


'''simple docstring'''





import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
 from PIL import Image


class  UpperCamelCase__  (    unittest.TestCase      ,     SCREAMING_SNAKE_CASE):






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







       '''simple docstring'''



       __A       	=			load_tool('image-segmentation'			)
       self.tool.setup()
       __A       	=			load_tool('image-segmentation'	,				remote=_A			)






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







       '''simple docstring'''



       __A       	=			Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'			)			) / '000000039769.png'			)
       __A       	=			self.tool(_A	,				'cat'			)
       self.assertTrue(isinstance(_A	,				Image.Image			)			)






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







       '''simple docstring'''



       __A       	=			Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'			)			) / '000000039769.png'			)
       __A       	=			self.remote_tool(_A	,				'cat'			)
       self.assertTrue(isinstance(_A	,				Image.Image			)			)






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







       '''simple docstring'''



       __A       	=			Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'			)			) / '000000039769.png'			)
       __A       	=			self.tool(image=_A	,				label='cat'			)
       self.assertTrue(isinstance(_A	,				Image.Image			)			)






 def 	lowercase_						(			self  :str			)				->			Any:







       '''simple docstring'''



       __A       	=			Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'			)			) / '000000039769.png'			)
       __A       	=			self.remote_tool(image=_A	,				label='cat'			)
       self.assertTrue(isinstance(_A	,				Image.Image			)			)








'''simple docstring'''





import unittest

import numpy as np

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 DeiTImageProcessor


class  UpperCamelCase__  (    unittest.TestCase):






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







       '''simple docstring'''



       __A       	=			size if size is not None else {'height': 20, 'width': 20}
       __A       	=			crop_size if crop_size is not None else {'height': 18, 'width': 18}

       __A       	=			parent
       __A       	=			batch_size
       __A       	=			num_channels
       __A       	=			image_size
       __A       	=			min_resolution
       __A       	=			max_resolution
       __A       	=			do_resize
       __A       	=			size
       __A       	=			do_center_crop
       __A       	=			crop_size
       __A       	=			do_normalize
       __A       	=			image_mean
       __A       	=			image_std






 def 	lowercase_						(			self  :Dict			)				->			List[str]:







       '''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,
       }


@require_torch
@require_vision
class  UpperCamelCase__  (    SCREAMING_SNAKE_CASE      ,     unittest.TestCase):
 UpperCAmelCase__    :    int										=					DeiTImageProcessor if is_vision_available() else None
 UpperCAmelCase__    :    Dict										=					True






 def 	lowercase_						(			self  :Dict			)				->			str:







       '''simple docstring'''



       __A       	=			DeiTImageProcessingTester(self			)






 @property
 def 	lowercase_						(			self  :Optional[Any]			)				->			Optional[Any]:







       '''simple docstring'''



       return self.image_processor_tester.prepare_image_processor_dict()






 def 	lowercase_						(			self  :Tuple			)				->			Tuple:







       '''simple docstring'''



       __A       	=			self.image_processing_class(**self.image_processor_dict			)
       self.assertTrue(hasattr(_A	,				'do_resize'			)			)
       self.assertTrue(hasattr(_A	,				'size'			)			)
       self.assertTrue(hasattr(_A	,				'do_center_crop'			)			)
       self.assertTrue(hasattr(_A	,				'center_crop'			)			)
       self.assertTrue(hasattr(_A	,				'do_normalize'			)			)
       self.assertTrue(hasattr(_A	,				'image_mean'			)			)
       self.assertTrue(hasattr(_A	,				'image_std'			)			)






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







       '''simple docstring'''



       __A       	=			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}			)

       __A       	=			self.image_processing_class.from_dict(self.image_processor_dict	,				size=42	,				crop_size=84			)
       self.assertEqual(image_processor.size	,				{'height': 42, 'width': 42}			)
       self.assertEqual(image_processor.crop_size	,				{'height': 84, 'width': 84}			)






 def 	lowercase_						(			self  :str			)				->			Any:







       '''simple docstring'''



       pass






 def 	lowercase_						(			self  :List[Any]			)				->			Optional[Any]:







       '''simple docstring'''



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

       # Test not batched input
       __A       	=			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
       __A       	=			image_processing(_A	,				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 	lowercase_						(			self  :Optional[Any]			)				->			Dict:







       '''simple docstring'''



       __A       	=			self.image_processing_class(**self.image_processor_dict			)
       # create random numpy tensors
       __A       	=			prepare_image_inputs(self.image_processor_tester	,				equal_resolution=_A	,				numpify=_A			)
       for image in image_inputs:
             self.assertIsInstance(_A	,				np.ndarray			)

       # Test not batched input
       __A       	=			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
       __A       	=			image_processing(_A	,				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 	lowercase_						(			self  :List[Any]			)				->			Dict:







       '''simple docstring'''



       __A       	=			self.image_processing_class(**self.image_processor_dict			)
       # create random PyTorch tensors
       __A       	=			prepare_image_inputs(self.image_processor_tester	,				equal_resolution=_A	,				torchify=_A			)
       for image in image_inputs:
             self.assertIsInstance(_A	,				torch.Tensor			)

       # Test not batched input
       __A       	=			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
       __A       	=			image_processing(_A	,				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'],
           )	,				)






