






import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
__magic_name__:					List[str]							=     logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt")

__magic_name__:					List[Any]							=     list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
__magic_name__:					int							=     tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)





@dataclass
class 	snake_case__ :
	lowercase__						:					Optional[str]											=   field(
	    default='''cifar10'''						,  metadata={'''help''': '''Name of a dataset from the datasets package'''}				)
	lowercase__						:					Optional[str]											=   field(
	    default=_lowerCAmelCase						,  metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}				)
	lowercase__						:					Optional[str]											=   field(
	    default=_lowerCAmelCase						,  metadata={'''help''': '''The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'''}						,  )
	lowercase__						:					Optional[str]											=   field(default=_lowerCAmelCase						,  metadata={'''help''': '''A folder containing the training data.'''}				)
	lowercase__						:					Optional[str]											=   field(default=_lowerCAmelCase						,  metadata={'''help''': '''A folder containing the validation data.'''}				)
	lowercase__						:					Optional[float]											=   field(
	    default=0.15						,  metadata={'''help''': '''Percent to split off of train for validation.'''}				)
	lowercase__						:					int											=   field(default=32						,  metadata={'''help''': '''The size of the square patches to use for masking.'''}				)
	lowercase__						:					float											=   field(
	    default=0.6						,  metadata={'''help''': '''Percentage of patches to mask.'''}						,  )
	lowercase__						:					Optional[int]											=   field(
	    default=_lowerCAmelCase						,  metadata={
	        '''help''': (
	            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
	            '''value if set.'''
	        )
	    }						,  )
	lowercase__						:					Optional[int]											=   field(
	    default=_lowerCAmelCase						,  metadata={
	        '''help''': (
	            '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
	            '''value if set.'''
	        )
	    }						,  )
	def       __magic_name__    (    self			)							->				Optional[Any]:
							__magic_name__      :       str       						= {}
							if self.train_dir is not None:
													__magic_name__      :       int       						= self.train_dir
							if self.validation_dir is not None:
													__magic_name__      :       Union[str, Any]       						= self.validation_dir
							__magic_name__      :       List[str]       						= data_files if data_files else None





@dataclass
class 	snake_case__ :
	lowercase__						:					str											=   field(
	    default=_lowerCAmelCase						,  metadata={
	        '''help''': (
	            '''The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a '''
	            '''checkpoint identifier on the hub. '''
	            '''Don\'t set if you want to train a model from scratch.'''
	        )
	    }						,  )
	lowercase__						:					Optional[str]											=   field(
	    default=_lowerCAmelCase						,  metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(_lowerCAmelCase				)}						,  )
	lowercase__						:					Optional[str]											=   field(
	    default=_lowerCAmelCase						,  metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}				)
	lowercase__						:					Optional[str]											=   field(
	    default=_lowerCAmelCase						,  metadata={
	        '''help''': (
	            '''Override some existing default config settings when a model is trained from scratch. Example: '''
	            '''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
	        )
	    }						,  )
	lowercase__						:					Optional[str]											=   field(
	    default=_lowerCAmelCase						,  metadata={'''help''': '''Where do you want to store (cache) the pretrained models/datasets downloaded from the hub'''}						,  )
	lowercase__						:					str											=   field(
	    default='''main'''						,  metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}						,  )
	lowercase__						:					str											=   field(default=_lowerCAmelCase						,  metadata={'''help''': '''Name or path of preprocessor config.'''}				)
	lowercase__						:					bool											=   field(
	    default=_lowerCAmelCase						,  metadata={
	        '''help''': (
	            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
	            '''with private models).'''
	        )
	    }						,  )
	lowercase__						:					Optional[int]											=   field(
	    default=_lowerCAmelCase						,  metadata={
	        '''help''': (
	            '''The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'''
	        )
	    }						,  )
	lowercase__						:					Optional[int]											=   field(
	    default=_lowerCAmelCase						,  metadata={
	        '''help''': (
	            '''The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'''
	        )
	    }						,  )
	lowercase__						:					Optional[int]											=   field(
	    default=_lowerCAmelCase						,  metadata={'''help''': '''Stride to use for the encoder.'''}						,  )





class 	snake_case__ :
	def __init__(    self      ,       lowerCAmelCase__=1_92      ,       lowerCAmelCase__=32      ,       lowerCAmelCase__=4      ,       lowerCAmelCase__=0.6			)							->				Optional[int]:
							__magic_name__      :       str       						= input_size
							__magic_name__      :       Dict       						= mask_patch_size
							__magic_name__      :       Optional[Any]       						= model_patch_size
							__magic_name__      :       Any       						= mask_ratio

							if self.input_size % self.mask_patch_size != 0:
													raise ValueError("""Input size must be divisible by mask patch size"""			)
							if self.mask_patch_size % self.model_patch_size != 0:
													raise ValueError("""Mask patch size must be divisible by model patch size"""			)

							__magic_name__      :       int       						= self.input_size // self.mask_patch_size
							__magic_name__      :       Dict       						= self.mask_patch_size // self.model_patch_size

							__magic_name__      :       List[Any]       						= self.rand_size**2
							__magic_name__      :       str       						= int(np.ceil(self.token_count * self.mask_ratio			)			)







	def __call__(    self			)							->				Dict:
							__magic_name__      :       Dict       						= np.random.permutation(self.token_count			)[: self.mask_count]
							__magic_name__      :       Optional[Any]       						= np.zeros(self.token_count      ,       dtype=lowerCAmelCase__			)
							__magic_name__      :       Dict       						= 1

							__magic_name__      :       int       						= mask.reshape((self.rand_size, self.rand_size)			)
							__magic_name__      :       int       						= mask.repeat(self.scale      ,       axis=0			).repeat(self.scale      ,       axis=1			)

							return torch.tensor(mask.flatten()			)



def    UpperCamelCase						(	_A			):






						"""simple docstring"""







						__magic_name__      :       Union[str, Any]       						= torch.stack([example["""pixel_values"""] for example in examples]			)
						__magic_name__      :       Any       						= torch.stack([example["""mask"""] for example in examples]			)
						return {"pixel_values": pixel_values, "bool_masked_pos": mask}

def    UpperCamelCase						(	):






						"""simple docstring"""







						__magic_name__      :       Any       						= HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)
						if len(sys.argv			) == 2 and sys.argv[1].endswith(""".json"""			):
												# If we pass only one argument to the script and it's the path to a json file,
												# let's parse it to get our arguments.
												__magic_name__						,__magic_name__						,__magic_name__      :       int       						= parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
						else:
												__magic_name__						,__magic_name__						,__magic_name__      :       List[Any]       						= parser.parse_args_into_dataclasses()

						# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
						# information sent is the one passed as arguments along with your Python/PyTorch versions.
						send_example_telemetry("""run_mim""",   _A,   _A			)

						# Setup logging
						logging.basicConfig(
						    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""",   datefmt="""%m/%d/%Y %H:%M:%S""",   handlers=[logging.StreamHandler(sys.stdout			)],   )

						if training_args.should_log:
												# The default of training_args.log_level is passive, so we set log level at info here to have that default.
												transformers.utils.logging.set_verbosity_info()

						__magic_name__      :       Any       						= training_args.get_process_log_level()
						logger.setLevel(_A			)
						transformers.utils.logging.set_verbosity(_A			)
						transformers.utils.logging.enable_default_handler()
						transformers.utils.logging.enable_explicit_format()

						# Log on each process the small summary:
						logger.warning(
						    f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
						    + f'distributed training: {bool(training_args.local_rank != -1			)}, 16-bits training: {training_args.fpaa}'			)
						logger.info(f'Training/evaluation parameters {training_args}'			)

						# Detecting last checkpoint.
						__magic_name__      :       Optional[int]       						= None
						if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
												__magic_name__      :       Optional[int]       						= get_last_checkpoint(training_args.output_dir			)
												if last_checkpoint is None and len(os.listdir(training_args.output_dir			)			) > 0:
																		raise ValueError(
																		    f'Output directory ({training_args.output_dir}) already exists and is not empty. '
																		    """Use --overwrite_output_dir to overcome."""			)
												elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
																		logger.info(
																		    f'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
																		    """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""			)

    # Initialize our dataset.
						__magic_name__      :       List[Any]       						= load_dataset(
						    data_args.dataset_name,   data_args.dataset_config_name,   data_files=data_args.data_files,   cache_dir=model_args.cache_dir,   use_auth_token=True if model_args.use_auth_token else None,   )

						# If we don't have a validation split, split off a percentage of train as validation.
						__magic_name__      :       Dict       						= None if """validation""" in ds.keys() else data_args.train_val_split
						if isinstance(data_args.train_val_split,   _A			) and data_args.train_val_split > 0.0:
												__magic_name__      :       Dict       						= ds["""train"""].train_test_split(data_args.train_val_split			)
												__magic_name__      :       str       						= split["""train"""]
												__magic_name__      :       Union[str, Any]       						= split["""test"""]

						# Create config
						# Distributed training:
						# The .from_pretrained methods guarantee that only one local process can concurrently
						# download model & vocab.
						__magic_name__      :       Any       						= {
						    """cache_dir""": model_args.cache_dir,
						    """revision""": model_args.model_revision,
						    """use_auth_token""": True if model_args.use_auth_token else None,
						}
						if model_args.config_name_or_path:
												__magic_name__      :       Any       						= AutoConfig.from_pretrained(model_args.config_name_or_path,   **_A			)
						elif model_args.model_name_or_path:
												__magic_name__      :       List[str]       						= AutoConfig.from_pretrained(model_args.model_name_or_path,   **_A			)
						else:
												__magic_name__      :       Optional[Any]       						= CONFIG_MAPPING[model_args.model_type]()
												logger.warning("""You are instantiating a new config instance from scratch."""			)
												if model_args.config_overrides is not None:
																		logger.info(f'Overriding config: {model_args.config_overrides}'			)
																		config.update_from_string(model_args.config_overrides			)
																		logger.info(f'New config: {config}'			)

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
						if hasattr(_A,   """decoder_type"""			):
												__magic_name__      :       Any       						= """simmim"""

						# adapt config
						__magic_name__      :       Any       						= model_args.image_size if model_args.image_size is not None else config.image_size
						__magic_name__      :       Optional[int]       						= model_args.patch_size if model_args.patch_size is not None else config.patch_size
						__magic_name__      :       Union[str, Any]       						= (
						    model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
						)

						config.update(
						    {
						        """image_size""": model_args.image_size,
						        """patch_size""": model_args.patch_size,
						        """encoder_stride""": model_args.encoder_stride,
						    }			)

						# create image processor
						if model_args.image_processor_name:
												__magic_name__      :       Any       						= AutoImageProcessor.from_pretrained(model_args.image_processor_name,   **_A			)
						elif model_args.model_name_or_path:
												__magic_name__      :       Any       						= AutoImageProcessor.from_pretrained(model_args.model_name_or_path,   **_A			)
						else:
												__magic_name__      :       Union[str, Any]       						= {
												    conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
												}
												__magic_name__      :       List[str]       						= IMAGE_PROCESSOR_TYPES[model_args.model_type]()

						# create model
						if model_args.model_name_or_path:
												__magic_name__      :       List[str]       						= AutoModelForMaskedImageModeling.from_pretrained(
												    model_args.model_name_or_path,   from_tf=bool(""".ckpt""" in model_args.model_name_or_path			),   config=_A,   cache_dir=model_args.cache_dir,   revision=model_args.model_revision,   use_auth_token=True if model_args.use_auth_token else None,   )
						else:
												logger.info("""Training new model from scratch"""			)
												__magic_name__      :       Optional[int]       						= AutoModelForMaskedImageModeling.from_config(_A			)

						if training_args.do_train:
												__magic_name__      :       Any       						= ds["""train"""].column_names
						else:
												__magic_name__      :       List[str]       						= ds["""validation"""].column_names

						if data_args.image_column_name is not None:
												__magic_name__      :       Any       						= data_args.image_column_name
						elif "image" in column_names:
												__magic_name__      :       Optional[int]       						= """image"""
						elif "img" in column_names:
												__magic_name__      :       Dict       						= """img"""
						else:
												__magic_name__      :       Union[str, Any]       						= column_names[0]

						# transformations as done in original SimMIM paper
						# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
						__magic_name__      :       Union[str, Any]       						= Compose(
						    [
						        Lambda(lambda _A			: img.convert("""RGB"""			) if img.mode != "RGB" else img			),
						        RandomResizedCrop(model_args.image_size,   scale=(0.67, 1.0),   ratio=(3.0 / 4.0, 4.0 / 3.0)			),
						        RandomHorizontalFlip(),
						        ToTensor(),
						        Normalize(mean=image_processor.image_mean,   std=image_processor.image_std			),
						    ]			)

						# create mask generator
						__magic_name__      :       Optional[Any]       						= MaskGenerator(
						    input_size=model_args.image_size,   mask_patch_size=data_args.mask_patch_size,   model_patch_size=model_args.patch_size,   mask_ratio=data_args.mask_ratio,   )

						def preprocess_images(_A			):
												__magic_name__      :       Dict       						= [transforms(_A			) for image in examples[image_column_name]]
												__magic_name__      :       Tuple       						= [mask_generator() for i in range(len(examples[image_column_name]			)			)]

												return examples

						if training_args.do_train:
												if "train" not in ds:
																		raise ValueError("""--do_train requires a train dataset"""			)
												if data_args.max_train_samples is not None:
																		__magic_name__      :       Tuple       						= ds["""train"""].shuffle(seed=training_args.seed			).select(range(data_args.max_train_samples			)			)
												# Set the training transforms
												ds["train"].set_transform(_A			)

						if training_args.do_eval:
												if "validation" not in ds:
																		raise ValueError("""--do_eval requires a validation dataset"""			)
												if data_args.max_eval_samples is not None:
																		__magic_name__      :       Tuple       						= (
																		    ds["""validation"""].shuffle(seed=training_args.seed			).select(range(data_args.max_eval_samples			)			)
																		)
												# Set the validation transforms
												ds["validation"].set_transform(_A			)

						# Initialize our trainer
						__magic_name__      :       Optional[Any]       						= Trainer(
						    model=_A,   args=_A,   train_dataset=ds["""train"""] if training_args.do_train else None,   eval_dataset=ds["""validation"""] if training_args.do_eval else None,   tokenizer=_A,   data_collator=_A,   )

						# Training
						if training_args.do_train:
												__magic_name__      :       List[Any]       						= None
												if training_args.resume_from_checkpoint is not None:
																		__magic_name__      :       int       						= training_args.resume_from_checkpoint
												elif last_checkpoint is not None:
																		__magic_name__      :       str       						= last_checkpoint
												__magic_name__      :       Union[str, Any]       						= trainer.train(resume_from_checkpoint=_A			)
												trainer.save_model()
												trainer.log_metrics("""train""",   train_result.metrics			)
												trainer.save_metrics("""train""",   train_result.metrics			)
												trainer.save_state()

						# Evaluation
						if training_args.do_eval:
												__magic_name__      :       Optional[Any]       						= trainer.evaluate()
												trainer.log_metrics("""eval""",   _A			)
												trainer.save_metrics("""eval""",   _A			)

						# Write model card and (optionally) push to hub
						__magic_name__      :       Optional[Any]       						= {
						    """finetuned_from""": model_args.model_name_or_path,
						    """tasks""": """masked-image-modeling""",
						    """dataset""": data_args.dataset_name,
						    """tags""": ["""masked-image-modeling"""],
						}
						if training_args.push_to_hub:
												trainer.push_to_hub(**_A			)
						else:
												trainer.create_model_card(**_A			)


if __name__ == "__main__":
		main()








from manim import *





class 	snake_case__ (							_lowerCAmelCase				):
	def       __magic_name__    (    self			)							->				Dict:
							__magic_name__      :       int       						= Rectangle(height=0.5      ,       width=0.5			)
							__magic_name__      :       Optional[int]       						= Rectangle(height=0.2_5      ,       width=0.2_5			)
							__magic_name__      :       str       						= Rectangle(height=0.4_6      ,       width=0.4_6			).set_stroke(width=0			)

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

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

							__magic_name__      :       Union[str, Any]       						= [mem.copy() for i in range(6			)]
							__magic_name__      :       Union[str, Any]       						= VGroup(*lowerCAmelCase__			).arrange(lowerCAmelCase__      ,       buff=0			)

							__magic_name__      :       str       						= Text("""Model"""      ,       font_size=24			)
							__magic_name__      :       Optional[int]       						= Group(lowerCAmelCase__      ,       lowerCAmelCase__			).arrange(lowerCAmelCase__      ,       buff=0.5      ,       aligned_edge=lowerCAmelCase__			)
							model.move_to([3, -1.0, 0]			)
							self.add(lowerCAmelCase__			)

							__magic_name__      :       str       						= []
							__magic_name__      :       Tuple       						= []
							__magic_name__      :       Union[str, Any]       						= []

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

													__magic_name__      :       Optional[Any]       						= Rectangle(height=0.4_6 / 4      ,       width=0.4_6 / 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=0.0_2      ,       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__			)

							__magic_name__      :       Optional[Any]       						= [mem.copy() for i in range(6			)]
							__magic_name__      :       Optional[Any]       						= VGroup(*lowerCAmelCase__			).arrange(lowerCAmelCase__      ,       buff=0			)

							__magic_name__      :       Any       						= Text("""Loaded Checkpoint"""      ,       font_size=24			)
							__magic_name__      :       Optional[int]       						= Group(lowerCAmelCase__      ,       lowerCAmelCase__			).arrange(lowerCAmelCase__      ,       buff=0.5      ,       aligned_edge=lowerCAmelCase__			)
							checkpoint.move_to([3, 0.5, 0]			)

							self.add(lowerCAmelCase__			)

							__magic_name__      :       Optional[int]       						= []
							__magic_name__      :       Tuple       						= []

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

													__magic_name__      :       int       						= 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__			)

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

							__magic_name__      :       str       						= 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__			)

							__magic_name__      :       Any       						= 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__			)

							__magic_name__      :       Optional[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=24      ,       )
							step_a.move_to([2, 2, 0]			)

							__magic_name__      :       int       						= [meta_mem.copy() for i in range(6			)]
							__magic_name__      :       Union[str, Any]       						= [meta_mem.copy() for i in range(6			)]
							__magic_name__      :       Any       						= VGroup(*lowerCAmelCase__			).arrange(lowerCAmelCase__      ,       buff=0			)
							__magic_name__      :       str       						= VGroup(*lowerCAmelCase__			).arrange(lowerCAmelCase__      ,       buff=0			)
							__magic_name__      :       Tuple       						= VGroup(lowerCAmelCase__      ,       lowerCAmelCase__			).arrange(lowerCAmelCase__      ,       buff=0			)
							__magic_name__      :       int       						= Text("""Disk"""      ,       font_size=24			)
							__magic_name__      :       Union[str, Any]       						= Group(lowerCAmelCase__      ,       lowerCAmelCase__			).arrange(lowerCAmelCase__      ,       buff=0.5      ,       aligned_edge=lowerCAmelCase__			)
							disk.move_to([-4.0, -1.2_5, 0]			)
							self.play(Write(lowerCAmelCase__      ,       run_time=3			)      ,       Write(lowerCAmelCase__      ,       run_time=1			)      ,       Create(lowerCAmelCase__      ,       run_time=1			)			)

							__magic_name__      :       List[Any]       						= []
							for i, rect in enumerate(lowerCAmelCase__			):
													__magic_name__      :       Dict       						= 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__			)			)

							__magic_name__      :       str       						= 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()
