

"""simple docstring"""





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    _lowerCAmelCase    (      unittest.TestCase  ):







 """simple docstring"""







 def __init__(     self				:     Dict,   UpperCAmelCase__				:     Tuple,   UpperCAmelCase__				:     str=7,   UpperCAmelCase__				:     int=3,   UpperCAmelCase__				:     Tuple=1_8,   UpperCAmelCase__				:     Optional[int]=3_0,   UpperCAmelCase__				:     str=4_0_0,   UpperCAmelCase__				:     Optional[Any]=True,   UpperCAmelCase__				:     List[Any]=None,   UpperCAmelCase__				:     Dict=2,   UpperCAmelCase__				:     Dict=True,   UpperCAmelCase__				:     List[Any]=[0.5, 0.5, 0.5],   UpperCAmelCase__				:     Optional[int]=[0.5, 0.5, 0.5],   ):
    __lowercase								=		size if size is not None else {"shortest_edge": 3_0}
    __lowercase								=		parent
    __lowercase								=		batch_size
    __lowercase								=		num_channels
    __lowercase								=		image_size
    __lowercase								=		min_resolution
    __lowercase								=		max_resolution
    __lowercase								=		do_resize
    __lowercase								=		size
    __lowercase								=		size_divisor
    __lowercase								=		do_normalize
    __lowercase								=		image_mean
    __lowercase								=		image_std


 def    _lowercase					(     self				:     Any      ):
    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    _lowercase					(     self				:     Optional[Any],   UpperCAmelCase__				:     Optional[Any],   UpperCAmelCase__				:     Dict=False      ):
    if not batched:
       __lowercase								=		self.size["shortest_edge"]
       __lowercase								=		image_inputs[0]
       if isinstance(UpperCAmelCase__,   Image.Image      ):
          __lowercase						,__lowercase								=		image.size
       else:
          __lowercase						,__lowercase								=		image.shape[1], image.shape[2]
       __lowercase								=		size / min(UpperCAmelCase__,   UpperCAmelCase__      )
       if h < w:
          __lowercase						,__lowercase								=		size, scale * w
       else:
          __lowercase						,__lowercase								=		scale * h, size

       __lowercase								=		int((1_3_3_3 / 8_0_0) * size      )
       if max(UpperCAmelCase__,   UpperCAmelCase__      ) > max_size:
          __lowercase								=		max_size / max(UpperCAmelCase__,   UpperCAmelCase__      )
          __lowercase								=		newh * scale
          __lowercase								=		neww * scale

       __lowercase						,__lowercase								=		int(newh + 0.5      ), int(neww + 0.5      )
       __lowercase						,__lowercase								=		(
           newh // self.size_divisor * self.size_divisor,
           neww // self.size_divisor * self.size_divisor,
       )

    else:
       __lowercase								=		[]
       for image in image_inputs:
          __lowercase						,__lowercase								=		self.get_expected_values([image]      )
          expected_values.append((expected_height, expected_width)      )
       __lowercase								=		max(UpperCAmelCase__,   key=lambda UpperCAmelCase__      : item[0]      )[0]
       __lowercase								=		max(UpperCAmelCase__,   key=lambda UpperCAmelCase__      : item[1]      )[1]

    return expected_height, expected_width







@require_torch
@require_vision
class    _lowerCAmelCase    (      lowercase	,unittest.TestCase  ):







 """simple docstring"""







 __UpperCAmelCase      :   Optional[int]  						=      ViltImageProcessor if is_vision_available() else None


 def    _lowercase					(     self				:     Union[str, Any]      ):
    __lowercase								=		ViltImageProcessingTester(self      )


 @property
 def    _lowercase					(     self				:     str      ):
    return self.image_processor_tester.prepare_image_processor_dict()


 def    _lowercase					(     self				:     List[str]      ):
    __lowercase								=		self.image_processing_class(**self.image_processor_dict      )
    self.assertTrue(hasattr(UpperCAmelCase__,   "image_mean"      )      )
    self.assertTrue(hasattr(UpperCAmelCase__,   "image_std"      )      )
    self.assertTrue(hasattr(UpperCAmelCase__,   "do_normalize"      )      )
    self.assertTrue(hasattr(UpperCAmelCase__,   "do_resize"      )      )
    self.assertTrue(hasattr(UpperCAmelCase__,   "size"      )      )
    self.assertTrue(hasattr(UpperCAmelCase__,   "size_divisor"      )      )


 def    _lowercase					(     self				:     int      ):
    __lowercase								=		self.image_processing_class.from_dict(self.image_processor_dict      )
    self.assertEqual(image_processor.size,   {"shortest_edge": 3_0}      )

    __lowercase								=		self.image_processing_class.from_dict(self.image_processor_dict,   size=4_2      )
    self.assertEqual(image_processor.size,   {"shortest_edge": 4_2}      )


 def    _lowercase					(     self				:     Optional[Any]      ):
    pass


 def    _lowercase					(     self				:     Tuple      ):
    # Initialize image_processing
    __lowercase								=		self.image_processing_class(**self.image_processor_dict      )
    # create random PIL images
    __lowercase								=		prepare_image_inputs(self.image_processor_tester,   equal_resolution=UpperCAmelCase__      )
    for image in image_inputs:
       self.assertIsInstance(UpperCAmelCase__,   Image.Image      )

    # Test not batched input
    __lowercase								=		image_processing(image_inputs[0],   return_tensors="pt"      ).pixel_values

    __lowercase						,__lowercase								=		self.image_processor_tester.get_expected_values(UpperCAmelCase__      )
    self.assertEqual(
        encoded_images.shape,   (1, self.image_processor_tester.num_channels, expected_height, expected_width),   )

    # Test batched
    __lowercase								=		image_processing(UpperCAmelCase__,   return_tensors="pt"      ).pixel_values

    __lowercase						,__lowercase								=		self.image_processor_tester.get_expected_values(UpperCAmelCase__,   batched=UpperCAmelCase__      )
    self.assertEqual(
        encoded_images.shape,   (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        ),   )


 def    _lowercase					(     self				:     List[Any]      ):
    # Initialize image_processing
    __lowercase								=		self.image_processing_class(**self.image_processor_dict      )
    # create random numpy tensors
    __lowercase								=		prepare_image_inputs(self.image_processor_tester,   equal_resolution=UpperCAmelCase__,   numpify=UpperCAmelCase__      )
    for image in image_inputs:
       self.assertIsInstance(UpperCAmelCase__,   np.ndarray      )

    # Test not batched input
    __lowercase								=		image_processing(image_inputs[0],   return_tensors="pt"      ).pixel_values

    __lowercase						,__lowercase								=		self.image_processor_tester.get_expected_values(UpperCAmelCase__      )
    self.assertEqual(
        encoded_images.shape,   (1, self.image_processor_tester.num_channels, expected_height, expected_width),   )

    # Test batched
    __lowercase								=		image_processing(UpperCAmelCase__,   return_tensors="pt"      ).pixel_values

    __lowercase						,__lowercase								=		self.image_processor_tester.get_expected_values(UpperCAmelCase__,   batched=UpperCAmelCase__      )
    self.assertEqual(
        encoded_images.shape,   (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        ),   )



 def    _lowercase					(     self				:     List[Any]      ):
    # Initialize image_processing
    __lowercase								=		self.image_processing_class(**self.image_processor_dict      )
    # create random PyTorch tensors
    __lowercase								=		prepare_image_inputs(self.image_processor_tester,   equal_resolution=UpperCAmelCase__,   torchify=UpperCAmelCase__      )
    for image in image_inputs:
       self.assertIsInstance(UpperCAmelCase__,   torch.Tensor      )

    # Test not batched input
    __lowercase								=		image_processing(image_inputs[0],   return_tensors="pt"      ).pixel_values

    __lowercase						,__lowercase								=		self.image_processor_tester.get_expected_values(UpperCAmelCase__      )
    self.assertEqual(
        encoded_images.shape,   (1, self.image_processor_tester.num_channels, expected_height, expected_width),   )

    # Test batched
    __lowercase								=		image_processing(UpperCAmelCase__,   return_tensors="pt"      ).pixel_values

    __lowercase						,__lowercase								=		self.image_processor_tester.get_expected_values(UpperCAmelCase__,   batched=UpperCAmelCase__      )
    self.assertEqual(
        encoded_images.shape,   (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        ),   )







"""simple docstring"""





def     _A    (    UpperCamelCase_	:			str)      ->		int:
   '''simple docstring'''


   __lowercase								=		0
   for plain_chr in plain_text:
      __lowercase								=		(
          ord(UpperCamelCase_) + (hash_value << 6) + (hash_value << 16) - hash_value
      )
   return hash_value




