



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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
				import PIL


lowerCAmelCase__	    =    logging.get_logger(__name__)


def    __lowerCamelCase   (  lowerCamelCase__	,					lowerCamelCase__		):

				"""simple docstring"""


				lowercase__	:       str						=	b.T
				lowercase__	:       Union[str, Any]						=	np.sum(np.square(lowerCAmelCase__		)	,					axis=1		)
				lowercase__	:       Any						=	np.sum(np.square(lowerCAmelCase__		)	,					axis=0		)
				lowercase__	:       List[Any]						=	np.matmul(lowerCAmelCase__	,					lowerCAmelCase__		)
				lowercase__	:       Any						=	aa[:, None] - 2 * ab + ba[None, :]
				return d


def    __lowerCamelCase   (  lowerCamelCase__	,					lowerCamelCase__		):

				"""simple docstring"""


				lowercase__	:       Optional[int]						=	x.reshape(-1	,					3		)
				lowercase__	:       List[Any]						=	squared_euclidean_distance(lowerCAmelCase__	,					lowerCAmelCase__		)
				return np.argmin(lowerCAmelCase__	,					axis=1		)

class  snake_case__(_UpperCamelCase     ):

					"""simple docstring"""






					lowercase_								=						['pixel_values']







					def __init__(				self   :      str ,  SCREAMING_SNAKE_CASE   :      Dict = None ,  SCREAMING_SNAKE_CASE   :      Optional[Any] = True ,  SCREAMING_SNAKE_CASE   :      int = None ,  SCREAMING_SNAKE_CASE   :      Dict = PILImageResampling.BILINEAR ,  SCREAMING_SNAKE_CASE   :      Dict = True ,  SCREAMING_SNAKE_CASE   :      List[Any] = True ,  **SCREAMING_SNAKE_CASE   :      Optional[int] ,  ):
									super().__init__(**__lowerCAmelCase	)
									lowercase__	:       Optional[int]						=	size if size is not None else {"height": 256, "width": 256}
									lowercase__	:       List[Any]						=	get_size_dict(__lowerCAmelCase	)
									lowercase__	:       List[str]						=	np.array(__lowerCAmelCase	) if clusters is not None else None
									lowercase__	:       List[str]						=	do_resize
									lowercase__	:       Tuple						=	size
									lowercase__	:       Any						=	resample
									lowercase__	:       List[str]						=	do_normalize
									lowercase__	:       List[Any]						=	do_color_quantize







					def 					snake_case	(				self   :      List[Any] ,  SCREAMING_SNAKE_CASE   :      Any ,  SCREAMING_SNAKE_CASE   :      Dict ,  SCREAMING_SNAKE_CASE   :      Optional[Any] = PILImageResampling.BILINEAR ,  SCREAMING_SNAKE_CASE   :      List[str] = None ,  **SCREAMING_SNAKE_CASE   :      Tuple ,  ):
									lowercase__	:       Optional[int]						=	get_size_dict(__lowerCAmelCase	)
									if "height" not in size or "width" not in size:
													raise ValueError(f"""Size dictionary must contain both height and width keys. Got {size.keys()}"""	)
									return resize(
									    __lowerCAmelCase ,  size=(size["height"], size["width"]) ,  resample=__lowerCAmelCase ,  data_format=__lowerCAmelCase ,  **__lowerCAmelCase	)







					def 					snake_case	(				self   :      Optional[int] ,  SCREAMING_SNAKE_CASE   :      List[Any] ,  SCREAMING_SNAKE_CASE   :      List[str] = None ,  ):
									lowercase__	:       Dict						=	rescale(image=__lowerCAmelCase ,  scale=1 / 127.5 ,  data_format=__lowerCAmelCase	)
									lowercase__	:       Optional[int]						=	image - 1
									return image







					def 					snake_case	(				self   :      Union[str, Any] ,  SCREAMING_SNAKE_CASE   :      Optional[Any] ,  SCREAMING_SNAKE_CASE   :      Dict = None ,  SCREAMING_SNAKE_CASE   :      Any = None ,  SCREAMING_SNAKE_CASE   :      Optional[Any] = None ,  SCREAMING_SNAKE_CASE   :      Tuple = None ,  SCREAMING_SNAKE_CASE   :      Union[str, Any] = None ,  SCREAMING_SNAKE_CASE   :      Tuple = None ,  SCREAMING_SNAKE_CASE   :      Optional[Any] = None ,  SCREAMING_SNAKE_CASE   :      Optional[Any] = ChannelDimension.FIRST ,  **SCREAMING_SNAKE_CASE   :      str ,  ):
									lowercase__	:       Tuple						=	do_resize if do_resize is not None else self.do_resize
									lowercase__	:       str						=	size if size is not None else self.size
									lowercase__	:       Dict						=	get_size_dict(__lowerCAmelCase	)
									lowercase__	:       Union[str, Any]						=	resample if resample is not None else self.resample
									lowercase__	:       List[Any]						=	do_normalize if do_normalize is not None else self.do_normalize
									lowercase__	:       Optional[int]						=	do_color_quantize if do_color_quantize is not None else self.do_color_quantize
									lowercase__	:       Union[str, Any]						=	clusters if clusters is not None else self.clusters
									lowercase__	:       Optional[int]						=	np.array(__lowerCAmelCase	)

									lowercase__	:       Any						=	make_list_of_images(__lowerCAmelCase	)

									if not valid_images(__lowerCAmelCase	):
													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 or resample is None:
													raise ValueError("Size and resample must be specified if do_resize is True."	)

									if do_color_quantize and clusters is None:
													raise ValueError("Clusters must be specified if do_color_quantize is True."	)

									# All transformations expect numpy arrays.
									lowercase__	:       str						=	[to_numpy_array(__lowerCAmelCase	) for image in images]

									if do_resize:
													lowercase__	:       str						=	[self.resize(image=__lowerCAmelCase ,  size=__lowerCAmelCase ,  resample=__lowerCAmelCase	) for image in images]

									if do_normalize:
													lowercase__	:       List[str]						=	[self.normalize(image=__lowerCAmelCase	) for image in images]

									if do_color_quantize:
													lowercase__	:       int						=	[to_channel_dimension_format(__lowerCAmelCase ,  ChannelDimension.LAST	) for image in images]
													# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
													lowercase__	:       Optional[Any]						=	np.array(__lowerCAmelCase	)
													lowercase__	:       Optional[Any]						=	color_quantize(__lowerCAmelCase ,  __lowerCAmelCase	).reshape(images.shape[:-1]	)

													# flatten to (batch_size, height*width)
													lowercase__	:       Optional[int]						=	images.shape[0]
													lowercase__	:       Any						=	images.reshape(__lowerCAmelCase ,  -1	)

													# We need to convert back to a list of images to keep consistent behaviour across processors.
													lowercase__	:       str						=	list(__lowerCAmelCase	)
									else:
													lowercase__	:       str						=	[to_channel_dimension_format(__lowerCAmelCase ,  __lowerCAmelCase	) for image in images]

									lowercase__	:       Optional[int]						=	{"input_ids": images}
									return BatchFeature(data=__lowerCAmelCase ,  tensor_type=__lowerCAmelCase	)





"""simple docstring"""






from math import pow, sqrt


def      UpperCAmelCase__			(							*lowerCAmelCase__	:float						)					->       bool:
      '''simple docstring'''


      lowercase								=      len(lowerCAmelCase__						) > 0 and all(value > 0.0 for value in values						)
      return result


def      UpperCAmelCase__			(							lowerCAmelCase__	:float   ,						lowerCAmelCase__	:float						)					->       float | ValueError:
      '''simple docstring'''


      return (
          round(sqrt(molar_mass_a / molar_mass_a						)   ,						6						)
          if validate(lowerCAmelCase__   ,						lowerCAmelCase__						)
          else ValueError("""Input Error: Molar mass values must greater than 0."""						)
      )


def      UpperCAmelCase__			(							lowerCAmelCase__	:float   ,						lowerCAmelCase__	:float   ,						lowerCAmelCase__	:float						)					->       float | ValueError:
      '''simple docstring'''


      return (
          round(effusion_rate * sqrt(molar_mass_a / molar_mass_a						)   ,						6						)
          if validate(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__						)
          else ValueError(
              """Input Error: Molar mass and effusion rate values must greater than 0."""						)
      )


def      UpperCAmelCase__			(							lowerCAmelCase__	:float   ,						lowerCAmelCase__	:float   ,						lowerCAmelCase__	:float						)					->       float | ValueError:
      '''simple docstring'''


      return (
          round(effusion_rate / sqrt(molar_mass_a / molar_mass_a						)   ,						6						)
          if validate(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__						)
          else ValueError(
              """Input Error: Molar mass and effusion rate values must greater than 0."""						)
      )


def      UpperCAmelCase__			(							lowerCAmelCase__	:float   ,						lowerCAmelCase__	:float   ,						lowerCAmelCase__	:float						)					->       float | ValueError:
      '''simple docstring'''


      return (
          round(molar_mass / pow(effusion_rate_a / effusion_rate_a   ,						2						)   ,						6						)
          if validate(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__						)
          else ValueError(
              """Input Error: Molar mass and effusion rate values must greater than 0."""						)
      )






def      UpperCAmelCase__			(							lowerCAmelCase__	:float   ,						lowerCAmelCase__	:float   ,						lowerCAmelCase__	:float						)					->       float | ValueError:
      '''simple docstring'''


      return (
          round(pow(effusion_rate_a / effusion_rate_a   ,						2						) / molar_mass   ,						6						)
          if validate(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__						)
          else ValueError(
              """Input Error: Molar mass and effusion rate values must greater than 0."""						)
      )



