



'''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      __UpperCamelCase       (unittest.TestCase			):







 def __init__(    self ,				_lowerCAmelCase ,				_lowerCAmelCase=7 ,				_lowerCAmelCase=3 ,				_lowerCAmelCase=18 ,				_lowerCAmelCase=30 ,				_lowerCAmelCase=400 ,				_lowerCAmelCase=True ,				_lowerCAmelCase=None ,				_lowerCAmelCase=True ,				_lowerCAmelCase=None ,				_lowerCAmelCase=True ,				_lowerCAmelCase=[0.5, 0.5, 0.5] ,				_lowerCAmelCase=[0.5, 0.5, 0.5] ,				_lowerCAmelCase=False ,				)    ->    Optional[Any]:
       '''simple docstring'''






       lowercase        =							size if size is not None else {"""height""": 20, """width""": 20}
       lowercase        =							crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
       lowercase        =							parent
       lowercase        =							batch_size
       lowercase        =							num_channels
       lowercase        =							image_size
       lowercase        =							min_resolution
       lowercase        =							max_resolution
       lowercase        =							do_resize
       lowercase        =							size
       lowercase        =							do_center_crop
       lowercase        =							crop_size
       lowercase        =							do_normalize
       lowercase        =							image_mean
       lowercase        =							image_std
       lowercase        =							do_reduce_labels







 def   _a   (    self						)    ->    Optional[int]:
       '''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      SCREAMING_SNAKE_CASE				(				):
      lowercase        =							load_dataset("""hf-internal-testing/fixtures_ade20k""" ,  split="""test"""						)

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

      return image, map







def      SCREAMING_SNAKE_CASE				(				):
      lowercase        =							load_dataset("""hf-internal-testing/fixtures_ade20k""" ,  split="""test"""						)

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

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




@require_torch
@require_vision
class      __UpperCamelCase       (_UpperCAmelCase			,   unittest.TestCase			):
 __A			  =					BeitImageProcessor if is_vision_available() else None







 def   _a   (    self						)    ->    int:
       '''simple docstring'''






       lowercase        =							BeitImageProcessingTester(self						)







 @property
 def   _a   (    self						)    ->    List[str]:
       '''simple docstring'''






       return self.image_processor_tester.prepare_image_processor_dict()







 def   _a   (    self						)    ->    List[str]:
       '''simple docstring'''






       lowercase        =							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   _a   (    self						)    ->    Optional[int]:
       '''simple docstring'''






       lowercase        =							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						)

       lowercase        =							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   _a   (    self						)    ->    int:
       '''simple docstring'''






       pass







 def   _a   (    self						)    ->    Tuple:
       '''simple docstring'''






       lowercase        =							self.image_processing_class(**self.image_processor_dict						)
       # create random PIL images
       lowercase        =							prepare_image_inputs(self.image_processor_tester ,				equal_resolution=_lowerCAmelCase						)
       for image in image_inputs:
             self.assertIsInstance(_lowerCAmelCase ,				Image.Image						)

       # Test not batched input
       lowercase        =							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
       lowercase        =							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   _a   (    self						)    ->    List[Any]:
       '''simple docstring'''






       lowercase        =							self.image_processing_class(**self.image_processor_dict						)
       # create random numpy tensors
       lowercase        =							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
       lowercase        =							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
       lowercase        =							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   _a   (    self						)    ->    List[Any]:
       '''simple docstring'''






       lowercase        =							self.image_processing_class(**self.image_processor_dict						)
       # create random PyTorch tensors
       lowercase        =							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
       lowercase        =							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
       lowercase        =							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   _a   (    self						)    ->    List[str]:
       '''simple docstring'''






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

       # Test not batched input
       lowercase        =							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
       lowercase        =							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() <= 255						)

       # Test not batched input (PIL images)
       lowercase     ,   lowercase        =							prepare_semantic_single_inputs()

       lowercase        =							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() <= 255						)

       # Test batched input (PIL images)
       lowercase     ,   lowercase        =							prepare_semantic_batch_inputs()

       lowercase        =							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() <= 255						)







 def   _a   (    self						)    ->    Any:
       '''simple docstring'''






       lowercase        =							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
       lowercase     ,   lowercase        =							prepare_semantic_single_inputs()
       lowercase        =							image_processing(_lowerCAmelCase ,				_lowerCAmelCase ,				return_tensors="""pt"""						)
       self.assertTrue(encoding["""labels"""].min().item() >= 0						)
       self.assertTrue(encoding["""labels"""].max().item() <= 150						)

       lowercase        =							True
       lowercase        =							image_processing(_lowerCAmelCase ,				_lowerCAmelCase ,				return_tensors="""pt"""						)
       self.assertTrue(encoding["""labels"""].min().item() >= 0						)
       self.assertTrue(encoding["""labels"""].max().item() <= 255						)







'''simple docstring'''


import os







def      SCREAMING_SNAKE_CASE				(				):
						lowercase        =							os.path.join(os.path.dirname(lowercase_						) ,  """num.txt"""						)
						with open(lowercase_						) as file_hand:
												return str(sum(int(lowercase_						) for line in file_hand						)						)[:10]


if __name__ == "__main__":
						print(solution())


