




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 						SCREAMING_SNAKE_CASE__		(unittest.TestCase ):
     def __init__(	self  :		str					,	__lowerCamelCase  :		Union[str, Any]					,	__lowerCamelCase  :		Optional[Any]=7					,	__lowerCamelCase  :		Union[str, Any]=3					,	__lowerCamelCase  :		Tuple=18					,	__lowerCamelCase  :		Tuple=30					,	__lowerCamelCase  :		Optional[int]=4_00					,	__lowerCamelCase  :		Tuple=True					,	__lowerCamelCase  :		Any=None					,	__lowerCamelCase  :		List[Any]=2					,	__lowerCamelCase  :		Any=True					,	__lowerCamelCase  :		int=[0.5, 0.5, 0.5]					,	__lowerCamelCase  :		Union[str, Any]=[0.5, 0.5, 0.5]					,	):



       """simple docstring"""
       lowerCAmelCase__								=			size if size is not None else {'''shortest_edge''': 30}
       lowerCAmelCase__								=			parent
       lowerCAmelCase__								=			batch_size
       lowerCAmelCase__								=			num_channels
       lowerCAmelCase__								=			image_size
       lowerCAmelCase__								=			min_resolution
       lowerCAmelCase__								=			max_resolution
       lowerCAmelCase__								=			do_resize
       lowerCAmelCase__								=			size
       lowerCAmelCase__								=			size_divisor
       lowerCAmelCase__								=			do_normalize
       lowerCAmelCase__								=			image_mean
       lowerCAmelCase__								=			image_std
     def 					A__			(	self  :		List[Any]	):



       """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,
           "size_divisor": self.size_divisor,
       }



     def 					A__			(	self  :		Tuple					,	__lowerCamelCase  :		int					,	__lowerCamelCase  :		Tuple=False	):



       """simple docstring"""
       if not batched:
         lowerCAmelCase__								=			self.size['''shortest_edge''']
         lowerCAmelCase__								=			image_inputs[0]
         if isinstance(__lowerCamelCase					,	Image.Image	):
           lowerCAmelCase__								=			image.size
         else:
           lowerCAmelCase__								=			image.shape[1], image.shape[2]
         lowerCAmelCase__								=			size / min(__lowerCamelCase					,	__lowerCamelCase	)
         if h < w:
           lowerCAmelCase__								=			size, scale * w
         else:
           lowerCAmelCase__								=			scale * h, size

         lowerCAmelCase__								=			int((13_33 / 8_00) * size	)
         if max(__lowerCamelCase					,	__lowerCamelCase	) > max_size:
           lowerCAmelCase__								=			max_size / max(__lowerCamelCase					,	__lowerCamelCase	)
           lowerCAmelCase__								=			newh * scale
           lowerCAmelCase__								=			neww * scale

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

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

       return expected_height, expected_width





@require_torch
@require_vision
class 						SCREAMING_SNAKE_CASE__		(_a						,				unittest.TestCase ):
     lowercase_   :			List[Any]   =   ViltImageProcessor if is_vision_available() else None
     def 					A__			(	self  :		int	):



       """simple docstring"""
       lowerCAmelCase__								=			ViltImageProcessingTester(self	)
     @property
     def 					A__			(	self  :		str	):



       """simple docstring"""
       return self.image_processor_tester.prepare_image_processor_dict()
     def 					A__			(	self  :		Optional[Any]	):



       """simple docstring"""
       lowerCAmelCase__								=			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'''	)	)
       self.assertTrue(hasattr(__lowerCamelCase					,	'''size_divisor'''	)	)
     def 					A__			(	self  :		List[str]	):



       """simple docstring"""
       lowerCAmelCase__								=			self.image_processing_class.from_dict(self.image_processor_dict	)
       self.assertEqual(image_processor.size					,	{'''shortest_edge''': 30}	)

       lowerCAmelCase__								=			self.image_processing_class.from_dict(self.image_processor_dict					,	size=42	)
       self.assertEqual(image_processor.size					,	{'''shortest_edge''': 42}	)
     def 					A__			(	self  :		Tuple	):



       """simple docstring"""
       pass
     def 					A__			(	self  :		List[Any]	):



       """simple docstring"""
       # Initialize image_processing
       lowerCAmelCase__								=			self.image_processing_class(**self.image_processor_dict	)
       # create random PIL images
       lowerCAmelCase__								=			prepare_image_inputs(self.image_processor_tester					,	equal_resolution=__lowerCamelCase	)
       for image in image_inputs:
         self.assertIsInstance(__lowerCamelCase					,	Image.Image	)

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

       lowerCAmelCase__								=			self.image_processor_tester.get_expected_values(__lowerCamelCase	)
       self.assertEqual(
           encoded_images.shape					,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)					,	)

       # Test batched
       lowerCAmelCase__								=			image_processing(__lowerCamelCase					,	return_tensors='''pt'''	).pixel_values

       lowerCAmelCase__								=			self.image_processor_tester.get_expected_values(__lowerCamelCase					,	batched=__lowerCamelCase	)
       self.assertEqual(
           encoded_images.shape					,	(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )					,	)
     def 					A__			(	self  :		Union[str, Any]	):



       """simple docstring"""
       # Initialize image_processing
       lowerCAmelCase__								=			self.image_processing_class(**self.image_processor_dict	)
       # create random numpy tensors
       lowerCAmelCase__								=			prepare_image_inputs(self.image_processor_tester					,	equal_resolution=__lowerCamelCase					,	numpify=__lowerCamelCase	)
       for image in image_inputs:
         self.assertIsInstance(__lowerCamelCase					,	np.ndarray	)

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

       lowerCAmelCase__								=			self.image_processor_tester.get_expected_values(__lowerCamelCase	)
       self.assertEqual(
           encoded_images.shape					,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)					,	)

       # Test batched
       lowerCAmelCase__								=			image_processing(__lowerCamelCase					,	return_tensors='''pt'''	).pixel_values

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



     def 					A__			(	self  :		Dict	):



       """simple docstring"""
       # Initialize image_processing
       lowerCAmelCase__								=			self.image_processing_class(**self.image_processor_dict	)
       # create random PyTorch tensors
       lowerCAmelCase__								=			prepare_image_inputs(self.image_processor_tester					,	equal_resolution=__lowerCamelCase					,	torchify=__lowerCamelCase	)
       for image in image_inputs:
         self.assertIsInstance(__lowerCamelCase					,	torch.Tensor	)

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

       lowerCAmelCase__								=			self.image_processor_tester.get_expected_values(__lowerCamelCase	)
       self.assertEqual(
           encoded_images.shape					,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)					,	)

       # Test batched
       lowerCAmelCase__								=			image_processing(__lowerCamelCase					,	return_tensors='''pt'''	).pixel_values

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







__magic_name__     :     dict[str, float]  							=					{
    "kilogram": 1,
    "gram": pow(10, 3),
    "milligram": pow(10, 6),
    "metric-ton": pow(10, -3),
    "long-ton": 0.0009842073,
    "short-ton": 0.0011023122,
    "pound": 2.2046244202,
    "stone": 0.1574731728,
    "ounce": 35.273990723,
    "carrat": 50_00,
    "atomic-mass-unit": 6.022_136_652E26,
}

__magic_name__     :     dict[str, float]  							=					{
    "kilogram": 1,
    "gram": pow(10, -3),
    "milligram": pow(10, -6),
    "metric-ton": pow(10, 3),
    "long-ton": 1016.04608,
    "short-ton": 907.184,
    "pound": 0.453592,
    "stone": 6.35029,
    "ounce": 0.0283495,
    "carrat": 0.0002,
    "atomic-mass-unit": 1.660_540_199E-27,
}
def 		a_       (   __lowerCAmelCase      ,   __lowerCAmelCase      ,   __lowerCAmelCase							):
  if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
    lowerCAmelCase__								=			(
        F"""Invalid 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"""
        F"""Supported values are: {', '.join(__lowerCAmelCase							)}"""
    )
    raise ValueError(__lowerCAmelCase							)
  return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
      import doctest

      doctest.testmod()

