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 					__snake_case    (     unittest.TestCase      ):




					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=False       ,			)	->    int:



									'''simple docstring'''







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




					def      __a     (  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__		(							)  ->   int:
				snake_case__   :      int   				= load_dataset('hf-internal-testing/fixtures_ade20k'     ,					split='test'       )

				snake_case__   :      int   				= Image.open(dataset[0]['file']       )
				snake_case__   :      List[str]   				= Image.open(dataset[1]['file']       )

				return image, map
def 					UpperCamelCase__		(							)  ->   Union[str, Any]:
				snake_case__   :      Optional[int]   				= load_dataset('hf-internal-testing/fixtures_ade20k'     ,					split='test'       )

				snake_case__   :      Any   				= Image.open(dataset[0]['file']       )
				snake_case__   :      List[Any]   				= Image.open(dataset[1]['file']       )
				snake_case__   :      int   				= Image.open(dataset[2]['file']       )
				snake_case__   :      List[Any]   				= Image.open(dataset[3]['file']       )

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





@require_torch
@require_vision
class 					__snake_case    (     snake_case__       ,unittest.TestCase      ):
					__lowerCamelCase			   =  SegformerImageProcessor if is_vision_available() else None




					def      __a     (  self						)	->    int:



									'''simple docstring'''







									snake_case__   :      List[str]   				= SegformerImageProcessingTester(self						)




					@property
					def      __a     (  self						)	->    int:



									'''simple docstring'''







									return self.image_processor_tester.prepare_image_processor_dict()




					def      __a     (  self						)	->    Any:



									'''simple docstring'''







									snake_case__   :      List[str]   				= 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      __a     (  self						)	->    int:



									'''simple docstring'''







									snake_case__   :      List[Any]   				= 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_						)

									snake_case__   :      Any   				= 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      __a     (  self						)	->    Optional[int]:



									'''simple docstring'''







									pass




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



									'''simple docstring'''







									snake_case__   :      str   				= self.image_processing_class(**self.image_processor_dict						)
									# create random PIL images
									snake_case__   :      List[str]   				= prepare_image_inputs(self.image_processor_tester       ,			equal_resolution=UpperCAmelCase_						)
									for image in image_inputs:
													self.assertIsInstance(UpperCAmelCase_       ,			Image.Image						)

									# Test not batched input
									snake_case__   :      Dict   				= 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
									snake_case__   :      Tuple   				= 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      __a     (  self						)	->    Any:



									'''simple docstring'''







									snake_case__   :      int   				= self.image_processing_class(**self.image_processor_dict						)
									# create random numpy tensors
									snake_case__   :      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
									snake_case__   :      List[str]   				= 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
									snake_case__   :      List[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      __a     (  self						)	->    Dict:



									'''simple docstring'''







									snake_case__   :      Tuple   				= self.image_processing_class(**self.image_processor_dict						)
									# create random PyTorch tensors
									snake_case__   :      List[str]   				= 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
									snake_case__   :      str   				= 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
									snake_case__   :      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      __a     (  self						)	->    Tuple:



									'''simple docstring'''







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

									# Test not batched input
									snake_case__   :      Optional[int]   				= 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() <= 255						)

									# Test batched
									snake_case__   :      str   				= 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() <= 255						)

									# Test not batched input (PIL images)
									snake_case__				,    snake_case__   :      List[Any]   				= prepare_semantic_single_inputs()

									snake_case__   :      Optional[Any]   				= 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() <= 255						)

									# Test batched input (PIL images)
									snake_case__				,    snake_case__   :      List[Any]   				= prepare_semantic_batch_inputs()

									snake_case__   :      Optional[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() <= 255						)




					def      __a     (  self						)	->    str:



									'''simple docstring'''







									snake_case__   :      Optional[int]   				= 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
									snake_case__				,    snake_case__   :      Dict   				= prepare_semantic_single_inputs()
									snake_case__   :      Union[str, Any]   				= image_processing(UpperCAmelCase_       ,			UpperCAmelCase_       ,			return_tensors='pt'						)
									self.assertTrue(encoding['labels'].min().item() >= 0						)
									self.assertTrue(encoding['labels'].max().item() <= 150						)

									snake_case__   :      Union[str, Any]   				= True
									snake_case__   :      List[str]   				= image_processing(UpperCAmelCase_       ,			UpperCAmelCase_       ,			return_tensors='pt'						)
									self.assertTrue(encoding['labels'].min().item() >= 0						)
									self.assertTrue(encoding['labels'].max().item() <= 255						)






"""simple docstring"""
from typing import Any





class   __A  :





							'''simple docstring'''

							def __init__(					self				:   List[Any]							)       ->int:



														"""simple docstring"""



														snake_case_     							=		[]
														snake_case_     							=		0



							def __str__(					self				:   List[str]							)       ->Union[str, Any]:



														"""simple docstring"""



														snake_case_     							=		"""<""" + str(self.stack							)[1:-1] + """>"""
														return printed



							def 		lowerCAmelCase							(					self				:   List[Any]		,							UpperCAmelCase_				:   Any							)       ->None:



														"""simple docstring"""



														self.stack.append(UpperCAmelCase_							)
														snake_case_     							=		self.length + 1



							def 		lowerCAmelCase							(					self				:   List[str]							)       ->Any:



														"""simple docstring"""



														self.rotate(1							)
														snake_case_     							=		self.stack[self.length - 1]
														snake_case_     							=		self.stack[:-1]
														self.rotate(self.length - 1							)
														snake_case_     							=		self.length - 1
														return dequeued



							def 		lowerCAmelCase							(					self				:   List[str]		,							UpperCAmelCase_				:   int							)       ->None:



														"""simple docstring"""



														for _ in range(UpperCAmelCase_							):
																					snake_case_     							=		self.stack[0]
																					snake_case_     							=		self.stack[1:]
																					self.put(UpperCAmelCase_							)
																					snake_case_     							=		self.length - 1



							def 		lowerCAmelCase							(					self				:   Dict							)       ->Any:



														"""simple docstring"""



														snake_case_     							=		self.get()
														self.put(UpperCAmelCase_							)
														self.rotate(self.length - 1							)
														return front



							def 		lowerCAmelCase							(					self				:   Optional[Any]							)       ->int:



														"""simple docstring"""



														return self.length



