




from manim import *
class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						def 			snake_case__		(		self				:	Dict		):






							"""simple docstring"""






							snake_case_						       =	Rectangle(height=0.5			,      width=0.5		)
							snake_case_						       =	Rectangle(height=0.46			,      width=0.46		).set_stroke(width=0		)

							snake_case_						       =	Rectangle(height=0.25			,      width=0.25		)

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

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

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

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

							snake_case_						       =	[]
							snake_case_						       =	[]

							for i, rect in enumerate(__lowercase		):
								snake_case_						       =	fill.copy().set_fill(__lowercase			,      opacity=0.8		)
								target.move_to(__lowercase		)
								model_arr.append(__lowercase		)

								snake_case_						       =	Rectangle(height=0.46			,      width=0.46		).set_stroke(width=0.0		).set_fill(__lowercase			,      opacity=0.8		)
								cpu_target.move_to(cpu_left_col_base[i]		)
								model_cpu_arr.append(__lowercase		)

							self.add(*__lowercase			,      *__lowercase		)

							snake_case_						       =	[meta_mem.copy() for i in range(6		)]
							snake_case_						       =	[meta_mem.copy() for i in range(6		)]
							snake_case_						       =	VGroup(*__lowercase		).arrange(__lowercase			,      buff=0		)
							snake_case_						       =	VGroup(*__lowercase		).arrange(__lowercase			,      buff=0		)
							snake_case_						       =	VGroup(__lowercase			,      __lowercase		).arrange(__lowercase			,      buff=0		)
							snake_case_						       =	Text("Disk"			,      font_size=24		)
							snake_case_						       =	Group(__lowercase			,      __lowercase		).arrange(__lowercase			,      buff=0.5			,      aligned_edge=__lowercase		)
							disk.move_to([-4, -1.25, 0]		)
							self.add(__lowercase			,      __lowercase		)

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

							snake_case_						       =	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(__lowercase			,      __lowercase		)

							snake_case_						       =	MarkupText(
							    f"<span fgcolor='{BLUE}'>●</span> Checkpoint"			,      font_size=18			,      )

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

							snake_case_						       =	MarkupText(
							    f"Now watch as an input is passed through the model\nand how the memory is utilized and handled."			,      font_size=24			,      )
							step_a.move_to([2, 2, 0]		)

							self.play(Write(__lowercase		)		)

							snake_case_						       =	Square(0.3		)
							input.set_fill(__lowercase			,      opacity=1.0		)
							input.set_stroke(width=0.0		)
							input.next_to(model_base[0]			,      __lowercase			,      buff=0.5		)

							self.play(Write(__lowercase		)		)

							input.generate_target()
							input.target.next_to(model_arr[0]			,      direction=__lowercase			,      buff=0.02		)
							self.play(MoveToTarget(__lowercase		)		)

							self.play(FadeOut(__lowercase		)		)

							snake_case_						       =	Arrow(start=__lowercase			,      end=__lowercase			,      color=__lowercase			,      buff=0.5		)
							a.next_to(model_arr[0].get_left()			,      __lowercase			,      buff=0.2		)

							model_cpu_arr[0].generate_target()
							model_cpu_arr[0].target.move_to(gpu_rect[0]		)

							snake_case_						       =	MarkupText(
							    f"As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back."			,      font_size=24			,      )
							step_a.move_to([2, 2, 0]		)

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

							snake_case_						       =	{"run_time": 1, "fade_in": True, "fade_out": True, "buff": 0.02}

							self.play(
							    Write(__lowercase		)			,      Circumscribe(model_arr[0]			,      color=__lowercase			,      **__lowercase		)			,      Circumscribe(model_cpu_arr[0]			,      color=__lowercase			,      **__lowercase		)			,      Circumscribe(gpu_rect[0]			,      color=__lowercase			,      **__lowercase		)			,      )
							self.play(MoveToTarget(model_cpu_arr[0]		)		)

							snake_case_						       =	a.copy()
							for i in range(6		):
								a_c.next_to(model_arr[i].get_right() + 0.02			,      __lowercase			,      buff=0.2		)

								input.generate_target()
								input.target.move_to(model_arr[i].get_right() + 0.02		)

								snake_case_						       =	AnimationGroup(
								    FadeOut(__lowercase			,      run_time=0.5		)			,      MoveToTarget(__lowercase			,      run_time=0.5		)			,      FadeIn(__lowercase			,      run_time=0.5		)			,      lag_ratio=0.2		)

								self.play(__lowercase		)

								model_cpu_arr[i].generate_target()
								model_cpu_arr[i].target.move_to(cpu_left_col_base[i]		)

								if i < 5:
									model_cpu_arr[i + 1].generate_target()
									model_cpu_arr[i + 1].target.move_to(gpu_rect[0]		)
									if i >= 1:
										snake_case_						       =	0.7

									self.play(
									    Circumscribe(model_arr[i]			,      **__lowercase		)			,      Circumscribe(cpu_left_col_base[i]			,      **__lowercase		)			,      Circumscribe(cpu_left_col_base[i + 1]			,      color=__lowercase			,      **__lowercase		)			,      Circumscribe(gpu_rect[0]			,      color=__lowercase			,      **__lowercase		)			,      Circumscribe(model_arr[i + 1]			,      color=__lowercase			,      **__lowercase		)			,      )
									if i < 1:
										self.play(
										    MoveToTarget(model_cpu_arr[i]		)			,      MoveToTarget(model_cpu_arr[i + 1]		)			,      )
									else:
										self.play(
										    MoveToTarget(model_cpu_arr[i]			,      run_time=0.7		)			,      MoveToTarget(model_cpu_arr[i + 1]			,      run_time=0.7		)			,      )
								else:
									model_cpu_arr[i].generate_target()
									model_cpu_arr[i].target.move_to(cpu_left_col_base[-1]		)
									input.generate_target()
									input.target.next_to(model_arr[-1].get_right()			,      RIGHT + 0.02			,      buff=0.2		)

									self.play(
									    Circumscribe(model_arr[-1]			,      color=__lowercase			,      **__lowercase		)			,      Circumscribe(cpu_left_col_base[-1]			,      color=__lowercase			,      **__lowercase		)			,      Circumscribe(gpu_rect[0]			,      color=__lowercase			,      **__lowercase		)			,      )

									self.play(MoveToTarget(model_cpu_arr[i]		)		)

								snake_case_						       =	a_c
								snake_case_						       =	a_c.copy()

							input.generate_target()
							input.target.next_to(model_base[-1]			,      RIGHT + 0.02			,      buff=0.5		)
							self.play(
							    FadeOut(__lowercase		)			,      FadeOut(__lowercase			,      run_time=0.5		)			,      )

							snake_case_						       =	MarkupText(f"Inference on a model too large for GPU memory\nis successfully completed."			,      font_size=24		)
							step_a.move_to([2, 2, 0]		)

							self.play(Write(__lowercase			,      run_time=3		)			,      MoveToTarget(__lowercase		)		)

							self.wait()






import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


lowercase__							:	Dict											=		16
lowercase__							:	str											=		32


def 		lowerCamelCase__       (						_A	,      _A = 16	,      _A = "bert-base-cased"				):







	'''simple docstring'''





	snake_case_						       =	AutoTokenizer.from_pretrained(_A				)
	snake_case_						       =	load_dataset("glue"	,      "mrpc"				)

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

	# Apply the method we just defined to all the examples in all the splits of the dataset
	snake_case_						       =	datasets.map(
	    _A	,      batched=_A	,      remove_columns=["idx", "sentence1", "sentence2"]	,      load_from_cache_file=_A				)

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

	def collate_fn(_A				):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		if accelerator.distributed_type == DistributedType.TPU:
			return tokenizer.pad(_A	,      padding="max_length"	,      max_length=128	,      return_tensors="pt"				)
		return tokenizer.pad(_A	,      padding="longest"	,      return_tensors="pt"				)

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

	return train_dataloader, eval_dataloader


def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	snake_case_						       =	Accelerator()

	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	snake_case_						       =	config["lr"]
	snake_case_						       =	int(config["num_epochs"]				)
	snake_case_						       =	int(config["seed"]				)
	snake_case_						       =	int(config["batch_size"]				)
	snake_case_						       =	args.model_name_or_path

	set_seed(_A				)
	snake_case_       , snake_case_						       =	get_dataloaders(_A	,      _A	,      _A				)

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	snake_case_						       =	AutoModelForSequenceClassification.from_pretrained(_A	,      return_dict=_A				)

	# Instantiate optimizer
	snake_case_						       =	(
	    AdamW
	    if accelerator.state.deepspeed_plugin is None
	    or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
	    else DummyOptim
	)
	snake_case_						       =	optimizer_cls(params=model.parameters()	,      lr=_A				)

	if accelerator.state.deepspeed_plugin is not None:
		snake_case_						       =	accelerator.state.deepspeed_plugin.deepspeed_config[
		    "gradient_accumulation_steps"
		]
	else:
		snake_case_						       =	1
	snake_case_						       =	(len(_A				) * num_epochs) // gradient_accumulation_steps

	# Instantiate scheduler
	if (
	    accelerator.state.deepspeed_plugin is None
	    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
	):
		snake_case_						       =	get_linear_schedule_with_warmup(
		    optimizer=_A	,      num_warmup_steps=0	,      num_training_steps=_A	,      )
	else:
		snake_case_						       =	DummyScheduler(_A	,      total_num_steps=_A	,      warmup_num_steps=0				)

	# 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.
	snake_case_       , snake_case_       , snake_case_       , snake_case_       , snake_case_						       =	accelerator.prepare(
	    _A	,      _A	,      _A	,      _A	,      _A				)

	# We need to keep track of how many total steps we have iterated over
	snake_case_						       =	0
	# We also need to keep track of the stating epoch so files are named properly
	snake_case_						       =	0

	# Now we train the model
	snake_case_						       =	evaluate.load("glue"	,      "mrpc"				)
	snake_case_						       =	0
	snake_case_						       =	{}
	for epoch in range(_A	,      _A				):
		model.train()
		for step, batch in enumerate(_A				):
			snake_case_						       =	model(**_A				)
			snake_case_						       =	outputs.loss
			snake_case_						       =	loss / gradient_accumulation_steps
			accelerator.backward(_A				)
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

			overall_step += 1

		model.eval()
		snake_case_						       =	0
		for step, batch in enumerate(_A				):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device				)
			with torch.no_grad():
				snake_case_						       =	model(**_A				)
			snake_case_						       =	outputs.logits.argmax(dim=-1				)
			# It is slightly faster to call this once, than multiple times
			snake_case_       , snake_case_						       =	accelerator.gather(
			    (predictions, batch["labels"])				)  # If we are in a multiprocess environment, the last batch has duplicates
			if accelerator.use_distributed:
				if step == len(_A				) - 1:
					snake_case_						       =	predictions[: len(eval_dataloader.dataset				) - samples_seen]
					snake_case_						       =	references[: len(eval_dataloader.dataset				) - samples_seen]
				else:
					samples_seen += references.shape[0]
			metric.add_batch(
			    predictions=_A	,      references=_A	,      )

		snake_case_						       =	metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f"epoch {epoch}:"	,      _A				)
		snake_case_						       =	eval_metric["accuracy"]

		if best_performance < eval_metric["accuracy"]:
			snake_case_						       =	eval_metric["accuracy"]

	if args.performance_lower_bound is not None:
		assert (
		    args.performance_lower_bound <= best_performance
		), f"Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"

	accelerator.wait_for_everyone()
	if accelerator.is_main_process:
		with open(os.path.join(args.output_dir	,      "all_results.json"				)	,      "w"				) as f:
			json.dump(_A	,      _A				)


def 		lowerCamelCase__       (						):







	'''simple docstring'''





	snake_case_						       =	argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."				)
	parser.add_argument(
	    "--model_name_or_path"	,      type=_A	,      default="bert-base-cased"	,      help="Path to pretrained model or model identifier from huggingface.co/models."	,      required=_A	,      )
	parser.add_argument(
	    "--output_dir"	,      type=_A	,      default="."	,      help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."	,      )
	parser.add_argument(
	    "--performance_lower_bound"	,      type=_A	,      default=_A	,      help="Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."	,      )
	parser.add_argument(
	    "--num_epochs"	,      type=_A	,      default=3	,      help="Number of train epochs."	,      )
	snake_case_						       =	parser.parse_args()
	snake_case_						       =	{"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
	training_function(_A	,      _A				)


if __name__ == "__main__":
		main()
