

'''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					):



 """simple docstring"""



 def __init__(		self					:       Any					,							__SCREAMING_SNAKE_CASE					:       Tuple					,							__SCREAMING_SNAKE_CASE					:       bool = True					,							__SCREAMING_SNAKE_CASE					:       Dict[str, int] = None					,							__SCREAMING_SNAKE_CASE					:       int = 32					,							__SCREAMING_SNAKE_CASE					:       bool = True					,							__SCREAMING_SNAKE_CASE					:       Union[int, float] = 1 / 255					,							__SCREAMING_SNAKE_CASE					:       bool = True					,							__SCREAMING_SNAKE_CASE					:       bool = True					,							__SCREAMING_SNAKE_CASE					:       Optional[Union[float, List[float]]] = [0.48145466, 0.4578275, 0.40821073]					,							__SCREAMING_SNAKE_CASE					:       Optional[Union[float, List[float]]] = [0.26862954, 0.26130258, 0.27577711]					,							__SCREAMING_SNAKE_CASE					:       bool = True					,							__SCREAMING_SNAKE_CASE					:       Tuple=7					,							__SCREAMING_SNAKE_CASE					:       Any=30					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]=400					,							__SCREAMING_SNAKE_CASE					:       List[str]=3					,							)  -> List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      parent
      __SCREAMING_SNAKE_CASE     =      do_resize
      __SCREAMING_SNAKE_CASE     =      size if size is not None else {'''shortest_edge''': 288}
      __SCREAMING_SNAKE_CASE     =      size_divisor
      __SCREAMING_SNAKE_CASE     =      do_rescale
      __SCREAMING_SNAKE_CASE     =      rescale_factor
      __SCREAMING_SNAKE_CASE     =      do_normalize
      __SCREAMING_SNAKE_CASE     =      do_center_crop
      __SCREAMING_SNAKE_CASE     =      image_mean
      __SCREAMING_SNAKE_CASE     =      image_std
      __SCREAMING_SNAKE_CASE     =      do_pad
      __SCREAMING_SNAKE_CASE     =      batch_size
      __SCREAMING_SNAKE_CASE     =      num_channels
      __SCREAMING_SNAKE_CASE     =      min_resolution
      __SCREAMING_SNAKE_CASE     =      max_resolution
 def       UpperCAmelCase__		(		self					:       str			)  -> 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       UpperCAmelCase__		(		self					:       Optional[Any]					,							__SCREAMING_SNAKE_CASE					:       List[str]					,							__SCREAMING_SNAKE_CASE					:       Tuple=False			)  -> List[Any]:


      """simple docstring"""




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

           __SCREAMING_SNAKE_CASE     =      int((1_333 / 800) * size			)
           if max(lowerCAmelCase__					,							lowerCAmelCase__			) > max_size:
                __SCREAMING_SNAKE_CASE     =      max_size / max(lowerCAmelCase__					,							lowerCAmelCase__			)
                __SCREAMING_SNAKE_CASE     =      newh * scale
                __SCREAMING_SNAKE_CASE     =      neww * scale

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

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

      return expected_height, expected_width







@require_torch
@require_vision
class 						lowerCAmelCase__   (      A__ ,					unittest.TestCase					):



 """simple docstring"""



 lowerCAmelCase__  					=      BridgeTowerImageProcessor if is_vision_available() else None
 def       UpperCAmelCase__		(		self					:       Optional[int]			)  -> Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      BridgeTowerImageProcessingTester(self			)
 @property
 def       UpperCAmelCase__		(		self					:       Dict			)  -> Optional[int]:


      """simple docstring"""




      return self.image_processor_tester.prepare_image_processor_dict()
 def       UpperCAmelCase__		(		self					:       List[str]			)  -> List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      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       UpperCAmelCase__		(		self					:       int			)  -> List[str]:


      """simple docstring"""




      pass
 def       UpperCAmelCase__		(		self					:       Optional[int]			)  -> int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.image_processing_class(**self.image_processor_dict			)
      # create random PIL images
      __SCREAMING_SNAKE_CASE     =      prepare_image_inputs(self.image_processor_tester					,							equal_resolution=lowerCAmelCase__			)
      for image in image_inputs:
           self.assertIsInstance(lowerCAmelCase__					,							Image.Image			)

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

      __SCREAMING_SNAKE_CASE     =      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
      __SCREAMING_SNAKE_CASE     =      image_processing(lowerCAmelCase__					,							return_tensors="""pt"""			).pixel_values

      __SCREAMING_SNAKE_CASE     =      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       UpperCAmelCase__		(		self					:       str			)  -> Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.image_processing_class(**self.image_processor_dict			)
      # create random numpy tensors
      __SCREAMING_SNAKE_CASE     =      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
      __SCREAMING_SNAKE_CASE     =      image_processing(image_inputs[0]					,							return_tensors="""pt"""			).pixel_values

      __SCREAMING_SNAKE_CASE     =      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
      __SCREAMING_SNAKE_CASE     =      image_processing(lowerCAmelCase__					,							return_tensors="""pt"""			).pixel_values

      __SCREAMING_SNAKE_CASE     =      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       UpperCAmelCase__		(		self					:       List[str]			)  -> Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.image_processing_class(**self.image_processor_dict			)
      # create random PyTorch tensors
      __SCREAMING_SNAKE_CASE     =      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
      __SCREAMING_SNAKE_CASE     =      image_processing(image_inputs[0]					,							return_tensors="""pt"""			).pixel_values

      __SCREAMING_SNAKE_CASE     =      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
      __SCREAMING_SNAKE_CASE     =      image_processing(lowerCAmelCase__					,							return_tensors="""pt"""			).pixel_values

      __SCREAMING_SNAKE_CASE     =      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,
          )					,							)










from __future__ import annotations


def        _UpperCamelCase							(      lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Dict					=		0.00
						__SCREAMING_SNAKE_CASE						:     List[str]					=		0
						for resistor in resistors:
												if resistor <= 0:
																		__SCREAMING_SNAKE_CASE						:     Any					=		F'''Resistor at index {index} has a negative or zero value!'''
																		raise ValueError(lowercase__      )
												first_sum += 1 / float(lowercase__      )
												index += 1
						return 1 / first_sum


def        _UpperCamelCase							(      lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Tuple					=		0.00
						__SCREAMING_SNAKE_CASE						:     int					=		0
						for resistor in resistors:
												sum_r += resistor
												if resistor < 0:
																		__SCREAMING_SNAKE_CASE						:     Tuple					=		F'''Resistor at index {index} has a negative value!'''
																		raise ValueError(lowercase__      )
												index += 1
						return sum_r


if __name__ == "__main__":
						import doctest

						doctest.testmod()
