




import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
					import tensorflow as tf
					from tensorflow.python.eager import context
					from tensorflow.python.framework import ops

					from transformers import GradientAccumulator, create_optimizer
@require_tf
class 			a__    (					unittest.TestCase      ):






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A			):




											"""simple docstring"""



											self.assertEqual(len(_A			)		,			len(_A			)			)
											for a, b in zip(_A		,			_A			):
																self.assertAlmostEqual(_A		,			_A		,			delta=_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       GradientAccumulator()
											accumulator([tf.constant([1.0, 2.0]			)]			)
											accumulator([tf.constant([-2.0, 1.0]			)]			)
											accumulator([tf.constant([-1.0, 2.0]			)]			)
											with self.assertRaises(_A			):
																accumulator([tf.constant([1.0, 1.0]			), tf.constant([2.0, 2.0]			)]			)
											self.assertEqual(accumulator.step		,			3			)
											self.assertEqual(len(accumulator.gradients			)		,			1			)
											self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()		,			[-2.0, 5.0]		,			tol=1E-2			)
											accumulator.reset()
											self.assertEqual(accumulator.step		,			0			)
											self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()		,			[0.0, 0.0]		,			tol=1E-2			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       None
											ops.enable_eager_execution_internal()
											__lowerCAmelCase				   =       tf.config.list_physical_devices("CPU"			)
											if len(_A			) == 1:
																tf.config.set_logical_device_configuration(
																    physical_devices[0]		,			[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]			)
											__lowerCAmelCase				   =       tf.config.list_logical_devices(device_type="CPU"			)
											__lowerCAmelCase				   =       tf.distribute.MirroredStrategy(devices=devices[:2]			)

											with strategy.scope():
																__lowerCAmelCase				   =       GradientAccumulator()
																__lowerCAmelCase				   =       tf.Variable([4.0, 3.0]			)
																__lowerCAmelCase				   =       create_optimizer(5E-5		,			1_0		,			5			)
																__lowerCAmelCase				   =       tf.Variable([0.0, 0.0]		,			trainable=_A			)

											def accumulate_on_replica(_A			):
																accumulator([gradient]			)

											def apply_on_replica():
																optimizer.apply_gradients(list(zip(accumulator.gradients		,			[variable]			)			)			)

											@tf.function
											def accumulate(_A		,			_A			):
																with strategy.scope():
																					__lowerCAmelCase				   =       strategy.experimental_local_results(_A			)
																					local_variables[0].assign(_A			)
																					local_variables[1].assign(_A			)
																					strategy.run(_A		,			args=(gradient_placeholder,)			)

											@tf.function
											def apply_grad():
																with strategy.scope():
																					strategy.run(_A			)

											def _check_local_values(_A		,			_A			):
																__lowerCAmelCase				   =       strategy.experimental_local_results(accumulator._gradients[0]			)
																self.assertListAlmostEqual(values[0].value()		,			_A		,			tol=1E-2			)
																self.assertListAlmostEqual(values[1].value()		,			_A		,			tol=1E-2			)

											accumulate([1.0, 2.0]		,			[-1.0, 1.0]			)
											accumulate([3.0, -1.0]		,			[-1.0, -1.0]			)
											accumulate([-2.0, 2.0]		,			[3.0, -2.0]			)
											self.assertEqual(accumulator.step		,			3			)
											_check_local_values([2.0, 3.0]		,			[1.0, -2.0]			)
											apply_grad()
											self.assertListAlmostEqual(variable.value()		,			[4.0, 3.0]		,			tol=1E-2			)
											accumulator.reset()
											self.assertEqual(accumulator.step		,			0			)
											_check_local_values([0.0, 0.0]		,			[0.0, 0.0]			)








'''simple docstring'''
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
  UpperCamelCase__    					=      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''')
  UpperCamelCase__					,   UpperCamelCase__    					=      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''')
    UpperCamelCase__    					=      rh.cluster(
        name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
    )
  else:
    UpperCamelCase__    					=      rh.cluster(
        name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
    )
  UpperCamelCase__    					=      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)
