'''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 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

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





      def      _A	(       self						:				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 	lowerCamelCase_	(__lowerCAmelCase							, unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  int						=						DonutImageProcessor if is_vision_available() else None




      def      _A	(       self						:				Dict       ):
             _UpperCAmelCase     :  str							      =    DonutImageProcessingTester(self       )




      @property
      def      _A	(       self						:				Dict       ):
             return self.image_processor_tester.prepare_image_processor_dict()




      def      _A	(       self						:				Optional[Any]       ):
             _UpperCAmelCase     :  str							      =    self.image_processing_class(**self.image_processor_dict       )
             self.assertTrue(hasattr(A						,					"do_resize"       )       )
             self.assertTrue(hasattr(A						,					"size"       )       )
             self.assertTrue(hasattr(A						,					"do_thumbnail"       )       )
             self.assertTrue(hasattr(A						,					"do_align_long_axis"       )       )
             self.assertTrue(hasattr(A						,					"do_pad"       )       )
             self.assertTrue(hasattr(A						,					"do_normalize"       )       )
             self.assertTrue(hasattr(A						,					"image_mean"       )       )
             self.assertTrue(hasattr(A						,					"image_std"       )       )




      def      _A	(       self						:				Tuple       ):
             _UpperCAmelCase     :  List[str]							      =    self.image_processing_class.from_dict(self.image_processor_dict       )
             self.assertEqual(image_processor.size						,					{"height": 18, "width": 20}       )

             _UpperCAmelCase     :  Optional[int]							      =    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
             _UpperCAmelCase     :  Optional[int]							      =    self.image_processing_class.from_dict(self.image_processor_dict						,					size=(42, 84)       )
             self.assertEqual(image_processor.size						,					{"height": 84, "width": 42}       )




      def      _A	(       self						:				int       ):
             pass




      @is_flaky()
      def      _A	(       self						:				Tuple       ):
             _UpperCAmelCase     :  Dict							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random PIL images
             _UpperCAmelCase     :  Optional[int]							      =    prepare_image_inputs(self.image_processor_tester						,					equal_resolution=A       )
             for image in image_inputs:
                    self.assertIsInstance(A						,					Image.Image       )

             # Test not batched input
             _UpperCAmelCase     :  int							      =    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
             _UpperCAmelCase     :  Dict							      =    image_processing(A						,					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      _A	(       self						:				Tuple       ):
             _UpperCAmelCase     :  str							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random numpy tensors
             _UpperCAmelCase     :  str							      =    prepare_image_inputs(self.image_processor_tester						,					equal_resolution=A						,					numpify=A       )
             for image in image_inputs:
                    self.assertIsInstance(A						,					np.ndarray       )

             # Test not batched input
             _UpperCAmelCase     :  str							      =    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
             _UpperCAmelCase     :  Tuple							      =    image_processing(A						,					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      _A	(       self						:				Any       ):
             _UpperCAmelCase     :  Optional[Any]							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random PyTorch tensors
             _UpperCAmelCase     :  str							      =    prepare_image_inputs(self.image_processor_tester						,					equal_resolution=A						,					torchify=A       )
             for image in image_inputs:
                    self.assertIsInstance(A						,					torch.Tensor       )

             # Test not batched input
             _UpperCAmelCase     :  Optional[int]							      =    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
             _UpperCAmelCase     :  List[Any]							      =    image_processing(A						,					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 						__a   (	*_SCREAMING_SNAKE_CASE   )	->bool:
  a__:     Union[str, Any]										=	len(_SCREAMING_SNAKE_CASE   ) > 0 and all(value > 0.0 for value in values   )
  return result







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







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







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







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







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


