



'''simple docstring'''





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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
   import PIL


_UpperCamelCase											=		logging.get_logger(__name__)






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   =["""pixel_values"""]




  def __init__(					self :      Tuple     ,      _a :      bool = True     ,      _a :      Dict[str, int] = None     ,      _a :      float = None     ,      _a :      PILImageResampling = PILImageResampling.BILINEAR     ,      _a :      bool = True     ,      _a :      Union[int, float] = 1 / 255     ,      _a :      bool = True     ,      _a :      Optional[Union[float, List[float]]] = None     ,      _a :      Optional[Union[float, List[float]]] = None     ,      **_a :      Optional[int]     ,      )   ->   None:
    super().__init__(**_a  )
    __lowerCamelCase			:			Any       			=					size if size is not None else {'shortest_edge': 384}
    __lowerCamelCase			:			Union[str, Any]       			=					get_size_dict(_a     ,      default_to_square=_a  )

    __lowerCamelCase			:			Optional[int]       			=					do_resize
    __lowerCamelCase			:			Optional[Any]       			=					size
    # Default value set here for backwards compatibility where the value in config is None
    __lowerCamelCase			:			Union[str, Any]       			=					crop_pct if crop_pct is not None else 224 / 256
    __lowerCamelCase			:			Tuple       			=					resample
    __lowerCamelCase			:			str       			=					do_rescale
    __lowerCamelCase			:			Optional[Any]       			=					rescale_factor
    __lowerCamelCase			:			Optional[Any]       			=					do_normalize
    __lowerCamelCase			:			Optional[int]       			=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    __lowerCamelCase			:			int       			=					image_std if image_std is not None else IMAGENET_STANDARD_STD




  def 		_lowercase	(					self :      int     ,      _a :      np.ndarray     ,      _a :      Dict[str, int]     ,      _a :      float     ,      _a :      PILImageResampling = PILImageResampling.BICUBIC     ,      _a :      Optional[Union[str, ChannelDimension]] = None     ,      **_a :      List[Any]     ,      )   ->   np.ndarray:
    __lowerCamelCase			:			Optional[int]       			=					get_size_dict(_a     ,      default_to_square=_a  )
    if "shortest_edge" not in size:
      raise ValueError(f'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'  )
    __lowerCamelCase			:			Any       			=					size['shortest_edge']

    if shortest_edge < 384:
      # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
      __lowerCamelCase			:			List[str]       			=					int(shortest_edge / crop_pct  )
      __lowerCamelCase			:			Optional[int]       			=					get_resize_output_image_size(_a     ,      size=_a     ,      default_to_square=_a  )
      __lowerCamelCase			:			Any       			=					resize(image=_a     ,      size=_a     ,      resample=_a     ,      data_format=_a     ,      **_a  )
      # then crop to (shortest_edge, shortest_edge)
      return center_crop(image=_a     ,      size=(shortest_edge, shortest_edge)     ,      data_format=_a     ,      **_a  )
    else:
      # warping (no cropping) when evaluated at 384 or larger
      return resize(
          _a     ,      size=(shortest_edge, shortest_edge)     ,      resample=_a     ,      data_format=_a     ,      **_a  )




  def 		_lowercase	(					self :      Optional[Any]     ,      _a :      np.ndarray     ,      _a :      Union[int, float]     ,      _a :      Optional[Union[str, ChannelDimension]] = None     ,      **_a :      List[str]     ,      )   ->   Union[str, Any]:
    return rescale(_a     ,      scale=_a     ,      data_format=_a     ,      **_a  )




  def 		_lowercase	(					self :      Any     ,      _a :      np.ndarray     ,      _a :      Union[float, List[float]]     ,      _a :      Union[float, List[float]]     ,      _a :      Optional[Union[str, ChannelDimension]] = None     ,      **_a :      int     ,      )   ->   np.ndarray:
    return normalize(_a     ,      mean=_a     ,      std=_a     ,      data_format=_a     ,      **_a  )




  def 		_lowercase	(					self :      List[Any]     ,      _a :      ImageInput     ,      _a :      bool = None     ,      _a :      Dict[str, int] = None     ,      _a :      float = None     ,      _a :      PILImageResampling = None     ,      _a :      bool = None     ,      _a :      float = None     ,      _a :      bool = None     ,      _a :      Optional[Union[float, List[float]]] = None     ,      _a :      Optional[Union[float, List[float]]] = None     ,      _a :      Optional[Union[str, TensorType]] = None     ,      _a :      ChannelDimension = ChannelDimension.FIRST     ,      **_a :      Any     ,      )   ->   PIL.Image.Image:
    __lowerCamelCase			:			int       			=					do_resize if do_resize is not None else self.do_resize
    __lowerCamelCase			:			Tuple       			=					crop_pct if crop_pct is not None else self.crop_pct
    __lowerCamelCase			:			Union[str, Any]       			=					resample if resample is not None else self.resample
    __lowerCamelCase			:			Any       			=					do_rescale if do_rescale is not None else self.do_rescale
    __lowerCamelCase			:			str       			=					rescale_factor if rescale_factor is not None else self.rescale_factor
    __lowerCamelCase			:			int       			=					do_normalize if do_normalize is not None else self.do_normalize
    __lowerCamelCase			:			Union[str, Any]       			=					image_mean if image_mean is not None else self.image_mean
    __lowerCamelCase			:			int       			=					image_std if image_std is not None else self.image_std

    __lowerCamelCase			:			List[Any]       			=					size if size is not None else self.size
    __lowerCamelCase			:			Any       			=					get_size_dict(_a     ,      default_to_square=_a  )

    __lowerCamelCase			:			str       			=					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 or resample is None:
      raise ValueError('Size and resample must be specified if do_resize is True.'  )

    if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
      raise ValueError('crop_pct must be specified if size < 384.'  )

    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			:			str       			=					[to_numpy_array(_a  ) for image in images]

    if do_resize:
      __lowerCamelCase			:			List[Any]       			=					[self.resize(image=_a     ,      size=_a     ,      crop_pct=_a     ,      resample=_a  ) for image in images]

    if do_rescale:
      __lowerCamelCase			:			str       			=					[self.rescale(image=_a     ,      scale=_a  ) for image in images]

    if do_normalize:
      __lowerCamelCase			:			Any       			=					[self.normalize(image=_a     ,      mean=_a     ,      std=_a  ) for image in images]

    __lowerCamelCase			:			int       			=					[to_channel_dimension_format(_a     ,      _a  ) for image in images]

    __lowerCamelCase			:			int       			=					{'pixel_values': images}
    return BatchFeature(data=_a     ,      tensor_type=_a  )











'''simple docstring'''





def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			) -> tuple[float, float]:

  # Check if the input is valid
  if not len(_lowerCAmelCase			) == len(_lowerCAmelCase			) == 3:
    raise ValueError('Please enter a valid equation.'			)
  if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
    raise ValueError('Both a & b of two equations can\'t be zero.'			)

  # Extract the coefficients
  __lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			:			Union[str, Any]       			=					equationa
  __lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			:			Any       			=					equationa

  # Calculate the determinants of the matrices
  __lowerCamelCase			:			Union[str, Any]       			=					aa * ba - aa * ba
  __lowerCamelCase			:			Dict       			=					ca * ba - ca * ba
  __lowerCamelCase			:			List[str]       			=					aa * ca - aa * ca

  # Check if the system of linear equations has a solution (using Cramer's rule)
  if determinant == 0:
    if determinant_x == determinant_y == 0:
      raise ValueError('Infinite solutions. (Consistent system)'			)
    else:
      raise ValueError('No solution. (Inconsistent system)'			)
  else:
    if determinant_x == determinant_y == 0:
      # Trivial solution (Inconsistent system)
      return (0.0, 0.0)
    else:
      __lowerCamelCase			:			Tuple       			=					determinant_x / determinant
      __lowerCamelCase			:			Union[str, Any]       			=					determinant_y / determinant
      # Non-Trivial Solution (Consistent system)
      return (x, y)






