

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   ):







						"""simple docstring"""




						def __init__(				self , __UpperCamelCase , __UpperCamelCase=7 , __UpperCamelCase=3 , __UpperCamelCase=18 , __UpperCamelCase=30 , __UpperCamelCase=4_00 , __UpperCamelCase=True , __UpperCamelCase=None , __UpperCamelCase=2 , __UpperCamelCase=True , __UpperCamelCase=[0.5, 0.5, 0.5] , __UpperCamelCase=[0.5, 0.5, 0.5] , )							->	List[Any]:




									'''simple docstring'''


									__UpperCamelCase    :    Any								=      size if size is not None else {"shortest_edge": 30}
									__UpperCamelCase    :    Tuple								=      parent
									__UpperCamelCase    :    str								=      batch_size
									__UpperCamelCase    :    int								=      num_channels
									__UpperCamelCase    :    List[Any]								=      image_size
									__UpperCamelCase    :    Optional[Any]								=      min_resolution
									__UpperCamelCase    :    Dict								=      max_resolution
									__UpperCamelCase    :    Union[str, Any]								=      do_resize
									__UpperCamelCase    :    str								=      size
									__UpperCamelCase    :    int								=      size_divisor
									__UpperCamelCase    :    Union[str, Any]								=      do_normalize
									__UpperCamelCase    :    List[str]								=      image_mean
									__UpperCamelCase    :    Union[str, Any]								=      image_std

						def __lowerCamelCase					(				self    )							->	Tuple:




									'''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 __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase=False    )							->	Tuple:




									'''simple docstring'''


									if not batched:
												__UpperCamelCase    :    Any								=      self.size["shortest_edge"]
												__UpperCamelCase    :    Any								=      image_inputs[0]
												if isinstance(__UpperCamelCase , Image.Image    ):
															__UpperCamelCase							,				__UpperCamelCase    :    List[Any]								=      image.size
												else:
															__UpperCamelCase							,				__UpperCamelCase    :    List[str]								=      image.shape[1], image.shape[2]
												__UpperCamelCase    :    Optional[int]								=      size / min(__UpperCamelCase , __UpperCamelCase    )
												if h < w:
															__UpperCamelCase							,				__UpperCamelCase    :    Optional[Any]								=      size, scale * w
												else:
															__UpperCamelCase							,				__UpperCamelCase    :    List[str]								=      scale * h, size

												__UpperCamelCase    :    Union[str, Any]								=      int((13_33 / 8_00) * size    )
												if max(__UpperCamelCase , __UpperCamelCase    ) > max_size:
															__UpperCamelCase    :    List[str]								=      max_size / max(__UpperCamelCase , __UpperCamelCase    )
															__UpperCamelCase    :    List[str]								=      newh * scale
															__UpperCamelCase    :    Optional[Any]								=      neww * scale

												__UpperCamelCase							,				__UpperCamelCase    :    Optional[Any]								=      int(newh + 0.5    ), int(neww + 0.5    )
												__UpperCamelCase							,				__UpperCamelCase    :    List[Any]								=      (
												    newh // self.size_divisor * self.size_divisor,
												    neww // self.size_divisor * self.size_divisor,
												)

									else:
												__UpperCamelCase    :    Union[str, Any]								=      []
												for image in image_inputs:
															__UpperCamelCase							,				__UpperCamelCase    :    Any								=      self.get_expected_values([image]    )
															expected_values.append((expected_height, expected_width)    )
												__UpperCamelCase    :    int								=      max(__UpperCamelCase , key=lambda __UpperCamelCase    : item[0]    )[0]
												__UpperCamelCase    :    Optional[int]								=      max(__UpperCamelCase , key=lambda __UpperCamelCase    : item[1]    )[1]

									return expected_height, expected_width







@require_torch
@require_vision
class 		SCREAMING_SNAKE_CASE__					(		lowerCamelCase__						,	unittest.TestCase   ):







						"""simple docstring"""




						lowercase  :				List[Any]											=							ViltImageProcessor if is_vision_available() else None

						def __lowerCamelCase					(				self    )							->	List[Any]:




									'''simple docstring'''


									__UpperCamelCase    :    List[str]								=      ViltImageProcessingTester(self    )

						@property
						def __lowerCamelCase					(				self    )							->	Tuple:




									'''simple docstring'''


									return self.image_processor_tester.prepare_image_processor_dict()

						def __lowerCamelCase					(				self    )							->	Union[str, Any]:




									'''simple docstring'''


									__UpperCamelCase    :    str								=      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 __lowerCamelCase					(				self    )							->	List[Any]:




									'''simple docstring'''


									__UpperCamelCase    :    Optional[Any]								=      self.image_processing_class.from_dict(self.image_processor_dict    )
									self.assertEqual(image_processor.size , {"shortest_edge": 30}    )

									__UpperCamelCase    :    Any								=      self.image_processing_class.from_dict(self.image_processor_dict , size=42    )
									self.assertEqual(image_processor.size , {"shortest_edge": 42}    )

						def __lowerCamelCase					(				self    )							->	Optional[int]:




									'''simple docstring'''


									pass

						def __lowerCamelCase					(				self    )							->	Optional[int]:




									'''simple docstring'''


									__UpperCamelCase    :    List[str]								=      self.image_processing_class(**self.image_processor_dict    )
									# create random PIL images
									__UpperCamelCase    :    Union[str, Any]								=      prepare_image_inputs(self.image_processor_tester , equal_resolution=__UpperCamelCase    )
									for image in image_inputs:
												self.assertIsInstance(__UpperCamelCase , Image.Image    )

									# Test not batched input
									__UpperCamelCase    :    List[str]								=      image_processing(image_inputs[0] , return_tensors="pt"    ).pixel_values

									__UpperCamelCase							,				__UpperCamelCase    :    List[str]								=      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
									__UpperCamelCase    :    Optional[Any]								=      image_processing(__UpperCamelCase , return_tensors="pt"    ).pixel_values

									__UpperCamelCase							,				__UpperCamelCase    :    List[Any]								=      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 __lowerCamelCase					(				self    )							->	Optional[Any]:




									'''simple docstring'''


									__UpperCamelCase    :    int								=      self.image_processing_class(**self.image_processor_dict    )
									# create random numpy tensors
									__UpperCamelCase    :    List[Any]								=      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
									__UpperCamelCase    :    Union[str, Any]								=      image_processing(image_inputs[0] , return_tensors="pt"    ).pixel_values

									__UpperCamelCase							,				__UpperCamelCase    :    Union[str, Any]								=      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
									__UpperCamelCase    :    Tuple								=      image_processing(__UpperCamelCase , return_tensors="pt"    ).pixel_values

									__UpperCamelCase							,				__UpperCamelCase    :    List[Any]								=      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 __lowerCamelCase					(				self    )							->	Dict:




									'''simple docstring'''


									__UpperCamelCase    :    Tuple								=      self.image_processing_class(**self.image_processor_dict    )
									# create random PyTorch tensors
									__UpperCamelCase    :    Tuple								=      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
									__UpperCamelCase    :    Optional[Any]								=      image_processing(image_inputs[0] , return_tensors="pt"    ).pixel_values

									__UpperCamelCase							,				__UpperCamelCase    :    Optional[Any]								=      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
									__UpperCamelCase    :    List[str]								=      image_processing(__UpperCamelCase , return_tensors="pt"    ).pixel_values

									__UpperCamelCase							,				__UpperCamelCase    :    Union[str, Any]								=      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,
									    ) , )


lowercase     :       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": 5000,
    "atomic-mass-unit": 6.0_22_13_66_52E26,
}

lowercase     :       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": 1.6_60_54_01_99E-27,
}







def  UpperCAmelCase_   (_lowerCAmelCase :		str     ,			_lowerCAmelCase :		str     ,			_lowerCAmelCase :		float		):
			if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
						__UpperCamelCase    :    Dict								=      (
						    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()