
'''simple docstring'''
import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


__lowerCAmelCase					=			'true'






def    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE=82				,   _SCREAMING_SNAKE_CASE=16 ):
							set_seed(42 )
							_snake_case  =    RegressionModel()
							_snake_case  =    deepcopy(_lowercase )
							_snake_case  =    RegressionDataset(length=_lowercase )
							_snake_case  =    DataLoader(_lowercase				,   batch_size=_lowercase )
							model.to(accelerator.device )
							_snake_case,				_snake_case  =    accelerator.prepare(_lowercase				,   _lowercase )
							return model, ddp_model, dataloader






def    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE=False ):
							_snake_case  =    AutoTokenizer.from_pretrained("""hf-internal-testing/mrpc-bert-base-cased""" )
							_snake_case  =    load_dataset("""glue"""				,   """mrpc"""				,   split="""validation""" )

							def tokenize_function(_SCREAMING_SNAKE_CASE ):
														_snake_case  =    tokenizer(examples["""sentence1"""]				,   examples["""sentence2"""]				,   truncation=_lowercase				,   max_length=_lowercase )
														return outputs

							with accelerator.main_process_first():
														_snake_case  =    dataset.map(
														    _lowercase				,   batched=_lowercase				,   remove_columns=["""idx""", """sentence1""", """sentence2"""]				,   )

							_snake_case  =    tokenized_datasets.rename_column("""label"""				,   """labels""" )

							def collate_fn(_SCREAMING_SNAKE_CASE ):
														if use_longest:
																					return tokenizer.pad(_lowercase				,   padding="""longest"""				,   return_tensors="""pt""" )
														return tokenizer.pad(_lowercase				,   padding="""max_length"""				,   max_length=128				,   return_tensors="""pt""" )

							return DataLoader(_lowercase				,   shuffle=_lowercase				,   collate_fn=_lowercase				,   batch_size=16 )






def    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE ):
							_snake_case  =    Accelerator(dispatch_batches=_lowercase				,   split_batches=_lowercase )
							_snake_case  =    get_dataloader(_lowercase				,   not dispatch_batches )
							_snake_case  =    AutoModelForSequenceClassification.from_pretrained(
							    """hf-internal-testing/mrpc-bert-base-cased"""				,   return_dict=_lowercase )
							_snake_case,				_snake_case  =    accelerator.prepare(_lowercase				,   _lowercase )
							return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator






def    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE ):
							_snake_case  =    []
							for batch in dataloader:
														_snake_case,				_snake_case  =    batch.values()
														with torch.no_grad():
																					_snake_case  =    model(_lowercase )
																					_snake_case,				_snake_case  =    accelerator.gather_for_metrics((logit, target) )
																					logits_and_targets.append((logit, target) )
							_snake_case,				_snake_case  =    [], []
							for logit, targ in logits_and_targets:
														logits.append(_lowercase )
														targs.append(_lowercase )
							_snake_case,				_snake_case  =    torch.cat(_lowercase ), torch.cat(_lowercase )
							return logits, targs






def    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE=82				,   _SCREAMING_SNAKE_CASE=False				,   _SCREAMING_SNAKE_CASE=False				,   _SCREAMING_SNAKE_CASE=16 ):
							_snake_case,				_snake_case,				_snake_case  =    get_basic_setup(_lowercase				,   _lowercase				,   _lowercase )
							_snake_case,				_snake_case  =    generate_predictions(_lowercase				,   _lowercase				,   _lowercase )
							assert (
							    len(_lowercase ) == num_samples
							), f"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(_lowercase )}"""






def    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE = False				,   _SCREAMING_SNAKE_CASE = False ):
							_snake_case  =    evaluate.load("""glue"""				,   """mrpc""" )
							_snake_case,				_snake_case  =    get_mrpc_setup(_lowercase				,   _lowercase )
							# First do baseline
							_snake_case,				_snake_case,				_snake_case  =    setup["""no"""]
							model.to(_lowercase )
							model.eval()
							for batch in dataloader:
														batch.to(_lowercase )
														with torch.inference_mode():
																					_snake_case  =    model(**_lowercase )
														_snake_case  =    outputs.logits.argmax(dim=-1 )
														metric.add_batch(predictions=_lowercase				,   references=batch["""labels"""] )
							_snake_case  =    metric.compute()

							# Then do distributed
							_snake_case,				_snake_case,				_snake_case  =    setup["""ddp"""]
							model.eval()
							for batch in dataloader:
														with torch.inference_mode():
																					_snake_case  =    model(**_lowercase )
														_snake_case  =    outputs.logits.argmax(dim=-1 )
														_snake_case  =    batch["""labels"""]
														_snake_case,				_snake_case  =    accelerator.gather_for_metrics((preds, references) )
														metric.add_batch(predictions=_lowercase				,   references=_lowercase )
							_snake_case  =    metric.compute()

							for key in "accuracy f1".split():
														assert math.isclose(
														    baseline[key]				,   distributed[key] ), f"""Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"""






def    __SCREAMING_SNAKE_CASE (    ):
							_snake_case  =    Accelerator(split_batches=_lowercase				,   dispatch_batches=_lowercase )
							if accelerator.is_local_main_process:
														datasets.utils.logging.set_verbosity_warning()
														transformers.utils.logging.set_verbosity_warning()
							else:
														datasets.utils.logging.set_verbosity_error()
														transformers.utils.logging.set_verbosity_error()
							# These are a bit slower so they should only be ran on the GPU or TPU
							if torch.cuda.is_available() or is_tpu_available():
														if accelerator.is_local_main_process:
																					print("""**Testing gather_for_metrics**""" )
														for split_batches in [True, False]:
																					for dispatch_batches in [True, False]:
																												if accelerator.is_local_main_process:
																																			print(f"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`""" )
																												test_mrpc(_lowercase				,   _lowercase )
																												accelerator.state._reset_state()
							if accelerator.is_local_main_process:
														print("""**Test torch metrics**""" )
							for split_batches in [True, False]:
														for dispatch_batches in [True, False]:
																					_snake_case  =    Accelerator(split_batches=_lowercase				,   dispatch_batches=_lowercase )
																					if accelerator.is_local_main_process:
																												print(f"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99""" )
																					test_torch_metrics(_lowercase				,   99 )
																					accelerator.state._reset_state()
							if accelerator.is_local_main_process:
														print("""**Test last batch is not dropped when perfectly divisible**""" )
							_snake_case  =    Accelerator()
							test_torch_metrics(_lowercase				,   512 )
							accelerator.state._reset_state()






def    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE ):
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
				main()

import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
 # Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
 # setup instructions, if using on-demand hardware

 # If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
 # If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
 # Throw an error if user passes both BYO and on-demand cluster args
 # Otherwise, use default values
 _snake_case      = argparse.ArgumentParser()
 parser.add_argument('''--user''', type=str, default='''ubuntu''')
 parser.add_argument('''--host''', type=str, default='''localhost''')
 parser.add_argument('''--key_path''', type=str, default=None)
 parser.add_argument('''--instance''', type=str, default='''V100:1''')
 parser.add_argument('''--provider''', type=str, default='''cheapest''')
 parser.add_argument('''--use_spot''', type=bool, default=False)
 parser.add_argument('''--example''', type=str, default='''pytorch/text-generation/run_generation.py''')
 _snake_case  ,      _snake_case      = parser.parse_known_args()
 if args.host != "localhost":
  if args.instance != "V100:1" or args.provider != "cheapest":
   raise ValueError('''Cannot specify both BYO and on-demand cluster args''')
  _snake_case      = rh.cluster(
      name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
  )
 else:
  _snake_case      = rh.cluster(
      name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
  )
 _snake_case      = args.example.rsplit('''/''', 1)[0]

 # Set up remote environment
 cluster.install_packages(['''pip:./'''])  # Installs transformers from local source
 # Note transformers is copied into the home directory on the remote machine, so we can install from there
 cluster.run([F"pip install -r transformers/examples/{example_dir}/requirements.txt"])
 cluster.run(['''pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'''])

 # Run example. You can bypass the CLI wrapper and paste your own code here.
 cluster.run([F"python transformers/examples/{args.example} {' '.join(shlex.quote(arg) for arg in unknown)}"])

 # Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
 # from my_script... import train
 # reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
 # launch_train_gpu = rh.function(fn=train,
 #                                system=gpu,
 #                                reqs=reqs,
 #                                name='train_bert_glue')
 #
 # We can pass in arguments just like we would to a function:
 # launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
 #                  stream_logs=True)


