import argparse
import os

# New Code #
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 import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# 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)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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   :     List[Any]       =				16
A   :     Optional[int]       =				32


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase = 1_6 ):
      SCREAMING_SNAKE_CASE_      =     AutoTokenizer.from_pretrained("bert-base-cased" )
      SCREAMING_SNAKE_CASE_      =     load_dataset("glue"   ,					"mrpc" )

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

      def collate_fn(__UpperCamelCase ):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            SCREAMING_SNAKE_CASE_      =     1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
            # When using mixed precision we want round multiples of 8/16
            if accelerator.mixed_precision == "fp8":
                  SCREAMING_SNAKE_CASE_      =     1_6
            elif accelerator.mixed_precision != "no":
                  SCREAMING_SNAKE_CASE_      =     8
            else:
                  SCREAMING_SNAKE_CASE_      =     None

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

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

      return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
      from accelerate.test_utils.training import mocked_dataloaders

      A   :     List[str]       =				mocked_dataloaders  # noqa: F811


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):





      if os.environ.get("TESTING_MOCKED_DATALOADERS"   ,					__UpperCamelCase ) == "1":
            SCREAMING_SNAKE_CASE_      =     2
      # Initialize accelerator
      SCREAMING_SNAKE_CASE_      =     Accelerator(cpu=args.cpu   ,					mixed_precision=args.mixed_precision )
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      SCREAMING_SNAKE_CASE_      =     config["""lr"""]
      SCREAMING_SNAKE_CASE_      =     int(config["num_epochs"] )
      SCREAMING_SNAKE_CASE_      =     int(config["seed"] )
      SCREAMING_SNAKE_CASE_      =     int(config["batch_size"] )

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

      # New Code #
      # We now can define an inner training loop function. It should take a batch size as the only parameter,
      # and build the dataloaders in there.
      # It also gets our decorator
      @find_executable_batch_size(starting_batch_size=__UpperCamelCase )
      def inner_training_loop(__UpperCamelCase ):
            # And now just move everything below under this function
            # We need to bring in the Accelerator object from earlier
            nonlocal accelerator
            # And reset all of its attributes that could hold onto any memory:
            accelerator.free_memory()

            # Then we can declare the model, optimizer, and everything else:
            set_seed(__UpperCamelCase )

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

            # 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).
            SCREAMING_SNAKE_CASE_      =     model.to(accelerator.device )

            # Instantiate optimizer
            SCREAMING_SNAKE_CASE_      =     AdamW(params=model.parameters()   ,					lr=__UpperCamelCase )
            SCREAMING_SNAKE_CASE_      =     get_dataloaders(__UpperCamelCase   ,					__UpperCamelCase )

            # Instantiate scheduler
            SCREAMING_SNAKE_CASE_      =     get_linear_schedule_with_warmup(
                optimizer=__UpperCamelCase   ,					num_warmup_steps=1_0_0   ,					num_training_steps=(len(__UpperCamelCase ) * num_epochs)   ,					)

            # 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.
            SCREAMING_SNAKE_CASE_      =     accelerator.prepare(
                __UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase )

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

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

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

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
      inner_training_loop()


def    a__       (						):
      SCREAMING_SNAKE_CASE_      =     argparse.ArgumentParser(description="Simple example of training script." )
      parser.add_argument(
          "--mixed_precision"   ,					type=__UpperCamelCase   ,					default=__UpperCamelCase   ,					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." )
      SCREAMING_SNAKE_CASE_      =     parser.parse_args()
      SCREAMING_SNAKE_CASE_      =     {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
      training_function(__UpperCamelCase   ,					__UpperCamelCase )


if __name__ == "__main__":
      main()







'''simple docstring'''





import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
 from sagemaker import Session, TrainingJobAnalytics
 from sagemaker.huggingface import HuggingFace






@pytest.mark.skipif(
    literal_eval(os.getenv('''TEST_SAGEMAKER'''						, '''False'''     )     ) is not True						, reason='''Skipping test because should only be run when releasing minor transformers version'''						, )
@pytest.mark.usefixtures('''sm_env'''     )
@parameterized_class(
    [
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_glue.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.p3.16xlarge''',
            '''results''': {'''train_runtime''': 6_5_0, '''eval_accuracy''': 0.7, '''eval_loss''': 0.6},
        },
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_ddp.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.p3.16xlarge''',
            '''results''': {'''train_runtime''': 6_0_0, '''eval_accuracy''': 0.7, '''eval_loss''': 0.6},
        },
        {
            '''framework''': '''tensorflow''',
            '''script''': '''run_tf_dist.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.p3.16xlarge''',
            '''results''': {'''train_runtime''': 6_0_0, '''eval_accuracy''': 0.6, '''eval_loss''': 0.7},
        },
    ]     )
class        lowerCAmelCase_(				unittest.TestCase     ):
  '''simple docstring'''




  def 							UpperCAmelCase_	(    self      )    ->		Optional[Any]:
      if self.framework == "pytorch":
          subprocess.run(
              F"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split()  ,encoding="""utf-8"""  ,check=__UpperCAmelCase  ,)
      assert hasattr(self  ,"""env"""      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Optional[Any]:
      lowerCAmelCase__ :  Optional[int]							      =					F"""{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"""
      # distributed data settings
      lowerCAmelCase__ :  Any							      =					{"""smdistributed""": {"""dataparallel""": {"""enabled""": True}}} if self.script != """run_ddp.py""" else None

      # creates estimator
      return HuggingFace(
          entry_point=self.script  ,source_dir=self.env.test_path  ,role=self.env.role  ,image_uri=self.env.image_uri  ,base_job_name=__UpperCAmelCase  ,instance_count=__UpperCAmelCase  ,instance_type=self.instance_type  ,debugger_hook_config=__UpperCAmelCase  ,hyperparameters={**self.env.distributed_hyperparameters, """model_name_or_path""": self.model_name_or_path}  ,metric_definitions=self.env.metric_definitions  ,distribution=__UpperCAmelCase  ,py_version="""py36"""  ,)




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Optional[Any]:
      TrainingJobAnalytics(__UpperCAmelCase      ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""      )






  @parameterized.expand([(2,)]      )
  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Any:
      # create estimator
      lowerCAmelCase__ :  List[Any]							      =					self.create_estimator(__UpperCAmelCase      )

      # run training
      estimator.fit()

      # result dataframe
      lowerCAmelCase__ :  Any							      =					TrainingJobAnalytics(estimator.latest_training_job.name      ).dataframe()

      # extract kpis
      lowerCAmelCase__ :  int							      =					list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]      )
      lowerCAmelCase__ :  List[Any]							      =					list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]      )
      # get train time from SageMaker job, this includes starting, preprocessing, stopping
      lowerCAmelCase__ :  List[str]							      =					(
          Session().describe_training_job(estimator.latest_training_job.name      ).get("""TrainingTimeInSeconds"""  ,99_9999      )
      )

      # assert kpis
      assert train_runtime <= self.results["train_runtime"]
      assert all(t >= self.results["""eval_accuracy"""] for t in eval_accuracy      )
      assert all(t <= self.results["""eval_loss"""] for t in eval_loss      )

      # dump tests result into json file to share in PR
      with open(F"""{estimator.latest_training_job.name}.json"""  ,"""w"""      ) as outfile:
          json.dump({"""train_time""": train_runtime, """eval_accuracy""": eval_accuracy, """eval_loss""": eval_loss}  ,__UpperCAmelCase      )


