


import warnings
from dataclasses import dataclass, field
from typing import Optional, Tuple

from .training_args import TrainingArguments
from .utils import cached_property, is_tf_available, logging, requires_backends


_UpperCAmelCase      :	Union[str, Any]  =logging.get_logger(__name__)

if is_tf_available():
						import tensorflow as tf







@dataclass
class 					snake_case__(				__SCREAMING_SNAKE_CASE				):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__   :	int       =				"""tf"""
						SCREAMING_SNAKE_CASE__   :	Optional[str]       =				field(
						    default=__SCREAMING_SNAKE_CASE,    metadata={"""help""": """Name of TPU"""},    )

						SCREAMING_SNAKE_CASE__   :	Optional[str]       =				field(
						    default=__SCREAMING_SNAKE_CASE,    metadata={"""help""": """Zone of TPU"""},    )

						SCREAMING_SNAKE_CASE__   :	Optional[str]       =				field(
						    default=__SCREAMING_SNAKE_CASE,    metadata={"""help""": """Name of Cloud TPU-enabled project"""},    )

						SCREAMING_SNAKE_CASE__   :	float       =				field(
						    default=1.0,    metadata={"""help""": """Power for the Polynomial decay LR scheduler."""},    )

						SCREAMING_SNAKE_CASE__   :	bool       =				field(default=__SCREAMING_SNAKE_CASE,    metadata={"""help""": """Whether to activate the XLA compilation or not"""}				)




						@cached_property
						def     lowercase_			(					self				)       ->     Tuple:
										requires_backends(self ,    ['''tf''']				)
										logger.info('''Tensorflow: setting up strategy'''				)

										lowerCAmelCase_			:   List[Any]     					=					tf.config.list_physical_devices('''GPU'''				)

										# Set to float16 at first
										if self.fpaa:
														tf.keras.mixed_precision.set_global_policy('''mixed_float16'''				)

										if self.no_cuda:
														lowerCAmelCase_			:   str     					=					tf.distribute.OneDeviceStrategy(device='''/cpu:0'''				)
										else:
														try:
																		if self.tpu_name:
																						lowerCAmelCase_			:   Dict     					=					tf.distribute.cluster_resolver.TPUClusterResolver(
																						    self.tpu_name ,    zone=self.tpu_zone ,    project=self.gcp_project				)
																		else:
																						lowerCAmelCase_			:   Union[str, Any]     					=					tf.distribute.cluster_resolver.TPUClusterResolver()
														except ValueError:
																		if self.tpu_name:
																						raise RuntimeError(f"""Couldn\'t connect to TPU {self.tpu_name}!"""				)
																		else:
																						lowerCAmelCase_			:   Optional[int]     					=					None

														if tpu:
																		# Set to bfloat16 in case of TPU
																		if self.fpaa:
																						tf.keras.mixed_precision.set_global_policy('''mixed_bfloat16'''				)

																		tf.config.experimental_connect_to_cluster(__UpperCamelCase				)
																		tf.tpu.experimental.initialize_tpu_system(__UpperCamelCase				)

																		lowerCAmelCase_			:   Optional[int]     					=					tf.distribute.TPUStrategy(__UpperCamelCase				)

														elif len(__UpperCamelCase				) == 0:
																		lowerCAmelCase_			:   Tuple     					=					tf.distribute.OneDeviceStrategy(device='''/cpu:0'''				)
														elif len(__UpperCamelCase				) == 1:
																		lowerCAmelCase_			:   str     					=					tf.distribute.OneDeviceStrategy(device='''/gpu:0'''				)
														elif len(__UpperCamelCase				) > 1:
																		# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
																		lowerCAmelCase_			:   Tuple     					=					tf.distribute.MirroredStrategy()
														else:
																		raise ValueError('''Cannot find the proper strategy, please check your environment properties.'''				)

										return strategy




						@property
						def     lowercase_			(					self				)       ->     Union[str, Any]:
										requires_backends(self ,    ['''tf''']				)
										return self._setup_strategy




						@property
						def     lowercase_			(					self				)       ->     Any:
										requires_backends(self ,    ['''tf''']				)
										return self._setup_strategy.num_replicas_in_sync




						@property
						def     lowercase_			(					self				)       ->     Dict:
										return False  # TF Logging is handled by Keras not the Trainer




						@property
						def     lowercase_			(					self				)       ->     List[str]:


										if self.per_gpu_train_batch_size:
														logger.warning(
														    '''Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future '''
														    '''version. Using `--per_device_train_batch_size` is preferred.'''				)
										lowerCAmelCase_			:   Any     					=					self.per_gpu_train_batch_size or self.per_device_train_batch_size
										return per_device_batch_size * self.n_replicas




						@property
						def     lowercase_			(					self				)       ->     Any:


										if self.per_gpu_eval_batch_size:
														logger.warning(
														    '''Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future '''
														    '''version. Using `--per_device_eval_batch_size` is preferred.'''				)
										lowerCAmelCase_			:   Tuple     					=					self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
										return per_device_batch_size * self.n_replicas




						@property
						def     lowercase_			(					self				)       ->     Dict:
										requires_backends(self ,    ['''tf''']				)
										warnings.warn(
										    '''The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead.''' ,    __UpperCamelCase ,    )
										return self._setup_strategy.num_replicas_in_sync

import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment




def 					lowerCamelCase__					(    *a__	:	Optional[Any]  )		->						Optional[Any]:
       UpperCamelCase_											=							torch.cuda.device_count()
       print(f'''Device count: {num_processes}'''  )
       with patch_environment(
           world_size=a__						,   master_addr="""127.0.01"""						,   master_port="""29500"""						,   mixed_precision=args[1].mixed_precision  ):
              UpperCamelCase_											=							PrepareForLaunch(a__						,   distributed_type="""MULTI_GPU"""  )
              torch.multiprocessing.start_processes(a__						,   args=a__						,   nprocs=a__						,   start_method="""spawn"""  )


if __name__ == "__main__":
      # Refer to https://runhouse-docs.readthedocs-hosted.com/en/main/rh_primitives/cluster.html#hardware-setup
      # for cloud access setup instructions (if using on-demand hardware), and for API specifications.

      # on-demand GPU
      # gpu = rh.cluster(name='rh-cluster', instance_type='V100:1', provider='cheapest', use_spot=False)  # single GPU
      _A							=       rh.cluster(name='''rh-cluster''', instance_type='''V100:4''', provider='''cheapest''', use_spot=False)  # multi GPU
      gpu.up_if_not()

      # on-prem GPU
      # gpu = rh.cluster(
      #           ips=["ip_addr"], ssh_creds={ssh_user:"<username>", ssh_private_key:"<key_path>"}, name="rh-cluster"
      #       )

      # Set up remote function
      _A							=       [
          '''pip:./''',
          '''transformers''',
          '''datasets''',
          '''evaluate''',
          '''tqdm''',
          '''scipy''',
          '''scikit-learn''',
          '''tensorboard''',
          '''torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117''',
      ]
      _A							=       rh.function(fn=launch_train, system=gpu, reqs=reqs, name='''train_bert_glue''')

      # Define train args/config, run train function
      _A							=       argparse.Namespace(cpu=False, mixed_precision='''fp16''')
      _A							=       {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
      launch_train_gpu(config, train_args, stream_logs=True)

      # Alternatively, we can just run as instructed in the README (but only because there's already a wrapper CLI):
      # gpu.install_packages(reqs)
      # gpu.run(['accelerate launch --multi_gpu accelerate/examples/nlp_example.py'])



