"""simple docstring"""




import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment






def 	UpperCAmelCase       (						*UpperCamelCase__					):






							"""simple docstring"""





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

				# Define train args/config, run train function
				__lowerCamelCase									=	argparse.Namespace(cpu=False, mixed_precision="fp16")
				__lowerCamelCase									=	{"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'])






"""simple docstring"""




from typing import TYPE_CHECKING

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


__lowerCamelCase									=	{
    "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
    "tokenization_layoutlm": ["LayoutLMTokenizer"],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase									=	["LayoutLMTokenizerFast"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase									=	[
				    "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "LayoutLMForMaskedLM",
				    "LayoutLMForSequenceClassification",
				    "LayoutLMForTokenClassification",
				    "LayoutLMForQuestionAnswering",
				    "LayoutLMModel",
				    "LayoutLMPreTrainedModel",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase									=	[
				    "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFLayoutLMForMaskedLM",
				    "TFLayoutLMForSequenceClassification",
				    "TFLayoutLMForTokenClassification",
				    "TFLayoutLMForQuestionAnswering",
				    "TFLayoutLMMainLayer",
				    "TFLayoutLMModel",
				    "TFLayoutLMPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
				from .tokenization_layoutlm import LayoutLMTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_layoutlm import (
								    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    LayoutLMForMaskedLM,
								    LayoutLMForQuestionAnswering,
								    LayoutLMForSequenceClassification,
								    LayoutLMForTokenClassification,
								    LayoutLMModel,
								    LayoutLMPreTrainedModel,
								)
				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_layoutlm import (
								    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFLayoutLMForMaskedLM,
								    TFLayoutLMForQuestionAnswering,
								    TFLayoutLMForSequenceClassification,
								    TFLayoutLMForTokenClassification,
								    TFLayoutLMMainLayer,
								    TFLayoutLMModel,
								    TFLayoutLMPreTrainedModel,
								)

else:
				import sys

				__lowerCamelCase									=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





