
"""simple docstring"""




import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
					import jax
					import jax.numpy as jnp
					import numpy as np

					from transformers.modeling_flax_pytorch_utils import (
					    convert_pytorch_state_dict_to_flax,
					    load_flax_weights_in_pytorch_model,
					)
					from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
					import torch



@require_flax
class      lowerCAmelCase_						:


				"""simple docstring"""



				def __init__(self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__=14       ,	SCREAMING_SNAKE_CASE__=7       ,	SCREAMING_SNAKE_CASE__=True       ,	SCREAMING_SNAKE_CASE__=True       ,	SCREAMING_SNAKE_CASE__=True       ,	SCREAMING_SNAKE_CASE__=99       ,	SCREAMING_SNAKE_CASE__=32       ,	SCREAMING_SNAKE_CASE__=5       ,	SCREAMING_SNAKE_CASE__=4       ,	SCREAMING_SNAKE_CASE__=37       ,	SCREAMING_SNAKE_CASE__="gelu"       ,	SCREAMING_SNAKE_CASE__=0.1       ,	SCREAMING_SNAKE_CASE__=0.1       ,	SCREAMING_SNAKE_CASE__=5_12       ,	SCREAMING_SNAKE_CASE__=0.02       ,	SCREAMING_SNAKE_CASE__=None       ,	) ->		List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	List[Any]									=       parent
											SCREAMING_SNAKE_CASE__						:	Dict									=       batch_size
											SCREAMING_SNAKE_CASE__						:	Dict									=       seq_length
											SCREAMING_SNAKE_CASE__						:	List[str]									=       is_training
											SCREAMING_SNAKE_CASE__						:	Any									=       use_input_mask
											SCREAMING_SNAKE_CASE__						:	str									=       use_labels
											SCREAMING_SNAKE_CASE__						:	int									=       vocab_size
											SCREAMING_SNAKE_CASE__						:	Dict									=       d_model
											SCREAMING_SNAKE_CASE__						:	List[Any]									=       num_hidden_layers
											SCREAMING_SNAKE_CASE__						:	Dict									=       num_attention_heads
											SCREAMING_SNAKE_CASE__						:	Dict									=       ffn_dim
											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       activation_function
											SCREAMING_SNAKE_CASE__						:	List[Any]									=       activation_dropout
											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       attention_dropout
											SCREAMING_SNAKE_CASE__						:	int									=       max_position_embeddings
											SCREAMING_SNAKE_CASE__						:	int									=       initializer_range
											SCREAMING_SNAKE_CASE__						:	int									=       None
											SCREAMING_SNAKE_CASE__						:	Dict									=       0
											SCREAMING_SNAKE_CASE__						:	List[str]									=       2
											SCREAMING_SNAKE_CASE__						:	str									=       1



				def      __magic_name__   (self   ) ->		Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Tuple									=       np.clip(ids_tensor([self.batch_size, self.seq_length]       ,	self.vocab_size   )       ,	3       ,	self.vocab_size   )

											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       None
											if self.use_input_mask:
																		SCREAMING_SNAKE_CASE__						:	List[Any]									=       random_attention_mask([self.batch_size, self.seq_length]   )

											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       XGLMConfig(
											    vocab_size=self.vocab_size       ,	d_model=self.hidden_size       ,	num_layers=self.num_hidden_layers       ,	attention_heads=self.num_attention_heads       ,	ffn_dim=self.ffn_dim       ,	activation_function=self.activation_function       ,	activation_dropout=self.activation_dropout       ,	attention_dropout=self.attention_dropout       ,	max_position_embeddings=self.max_position_embeddings       ,	initializer_range=self.initializer_range       ,	use_cache=SCREAMING_SNAKE_CASE__       ,	bos_token_id=self.bos_token_id       ,	eos_token_id=self.eos_token_id       ,	pad_token_id=self.pad_token_id       ,	)

											return (config, input_ids, input_mask)



				def      __magic_name__   (self   ) ->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       self.prepare_config_and_inputs()
											SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	Dict									=       config_and_inputs
											SCREAMING_SNAKE_CASE__						:	List[Any]									=       {"""input_ids""": input_ids, """attention_mask""": attention_mask}
											return config, inputs_dict



				def      __magic_name__   (self   ) ->		Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	Optional[int]									=       self.prepare_config_and_inputs()

											SCREAMING_SNAKE_CASE__						:	str									=       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
											SCREAMING_SNAKE_CASE__						:	List[str]									=       ids_tensor([self.batch_size, self.seq_length]       ,	vocab_size=2   )

											return (
											    config,
											    input_ids,
											    attention_mask,
											    encoder_hidden_states,
											    encoder_attention_mask,
											)



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ) ->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	int									=       20
											SCREAMING_SNAKE_CASE__						:	int									=       model_class_name(SCREAMING_SNAKE_CASE__   )

											SCREAMING_SNAKE_CASE__						:	Dict									=       model.init_cache(input_ids.shape[0]       ,	SCREAMING_SNAKE_CASE__   )
											SCREAMING_SNAKE_CASE__						:	List[str]									=       jnp.ones((input_ids.shape[0], max_decoder_length)       ,	dtype="""i4"""   )

											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       jnp.broadcast_to(
											    jnp.arange(input_ids.shape[-1] - 1   )[None, :]       ,	(input_ids.shape[0], input_ids.shape[-1] - 1)   )
											SCREAMING_SNAKE_CASE__						:	Tuple									=       model(
											    input_ids[:, :-1]       ,	attention_mask=SCREAMING_SNAKE_CASE__       ,	past_key_values=SCREAMING_SNAKE_CASE__       ,	position_ids=SCREAMING_SNAKE_CASE__       ,	)

											SCREAMING_SNAKE_CASE__						:	Tuple									=       jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,	dtype="""i4"""   )
											SCREAMING_SNAKE_CASE__						:	str									=       model(
											    input_ids[:, -1:]       ,	attention_mask=SCREAMING_SNAKE_CASE__       ,	past_key_values=outputs_cache.past_key_values       ,	position_ids=SCREAMING_SNAKE_CASE__       ,	)

											SCREAMING_SNAKE_CASE__						:	List[str]									=       model(SCREAMING_SNAKE_CASE__   )

											SCREAMING_SNAKE_CASE__						:	List[Any]									=       np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
											self.parent.assertTrue(diff < 1E-3       ,	msg=F'''Max diff is {diff}'''   )



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ) ->		List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	List[str]									=       20
											SCREAMING_SNAKE_CASE__						:	Any									=       model_class_name(SCREAMING_SNAKE_CASE__   )

											SCREAMING_SNAKE_CASE__						:	Any									=       jnp.concatenate(
											    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])   )]       ,	axis=-1       ,	)

											SCREAMING_SNAKE_CASE__						:	Dict									=       model.init_cache(input_ids.shape[0]       ,	SCREAMING_SNAKE_CASE__   )
											SCREAMING_SNAKE_CASE__						:	Tuple									=       jnp.broadcast_to(
											    jnp.arange(input_ids.shape[-1] - 1   )[None, :]       ,	(input_ids.shape[0], input_ids.shape[-1] - 1)   )

											SCREAMING_SNAKE_CASE__						:	Tuple									=       model(
											    input_ids[:, :-1]       ,	attention_mask=SCREAMING_SNAKE_CASE__       ,	past_key_values=SCREAMING_SNAKE_CASE__       ,	position_ids=SCREAMING_SNAKE_CASE__       ,	)
											SCREAMING_SNAKE_CASE__						:	int									=       jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,	dtype="""i4"""   )
											SCREAMING_SNAKE_CASE__						:	str									=       model(
											    input_ids[:, -1:]       ,	past_key_values=outputs_cache.past_key_values       ,	attention_mask=SCREAMING_SNAKE_CASE__       ,	position_ids=SCREAMING_SNAKE_CASE__       ,	)

											SCREAMING_SNAKE_CASE__						:	Tuple									=       model(SCREAMING_SNAKE_CASE__       ,	attention_mask=SCREAMING_SNAKE_CASE__   )
											SCREAMING_SNAKE_CASE__						:	int									=       np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
											self.parent.assertTrue(diff < 1E-3       ,	msg=F'''Max diff is {diff}'''   )






@require_sentencepiece
@require_flax
class      lowerCAmelCase_						(a__			, a__			, unittest.TestCase  ):


				"""simple docstring"""



				__UpperCamelCase				:						int 	=       (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
				__UpperCamelCase				:						Any 	=       (FlaxXGLMForCausalLM,) if is_flax_available() else ()



				def      __magic_name__   (self   ) ->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	List[Any]									=       FlaxXGLMModelTester(self   )



				def      __magic_name__   (self   ) ->		Tuple:



											"""simple docstring"""




											for model_class_name in self.all_model_classes:
																		SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	str									=       self.model_tester.prepare_config_and_inputs()
																		self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   )



				def      __magic_name__   (self   ) ->		Union[str, Any]:



											"""simple docstring"""




											for model_class_name in self.all_model_classes:
																		SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	int									=       self.model_tester.prepare_config_and_inputs()
																		self.model_tester.check_use_cache_forward_with_attn_mask(
																		    SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   )



				@slow
				def      __magic_name__   (self   ) ->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	int									=       XGLMTokenizer.from_pretrained("""XGLM"""       ,	padding_side="""left"""   )
											SCREAMING_SNAKE_CASE__						:	str									=       tokenizer(["""Hello this is a long string""", """Hey"""]       ,	return_tensors="""np"""       ,	padding=SCREAMING_SNAKE_CASE__       ,	truncation=SCREAMING_SNAKE_CASE__   )

											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       FlaxXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""   )
											SCREAMING_SNAKE_CASE__						:	Any									=       1
											SCREAMING_SNAKE_CASE__						:	List[Any]									=       False

											SCREAMING_SNAKE_CASE__						:	List[Any]									=       jax.jit(model.generate   )

											SCREAMING_SNAKE_CASE__						:	Any									=       jit_generate(inputs["""input_ids"""]       ,	attention_mask=inputs["""attention_mask"""]   ).sequences

											SCREAMING_SNAKE_CASE__						:	List[str]									=       tokenizer.batch_decode(SCREAMING_SNAKE_CASE__       ,	skip_special_tokens=SCREAMING_SNAKE_CASE__   )

											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       [
											    """Hello this is a long string of questions, but I'm not sure if I'm""",
											    """Hey, I'm a newbie to the forum and I'""",
											]

											self.assertListEqual(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   )



				@is_pt_flax_cross_test
				def      __magic_name__   (self   ) ->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	str									=       self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__   ):
																									# prepare inputs
																									SCREAMING_SNAKE_CASE__						:	Dict									=       self._prepare_for_class(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   )
																									SCREAMING_SNAKE_CASE__						:	int									=       {k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

																									# load corresponding PyTorch class
																									SCREAMING_SNAKE_CASE__						:	Tuple									=       model_class.__name__[4:]  # Skip the "Flax" at the beginning
																									SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       getattr(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   )

																									SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       pt_inputs["""input_ids"""].shape
																									SCREAMING_SNAKE_CASE__						:	Dict									=       np.random.randint(0       ,	seq_length - 1       ,	size=(batch_size,)   )
																									for batch_idx, start_index in enumerate(SCREAMING_SNAKE_CASE__   ):
																																SCREAMING_SNAKE_CASE__						:	Tuple									=       0
																																SCREAMING_SNAKE_CASE__						:	List[Any]									=       1
																																SCREAMING_SNAKE_CASE__						:	Optional[int]									=       0
																																SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       1
																									SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       pt_model_class(SCREAMING_SNAKE_CASE__   ).eval()
																									# Flax models don't use the `use_cache` option and cache is not returned as a default.
																									# So we disable `use_cache` here for PyTorch model.
																									SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       False
																									SCREAMING_SNAKE_CASE__						:	Optional[int]									=       model_class(SCREAMING_SNAKE_CASE__       ,	dtype=jnp.floataa   )
																									SCREAMING_SNAKE_CASE__						:	int									=       convert_pytorch_state_dict_to_flax(pt_model.state_dict()       ,	SCREAMING_SNAKE_CASE__   )
																									SCREAMING_SNAKE_CASE__						:	Optional[int]									=       fx_state

																									with torch.no_grad():
																																SCREAMING_SNAKE_CASE__						:	int									=       pt_model(**SCREAMING_SNAKE_CASE__   ).to_tuple()

																									SCREAMING_SNAKE_CASE__						:	List[str]									=       fx_model(**SCREAMING_SNAKE_CASE__   ).to_tuple()
																									self.assertEqual(len(SCREAMING_SNAKE_CASE__   )       ,	len(SCREAMING_SNAKE_CASE__   )       ,	"""Output lengths differ between Flax and PyTorch"""   )
																									for fx_output, pt_output in zip(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ):
																																self.assert_almost_equals(fx_output[:, -1]       ,	pt_output[:, -1].numpy()       ,	4E-2   )

																									with tempfile.TemporaryDirectory() as tmpdirname:
																																pt_model.save_pretrained(SCREAMING_SNAKE_CASE__   )
																																SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       model_class.from_pretrained(SCREAMING_SNAKE_CASE__       ,	from_pt=SCREAMING_SNAKE_CASE__   )

																									SCREAMING_SNAKE_CASE__						:	Optional[int]									=       fx_model_loaded(**SCREAMING_SNAKE_CASE__   ).to_tuple()
																									self.assertEqual(
																									    len(SCREAMING_SNAKE_CASE__   )       ,	len(SCREAMING_SNAKE_CASE__   )       ,	"""Output lengths differ between Flax and PyTorch"""   )
																									for fx_output_loaded, pt_output in zip(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ):
																																self.assert_almost_equals(fx_output_loaded[:, -1]       ,	pt_output[:, -1].numpy()       ,	4E-2   )



				@is_pt_flax_cross_test
				def      __magic_name__   (self   ) ->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	List[str]									=       self.model_tester.prepare_config_and_inputs_for_common()
											for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__   ):
																									# prepare inputs
																									SCREAMING_SNAKE_CASE__						:	str									=       self._prepare_for_class(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   )
																									SCREAMING_SNAKE_CASE__						:	str									=       {k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

																									# load corresponding PyTorch class
																									SCREAMING_SNAKE_CASE__						:	Dict									=       model_class.__name__[4:]  # Skip the "Flax" at the beginning
																									SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       getattr(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   )

																									SCREAMING_SNAKE_CASE__						:	Optional[int]									=       pt_model_class(SCREAMING_SNAKE_CASE__   ).eval()
																									SCREAMING_SNAKE_CASE__						:	int									=       False
																									SCREAMING_SNAKE_CASE__						:	List[str]									=       model_class(SCREAMING_SNAKE_CASE__       ,	dtype=jnp.floataa   )

																									SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       load_flax_weights_in_pytorch_model(SCREAMING_SNAKE_CASE__       ,	fx_model.params   )
																									SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	Optional[int]									=       pt_inputs["""input_ids"""].shape
																									SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       np.random.randint(0       ,	seq_length - 1       ,	size=(batch_size,)   )
																									for batch_idx, start_index in enumerate(SCREAMING_SNAKE_CASE__   ):
																																SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       0
																																SCREAMING_SNAKE_CASE__						:	Optional[int]									=       1
																																SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       0
																																SCREAMING_SNAKE_CASE__						:	List[Any]									=       1

																									# make sure weights are tied in PyTorch
																									pt_model.tie_weights()

																									with torch.no_grad():
																																SCREAMING_SNAKE_CASE__						:	List[str]									=       pt_model(**SCREAMING_SNAKE_CASE__   ).to_tuple()

																									SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       fx_model(**SCREAMING_SNAKE_CASE__   ).to_tuple()
																									self.assertEqual(len(SCREAMING_SNAKE_CASE__   )       ,	len(SCREAMING_SNAKE_CASE__   )       ,	"""Output lengths differ between Flax and PyTorch"""   )
																									for fx_output, pt_output in zip(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ):
																																self.assert_almost_equals(fx_output[:, -1]       ,	pt_output[:, -1].numpy()       ,	4E-2   )

																									with tempfile.TemporaryDirectory() as tmpdirname:
																																fx_model.save_pretrained(SCREAMING_SNAKE_CASE__   )
																																SCREAMING_SNAKE_CASE__						:	str									=       pt_model_class.from_pretrained(SCREAMING_SNAKE_CASE__       ,	from_flax=SCREAMING_SNAKE_CASE__   )

																									with torch.no_grad():
																																SCREAMING_SNAKE_CASE__						:	Dict									=       pt_model_loaded(**SCREAMING_SNAKE_CASE__   ).to_tuple()

																									self.assertEqual(
																									    len(SCREAMING_SNAKE_CASE__   )       ,	len(SCREAMING_SNAKE_CASE__   )       ,	"""Output lengths differ between Flax and PyTorch"""   )
																									for fx_output, pt_output in zip(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ):
																																self.assert_almost_equals(fx_output[:, -1]       ,	pt_output[:, -1].numpy()       ,	4E-2   )



				@slow
				def      __magic_name__   (self   ) ->		Union[str, Any]:



											"""simple docstring"""




											for model_class_name in self.all_model_classes:
																		SCREAMING_SNAKE_CASE__						:	Dict									=       model_class_name.from_pretrained("""facebook/xglm-564M"""   )
																		SCREAMING_SNAKE_CASE__						:	Any									=       model(np.ones((1, 1)   )   )
																		self.assertIsNotNone(SCREAMING_SNAKE_CASE__   )






"""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'])




