

import argparse
import os

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,
# specifically showcasing the experiment tracking capability,
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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[str]    			=     16
A_			:					Any    			=     32

def 		UpperCamelCase   (lowercase_:					Accelerator       , lowercase_:					int = 16   )	-> Union[str, Any]:
	A__				:       Optional[int]	  =						AutoTokenizer.from_pretrained("""bert-base-cased"""   )
	A__				:       Tuple	  =						load_dataset("""glue"""       , """mrpc"""   )

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

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

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

	# Instantiate dataloaders.
	A__				:       Optional[Any]	  =						DataLoader(
	    tokenized_datasets["""train"""]       , shuffle=lowercase_       , collate_fn=lowercase_       , batch_size=lowercase_   )
	A__				:       Tuple	  =						DataLoader(
	    tokenized_datasets["""validation"""]       , shuffle=lowercase_       , collate_fn=lowercase_       , batch_size=lowercase_   )

	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[Any]    			=     mocked_dataloaders  # noqa: F811

def 		UpperCamelCase   (lowercase_:					List[Any]       , lowercase_:					int   )	-> List[Any]:
	# For testing only
	if os.environ.get("""TESTING_MOCKED_DATALOADERS"""       , lowercase_   ) == "1":
		A__				:       Dict	  =						2
	# Initialize Accelerator

	# New Code #
	# We pass in "all" to `log_with` to grab all available trackers in the environment
	# Note: If using a custom `Tracker` class, should be passed in here such as:
	# >>> log_with = ["all", MyCustomTrackerClassInstance()]
	if args.with_tracking:
		A__				:       str	  =						Accelerator(
		    cpu=args.cpu       , mixed_precision=args.mixed_precision       , log_with="""all"""       , project_dir=args.project_dir   )
	else:
		A__				:       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
	A__				:       List[Any]	  =						config["""lr"""]
	A__				:       str	  =						int(config["""num_epochs"""]   )
	A__				:       Any	  =						int(config["""seed"""]   )
	A__				:       Optional[int]	  =						int(config["""batch_size"""]   )
	set_seed(lowercase_   )

	A__  ,  A__				:       List[Any]	  =						get_dataloaders(lowercase_       , lowercase_   )
	A__				:       Union[str, Any]	  =						evaluate.load("""glue"""       , """mrpc"""   )

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

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

	# 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__				:       Any	  =						model.to(accelerator.device   )

	# Instantiate optimizer
	A__				:       Optional[int]	  =						AdamW(params=model.parameters()       , lr=lowercase_   )

	# Instantiate scheduler
	A__				:       str	  =						get_linear_schedule_with_warmup(
	    optimizer=lowercase_       , num_warmup_steps=100       , num_training_steps=(len(lowercase_   ) * 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__  ,  A__  ,  A__  ,  A__  ,  A__				:       Union[str, Any]	  =						accelerator.prepare(
	    lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_   )

	# New Code #
	# We need to initialize the trackers we use. Overall configurations can also be stored
	if args.with_tracking:
		A__				:       Dict	  =						os.path.split(lowercase_   )[-1].split("""."""   )[0]
		accelerator.init_trackers(lowercase_       , lowercase_   )

	# Now we train the model
	for epoch in range(lowercase_   ):
		model.train()
		# New Code #
		# For our tracking example, we will log the total loss of each epoch
		if args.with_tracking:
			A__				:       str	  =						0
		for step, batch in enumerate(lowercase_   ):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device   )
			A__				:       Optional[Any]	  =						model(**lowercase_   )
			A__				:       Dict	  =						outputs.loss
			# New Code #
			if args.with_tracking:
				total_loss += loss.detach().float()
			A__				:       Optional[Any]	  =						loss / gradient_accumulation_steps
			accelerator.backward(lowercase_   )
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

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

		A__				:       Tuple	  =						metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f"""epoch {epoch}:"""       , lowercase_   )

		# New Code #
		# To actually log, we call `Accelerator.log`
		# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
		if args.with_tracking:
			accelerator.log(
			    {
			        """accuracy""": eval_metric["""accuracy"""],
			        """f1""": eval_metric["""f1"""],
			        """train_loss""": total_loss.item() / len(lowercase_   ),
			        """epoch""": epoch,
			    }       , step=lowercase_       , )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
	if args.with_tracking:
		accelerator.end_training()

def 		UpperCamelCase   ()	-> Dict:
	A__				:       Any	  =						argparse.ArgumentParser(description="""Simple example of training script."""   )
	parser.add_argument(
	    """--mixed_precision"""       , type=lowercase_       , default=lowercase_       , 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."""   )
	parser.add_argument(
	    """--with_tracking"""       , action="""store_true"""       , help="""Whether to load in all available experiment trackers from the environment and use them for logging."""       , )
	parser.add_argument(
	    """--project_dir"""       , type=lowercase_       , default="""logs"""       , help="""Location on where to store experiment tracking logs` and relevent project information"""       , )
	A__				:       Optional[int]	  =						parser.parse_args()
	A__				:       int	  =						{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
	training_function(lowercase_       , lowercase_   )


if __name__ == "__main__":
	main()






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_50, '''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_00, '''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_00, '''eval_accuracy''': 0.6, '''eval_loss''': 0.7},
        },
    ]							)
class        _a		(unittest.TestCase							):


					'''simple docstring'''






					def     __A     (					self     ):
						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=A__       ,			)
						assert hasattr(self       ,			"""env"""     )






					def     __A     (					self       ,			A__     ):
						A__				:       int	  =						F"""{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"""
						# distributed data settings
						A__				:       str	  =						{"""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=A__       ,			instance_count=A__       ,			instance_type=self.instance_type       ,			debugger_hook_config=A__       ,			hyperparameters={**self.env.distributed_hyperparameters, """model_name_or_path""": self.model_name_or_path}       ,			metric_definitions=self.env.metric_definitions       ,			distribution=A__       ,			py_version="""py36"""       ,			)






					def     __A     (					self       ,			A__     ):
						TrainingJobAnalytics(A__     ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""     )






					@parameterized.expand([(2,)]     )
					def     __A     (					self       ,			A__     ):
						# create estimator
						A__				:       str	  =						self.create_estimator(A__     )

						# run training
						estimator.fit()

						# result dataframe
						A__				:       str	  =						TrainingJobAnalytics(estimator.latest_training_job.name     ).dataframe()

						# extract kpis
						A__				:       List[Any]	  =						list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]     )
						A__				:       Optional[Any]	  =						list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]     )
						# get train time from SageMaker job, this includes starting, preprocessing, stopping
						A__				:       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}       ,			A__     )



