import unittest

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 LevitImageProcessor




class  __magic_name__  (		unittest.TestCase    ):





   """simple docstring"""




   def __init__(	self					:  List[str]		,  _lowercase					:  Dict		,  _lowercase					:  List[str]=7		,  _lowercase					:  Optional[int]=3		,  _lowercase					:  List[Any]=18		,  _lowercase					:  List[Any]=30		,  _lowercase					:  str=400		,  _lowercase					:  Union[str, Any]=True		,  _lowercase					:  List[Any]=None		,  _lowercase					:  Optional[Any]=True		,  _lowercase					:  Optional[int]=None		,  _lowercase					:  str=True		,  _lowercase					:  Optional[int]=[0.5, 0.5, 0.5]		,  _lowercase					:  Union[str, Any]=[0.5, 0.5, 0.5]		,  ):



    """simple docstring"""



    _UpperCamelCase:     List[Any]							= size if size is not None else {'''shortest_edge''': 18}
    _UpperCamelCase:     Any							= crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
    _UpperCamelCase:     List[str]							= parent
    _UpperCamelCase:     Any							= batch_size
    _UpperCamelCase:     List[Any]							= num_channels
    _UpperCamelCase:     Optional[Any]							= image_size
    _UpperCamelCase:     Union[str, Any]							= min_resolution
    _UpperCamelCase:     Tuple							= max_resolution
    _UpperCamelCase:     List[Any]							= do_resize
    _UpperCamelCase:     str							= size
    _UpperCamelCase:     Dict							= do_center_crop
    _UpperCamelCase:     int							= crop_size
    _UpperCamelCase:     str							= do_normalize
    _UpperCamelCase:     Union[str, Any]							= image_mean
    _UpperCamelCase:     int							= image_std



   def   lowerCAmelCase						(	self					:  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,
        "do_center_crop": self.do_center_crop,
        "size": self.size,
        "crop_size": self.crop_size,
    }




@require_torch
@require_vision
class  __magic_name__  (		__a      ,	unittest.TestCase    ):





   """simple docstring"""




   lowerCAmelCase      :    str  			=   LevitImageProcessor if is_vision_available() else None



   def   lowerCAmelCase						(	self					:  Union[str, Any]							):



    """simple docstring"""



    _UpperCamelCase:     List[Any]							= LevitImageProcessingTester(self							)



   @property
   def   lowerCAmelCase						(	self					:  Optional[Any]							):



    """simple docstring"""



    return self.image_processor_tester.prepare_image_processor_dict()



   def   lowerCAmelCase						(	self					:  Optional[int]							):



    """simple docstring"""



    _UpperCamelCase:     List[Any]							= self.image_processing_class(**self.image_processor_dict							)
    self.assertTrue(hasattr(_lowercase		,  '''image_mean'''							)							)
    self.assertTrue(hasattr(_lowercase		,  '''image_std'''							)							)
    self.assertTrue(hasattr(_lowercase		,  '''do_normalize'''							)							)
    self.assertTrue(hasattr(_lowercase		,  '''do_resize'''							)							)
    self.assertTrue(hasattr(_lowercase		,  '''do_center_crop'''							)							)
    self.assertTrue(hasattr(_lowercase		,  '''size'''							)							)



   def   lowerCAmelCase						(	self					:  Optional[Any]							):



    """simple docstring"""



    _UpperCamelCase:     Tuple							= self.image_processing_class.from_dict(self.image_processor_dict							)
    self.assertEqual(image_processor.size		,  {'''shortest_edge''': 18}							)
    self.assertEqual(image_processor.crop_size		,  {'''height''': 18, '''width''': 18}							)

    _UpperCamelCase:     List[Any]							= self.image_processing_class.from_dict(self.image_processor_dict		,  size=42		,  crop_size=84							)
    self.assertEqual(image_processor.size		,  {'''shortest_edge''': 42}							)
    self.assertEqual(image_processor.crop_size		,  {'''height''': 84, '''width''': 84}							)



   def   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



    pass



   def   lowerCAmelCase						(	self					:  Optional[Any]							):



    """simple docstring"""



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

    # Test not batched input
    _UpperCamelCase:     List[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.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )		,  )

    # Test batched
    _UpperCamelCase:     str							= image_processing(_lowercase		,  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.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )		,  )



   def   lowerCAmelCase						(	self					:  Tuple							):



    """simple docstring"""



    _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=_lowercase		,  numpify=_lowercase							)
    for image in image_inputs:
     self.assertIsInstance(_lowercase		,  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.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )		,  )

    # Test batched
    _UpperCamelCase:     int							= image_processing(_lowercase		,  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.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )		,  )



   def   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



    _UpperCamelCase:     Tuple							= self.image_processing_class(**self.image_processor_dict							)
    # create random PyTorch tensors
    _UpperCamelCase:     Dict							= prepare_image_inputs(self.image_processor_tester		,  equal_resolution=_lowercase		,  torchify=_lowercase							)
    for image in image_inputs:
     self.assertIsInstance(_lowercase		,  torch.Tensor							)

    # Test not batched input
    _UpperCamelCase:     List[Any]							= 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.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )		,  )

    # Test batched
    _UpperCamelCase:     Tuple							= image_processing(_lowercase		,  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.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )		,  )
from __future__ import annotations

UpperCAmelCase_			    =							1.6_0_2_1E-1_9  # units = C




def       lowerCAmelCase_     (		lowercase:							float  ,					lowercase:							float  ,					lowercase:							float  ,					)    ->				tuple[str, float]:







 '''simple docstring'''
 if (conductivity, electron_conc, mobility).count(0		) != 1:
  raise ValueError('''You cannot supply more or less than 2 values'''		)
 elif conductivity < 0:
  raise ValueError('''Conductivity cannot be negative'''		)
 elif electron_conc < 0:
  raise ValueError('''Electron concentration cannot be negative'''		)
 elif mobility < 0:
  raise ValueError('''mobility cannot be negative'''		)
 elif conductivity == 0:
  return (
      "conductivity",
      mobility * electron_conc * ELECTRON_CHARGE,
  )
 elif electron_conc == 0:
  return (
      "electron_conc",
      conductivity / (mobility * ELECTRON_CHARGE),
  )
 else:
  return (
      "mobility",
      conductivity / (electron_conc * ELECTRON_CHARGE),
  )


if __name__ == "__main__":
  import doctest

  doctest.testmod()