





"""simple docstring"""





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 BeitImageProcessor





class        _lowerCamelCase      (       unittest.TestCase ):


						def __init__(self   ,							__a   ,							__a=7   ,							__a=3   ,							__a=18   ,							__a=30   ,							__a=4_00   ,							__a=True   ,							__a=None   ,							__a=True   ,							__a=None   ,							__a=True   ,							__a=[0.5, 0.5, 0.5]   ,							__a=[0.5, 0.5, 0.5]   ,							__a=False   ,							) ->   Optional[Any]:
								UpperCamelCase   =	size if size is not None else {"height": 20, "width": 20}
								UpperCamelCase   =	crop_size if crop_size is not None else {"height": 18, "width": 18}
								UpperCamelCase   =	parent
								UpperCamelCase   =	batch_size
								UpperCamelCase   =	num_channels
								UpperCamelCase   =	image_size
								UpperCamelCase   =	min_resolution
								UpperCamelCase   =	max_resolution
								UpperCamelCase   =	do_resize
								UpperCamelCase   =	size
								UpperCamelCase   =	do_center_crop
								UpperCamelCase   =	crop_size
								UpperCamelCase   =	do_normalize
								UpperCamelCase   =	image_mean
								UpperCamelCase   =	image_std
								UpperCamelCase   =	do_reduce_labels




						def     snake_case_   (self		) ->   List[Any]:
								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_center_crop": self.do_center_crop,
								    "crop_size": self.crop_size,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_reduce_labels": self.do_reduce_labels,
								}



def       a__					(					):




		"""simple docstring"""


		UpperCamelCase   =	load_dataset("hf-internal-testing/fixtures_ade20k"  ,	split="test"    )

		UpperCamelCase   =	Image.open(dataset[0]["file"]    )
		UpperCamelCase   =	Image.open(dataset[1]["file"]    )

		return image, map


def       a__					(					):




		"""simple docstring"""


		UpperCamelCase   =	load_dataset("hf-internal-testing/fixtures_ade20k"  ,	split="test"    )

		UpperCamelCase   =	Image.open(ds[0]["file"]    )
		UpperCamelCase   =	Image.open(ds[1]["file"]    )
		UpperCamelCase   =	Image.open(ds[2]["file"]    )
		UpperCamelCase   =	Image.open(ds[3]["file"]    )

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






@require_torch
@require_vision
class        _lowerCamelCase      (       a__  ,			unittest.TestCase ):
						UpperCAmelCase_ =			BeitImageProcessor if is_vision_available() else None


						def     snake_case_   (self		) ->   str:
								UpperCamelCase   =	BeitImageProcessingTester(self		)


						@property
						def     snake_case_   (self		) ->   List[str]:
								return self.image_processor_tester.prepare_image_processor_dict()


						def     snake_case_   (self		) ->   int:
								UpperCamelCase   =	self.image_processing_class(**self.image_processor_dict		)
								self.assertTrue(hasattr(lowerCamelCase_   ,							"do_resize"		)		)
								self.assertTrue(hasattr(lowerCamelCase_   ,							"size"		)		)
								self.assertTrue(hasattr(lowerCamelCase_   ,							"do_center_crop"		)		)
								self.assertTrue(hasattr(lowerCamelCase_   ,							"center_crop"		)		)
								self.assertTrue(hasattr(lowerCamelCase_   ,							"do_normalize"		)		)
								self.assertTrue(hasattr(lowerCamelCase_   ,							"image_mean"		)		)
								self.assertTrue(hasattr(lowerCamelCase_   ,							"image_std"		)		)


						def     snake_case_   (self		) ->   Union[str, Any]:
								UpperCamelCase   =	self.image_processing_class.from_dict(self.image_processor_dict		)
								self.assertEqual(image_processor.size   ,							{"height": 20, "width": 20}		)
								self.assertEqual(image_processor.crop_size   ,							{"height": 18, "width": 18}		)
								self.assertEqual(image_processor.do_reduce_labels   ,							lowerCamelCase_		)

								UpperCamelCase   =	self.image_processing_class.from_dict(
								    self.image_processor_dict   ,							size=42   ,							crop_size=84   ,							reduce_labels=lowerCamelCase_		)
								self.assertEqual(image_processor.size   ,							{"height": 42, "width": 42}		)
								self.assertEqual(image_processor.crop_size   ,							{"height": 84, "width": 84}		)
								self.assertEqual(image_processor.do_reduce_labels   ,							lowerCamelCase_		)


						def     snake_case_   (self		) ->   Union[str, Any]:
								pass


						def     snake_case_   (self		) ->   List[Any]:
								# Initialize image_processing
								UpperCamelCase   =	self.image_processing_class(**self.image_processor_dict		)
								# create random PIL images
								UpperCamelCase   =	prepare_image_inputs(self.image_processor_tester   ,							equal_resolution=lowerCamelCase_		)
								for image in image_inputs:
										self.assertIsInstance(lowerCamelCase_   ,							Image.Image		)

								# Test not batched input
								UpperCamelCase   =	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.crop_size["height"],
								        self.image_processor_tester.crop_size["width"],
								    )   ,							)

								# Test batched
								UpperCamelCase   =	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,
								        self.image_processor_tester.crop_size["height"],
								        self.image_processor_tester.crop_size["width"],
								    )   ,							)


						def     snake_case_   (self		) ->   Union[str, Any]:
								# Initialize image_processing
								UpperCamelCase   =	self.image_processing_class(**self.image_processor_dict		)
								# create random numpy tensors
								UpperCamelCase   =	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
								UpperCamelCase   =	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.crop_size["height"],
								        self.image_processor_tester.crop_size["width"],
								    )   ,							)

								# Test batched
								UpperCamelCase   =	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,
								        self.image_processor_tester.crop_size["height"],
								        self.image_processor_tester.crop_size["width"],
								    )   ,							)


						def     snake_case_   (self		) ->   Union[str, Any]:
								# Initialize image_processing
								UpperCamelCase   =	self.image_processing_class(**self.image_processor_dict		)
								# create random PyTorch tensors
								UpperCamelCase   =	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
								UpperCamelCase   =	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.crop_size["height"],
								        self.image_processor_tester.crop_size["width"],
								    )   ,							)

								# Test batched
								UpperCamelCase   =	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,
								        self.image_processor_tester.crop_size["height"],
								        self.image_processor_tester.crop_size["width"],
								    )   ,							)


						def     snake_case_   (self		) ->   Optional[Any]:
								# Initialize image_processing
								UpperCamelCase   =	self.image_processing_class(**self.image_processor_dict		)
								# create random PyTorch tensors
								UpperCamelCase   =	prepare_image_inputs(self.image_processor_tester   ,							equal_resolution=lowerCamelCase_   ,							torchify=lowerCamelCase_		)
								UpperCamelCase   =	[]
								for image in image_inputs:
										self.assertIsInstance(lowerCamelCase_   ,							torch.Tensor		)
										maps.append(torch.zeros(image.shape[-2:]		).long()		)

								# Test not batched input
								UpperCamelCase   =	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.crop_size["height"],
								        self.image_processor_tester.crop_size["width"],
								    )   ,							)
								self.assertEqual(
								    encoding["labels"].shape   ,							(
								        1,
								        self.image_processor_tester.crop_size["height"],
								        self.image_processor_tester.crop_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   =	image_processing(lowerCamelCase_   ,							lowerCamelCase_   ,							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.crop_size["height"],
								        self.image_processor_tester.crop_size["width"],
								    )   ,							)
								self.assertEqual(
								    encoding["labels"].shape   ,							(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.crop_size["height"],
								        self.image_processor_tester.crop_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   =	prepare_semantic_single_inputs()

								UpperCamelCase   =	image_processing(lowerCamelCase_   ,							lowerCamelCase_   ,							return_tensors="pt"		)
								self.assertEqual(
								    encoding["pixel_values"].shape   ,							(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["height"],
								        self.image_processor_tester.crop_size["width"],
								    )   ,							)
								self.assertEqual(
								    encoding["labels"].shape   ,							(
								        1,
								        self.image_processor_tester.crop_size["height"],
								        self.image_processor_tester.crop_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   =	prepare_semantic_batch_inputs()

								UpperCamelCase   =	image_processing(lowerCamelCase_   ,							lowerCamelCase_   ,							return_tensors="pt"		)
								self.assertEqual(
								    encoding["pixel_values"].shape   ,							(
								        2,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["height"],
								        self.image_processor_tester.crop_size["width"],
								    )   ,							)
								self.assertEqual(
								    encoding["labels"].shape   ,							(
								        2,
								        self.image_processor_tester.crop_size["height"],
								        self.image_processor_tester.crop_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     snake_case_   (self		) ->   Any:
								# Initialize image_processing
								UpperCamelCase   =	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   =	prepare_semantic_single_inputs()
								UpperCamelCase   =	image_processing(lowerCamelCase_   ,							lowerCamelCase_   ,							return_tensors="pt"		)
								self.assertTrue(encoding["labels"].min().item() >= 0		)
								self.assertTrue(encoding["labels"].max().item() <= 1_50		)

								UpperCamelCase   =	True
								UpperCamelCase   =	image_processing(lowerCamelCase_   ,							lowerCamelCase_   ,							return_tensors="pt"		)
								self.assertTrue(encoding["labels"].min().item() >= 0		)
								self.assertTrue(encoding["labels"].max().item() <= 2_55		)













"""simple docstring"""





from typing import List

from .keymap import KEYMAP, get_character


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		def decorator(_SCREAMING_SNAKE_CASE    ):
				UpperCamelCase   =	getattr(_SCREAMING_SNAKE_CASE  ,	"handle_key"  ,	[]    )
				handle += [key]
				setattr(_SCREAMING_SNAKE_CASE  ,	"handle_key"  ,	_SCREAMING_SNAKE_CASE    )
				return func

		return decorator


def       a__					(					*_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		def decorator(_SCREAMING_SNAKE_CASE    ):
				UpperCamelCase   =	getattr(_SCREAMING_SNAKE_CASE  ,	"handle_key"  ,	[]    )
				handle += keys
				setattr(_SCREAMING_SNAKE_CASE  ,	"handle_key"  ,	_SCREAMING_SNAKE_CASE    )
				return func

		return decorator





class        _lowerCamelCase      (       _lowercase ):


						def __new__(cls   ,							__a   ,							__a   ,							__a		) ->   Any:
								UpperCamelCase   =	super().__new__(cls   ,							__a   ,							__a   ,							__a		)
								if not hasattr(__a   ,							"key_handler"		):
										setattr(__a   ,							"key_handler"   ,							{}		)
								setattr(__a   ,							"handle_input"   ,							KeyHandler.handle_input		)

								for value in attrs.values():
										UpperCamelCase   =	getattr(__a   ,							"handle_key"   ,							[]		)
										for key in handled_keys:
												UpperCamelCase   =	value
								return new_cls




						@staticmethod
						def     snake_case_   (cls		) ->   Optional[Any]:
								UpperCamelCase   =	get_character()
								if char != KEYMAP["undefined"]:
										UpperCamelCase   =	ord(__a		)
								UpperCamelCase   =	cls.key_handler.get(__a		)
								if handler:
										UpperCamelCase   =	char
										return handler(cls		)
								else:
										return None



def       a__					(					cls    ):




		"""simple docstring"""


		return KeyHandler(cls.__name__  ,	cls.__bases__  ,	cls.__dict__.copy()    )






