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 ViltImageProcessor



class 						snake_case_ (	unittest.TestCase ):






   def __init__(   self       :  Dict			,		lowercase_       :  Optional[Any]			,		lowercase_       :  str=7			,		lowercase_       :  List[Any]=3			,		lowercase_       :  Optional[Any]=18			,		lowercase_       :  List[str]=30			,		lowercase_       :  Tuple=4_00			,		lowercase_       :  Union[str, Any]=True			,		lowercase_       :  Union[str, Any]=None			,		lowercase_       :  Optional[int]=2			,		lowercase_       :  Optional[Any]=True			,		lowercase_       :  Optional[Any]=[0.5, 0.5, 0.5]			,		lowercase_       :  Dict=[0.5, 0.5, 0.5]			,		)      ->		Tuple:
       lowercase__				:     List[str]					      =       size if size is not None else {"shortest_edge": 30}
       lowercase__				:     Tuple					      =       parent
       lowercase__				:     Optional[int]					      =       batch_size
       lowercase__				:     str					      =       num_channels
       lowercase__				:     str					      =       image_size
       lowercase__				:     Tuple					      =       min_resolution
       lowercase__				:     List[Any]					      =       max_resolution
       lowercase__				:     Dict					      =       do_resize
       lowercase__				:     Union[str, Any]					      =       size
       lowercase__				:     Optional[int]					      =       size_divisor
       lowercase__				:     str					      =       do_normalize
       lowercase__				:     Any					      =       image_mean
       lowercase__				:     str					      =       image_std






   def    __UpperCamelCase  (   self       :  int						)      ->		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,
           "size_divisor": self.size_divisor,
       }







   def    __UpperCamelCase  (   self       :  Optional[int]			,		lowercase_       :  Tuple			,		lowercase_       :  List[str]=False						)      ->		Union[str, Any]:
       if not batched:
           lowercase__				:     Optional[int]					      =       self.size["shortest_edge"]
           lowercase__				:     Optional[int]					      =       image_inputs[0]
           if isinstance(lowercase_			,		Image.Image						):
               lowercase__      ,     lowercase__				:     Optional[int]					      =       image.size
           else:
               lowercase__      ,     lowercase__				:     Union[str, Any]					      =       image.shape[1], image.shape[2]
           lowercase__				:     Any					      =       size / min(lowercase_			,		lowercase_						)
           if h < w:
               lowercase__      ,     lowercase__				:     int					      =       size, scale * w
           else:
               lowercase__      ,     lowercase__				:     Optional[int]					      =       scale * h, size

           lowercase__				:     str					      =       int((13_33 / 8_00) * size						)
           if max(lowercase_			,		lowercase_						) > max_size:
               lowercase__				:     int					      =       max_size / max(lowercase_			,		lowercase_						)
               lowercase__				:     int					      =       newh * scale
               lowercase__				:     Union[str, Any]					      =       neww * scale

           lowercase__      ,     lowercase__				:     List[Any]					      =       int(newh + 0.5						), int(neww + 0.5						)
           lowercase__      ,     lowercase__				:     Optional[Any]					      =       (
               newh // self.size_divisor * self.size_divisor,
               neww // self.size_divisor * self.size_divisor,
           )

       else:
           lowercase__				:     Tuple					      =       []
           for image in image_inputs:
               lowercase__      ,     lowercase__				:     str					      =       self.get_expected_values([image]						)
               expected_values.append((expected_height, expected_width)						)
           lowercase__				:     Dict					      =       max(lowercase_			,		key=lambda lowercase_						: item[0]						)[0]
           lowercase__				:     Union[str, Any]					      =       max(lowercase_			,		key=lambda lowercase_						: item[1]						)[1]

       return expected_height, expected_width



@require_torch
@require_vision
class 						snake_case_ (	__A						,unittest.TestCase ):
   __A			:	Any        =       ViltImageProcessor if is_vision_available() else None






   def    __UpperCamelCase  (   self       :  Any						)      ->		Any:
       lowercase__				:     Dict					      =       ViltImageProcessingTester(self						)






   @property
   def    __UpperCamelCase  (   self       :  Optional[int]						)      ->		int:
       return self.image_processor_tester.prepare_image_processor_dict()






   def    __UpperCamelCase  (   self       :  Dict						)      ->		int:
       lowercase__				:     List[str]					      =       self.image_processing_class(**self.image_processor_dict						)
       self.assertTrue(hasattr(lowercase_			,		"image_mean"						)						)
       self.assertTrue(hasattr(lowercase_			,		"image_std"						)						)
       self.assertTrue(hasattr(lowercase_			,		"do_normalize"						)						)
       self.assertTrue(hasattr(lowercase_			,		"do_resize"						)						)
       self.assertTrue(hasattr(lowercase_			,		"size"						)						)
       self.assertTrue(hasattr(lowercase_			,		"size_divisor"						)						)






   def    __UpperCamelCase  (   self       :  Optional[Any]						)      ->		List[Any]:
       lowercase__				:     Dict					      =       self.image_processing_class.from_dict(self.image_processor_dict						)
       self.assertEqual(image_processor.size			,		{"shortest_edge": 30}						)

       lowercase__				:     Union[str, Any]					      =       self.image_processing_class.from_dict(self.image_processor_dict			,		size=42						)
       self.assertEqual(image_processor.size			,		{"shortest_edge": 42}						)






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






   def    __UpperCamelCase  (   self       :  List[str]						)      ->		List[Any]:
       # Initialize image_processing
       lowercase__				:     str					      =       self.image_processing_class(**self.image_processor_dict						)
       # create random PIL images
       lowercase__				:     Optional[int]					      =       prepare_image_inputs(self.image_processor_tester			,		equal_resolution=lowercase_						)
       for image in image_inputs:
           self.assertIsInstance(lowercase_			,		Image.Image						)

       # Test not batched input
       lowercase__				:     Optional[int]					      =       image_processing(image_inputs[0]			,		return_tensors="pt"						).pixel_values

       lowercase__      ,     lowercase__				:     Tuple					      =       self.image_processor_tester.get_expected_values(lowercase_						)
       self.assertEqual(
           encoded_images.shape			,		(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,		)

       # Test batched
       lowercase__				:     Union[str, Any]					      =       image_processing(lowercase_			,		return_tensors="pt"						).pixel_values

       lowercase__      ,     lowercase__				:     Any					      =       self.image_processor_tester.get_expected_values(lowercase_			,		batched=lowercase_						)
       self.assertEqual(
           encoded_images.shape			,		(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )			,		)






   def    __UpperCamelCase  (   self       :  Optional[Any]						)      ->		Union[str, Any]:
       # Initialize image_processing
       lowercase__				:     Dict					      =       self.image_processing_class(**self.image_processor_dict						)
       # create random numpy tensors
       lowercase__				:     List[str]					      =       prepare_image_inputs(self.image_processor_tester			,		equal_resolution=lowercase_			,		numpify=lowercase_						)
       for image in image_inputs:
           self.assertIsInstance(lowercase_			,		np.ndarray						)

       # Test not batched input
       lowercase__				:     List[str]					      =       image_processing(image_inputs[0]			,		return_tensors="pt"						).pixel_values

       lowercase__      ,     lowercase__				:     int					      =       self.image_processor_tester.get_expected_values(lowercase_						)
       self.assertEqual(
           encoded_images.shape			,		(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,		)

       # Test batched
       lowercase__				:     int					      =       image_processing(lowercase_			,		return_tensors="pt"						).pixel_values

       lowercase__      ,     lowercase__				:     Union[str, Any]					      =       self.image_processor_tester.get_expected_values(lowercase_			,		batched=lowercase_						)
       self.assertEqual(
           encoded_images.shape			,		(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )			,		)







   def    __UpperCamelCase  (   self       :  List[Any]						)      ->		int:
       # Initialize image_processing
       lowercase__				:     Dict					      =       self.image_processing_class(**self.image_processor_dict						)
       # create random PyTorch tensors
       lowercase__				:     Any					      =       prepare_image_inputs(self.image_processor_tester			,		equal_resolution=lowercase_			,		torchify=lowercase_						)
       for image in image_inputs:
           self.assertIsInstance(lowercase_			,		torch.Tensor						)

       # Test not batched input
       lowercase__				:     Optional[Any]					      =       image_processing(image_inputs[0]			,		return_tensors="pt"						).pixel_values

       lowercase__      ,     lowercase__				:     List[Any]					      =       self.image_processor_tester.get_expected_values(lowercase_						)
       self.assertEqual(
           encoded_images.shape			,		(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,		)

       # Test batched
       lowercase__				:     Dict					      =       image_processing(lowercase_			,		return_tensors="pt"						).pixel_values

       lowercase__      ,     lowercase__				:     Dict					      =       self.image_processor_tester.get_expected_values(lowercase_			,		batched=lowercase_						)
       self.assertEqual(
           encoded_images.shape			,		(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )			,		)




from typing import Callable

def     lowercase_       (    _lowerCamelCase							: Callable):

    def wrapper(_lowerCamelCase							: str):
        lowercase__				:     Tuple					      =       function_with_docstring.__doc__
        return target_function

    return wrapper



