





'''simple docstring'''


import argparse

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
#
# 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
#
########################################################################


lowerCAmelCase_   :   Optional[int]       =					16
lowerCAmelCase_   :   Any       =					32







def    _lowerCamelCase  (__lowerCamelCase  :     List[Any]					,  __lowerCamelCase  :     Dict = 16     )   ->    Dict:
			a__     = AutoTokenizer.from_pretrained("bert-base-cased"     )
			a__     = load_dataset("glue"					,  "mrpc"     )

			def tokenize_function(__lowerCamelCase  :     List[str]     ):
						# max_length=None => use the model max length (it's actually the default)
						a__     = tokenizer(examples["sentence1"]					,  examples["sentence2"]					,  truncation=lowerCAmelCase_					,  max_length=lowerCAmelCase_     )
						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():
						a__     = datasets.map(
						    lowerCAmelCase_					,  batched=lowerCAmelCase_					,  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
			a__     = tokenized_datasets.rename_column("label"					,  "labels"     )

			def collate_fn(__lowerCamelCase  :     Dict     ):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						a__     = 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":
									a__     = 16
						elif accelerator.mixed_precision != "no":
									a__     = 8
						else:
									a__     = None

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

			# Instantiate dataloaders.
			a__     = DataLoader(
			    tokenized_datasets["train"]					,  shuffle=lowerCAmelCase_					,  collate_fn=lowerCAmelCase_					,  batch_size=lowerCAmelCase_					,  drop_last=lowerCAmelCase_     )
			a__     = DataLoader(
			    tokenized_datasets["validation"]					,  shuffle=lowerCAmelCase_					,  collate_fn=lowerCAmelCase_					,  batch_size=lowerCAmelCase_					,  drop_last=(accelerator.mixed_precision == "fp8")					,  )

			return train_dataloader, eval_dataloader







def    _lowerCamelCase  (__lowerCamelCase  :     Optional[int]					,  __lowerCamelCase  :     Dict     )   ->    Any:
			a__     = Accelerator(cpu=args.cpu					,  mixed_precision=args.mixed_precision     )
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			a__     = config['''lr''']
			a__     = int(config["num_epochs"]     )
			a__     = int(config["seed"]     )
			a__     = int(config["batch_size"]     )

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

			# If the batch size is too big we use gradient accumulation
			a__     = 1
			if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
						a__     = batch_size // MAX_GPU_BATCH_SIZE
						a__     = MAX_GPU_BATCH_SIZE

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

			# 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).
			a__     = model.to(accelerator.device     )
			# Instantiate optimizer
			a__     = AdamW(params=model.parameters()					,  lr=lowerCAmelCase_     )

			# Instantiate scheduler
			a__     = get_linear_schedule_with_warmup(
			    optimizer=lowerCAmelCase_					,  num_warmup_steps=100					,  num_training_steps=(len(lowerCAmelCase_     ) * num_epochs) // gradient_accumulation_steps					,  )

			# 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.

			a__     = accelerator.prepare(
			    lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_     )

			# Now we train the model
			for epoch in range(lowerCAmelCase_     ):
						model.train()
						for step, batch in enumerate(lowerCAmelCase_     ):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device     )
									a__     = model(**lowerCAmelCase_     )
									a__     = outputs.loss
									a__     = loss / gradient_accumulation_steps
									accelerator.backward(lowerCAmelCase_     )
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

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

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







def    _lowerCamelCase  ()   ->    Dict:
			a__     = argparse.ArgumentParser(description="Simple example of training script."     )
			parser.add_argument(
			    "--mixed_precision"					,  type=lowerCAmelCase_					,  default=lowerCAmelCase_					,  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."					,  )
			parser.add_argument("--cpu"					,  action="store_true"					,  help="If passed, will train on the CPU."     )
			a__     = parser.parse_args()
			a__     = {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
			training_function(lowerCAmelCase_					,  lowerCAmelCase_     )


if __name__ == "__main__":
					main()










'''simple docstring'''


from manim import *






class       UpperCamelCase__  (		__lowerCAmelCase     ):







	def 						__a   (				self			:   List[Any]		):




				'''simple docstring'''
				a__     = Rectangle(height=0.5 , width=0.5		)
				a__     = Rectangle(height=0.25 , width=0.25		)
				a__     = Rectangle(height=0.46 , width=0.46		).set_stroke(width=0		)

				a__     = [mem.copy() for i in range(6		)]
				a__     = [mem.copy() for i in range(6		)]
				a__     = VGroup(*lowerCamelCase		).arrange(lowerCamelCase , buff=0		)
				a__     = VGroup(*lowerCamelCase		).arrange(lowerCamelCase , buff=0		)
				a__     = VGroup(lowerCamelCase , lowerCamelCase		).arrange(lowerCamelCase , buff=0		)
				a__     = Text("CPU" , font_size=2_4		)
				a__     = Group(lowerCamelCase , lowerCamelCase		).arrange(lowerCamelCase , buff=0.5 , aligned_edge=lowerCamelCase		)
				cpu.move_to([-2.5, -0.5, 0]		)
				self.add(lowerCamelCase		)

				a__     = [mem.copy() for i in range(4		)]
				a__     = VGroup(*lowerCamelCase		).arrange(lowerCamelCase , buff=0		)
				a__     = Text("GPU" , font_size=2_4		)
				a__     = Group(lowerCamelCase , lowerCamelCase		).arrange(lowerCamelCase , buff=0.5 , aligned_edge=lowerCamelCase		)
				gpu.move_to([-1, -1, 0]		)
				self.add(lowerCamelCase		)

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

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

				a__     = []
				a__     = []
				a__     = []

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

							a__     = 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		)

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

				a__     = Text("Loaded Checkpoint" , font_size=2_4		)
				a__     = Group(lowerCamelCase , lowerCamelCase		).arrange(lowerCamelCase , buff=0.5 , aligned_edge=lowerCamelCase		)
				checkpoint.move_to([3, 0.5, 0]		)

				self.add(lowerCamelCase		)

				a__     = []
				a__     = []

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

							a__     = 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		)

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

				a__     = 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(lowerCamelCase , lowerCamelCase		)

				a__     = MarkupText(
				    F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint''' , font_size=1_8 , )

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

				a__     = 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]		)

				a__     = [meta_mem.copy() for i in range(6		)]
				a__     = [meta_mem.copy() for i in range(6		)]
				a__     = VGroup(*lowerCamelCase		).arrange(lowerCamelCase , buff=0		)
				a__     = VGroup(*lowerCamelCase		).arrange(lowerCamelCase , buff=0		)
				a__     = VGroup(lowerCamelCase , lowerCamelCase		).arrange(lowerCamelCase , buff=0		)
				a__     = Text("Disk" , font_size=2_4		)
				a__     = 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		)		)

				a__     = []
				for i, rect in enumerate(lowerCamelCase		):
							a__     = 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		)		)

				a__     = 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(lowerCamelCase , run_time=3		)		)

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

				self.wait()



