





import random
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():
       import PIL

       from transformers import FlavaImageProcessor
       from transformers.image_utils import PILImageResampling
       from transformers.models.flava.image_processing_flava import (
           FLAVA_CODEBOOK_MEAN,
           FLAVA_CODEBOOK_STD,
           FLAVA_IMAGE_MEAN,
           FLAVA_IMAGE_STD,
       )
else:
       lowercase     :			Tuple     						=	None

class 			__A(    unittest.TestCase			):

     def __init__(   self,							A,							A=7,							A=3,							A=30,							A=400,							A=True,							A=None,							A=True,							A=None,							A=None,							A=True,							A=1 / 255,							A=True,							A=FLAVA_IMAGE_MEAN,							A=FLAVA_IMAGE_STD,							A=14,							A=75,							A=None,							A=16,							A=0.3,							A=None,							A=True,							A=None,							A=None,							A=True,							A=None,							A=True,							A=True,							A=FLAVA_CODEBOOK_MEAN,							A=FLAVA_CODEBOOK_STD,							):





            """simple docstring"""

            _UpperCamelCase														=    size if size is not None else {'''height''': 224, '''width''': 224}
            _UpperCamelCase														=    crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
            _UpperCamelCase														=    codebook_size if codebook_size is not None else {'''height''': 112, '''width''': 112}
            _UpperCamelCase														=    codebook_crop_size if codebook_crop_size is not None else {'''height''': 112, '''width''': 112}

            _UpperCamelCase														=    parent
            _UpperCamelCase														=    batch_size
            _UpperCamelCase														=    num_channels
            _UpperCamelCase														=    do_resize
            _UpperCamelCase														=    do_rescale
            _UpperCamelCase														=    rescale_factor
            _UpperCamelCase														=    min_resolution
            _UpperCamelCase														=    max_resolution
            _UpperCamelCase														=    size
            _UpperCamelCase														=    resample if resample is not None else PILImageResampling.BICUBIC
            _UpperCamelCase														=    do_normalize
            _UpperCamelCase														=    image_mean
            _UpperCamelCase														=    image_std
            _UpperCamelCase														=    do_center_crop
            _UpperCamelCase														=    crop_size

            _UpperCamelCase														=    input_size_patches
            _UpperCamelCase														=    total_mask_patches
            _UpperCamelCase														=    mask_group_max_patches
            _UpperCamelCase														=    mask_group_min_patches
            _UpperCamelCase														=    mask_group_min_aspect_ratio
            _UpperCamelCase														=    mask_group_max_aspect_ratio

            _UpperCamelCase														=    codebook_do_resize
            _UpperCamelCase														=    codebook_size
            _UpperCamelCase														=    codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
            _UpperCamelCase														=    codebook_do_center_crop
            _UpperCamelCase														=    codebook_crop_size
            _UpperCamelCase														=    codebook_do_map_pixels
            _UpperCamelCase														=    codebook_do_normalize
            _UpperCamelCase														=    codebook_image_mean
            _UpperCamelCase														=    codebook_image_std

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            return {
                "image_mean": self.image_mean,
                "image_std": self.image_std,
                "do_normalize": self.do_normalize,
                "do_resize": self.do_resize,
                "size": self.size,
                "resample": self.resample,
                "do_rescale": self.do_rescale,
                "rescale_factor": self.rescale_factor,
                "do_center_crop": self.do_center_crop,
                "crop_size": self.crop_size,
                "input_size_patches": self.input_size_patches,
                "total_mask_patches": self.total_mask_patches,
                "mask_group_max_patches": self.mask_group_max_patches,
                "mask_group_min_patches": self.mask_group_min_patches,
                "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
                "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
                "codebook_do_resize": self.codebook_do_resize,
                "codebook_size": self.codebook_size,
                "codebook_resample": self.codebook_resample,
                "codebook_do_center_crop": self.codebook_do_center_crop,
                "codebook_crop_size": self.codebook_crop_size,
                "codebook_do_map_pixels": self.codebook_do_map_pixels,
                "codebook_do_normalize": self.codebook_do_normalize,
                "codebook_image_mean": self.codebook_image_mean,
                "codebook_image_std": self.codebook_image_std,
            }

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            return (self.size["height"], self.size["width"])

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            return (
                (self.input_size_patches, self.input_size_patches)
                if not isinstance(self.input_size_patches,							__SCREAMING_SNAKE_CASE   )
                else self.input_size_patches
            )



     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            return (self.codebook_size["height"], self.codebook_size["width"])






@require_torch
@require_vision
class 			__A(    __UpperCAmelCase    ,      unittest.TestCase			):
     __A							=							FlavaImageProcessor if is_vision_available() else None
     __A							=							None

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    FlavaImageProcessingTester(self   )

     @property
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            return self.image_processor_tester.prepare_image_processor_dict()

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.image_processing_class(**self.image_processor_dict   )
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE,							'''image_mean'''   )   )
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE,							'''image_std'''   )   )
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE,							'''do_normalize'''   )   )
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE,							'''do_resize'''   )   )
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE,							'''resample'''   )   )
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE,							'''crop_size'''   )   )
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE,							'''do_center_crop'''   )   )
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE,							'''do_rescale'''   )   )
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE,							'''rescale_factor'''   )   )
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE,							'''masking_generator'''   )   )
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE,							'''codebook_do_resize'''   )   )
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE,							'''codebook_size'''   )   )
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE,							'''codebook_resample'''   )   )
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE,							'''codebook_do_center_crop'''   )   )
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE,							'''codebook_crop_size'''   )   )
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE,							'''codebook_do_map_pixels'''   )   )
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE,							'''codebook_do_normalize'''   )   )
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE,							'''codebook_image_mean'''   )   )
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE,							'''codebook_image_std'''   )   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.image_processing_class.from_dict(self.image_processor_dict   )
            self.assertEqual(image_processor.size,							{'''height''': 224, '''width''': 224}   )
            self.assertEqual(image_processor.crop_size,							{'''height''': 224, '''width''': 224}   )
            self.assertEqual(image_processor.codebook_size,							{'''height''': 112, '''width''': 112}   )
            self.assertEqual(image_processor.codebook_crop_size,							{'''height''': 112, '''width''': 112}   )

            _UpperCamelCase														=    self.image_processing_class.from_dict(
                self.image_processor_dict,							size=42,							crop_size=84,							codebook_size=33,							codebook_crop_size=66   )
            self.assertEqual(image_processor.size,							{'''height''': 42, '''width''': 42}   )
            self.assertEqual(image_processor.crop_size,							{'''height''': 84, '''width''': 84}   )
            self.assertEqual(image_processor.codebook_size,							{'''height''': 33, '''width''': 33}   )
            self.assertEqual(image_processor.codebook_crop_size,							{'''height''': 66, '''width''': 66}   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            pass

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

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

            # Test not batched input
            _UpperCamelCase														=    image_processing(image_inputs[0],							return_tensors='''pt'''   )

            # Test no bool masked pos
            self.assertFalse('''bool_masked_pos''' in encoded_images   )

            _UpperCamelCase							,		_UpperCamelCase														=    self.image_processor_tester.get_expected_image_size()

            self.assertEqual(
                encoded_images.pixel_values.shape,							(1, self.image_processor_tester.num_channels, expected_height, expected_width),							)

            # Test batched
            _UpperCamelCase														=    image_processing(__SCREAMING_SNAKE_CASE,							return_tensors='''pt'''   )
            _UpperCamelCase							,		_UpperCamelCase														=    self.image_processor_tester.get_expected_image_size()

            # Test no bool masked pos
            self.assertFalse('''bool_masked_pos''' in encoded_images   )

            self.assertEqual(
                encoded_images.pixel_values.shape,							(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                ),							)

     def 						_UpperCamelCase  (   self,							A,							A   ):





            """simple docstring"""

            _UpperCamelCase														=    self.image_processing_class(**self.image_processor_dict   )
            # create random tensors
            _UpperCamelCase														=    prepare_image_inputs(self.image_processor_tester,							equal_resolution=__SCREAMING_SNAKE_CASE,							**__SCREAMING_SNAKE_CASE   )
            for image in image_inputs:
                   self.assertIsInstance(__SCREAMING_SNAKE_CASE,							__SCREAMING_SNAKE_CASE   )

            # Test not batched input
            _UpperCamelCase														=    image_processing(image_inputs[0],							return_tensors='''pt'''   )

            _UpperCamelCase							,		_UpperCamelCase														=    self.image_processor_tester.get_expected_image_size()
            self.assertEqual(
                encoded_images.pixel_values.shape,							(1, self.image_processor_tester.num_channels, expected_height, expected_width),							)

            _UpperCamelCase														=    image_processing(__SCREAMING_SNAKE_CASE,							return_image_mask=__SCREAMING_SNAKE_CASE,							return_tensors='''pt'''   )

            _UpperCamelCase							,		_UpperCamelCase														=    self.image_processor_tester.get_expected_image_size()
            self.assertEqual(
                encoded_images.pixel_values.shape,							(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                ),							)

            _UpperCamelCase							,		_UpperCamelCase														=    self.image_processor_tester.get_expected_mask_size()
            self.assertEqual(
                encoded_images.bool_masked_pos.shape,							(
                    self.image_processor_tester.batch_size,
                    expected_height,
                    expected_width,
                ),							)

            # Test batched
            _UpperCamelCase														=    image_processing(__SCREAMING_SNAKE_CASE,							return_tensors='''pt'''   ).pixel_values

            _UpperCamelCase							,		_UpperCamelCase														=    self.image_processor_tester.get_expected_image_size()
            self.assertEqual(
                encoded_images.shape,							(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                ),							)

            # Test masking
            _UpperCamelCase														=    image_processing(__SCREAMING_SNAKE_CASE,							return_image_mask=__SCREAMING_SNAKE_CASE,							return_tensors='''pt'''   )

            _UpperCamelCase							,		_UpperCamelCase														=    self.image_processor_tester.get_expected_image_size()
            self.assertEqual(
                encoded_images.pixel_values.shape,							(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                ),							)

            _UpperCamelCase							,		_UpperCamelCase														=    self.image_processor_tester.get_expected_mask_size()
            self.assertEqual(
                encoded_images.bool_masked_pos.shape,							(
                    self.image_processor_tester.batch_size,
                    expected_height,
                    expected_width,
                ),							)

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            self._test_call_framework(np.ndarray,							prepare_kwargs={'''numpify''': True}   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            self._test_call_framework(torch.Tensor,							prepare_kwargs={'''torchify''': True}   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            random.seed(1234   )
            _UpperCamelCase														=    self.image_processing_class(**self.image_processor_dict   )
            _UpperCamelCase														=    prepare_image_inputs(self.image_processor_tester,							equal_resolution=__SCREAMING_SNAKE_CASE,							torchify=__SCREAMING_SNAKE_CASE   )

            # Test not batched input
            _UpperCamelCase														=    image_processing(image_inputs[0],							return_image_mask=__SCREAMING_SNAKE_CASE,							return_tensors='''pt'''   )
            self.assertEqual(encoded_images.bool_masked_pos.sum().item(),							75   )



     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

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

            # Test not batched input
            _UpperCamelCase														=    image_processing(image_inputs[0],							return_codebook_pixels=__SCREAMING_SNAKE_CASE,							return_tensors='''pt'''   )
            _UpperCamelCase							,		_UpperCamelCase														=    self.image_processor_tester.get_expected_codebook_image_size()
            self.assertEqual(
                encoded_images.codebook_pixel_values.shape,							(1, self.image_processor_tester.num_channels, expected_height, expected_width),							)

            # Test batched
            _UpperCamelCase														=    image_processing(__SCREAMING_SNAKE_CASE,							return_codebook_pixels=__SCREAMING_SNAKE_CASE,							return_tensors='''pt'''   )
            _UpperCamelCase							,		_UpperCamelCase														=    self.image_processor_tester.get_expected_codebook_image_size()
            self.assertEqual(
                encoded_images.codebook_pixel_values.shape,							(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                ),							)











from __future__ import annotations

class        _A			:



       def __init__(						self				:	str	,						__SCREAMING_SNAKE_CASE				:	int = 0):


           '''simple docstring'''

           __a					=							value
           __a					=							None
           __a					=							None





def 	__snake_case      (     _UpperCAmelCase  ,					_UpperCAmelCase							):
    if treea is None:
        return treea
    if treea is None:
        return treea

    __a					=							treea.value + treea.value
    __a					=							merge_two_binary_trees(treea.left  ,					treea.left							)
    __a					=							merge_two_binary_trees(treea.right  ,					treea.right							)
    return treea





def 	__snake_case      (     _UpperCAmelCase							):
    if root:
        print(root.value							)
        print_preorder(root.left							)
        print_preorder(root.right							)


if __name__ == "__main__":
 __snake_case		:List[Any] 						=	Node(1)
 __snake_case		:Optional[Any] 						=	Node(2)
 __snake_case		:Dict 						=	Node(3)
 __snake_case		:int 						=	Node(4)

 __snake_case		:List[str] 						=	Node(2)
 __snake_case		:List[str] 						=	Node(4)
 __snake_case		:Tuple 						=	Node(6)
 __snake_case		:str 						=	Node(9)
 __snake_case		:List[str] 						=	Node(5)

 print('''Tree1 is: ''')
 print_preorder(treea)
 print('''Tree2 is: ''')
 print_preorder(treea)
 __snake_case		:Union[str, Any] 						=	merge_two_binary_trees(treea, treea)
 print('''Merged Tree is: ''')
 print_preorder(merged_tree)





