
from manim import *



class    _snake_case (						UpperCAmelCase_				):


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   Rectangle(height=0.5       ,	width=0.5)
   lowercase__	: Tuple     		=   Rectangle(height=0.4_6       ,	width=0.4_6).set_stroke(width=0)

   lowercase__	: List[Any]     		=   [mem.copy() for i in range(6)]
   lowercase__	: Tuple     		=   [mem.copy() for i in range(6)]
   lowercase__	: List[str]     		=   VGroup(*SCREAMING_SNAKE_CASE_).arrange(SCREAMING_SNAKE_CASE_       ,	buff=0)
   lowercase__	: Tuple     		=   VGroup(*SCREAMING_SNAKE_CASE_).arrange(SCREAMING_SNAKE_CASE_       ,	buff=0)
   lowercase__	: List[Any]     		=   VGroup(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_).arrange(SCREAMING_SNAKE_CASE_       ,	buff=0)
   lowercase__	: Dict     		=   Text("""CPU"""       ,	font_size=24)
   lowercase__	: List[str]     		=   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_)

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

   self.add(SCREAMING_SNAKE_CASE_)

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

   lowercase__	: Optional[Any]     		=   Text("""Model"""       ,	font_size=24)
   lowercase__	: Tuple     		=   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.play(
       Create(SCREAMING_SNAKE_CASE_       ,	run_time=1)       ,	Create(SCREAMING_SNAKE_CASE_       ,	run_time=1)       ,	Create(SCREAMING_SNAKE_CASE_       ,	run_time=1)       ,	)

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

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

   lowercase__	: Optional[int]     		=   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(SCREAMING_SNAKE_CASE_       ,	run_time=2.5)       ,	Write(SCREAMING_SNAKE_CASE_)       ,	Write(SCREAMING_SNAKE_CASE_))

   self.add(SCREAMING_SNAKE_CASE_)

   lowercase__	: List[Any]     		=   []
   lowercase__	: Union[str, Any]     		=   []
   lowercase__	: str     		=   []
   for i, rect in enumerate(SCREAMING_SNAKE_CASE_):
     lowercase__	: Union[str, Any]     		=   Rectangle(height=0.4_6       ,	width=0.4_6).set_stroke(width=0.0).set_fill(SCREAMING_SNAKE_CASE_       ,	opacity=0.7)
     cpu_target.move_to(SCREAMING_SNAKE_CASE_)
     cpu_target.generate_target()
     lowercase__	: Union[str, Any]     		=   0.4_6 / 4
     lowercase__	: Tuple     		=   0.4_6 / 3

     if i == 0:
       cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT)       ,	buff=0.0_2       ,	direction=SCREAMING_SNAKE_CASE_)
       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=SCREAMING_SNAKE_CASE_       ,	buff=0.0)
     else:
       cpu_target.target.next_to(cpu_targs[i - 1].target       ,	direction=SCREAMING_SNAKE_CASE_       ,	buff=0.0)
     cpu_targs.append(SCREAMING_SNAKE_CASE_)

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

   self.play(*SCREAMING_SNAKE_CASE_)
   self.play(*SCREAMING_SNAKE_CASE_)

   self.wait()








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


lowerCamelCase__     :		Union[str, Any]        = logging.get_logger(__name__)



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  Any	   =      ['pixel_values']


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_ = True       ,	SCREAMING_SNAKE_CASE_ = 1 / 2_55       ,	SCREAMING_SNAKE_CASE_ = True       ,	SCREAMING_SNAKE_CASE_ = 8       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   super().__init__(**SCREAMING_SNAKE_CASE_)

   lowercase__	: List[str]     		=   do_rescale
   lowercase__	: List[Any]     		=   rescale_factor
   lowercase__	: Tuple     		=   do_pad
   lowercase__	: Optional[Any]     		=   pad_size


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




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


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None):


   '''simple docstring'''




   lowercase__		,     lowercase__	: Optional[int]     		=   get_image_size(SCREAMING_SNAKE_CASE_)
   lowercase__	: Optional[Any]     		=   (old_height // size + 1) * size - old_height
   lowercase__	: str     		=   (old_width // size + 1) * size - old_width

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



 def   lowercase__  (       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_ = ChannelDimension.FIRST       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   do_rescale if do_rescale is not None else self.do_rescale
   lowercase__	: int     		=   rescale_factor if rescale_factor is not None else self.rescale_factor
   lowercase__	: Union[str, Any]     		=   do_pad if do_pad is not None else self.do_pad
   lowercase__	: Optional[Any]     		=   pad_size if pad_size is not None else self.pad_size

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

   # All transformations expect numpy arrays.
   lowercase__	: List[Any]     		=   [to_numpy_array(SCREAMING_SNAKE_CASE_) for image in images]

   if do_rescale:
     lowercase__	: str     		=   [self.rescale(image=SCREAMING_SNAKE_CASE_       ,	scale=SCREAMING_SNAKE_CASE_) for image in images]

   if do_pad:
     lowercase__	: List[str]     		=   [self.pad(SCREAMING_SNAKE_CASE_       ,	size=SCREAMING_SNAKE_CASE_) for image in images]

   lowercase__	: Optional[Any]     		=   [to_channel_dimension_format(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_) for image in images]

   lowercase__	: Dict     		=   {"""pixel_values""": images}
   return BatchFeature(data=SCREAMING_SNAKE_CASE_       ,	tensor_type=SCREAMING_SNAKE_CASE_)






