'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__a:					Tuple			  =  logging.get_logger(__name__)


if is_vision_available():
  import PIL



class      UpperCAmelCase    (		_a   ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE   						=       ["""pixel_values"""]

       def __init__(		self      ,  __lowerCAmelCase = True      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = PILImageResampling.BICUBIC      ,  __lowerCAmelCase = True      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = True      ,  __lowerCAmelCase = 1 / 255      ,  __lowerCAmelCase = True      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = True      ,  **__lowerCAmelCase      ,  )    ->    Optional[Any]:
        super().__init__(**__lowerCAmelCase					)
        lowercase__							:       str										=   size if size is not None else {'''shortest_edge''': 224}
        lowercase__							:       List[str]										=   get_size_dict(__lowerCAmelCase      ,  default_to_square=__lowerCAmelCase					)
        lowercase__							:       Any										=   crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
        lowercase__							:       Any										=   get_size_dict(__lowerCAmelCase      ,  default_to_square=__lowerCAmelCase      ,  param_name='''crop_size'''					)

        lowercase__							:       Tuple										=   do_resize
        lowercase__							:       Any										=   size
        lowercase__							:       Tuple										=   resample
        lowercase__							:       int										=   do_center_crop
        lowercase__							:       Optional[Any]										=   crop_size
        lowercase__							:       Dict										=   do_rescale
        lowercase__							:       Dict										=   rescale_factor
        lowercase__							:       Optional[Any]										=   do_normalize
        lowercase__							:       Any										=   image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        lowercase__							:       Optional[Any]										=   image_std if image_std is not None else OPENAI_CLIP_STD
        lowercase__							:       List[str]										=   do_convert_rgb

       def _lowerCAmelCase(		self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase = PILImageResampling.BICUBIC      ,  __lowerCAmelCase = None      ,  **__lowerCAmelCase      ,  )    ->    Union[str, Any]:
        lowercase__							:       str										=   get_size_dict(__lowerCAmelCase      ,  default_to_square=__lowerCAmelCase					)
        if "shortest_edge" not in size:
         raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""					)
        lowercase__							:       int										=   get_resize_output_image_size(__lowerCAmelCase      ,  size=size['''shortest_edge''']      ,  default_to_square=__lowerCAmelCase					)
        return resize(__lowerCAmelCase      ,  size=__lowerCAmelCase      ,  resample=__lowerCAmelCase      ,  data_format=__lowerCAmelCase      ,  **__lowerCAmelCase					)

       def _lowerCAmelCase(		self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase = None      ,  **__lowerCAmelCase      ,  )    ->    List[Any]:
        lowercase__							:       Optional[Any]										=   get_size_dict(__lowerCAmelCase					)
        if "height" not in size or "width" not in size:
         raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""					)
        return center_crop(__lowerCAmelCase      ,  size=(size['''height'''], size['''width'''])      ,  data_format=__lowerCAmelCase      ,  **__lowerCAmelCase					)

       def _lowerCAmelCase(		self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase = None      ,  **__lowerCAmelCase      ,  )    ->    Dict:
        return rescale(__lowerCAmelCase      ,  scale=__lowerCAmelCase      ,  data_format=__lowerCAmelCase      ,  **__lowerCAmelCase					)

       def _lowerCAmelCase(		self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase = None      ,  **__lowerCAmelCase      ,  )    ->    Dict:
        return normalize(__lowerCAmelCase      ,  mean=__lowerCAmelCase      ,  std=__lowerCAmelCase      ,  data_format=__lowerCAmelCase      ,  **__lowerCAmelCase					)


       def _lowerCAmelCase(		self      ,  __lowerCAmelCase      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = ChannelDimension.FIRST      ,  **__lowerCAmelCase      ,  )    ->    Optional[Any]:
        lowercase__							:       Optional[Any]										=   do_resize if do_resize is not None else self.do_resize
        lowercase__							:       int										=   size if size is not None else self.size
        lowercase__							:       int										=   get_size_dict(__lowerCAmelCase      ,  param_name='''size'''      ,  default_to_square=__lowerCAmelCase					)
        lowercase__							:       Optional[Any]										=   resample if resample is not None else self.resample
        lowercase__							:       Tuple										=   do_center_crop if do_center_crop is not None else self.do_center_crop
        lowercase__							:       str										=   crop_size if crop_size is not None else self.crop_size
        lowercase__							:       Optional[Any]										=   get_size_dict(__lowerCAmelCase      ,  param_name='''crop_size'''      ,  default_to_square=__lowerCAmelCase					)
        lowercase__							:       Optional[Any]										=   do_rescale if do_rescale is not None else self.do_rescale
        lowercase__							:       Tuple										=   rescale_factor if rescale_factor is not None else self.rescale_factor
        lowercase__							:       Optional[int]										=   do_normalize if do_normalize is not None else self.do_normalize
        lowercase__							:       Optional[int]										=   image_mean if image_mean is not None else self.image_mean
        lowercase__							:       str										=   image_std if image_std is not None else self.image_std
        lowercase__							:       List[Any]										=   do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        lowercase__							:       Optional[int]										=   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_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.'''					)

        # PIL RGBA images are converted to RGB
        if do_convert_rgb:
         lowercase__							:       int										=   [convert_to_rgb(__lowerCAmelCase					) for image in images]

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

        if do_resize:
         lowercase__							:       List[str]										=   [self.resize(image=__lowerCAmelCase      ,  size=__lowerCAmelCase      ,  resample=__lowerCAmelCase					) for image in images]

        if do_center_crop:
         lowercase__							:       Tuple										=   [self.center_crop(image=__lowerCAmelCase      ,  size=__lowerCAmelCase					) for image in images]

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

        if do_normalize:
         lowercase__							:       List[Any]										=   [self.normalize(image=__lowerCAmelCase      ,  mean=__lowerCAmelCase      ,  std=__lowerCAmelCase					) for image in images]

        lowercase__							:       List[str]										=   [to_channel_dimension_format(__lowerCAmelCase      ,  __lowerCAmelCase					) for image in images]

        lowercase__							:       Tuple										=   {'''pixel_values''': images}
        return BatchFeature(data=__lowerCAmelCase      ,  tensor_type=__lowerCAmelCase					)








from manim import *

class 							__SCREAMING_SNAKE_CASE       (  _a							):






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      Rectangle(height=0.5	,			width=0.5	)
														UpperCamelCase__					  =      Rectangle(height=0.25	,			width=0.25	)
														UpperCamelCase__					  =      Rectangle(height=0.46	,			width=0.46	).set_stroke(width=0	)

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

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

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

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

														UpperCamelCase__					  =      []
														UpperCamelCase__					  =      []
														UpperCamelCase__					  =      []

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

																					UpperCamelCase__					  =      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	)

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

														UpperCamelCase__					  =      Text("""Loaded Checkpoint"""	,			font_size=24	)
														UpperCamelCase__					  =      Group(__lowerCAmelCase	,			__lowerCAmelCase	).arrange(__lowerCAmelCase	,			buff=0.5	,			aligned_edge=__lowerCAmelCase	)
														checkpoint.move_to([3, 0.5, 0]	)

														self.add(__lowerCAmelCase	)

														UpperCamelCase__					  =      []
														UpperCamelCase__					  =      []

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

																					UpperCamelCase__					  =      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	)

														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(__lowerCAmelCase	,			__lowerCAmelCase	)

														UpperCamelCase__					  =      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__					  =      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]	)

														UpperCamelCase__					  =      [meta_mem.copy() for i in range(6	)]
														UpperCamelCase__					  =      [meta_mem.copy() for i in range(6	)]
														UpperCamelCase__					  =      VGroup(*__lowerCAmelCase	).arrange(__lowerCAmelCase	,			buff=0	)
														UpperCamelCase__					  =      VGroup(*__lowerCAmelCase	).arrange(__lowerCAmelCase	,			buff=0	)
														UpperCamelCase__					  =      VGroup(__lowerCAmelCase	,			__lowerCAmelCase	).arrange(__lowerCAmelCase	,			buff=0	)
														UpperCamelCase__					  =      Text("""Disk"""	,			font_size=24	)
														UpperCamelCase__					  =      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	)	)

														UpperCamelCase__					  =      []
														for i, rect in enumerate(__lowerCAmelCase	):
																					UpperCamelCase__					  =      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	)	)

														UpperCamelCase__					  =      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()




