


"""simple docstring"""



import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor







class _lowerCAmelCase							( unittest.TestCase      ):







   def __init__(      self		,      UpperCamelCase__		,      UpperCamelCase__ = True		,      UpperCamelCase__ = None		,      UpperCamelCase__ = 32		,      UpperCamelCase__ = True		,      UpperCamelCase__ = 1 / 255		,      UpperCamelCase__ = True		,      UpperCamelCase__ = True		,      UpperCamelCase__ = [0.48145466, 0.4578275, 0.40821073]		,      UpperCamelCase__ = [0.26862954, 0.26130258, 0.27577711]		,      UpperCamelCase__ = True		,      UpperCamelCase__=7		,      UpperCamelCase__=30		,      UpperCamelCase__=400		,      UpperCamelCase__=3		,      )      ->						Optional[int]:

       '''simple docstring'''




       snake_case       : Any    =       parent
       snake_case       : Dict    =       do_resize
       snake_case       : Optional[int]    =       size if size is not None else {"""shortest_edge""": 288}
       snake_case       : List[Any]    =       size_divisor
       snake_case       : Any    =       do_rescale
       snake_case       : Union[str, Any]    =       rescale_factor
       snake_case       : int    =       do_normalize
       snake_case       : List[Any]    =       do_center_crop
       snake_case       : str    =       image_mean
       snake_case       : Union[str, Any]    =       image_std
       snake_case       : Optional[int]    =       do_pad
       snake_case       : Tuple    =       batch_size
       snake_case       : int    =       num_channels
       snake_case       : Any    =       min_resolution
       snake_case       : List[str]    =       max_resolution







   def 					lowerCamelCase						(      self		)      ->						Optional[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 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__=False		)      ->						Optional[int]:

       '''simple docstring'''




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

           snake_case       : Any    =       int((1333 / 800) * size		)
           if max(__lowerCAmelCase		,      __lowerCAmelCase		) > max_size:
               snake_case       : Any    =       max_size / max(__lowerCAmelCase		,      __lowerCAmelCase		)
               snake_case       : Tuple    =       newh * scale
               snake_case       : List[str]    =       neww * scale

           snake_case       : List[str]    =       int(newh + 0.5		), int(neww + 0.5		)
           snake_case       : int    =       (
               newh // self.size_divisor * self.size_divisor,
               neww // self.size_divisor * self.size_divisor,
           )

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

       return expected_height, expected_width







@require_torch
@require_vision
class _lowerCAmelCase							( SCREAMING_SNAKE_CASE__       ,				unittest.TestCase      ):
   __UpperCAmelCase					:			Tuple				    =    BridgeTowerImageProcessor if is_vision_available() else None







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

       '''simple docstring'''




       snake_case       : Optional[int]    =       BridgeTowerImageProcessingTester(self		)







   @property
   def 					lowerCamelCase						(      self		)      ->						List[Any]:

       '''simple docstring'''




       return self.image_processor_tester.prepare_image_processor_dict()







   def 					lowerCamelCase						(      self		)      ->						Dict:

       '''simple docstring'''




       snake_case       : Dict    =       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 					lowerCamelCase						(      self		)      ->						int:

       '''simple docstring'''




       pass







   def 					lowerCamelCase						(      self		)      ->						Optional[Any]:

       '''simple docstring'''




       snake_case       : Optional[Any]    =       self.image_processing_class(**self.image_processor_dict		)
       # create random PIL images
       snake_case       : Tuple    =       prepare_image_inputs(self.image_processor_tester		,      equal_resolution=__lowerCAmelCase		)
       for image in image_inputs:
           self.assertIsInstance(__lowerCAmelCase		,      Image.Image		)

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

       snake_case       : Optional[Any]    =       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
       snake_case       : int    =       image_processing(__lowerCAmelCase		,      return_tensors="pt"		).pixel_values

       snake_case       : Union[str, Any]    =       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 					lowerCamelCase						(      self		)      ->						Dict:

       '''simple docstring'''




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

       snake_case       : Optional[int]    =       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
       snake_case       : Optional[Any]    =       image_processing(__lowerCAmelCase		,      return_tensors="pt"		).pixel_values

       snake_case       : Tuple    =       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 					lowerCamelCase						(      self		)      ->						Any:

       '''simple docstring'''




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

       snake_case       : int    =       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
       snake_case       : Optional[int]    =       image_processing(__lowerCAmelCase		,      return_tensors="pt"		).pixel_values

       snake_case       : str    =       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,
           )		,      )













'''simple docstring'''

from __future__ import annotations

from collections import namedtuple



def 				snake_case       (  a_				:			float     , a_				:			float     , a_				:			float						)							->					tuple:





      """simple docstring"""

      UpperCamelCase_					: Tuple            =				namedtuple("""result"""     , """name value"""						)
      if (voltage, current, power).count(0						) != 1:
            raise ValueError("""Only one argument must be 0"""						)
      elif power < 0:
            raise ValueError(
                """Power cannot be negative in any electrical/electronics system"""						)
      elif voltage == 0:
            return result("""voltage"""     , power / current						)
      elif current == 0:
            return result("""current"""     , power / voltage						)
      elif power == 0:
            return result("""power"""     , float(round(abs(voltage * current						)     , 2						)						)						)
      else:
            raise ValueError("""Exactly one argument must be 0"""						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()






