






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  _lowercase   (   unittest.TestCase						):
      def __init__(     self				,			a				,			a=1_3				,			a=3				,			a=2_2_4				,			a=3_0				,			a=4_0_0				,			a=True				,			a=None				,			a=True				,			a=[0.5, 0.5, 0.5]				,			a=[0.5, 0.5, 0.5]				,			):
       snake_case__  :    Optional[Any]							       =size if size is not None else {"""height""": 1_8, """width""": 1_8}
       snake_case__  :    Optional[int]							       =parent
       snake_case__  :    str							       =batch_size
       snake_case__  :    Union[str, Any]							       =num_channels
       snake_case__  :    Any							       =image_size
       snake_case__  :    Optional[Any]							       =min_resolution
       snake_case__  :    Tuple							       =max_resolution
       snake_case__  :    List[Any]							       =do_resize
       snake_case__  :    Optional[int]							       =size
       snake_case__  :    Optional[int]							       =do_normalize
       snake_case__  :    Optional[int]							       =image_mean
       snake_case__  :    Dict							       =image_std



      def 						lowercase__					(     self   ):
       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  _lowercase   (   a__						,				unittest.TestCase						):
      _a							:						Union[str, Any]										=							ViTImageProcessor if is_vision_available() else None
      def 						lowercase__					(     self   ):
       snake_case__  :    Union[str, Any]							       =EfficientFormerImageProcessorTester(self   )
      @property
      def 						lowercase__					(     self   ):
       return self.image_proc_tester.prepare_image_processor_dict()
      def 						lowercase__					(     self   ):
       snake_case__  :    List[Any]							       =self.image_processing_class(**self.image_processor_dict   )
       self.assertTrue(hasattr(lowerCamelCase_				,			"""image_mean"""   )   )
       self.assertTrue(hasattr(lowerCamelCase_				,			"""image_std"""   )   )
       self.assertTrue(hasattr(lowerCamelCase_				,			"""do_normalize"""   )   )
       self.assertTrue(hasattr(lowerCamelCase_				,			"""do_resize"""   )   )
       self.assertTrue(hasattr(lowerCamelCase_				,			"""size"""   )   )
      def 						lowercase__					(     self   ):
       pass
      def 						lowercase__					(     self   ):
       # Initialize image_processor
       snake_case__  :    List[Any]							       =self.image_processing_class(**self.image_processor_dict   )
       # create random PIL images
       snake_case__  :    Union[str, Any]							       =prepare_image_inputs(self.image_proc_tester				,			equal_resolution=lowerCamelCase_   )
       for image in image_inputs:
        self.assertIsInstance(lowerCamelCase_				,			Image.Image   )

       # Test not batched input
       snake_case__  :    Optional[Any]							       =image_processor(image_inputs[0]				,			return_tensors="""pt"""   ).pixel_values
       self.assertEqual(
           encoded_images.shape				,			(
               1,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["""height"""],
               self.image_proc_tester.size["""width"""],
           )				,			)

       # Test batched
       snake_case__  :    Any							       =image_processor(lowerCamelCase_				,			return_tensors="""pt"""   ).pixel_values
       self.assertEqual(
           encoded_images.shape				,			(
               self.image_proc_tester.batch_size,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["""height"""],
               self.image_proc_tester.size["""width"""],
           )				,			)
      def 						lowercase__					(     self   ):
       # Initialize image_processor
       snake_case__  :    Union[str, Any]							       =self.image_processing_class(**self.image_processor_dict   )
       # create random numpy tensors
       snake_case__  :    Dict							       =prepare_image_inputs(self.image_proc_tester				,			equal_resolution=lowerCamelCase_				,			numpify=lowerCamelCase_   )
       for image in image_inputs:
        self.assertIsInstance(lowerCamelCase_				,			np.ndarray   )

       # Test not batched input
       snake_case__  :    Optional[Any]							       =image_processor(image_inputs[0]				,			return_tensors="""pt"""   ).pixel_values
       self.assertEqual(
           encoded_images.shape				,			(
               1,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["""height"""],
               self.image_proc_tester.size["""width"""],
           )				,			)

       # Test batched
       snake_case__  :    str							       =image_processor(lowerCamelCase_				,			return_tensors="""pt"""   ).pixel_values
       self.assertEqual(
           encoded_images.shape				,			(
               self.image_proc_tester.batch_size,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["""height"""],
               self.image_proc_tester.size["""width"""],
           )				,			)



      def 						lowercase__					(     self   ):
       # Initialize image_processor
       snake_case__  :    Optional[Any]							       =self.image_processing_class(**self.image_processor_dict   )
       # create random PyTorch tensors
       snake_case__  :    Tuple							       =prepare_image_inputs(self.image_proc_tester				,			equal_resolution=lowerCamelCase_				,			torchify=lowerCamelCase_   )
       for image in image_inputs:
        self.assertIsInstance(lowerCamelCase_				,			torch.Tensor   )

       # Test not batched input
       snake_case__  :    List[str]							       =image_processor(image_inputs[0]				,			return_tensors="""pt"""   ).pixel_values
       self.assertEqual(
           encoded_images.shape				,			(
               1,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["""height"""],
               self.image_proc_tester.size["""width"""],
           )				,			)

       # Test batched
       snake_case__  :    Dict							       =image_processor(lowerCamelCase_				,			return_tensors="""pt"""   ).pixel_values
       self.assertEqual(
           encoded_images.shape				,			(
               self.image_proc_tester.batch_size,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["""height"""],
               self.image_proc_tester.size["""width"""],
           )				,			)













class  _lowercase   :
      def __init__(     self				,			a   ):
       snake_case__  :    Optional[int]							       =size
       snake_case__  :    List[Any]							       =[0] * size
       snake_case__  :    List[Any]							       =[0] * size
      @staticmethod
      def 						lowercase__					(     a   ):
       return index | (index + 1)
      @staticmethod
      def 						lowercase__					(     a   ):
       return (index & (index + 1)) - 1
      def 						lowercase__					(     self				,			a				,			a   ):
       snake_case__  :    Optional[Any]							       =value
       while index < self.size:
        snake_case__  :    str							       =self.get_prev(a   ) + 1
        if current_left_border == index:
         snake_case__  :    Dict							       =value
        else:
         snake_case__  :    str							       =max(a				,			a				,			a   )
        snake_case__  :    Dict							       =self.get_next(a   )



      def 						lowercase__					(     self				,			a				,			a   ):
       right -= 1  # Because of right is exclusive
       snake_case__  :    Union[str, Any]							       =0
       while left <= right:
        snake_case__  :    List[str]							       =self.get_prev(a   )
        if left <= current_left:
         snake_case__  :    Optional[Any]							       =max(a				,			self.tree[right]   )
         snake_case__  :    Optional[Any]							       =current_left
        else:
         snake_case__  :    Tuple							       =max(a				,			self.arr[right]   )
         right -= 1
       return result


if __name__ == "__main__":
       import doctest

       doctest.testmod()





