs[0]      ,     return_tensors="""pt"""		).pixel_values

													a_		      =		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_		      =		self.image_processor_tester.get_expected_values(lowerCamelCase__      ,     batched=lowerCamelCase__		)

													a_		      =		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     lowercase__  ( self		):







													"""simple docstring"""


													a_		      =		self.image_processing_class(**self.image_processor_dict		)
													# create random numpy tensors
													a_		      =		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_		      =		image_processing(image_inputs[0]      ,     return_tensors="""pt"""		).pixel_values

													a_		      =		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_		      =		image_processing(lowerCamelCase__      ,     return_tensors="""pt"""		).pixel_values

													a_		      =		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     lowercase__  ( self		):







													"""simple docstring"""


													a_		      =		self.image_processing_class(**self.image_processor_dict		)
													# create random PyTorch tensors
													a_		      =		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_		      =		image_processing(image_inputs[0]      ,     return_tensors="""pt"""		).pixel_values

													a_		      =		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_		      =		image_processing(lowerCamelCase__      ,     return_tensors="""pt"""		).pixel_values

													a_		      =		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     lowercase__  ( self		):







													"""simple docstring"""


													a_		      =		self.image_processing_class(**self.image_processor_dict		)
													a_		      =		self.image_processing_class(do_resize=lowerCamelCase__      ,     do_normalize=lowerCamelCase__      ,     do_rescale=lowerCamelCase__		)
													# create random PyTorch tensors
													a_		      =		prepare_image_inputs(self.image_processor_tester      ,     equal_resolution=lowerCamelCase__      ,     torchify=lowerCamelCase__		)
													for image in image_inputs:
																			self.assertIsInstance(lowerCamelCase__      ,     torch.Tensor		)

													# Test whether the method "pad" and calling the image processor return the same tensors
													a_		      =		image_processing_a.pad(lowerCamelCase__      ,     return_tensors="""pt"""		)
													a_		      =		image_processing_a(lowerCamelCase__      ,     return_tensors="""pt"""		)

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


							@slow
							def     lowercase__  ( self		):







													"""simple docstring"""


													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": 39_769, "annotations": target}

													# encode them
													a_		      =		YolosImageProcessor.from_pretrained("""hustvl/yolos-small"""		)
													a_		      =		image_processing(images=lowerCamelCase__      ,     annotations=lowerCamelCase__      ,     return_tensors="""pt"""		)

													# verify pixel values
													a_		      =		torch.Size([1, 3, 800, 1_066]		)
													self.assertEqual(encoding["""pixel_values"""].shape      ,     lowerCamelCase__		)

													a_		      =		torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]		)
													self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]      ,     lowerCamelCase__      ,     atol=1e-4		)		)

													# verify area
													a_		      =		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_		      =		torch.Size([6, 4]		)
													self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape      ,     lowerCamelCase__		)
													a_		      =		torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]		)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]      ,     lowerCamelCase__      ,     atol=1e-3		)		)
													# verify image_id
													a_		      =		torch.tensor([39_769]		)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]      ,     lowerCamelCase__		)		)
													# verify is_crowd
													a_		      =		torch.tensor([0, 0, 0, 0, 0, 0]		)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]      ,     lowerCamelCase__		)		)
													# verify class_labels
													a_		      =		torch.tensor([75, 75, 63, 65, 17, 17]		)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]      ,     lowerCamelCase__		)		)
													# verify orig_size
													a_		      =		torch.tensor([480, 640]		)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]      ,     lowerCamelCase__		)		)
													# verify size
													a_		      =		torch.tensor([800, 1_066]		)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]      ,     lowerCamelCase__		)		)






							@slow
							def     lowercase__  ( self		):







													"""simple docstring"""


													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": 39_769, "segments_info": target}

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

													# encode them
													a_		      =		YolosImageProcessor(format="""coco_panoptic"""		)
													a_		      =		image_processing(images=lowerCamelCase__      ,     annotations=lowerCamelCase__      ,     masks_path=lowerCamelCase__      ,     return_tensors="""pt"""		)

													# verify pixel values
													a_		      =		torch.Size([1, 3, 800, 1_066]		)
													self.assertEqual(encoding["""pixel_values"""].shape      ,     lowerCamelCase__		)

													a_		      =		torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]		)
													self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]      ,     lowerCamelCase__      ,     atol=1e-4		)		)

													# verify area
													a_		      =		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_		      =		torch.Size([6, 4]		)
													self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape      ,     lowerCamelCase__		)
													a_		      =		torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]		)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]      ,     lowerCamelCase__      ,     atol=1e-3		)		)
													# verify image_id
													a_		      =		torch.tensor([39_769]		)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]      ,     lowerCamelCase__		)		)
													# verify is_crowd
													a_		      =		torch.tensor([0, 0, 0, 0, 0, 0]		)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]      ,     lowerCamelCase__		)		)
													# verify class_labels
													a_		      =		torch.tensor([17, 17, 63, 75, 75, 93]		)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]      ,     lowerCamelCase__		)		)
													# verify masks
													a_		      =		822_873
													self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()      ,     lowerCamelCase__		)
													# verify orig_size
													a_		      =		torch.tensor([480, 640]		)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]      ,     lowerCamelCase__		)		)
													# verify size
													a_		      =		torch.tensor([800, 1_066]		)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]      ,     lowerCamelCase__		)		)
class 					_lowercase    :






    '''simple docstring'''




    def __init__(    self ):
       lowerCAmelCase_:							dict[str, TrieNode]       =	{}  # Mapping from char to TrieNode
       lowerCAmelCase_:							str       =	False




    def      _a    (    self       ,						lowerCamelCase__ ):
       for word in words:
          self.insert(lowerCamelCase__ )




    def      _a    (    self       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							Union[str, Any]       =	self
       for char in word:
          if char not in curr.nodes:
             lowerCAmelCase_:							List[Any]       =	TrieNode()
          lowerCAmelCase_:							Optional[Any]       =	curr.nodes[char]
       lowerCAmelCase_:							Dict       =	True




    def      _a    (    self       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							Optional[Any]       =	self
       for char in word:
          if char not in curr.nodes:
             return False
          lowerCAmelCase_:							Dict       =	curr.nodes[char]
       return curr.is_leaf




    def      _a    (    self       ,						lowerCamelCase__ ):

       def _delete(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ) -> bool:
          if index == len(lowerCamelCase__ ):
             # If word does not exist
             if not curr.is_leaf:
                return False
             lowerCAmelCase_:							List[str]       =	False
             return len(curr.nodes ) == 0
          lowerCAmelCase_:							Union[str, Any]       =	word[index]
          lowerCAmelCase_:							Optional[int]       =	curr.nodes.get(lowerCamelCase__ )
          # If char not in current trie node
          if not char_node:
             return False
          # Flag to check if node can be deleted
          lowerCAmelCase_:							List[Any]       =	_delete(lowerCamelCase__       ,						lowerCamelCase__       ,						index + 1 )
          if delete_curr:
             del curr.nodes[char]
             return len(curr.nodes ) == 0
          return delete_curr

       _delete(self       ,						lowerCamelCase__       ,						0 )





def 		snake_case__	(		lowercase							,				lowercase					):
   if node.is_leaf:
      print(lowercase							,				end=" "					)

   for key, value in node.nodes.items():
      print_words(lowercase							,				word + key					)
def 		snake_case__	(		):
   lowerCAmelCase_:							Any       =	"banana bananas bandana band apple all beast".split()
   lowerCAmelCase_:							Optional[Any]       =	TrieNode()
   root.insert_many(lowercase					)
   # print_words(root, "")
   assert all(root.find(lowercase					) for word in words					)
   assert root.find("banana"					)
   assert not root.find("bandanas"					)
   assert not root.find("apps"					)
   assert root.find("apple"					)
   assert root.find("all"					)
   root.delete("all"					)
   assert not root.find("all"					)
   root.delete("banana"					)
   assert not root.find("banana"					)
   assert root.find("bananas"					)
   return True
def 		snake_case__	(		lowercase							,				lowercase					):
   print(str(lowercase					)							,				"works!" if passes else "doesn't work :("					)
def 		snake_case__	(		):
   assert test_trie()
def 		snake_case__	(		):
   print_results("Testing trie functionality"							,				test_trie()					)


if __name__ == "__main__":
 main()