




'''simple docstring'''



from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCamelCase_    	=				logging.get_logger(__name__)






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       A						:				List[str]        =						['''pixel_values''']







       def __init__(      self,							A = True,							A = None,							A = PILImageResampling.BICUBIC,							A = True,							A = None,							A = True,							A = 1 / 255,							A = True,							A = IMAGENET_DEFAULT_MEAN,							A = IMAGENET_DEFAULT_STD,							**A,							):







             '''simple docstring'''







             super().__init__(**A  )
             SCREAMING_SNAKE_CASE   :      Any    					=						size if size is not None else {'shortest_edge': 224}
             SCREAMING_SNAKE_CASE   :      List[str]    					=						get_size_dict(A,							default_to_square=A  )
             SCREAMING_SNAKE_CASE   :      Tuple    					=						crop_size if crop_size is not None else {'height': 224, 'width': 224}
             SCREAMING_SNAKE_CASE   :      List[str]    					=						get_size_dict(A,							param_name='crop_size'  )

             SCREAMING_SNAKE_CASE   :      Any    					=						do_resize
             SCREAMING_SNAKE_CASE   :      List[str]    					=						size
             SCREAMING_SNAKE_CASE   :      Any    					=						resample
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						do_center_crop
             SCREAMING_SNAKE_CASE   :      Any    					=						crop_size
             SCREAMING_SNAKE_CASE   :      Tuple    					=						do_rescale
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						rescale_factor
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						do_normalize
             SCREAMING_SNAKE_CASE   :      int    					=						image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
             SCREAMING_SNAKE_CASE   :      List[str]    					=						image_std if image_std is not None else IMAGENET_DEFAULT_STD







       def        UpperCamelCase_							(      self,							A,							A,							A = PILImageResampling.BICUBIC,							A = None,							**A,							):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      int    					=						get_size_dict(A,							default_to_square=A  )
             # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
             if "shortest_edge" in size:
                   SCREAMING_SNAKE_CASE   :      str    					=						int((256 / 224) * size['shortest_edge']  )
                   SCREAMING_SNAKE_CASE   :      str    					=						get_resize_output_image_size(A,							size=A,							default_to_square=A  )
                   SCREAMING_SNAKE_CASE   :      Dict    					=						{'height': output_size[0], 'width': output_size[1]}
             if "height" not in size_dict or "width" not in size_dict:
                   raise ValueError(
                       F"Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"  )
             return resize(
                 A,							size=(size_dict['height'], size_dict['width']),							resample=A,							data_format=A,							**A  )







       def        UpperCamelCase_							(      self,							A,							A,							A = None,							**A,							):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						get_size_dict(A  )
             if "height" not in size or "width" not in size:
                   raise ValueError(F"Size dict must have keys 'height' and 'width'. Got {size.keys()}"  )
             return center_crop(A,							size=(size['height'], size['width']),							data_format=A,							**A  )







       def        UpperCamelCase_							(      self,							A,							A,							A = None,							**A,							):







             '''simple docstring'''







             return rescale(A,							scale=A,							data_format=A,							**A  )







       def        UpperCamelCase_							(      self,							A,							A,							A,							A = None,							**A,							):







             '''simple docstring'''







             return normalize(A,							mean=A,							std=A,							data_format=A,							**A  )







       def        UpperCamelCase_							(      self,							A,							A = None,							A = None,							A = None,							A = None,							A = None,							A = None,							A = None,							A = None,							A = None,							A = None,							A = None,							A = ChannelDimension.FIRST,							**A,							):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						do_resize if do_resize is not None else self.do_resize
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						resample if resample is not None else self.resample
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						do_center_crop if do_center_crop is not None else self.do_center_crop
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						do_rescale if do_rescale is not None else self.do_rescale
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						rescale_factor if rescale_factor is not None else self.rescale_factor
             SCREAMING_SNAKE_CASE   :      List[str]    					=						do_normalize if do_normalize is not None else self.do_normalize
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						image_mean if image_mean is not None else self.image_mean
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						image_std if image_std is not None else self.image_std

             SCREAMING_SNAKE_CASE   :      Dict    					=						size if size is not None else self.size
             SCREAMING_SNAKE_CASE   :      List[str]    					=						get_size_dict(A,							default_to_square=A  )
             SCREAMING_SNAKE_CASE   :      Tuple    					=						crop_size if crop_size is not None else self.crop_size
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						get_size_dict(A,							param_name='crop_size'  )

             SCREAMING_SNAKE_CASE   :      int    					=						make_list_of_images(A  )

             if not valid_images(A  ):
                   raise ValueError(
                       'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                       'torch.Tensor, tf.Tensor or jax.ndarray.'  )

             if do_resize and size is None:
                   raise ValueError('Size must be specified if do_resize is True.'  )

             if do_center_crop and crop_size is None:
                   raise ValueError('Crop size must be specified if do_center_crop is True.'  )

             if do_rescale and rescale_factor is None:
                   raise ValueError('Rescale factor must be specified if do_rescale is True.'  )

             if do_normalize and (image_mean is None or image_std is None):
                   raise ValueError('Image mean and std must be specified if do_normalize is True.'  )

             # All transformations expect numpy arrays.
             SCREAMING_SNAKE_CASE   :      Tuple    					=						[to_numpy_array(A  ) for image in images]

             if do_resize:
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						[self.resize(A,							A,							A  ) for image in images]

             if do_center_crop:
                   SCREAMING_SNAKE_CASE   :      int    					=						[self.center_crop(A,							A  ) for image in images]

             if do_rescale:
                   SCREAMING_SNAKE_CASE   :      int    					=						[self.rescale(A,							A  ) for image in images]

             if do_normalize:
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						[self.normalize(A,							A,							A  ) for image in images]

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						[to_channel_dimension_format(A,							A  ) for image in images]

             SCREAMING_SNAKE_CASE   :      Tuple    					=						{'pixel_values': images}
             return BatchFeature(data=A,							tensor_type=A  )









'''simple docstring'''




from __future__ import annotations

_SCREAMING_SNAKE_CASE							     =			1.6021E-19  # units = C





def 	__lowerCamelCase			(      __lowerCAmelCase       :							float      ,    __lowerCAmelCase       :							float      ,    __lowerCAmelCase       :							float      ,    )		->  tuple[str, float]:
   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()

