

"""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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


SCREAMING_SNAKE_CASE_  :      Any							=      logging.get_logger(__name__)


if is_vision_available():
     import PIL


class 		_A							(      __a       ):
      __a             =						['pixel_values']





      def __init__(	self     ,    SCREAMING_SNAKE_CASE__ = True     ,    SCREAMING_SNAKE_CASE__ = None     ,    SCREAMING_SNAKE_CASE__ = PILImageResampling.BILINEAR     ,    SCREAMING_SNAKE_CASE__ = True     ,    SCREAMING_SNAKE_CASE__ = False     ,    SCREAMING_SNAKE_CASE__ = True     ,    SCREAMING_SNAKE_CASE__ = True     ,    SCREAMING_SNAKE_CASE__ = 1 / 255     ,    SCREAMING_SNAKE_CASE__ = True     ,    SCREAMING_SNAKE_CASE__ = None     ,    SCREAMING_SNAKE_CASE__ = None     ,    **SCREAMING_SNAKE_CASE__     ,    ) ->       None:
         super().__init__(**SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						       =      size if size is not None else {"height": 2560, "width": 1920}
         if isinstance(SCREAMING_SNAKE_CASE__     ,    (tuple, list)		):
            # The previous feature extractor size parameter was in (width, height) format
            lowerCamelCase__						       =      size[::-1]
         lowerCamelCase__						       =      get_size_dict(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						       =      do_resize
         lowerCamelCase__						       =      size
         lowerCamelCase__						       =      resample
         lowerCamelCase__						       =      do_thumbnail
         lowerCamelCase__						       =      do_align_long_axis
         lowerCamelCase__						       =      do_pad
         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        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__ = None		) ->       np.ndarray:
         lowerCamelCase__       ,							lowerCamelCase__						       =      get_image_size(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__       ,							lowerCamelCase__						       =      size["height"], size["width"]

         if (output_width < output_height and input_width > input_height) or (
             output_width > output_height and input_width < input_height
         ):
            lowerCamelCase__						       =      np.rotaa(SCREAMING_SNAKE_CASE__     ,    3		)

         if data_format is not None:
            lowerCamelCase__						       =      to_channel_dimension_format(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		)

         return image





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__ = False     ,    SCREAMING_SNAKE_CASE__ = None     ,    ) ->       np.ndarray:
         lowerCamelCase__       ,							lowerCamelCase__						       =      size["height"], size["width"]
         lowerCamelCase__       ,							lowerCamelCase__						       =      get_image_size(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						       =      output_width - input_width
         lowerCamelCase__						       =      output_height - input_height

         if random_padding:
            lowerCamelCase__						       =      np.random.randint(low=0     ,    high=delta_height + 1		)
            lowerCamelCase__						       =      np.random.randint(low=0     ,    high=delta_width + 1		)
         else:
            lowerCamelCase__						       =      delta_height // 2
            lowerCamelCase__						       =      delta_width // 2

         lowerCamelCase__						       =      delta_height - pad_top
         lowerCamelCase__						       =      delta_width - pad_left

         lowerCamelCase__						       =      ((pad_top, pad_bottom), (pad_left, pad_right))
         return pad(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    data_format=SCREAMING_SNAKE_CASE__		)





      def        _lowerCamelCase	(	self     ,    *SCREAMING_SNAKE_CASE__     ,    **SCREAMING_SNAKE_CASE__		) ->       int:
         logger.info("pad is deprecated and will be removed in version 4.27. Please use pad_image instead."		)
         return self.pad_image(*SCREAMING_SNAKE_CASE__     ,    **SCREAMING_SNAKE_CASE__		)





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__ = PILImageResampling.BICUBIC     ,    SCREAMING_SNAKE_CASE__ = None     ,    **SCREAMING_SNAKE_CASE__     ,    ) ->       np.ndarray:
         lowerCamelCase__       ,							lowerCamelCase__						       =      get_image_size(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__       ,							lowerCamelCase__						       =      size["height"], size["width"]

         # We always resize to the smallest of either the input or output size.
         lowerCamelCase__						       =      min(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      min(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		)

         if height == input_height and width == input_width:
            return image

         if input_height > input_width:
            lowerCamelCase__						       =      int(input_width * height / input_height		)
         elif input_width > input_height:
            lowerCamelCase__						       =      int(input_height * width / input_width		)

         return resize(
             SCREAMING_SNAKE_CASE__     ,    size=(height, width)     ,    resample=SCREAMING_SNAKE_CASE__     ,    reducing_gap=2.0     ,    data_format=SCREAMING_SNAKE_CASE__     ,    **SCREAMING_SNAKE_CASE__		)





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__ = PILImageResampling.BICUBIC     ,    SCREAMING_SNAKE_CASE__ = None     ,    **SCREAMING_SNAKE_CASE__     ,    ) ->       np.ndarray:
         lowerCamelCase__						       =      get_size_dict(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      min(size["height"]     ,    size["width"]		)
         lowerCamelCase__						       =      get_resize_output_image_size(SCREAMING_SNAKE_CASE__     ,    size=SCREAMING_SNAKE_CASE__     ,    default_to_square=SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      resize(SCREAMING_SNAKE_CASE__     ,    size=SCREAMING_SNAKE_CASE__     ,    resample=SCREAMING_SNAKE_CASE__     ,    data_format=SCREAMING_SNAKE_CASE__     ,    **SCREAMING_SNAKE_CASE__		)
         return resized_image





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__ = None     ,    **SCREAMING_SNAKE_CASE__     ,    ) ->       int:
         return rescale(SCREAMING_SNAKE_CASE__     ,    scale=SCREAMING_SNAKE_CASE__     ,    data_format=SCREAMING_SNAKE_CASE__     ,    **SCREAMING_SNAKE_CASE__		)





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__ = None     ,    **SCREAMING_SNAKE_CASE__     ,    ) ->       np.ndarray:
         return normalize(SCREAMING_SNAKE_CASE__     ,    mean=SCREAMING_SNAKE_CASE__     ,    std=SCREAMING_SNAKE_CASE__     ,    data_format=SCREAMING_SNAKE_CASE__     ,    **SCREAMING_SNAKE_CASE__		)





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__ = None     ,    SCREAMING_SNAKE_CASE__ = None     ,    SCREAMING_SNAKE_CASE__ = None     ,    SCREAMING_SNAKE_CASE__ = None     ,    SCREAMING_SNAKE_CASE__ = None     ,    SCREAMING_SNAKE_CASE__ = None     ,    SCREAMING_SNAKE_CASE__ = False     ,    SCREAMING_SNAKE_CASE__ = None     ,    SCREAMING_SNAKE_CASE__ = None     ,    SCREAMING_SNAKE_CASE__ = None     ,    SCREAMING_SNAKE_CASE__ = None     ,    SCREAMING_SNAKE_CASE__ = None     ,    SCREAMING_SNAKE_CASE__ = None     ,    SCREAMING_SNAKE_CASE__ = ChannelDimension.FIRST     ,    **SCREAMING_SNAKE_CASE__     ,    ) ->       PIL.Image.Image:
         lowerCamelCase__						       =      do_resize if do_resize is not None else self.do_resize
         lowerCamelCase__						       =      size if size is not None else self.size
         if isinstance(SCREAMING_SNAKE_CASE__     ,    (tuple, list)		):
            # Previous feature extractor had size in (width, height) format
            lowerCamelCase__						       =      size[::-1]
         lowerCamelCase__						       =      get_size_dict(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      resample if resample is not None else self.resample
         lowerCamelCase__						       =      do_thumbnail if do_thumbnail is not None else self.do_thumbnail
         lowerCamelCase__						       =      do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
         lowerCamelCase__						       =      do_pad if do_pad is not None else self.do_pad
         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__						       =      make_list_of_images(SCREAMING_SNAKE_CASE__		)

         if not valid_images(SCREAMING_SNAKE_CASE__		):
            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_rescale and rescale_factor is None:
            raise ValueError("Rescale factor must be specified if do_rescale is True."		)

         if do_pad and size is None:
            raise ValueError("Size must be specified if do_pad 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(SCREAMING_SNAKE_CASE__		) for image in images]

         if do_align_long_axis:
            lowerCamelCase__						       =      [self.align_long_axis(SCREAMING_SNAKE_CASE__     ,    size=SCREAMING_SNAKE_CASE__		) for image in images]

         if do_resize:
            lowerCamelCase__						       =      [self.resize(image=SCREAMING_SNAKE_CASE__     ,    size=SCREAMING_SNAKE_CASE__     ,    resample=SCREAMING_SNAKE_CASE__		) for image in images]

         if do_thumbnail:
            lowerCamelCase__						       =      [self.thumbnail(image=SCREAMING_SNAKE_CASE__     ,    size=SCREAMING_SNAKE_CASE__		) for image in images]

         if do_pad:
            lowerCamelCase__						       =      [self.pad_image(image=SCREAMING_SNAKE_CASE__     ,    size=SCREAMING_SNAKE_CASE__     ,    random_padding=SCREAMING_SNAKE_CASE__		) for image in images]

         if do_rescale:
            lowerCamelCase__						       =      [self.rescale(image=SCREAMING_SNAKE_CASE__     ,    scale=SCREAMING_SNAKE_CASE__		) for image in images]

         if do_normalize:
            lowerCamelCase__						       =      [self.normalize(image=SCREAMING_SNAKE_CASE__     ,    mean=SCREAMING_SNAKE_CASE__     ,    std=SCREAMING_SNAKE_CASE__		) for image in images]

         lowerCamelCase__						       =      [to_channel_dimension_format(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		) for image in images]

         lowerCamelCase__						       =      {"pixel_values": images}
         return BatchFeature(data=SCREAMING_SNAKE_CASE__     ,    tensor_type=SCREAMING_SNAKE_CASE__		)







"""simple docstring"""




import numpy as np
from numpy import floataa
from numpy.typing import NDArray



def 						UpperCAmelCase__ (					A__						,     A__ )   ->   NDArray[floataa]:




   """simple docstring"""
   lowerCamelCase__       ,							lowerCamelCase__						       =      np.shape(A__ )

   lowerCamelCase__						       =      np.zeros((rows, 1)						,     dtype=A__ )
   for row in reversed(range(A__ ) ):
      lowerCamelCase__						       =      0
      for col in range(row + 1						,     A__ ):
         total += coefficients[row, col] * x[col]

      lowerCamelCase__						       =      (vector[row] - total) / coefficients[row, row]

   return x



def 						UpperCAmelCase__ (					A__						,     A__ )   ->   NDArray[floataa]:




   """simple docstring"""
   lowerCamelCase__       ,							lowerCamelCase__						       =      np.shape(A__ )
   if rows != columns:
      return np.array(()						,     dtype=A__ )

   # augmented matrix
   lowerCamelCase__						       =      np.concatenate((coefficients, vector)						,     axis=1 )
   lowerCamelCase__						       =      augmented_mat.astype("float64" )

   # scale the matrix leaving it triangular
   for row in range(rows - 1 ):
      lowerCamelCase__						       =      augmented_mat[row, row]
      for col in range(row + 1						,     A__ ):
         lowerCamelCase__						       =      augmented_mat[col, row] / pivot
         augmented_mat[col, :] -= factor * augmented_mat[row, :]

   lowerCamelCase__						       =      retroactive_resolution(
       augmented_mat[:, 0:columns]						,     augmented_mat[:, columns : columns + 1] )

   return x


if __name__ == "__main__":
     import doctest

     doctest.testmod()




