




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 ViTImageProcessor






class       A      (		unittest.TestCase		):
     """simple docstring"""

     def __init__(     self	: int						,	A__	: Any						,	A__	: Dict=7						,	A__	: Tuple=3						,	A__	: List[Any]=1_8						,	A__	: Optional[int]=3_0						,	A__	: str=4_0_0						,	A__	: List[Any]=True						,	A__	: Union[str, Any]=None						,	A__	: Any=True						,	A__	: Dict=[0.5, 0.5, 0.5]						,	A__	: Optional[int]=[0.5, 0.5, 0.5]						,	)					->							Any:
           lowerCamelCase__   	=					size if size is not None else {"""height""": 1_8, """width""": 1_8}
           lowerCamelCase__   	=					parent
           lowerCamelCase__   	=					batch_size
           lowerCamelCase__   	=					num_channels
           lowerCamelCase__   	=					image_size
           lowerCamelCase__   	=					min_resolution
           lowerCamelCase__   	=					max_resolution
           lowerCamelCase__   	=					do_resize
           lowerCamelCase__   	=					size
           lowerCamelCase__   	=					do_normalize
           lowerCamelCase__   	=					image_mean
           lowerCamelCase__   	=					image_std




     def 			__snake_case (     self	: Optional[Any]      )					->							Dict:
           return {
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_normalize": self.do_normalize,
               "do_resize": self.do_resize,
               "size": self.size,
           }






@require_torch
@require_vision
class       A      (		_lowerCAmelCase      ,    unittest.TestCase		):
     """simple docstring"""

     _snake_case:						List[str]  =ViTImageProcessor if is_vision_available() else None




     def 			__snake_case (     self	: str      )					->							List[Any]:
           lowerCamelCase__   	=					ViTImageProcessingTester(self      )




     @property
     def 			__snake_case (     self	: Dict      )					->							Dict:
           return self.image_processor_tester.prepare_image_processor_dict()




     def 			__snake_case (     self	: Dict      )					->							List[Any]:
           lowerCamelCase__   	=					self.image_processing_class(**self.image_processor_dict      )
           self.assertTrue(hasattr(UpperCAmelCase_						,	'image_mean'      )      )
           self.assertTrue(hasattr(UpperCAmelCase_						,	'image_std'      )      )
           self.assertTrue(hasattr(UpperCAmelCase_						,	'do_normalize'      )      )
           self.assertTrue(hasattr(UpperCAmelCase_						,	'do_resize'      )      )
           self.assertTrue(hasattr(UpperCAmelCase_						,	'size'      )      )




     def 			__snake_case (     self	: List[Any]      )					->							Union[str, Any]:
           lowerCamelCase__   	=					self.image_processing_class.from_dict(self.image_processor_dict      )
           self.assertEqual(image_processor.size						,	{'height': 1_8, 'width': 1_8}      )

           lowerCamelCase__   	=					self.image_processing_class.from_dict(self.image_processor_dict						,	size=4_2      )
           self.assertEqual(image_processor.size						,	{'height': 4_2, 'width': 4_2}      )




     def 			__snake_case (     self	: Dict      )					->							Optional[Any]:
           pass




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

           # Test not batched input
           lowerCamelCase__   	=					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
           lowerCamelCase__   	=					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 			__snake_case (     self	: str      )					->							Tuple:
           # Initialize image_processing
           lowerCamelCase__   	=					self.image_processing_class(**self.image_processor_dict      )
           # create random numpy tensors
           lowerCamelCase__   	=					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
           lowerCamelCase__   	=					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
           lowerCamelCase__   	=					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 			__snake_case (     self	: Tuple      )					->							List[str]:
           # Initialize image_processing
           lowerCamelCase__   	=					self.image_processing_class(**self.image_processor_dict      )
           # create random PyTorch tensors
           lowerCamelCase__   	=					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
           lowerCamelCase__   	=					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
           lowerCamelCase__   	=					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'],
               )						,	)




"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import _LazyModule


UpperCAmelCase:  Union[str, Any]			     =				{"""tokenization_phobert""": ["""PhobertTokenizer"""]}


if TYPE_CHECKING:
		from .tokenization_phobert import PhobertTokenizer

else:
		import sys

		UpperCAmelCase:  Optional[int]			     =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



