




from typing import Dict, 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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
							import PIL


UpperCamelCase__         =      logging.get_logger(__name__)







def      _a				(						SCREAMING_SNAKE_CASE_							:    Dict					):





					if isinstance(UpperCAmelCase_				,				(list, tuple)					) and isinstance(videos[0]				,				(list, tuple)					) and is_valid_image(videos[0][0]					):
										return videos

					elif isinstance(UpperCAmelCase_				,				(list, tuple)					) and is_valid_image(videos[0]					):
										return [videos]

					elif is_valid_image(UpperCAmelCase_					):
										return [[videos]]

					raise ValueError(F"""Could not make batched video from {videos}"""					)


class       a__   ( snake_case__				):
						_a							:   Optional[int] =   ["""pixel_values"""]






						def __init__(       self		,      _A = True		,      _A = None		,      _A = PILImageResampling.BILINEAR		,      _A = True		,      _A = None		,      _A = True		,      _A = 1 / 2_5_5		,      _A = True		,      _A = None		,      _A = None		,      **_A		,      ):




											"""simple docstring"""



											super().__init__(**_A      )
											__lowerCAmelCase           =	size if size is not None else {'shortest_edge': 2_2_4}
											__lowerCAmelCase           =	get_size_dict(_A		,      default_to_square=_A      )
											__lowerCAmelCase           =	crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
											__lowerCAmelCase           =	get_size_dict(_A		,      param_name="crop_size"      )

											__lowerCAmelCase           =	do_resize
											__lowerCAmelCase           =	size
											__lowerCAmelCase           =	do_center_crop
											__lowerCAmelCase           =	crop_size
											__lowerCAmelCase           =	resample
											__lowerCAmelCase           =	do_rescale
											__lowerCAmelCase           =	rescale_factor
											__lowerCAmelCase           =	do_normalize
											__lowerCAmelCase           =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											__lowerCAmelCase           =	image_std if image_std is not None else IMAGENET_STANDARD_STD






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A = PILImageResampling.BILINEAR		,      _A = None		,      **_A		,      ):




											"""simple docstring"""



											__lowerCAmelCase           =	get_size_dict(_A		,      default_to_square=_A      )
											if "shortest_edge" in size:
																__lowerCAmelCase           =	get_resize_output_image_size(_A		,      size["shortest_edge"]		,      default_to_square=_A      )
											elif "height" in size and "width" in size:
																__lowerCAmelCase           =	(size['height'], size['width'])
											else:
																raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""      )
											return resize(_A		,      size=_A		,      resample=_A		,      data_format=_A		,      **_A      )






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A = None		,      **_A		,      ):




											"""simple docstring"""



											__lowerCAmelCase           =	get_size_dict(_A      )
											if "height" not in size or "width" not in size:
																raise ValueError(f"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""      )
											return center_crop(_A		,      size=(size["height"], size["width"])		,      data_format=_A		,      **_A      )






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A = None		,      **_A		,      ):




											"""simple docstring"""



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






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A		,      _A = None		,      **_A		,      ):




											"""simple docstring"""



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






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A = None		,      _A = None		,      _A = None		,      _A = None		,      _A = None		,      _A = None		,      _A = None		,      _A = None		,      _A = None		,      _A = None		,      _A = ChannelDimension.FIRST		,      ):




											"""simple docstring"""



											if do_resize and size is None or resample is None:
																raise ValueError("Size and resample 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.
											__lowerCAmelCase           =	to_numpy_array(_A      )

											if do_resize:
																__lowerCAmelCase           =	self.resize(image=_A		,      size=_A		,      resample=_A      )

											if do_center_crop:
																__lowerCAmelCase           =	self.center_crop(_A		,      size=_A      )

											if do_rescale:
																__lowerCAmelCase           =	self.rescale(image=_A		,      scale=_A      )

											if do_normalize:
																__lowerCAmelCase           =	self.normalize(image=_A		,      mean=_A		,      std=_A      )

											__lowerCAmelCase           =	to_channel_dimension_format(_A		,      _A      )
											return image






						def     __SCREAMING_SNAKE_CASE(       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"""



											__lowerCAmelCase           =	do_resize if do_resize is not None else self.do_resize
											__lowerCAmelCase           =	resample if resample is not None else self.resample
											__lowerCAmelCase           =	do_center_crop if do_center_crop is not None else self.do_center_crop
											__lowerCAmelCase           =	do_rescale if do_rescale is not None else self.do_rescale
											__lowerCAmelCase           =	rescale_factor if rescale_factor is not None else self.rescale_factor
											__lowerCAmelCase           =	do_normalize if do_normalize is not None else self.do_normalize
											__lowerCAmelCase           =	image_mean if image_mean is not None else self.image_mean
											__lowerCAmelCase           =	image_std if image_std is not None else self.image_std

											__lowerCAmelCase           =	size if size is not None else self.size
											__lowerCAmelCase           =	get_size_dict(_A		,      default_to_square=_A      )
											__lowerCAmelCase           =	crop_size if crop_size is not None else self.crop_size
											__lowerCAmelCase           =	get_size_dict(_A		,      param_name="crop_size"      )

											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."      )

											__lowerCAmelCase           =	make_batched(_A      )

											__lowerCAmelCase           =	[
											    [
											        self._preprocess_image(
											            image=_A		,      do_resize=_A		,      size=_A		,      resample=_A		,      do_center_crop=_A		,      crop_size=_A		,      do_rescale=_A		,      rescale_factor=_A		,      do_normalize=_A		,      image_mean=_A		,      image_std=_A		,      data_format=_A		,      )
											        for img in video
											    ]
											    for video in videos
											]

											__lowerCAmelCase           =	{'pixel_values': videos}
											return BatchFeature(data=_A		,      tensor_type=_A      )










from __future__ import annotations







def      _a				(						SCREAMING_SNAKE_CASE_							:    float				,				SCREAMING_SNAKE_CASE_							:    float				,				SCREAMING_SNAKE_CASE_							:    float				,				):
					if (electron_conc, hole_conc, intrinsic_conc).count(0					) != 1:
										raise ValueError("You cannot supply more or less than 2 values"					)
					elif electron_conc < 0:
										raise ValueError("Electron concentration cannot be negative in a semiconductor"					)
					elif hole_conc < 0:
										raise ValueError("Hole concentration cannot be negative in a semiconductor"					)
					elif intrinsic_conc < 0:
										raise ValueError(
										    "Intrinsic concentration cannot be negative in a semiconductor"					)
					elif electron_conc == 0:
										return (
										    "electron_conc",
										    intrinsic_conc**2 / hole_conc,
										)
					elif hole_conc == 0:
										return (
										    "hole_conc",
										    intrinsic_conc**2 / electron_conc,
										)
					elif intrinsic_conc == 0:
										return (
										    "intrinsic_conc",
										    (electron_conc * hole_conc) ** 0.5,
										)
					else:
										return (-1, -1)


if __name__ == "__main__":
					import doctest

					doctest.testmod()




