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


_A  =	16
_A  =	32






def   lowerCamelCase__							(							__lowerCAmelCase    :    Accelerator       ,				__lowerCAmelCase    :    int = 16							):




			"""simple docstring"""


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

			def tokenize_function(__lowerCAmelCase    :    Optional[Any]							):
						# max_length=None => use the model max length (it's actually the default)
						lowerCAmelCase_         =      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():
						lowerCAmelCase_         =      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
			lowerCAmelCase_         =      tokenized_datasets.rename_column("label"       ,				"labels"							)

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

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

			# Instantiate dataloaders.
			lowerCAmelCase_         =      DataLoader(
			    tokenized_datasets["train"]       ,				shuffle=__lowerCAmelCase       ,				collate_fn=__lowerCAmelCase       ,				batch_size=__lowerCAmelCase       ,				drop_last=__lowerCAmelCase							)
			lowerCAmelCase_         =      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    :    List[str]       ,				__lowerCAmelCase    :    Tuple							):




			"""simple docstring"""


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

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

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

			set_seed(__lowerCAmelCase							)
			lowerCAmelCase_      ,    lowerCAmelCase_         =      get_dataloaders(__lowerCAmelCase       ,				__lowerCAmelCase							)
			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			lowerCAmelCase_         =      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).
			lowerCAmelCase_         =      model.to(accelerator.device							)
			# Instantiate optimizer
			lowerCAmelCase_         =      AdamW(params=model.parameters()       ,				lr=__lowerCAmelCase							)

			# Instantiate scheduler
			lowerCAmelCase_         =      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.

			lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_         =      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							)
									lowerCAmelCase_         =      model(**__lowerCAmelCase							)
									lowerCAmelCase_         =      outputs.loss
									lowerCAmelCase_         =      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():
												lowerCAmelCase_         =      model(**__lowerCAmelCase							)
									lowerCAmelCase_         =      outputs.logits.argmax(dim=-1							)
									lowerCAmelCase_      ,    lowerCAmelCase_         =      accelerator.gather_for_metrics((predictions, batch["labels"])							)
									metric.add_batch(
									    predictions=__lowerCAmelCase       ,				references=__lowerCAmelCase       ,				)

						lowerCAmelCase_         =      metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(F"""epoch {epoch}:"""       ,				__lowerCAmelCase							)






def   lowerCamelCase__							(							):




			"""simple docstring"""


			lowerCAmelCase_         =      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."							)
			lowerCAmelCase_         =      parser.parse_args()
			lowerCAmelCase_         =      {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
			training_function(__lowerCAmelCase       ,				__lowerCAmelCase							)


if __name__ == "__main__":
			main()





from typing import Optional
from urllib.parse import quote

import huggingface_hub as hfh
from packaging import version






def   lowerCamelCase__							(							__lowerCAmelCase    :    str       ,				__lowerCAmelCase    :    str       ,				__lowerCAmelCase    :    Optional[str] = None							):




			"""simple docstring"""


			if version.parse(hfh.__version__							).release < version.parse("0.11.0"							).release:
						# old versions of hfh don't url-encode the file path
						lowerCAmelCase_         =      quote(__lowerCAmelCase							)
			return hfh.hf_hub_url(__lowerCAmelCase       ,				__lowerCAmelCase       ,				repo_type="dataset"       ,				revision=__lowerCAmelCase							)



