






from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
       import PIL

UpperCamelCase    							=      logging.get_logger(__name__)


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Dict							)     ->  List[List[ImageInput]]:
   if isinstance(__lowerCAmelCase					,							(list, tuple)							) and isinstance(videos[0]					,							(list, tuple)							) and is_valid_image(videos[0][0]							):
      return videos

   elif isinstance(__lowerCAmelCase					,							(list, tuple)							) and is_valid_image(videos[0]							):
      return [videos]

   elif is_valid_image(__lowerCAmelCase							):
      return [[videos]]

   raise ValueError(f'Could not make batched video from {videos}'							)







class 		_A						( UpperCAmelCase_ ):
  lowercase_						:			Dict						=					['''pixel_values''']

  def __init__(    self						:	Optional[int]   ,				lowerCamelCase__						:	bool = True   ,				lowerCamelCase__						:	Dict[str, int] = None   ,				lowerCamelCase__						:	PILImageResampling = PILImageResampling.BILINEAR   ,				lowerCamelCase__						:	bool = True   ,				lowerCamelCase__						:	Dict[str, int] = None   ,				lowerCamelCase__						:	bool = True   ,				lowerCamelCase__						:	Union[int, float] = 1 / 2_55   ,				lowerCamelCase__						:	bool = True   ,				lowerCamelCase__						:	bool = True   ,				lowerCamelCase__						:	Optional[Union[float, List[float]]] = None   ,				lowerCamelCase__						:	Optional[Union[float, List[float]]] = None   ,				**lowerCamelCase__						:	str   ,				):
     """simple docstring"""

     super().__init__(**lowerCamelCase__			)
     __UpperCamelCase	:			Any					=					size if size is not None else {"""shortest_edge""": 2_56}
     __UpperCamelCase	:			Any					=					get_size_dict(lowerCamelCase__   ,				default_to_square=lowerCamelCase__			)
     __UpperCamelCase	:			Dict					=					crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
     __UpperCamelCase	:			List[str]					=					get_size_dict(lowerCamelCase__   ,				param_name="""crop_size"""			)

     __UpperCamelCase	:			List[str]					=					do_resize
     __UpperCamelCase	:			List[Any]					=					size
     __UpperCamelCase	:			Union[str, Any]					=					do_center_crop
     __UpperCamelCase	:			List[Any]					=					crop_size
     __UpperCamelCase	:			Any					=					resample
     __UpperCamelCase	:			int					=					do_rescale
     __UpperCamelCase	:			Tuple					=					rescale_factor
     __UpperCamelCase	:			Union[str, Any]					=					offset
     __UpperCamelCase	:			Tuple					=					do_normalize
     __UpperCamelCase	:			str					=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     __UpperCamelCase	:			Any					=					image_std if image_std is not None else IMAGENET_STANDARD_STD

  def    a					(    self						:	List[Any]   ,				lowerCamelCase__						:	np.ndarray   ,				lowerCamelCase__						:	Dict[str, int]   ,				lowerCamelCase__						:	PILImageResampling = PILImageResampling.BILINEAR   ,				lowerCamelCase__						:	Optional[Union[str, ChannelDimension]] = None   ,				**lowerCamelCase__						:	Optional[int]   ,				):
     """simple docstring"""

     __UpperCamelCase	:			Union[str, Any]					=					get_size_dict(lowerCamelCase__   ,				default_to_square=lowerCamelCase__			)
     if "shortest_edge" in size:
        __UpperCamelCase	:			List[Any]					=					get_resize_output_image_size(lowerCamelCase__   ,				size["""shortest_edge"""]   ,				default_to_square=lowerCamelCase__			)
     elif "height" in size and "width" in size:
        __UpperCamelCase	:			int					=					(size["""height"""], size["""width"""])
     else:
        raise ValueError(f'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'			)
     return resize(lowerCamelCase__   ,				size=lowerCamelCase__   ,				resample=lowerCamelCase__   ,				data_format=lowerCamelCase__   ,				**lowerCamelCase__			)

  def    a					(    self						:	str   ,				lowerCamelCase__						:	np.ndarray   ,				lowerCamelCase__						:	Dict[str, int]   ,				lowerCamelCase__						:	Optional[Union[str, ChannelDimension]] = None   ,				**lowerCamelCase__						:	Tuple   ,				):
     """simple docstring"""

     __UpperCamelCase	:			Dict					=					get_size_dict(lowerCamelCase__			)
     if "height" not in size or "width" not in size:
        raise ValueError(f'Size must have \'height\' and \'width\' as keys. Got {size.keys()}'			)
     return center_crop(lowerCamelCase__   ,				size=(size["""height"""], size["""width"""])   ,				data_format=lowerCamelCase__   ,				**lowerCamelCase__			)

  def    a					(    self						:	Optional[Any]   ,				lowerCamelCase__						:	np.ndarray   ,				lowerCamelCase__						:	Union[int, float]   ,				lowerCamelCase__						:	bool = True   ,				lowerCamelCase__						:	Optional[Union[str, ChannelDimension]] = None   ,				**lowerCamelCase__						:	int   ,				):
     """simple docstring"""

     __UpperCamelCase	:			Dict					=					image.astype(np.floataa			)
     if offset:
        __UpperCamelCase	:			str					=					image - (scale / 2)
     return rescale(lowerCamelCase__   ,				scale=lowerCamelCase__   ,				data_format=lowerCamelCase__   ,				**lowerCamelCase__			)

  def    a					(    self						:	Dict   ,				lowerCamelCase__						:	np.ndarray   ,				lowerCamelCase__						:	Union[float, List[float]]   ,				lowerCamelCase__						:	Union[float, List[float]]   ,				lowerCamelCase__						:	Optional[Union[str, ChannelDimension]] = None   ,				**lowerCamelCase__						:	Tuple   ,				):
     """simple docstring"""

     return normalize(lowerCamelCase__   ,				mean=lowerCamelCase__   ,				std=lowerCamelCase__   ,				data_format=lowerCamelCase__   ,				**lowerCamelCase__			)

  def    a					(    self						:	Any   ,				lowerCamelCase__						:	ImageInput   ,				lowerCamelCase__						:	bool = None   ,				lowerCamelCase__						:	Dict[str, int] = None   ,				lowerCamelCase__						:	PILImageResampling = None   ,				lowerCamelCase__						:	bool = None   ,				lowerCamelCase__						:	Dict[str, int] = None   ,				lowerCamelCase__						:	bool = None   ,				lowerCamelCase__						:	float = None   ,				lowerCamelCase__						:	bool = None   ,				lowerCamelCase__						:	bool = None   ,				lowerCamelCase__						:	Optional[Union[float, List[float]]] = None   ,				lowerCamelCase__						:	Optional[Union[float, List[float]]] = None   ,				lowerCamelCase__						:	Optional[ChannelDimension] = ChannelDimension.FIRST   ,				):
     """simple docstring"""

     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_center_crop and crop_size is None:
        raise ValueError("""Crop size must be specified if do_center_crop is True."""			)

     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."""			)

     if offset and not do_rescale:
        raise ValueError("""For offset, do_rescale must also be set to True."""			)

     # All transformations expect numpy arrays.
     __UpperCamelCase	:			str					=					to_numpy_array(lowerCamelCase__			)

     if do_resize:
        __UpperCamelCase	:			Optional[Any]					=					self.resize(image=lowerCamelCase__   ,				size=lowerCamelCase__   ,				resample=lowerCamelCase__			)

     if do_center_crop:
        __UpperCamelCase	:			Optional[int]					=					self.center_crop(lowerCamelCase__   ,				size=lowerCamelCase__			)

     if do_rescale:
        __UpperCamelCase	:			List[Any]					=					self.rescale(image=lowerCamelCase__   ,				scale=lowerCamelCase__   ,				offset=lowerCamelCase__			)

     if do_normalize:
        __UpperCamelCase	:			List[str]					=					self.normalize(image=lowerCamelCase__   ,				mean=lowerCamelCase__   ,				std=lowerCamelCase__			)

     __UpperCamelCase	:			Union[str, Any]					=					to_channel_dimension_format(lowerCamelCase__   ,				lowerCamelCase__			)
     return image

  def    a					(    self						:	List[Any]   ,				lowerCamelCase__						:	ImageInput   ,				lowerCamelCase__						:	bool = None   ,				lowerCamelCase__						:	Dict[str, int] = None   ,				lowerCamelCase__						:	PILImageResampling = None   ,				lowerCamelCase__						:	bool = None   ,				lowerCamelCase__						:	Dict[str, int] = None   ,				lowerCamelCase__						:	bool = None   ,				lowerCamelCase__						:	float = None   ,				lowerCamelCase__						:	bool = None   ,				lowerCamelCase__						:	bool = None   ,				lowerCamelCase__						:	Optional[Union[float, List[float]]] = None   ,				lowerCamelCase__						:	Optional[Union[float, List[float]]] = None   ,				lowerCamelCase__						:	Optional[Union[str, TensorType]] = None   ,				lowerCamelCase__						:	ChannelDimension = ChannelDimension.FIRST   ,				**lowerCamelCase__						:	Tuple   ,				):
     """simple docstring"""

     __UpperCamelCase	:			List[str]					=					do_resize if do_resize is not None else self.do_resize
     __UpperCamelCase	:			List[Any]					=					resample if resample is not None else self.resample
     __UpperCamelCase	:			Optional[int]					=					do_center_crop if do_center_crop is not None else self.do_center_crop
     __UpperCamelCase	:			int					=					do_rescale if do_rescale is not None else self.do_rescale
     __UpperCamelCase	:			str					=					rescale_factor if rescale_factor is not None else self.rescale_factor
     __UpperCamelCase	:			Any					=					offset if offset is not None else self.offset
     __UpperCamelCase	:			List[Any]					=					do_normalize if do_normalize is not None else self.do_normalize
     __UpperCamelCase	:			List[str]					=					image_mean if image_mean is not None else self.image_mean
     __UpperCamelCase	:			int					=					image_std if image_std is not None else self.image_std

     __UpperCamelCase	:			str					=					size if size is not None else self.size
     __UpperCamelCase	:			List[Any]					=					get_size_dict(lowerCamelCase__   ,				default_to_square=lowerCamelCase__			)
     __UpperCamelCase	:			Optional[Any]					=					crop_size if crop_size is not None else self.crop_size
     __UpperCamelCase	:			Dict					=					get_size_dict(lowerCamelCase__   ,				param_name="""crop_size"""			)

     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."""			)

     __UpperCamelCase	:			List[Any]					=					make_batched(lowerCamelCase__			)

     __UpperCamelCase	:			Any					=					[
         [
             self._preprocess_image(
                 image=lowerCamelCase__   ,				do_resize=lowerCamelCase__   ,				size=lowerCamelCase__   ,				resample=lowerCamelCase__   ,				do_center_crop=lowerCamelCase__   ,				crop_size=lowerCamelCase__   ,				do_rescale=lowerCamelCase__   ,				rescale_factor=lowerCamelCase__   ,				offset=lowerCamelCase__   ,				do_normalize=lowerCamelCase__   ,				image_mean=lowerCamelCase__   ,				image_std=lowerCamelCase__   ,				data_format=lowerCamelCase__   ,				)
             for img in video
         ]
         for video in videos
     ]

     __UpperCamelCase	:			int					=					{"""pixel_values""": videos}
     return BatchFeature(data=lowerCamelCase__   ,				tensor_type=lowerCamelCase__			)











from manim import *







class 		_A						( UpperCAmelCase_ ):

  def    a					(    self						:	Dict			):
     """simple docstring"""

     __UpperCamelCase	:			Tuple					=					Rectangle(height=0.5   ,				width=0.5			)
     __UpperCamelCase	:			Tuple					=					Rectangle(height=0.46   ,				width=0.46			).set_stroke(width=0			)

     __UpperCamelCase	:			Optional[Any]					=					Rectangle(height=0.25   ,				width=0.25			)

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

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

     __UpperCamelCase	:			Dict					=					[mem.copy() for i in range(6			)]
     __UpperCamelCase	:			Tuple					=					VGroup(*lowerCamelCase__			).arrange(lowerCamelCase__   ,				buff=0			)

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

     __UpperCamelCase	:			Tuple					=					[]
     __UpperCamelCase	:			str					=					[]

     for i, rect in enumerate(lowerCamelCase__			):
        __UpperCamelCase	:			Optional[Any]					=					fill.copy().set_fill(lowerCamelCase__   ,				opacity=0.8			)
        target.move_to(lowerCamelCase__			)
        model_arr.append(lowerCamelCase__			)

        __UpperCamelCase	:			Tuple					=					Rectangle(height=0.46   ,				width=0.46			).set_stroke(width=0.0			).set_fill(lowerCamelCase__   ,				opacity=0.8			)
        cpu_target.move_to(cpu_left_col_base[i]			)
        model_cpu_arr.append(lowerCamelCase__			)

     self.add(*lowerCamelCase__   ,				*lowerCamelCase__			)

     __UpperCamelCase	:			Union[str, Any]					=					[meta_mem.copy() for i in range(6			)]
     __UpperCamelCase	:			Any					=					[meta_mem.copy() for i in range(6			)]
     __UpperCamelCase	:			int					=					VGroup(*lowerCamelCase__			).arrange(lowerCamelCase__   ,				buff=0			)
     __UpperCamelCase	:			Union[str, Any]					=					VGroup(*lowerCamelCase__			).arrange(lowerCamelCase__   ,				buff=0			)
     __UpperCamelCase	:			str					=					VGroup(lowerCamelCase__   ,				lowerCamelCase__			).arrange(lowerCamelCase__   ,				buff=0			)
     __UpperCamelCase	:			Optional[Any]					=					Text("""Disk"""   ,				font_size=24			)
     __UpperCamelCase	:			Optional[Any]					=					Group(lowerCamelCase__   ,				lowerCamelCase__			).arrange(lowerCamelCase__   ,				buff=0.5   ,				aligned_edge=lowerCamelCase__			)
     disk.move_to([-4, -1.25, 0]			)
     self.add(lowerCamelCase__   ,				lowerCamelCase__			)

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

     __UpperCamelCase	:			Optional[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__			)

     __UpperCamelCase	:			Optional[int]					=					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__			)

     __UpperCamelCase	:			Any					=					MarkupText(
         f'Now watch as an input is passed through the model\nand how the memory is utilized and handled.'   ,				font_size=24   ,				)
     step_a.move_to([2, 2, 0]			)

     self.play(Write(lowerCamelCase__			)			)

     __UpperCamelCase	:			List[str]					=					Square(0.3			)
     input.set_fill(lowerCamelCase__   ,				opacity=1.0			)
     input.set_stroke(width=0.0			)
     input.next_to(model_base[0]   ,				lowerCamelCase__   ,				buff=0.5			)

     self.play(Write(lowerCamelCase__			)			)

     input.generate_target()
     input.target.next_to(model_arr[0]   ,				direction=lowerCamelCase__   ,				buff=0.02			)
     self.play(MoveToTarget(lowerCamelCase__			)			)

     self.play(FadeOut(lowerCamelCase__			)			)

     __UpperCamelCase	:			Dict					=					Arrow(start=lowerCamelCase__   ,				end=lowerCamelCase__   ,				color=lowerCamelCase__   ,				buff=0.5			)
     a.next_to(model_arr[0].get_left()   ,				lowerCamelCase__   ,				buff=0.2			)

     model_cpu_arr[0].generate_target()
     model_cpu_arr[0].target.move_to(gpu_rect[0]			)

     __UpperCamelCase	:			int					=					MarkupText(
         f'As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back.'   ,				font_size=24   ,				)
     step_a.move_to([2, 2, 0]			)

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

     __UpperCamelCase	:			Dict					=					{"""run_time""": 1, """fade_in""": True, """fade_out""": True, """buff""": 0.02}

     self.play(
         Write(lowerCamelCase__			)   ,				Circumscribe(model_arr[0]   ,				color=lowerCamelCase__   ,				**lowerCamelCase__			)   ,				Circumscribe(model_cpu_arr[0]   ,				color=lowerCamelCase__   ,				**lowerCamelCase__			)   ,				Circumscribe(gpu_rect[0]   ,				color=lowerCamelCase__   ,				**lowerCamelCase__			)   ,				)
     self.play(MoveToTarget(model_cpu_arr[0]			)			)

     __UpperCamelCase	:			int					=					a.copy()
     for i in range(6			):
        a_c.next_to(model_arr[i].get_right() + 0.02   ,				lowerCamelCase__   ,				buff=0.2			)

        input.generate_target()
        input.target.move_to(model_arr[i].get_right() + 0.02			)

        __UpperCamelCase	:			Tuple					=					AnimationGroup(
            FadeOut(lowerCamelCase__   ,				run_time=0.5			)   ,				MoveToTarget(lowerCamelCase__   ,				run_time=0.5			)   ,				FadeIn(lowerCamelCase__   ,				run_time=0.5			)   ,				lag_ratio=0.2			)

        self.play(lowerCamelCase__			)

        model_cpu_arr[i].generate_target()
        model_cpu_arr[i].target.move_to(cpu_left_col_base[i]			)

        if i < 5:
           model_cpu_arr[i + 1].generate_target()
           model_cpu_arr[i + 1].target.move_to(gpu_rect[0]			)
           if i >= 1:
              __UpperCamelCase	:			Any					=					0.7

           self.play(
               Circumscribe(model_arr[i]   ,				**lowerCamelCase__			)   ,				Circumscribe(cpu_left_col_base[i]   ,				**lowerCamelCase__			)   ,				Circumscribe(cpu_left_col_base[i + 1]   ,				color=lowerCamelCase__   ,				**lowerCamelCase__			)   ,				Circumscribe(gpu_rect[0]   ,				color=lowerCamelCase__   ,				**lowerCamelCase__			)   ,				Circumscribe(model_arr[i + 1]   ,				color=lowerCamelCase__   ,				**lowerCamelCase__			)   ,				)
           if i < 1:
              self.play(
                  MoveToTarget(model_cpu_arr[i]			)   ,				MoveToTarget(model_cpu_arr[i + 1]			)   ,				)
           else:
              self.play(
                  MoveToTarget(model_cpu_arr[i]   ,				run_time=0.7			)   ,				MoveToTarget(model_cpu_arr[i + 1]   ,				run_time=0.7			)   ,				)
        else:
           model_cpu_arr[i].generate_target()
           model_cpu_arr[i].target.move_to(cpu_left_col_base[-1]			)
           input.generate_target()
           input.target.next_to(model_arr[-1].get_right()   ,				RIGHT + 0.02   ,				buff=0.2			)

           self.play(
               Circumscribe(model_arr[-1]   ,				color=lowerCamelCase__   ,				**lowerCamelCase__			)   ,				Circumscribe(cpu_left_col_base[-1]   ,				color=lowerCamelCase__   ,				**lowerCamelCase__			)   ,				Circumscribe(gpu_rect[0]   ,				color=lowerCamelCase__   ,				**lowerCamelCase__			)   ,				)

           self.play(MoveToTarget(model_cpu_arr[i]			)			)

        __UpperCamelCase	:			str					=					a_c
        __UpperCamelCase	:			List[Any]					=					a_c.copy()

     input.generate_target()
     input.target.next_to(model_base[-1]   ,				RIGHT + 0.02   ,				buff=0.5			)
     self.play(
         FadeOut(lowerCamelCase__			)   ,				FadeOut(lowerCamelCase__   ,				run_time=0.5			)   ,				)

     __UpperCamelCase	:			List[str]					=					MarkupText(f'Inference on a model too large for GPU memory\nis successfully completed.'   ,				font_size=24			)
     step_a.move_to([2, 2, 0]			)

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

     self.wait()



