
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[Any]         =  16
lowerCamelCase_						: int         =  32







def        A__					(						lowerCamelCase     ,    lowerCamelCase = 16						)  ->	List[str]:
				UpperCamelCase_:							Dict	=						AutoTokenizer.from_pretrained("""bert-base-cased"""						)
				UpperCamelCase_:							Any	=						load_dataset("""glue"""     ,    """mrpc"""						)

				def tokenize_function(lowerCamelCase						):
								# max_length=None => use the model max length (it's actually the default)
								UpperCamelCase_:							List[Any]	=						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():
								UpperCamelCase_:							Optional[int]	=						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
				UpperCamelCase_:							Union[str, Any]	=						tokenized_datasets.rename_column("""label"""     ,    """labels"""						)

				def collate_fn(lowerCamelCase						):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								UpperCamelCase_:							Optional[Any]	=						1_28 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												UpperCamelCase_:							List[Any]	=						16
								elif accelerator.mixed_precision != "no":
												UpperCamelCase_:							Any	=						8
								else:
												UpperCamelCase_:							Optional[Any]	=						None

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

				# Instantiate dataloaders.
				UpperCamelCase_:							Any	=						DataLoader(
				    tokenized_datasets["""train"""]     ,    shuffle=lowerCamelCase     ,    collate_fn=lowerCamelCase     ,    batch_size=lowerCamelCase     ,    drop_last=lowerCamelCase						)
				UpperCamelCase_:							List[Any]	=						DataLoader(
				    tokenized_datasets["""validation"""]     ,    shuffle=lowerCamelCase     ,    collate_fn=lowerCamelCase     ,    batch_size=lowerCamelCase     ,    drop_last=(accelerator.mixed_precision == """fp8""")     ,    )

				return train_dataloader, eval_dataloader







def        A__					(						lowerCamelCase     ,    lowerCamelCase						)  ->	Optional[int]:
				# Initialize accelerator
				UpperCamelCase_:							List[str]	=						Accelerator(cpu=args.cpu     ,    mixed_precision=args.mixed_precision						)
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				UpperCamelCase_:							List[Any]	=						config["""lr"""]
				UpperCamelCase_:							List[Any]	=						int(config["""num_epochs"""]						)
				UpperCamelCase_:							Optional[Any]	=						int(config["""seed"""]						)
				UpperCamelCase_:							List[Any]	=						int(config["""batch_size"""]						)

				UpperCamelCase_:							Optional[int]	=						evaluate.load("""glue"""     ,    """mrpc"""						)

				# If the batch size is too big we use gradient accumulation
				UpperCamelCase_:							Any	=						1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								UpperCamelCase_:							Union[str, Any]	=						batch_size // MAX_GPU_BATCH_SIZE
								UpperCamelCase_:							str	=						MAX_GPU_BATCH_SIZE

				set_seed(lowerCamelCase						)
				UpperCamelCase_,   UpperCamelCase_:							Tuple	=						get_dataloaders(lowerCamelCase     ,    lowerCamelCase						)
				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				UpperCamelCase_:							Any	=						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).
				UpperCamelCase_:							Dict	=						model.to(accelerator.device						)
				# Instantiate optimizer
				UpperCamelCase_:							List[Any]	=						AdamW(params=model.parameters()     ,    lr=lowerCamelCase						)

				# Instantiate scheduler
				UpperCamelCase_:							Any	=						get_linear_schedule_with_warmup(
				    optimizer=lowerCamelCase     ,    num_warmup_steps=1_00     ,    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.

				UpperCamelCase_,   UpperCamelCase_,   UpperCamelCase_,   UpperCamelCase_,   UpperCamelCase_:							int	=						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						)
												UpperCamelCase_:							str	=						model(**lowerCamelCase						)
												UpperCamelCase_:							Union[str, Any]	=						outputs.loss
												UpperCamelCase_:							Optional[Any]	=						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():
																UpperCamelCase_:							Dict	=						model(**lowerCamelCase						)
												UpperCamelCase_:							Optional[int]	=						outputs.logits.argmax(dim=-1						)
												UpperCamelCase_,   UpperCamelCase_:							Any	=						accelerator.gather_for_metrics((predictions, batch["""labels"""])						)
												metric.add_batch(
												    predictions=lowerCamelCase     ,    references=lowerCamelCase     ,    )

								UpperCamelCase_:							Any	=						metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F'''epoch {epoch}:'''     ,    lowerCamelCase						)







def        A__					(						)  ->	Union[str, Any]:
				UpperCamelCase_:							int	=						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."""						)
				UpperCamelCase_:							Optional[Any]	=						parser.parse_args()
				UpperCamelCase_:							Tuple	=						{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
				training_function(lowerCamelCase     ,    lowerCamelCase						)


if __name__ == "__main__":
							main()








from manim import *

class        _UpperCamelCase				(					_A  ):






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       int      ):
							UpperCamelCase_:							Dict	=						Rectangle(height=0.5						,	width=0.5      )
							UpperCamelCase_:							Dict	=						Rectangle(height=0.46						,	width=0.46      ).set_stroke(width=0      )

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

							UpperCamelCase_:							Optional[int]	=						[mem.copy() for i in range(1      )]
							UpperCamelCase_:							Dict	=						VGroup(*snake_case_      ).arrange(snake_case_						,	buff=0      )
							UpperCamelCase_:							Optional[int]	=						Text("""GPU"""						,	font_size=24      )
							UpperCamelCase_:							Optional[int]	=						Group(snake_case_						,	snake_case_      ).arrange(snake_case_						,	buff=0.5						,	aligned_edge=snake_case_      )
							gpu.align_to(snake_case_						,	snake_case_      )
							gpu.set_x(gpu.get_x() - 1      )

							self.add(snake_case_      )

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

							UpperCamelCase_:							Any	=						Text("""Model"""						,	font_size=24      )
							UpperCamelCase_:							Optional[Any]	=						Group(snake_case_						,	snake_case_      ).arrange(snake_case_						,	buff=0.5						,	aligned_edge=snake_case_      )
							model.move_to([3, -1.0, 0]      )

							self.play(
							    Create(snake_case_						,	run_time=1      )						,	Create(snake_case_						,	run_time=1      )						,	Create(snake_case_						,	run_time=1      )						,	)

							UpperCamelCase_:							List[Any]	=						MarkupText(
							    f'''First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.'''						,	font_size=24						,	)

							UpperCamelCase_:							Optional[Any]	=						Square(side_length=2.2      )
							key.move_to([-5, 2, 0]      )

							UpperCamelCase_:							Union[str, 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]      )

							step_a.move_to([2, 2, 0]      )
							self.play(Write(snake_case_						,	run_time=2.5      )						,	Write(snake_case_      )						,	Write(snake_case_      )      )

							self.add(snake_case_      )

							UpperCamelCase_:							Union[str, Any]	=						[]
							UpperCamelCase_:							Union[str, Any]	=						[]
							UpperCamelCase_:							Tuple	=						[]
							for i, rect in enumerate(snake_case_      ):
											UpperCamelCase_:							Tuple	=						Rectangle(height=0.46						,	width=0.46      ).set_stroke(width=0.0      ).set_fill(snake_case_						,	opacity=0.7      )
											cpu_target.move_to(snake_case_      )
											cpu_target.generate_target()
											UpperCamelCase_:							int	=						0.46 / 4
											UpperCamelCase_:							Optional[int]	=						0.46 / 3

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

											first_animations.append(rect.animate(run_time=0.5      ).set_stroke(snake_case_      )      )
											second_animations.append(MoveToTarget(snake_case_						,	run_time=1.5      )      )

							self.play(*snake_case_      )
							self.play(*snake_case_      )

							self.wait()






