





"""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
		_A		:				Dict  			=    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''')
		_A			,				_A		:				Dict  			=    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''')
				_A		:				Optional[Any]  			=    rh.cluster(
				    name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
				)
		else:
				_A		:				Dict  			=    rh.cluster(
				    name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
				)
		_A		:				Tuple  			=    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)









"""simple docstring"""




from typing import TYPE_CHECKING

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


if is_sentencepiece_available():
		from ..ta.tokenization_ta import TaTokenizer
else:
		from ...utils.dummy_sentencepiece_objects import TaTokenizer

a		:				List[Any]  			=    TaTokenizer

if is_tokenizers_available():
		from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
		from ...utils.dummy_tokenizers_objects import TaTokenizerFast

a		:				Union[str, Any]  			=    TaTokenizerFast

a		:				Union[str, Any]  			=    {'''configuration_mt5''': ['''MT5Config''', '''MT5OnnxConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a		:				str  			=    [
		    '''MT5EncoderModel''',
		    '''MT5ForConditionalGeneration''',
		    '''MT5ForQuestionAnswering''',
		    '''MT5Model''',
		    '''MT5PreTrainedModel''',
		    '''MT5Stack''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a		:				List[str]  			=    ['''TFMT5EncoderModel''', '''TFMT5ForConditionalGeneration''', '''TFMT5Model''']

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a		:				Optional[Any]  			=    ['''FlaxMT5EncoderModel''', '''FlaxMT5ForConditionalGeneration''', '''FlaxMT5Model''']


if TYPE_CHECKING:
		from .configuration_mta import MTaConfig, MTaOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mta import (
				    MTaEncoderModel,
				    MTaForConditionalGeneration,
				    MTaForQuestionAnswering,
				    MTaModel,
				    MTaPreTrainedModel,
				    MTaStack,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
		import sys

		a		:				List[Any]  			=    _LazyModule(
		    __name__,
		    globals()['''__file__'''],
		    _import_structure,
		    extra_objects={'''MT5Tokenizer''': MTaTokenizer, '''MT5TokenizerFast''': MTaTokenizerFast},
		    module_spec=__spec__,
		)


