

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,
    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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
			import PIL


a__												=						logging.get_logger(__name__)

class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		int             =       ["pixel_values"]


							def __init__(     self			,				_a = True			,				_a = None			,				_a = None			,				_a = PILImageResampling.BILINEAR			,				_a = True			,				_a = 1 / 2_5_5			,				_a = True			,				_a = None			,				_a = None			,				**_a			,				)       ->       None:
												super().__init__(**_a			)
												_a      :       Tuple   =						size if size is not None else {'''shortest_edge''': 3_8_4}
												_a      :       Optional[Any]   =						get_size_dict(_a			,				default_to_square=_a			)

												_a      :       int   =						do_resize
												_a      :       Union[str, Any]   =						size
												# Default value set here for backwards compatibility where the value in config is None
												_a      :       str   =						crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
												_a      :       List[Any]   =						resample
												_a      :       Optional[Any]   =						do_rescale
												_a      :       str   =						rescale_factor
												_a      :       str   =						do_normalize
												_a      :       int   =						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												_a      :       Any   =						image_std if image_std is not None else IMAGENET_STANDARD_STD


							def 							__lowercase							(     self			,				_a			,				_a			,				_a			,				_a = PILImageResampling.BICUBIC			,				_a = None			,				**_a			,				)       ->       np.ndarray:
												_a      :       List[str]   =						get_size_dict(_a			,				default_to_square=_a			)
												if "shortest_edge" not in size:
																	raise ValueError(F"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"""			)
												_a      :       str   =						size['''shortest_edge''']

												if shortest_edge < 3_8_4:
																	# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
																	_a      :       Dict   =						int(shortest_edge / crop_pct			)
																	_a      :       Optional[Any]   =						get_resize_output_image_size(_a			,				size=_a			,				default_to_square=_a			)
																	_a      :       Dict   =						resize(image=_a			,				size=_a			,				resample=_a			,				data_format=_a			,				**_a			)
																	# then crop to (shortest_edge, shortest_edge)
																	return center_crop(image=_a			,				size=(shortest_edge, shortest_edge)			,				data_format=_a			,				**_a			)
												else:
																	# warping (no cropping) when evaluated at 384 or larger
																	return resize(
																	    _a			,				size=(shortest_edge, shortest_edge)			,				resample=_a			,				data_format=_a			,				**_a			)


							def 							__lowercase							(     self			,				_a			,				_a			,				_a = None			,				**_a			,				)       ->       str:
												return rescale(_a			,				scale=_a			,				data_format=_a			,				**_a			)


							def 							__lowercase							(     self			,				_a			,				_a			,				_a			,				_a = None			,				**_a			,				)       ->       np.ndarray:
												return normalize(_a			,				mean=_a			,				std=_a			,				data_format=_a			,				**_a			)




							def 							__lowercase							(     self			,				_a			,				_a = None			,				_a = None			,				_a = None			,				_a = None			,				_a = None			,				_a = None			,				_a = None			,				_a = None			,				_a = None			,				_a = None			,				_a = ChannelDimension.FIRST			,				**_a			,				)       ->       PIL.Image.Image:
												_a      :       List[Any]   =						do_resize if do_resize is not None else self.do_resize
												_a      :       str   =						crop_pct if crop_pct is not None else self.crop_pct
												_a      :       int   =						resample if resample is not None else self.resample
												_a      :       Optional[Any]   =						do_rescale if do_rescale is not None else self.do_rescale
												_a      :       Optional[int]   =						rescale_factor if rescale_factor is not None else self.rescale_factor
												_a      :       Optional[int]   =						do_normalize if do_normalize is not None else self.do_normalize
												_a      :       List[str]   =						image_mean if image_mean is not None else self.image_mean
												_a      :       List[str]   =						image_std if image_std is not None else self.image_std

												_a      :       Optional[Any]   =						size if size is not None else self.size
												_a      :       Optional[int]   =						get_size_dict(_a			,				default_to_square=_a			)

												_a      :       Any   =						make_list_of_images(_a			)

												if not valid_images(_a			):
																	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 or resample is None:
																	raise ValueError('''Size and resample must be specified if do_resize is True.'''			)

												if do_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
																	raise ValueError('''crop_pct must be specified if size < 384.'''			)

												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.
												_a      :       Union[str, Any]   =						[to_numpy_array(_a			) for image in images]

												if do_resize:
																	_a      :       Optional[int]   =						[self.resize(image=_a			,				size=_a			,				crop_pct=_a			,				resample=_a			) for image in images]

												if do_rescale:
																	_a      :       int   =						[self.rescale(image=_a			,				scale=_a			) for image in images]

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

												_a      :       Optional[int]   =						[to_channel_dimension_format(_a			,				_a			) for image in images]

												_a      :       int   =						{'''pixel_values''': images}
												return BatchFeature(data=_a			,				tensor_type=_a			)








"""simple docstring"""




from manim import *






class       UpperCAmelCase_    (							_UpperCamelCase		):



      def 		snake_case_   (	self	:      Tuple		):
           _UpperCAmelCase     :      str														=       Rectangle(height=0.5	,      width=0.5		)
           _UpperCAmelCase     :      List[Any]														=       Rectangle(height=0.25	,      width=0.25		)
           _UpperCAmelCase     :      Tuple														=       Rectangle(height=0.46	,      width=0.46		).set_stroke(width=0		)

           _UpperCAmelCase     :      List[Any]														=       [mem.copy() for i in range(6		)]
           _UpperCAmelCase     :      List[str]														=       [mem.copy() for i in range(6		)]
           _UpperCAmelCase     :      int														=       VGroup(*A		).arrange(A	,      buff=0		)
           _UpperCAmelCase     :      Dict														=       VGroup(*A		).arrange(A	,      buff=0		)
           _UpperCAmelCase     :      int														=       VGroup(A	,      A		).arrange(A	,      buff=0		)
           _UpperCAmelCase     :      str														=       Text("CPU"	,      font_size=2_4		)
           _UpperCAmelCase     :      Union[str, Any]														=       Group(A	,      A		).arrange(A	,      buff=0.5	,      aligned_edge=A		)
           cpu.move_to([-2.5, -0.5, 0]		)
           self.add(A		)

           _UpperCAmelCase     :      Union[str, Any]														=       [mem.copy() for i in range(4		)]
           _UpperCAmelCase     :      Optional[int]														=       VGroup(*A		).arrange(A	,      buff=0		)
           _UpperCAmelCase     :      Any														=       Text("GPU"	,      font_size=2_4		)
           _UpperCAmelCase     :      Dict														=       Group(A	,      A		).arrange(A	,      buff=0.5	,      aligned_edge=A		)
           gpu.move_to([-1, -1, 0]		)
           self.add(A		)

           _UpperCAmelCase     :      Tuple														=       [mem.copy() for i in range(6		)]
           _UpperCAmelCase     :      str														=       VGroup(*A		).arrange(A	,      buff=0		)

           _UpperCAmelCase     :      Any														=       Text("Model"	,      font_size=2_4		)
           _UpperCAmelCase     :      Any														=       Group(A	,      A		).arrange(A	,      buff=0.5	,      aligned_edge=A		)
           model.move_to([3, -1.0, 0]		)
           self.add(A		)

           _UpperCAmelCase     :      List[str]														=       []
           _UpperCAmelCase     :      Optional[int]														=       []
           _UpperCAmelCase     :      str														=       []

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

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

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

           self.add(*A	,      *A	,      *A		)

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

           _UpperCAmelCase     :      List[str]														=       Text("Loaded Checkpoint"	,      font_size=2_4		)
           _UpperCAmelCase     :      Optional[int]														=       Group(A	,      A		).arrange(A	,      buff=0.5	,      aligned_edge=A		)
           checkpoint.move_to([3, 0.5, 0]		)

           self.add(A		)

           _UpperCAmelCase     :      Union[str, Any]														=       []
           _UpperCAmelCase     :      Union[str, Any]														=       []

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

                _UpperCAmelCase     :      Dict														=       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(A		)
           self.add(*A	,      *A		)

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

           _UpperCAmelCase     :      List[Any]														=       MarkupText(
               f'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'	,      font_size=1_8	,      )

           key_text.move_to([-5, 2.4, 0]		)

           self.add(A	,      A		)

           _UpperCAmelCase     :      Tuple														=       MarkupText(
               f'<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'	,      font_size=1_8	,      )

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

           _UpperCAmelCase     :      Any														=       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=2_4	,      )
           step_a.move_to([2, 2, 0]		)

           _UpperCAmelCase     :      int														=       [meta_mem.copy() for i in range(6		)]
           _UpperCAmelCase     :      Optional[int]														=       [meta_mem.copy() for i in range(6		)]
           _UpperCAmelCase     :      List[str]														=       VGroup(*A		).arrange(A	,      buff=0		)
           _UpperCAmelCase     :      Optional[int]														=       VGroup(*A		).arrange(A	,      buff=0		)
           _UpperCAmelCase     :      List[Any]														=       VGroup(A	,      A		).arrange(A	,      buff=0		)
           _UpperCAmelCase     :      Union[str, Any]														=       Text("Disk"	,      font_size=2_4		)
           _UpperCAmelCase     :      int														=       Group(A	,      A		).arrange(A	,      buff=0.5	,      aligned_edge=A		)
           disk.move_to([-4.0, -1.25, 0]		)
           self.play(Write(A	,      run_time=3		)	,      Write(A	,      run_time=1		)	,      Create(A	,      run_time=1		)		)

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

           self.play(FadeOut(A		)		)

           _UpperCAmelCase     :      int														=       MarkupText(f'Then, the checkpoint is removed from memory\nthrough garbage collection.'	,      font_size=2_4		)
           step_a.move_to([2, 2, 0]		)

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

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

           self.wait()
