






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			:					int         =	16
lowerCamelCase			:					str         =	32





def       __lowerCAmelCase      (						__snake_case					, __snake_case = 16       ):
     __lowerCAmelCase								=					AutoTokenizer.from_pretrained("bert-base-cased"       )
     __lowerCAmelCase								=					load_dataset("glue"					, "mrpc"       )

     def tokenize_function(__snake_case       ):
          # max_length=None => use the model max length (it's actually the default)
          __lowerCAmelCase								=					tokenizer(examples["sentence1"]					, examples["sentence2"]					, truncation=__snake_case					, max_length=__snake_case       )
          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(
              __snake_case					, batched=__snake_case					, 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(__snake_case       ):
          # 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(
              __snake_case					, padding="longest"					, max_length=__snake_case					, pad_to_multiple_of=__snake_case					, return_tensors="pt"					, )

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

     return train_dataloader, eval_dataloader





def       __lowerCAmelCase      (						__snake_case					, __snake_case       ):
     # Initialize accelerator
     __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(__snake_case       )
     __lowerCAmelCase			, __lowerCAmelCase								=					get_dataloaders(__snake_case					, __snake_case       )
     # 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=__snake_case       )

     # 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=__snake_case       )

     # Instantiate scheduler
     __lowerCAmelCase								=					get_linear_schedule_with_warmup(
         optimizer=__snake_case					, num_warmup_steps=100					, num_training_steps=(len(__snake_case       ) * 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(
         __snake_case					, __snake_case					, __snake_case					, __snake_case					, __snake_case       )

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

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

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





def       __lowerCAmelCase      (						):
     __lowerCAmelCase								=					argparse.ArgumentParser(description="Simple example of training script."       )
     parser.add_argument(
         "--mixed_precision"					, type=__snake_case					, default=__snake_case					, 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(__snake_case					, __snake_case       )


if __name__ == "__main__":
 main()














# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


lowerCamelCase			:					List[str]         =	'''Create a default config file for Accelerate with only a few flags set.'''





def       __lowerCAmelCase      (						__snake_case="no"					, __snake_case = default_json_config_file					, __snake_case = False       ):
     __lowerCAmelCase								=					Path(__snake_case       )
     path.parent.mkdir(parents=__snake_case					, exist_ok=__snake_case       )
     if path.exists():
          print(
              F"""Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`."""       )
          return False
     __lowerCAmelCase								=					mixed_precision.lower()
     if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
          raise ValueError(
              F"""`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}"""       )
     __lowerCAmelCase								=					{
         "compute_environment": "LOCAL_MACHINE",
         "mixed_precision": mixed_precision,
     }
     if torch.cuda.is_available():
          __lowerCAmelCase								=					torch.cuda.device_count()
          __lowerCAmelCase								=					num_gpus
          __lowerCAmelCase								=					False
          if num_gpus > 1:
               __lowerCAmelCase								=					"MULTI_GPU"
          else:
               __lowerCAmelCase								=					"NO"
     elif is_xpu_available() and use_xpu:
          __lowerCAmelCase								=					torch.xpu.device_count()
          __lowerCAmelCase								=					num_xpus
          __lowerCAmelCase								=					False
          if num_xpus > 1:
               __lowerCAmelCase								=					"MULTI_XPU"
          else:
               __lowerCAmelCase								=					"NO"
     elif is_npu_available():
          __lowerCAmelCase								=					torch.npu.device_count()
          __lowerCAmelCase								=					num_npus
          __lowerCAmelCase								=					False
          if num_npus > 1:
               __lowerCAmelCase								=					"MULTI_NPU"
          else:
               __lowerCAmelCase								=					"NO"
     else:
          __lowerCAmelCase								=					0
          __lowerCAmelCase								=					True
          __lowerCAmelCase								=					1
          __lowerCAmelCase								=					"NO"
     __lowerCAmelCase								=					ClusterConfig(**__snake_case       )
     config.to_json_file(__snake_case       )
     return path





def       __lowerCAmelCase      (						__snake_case					, __snake_case       ):
     __lowerCAmelCase								=					parser.add_parser("default"					, parents=__snake_case					, help=__snake_case					, formatter_class=__snake_case       )
     parser.add_argument(
         "--config_file"					, default=__snake_case					, help=(
             "The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
             "location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
             "such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
             "with 'huggingface'."
         )					, dest="save_location"					, )

     parser.add_argument(
         "--mixed_precision"					, choices=["no", "fp16", "bf16"]					, type=__snake_case					, help="Whether or not to use mixed precision training. "
         "Choose between FP16 and BF16 (bfloat16) training. "
         "BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later."					, default="no"					, )
     parser.set_defaults(func=__snake_case       )
     return parser





def       __lowerCAmelCase      (						__snake_case       ):
     __lowerCAmelCase								=					write_basic_config(args.mixed_precision					, args.save_location       )
     if config_file:
          print(F"""accelerate configuration saved at {config_file}"""       )






