
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


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import BlipImageProcessor

class 		lowercase_							(							unittest.TestCase					):

 def __init__(   self  ,						__UpperCamelCase  ,						__UpperCamelCase=7  ,						__UpperCamelCase=3  ,						__UpperCamelCase=1_8  ,						__UpperCamelCase=3_0  ,						__UpperCamelCase=4_0_0  ,						__UpperCamelCase=True  ,						__UpperCamelCase=None  ,						__UpperCamelCase=True  ,						__UpperCamelCase=False  ,						__UpperCamelCase=[0.48_145_466, 0.4_578_275, 0.40_821_073]  ,						__UpperCamelCase=[0.26_862_954, 0.26_130_258, 0.27_577_711]  ,						__UpperCamelCase=True  ,						):




        """simple docstring"""


        UpperCamelCase_											=							size if size is not None else {"""height""": 2_0, """width""": 2_0}
        UpperCamelCase_											=							parent
        UpperCamelCase_											=							batch_size
        UpperCamelCase_											=							num_channels
        UpperCamelCase_											=							image_size
        UpperCamelCase_											=							min_resolution
        UpperCamelCase_											=							max_resolution
        UpperCamelCase_											=							do_resize
        UpperCamelCase_											=							size
        UpperCamelCase_											=							do_normalize
        UpperCamelCase_											=							image_mean
        UpperCamelCase_											=							image_std
        UpperCamelCase_											=							do_pad
        UpperCamelCase_											=							do_convert_rgb

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_convert_rgb": self.do_convert_rgb,
            "do_pad": self.do_pad,
        }

 def lowerCamelCase_					(   self  ,						__UpperCamelCase=False  ,						__UpperCamelCase=False  ,						__UpperCamelCase=False  ):




        """simple docstring"""


        assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

        if equal_resolution:
               UpperCamelCase_											=							[]
               for i in range(self.batch_size  ):
                      image_inputs.append(
                          np.random.randint(
                              2_5_5  ,						size=(self.num_channels, self.max_resolution, self.max_resolution)  ,						dtype=np.uinta  )  )
        else:
               UpperCamelCase_											=							[]
               for i in range(self.batch_size  ):
                      UpperCamelCase_											=							np.random.choice(np.arange(self.min_resolution  ,						self.max_resolution  )  ,						2  )
                      image_inputs.append(np.random.randint(2_5_5  ,						size=(self.num_channels, width, height)  ,						dtype=np.uinta  )  )

        if not numpify and not torchify:
               # PIL expects the channel dimension as last dimension
               UpperCamelCase_											=							[Image.fromarray(np.moveaxis(a_  ,						0  ,						-1  )  ) for x in image_inputs]

        if torchify:
               UpperCamelCase_											=							[torch.from_numpy(a_  ) for x in image_inputs]

        return image_inputs



@require_torch
@require_vision
class 		lowercase_							(							_lowerCamelCase				,						unittest.TestCase					):
 A__    :					str    =     BlipImageProcessor if is_vision_available() else None

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							BlipImageProcessingTester(self  )

 @property
 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        return self.image_processor_tester.prepare_image_processor_dict()

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.image_processing_class(**self.image_processor_dict  )
        self.assertTrue(hasattr(a_  ,						"""do_resize"""  )  )
        self.assertTrue(hasattr(a_  ,						"""size"""  )  )
        self.assertTrue(hasattr(a_  ,						"""do_normalize"""  )  )
        self.assertTrue(hasattr(a_  ,						"""image_mean"""  )  )
        self.assertTrue(hasattr(a_  ,						"""image_std"""  )  )
        self.assertTrue(hasattr(a_  ,						"""do_convert_rgb"""  )  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        pass

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.image_processing_class(**self.image_processor_dict  )
        # create random PIL images
        UpperCamelCase_											=							self.image_processor_tester.prepare_inputs(equal_resolution=a_  )
        for image in image_inputs:
               self.assertIsInstance(a_  ,						Image.Image  )

        # Test not batched input
        UpperCamelCase_											=							image_processor(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
        UpperCamelCase_											=							image_processor(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.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )  ,						)

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


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

        # Test not batched input
        UpperCamelCase_											=							image_processor(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
        UpperCamelCase_											=							image_processor(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.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )  ,						)

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


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

        # Test not batched input
        UpperCamelCase_											=							image_processor(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
        UpperCamelCase_											=							image_processor(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.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )  ,						)



@require_torch
@require_vision
class 		lowercase_							(							_lowerCamelCase				,						unittest.TestCase					):
 A__    :					Union[str, Any]    =     BlipImageProcessor if is_vision_available() else None

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							BlipImageProcessingTester(self  ,						num_channels=4  )
        UpperCamelCase_											=							3

 @property
 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        return self.image_processor_tester.prepare_image_processor_dict()

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.image_processing_class(**self.image_processor_dict  )
        self.assertTrue(hasattr(a_  ,						"""do_resize"""  )  )
        self.assertTrue(hasattr(a_  ,						"""size"""  )  )
        self.assertTrue(hasattr(a_  ,						"""do_normalize"""  )  )
        self.assertTrue(hasattr(a_  ,						"""image_mean"""  )  )
        self.assertTrue(hasattr(a_  ,						"""image_std"""  )  )
        self.assertTrue(hasattr(a_  ,						"""do_convert_rgb"""  )  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        pass

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.image_processing_class(**self.image_processor_dict  )
        # create random PIL images
        UpperCamelCase_											=							self.image_processor_tester.prepare_inputs(equal_resolution=a_  )
        for image in image_inputs:
               self.assertIsInstance(a_  ,						Image.Image  )

        # Test not batched input
        UpperCamelCase_											=							image_processor(image_inputs[0]  ,						return_tensors="""pt"""  ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,						(
                1,
                self.expected_encoded_image_num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )  ,						)

        # Test batched
        UpperCamelCase_											=							image_processor(a_  ,						return_tensors="""pt"""  ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,						(
                self.image_processor_tester.batch_size,
                self.expected_encoded_image_num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )  ,						)








'''simple docstring'''






from __future__ import annotations

A_				:					Tuple							=      """Alexander Joslin"""




def        snake_case_   (	lowerCAmelCase_						)->						int:





       '''simple docstring'''


       if number <= 0:
              _UpperCAmelCase    :		Union[str, Any]     	=					F'''n must be greater than 0. Got n = {number}'''
              raise ValueError(lowerCAmelCase_						)

       _UpperCAmelCase    :		List[str]     	=					[number + 1] * (number + 1)

       # starting position
       _UpperCAmelCase    :		Dict     	=					0
       for i in range(1			,  lowerCAmelCase_						):
              _UpperCAmelCase    :		int     	=					min(table[i + 1]			,  table[i] + 1						)
              # check if out of bounds
              if i * 2 <= number:
                     _UpperCAmelCase    :		Dict     	=					min(table[i * 2]			,  table[i] + 1						)
              # check if out of bounds
              if i * 3 <= number:
                     _UpperCAmelCase    :		Tuple     	=					min(table[i * 3]			,  table[i] + 1						)
       return table[number]


if __name__ == "__main__":
   import doctest

   doctest.testmod()



