






from typing import Dict, Iterable, 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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__magic_name__   :  str        =							logging.get_logger(__name__)





class 					A__     (     __snake_case ):

     '''simple docstring'''






     snake_case__					=					["""pixel_values"""]







     def __init__( self      :    Union[str, Any]   ,						_SCREAMING_SNAKE_CASE      :    bool = True   ,						_SCREAMING_SNAKE_CASE      :    Dict[str, int] = None   ,						_SCREAMING_SNAKE_CASE      :    PILImageResampling = PILImageResampling.BICUBIC   ,						_SCREAMING_SNAKE_CASE      :    bool = True   ,						_SCREAMING_SNAKE_CASE      :    Dict[str, int] = None   ,						_SCREAMING_SNAKE_CASE      :    bool = True   ,						_SCREAMING_SNAKE_CASE      :    Union[int, float] = 1 / 255   ,						_SCREAMING_SNAKE_CASE      :    bool = True   ,						_SCREAMING_SNAKE_CASE      :    Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN   ,						_SCREAMING_SNAKE_CASE      :    Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD   ,						**_SCREAMING_SNAKE_CASE      :    List[str]   ,						):






           """simple docstring"""




           super().__init__(**_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  size if size is not None else {'shortest_edge': 224}
           UpperCamelCase					       =  get_size_dict(_SCREAMING_SNAKE_CASE   ,						default_to_square=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  crop_size if crop_size is not None else {'height': 224, 'width': 224}
           UpperCamelCase					       =  get_size_dict(_SCREAMING_SNAKE_CASE   ,						param_name='crop_size' )

           UpperCamelCase					       =  do_resize
           UpperCamelCase					       =  size
           UpperCamelCase					       =  resample
           UpperCamelCase					       =  do_center_crop
           UpperCamelCase					       =  crop_size
           UpperCamelCase					       =  do_rescale
           UpperCamelCase					       =  rescale_factor
           UpperCamelCase					       =  do_normalize
           UpperCamelCase					       =  image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
           UpperCamelCase					       =  image_std if image_std is not None else IMAGENET_DEFAULT_STD







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[str]   ,						_SCREAMING_SNAKE_CASE      :    np.ndarray   ,						_SCREAMING_SNAKE_CASE      :    Dict[str, int]   ,						_SCREAMING_SNAKE_CASE      :    PILImageResampling = PILImageResampling.BICUBIC   ,						_SCREAMING_SNAKE_CASE      :    Optional[Union[str, ChannelDimension]] = None   ,						**_SCREAMING_SNAKE_CASE      :    Dict   ,						):






           """simple docstring"""




           UpperCamelCase					       =  get_size_dict(_SCREAMING_SNAKE_CASE   ,						default_to_square=_SCREAMING_SNAKE_CASE )
           # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
           if "shortest_edge" in size:
                 UpperCamelCase					       =  int((256 / 224) * size['shortest_edge'] )
                 UpperCamelCase					       =  get_resize_output_image_size(_SCREAMING_SNAKE_CASE   ,						size=_SCREAMING_SNAKE_CASE   ,						default_to_square=_SCREAMING_SNAKE_CASE )
                 UpperCamelCase					       =  {'height': output_size[0], 'width': output_size[1]}
           if "height" not in size_dict or "width" not in size_dict:
                 raise ValueError(
                     f'Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}' )
           return resize(
               _SCREAMING_SNAKE_CASE   ,						size=(size_dict['height'], size_dict['width'])   ,						resample=_SCREAMING_SNAKE_CASE   ,						data_format=_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any]   ,						_SCREAMING_SNAKE_CASE      :    np.ndarray   ,						_SCREAMING_SNAKE_CASE      :    Dict[str, int]   ,						_SCREAMING_SNAKE_CASE      :    Optional[Union[str, ChannelDimension]] = None   ,						**_SCREAMING_SNAKE_CASE      :    Dict   ,						):






           """simple docstring"""




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







     def   _SCREAMING_SNAKE_CASE     ( self      :    Any   ,						_SCREAMING_SNAKE_CASE      :    np.ndarray   ,						_SCREAMING_SNAKE_CASE      :    Union[int, float]   ,						_SCREAMING_SNAKE_CASE      :    Optional[Union[str, ChannelDimension]] = None   ,						**_SCREAMING_SNAKE_CASE      :    str   ,						):






           """simple docstring"""




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







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[int]   ,						_SCREAMING_SNAKE_CASE      :    np.ndarray   ,						_SCREAMING_SNAKE_CASE      :    Union[float, List[float]]   ,						_SCREAMING_SNAKE_CASE      :    Union[float, List[float]]   ,						_SCREAMING_SNAKE_CASE      :    Optional[Union[str, ChannelDimension]] = None   ,						**_SCREAMING_SNAKE_CASE      :    Dict   ,						):






           """simple docstring"""




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







     def   _SCREAMING_SNAKE_CASE     ( self      :    int   ,						_SCREAMING_SNAKE_CASE      :    ImageInput   ,						_SCREAMING_SNAKE_CASE      :    Optional[bool] = None   ,						_SCREAMING_SNAKE_CASE      :    Optional[Dict[str, int]] = None   ,						_SCREAMING_SNAKE_CASE      :    PILImageResampling = None   ,						_SCREAMING_SNAKE_CASE      :    Optional[bool] = None   ,						_SCREAMING_SNAKE_CASE      :    Optional[Dict[str, int]] = None   ,						_SCREAMING_SNAKE_CASE      :    Optional[bool] = None   ,						_SCREAMING_SNAKE_CASE      :    Optional[float] = None   ,						_SCREAMING_SNAKE_CASE      :    Optional[bool] = None   ,						_SCREAMING_SNAKE_CASE      :    Optional[Union[float, Iterable[float]]] = None   ,						_SCREAMING_SNAKE_CASE      :    Optional[Union[float, Iterable[float]]] = None   ,						_SCREAMING_SNAKE_CASE      :    Optional[TensorType] = None   ,						_SCREAMING_SNAKE_CASE      :    ChannelDimension = ChannelDimension.FIRST   ,						**_SCREAMING_SNAKE_CASE      :    int   ,						):






           """simple docstring"""




           UpperCamelCase					       =  do_resize if do_resize is not None else self.do_resize
           UpperCamelCase					       =  resample if resample is not None else self.resample
           UpperCamelCase					       =  do_center_crop if do_center_crop is not None else self.do_center_crop
           UpperCamelCase					       =  do_rescale if do_rescale is not None else self.do_rescale
           UpperCamelCase					       =  rescale_factor if rescale_factor is not None else self.rescale_factor
           UpperCamelCase					       =  do_normalize if do_normalize is not None else self.do_normalize
           UpperCamelCase					       =  image_mean if image_mean is not None else self.image_mean
           UpperCamelCase					       =  image_std if image_std is not None else self.image_std

           UpperCamelCase					       =  size if size is not None else self.size
           UpperCamelCase					       =  get_size_dict(_SCREAMING_SNAKE_CASE   ,						default_to_square=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  crop_size if crop_size is not None else self.crop_size
           UpperCamelCase					       =  get_size_dict(_SCREAMING_SNAKE_CASE   ,						param_name='crop_size' )

           UpperCamelCase					       =  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_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.' )

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

           if do_resize:
                 UpperCamelCase					       =  [self.resize(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ) for image in images]

           if do_center_crop:
                 UpperCamelCase					       =  [self.center_crop(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ) for image in images]

           if do_rescale:
                 UpperCamelCase					       =  [self.rescale(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ) for image in images]

           if do_normalize:
                 UpperCamelCase					       =  [self.normalize(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ) for image in images]

           UpperCamelCase					       =  [to_channel_dimension_format(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ) for image in images]

           UpperCamelCase					       =  {'pixel_values': images}
           return BatchFeature(data=_SCREAMING_SNAKE_CASE   ,						tensor_type=_SCREAMING_SNAKE_CASE )











from manim import *





class 					A__     (     __snake_case ):

     '''simple docstring'''






     def   _SCREAMING_SNAKE_CASE     ( self      :    Union[str, Any] ):






           """simple docstring"""




           UpperCamelCase					       =  Rectangle(height=0.5   ,						width=0.5 )
           UpperCamelCase					       =  Rectangle(height=0.4_6   ,						width=0.4_6 ).set_stroke(width=0 )

           UpperCamelCase					       =  Rectangle(height=0.2_5   ,						width=0.2_5 )

           UpperCamelCase					       =  [mem.copy() for i in range(6 )]
           UpperCamelCase					       =  [mem.copy() for i in range(6 )]
           UpperCamelCase					       =  VGroup(*_SCREAMING_SNAKE_CASE ).arrange(_SCREAMING_SNAKE_CASE   ,						buff=0 )
           UpperCamelCase					       =  VGroup(*_SCREAMING_SNAKE_CASE ).arrange(_SCREAMING_SNAKE_CASE   ,						buff=0 )
           UpperCamelCase					       =  VGroup(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ).arrange(_SCREAMING_SNAKE_CASE   ,						buff=0 )
           UpperCamelCase					       =  Text('CPU'   ,						font_size=24 )
           UpperCamelCase					       =  Group(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ).arrange(_SCREAMING_SNAKE_CASE   ,						buff=0.5   ,						aligned_edge=_SCREAMING_SNAKE_CASE )
           cpu.move_to([-2.5, -0.5, 0] )
           self.add(_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  [mem.copy() for i in range(4 )]
           UpperCamelCase					       =  VGroup(*_SCREAMING_SNAKE_CASE ).arrange(_SCREAMING_SNAKE_CASE   ,						buff=0 )
           UpperCamelCase					       =  Text('GPU'   ,						font_size=24 )
           UpperCamelCase					       =  Group(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ).arrange(_SCREAMING_SNAKE_CASE   ,						buff=0.5   ,						aligned_edge=_SCREAMING_SNAKE_CASE )
           gpu.move_to([-1, -1, 0] )
           self.add(_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  [mem.copy() for i in range(6 )]
           UpperCamelCase					       =  VGroup(*_SCREAMING_SNAKE_CASE ).arrange(_SCREAMING_SNAKE_CASE   ,						buff=0 )

           UpperCamelCase					       =  Text('Model'   ,						font_size=24 )
           UpperCamelCase					       =  Group(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ).arrange(_SCREAMING_SNAKE_CASE   ,						buff=0.5   ,						aligned_edge=_SCREAMING_SNAKE_CASE )
           model.move_to([3, -1.0, 0] )
           self.add(_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  []
           UpperCamelCase					       =  []

           for i, rect in enumerate(_SCREAMING_SNAKE_CASE ):
                 UpperCamelCase					       =  fill.copy().set_fill(_SCREAMING_SNAKE_CASE   ,						opacity=0.8 )
                 target.move_to(_SCREAMING_SNAKE_CASE )
                 model_arr.append(_SCREAMING_SNAKE_CASE )

                 UpperCamelCase					       =  Rectangle(height=0.4_6   ,						width=0.4_6 ).set_stroke(width=0.0 ).set_fill(_SCREAMING_SNAKE_CASE   ,						opacity=0.8 )
                 cpu_target.move_to(cpu_left_col_base[i] )
                 model_cpu_arr.append(_SCREAMING_SNAKE_CASE )

           self.add(*_SCREAMING_SNAKE_CASE   ,						*_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  [meta_mem.copy() for i in range(6 )]
           UpperCamelCase					       =  [meta_mem.copy() for i in range(6 )]
           UpperCamelCase					       =  VGroup(*_SCREAMING_SNAKE_CASE ).arrange(_SCREAMING_SNAKE_CASE   ,						buff=0 )
           UpperCamelCase					       =  VGroup(*_SCREAMING_SNAKE_CASE ).arrange(_SCREAMING_SNAKE_CASE   ,						buff=0 )
           UpperCamelCase					       =  VGroup(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ).arrange(_SCREAMING_SNAKE_CASE   ,						buff=0 )
           UpperCamelCase					       =  Text('Disk'   ,						font_size=24 )
           UpperCamelCase					       =  Group(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ).arrange(_SCREAMING_SNAKE_CASE   ,						buff=0.5   ,						aligned_edge=_SCREAMING_SNAKE_CASE )
           disk.move_to([-4, -1.2_5, 0] )
           self.add(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

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

           UpperCamelCase					       =  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(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  MarkupText(
               f'<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'   ,						font_size=18   ,						)

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

           UpperCamelCase					       =  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(_SCREAMING_SNAKE_CASE ) )

           UpperCamelCase					       =  Square(0.3 )
           input.set_fill(_SCREAMING_SNAKE_CASE   ,						opacity=1.0 )
           input.set_stroke(width=0.0 )
           input.next_to(model_base[0]   ,						_SCREAMING_SNAKE_CASE   ,						buff=0.5 )

           self.play(Write(_SCREAMING_SNAKE_CASE ) )

           input.generate_target()
           input.target.next_to(model_arr[0]   ,						direction=_SCREAMING_SNAKE_CASE   ,						buff=0.0_2 )
           self.play(MoveToTarget(_SCREAMING_SNAKE_CASE ) )

           self.play(FadeOut(_SCREAMING_SNAKE_CASE ) )

           UpperCamelCase					       =  Arrow(start=_SCREAMING_SNAKE_CASE   ,						end=_SCREAMING_SNAKE_CASE   ,						color=_SCREAMING_SNAKE_CASE   ,						buff=0.5 )
           a.next_to(model_arr[0].get_left()   ,						_SCREAMING_SNAKE_CASE   ,						buff=0.2 )

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

           UpperCamelCase					       =  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(_SCREAMING_SNAKE_CASE   ,						run_time=3 ) )

           UpperCamelCase					       =  {'run_time': 1, 'fade_in': True, 'fade_out': True, 'buff': 0.0_2}

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

           UpperCamelCase					       =  a.copy()
           for i in range(6 ):
                 a_c.next_to(model_arr[i].get_right() + 0.0_2   ,						_SCREAMING_SNAKE_CASE   ,						buff=0.2 )

                 input.generate_target()
                 input.target.move_to(model_arr[i].get_right() + 0.0_2 )

                 UpperCamelCase					       =  AnimationGroup(
                     FadeOut(_SCREAMING_SNAKE_CASE   ,						run_time=0.5 )   ,						MoveToTarget(_SCREAMING_SNAKE_CASE   ,						run_time=0.5 )   ,						FadeIn(_SCREAMING_SNAKE_CASE   ,						run_time=0.5 )   ,						lag_ratio=0.2 )

                 self.play(_SCREAMING_SNAKE_CASE )

                 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					       =  0.7

                       self.play(
                           Circumscribe(model_arr[i]   ,						**_SCREAMING_SNAKE_CASE )   ,						Circumscribe(cpu_left_col_base[i]   ,						**_SCREAMING_SNAKE_CASE )   ,						Circumscribe(cpu_left_col_base[i + 1]   ,						color=_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE )   ,						Circumscribe(gpu_rect[0]   ,						color=_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE )   ,						Circumscribe(model_arr[i + 1]   ,						color=_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE )   ,						)
                       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 + 0.0_2   ,						buff=0.2 )

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

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

                 UpperCamelCase					       =  a_c
                 UpperCamelCase					       =  a_c.copy()

           input.generate_target()
           input.target.next_to(model_base[-1]   ,						RIGHT + 0.0_2   ,						buff=0.5 )
           self.play(
               FadeOut(_SCREAMING_SNAKE_CASE )   ,						FadeOut(_SCREAMING_SNAKE_CASE   ,						run_time=0.5 )   ,						)

           UpperCamelCase					       =  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(_SCREAMING_SNAKE_CASE   ,						run_time=3 )   ,						MoveToTarget(_SCREAMING_SNAKE_CASE ) )

           self.wait()



