
"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase__				:   Optional[int]   							=			{'configuration_gptj': ['GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTJConfig', 'GPTJOnnxConfig']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__				:   List[Any]   							=			[
					    'GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'GPTJForCausalLM',
					    'GPTJForQuestionAnswering',
					    'GPTJForSequenceClassification',
					    'GPTJModel',
					    'GPTJPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__				:   Dict   							=			[
					    'TFGPTJForCausalLM',
					    'TFGPTJForQuestionAnswering',
					    'TFGPTJForSequenceClassification',
					    'TFGPTJModel',
					    'TFGPTJPreTrainedModel',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__				:   Optional[int]   							=			[
					    'FlaxGPTJForCausalLM',
					    'FlaxGPTJModel',
					    'FlaxGPTJPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_gptj import (
										    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GPTJForCausalLM,
										    GPTJForQuestionAnswering,
										    GPTJForSequenceClassification,
										    GPTJModel,
										    GPTJPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_gptj import (
										    TFGPTJForCausalLM,
										    TFGPTJForQuestionAnswering,
										    TFGPTJForSequenceClassification,
										    TFGPTJModel,
										    TFGPTJPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
					import sys

					UpperCAmelCase__				:   Any   							=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




"""simple docstring"""




import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment

def lowercase_  (       *_snake_case						):
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.cuda.device_count()
							print(f'''Device count: {num_processes}'''						)
							with patch_environment(
							    world_size=_snake_case       ,master_addr="""127.0.01"""       ,master_port="""29500"""       ,mixed_precision=args[1].mixed_precision						):
														SCREAMING_SNAKE_CASE__						:	int									=       PrepareForLaunch(_snake_case       ,distributed_type="""MULTI_GPU"""						)
														torch.multiprocessing.start_processes(_snake_case       ,args=_snake_case       ,nprocs=_snake_case       ,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
					UpperCAmelCase__				:   str   							=			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
					UpperCAmelCase__				:   int   							=			[
					    'pip:./',
					    'transformers',
					    'datasets',
					    'evaluate',
					    'tqdm',
					    'scipy',
					    'scikit-learn',
					    'tensorboard',
					    'torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117',
					]
					UpperCAmelCase__				:   List[str]   							=			rh.function(fn=launch_train, system=gpu, reqs=reqs, name='train_bert_glue')

					# Define train args/config, run train function
					UpperCAmelCase__				:   Dict   							=			argparse.Namespace(cpu=False, mixed_precision='fp16')
					UpperCAmelCase__				:   Any   							=			{'lr': 2E-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
					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'])




