



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 				snake_case__(unittest.TestCase      ):

     """simple docstring"""






     def __init__(							self :     Optional[int]				,       SCREAMING_SNAKE_CASE :     Any				,       SCREAMING_SNAKE_CASE :     int=7				,       SCREAMING_SNAKE_CASE :     int=3				,       SCREAMING_SNAKE_CASE :     Optional[Any]=30				,       SCREAMING_SNAKE_CASE :     str=400				,       SCREAMING_SNAKE_CASE :     List[Any]=True				,       SCREAMING_SNAKE_CASE :     List[Any]=None				,       SCREAMING_SNAKE_CASE :     Optional[int]=True				,       SCREAMING_SNAKE_CASE :     str=[0.5, 0.5, 0.5]				,       SCREAMING_SNAKE_CASE :     Tuple=[0.5, 0.5, 0.5]				,       SCREAMING_SNAKE_CASE :     Dict=True				,       SCREAMING_SNAKE_CASE :     Dict=1 / 255				,       SCREAMING_SNAKE_CASE :     int=True				,       ):
         # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
         lowercase__					:   List[Any]													=					size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
         lowercase__					:   str													=					parent
         lowercase__					:   List[str]													=					batch_size
         lowercase__					:   int													=					num_channels
         lowercase__					:   Any													=					min_resolution
         lowercase__					:   Optional[int]													=					max_resolution
         lowercase__					:   Optional[int]													=					do_resize
         lowercase__					:   Optional[int]													=					size
         lowercase__					:   Any													=					do_normalize
         lowercase__					:   int													=					image_mean
         lowercase__					:   Tuple													=					image_std
         lowercase__					:   Optional[Any]													=					do_rescale
         lowercase__					:   Tuple													=					rescale_factor
         lowercase__					:   Optional[int]													=					do_pad







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

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

         return expected_height, expected_width






@require_torch
@require_vision
class 				snake_case__(_UpperCamelCase    ,      unittest.TestCase      ):

     """simple docstring"""






     lowercase_								=		DetaImageProcessor if is_vision_available() else None







     def 			snake_case					(							self :     str					):
         lowercase__					:   Any													=					DetaImageProcessingTester(self					)







     @property
     def 			snake_case					(							self :     List[str]					):
         return self.image_processor_tester.prepare_image_processor_dict()







     def 			snake_case					(							self :     List[str]					):
         lowercase__					:   List[Any]													=					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_resize"					)					)
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE				,       "do_rescale"					)					)
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE				,       "do_pad"					)					)
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE				,       "size"					)					)







     def 			snake_case					(							self :     Any					):
         lowercase__					:   Optional[int]													=					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				,       _SCREAMING_SNAKE_CASE					)







     def 			snake_case					(							self :     Any					):
         pass







     def 			snake_case					(							self :     List[Any]					):
         # Initialize image_processing
         lowercase__					:   Dict													=					self.image_processing_class(**self.image_processor_dict					)
         # create random PIL images
         lowercase__					:   Optional[Any]													=					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
         lowercase__					:   List[str]													=					image_processing(image_inputs[0]				,       return_tensors="pt"					).pixel_values

         lowercase__							,							lowercase__					:   Optional[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
         lowercase__							,							lowercase__					:   Dict													=					self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE				,       batched=_SCREAMING_SNAKE_CASE					)

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

         lowercase__							,							lowercase__					:   str													=					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
         lowercase__					:   Any													=					image_processing(_SCREAMING_SNAKE_CASE				,       return_tensors="pt"					).pixel_values

         lowercase__							,							lowercase__					:   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 			snake_case					(							self :     Dict					):
         # Initialize image_processing
         lowercase__					:   str													=					self.image_processing_class(**self.image_processor_dict					)
         # create random PyTorch tensors
         lowercase__					:   Any													=					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
         lowercase__					:   Any													=					image_processing(image_inputs[0]				,       return_tensors="pt"					).pixel_values

         lowercase__							,							lowercase__					:   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
         lowercase__					:   Tuple													=					image_processing(_SCREAMING_SNAKE_CASE				,       return_tensors="pt"					).pixel_values

         lowercase__							,							lowercase__					:   str													=					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 			snake_case					(							self :     Dict					):
         # prepare image and target
         lowercase__					:   str													=					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
         with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"				,       "r"					) as f:
             lowercase__					:   str													=					json.loads(f.read()					)

         lowercase__					:   Dict													=					{"image_id": 39_769, "annotations": target}

         # encode them
         lowercase__					:   Tuple													=					DetaImageProcessor()
         lowercase__					:   List[str]													=					image_processing(images=_SCREAMING_SNAKE_CASE				,       annotations=_SCREAMING_SNAKE_CASE				,       return_tensors="pt"					)

         # verify pixel values
         lowercase__					:   List[str]													=					torch.Size([1, 3, 800, 1_066]					)
         self.assertEqual(encoding["pixel_values"].shape				,       _SCREAMING_SNAKE_CASE					)

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

         # verify area
         lowercase__					:   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"]				,       _SCREAMING_SNAKE_CASE					)					)
         # verify boxes
         lowercase__					:   Union[str, Any]													=					torch.Size([6, 4]					)
         self.assertEqual(encoding["labels"][0]["boxes"].shape				,       _SCREAMING_SNAKE_CASE					)
         lowercase__					:   Tuple													=					torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]					)
         self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]				,       _SCREAMING_SNAKE_CASE				,       atol=1E-3					)					)
         # verify image_id
         lowercase__					:   str													=					torch.tensor([39_769]					)
         self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]				,       _SCREAMING_SNAKE_CASE					)					)
         # verify is_crowd
         lowercase__					:   Optional[int]													=					torch.tensor([0, 0, 0, 0, 0, 0]					)
         self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]				,       _SCREAMING_SNAKE_CASE					)					)
         # verify class_labels
         lowercase__					:   Optional[Any]													=					torch.tensor([75, 75, 63, 65, 17, 17]					)
         self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]				,       _SCREAMING_SNAKE_CASE					)					)
         # verify orig_size
         lowercase__					:   int													=					torch.tensor([480, 640]					)
         self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]				,       _SCREAMING_SNAKE_CASE					)					)
         # verify size
         lowercase__					:   Optional[int]													=					torch.tensor([800, 1_066]					)
         self.assertTrue(torch.allclose(encoding["labels"][0]["size"]				,       _SCREAMING_SNAKE_CASE					)					)







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

         lowercase__					:   str													=					{"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}

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

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

         # verify pixel values
         lowercase__					:   Union[str, Any]													=					torch.Size([1, 3, 800, 1_066]					)
         self.assertEqual(encoding["pixel_values"].shape				,       _SCREAMING_SNAKE_CASE					)

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

         # verify area
         lowercase__					:   Any													=					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"]				,       _SCREAMING_SNAKE_CASE					)					)
         # verify boxes
         lowercase__					:   Dict													=					torch.Size([6, 4]					)
         self.assertEqual(encoding["labels"][0]["boxes"].shape				,       _SCREAMING_SNAKE_CASE					)
         lowercase__					:   Tuple													=					torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]					)
         self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]				,       _SCREAMING_SNAKE_CASE				,       atol=1E-3					)					)
         # verify image_id
         lowercase__					:   Optional[int]													=					torch.tensor([39_769]					)
         self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]				,       _SCREAMING_SNAKE_CASE					)					)
         # verify is_crowd
         lowercase__					:   Optional[Any]													=					torch.tensor([0, 0, 0, 0, 0, 0]					)
         self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]				,       _SCREAMING_SNAKE_CASE					)					)
         # verify class_labels
         lowercase__					:   Tuple													=					torch.tensor([17, 17, 63, 75, 75, 93]					)
         self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]				,       _SCREAMING_SNAKE_CASE					)					)
         # verify masks
         lowercase__					:   Dict													=					822_873
         self.assertEqual(encoding["labels"][0]["masks"].sum().item()				,       _SCREAMING_SNAKE_CASE					)
         # verify orig_size
         lowercase__					:   Any													=					torch.tensor([480, 640]					)
         self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]				,       _SCREAMING_SNAKE_CASE					)					)
         # verify size
         lowercase__					:   List[str]													=					torch.tensor([800, 1_066]					)
         self.assertTrue(torch.allclose(encoding["labels"][0]["size"]				,       _SCREAMING_SNAKE_CASE					)					)









def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   Union[str, Any]													=					[]
				lowercase__					:   Tuple													=					[]
				lowercase__					:   Any													=					{
				    "^": 3,
				    "*": 2,
				    "/": 2,
				    "%": 2,
				    "+": 1,
				    "-": 1,
				}  # Priority of each operator
				lowercase__					:   Any													=					len(lowerCamelCase__						) if (len(lowerCamelCase__						) > 7) else 7

				# Print table header for output
				print(
				    "Symbol".center(8						)					,			"Stack".center(lowerCamelCase__						)					,			"Postfix".center(lowerCamelCase__						)					,			sep=" | "					,			)
				print("-" * (print_width * 3 + 7)						)

				for x in infix:
								if x.isalpha() or x.isdigit():
												post_fix.append(lowerCamelCase__						)  # if x is Alphabet / Digit, add it to Postfix
								elif x == "(":
												stack.append(lowerCamelCase__						)  # if x is "(" push to Stack
								elif x == ")":  # if x is ")" pop stack until "(" is encountered
												while stack[-1] != "(":
																post_fix.append(stack.pop()						)  # Pop stack & add the content to Postfix
												stack.pop()
								else:
												if len(lowerCamelCase__						) == 0:
																stack.append(lowerCamelCase__						)  # If stack is empty, push x to stack
												else:  # while priority of x is not > priority of element in the stack
																while len(lowerCamelCase__						) > 0 and priority[x] <= priority[stack[-1]]:
																				post_fix.append(stack.pop()						)  # pop stack & add to Postfix
																stack.append(lowerCamelCase__						)  # push x to stack

								print(
								    x.center(8						)					,			("".join(lowerCamelCase__						)).ljust(lowerCamelCase__						)					,			("".join(lowerCamelCase__						)).ljust(lowerCamelCase__						)					,			sep=" | "					,			)  # Output in tabular format

				while len(lowerCamelCase__						) > 0:  # while stack is not empty
								post_fix.append(stack.pop()						)  # pop stack & add to Postfix
								print(
								    " ".center(8						)					,			("".join(lowerCamelCase__						)).ljust(lowerCamelCase__						)					,			("".join(lowerCamelCase__						)).ljust(lowerCamelCase__						)					,			sep=" | "					,			)  # Output in tabular format

				return "".join(lowerCamelCase__						)  # return Postfix as str


def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   Optional[int]													=					list(infix[::-1]						)  # reverse the infix equation

				for i in range(len(lowerCamelCase__						)						):
								if infix[i] == "(":
												lowercase__					:   Tuple													=					")"  # change "(" to ")"
								elif infix[i] == ")":
												lowercase__					:   Optional[Any]													=					"("  # change ")" to "("

				return (infix_2_postfix("".join(lowerCamelCase__						)						))[
				    ::-1
				]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
				lowerCAmelCase__					  =  input('''\nEnter an Infix Equation = ''')  # Input an Infix equation
				lowerCAmelCase__					  =  ''''''.join(Infix.split())  # Remove spaces from the input
				print('''\n\t''', Infix, '''(Infix) -> ''', infix_2_prefix(Infix), '''(Prefix)''')




