






"""simple docstring"""





import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor






class      a						(					unittest.TestCase		):






    def __init__(       self     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							Any       ,						__SCREAMING_SNAKE_CASE     :							List[str]=7       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]=3       ,						__SCREAMING_SNAKE_CASE     :							Dict=18       ,						__SCREAMING_SNAKE_CASE     :							Dict=30       ,						__SCREAMING_SNAKE_CASE     :							str=400       ,						__SCREAMING_SNAKE_CASE     :							int=True       ,						__SCREAMING_SNAKE_CASE     :							Dict=None       ,						__SCREAMING_SNAKE_CASE     :							Optional[int]=True       ,						__SCREAMING_SNAKE_CASE     :							List[Any]=False       ,						__SCREAMING_SNAKE_CASE     :							Dict=True       ,						__SCREAMING_SNAKE_CASE     :							List[str]=True       ,						__SCREAMING_SNAKE_CASE     :							Dict=[0.5, 0.5, 0.5]       ,						__SCREAMING_SNAKE_CASE     :							Optional[int]=[0.5, 0.5, 0.5]       ,						)     ->		int:
      lowerCamelCase_         =						parent
      lowerCamelCase_         =						batch_size
      lowerCamelCase_         =						num_channels
      lowerCamelCase_         =						image_size
      lowerCamelCase_         =						min_resolution
      lowerCamelCase_         =						max_resolution
      lowerCamelCase_         =						do_resize
      lowerCamelCase_         =						size if size is not None else {'height': 18, 'width': 20}
      lowerCamelCase_         =						do_thumbnail
      lowerCamelCase_         =						do_align_axis
      lowerCamelCase_         =						do_pad
      lowerCamelCase_         =						do_normalize
      lowerCamelCase_         =						image_mean
      lowerCamelCase_         =						image_std






    def    UpperCamelCase       (       self     :							str					)     ->		List[str]:
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_thumbnail": self.do_thumbnail,
          "do_align_long_axis": self.do_align_axis,
          "do_pad": self.do_pad,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
      }






@require_torch
@require_vision
class      a						(					__snake_case  ,      unittest.TestCase		):
    SCREAMING_SNAKE_CASE						:     List[str]						= DonutImageProcessor if is_vision_available() else None






    def    UpperCamelCase       (       self     :							Optional[Any]					)     ->		List[str]:
      lowerCamelCase_         =						DonutImageProcessingTester(self					)






    @property
    def    UpperCamelCase       (       self     :							Optional[int]					)     ->		List[str]:
      return self.image_processor_tester.prepare_image_processor_dict()






    def    UpperCamelCase       (       self     :							Any					)     ->		str:
      lowerCamelCase_         =						self.image_processing_class(**self.image_processor_dict					)
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,						'do_resize'					)					)
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,						'size'					)					)
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,						'do_thumbnail'					)					)
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,						'do_align_long_axis'					)					)
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,						'do_pad'					)					)
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,						'do_normalize'					)					)
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,						'image_mean'					)					)
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,						'image_std'					)					)






    def    UpperCamelCase       (       self     :							Optional[Any]					)     ->		Union[str, Any]:
      lowerCamelCase_         =						self.image_processing_class.from_dict(self.image_processor_dict					)
      self.assertEqual(image_processor.size       ,						{'height': 18, 'width': 20}					)

      lowerCamelCase_         =						self.image_processing_class.from_dict(self.image_processor_dict       ,						size=42					)
      self.assertEqual(image_processor.size       ,						{'height': 42, 'width': 42}					)

      # Previous config had dimensions in (width, height) order
      lowerCamelCase_         =						self.image_processing_class.from_dict(self.image_processor_dict       ,						size=(42, 84)					)
      self.assertEqual(image_processor.size       ,						{'height': 84, 'width': 42}					)






    def    UpperCamelCase       (       self     :							Tuple					)     ->		Any:
      pass






    @is_flaky()
    def    UpperCamelCase       (       self     :							int					)     ->		str:
      # 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=__SCREAMING_SNAKE_CASE					)
      for image in image_inputs:
        self.assertIsInstance(__SCREAMING_SNAKE_CASE       ,						Image.Image					)

      # Test not batched input
      lowerCamelCase_         =						image_processing(image_inputs[0]       ,						return_tensors='pt'					).pixel_values
      self.assertEqual(
          encoded_images.shape       ,						(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )       ,						)

      # Test batched
      lowerCamelCase_         =						image_processing(__SCREAMING_SNAKE_CASE       ,						return_tensors='pt'					).pixel_values
      self.assertEqual(
          encoded_images.shape       ,						(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )       ,						)






    @is_flaky()
    def    UpperCamelCase       (       self     :							List[str]					)     ->		Dict:
      # 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=__SCREAMING_SNAKE_CASE       ,						numpify=__SCREAMING_SNAKE_CASE					)
      for image in image_inputs:
        self.assertIsInstance(__SCREAMING_SNAKE_CASE       ,						np.ndarray					)

      # Test not batched input
      lowerCamelCase_         =						image_processing(image_inputs[0]       ,						return_tensors='pt'					).pixel_values
      self.assertEqual(
          encoded_images.shape       ,						(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )       ,						)

      # Test batched
      lowerCamelCase_         =						image_processing(__SCREAMING_SNAKE_CASE       ,						return_tensors='pt'					).pixel_values
      self.assertEqual(
          encoded_images.shape       ,						(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )       ,						)






    @is_flaky()
    def    UpperCamelCase       (       self     :							Union[str, Any]					)     ->		List[str]:
      # 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=__SCREAMING_SNAKE_CASE       ,						torchify=__SCREAMING_SNAKE_CASE					)
      for image in image_inputs:
        self.assertIsInstance(__SCREAMING_SNAKE_CASE       ,						torch.Tensor					)

      # Test not batched input
      lowerCamelCase_         =						image_processing(image_inputs[0]       ,						return_tensors='pt'					).pixel_values
      self.assertEqual(
          encoded_images.shape       ,						(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )       ,						)

      # Test batched
      lowerCamelCase_         =						image_processing(__SCREAMING_SNAKE_CASE       ,						return_tensors='pt'					).pixel_values
      self.assertEqual(
          encoded_images.shape       ,						(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )       ,						)












"""simple docstring"""





from math import pow, sqrt







def      lowerCamelCase__       (							*_lowerCamelCase       :		float )      ->				bool:
  lowerCamelCase_         =						len(_lowerCamelCase ) > 0 and all(value > 0.0 for value in values )
  return result







def      lowerCamelCase__       (							_lowerCamelCase       :		float		,				_lowerCamelCase       :		float )      ->				float | ValueError:
  return (
      round(sqrt(molar_mass_a / molar_mass_a )		,				6 )
      if validate(_lowerCamelCase		,				_lowerCamelCase )
      else ValueError('Input Error: Molar mass values must greater than 0.' )
  )







def      lowerCamelCase__       (							_lowerCamelCase       :		float		,				_lowerCamelCase       :		float		,				_lowerCamelCase       :		float )      ->				float | ValueError:
  return (
      round(effusion_rate * sqrt(molar_mass_a / molar_mass_a )		,				6 )
      if validate(_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase )
      else ValueError(
          'Input Error: Molar mass and effusion rate values must greater than 0.' )
  )







def      lowerCamelCase__       (							_lowerCamelCase       :		float		,				_lowerCamelCase       :		float		,				_lowerCamelCase       :		float )      ->				float | ValueError:
  return (
      round(effusion_rate / sqrt(molar_mass_a / molar_mass_a )		,				6 )
      if validate(_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase )
      else ValueError(
          'Input Error: Molar mass and effusion rate values must greater than 0.' )
  )







def      lowerCamelCase__       (							_lowerCamelCase       :		float		,				_lowerCamelCase       :		float		,				_lowerCamelCase       :		float )      ->				float | ValueError:
  return (
      round(molar_mass / pow(effusion_rate_a / effusion_rate_a		,				2 )		,				6 )
      if validate(_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase )
      else ValueError(
          'Input Error: Molar mass and effusion rate values must greater than 0.' )
  )







def      lowerCamelCase__       (							_lowerCamelCase       :		float		,				_lowerCamelCase       :		float		,				_lowerCamelCase       :		float )      ->				float | ValueError:
  return (
      round(pow(effusion_rate_a / effusion_rate_a		,				2 ) / molar_mass		,				6 )
      if validate(_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase )
      else ValueError(
          'Input Error: Molar mass and effusion rate values must greater than 0.' )
  )




