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 	__snake_case	( unittest.TestCase		):





   '''simple docstring'''






   def __init__(					self     :				List[Any]    ,  _UpperCamelCase     :				Union[str, Any]    ,  _UpperCamelCase     :				Optional[int] = True    ,  _UpperCamelCase     :				Any = None    ,  _UpperCamelCase     :				Any = 32    ,  _UpperCamelCase     :				Optional[int] = True    ,  _UpperCamelCase     :				Union[str, Any] = 1 / 255    ,  _UpperCamelCase     :				Union[str, Any] = True    ,  _UpperCamelCase     :				List[Any] = True    ,  _UpperCamelCase     :				List[Any] = [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]    ,  _UpperCamelCase     :				Tuple = [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]    ,  _UpperCamelCase     :				Optional[int] = True    ,  _UpperCamelCase     :				List[str]=7    ,  _UpperCamelCase     :				int=30    ,  _UpperCamelCase     :				Any=400    ,  _UpperCamelCase     :				Dict=3    ,  )				->Any:


       """simple docstring"""


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



   def 	_SCREAMING_SNAKE_CASE    (					self     :				int)				->List[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 	_SCREAMING_SNAKE_CASE    (					self     :				str    ,  _UpperCamelCase     :				List[Any]    ,  _UpperCamelCase     :				Tuple=False)				->Optional[int]:


       """simple docstring"""


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

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

           _lowerCamelCase     :		Optional[int] 						=						int(newh + 0.5), int(neww + 0.5)
           _lowerCamelCase     :		Dict 						=						(
               newh // self.size_divisor * self.size_divisor,
               neww // self.size_divisor * self.size_divisor,
           )

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

       return expected_height, expected_width





@require_torch
@require_vision
class 	__snake_case	( _A ,  unittest.TestCase		):





   '''simple docstring'''






   _snake_case    		=       BridgeTowerImageProcessor if is_vision_available() else None



   def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->List[str]:


       """simple docstring"""


       _lowerCamelCase     :		str 						=						BridgeTowerImageProcessingTester(self)



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


       """simple docstring"""


       return self.image_processor_tester.prepare_image_processor_dict()



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


       """simple docstring"""


       _lowerCamelCase     :		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 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->Union[str, Any]:


       """simple docstring"""


       pass



   def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->List[Any]:


       """simple docstring"""


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

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

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

       _lowerCamelCase     :		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 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->List[str]:


       """simple docstring"""


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

       _lowerCamelCase     :		int 						=						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
       _lowerCamelCase     :		Optional[int] 						=						image_processing(_UpperCamelCase    ,  return_tensors="""pt""").pixel_values

       _lowerCamelCase     :		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 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->List[str]:


       """simple docstring"""


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

       _lowerCamelCase     :		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
       _lowerCamelCase     :		str 						=						image_processing(_UpperCamelCase    ,  return_tensors="""pt""").pixel_values

       _lowerCamelCase     :		List[str] 						=						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,
           )    ,  )





from math import log

from scipy.constants import Boltzmann, physical_constants

lowerCAmelCase			:							List[Any]     =300  # TEMPERATURE (unit = K)







def 						A__  (					__A   , __A   , __A   , ):







    '''simple docstring'''

    if donor_conc <= 0:
        raise ValueError("""Donor concentration should be positive"""	)
    elif acceptor_conc <= 0:
        raise ValueError("""Acceptor concentration should be positive"""	)
    elif intrinsic_conc <= 0:
        raise ValueError("""Intrinsic concentration should be positive"""	)
    elif donor_conc <= intrinsic_conc:
        raise ValueError(
            """Donor concentration should be greater than intrinsic concentration"""	)
    elif acceptor_conc <= intrinsic_conc:
        raise ValueError(
            """Acceptor concentration should be greater than intrinsic concentration"""	)
    else:
        return (
            Boltzmann
            * T
            * log((donor_conc * acceptor_conc) / intrinsic_conc**2	)
            / physical_constants["electron volt"][0]
        )


if __name__ == "__main__":
    import doctest

    doctest.testmod()




