

import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
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 SegformerImageProcessor
class 		SCREAMING_SNAKE_CASE__					(		unittest.TestCase   ):







						"""simple docstring"""




						def __init__(				self , __UpperCamelCase , __UpperCamelCase=7 , __UpperCamelCase=3 , __UpperCamelCase=30 , __UpperCamelCase=4_00 , __UpperCamelCase=True , __UpperCamelCase=None , __UpperCamelCase=True , __UpperCamelCase=[0.5, 0.5, 0.5] , __UpperCamelCase=[0.5, 0.5, 0.5] , __UpperCamelCase=False , )							->	List[str]:




									'''simple docstring'''


									__UpperCamelCase    :    List[Any]								=      size if size is not None else {"height": 30, "width": 30}
									__UpperCamelCase    :    Dict								=      parent
									__UpperCamelCase    :    List[str]								=      batch_size
									__UpperCamelCase    :    List[str]								=      num_channels
									__UpperCamelCase    :    int								=      min_resolution
									__UpperCamelCase    :    List[str]								=      max_resolution
									__UpperCamelCase    :    List[Any]								=      do_resize
									__UpperCamelCase    :    Union[str, Any]								=      size
									__UpperCamelCase    :    List[Any]								=      do_normalize
									__UpperCamelCase    :    Dict								=      image_mean
									__UpperCamelCase    :    Optional[Any]								=      image_std
									__UpperCamelCase    :    Union[str, Any]								=      do_reduce_labels




						def __lowerCamelCase					(				self    )							->	Optional[Any]:




									'''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_reduce_labels": self.do_reduce_labels,
									}







def  UpperCAmelCase_   ():
			__UpperCamelCase    :    Dict								=      load_dataset("hf-internal-testing/fixtures_ade20k"     ,			split="test"		)

			__UpperCamelCase    :    Tuple								=      Image.open(dataset[0]["file"]		)
			__UpperCamelCase    :    List[str]								=      Image.open(dataset[1]["file"]		)

			return image, map







def  UpperCAmelCase_   ():
			__UpperCamelCase    :    Tuple								=      load_dataset("hf-internal-testing/fixtures_ade20k"     ,			split="test"		)

			__UpperCamelCase    :    Dict								=      Image.open(dataset[0]["file"]		)
			__UpperCamelCase    :    Union[str, Any]								=      Image.open(dataset[1]["file"]		)
			__UpperCamelCase    :    List[str]								=      Image.open(dataset[2]["file"]		)
			__UpperCamelCase    :    List[str]								=      Image.open(dataset[3]["file"]		)

			return [imagea, imagea], [mapa, mapa]

@require_torch
@require_vision
class 		SCREAMING_SNAKE_CASE__					(		_UpperCAmelCase						,	unittest.TestCase   ):







						"""simple docstring"""




						lowercase  :				Tuple											=							SegformerImageProcessor if is_vision_available() else None

						def __lowerCamelCase					(				self    )							->	Optional[int]:




									'''simple docstring'''


									__UpperCamelCase    :    Tuple								=      SegformerImageProcessingTester(self    )

						@property
						def __lowerCamelCase					(				self    )							->	str:




									'''simple docstring'''


									return self.image_processor_tester.prepare_image_processor_dict()

						def __lowerCamelCase					(				self    )							->	List[Any]:




									'''simple docstring'''


									__UpperCamelCase    :    Tuple								=      self.image_processing_class(**self.image_processor_dict    )
									self.assertTrue(hasattr(__UpperCamelCase , "do_resize"    )    )
									self.assertTrue(hasattr(__UpperCamelCase , "size"    )    )
									self.assertTrue(hasattr(__UpperCamelCase , "do_normalize"    )    )
									self.assertTrue(hasattr(__UpperCamelCase , "image_mean"    )    )
									self.assertTrue(hasattr(__UpperCamelCase , "image_std"    )    )
									self.assertTrue(hasattr(__UpperCamelCase , "do_reduce_labels"    )    )

						def __lowerCamelCase					(				self    )							->	List[str]:




									'''simple docstring'''


									__UpperCamelCase    :    Dict								=      self.image_processing_class.from_dict(self.image_processor_dict    )
									self.assertEqual(image_processor.size , {"height": 30, "width": 30}    )
									self.assertEqual(image_processor.do_reduce_labels , __UpperCamelCase    )

									__UpperCamelCase    :    Dict								=      self.image_processing_class.from_dict(self.image_processor_dict , size=42 , reduce_labels=__UpperCamelCase    )
									self.assertEqual(image_processor.size , {"height": 42, "width": 42}    )
									self.assertEqual(image_processor.do_reduce_labels , __UpperCamelCase    )

						def __lowerCamelCase					(				self    )							->	List[Any]:




									'''simple docstring'''


									pass

						def __lowerCamelCase					(				self    )							->	str:




									'''simple docstring'''


									__UpperCamelCase    :    Union[str, Any]								=      self.image_processing_class(**self.image_processor_dict    )
									# create random PIL images
									__UpperCamelCase    :    int								=      prepare_image_inputs(self.image_processor_tester , equal_resolution=__UpperCamelCase    )
									for image in image_inputs:
												self.assertIsInstance(__UpperCamelCase , Image.Image    )

									# Test not batched input
									__UpperCamelCase    :    Any								=      image_processing(image_inputs[0] , return_tensors="pt"    ).pixel_values
									self.assertEqual(
									    encoded_images.shape , (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["height"],
									        self.image_processor_tester.size["width"],
									    ) , )

									# Test batched
									__UpperCamelCase    :    Dict								=      image_processing(__UpperCamelCase , return_tensors="pt"    ).pixel_values
									self.assertEqual(
									    encoded_images.shape , (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["height"],
									        self.image_processor_tester.size["width"],
									    ) , )

						def __lowerCamelCase					(				self    )							->	Dict:




									'''simple docstring'''


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

									# Test not batched input
									__UpperCamelCase    :    Tuple								=      image_processing(image_inputs[0] , return_tensors="pt"    ).pixel_values
									self.assertEqual(
									    encoded_images.shape , (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["height"],
									        self.image_processor_tester.size["width"],
									    ) , )

									# Test batched
									__UpperCamelCase    :    Optional[int]								=      image_processing(__UpperCamelCase , return_tensors="pt"    ).pixel_values
									self.assertEqual(
									    encoded_images.shape , (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["height"],
									        self.image_processor_tester.size["width"],
									    ) , )

						def __lowerCamelCase					(				self    )							->	List[Any]:




									'''simple docstring'''


									__UpperCamelCase    :    Any								=      self.image_processing_class(**self.image_processor_dict    )
									# create random PyTorch tensors
									__UpperCamelCase    :    Optional[Any]								=      prepare_image_inputs(self.image_processor_tester , equal_resolution=__UpperCamelCase , torchify=__UpperCamelCase    )
									for image in image_inputs:
												self.assertIsInstance(__UpperCamelCase , torch.Tensor    )

									# Test not batched input
									__UpperCamelCase    :    Optional[int]								=      image_processing(image_inputs[0] , return_tensors="pt"    ).pixel_values
									self.assertEqual(
									    encoded_images.shape , (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["height"],
									        self.image_processor_tester.size["width"],
									    ) , )

									# Test batched
									__UpperCamelCase    :    Union[str, Any]								=      image_processing(__UpperCamelCase , return_tensors="pt"    ).pixel_values
									self.assertEqual(
									    encoded_images.shape , (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["height"],
									        self.image_processor_tester.size["width"],
									    ) , )

						def __lowerCamelCase					(				self    )							->	Optional[Any]:




									'''simple docstring'''


									__UpperCamelCase    :    Optional[int]								=      self.image_processing_class(**self.image_processor_dict    )
									# create random PyTorch tensors
									__UpperCamelCase    :    Optional[int]								=      prepare_image_inputs(self.image_processor_tester , equal_resolution=__UpperCamelCase , torchify=__UpperCamelCase    )
									__UpperCamelCase    :    List[Any]								=      []
									for image in image_inputs:
												self.assertIsInstance(__UpperCamelCase , torch.Tensor    )
												maps.append(torch.zeros(image.shape[-2:]    ).long()    )

									# Test not batched input
									__UpperCamelCase    :    Any								=      image_processing(image_inputs[0] , maps[0] , return_tensors="pt"    )
									self.assertEqual(
									    encoding["pixel_values"].shape , (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["height"],
									        self.image_processor_tester.size["width"],
									    ) , )
									self.assertEqual(
									    encoding["labels"].shape , (
									        1,
									        self.image_processor_tester.size["height"],
									        self.image_processor_tester.size["width"],
									    ) , )
									self.assertEqual(encoding["labels"].dtype , torch.long    )
									self.assertTrue(encoding["labels"].min().item() >= 0    )
									self.assertTrue(encoding["labels"].max().item() <= 2_55    )

									# Test batched
									__UpperCamelCase    :    List[Any]								=      image_processing(__UpperCamelCase , __UpperCamelCase , return_tensors="pt"    )
									self.assertEqual(
									    encoding["pixel_values"].shape , (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["height"],
									        self.image_processor_tester.size["width"],
									    ) , )
									self.assertEqual(
									    encoding["labels"].shape , (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.size["height"],
									        self.image_processor_tester.size["width"],
									    ) , )
									self.assertEqual(encoding["labels"].dtype , torch.long    )
									self.assertTrue(encoding["labels"].min().item() >= 0    )
									self.assertTrue(encoding["labels"].max().item() <= 2_55    )

									# Test not batched input (PIL images)
									__UpperCamelCase							,				__UpperCamelCase    :    int								=      prepare_semantic_single_inputs()

									__UpperCamelCase    :    Tuple								=      image_processing(__UpperCamelCase , __UpperCamelCase , return_tensors="pt"    )
									self.assertEqual(
									    encoding["pixel_values"].shape , (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["height"],
									        self.image_processor_tester.size["width"],
									    ) , )
									self.assertEqual(
									    encoding["labels"].shape , (
									        1,
									        self.image_processor_tester.size["height"],
									        self.image_processor_tester.size["width"],
									    ) , )
									self.assertEqual(encoding["labels"].dtype , torch.long    )
									self.assertTrue(encoding["labels"].min().item() >= 0    )
									self.assertTrue(encoding["labels"].max().item() <= 2_55    )

									# Test batched input (PIL images)
									__UpperCamelCase							,				__UpperCamelCase    :    int								=      prepare_semantic_batch_inputs()

									__UpperCamelCase    :    Any								=      image_processing(__UpperCamelCase , __UpperCamelCase , return_tensors="pt"    )
									self.assertEqual(
									    encoding["pixel_values"].shape , (
									        2,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["height"],
									        self.image_processor_tester.size["width"],
									    ) , )
									self.assertEqual(
									    encoding["labels"].shape , (
									        2,
									        self.image_processor_tester.size["height"],
									        self.image_processor_tester.size["width"],
									    ) , )
									self.assertEqual(encoding["labels"].dtype , torch.long    )
									self.assertTrue(encoding["labels"].min().item() >= 0    )
									self.assertTrue(encoding["labels"].max().item() <= 2_55    )




						def __lowerCamelCase					(				self    )							->	List[str]:




									'''simple docstring'''


									__UpperCamelCase    :    List[str]								=      self.image_processing_class(**self.image_processor_dict    )

									# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
									__UpperCamelCase							,				__UpperCamelCase    :    int								=      prepare_semantic_single_inputs()
									__UpperCamelCase    :    Tuple								=      image_processing(__UpperCamelCase , __UpperCamelCase , return_tensors="pt"    )
									self.assertTrue(encoding["labels"].min().item() >= 0    )
									self.assertTrue(encoding["labels"].max().item() <= 1_50    )

									__UpperCamelCase    :    Dict								=      True
									__UpperCamelCase    :    Optional[Any]								=      image_processing(__UpperCamelCase , __UpperCamelCase , return_tensors="pt"    )
									self.assertTrue(encoding["labels"].min().item() >= 0    )
									self.assertTrue(encoding["labels"].max().item() <= 2_55    )


from .balanced_parentheses import balanced_parentheses
from .stack import Stack







def  UpperCAmelCase_   (_lowerCAmelCase :		str		):
			return {"+": 1, "-": 1, "*": 2, "/": 2, "^": 3}.get(_lowerCAmelCase     ,			-1		)







def  UpperCAmelCase_   (_lowerCAmelCase :		str		):
			if not balanced_parentheses(_lowerCAmelCase		):
						raise ValueError("Mismatched parentheses"		)
			__UpperCamelCase    :    Stack[str]								=      Stack()
			__UpperCamelCase    :    int								=      []
			for char in expression_str:
						if char.isalpha() or char.isdigit():
									postfix.append(_lowerCAmelCase		)
						elif char == "(":
									stack.push(_lowerCAmelCase		)
						elif char == ")":
									while not stack.is_empty() and stack.peek() != "(":
												postfix.append(stack.pop()		)
									stack.pop()
						else:
									while not stack.is_empty() and precedence(_lowerCAmelCase		) <= precedence(stack.peek()		):
												postfix.append(stack.pop()		)
									stack.push(_lowerCAmelCase		)
			while not stack.is_empty():
						postfix.append(stack.pop()		)
			return " ".join(_lowerCAmelCase		)


if __name__ == "__main__":
					from doctest import testmod

					testmod()
					lowercase     :       int        =       "a+b*(c^d-e)^(f+g*h)-i"

					print("Infix to Postfix Notation demonstration:\n")
					print("Infix notation: " + expression)
					print("Postfix notation: " + infix_to_postfix(expression))