






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:
       a		:	Any      					=			None
class    a   (			unittest.TestCase       ):



      """simple docstring"""


      def __init__(				self :      int		,				__lowercase :      List[Any]		,				__lowercase :      List[str]=7		,				__lowercase :      List[str]=3		,				__lowercase :      int=30		,				__lowercase :      Optional[Any]=400		,				__lowercase :      Any=True		,				__lowercase :      Tuple=None		,				__lowercase :      List[str]=True		,				__lowercase :      Tuple=None		,				__lowercase :      Tuple=None		,				__lowercase :      Dict=True		,				__lowercase :      str=1 / 255		,				__lowercase :      Dict=True		,				__lowercase :      Tuple=FLAVA_IMAGE_MEAN		,				__lowercase :      str=FLAVA_IMAGE_STD		,				__lowercase :      Optional[int]=14		,				__lowercase :      Dict=75		,				__lowercase :      Union[str, Any]=None		,				__lowercase :      Any=16		,				__lowercase :      Any=0.3		,				__lowercase :      Optional[int]=None		,				__lowercase :      Optional[int]=True		,				__lowercase :      List[str]=None		,				__lowercase :      Union[str, Any]=None		,				__lowercase :      List[Any]=True		,				__lowercase :      List[Any]=None		,				__lowercase :      List[Any]=True		,				__lowercase :      str=True		,				__lowercase :      List[Any]=FLAVA_CODEBOOK_MEAN		,				__lowercase :      Union[str, Any]=FLAVA_CODEBOOK_STD		,				)							->				Tuple:
        __UpperCAmelCase							:     Dict									=  size if size is not None else {"""height""": 224, """width""": 224}
        __UpperCAmelCase							:     Optional[int]									=  crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
        __UpperCAmelCase							:     Dict									=  codebook_size if codebook_size is not None else {"""height""": 112, """width""": 112}
        __UpperCAmelCase							:     Optional[int]									=  codebook_crop_size if codebook_crop_size is not None else {"""height""": 112, """width""": 112}

        __UpperCAmelCase							:     str									=  parent
        __UpperCAmelCase							:     List[str]									=  batch_size
        __UpperCAmelCase							:     str									=  num_channels
        __UpperCAmelCase							:     Dict									=  do_resize
        __UpperCAmelCase							:     Union[str, Any]									=  do_rescale
        __UpperCAmelCase							:     Tuple									=  rescale_factor
        __UpperCAmelCase							:     Tuple									=  min_resolution
        __UpperCAmelCase							:     Union[str, Any]									=  max_resolution
        __UpperCAmelCase							:     int									=  size
        __UpperCAmelCase							:     Optional[Any]									=  resample if resample is not None else PILImageResampling.BICUBIC
        __UpperCAmelCase							:     Dict									=  do_normalize
        __UpperCAmelCase							:     Tuple									=  image_mean
        __UpperCAmelCase							:     Optional[int]									=  image_std
        __UpperCAmelCase							:     List[Any]									=  do_center_crop
        __UpperCAmelCase							:     List[str]									=  crop_size

        __UpperCAmelCase							:     List[str]									=  input_size_patches
        __UpperCAmelCase							:     Optional[int]									=  total_mask_patches
        __UpperCAmelCase							:     str									=  mask_group_max_patches
        __UpperCAmelCase							:     Union[str, Any]									=  mask_group_min_patches
        __UpperCAmelCase							:     Optional[Any]									=  mask_group_min_aspect_ratio
        __UpperCAmelCase							:     Union[str, Any]									=  mask_group_max_aspect_ratio

        __UpperCAmelCase							:     Dict									=  codebook_do_resize
        __UpperCAmelCase							:     int									=  codebook_size
        __UpperCAmelCase							:     List[str]									=  codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
        __UpperCAmelCase							:     List[str]									=  codebook_do_center_crop
        __UpperCAmelCase							:     Optional[int]									=  codebook_crop_size
        __UpperCAmelCase							:     Tuple									=  codebook_do_map_pixels
        __UpperCAmelCase							:     Union[str, Any]									=  codebook_do_normalize
        __UpperCAmelCase							:     Optional[int]									=  codebook_image_mean
        __UpperCAmelCase							:     Tuple									=  codebook_image_std
      def      UpperCAmelCase				(				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,
            "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 :      List[str] )							->				Any:
        return (self.size["height"], self.size["width"])
      def      UpperCAmelCase				(				self :      Any )							->				Optional[int]:
        return (
            (self.input_size_patches, self.input_size_patches)
            if not isinstance(self.input_size_patches		,				__lowercase )
            else self.input_size_patches
        )




      def      UpperCAmelCase				(				self :      Dict )							->				List[str]:
        return (self.codebook_size["height"], self.codebook_size["width"])


@require_torch
@require_vision
class    a   (			lowercase__							,	unittest.TestCase       ):



      """simple docstring"""


      a  :			str								=     FlavaImageProcessor if is_vision_available() else None
      a  :			List[Any]								=     None
      def      UpperCAmelCase				(				self :      int )							->				List[Any]:
        __UpperCAmelCase							:     Optional[int]									=  FlavaImageProcessingTester(self )
      @property
      def      UpperCAmelCase				(				self :      Any )							->				int:
        return self.image_processor_tester.prepare_image_processor_dict()
      def      UpperCAmelCase				(				self :      Optional[Any] )							->				List[str]:
        __UpperCAmelCase							:     Optional[int]									=  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		,				"""resample""" ) )
        self.assertTrue(hasattr(__lowercase		,				"""crop_size""" ) )
        self.assertTrue(hasattr(__lowercase		,				"""do_center_crop""" ) )
        self.assertTrue(hasattr(__lowercase		,				"""do_rescale""" ) )
        self.assertTrue(hasattr(__lowercase		,				"""rescale_factor""" ) )
        self.assertTrue(hasattr(__lowercase		,				"""masking_generator""" ) )
        self.assertTrue(hasattr(__lowercase		,				"""codebook_do_resize""" ) )
        self.assertTrue(hasattr(__lowercase		,				"""codebook_size""" ) )
        self.assertTrue(hasattr(__lowercase		,				"""codebook_resample""" ) )
        self.assertTrue(hasattr(__lowercase		,				"""codebook_do_center_crop""" ) )
        self.assertTrue(hasattr(__lowercase		,				"""codebook_crop_size""" ) )
        self.assertTrue(hasattr(__lowercase		,				"""codebook_do_map_pixels""" ) )
        self.assertTrue(hasattr(__lowercase		,				"""codebook_do_normalize""" ) )
        self.assertTrue(hasattr(__lowercase		,				"""codebook_image_mean""" ) )
        self.assertTrue(hasattr(__lowercase		,				"""codebook_image_std""" ) )
      def      UpperCAmelCase				(				self :      Optional[int] )							->				Any:
        __UpperCAmelCase							:     Optional[Any]									=  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							:     str									=  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 :      Dict )							->				List[Any]:
        pass
      def      UpperCAmelCase				(				self :      Dict )							->				Union[str, Any]:
        # Initialize image_processing
        __UpperCAmelCase							:     int									=  self.image_processing_class(**self.image_processor_dict )
        # create random PIL images
        __UpperCAmelCase							:     Tuple									=  prepare_image_inputs(self.image_processor_tester		,				equal_resolution=__lowercase )
        for image in image_inputs:
          self.assertIsInstance(__lowercase		,				PIL.Image.Image )

        # Test not batched input
        __UpperCAmelCase							:     Any									=  image_processing(image_inputs[0]		,				return_tensors="""pt""" )

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

        __UpperCAmelCase				,			__UpperCAmelCase							:     Union[str, Any]									=  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							:     Any									=  image_processing(__lowercase		,				return_tensors="""pt""" )
        __UpperCAmelCase				,			__UpperCAmelCase							:     Union[str, Any]									=  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 :      List[Any]		,				__lowercase :      Optional[Any]		,				__lowercase :      Tuple )							->				List[Any]:
        # Initialize image_processing
        __UpperCAmelCase							:     Any									=  self.image_processing_class(**self.image_processor_dict )
        # create random tensors
        __UpperCAmelCase							:     Optional[Any]									=  prepare_image_inputs(self.image_processor_tester		,				equal_resolution=__lowercase		,				**__lowercase )
        for image in image_inputs:
          self.assertIsInstance(__lowercase		,				__lowercase )

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

        __UpperCAmelCase				,			__UpperCAmelCase							:     Optional[int]									=  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							:     List[str]									=  image_processing(__lowercase		,				return_image_mask=__lowercase		,				return_tensors="""pt""" )

        __UpperCAmelCase				,			__UpperCAmelCase							:     Tuple									=  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							:     Optional[Any]									=  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							:     str									=  image_processing(__lowercase		,				return_tensors="""pt""" ).pixel_values

        __UpperCAmelCase				,			__UpperCAmelCase							:     Tuple									=  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							:     int									=  image_processing(__lowercase		,				return_image_mask=__lowercase		,				return_tensors="""pt""" )

        __UpperCAmelCase				,			__UpperCAmelCase							:     Any									=  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							:     Union[str, Any]									=  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 :      str )							->				str:
        self._test_call_framework(np.ndarray		,				prepare_kwargs={"""numpify""": True} )
      def      UpperCAmelCase				(				self :      Optional[int] )							->				List[Any]:
        self._test_call_framework(torch.Tensor		,				prepare_kwargs={"""torchify""": True} )
      def      UpperCAmelCase				(				self :      Union[str, Any] )							->				List[Any]:
        # Initialize image_processing
        random.seed(1234 )
        __UpperCAmelCase							:     str									=  self.image_processing_class(**self.image_processor_dict )
        __UpperCAmelCase							:     Optional[Any]									=  prepare_image_inputs(self.image_processor_tester		,				equal_resolution=__lowercase		,				torchify=__lowercase )

        # Test not batched input
        __UpperCAmelCase							:     Optional[int]									=  image_processing(image_inputs[0]		,				return_image_mask=__lowercase		,				return_tensors="""pt""" )
        self.assertEqual(encoded_images.bool_masked_pos.sum().item()		,				75 )




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

        # Test not batched input
        __UpperCAmelCase							:     Optional[Any]									=  image_processing(image_inputs[0]		,				return_codebook_pixels=__lowercase		,				return_tensors="""pt""" )
        __UpperCAmelCase				,			__UpperCAmelCase							:     int									=  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							:     Tuple									=  image_processing(__lowercase		,				return_codebook_pixels=__lowercase		,				return_tensors="""pt""" )
        __UpperCAmelCase				,			__UpperCAmelCase							:     str									=  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,
            )		,				)










def     lowerCamelCase__				(   ):

  for a in range(300		):
    for b in range(a + 1  ,		400		):
      for c in range(b + 1  ,		500		):
        if (a + b + c) == 1000 and (a**2) + (b**2) == (c**2):
          return a * b * c

  return -1






def     lowerCamelCase__				(   ):

  for a in range(300		):
    for b in range(400		):
      __UpperCAmelCase							:     Optional[Any]									=  1000 - a - b
      if a < b < c and (a**2) + (b**2) == (c**2):
        return a * b * c

  return -1






def     lowerCamelCase__				(   ):
  import timeit

  print(
      timeit.timeit("""solution()"""  ,		setup="""from __main__ import solution"""  ,		number=1000		)		)
  print(
      timeit.timeit(
          """solution_fast()"""  ,		setup="""from __main__ import solution_fast"""  ,		number=1000		)		)


if __name__ == "__main__":
       print(f"""{solution() = }""")


