



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


SCREAMING_SNAKE_CASE_:str             =						logging.get_logger(__name__)






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Any					=      ["pixel_values"]






    def __init__(       self,		lowerCamelCase__ = True,		lowerCamelCase__ = 1 / 255,		lowerCamelCase__ = True,		lowerCamelCase__ = 8,		**lowerCamelCase__,		):
        super().__init__(**lowerCamelCase__		)

        A :       Optional[Any]          =   do_rescale
        A :       str          =   rescale_factor
        A :       List[str]          =   do_pad
        A :       Optional[int]          =   pad_size






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = None,		**lowerCamelCase__		):
        return rescale(lowerCamelCase__,		scale=lowerCamelCase__,		data_format=lowerCamelCase__,		**lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = None		):
        A     ,							A :       Optional[Any]          =   get_image_size(lowerCamelCase__		)
        A :       str          =   (old_height // size + 1) * size - old_height
        A :       List[str]          =   (old_width // size + 1) * size - old_width

        return pad(lowerCamelCase__,		((0, pad_height), (0, pad_width)),		mode="""symmetric""",		data_format=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = ChannelDimension.FIRST,		**lowerCamelCase__,		):
        A :       Dict          =   do_rescale if do_rescale is not None else self.do_rescale
        A :       Optional[Any]          =   rescale_factor if rescale_factor is not None else self.rescale_factor
        A :       Dict          =   do_pad if do_pad is not None else self.do_pad
        A :       List[str]          =   pad_size if pad_size is not None else self.pad_size

        A :       str          =   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_rescale and rescale_factor is None:
            raise ValueError("""Rescale factor must be specified if do_rescale is True."""		)

        # All transformations expect numpy arrays.
        A :       List[str]          =   [to_numpy_array(lowerCamelCase__		) for image in images]

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

        if do_pad:
            A :       Union[str, Any]          =   [self.pad(lowerCamelCase__,		size=lowerCamelCase__		) for image in images]

        A :       List[str]          =   [to_channel_dimension_format(lowerCamelCase__,		lowerCamelCase__		) for image in images]

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







from manim import *






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    def 			_lowerCAmelCase					(       self		):
        A :       Union[str, Any]          =   Rectangle(height=0.5,		width=0.5		)
        A :       Optional[int]          =   Rectangle(height=0.25,		width=0.25		)
        A :       Optional[Any]          =   Rectangle(height=0.46,		width=0.46		).set_stroke(width=0		)

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

        A :       List[Any]          =   [mem.copy() for i in range(4		)]
        A :       Optional[Any]          =   VGroup(*lowerCamelCase__		).arrange(lowerCamelCase__,		buff=0		)
        A :       Dict          =   Text("""GPU""",		font_size=24		)
        A :       Any          =   Group(lowerCamelCase__,		lowerCamelCase__		).arrange(lowerCamelCase__,		buff=0.5,		aligned_edge=lowerCamelCase__		)
        gpu.move_to([-1, -1, 0]		)
        self.add(lowerCamelCase__		)

        A :       Optional[int]          =   [mem.copy() for i in range(6		)]
        A :       List[str]          =   VGroup(*lowerCamelCase__		).arrange(lowerCamelCase__,		buff=0		)

        A :       Optional[int]          =   Text("""Model""",		font_size=24		)
        A :       List[Any]          =   Group(lowerCamelCase__,		lowerCamelCase__		).arrange(lowerCamelCase__,		buff=0.5,		aligned_edge=lowerCamelCase__		)
        model.move_to([3, -1.0, 0]		)
        self.add(lowerCamelCase__		)

        A :       Tuple          =   []
        A :       Tuple          =   []
        A :       Any          =   []

        for i, rect in enumerate(lowerCamelCase__		):
            rect.set_stroke(lowerCamelCase__		)

            A :       Any          =   Rectangle(height=0.46 / 4,		width=0.46 / 3		).set_stroke(width=0.0		).set_fill(lowerCamelCase__,		opacity=0.7		)

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

        self.add(*lowerCamelCase__,		*lowerCamelCase__,		*lowerCamelCase__		)

        A :       int          =   [mem.copy() for i in range(6		)]
        A :       List[Any]          =   VGroup(*lowerCamelCase__		).arrange(lowerCamelCase__,		buff=0		)

        A :       str          =   Text("""Loaded Checkpoint""",		font_size=24		)
        A :       List[str]          =   Group(lowerCamelCase__,		lowerCamelCase__		).arrange(lowerCamelCase__,		buff=0.5,		aligned_edge=lowerCamelCase__		)
        checkpoint.move_to([3, 0.5, 0]		)

        self.add(lowerCamelCase__		)

        A :       Optional[int]          =   []
        A :       List[Any]          =   []

        for i, rect in enumerate(lowerCamelCase__		):
            A :       int          =   fill.copy().set_fill(lowerCamelCase__,		opacity=0.7		)
            target.move_to(lowerCamelCase__		)
            ckpt_arr.append(lowerCamelCase__		)

            A :       List[Any]          =   target.copy()
            if i < 5:
                cpu_target.move_to(cpu_left_col_base[i + 1]		)
            else:
                cpu_target.move_to(cpu_right_col_base[i - 5]		)
            ckpt_cpu_arr.append(lowerCamelCase__		)
        self.add(*lowerCamelCase__,		*lowerCamelCase__		)

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

        A :       List[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]		)

        self.add(lowerCamelCase__,		lowerCamelCase__		)

        A :       Union[str, Any]          =   MarkupText(
            f'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint''',		font_size=18,		)

        blue_text.next_to(lowerCamelCase__,		DOWN * 2.4,		aligned_edge=key_text.get_left()		)
        self.add(lowerCamelCase__		)

        A :       List[str]          =   MarkupText(
            f'''Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device.''',		font_size=24,		)
        step_a.move_to([2, 2, 0]		)

        A :       List[str]          =   [meta_mem.copy() for i in range(6		)]
        A :       List[Any]          =   [meta_mem.copy() for i in range(6		)]
        A :       List[Any]          =   VGroup(*lowerCamelCase__		).arrange(lowerCamelCase__,		buff=0		)
        A :       List[str]          =   VGroup(*lowerCamelCase__		).arrange(lowerCamelCase__,		buff=0		)
        A :       Dict          =   VGroup(lowerCamelCase__,		lowerCamelCase__		).arrange(lowerCamelCase__,		buff=0		)
        A :       Optional[Any]          =   Text("""Disk""",		font_size=24		)
        A :       List[str]          =   Group(lowerCamelCase__,		lowerCamelCase__		).arrange(lowerCamelCase__,		buff=0.5,		aligned_edge=lowerCamelCase__		)
        disk.move_to([-4.0, -1.25, 0]		)
        self.play(Write(lowerCamelCase__,		run_time=3		),		Write(lowerCamelCase__,		run_time=1		),		Create(lowerCamelCase__,		run_time=1		)		)

        A :       str          =   []
        for i, rect in enumerate(lowerCamelCase__		):
            A :       Optional[Any]          =   rect.copy()
            target.generate_target()
            target.target.move_to(disk_left_col_base[i]		).scale(0.5		)
            animations.append(MoveToTarget(lowerCamelCase__,		run_time=1.5		)		)
        self.play(*lowerCamelCase__		)

        self.play(FadeOut(lowerCamelCase__		)		)

        A :       List[str]          =   MarkupText(f'''Then, the checkpoint is removed from memory\nthrough garbage collection.''',		font_size=24		)
        step_a.move_to([2, 2, 0]		)

        self.play(Write(lowerCamelCase__,		run_time=3		)		)

        self.play(
            FadeOut(lowerCamelCase__,		lowerCamelCase__,		*lowerCamelCase__,		*lowerCamelCase__		),		)

        self.wait()


