




"""simple docstring"""




from manim import *

class   UpperCAmelCase_    (			snake_case		):
		def 	_lowerCamelCase							(						self       )						->       List[str]:
					__lowercase			:  Tuple								=       Rectangle(height=0.5				,						width=0.5       )
					__lowercase			:  Tuple								=       Rectangle(height=0.2_5				,						width=0.2_5       )
					__lowercase			:  List[Any]								=       Rectangle(height=0.4_6				,						width=0.4_6       ).set_stroke(width=0       )

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

					__lowercase			:  Optional[Any]								=       [mem.copy() for i in range(4       )]
					__lowercase			:  List[Any]								=       VGroup(*UpperCamelCase_       ).arrange(UpperCamelCase_				,						buff=0       )
					__lowercase			:  Optional[Any]								=       Text('''GPU'''				,						font_size=24       )
					__lowercase			:  Dict								=       Group(UpperCamelCase_				,						UpperCamelCase_       ).arrange(UpperCamelCase_				,						buff=0.5				,						aligned_edge=UpperCamelCase_       )
					gpu.move_to([-1, -1, 0]       )
					self.add(UpperCamelCase_       )

					__lowercase			:  Any								=       [mem.copy() for i in range(6       )]
					__lowercase			:  Dict								=       VGroup(*UpperCamelCase_       ).arrange(UpperCamelCase_				,						buff=0       )

					__lowercase			:  Dict								=       Text('''Model'''				,						font_size=24       )
					__lowercase			:  List[str]								=       Group(UpperCamelCase_				,						UpperCamelCase_       ).arrange(UpperCamelCase_				,						buff=0.5				,						aligned_edge=UpperCamelCase_       )
					model.move_to([3, -1.0, 0]       )
					self.add(UpperCamelCase_       )

					__lowercase			:  int								=       []
					__lowercase			:  Dict								=       []
					__lowercase			:  Optional[Any]								=       []

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

								__lowercase			:  List[str]								=       Rectangle(height=0.4_6 / 4				,						width=0.4_6 / 3       ).set_stroke(width=0.0       ).set_fill(UpperCamelCase_				,						opacity=0.7       )

								if i == 0:
											cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT       )				,						buff=0.0_2				,						direction=UpperCamelCase_       )
											cpu_target.set_x(cpu_target.get_x() + 0.1       )
								elif i == 3:
											cpu_target.next_to(model_cpu_arr[0]				,						direction=UpperCamelCase_				,						buff=0.0       )
								else:
											cpu_target.next_to(model_cpu_arr[i - 1]				,						direction=UpperCamelCase_				,						buff=0.0       )
								self.add(UpperCamelCase_       )
								model_cpu_arr.append(UpperCamelCase_       )

					self.add(*UpperCamelCase_				,						*UpperCamelCase_				,						*UpperCamelCase_       )

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

					__lowercase			:  List[str]								=       Text('''Loaded Checkpoint'''				,						font_size=24       )
					__lowercase			:  Dict								=       Group(UpperCamelCase_				,						UpperCamelCase_       ).arrange(UpperCamelCase_				,						buff=0.5				,						aligned_edge=UpperCamelCase_       )
					checkpoint.move_to([3, 0.5, 0]       )

					self.add(UpperCamelCase_       )

					__lowercase			:  List[Any]								=       []
					__lowercase			:  str								=       []

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

								__lowercase			:  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(UpperCamelCase_       )
					self.add(*UpperCamelCase_				,						*UpperCamelCase_       )

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

					__lowercase			:  List[Any]								=       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(UpperCamelCase_				,						UpperCamelCase_       )

					__lowercase			:  Any								=       MarkupText(
					    F"""<span fgcolor='{BLUE}'>●</span> Checkpoint"""				,						font_size=18				,						)

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

					__lowercase			:  int								=       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]       )

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

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

					self.play(FadeOut(UpperCamelCase_       )       )

					__lowercase			:  List[Any]								=       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(UpperCamelCase_				,						run_time=3       )       )

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

					self.wait()







import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


A			:						Optional[Any]        =  1_6
A			:						str        =  3_2






def 					UpperCamelCase   (      __magic_name__	:				Accelerator       ,       __magic_name__	:				int = 16	)  ->    List[str]:
		"""simple docstring"""




		lowercase__	     =						AutoTokenizer.from_pretrained("""bert-base-cased"""	)
		lowercase__	     =						load_dataset("""glue"""       ,       """mrpc"""	)

		def tokenize_function(__magic_name__	:				int	):
				# max_length=None => use the model max length (it's actually the default)
				lowercase__	     =						tokenizer(examples["""sentence1"""]       ,       examples["""sentence2"""]       ,       truncation=__magic_name__       ,       max_length=__magic_name__	)
				return outputs

		# Apply the method we just defined to all the examples in all the splits of the dataset
		# starting with the main process first:
		with accelerator.main_process_first():
				lowercase__	     =						datasets.map(
				    __magic_name__       ,       batched=__magic_name__       ,       remove_columns=["""idx""", """sentence1""", """sentence2"""]       ,       )

		# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
		# transformers library
		lowercase__	     =						tokenized_datasets.rename_column("""label"""       ,       """labels"""	)

		def collate_fn(__magic_name__	:				Optional[int]	):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				lowercase__	     =						128 if accelerator.distributed_type == DistributedType.TPU else None
				# When using mixed precision we want round multiples of 8/16
				if accelerator.mixed_precision == "fp8":
						lowercase__	     =						16
				elif accelerator.mixed_precision != "no":
						lowercase__	     =						8
				else:
						lowercase__	     =						None

				return tokenizer.pad(
				    __magic_name__       ,       padding="""longest"""       ,       max_length=__magic_name__       ,       pad_to_multiple_of=__magic_name__       ,       return_tensors="""pt"""       ,       )

		# Instantiate dataloaders.
		lowercase__	     =						DataLoader(
		    tokenized_datasets["""train"""]       ,       shuffle=__magic_name__       ,       collate_fn=__magic_name__       ,       batch_size=__magic_name__	)
		lowercase__	     =						DataLoader(
		    tokenized_datasets["""validation"""]       ,       shuffle=__magic_name__       ,       collate_fn=__magic_name__       ,       batch_size=__magic_name__	)

		return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
	from accelerate.test_utils.training import mocked_dataloaders

	A			:						Union[str, Any]        =  mocked_dataloaders  # noqa: F811






def 					UpperCamelCase   (      __magic_name__	:				Optional[Any]       ,       __magic_name__	:				List[Any]	)  ->    Dict:
		"""simple docstring"""




		if os.environ.get("""TESTING_MOCKED_DATALOADERS"""       ,       __magic_name__	) == "1":
				lowercase__	     =						2
		# New Code #
		lowercase__	     =						int(args.gradient_accumulation_steps	)
		# Initialize accelerator
		lowercase__	     =						Accelerator(
		    cpu=args.cpu       ,       mixed_precision=args.mixed_precision       ,       gradient_accumulation_steps=__magic_name__	)
		if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
				raise NotImplementedError(
				    """Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"""	)
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		lowercase__	     =						config["""lr"""]
		lowercase__	     =						int(config["""num_epochs"""]	)
		lowercase__	     =						int(config["""seed"""]	)
		lowercase__	     =						int(config["""batch_size"""]	)

		lowercase__	     =						evaluate.load("""glue"""       ,       """mrpc"""	)

		set_seed(__magic_name__	)
		lowercase__     ,      lowercase__	     =						get_dataloaders(__magic_name__       ,       __magic_name__	)
		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		lowercase__	     =						AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""       ,       return_dict=__magic_name__	)

		# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
		# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
		# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
		lowercase__	     =						model.to(accelerator.device	)

		# Instantiate optimizer
		lowercase__	     =						AdamW(params=model.parameters()       ,       lr=__magic_name__	)

		# Instantiate scheduler
		lowercase__	     =						get_linear_schedule_with_warmup(
		    optimizer=__magic_name__       ,       num_warmup_steps=100       ,       num_training_steps=(len(__magic_name__	) * num_epochs)       ,       )

		# Prepare everything
		# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
		# prepare method.
		lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__	     =						accelerator.prepare(
		    __magic_name__       ,       __magic_name__       ,       __magic_name__       ,       __magic_name__       ,       __magic_name__	)

		# Now we train the model
		for epoch in range(__magic_name__	):
				model.train()
				for step, batch in enumerate(__magic_name__	):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device	)
						# New code #
						# We use the new `accumulate` context manager to perform gradient accumulation
						# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
						with accelerator.accumulate(__magic_name__	):
								lowercase__	     =						model(**__magic_name__	)
								lowercase__	     =						output.loss
								accelerator.backward(__magic_name__	)
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

				model.eval()
				for step, batch in enumerate(__magic_name__	):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device	)
						with torch.no_grad():
								lowercase__	     =						model(**__magic_name__	)
						lowercase__	     =						outputs.logits.argmax(dim=-1	)
						lowercase__     ,      lowercase__	     =						accelerator.gather_for_metrics((predictions, batch["""labels"""])	)
						metric.add_batch(
						    predictions=__magic_name__       ,       references=__magic_name__       ,       )

				lowercase__	     =						metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(f'''epoch {epoch}:'''       ,       __magic_name__	)






def 					UpperCamelCase   (      )  ->    Optional[Any]:
		"""simple docstring"""




		lowercase__	     =						argparse.ArgumentParser(description="""Simple example of training script."""	)
		parser.add_argument(
		    """--mixed_precision"""       ,       type=__magic_name__       ,       default=__magic_name__       ,       choices=["""no""", """fp16""", """bf16""", """fp8"""]       ,       help="""Whether to use mixed precision. Choose"""
		    """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
		    """and an Nvidia Ampere GPU."""       ,       )
		# New Code #
		parser.add_argument(
		    """--gradient_accumulation_steps"""       ,       type=__magic_name__       ,       default=1       ,       help="""The number of minibatches to be ran before gradients are accumulated."""       ,       )
		parser.add_argument("""--cpu"""       ,       action="""store_true"""       ,       help="""If passed, will train on the CPU."""	)
		lowercase__	     =						parser.parse_args()
		lowercase__	     =						{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
		training_function(__magic_name__       ,       __magic_name__	)


if __name__ == "__main__":
	main()

