

'''simple docstring'''

from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


UpperCAmelCase					 =	logging.get_logger(__name__)


class 	lowerCAmelCase  (   snake_case_ ):
       lowerCAmelCase_												=			['''pixel_values''']







       def __init__(				self      :			int    ,   __lowercase      :			Optional[int] = True    ,   __lowercase      :			Optional[int] = 1 / 255    ,   __lowercase      :			int = True    ,   __lowercase      :			List[Any] = 8    ,   **__lowercase      :			str    ,   ):







            """simple docstring"""







            super().__init__(**UpperCamelCase__   )

            __lowercase      		=do_rescale
            __lowercase      		=rescale_factor
            __lowercase      		=do_pad
            __lowercase      		=pad_size







       def       snake_case (				self      :			Tuple    ,   __lowercase      :			List[str]    ,   __lowercase      :			List[Any]    ,   __lowercase      :			int = None    ,   **__lowercase      :			List[str]   ):







            """simple docstring"""







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







       def       snake_case (				self      :			Union[str, Any]    ,   __lowercase      :			Union[str, Any]    ,   __lowercase      :			List[str]    ,   __lowercase      :			int = None   ):







            """simple docstring"""







            __lowercase      		=get_image_size(UpperCamelCase__   )
            __lowercase      		=(old_height // size + 1) * size - old_height
            __lowercase      		=(old_width // size + 1) * size - old_width

            return pad(UpperCamelCase__    ,   ((0, pad_height), (0, pad_width))    ,   mode='symmetric'    ,   data_format=UpperCamelCase__   )







       def       snake_case (				self      :			List[str]    ,   __lowercase      :			Optional[int]    ,   __lowercase      :			str = None    ,   __lowercase      :			Optional[int] = None    ,   __lowercase      :			Tuple = None    ,   __lowercase      :			List[str] = None    ,   __lowercase      :			str = None    ,   __lowercase      :			List[str] = ChannelDimension.FIRST    ,   **__lowercase      :			Optional[Any]    ,   ):







            """simple docstring"""







            __lowercase      		=do_rescale if do_rescale is not None else self.do_rescale
            __lowercase      		=rescale_factor if rescale_factor is not None else self.rescale_factor
            __lowercase      		=do_pad if do_pad is not None else self.do_pad
            __lowercase      		=pad_size if pad_size is not None else self.pad_size

            __lowercase      		=make_list_of_images(UpperCamelCase__   )

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

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

            # All transformations expect numpy arrays.
            __lowercase      		=[to_numpy_array(UpperCamelCase__   ) for image in images]

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

            if do_pad:
                 __lowercase      		=[self.pad(UpperCamelCase__    ,   size=UpperCamelCase__   ) for image in images]

            __lowercase      		=[to_channel_dimension_format(UpperCamelCase__    ,   UpperCamelCase__   ) for image in images]

            __lowercase      		={"pixel_values": images}
            return BatchFeature(data=UpperCamelCase__    ,   tensor_type=UpperCamelCase__   )






"""simple docstring"""



from manim import *







class _lowerCAmelCase							( snake_case_      ):







   def 					lowerCamelCase						(      self		)      ->						Any:

       '''simple docstring'''




       snake_case       : List[str]    =       Rectangle(height=0.5		,      width=0.5		)
       snake_case       : Dict    =       Rectangle(height=0.46		,      width=0.46		).set_stroke(width=0		)

       snake_case       : Tuple    =       [mem.copy() for i in range(6		)]
       snake_case       : Any    =       [mem.copy() for i in range(6		)]
       snake_case       : Tuple    =       VGroup(*UpperCamelCase__		).arrange(UpperCamelCase__		,      buff=0		)
       snake_case       : Optional[Any]    =       VGroup(*UpperCamelCase__		).arrange(UpperCamelCase__		,      buff=0		)
       snake_case       : Optional[Any]    =       VGroup(UpperCamelCase__		,      UpperCamelCase__		).arrange(UpperCamelCase__		,      buff=0		)
       snake_case       : Optional[Any]    =       Text("CPU"		,      font_size=24		)
       snake_case       : Optional[int]    =       Group(UpperCamelCase__		,      UpperCamelCase__		).arrange(UpperCamelCase__		,      buff=0.5		,      aligned_edge=UpperCamelCase__		)
       cpu.move_to([-2.5, -0.5, 0]		)
       self.add(UpperCamelCase__		)

       snake_case       : Optional[Any]    =       [mem.copy() for i in range(1		)]
       snake_case       : List[str]    =       VGroup(*UpperCamelCase__		).arrange(UpperCamelCase__		,      buff=0		)
       snake_case       : Optional[int]    =       Text("GPU"		,      font_size=24		)
       snake_case       : Tuple    =       Group(UpperCamelCase__		,      UpperCamelCase__		).arrange(UpperCamelCase__		,      buff=0.5		,      aligned_edge=UpperCamelCase__		)
       gpu.align_to(UpperCamelCase__		,      UpperCamelCase__		)
       gpu.set_x(gpu.get_x() - 1		)

       self.add(UpperCamelCase__		)

       snake_case       : Any    =       [mem.copy() for i in range(6		)]
       snake_case       : Any    =       VGroup(*UpperCamelCase__		).arrange(UpperCamelCase__		,      buff=0		)

       snake_case       : Dict    =       Text("Model"		,      font_size=24		)
       snake_case       : Optional[Any]    =       Group(UpperCamelCase__		,      UpperCamelCase__		).arrange(UpperCamelCase__		,      buff=0.5		,      aligned_edge=UpperCamelCase__		)
       model.move_to([3, -1.0, 0]		)

       self.play(
           Create(UpperCamelCase__		,      run_time=1		)		,      Create(UpperCamelCase__		,      run_time=1		)		,      Create(UpperCamelCase__		,      run_time=1		)		,      )

       snake_case       : Optional[Any]    =       MarkupText(
           F'First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.'		,      font_size=24		,      )

       snake_case       : Any    =       Square(side_length=2.2		)
       key.move_to([-5, 2, 0]		)

       snake_case       : Any    =       MarkupText(
           F'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'		,      font_size=18		,      )

       key_text.move_to([-5, 2.4, 0]		)

       step_a.move_to([2, 2, 0]		)
       self.play(Write(UpperCamelCase__		,      run_time=2.5		)		,      Write(UpperCamelCase__		)		,      Write(UpperCamelCase__		)		)

       self.add(UpperCamelCase__		)

       snake_case       : Optional[Any]    =       []
       snake_case       : Dict    =       []
       snake_case       : Union[str, Any]    =       []
       for i, rect in enumerate(UpperCamelCase__		):
           snake_case       : Any    =       Rectangle(height=0.46		,      width=0.46		).set_stroke(width=0.0		).set_fill(UpperCamelCase__		,      opacity=0.7		)
           cpu_target.move_to(UpperCamelCase__		)
           cpu_target.generate_target()
           snake_case       : Optional[int]    =       0.46 / 4
           snake_case       : Any    =       0.46 / 3

           if i == 0:
               cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT		)		,      buff=0.02		,      direction=UpperCamelCase__		)
               cpu_target.target.set_x(cpu_target.target.get_x() + 0.1		)
           elif i == 3:
               cpu_target.target.next_to(cpu_targs[0].target		,      direction=UpperCamelCase__		,      buff=0.0		)
           else:
               cpu_target.target.next_to(cpu_targs[i - 1].target		,      direction=UpperCamelCase__		,      buff=0.0		)
           cpu_targs.append(UpperCamelCase__		)

           first_animations.append(rect.animate(run_time=0.5		).set_stroke(UpperCamelCase__		)		)
           second_animations.append(MoveToTarget(UpperCamelCase__		,      run_time=1.5		)		)

       self.play(*UpperCamelCase__		)
       self.play(*UpperCamelCase__		)

       self.wait()





