




"""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      a__				(	unittest.TestCase   ):


			def __init__(      self      :    int      ,  UpperCamelCase_      :    Optional[Any]      ,  UpperCamelCase_      :    Optional[int]=7      ,  UpperCamelCase_      :    Union[str, Any]=3      ,  UpperCamelCase_      :    List[Any]=18      ,  UpperCamelCase_      :    Any=30      ,  UpperCamelCase_      :    int=400      ,  UpperCamelCase_      :    Tuple=True      ,  UpperCamelCase_      :    Tuple=None      ,  UpperCamelCase_      :    Optional[int]=True      ,  UpperCamelCase_      :    Dict=None      ,  UpperCamelCase_      :    Optional[int]=True      ,  UpperCamelCase_      :    List[str]=[0.5, 0.5, 0.5]      ,  UpperCamelCase_      :    Tuple=[0.5, 0.5, 0.5]      ,  UpperCamelCase_      :    Dict=False      ,  ):


						"""simple docstring"""




						__UpperCAmelCase	: int		=					size if size is not None else {"height": 20, "width": 20}
						__UpperCAmelCase	: Any		=					crop_size if crop_size is not None else {"height": 18, "width": 18}
						__UpperCAmelCase	: Optional[int]		=					parent
						__UpperCAmelCase	: Any		=					batch_size
						__UpperCAmelCase	: List[Any]		=					num_channels
						__UpperCAmelCase	: Any		=					image_size
						__UpperCAmelCase	: Optional[Any]		=					min_resolution
						__UpperCAmelCase	: str		=					max_resolution
						__UpperCAmelCase	: Union[str, Any]		=					do_resize
						__UpperCAmelCase	: Dict		=					size
						__UpperCAmelCase	: Dict		=					do_center_crop
						__UpperCAmelCase	: str		=					crop_size
						__UpperCAmelCase	: List[str]		=					do_normalize
						__UpperCAmelCase	: Tuple		=					image_mean
						__UpperCAmelCase	: Tuple		=					image_std
						__UpperCAmelCase	: List[str]		=					do_reduce_labels


			def       a_     (      self      :    Dict):


						"""simple docstring"""




						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 					_UpperCamelCase    (			)			->			List[Any]:


			"""simple docstring"""
			__UpperCAmelCase	: Optional[int]		=					load_dataset("hf-internal-testing/fixtures_ade20k"				,       split="test"							)

			__UpperCAmelCase	: Optional[Any]		=					Image.open(dataset[0]["file"]							)
			__UpperCAmelCase	: Optional[int]		=					Image.open(dataset[1]["file"]							)

			return image, map

def 					_UpperCamelCase    (			)			->			Tuple:


			"""simple docstring"""
			__UpperCAmelCase	: Any		=					load_dataset("hf-internal-testing/fixtures_ade20k"				,       split="test"							)

			__UpperCAmelCase	: Tuple		=					Image.open(ds[0]["file"]							)
			__UpperCAmelCase	: Optional[Any]		=					Image.open(ds[1]["file"]							)
			__UpperCAmelCase	: Tuple		=					Image.open(ds[2]["file"]							)
			__UpperCAmelCase	: Any		=					Image.open(ds[3]["file"]							)

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





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


			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase	: List[Any]		=					BeitImageProcessingTester(self)


			@property
			def       a_     (      self      :    List[str]):


						"""simple docstring"""




						return self.image_processor_tester.prepare_image_processor_dict()


			def       a_     (      self      :    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_center_crop"))
						self.assertTrue(hasattr(UpperCamelCase_      ,  "center_crop"))
						self.assertTrue(hasattr(UpperCamelCase_      ,  "do_normalize"))
						self.assertTrue(hasattr(UpperCamelCase_      ,  "image_mean"))
						self.assertTrue(hasattr(UpperCamelCase_      ,  "image_std"))


			def       a_     (      self      :    str):


						"""simple docstring"""




						__UpperCAmelCase	: int		=					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      ,  UpperCamelCase_)

						__UpperCAmelCase	: Optional[Any]		=					self.image_processing_class.from_dict(
						    self.image_processor_dict      ,  size=42      ,  crop_size=84      ,  reduce_labels=UpperCamelCase_)
						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      ,  UpperCamelCase_)


			def       a_     (      self      :    Dict):


						"""simple docstring"""




						pass


			def       a_     (      self      :    str):


						"""simple docstring"""




						__UpperCAmelCase	: List[Any]		=					self.image_processing_class(**self.image_processor_dict)
						# create random PIL images
						__UpperCAmelCase	: Union[str, Any]		=					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	: 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.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )      ,  )

						# Test batched
						__UpperCAmelCase	: 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.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )      ,  )


			def       a_     (      self      :    List[Any]):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[int]		=					self.image_processing_class(**self.image_processor_dict)
						# create random numpy tensors
						__UpperCAmelCase	: 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	: 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.crop_size["height"],
						        self.image_processor_tester.crop_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.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )      ,  )


			def       a_     (      self      :    List[str]):


						"""simple docstring"""




						__UpperCAmelCase	: List[Any]		=					self.image_processing_class(**self.image_processor_dict)
						# create random PyTorch tensors
						__UpperCAmelCase	: Union[str, 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[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.crop_size["height"],
						        self.image_processor_tester.crop_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.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )      ,  )


			def       a_     (      self      :    Dict):


						"""simple docstring"""




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

						# Test not batched input
						__UpperCAmelCase	: Dict		=					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() <= 255)

						# Test batched
						__UpperCAmelCase	: Optional[int]		=					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.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() <= 255)

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

						__UpperCAmelCase	: 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.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() <= 255)

						# Test batched input (PIL images)
						__UpperCAmelCase	,     __UpperCAmelCase	: str		=					prepare_semantic_batch_inputs()

						__UpperCAmelCase	: List[str]		=					image_processing(UpperCamelCase_      ,  UpperCamelCase_      ,  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() <= 255)


			def       a_     (      self      :    Optional[Any]):


						"""simple docstring"""




						__UpperCAmelCase	: Tuple		=					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	: str		=					prepare_semantic_single_inputs()
						__UpperCAmelCase	: Optional[Any]		=					image_processing(UpperCamelCase_      ,  UpperCamelCase_      ,  return_tensors="pt")
						self.assertTrue(encoding["labels"].min().item() >= 0)
						self.assertTrue(encoding["labels"].max().item() <= 150)

						__UpperCAmelCase	: Dict		=					True
						__UpperCAmelCase	: List[Any]		=					image_processing(UpperCamelCase_      ,  UpperCamelCase_      ,  return_tensors="pt")
						self.assertTrue(encoding["labels"].min().item() >= 0)
						self.assertTrue(encoding["labels"].max().item() <= 255)








"""simple docstring"""





def 					_UpperCamelCase    (			UpperCamelCase							)			->			int:


			"""simple docstring"""

			if not grid or not grid[0]:
						raise TypeError("The grid does not contain the appropriate information"							)

			for cell_n in range(1				,       len(grid[0]							)							):
						grid[0][cell_n] += grid[0][cell_n - 1]
			__UpperCAmelCase	: int		=					grid[0]

			for row_n in range(1				,       len(UpperCamelCase							)							):
						__UpperCAmelCase	: int		=					grid[row_n]
						__UpperCAmelCase	: str		=					fill_row(UpperCamelCase				,       UpperCamelCase							)
						__UpperCAmelCase	: List[str]		=					grid[row_n]

			return grid[-1][-1]

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase							)			->			list:


			"""simple docstring"""
			current_row[0] += row_above[0]
			for cell_n in range(1				,       len(UpperCamelCase							)							):
						current_row[cell_n] += min(current_row[cell_n - 1]				,       row_above[cell_n]							)

			return current_row


if __name__ == "__main__":
					import doctest

					doctest.testmod()


