





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






import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class   UpperCamelCase_       (     UpperCamelCase__				,  unittest.TestCase    ):
							lowerCamelCase_           =					OpenAIGPTTokenizer
							lowerCamelCase_           =					OpenAIGPTTokenizerFast
							lowerCamelCase_           =					True
							lowerCamelCase_           =					False


							def      _snake_case  (							self    :Optional[Any]		)   ->   Dict:



									"""simple docstring"""



									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									SCREAMING_SNAKE_CASE__			=      [
									    """l""",
									    """o""",
									    """w""",
									    """e""",
									    """r""",
									    """s""",
									    """t""",
									    """i""",
									    """d""",
									    """n""",
									    """w</w>""",
									    """r</w>""",
									    """t</w>""",
									    """lo""",
									    """low""",
									    """er</w>""",
									    """low</w>""",
									    """lowest</w>""",
									    """newer</w>""",
									    """wider</w>""",
									    """<unk>""",
									]
									SCREAMING_SNAKE_CASE__			=      dict(zip(__A							,				range(len(__A		)		)		)		)
									SCREAMING_SNAKE_CASE__			=      ["""#version: 0.2""", """l o""", """lo w""", """e r</w>""", """"""]

									SCREAMING_SNAKE_CASE__			=      os.path.join(self.tmpdirname							,				VOCAB_FILES_NAMES["""vocab_file"""]		)
									SCREAMING_SNAKE_CASE__			=      os.path.join(self.tmpdirname							,				VOCAB_FILES_NAMES["""merges_file"""]		)
									with open(self.vocab_file							,				"""w"""		) as fp:
											fp.write(json.dumps(__A		)		)
									with open(self.merges_file							,				"""w"""		) as fp:
											fp.write("""\n""".join(__A		)		)


							def      _snake_case  (							self    :Union[str, Any]							,				__A    :str		)   ->   List[Any]:



									"""simple docstring"""



									return "lower newer", "lower newer"


							def      _snake_case  (							self    :Optional[Any]		)   ->   Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      OpenAIGPTTokenizer(self.vocab_file							,				self.merges_file		)

									SCREAMING_SNAKE_CASE__			=      """lower"""
									SCREAMING_SNAKE_CASE__			=      ["""low""", """er</w>"""]
									SCREAMING_SNAKE_CASE__			=      tokenizer.tokenize(__A		)
									self.assertListEqual(__A							,				__A		)

									SCREAMING_SNAKE_CASE__			=      tokens + ["""<unk>"""]
									SCREAMING_SNAKE_CASE__			=      [14, 15, 20]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(__A		)							,				__A		)


							def      _snake_case  (							self    :Optional[Any]							,				__A    :Optional[Any]=15		)   ->   Any:



									"""simple docstring"""



									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''		):
													SCREAMING_SNAKE_CASE__			=      self.rust_tokenizer_class.from_pretrained(__A							,				**__A		)

													# Simple input
													SCREAMING_SNAKE_CASE__			=      """This is a simple input"""
													SCREAMING_SNAKE_CASE__			=      ["""This is a simple input 1""", """This is a simple input 2"""]
													SCREAMING_SNAKE_CASE__			=      ("""This is a simple input""", """This is a pair""")
													SCREAMING_SNAKE_CASE__			=      [
													    ("""This is a simple input 1""", """This is a simple input 2"""),
													    ("""This is a simple pair 1""", """This is a simple pair 2"""),
													]

													# Simple input tests
													self.assertRaises(__A							,				tokenizer_r.encode							,				__A							,				max_length=__A							,				padding="""max_length"""		)

													# Simple input
													self.assertRaises(__A							,				tokenizer_r.encode_plus							,				__A							,				max_length=__A							,				padding="""max_length"""		)

													# Simple input
													self.assertRaises(
													    __A							,				tokenizer_r.batch_encode_plus							,				__A							,				max_length=__A							,				padding="""max_length"""							,				)

													# Pair input
													self.assertRaises(__A							,				tokenizer_r.encode							,				__A							,				max_length=__A							,				padding="""max_length"""		)

													# Pair input
													self.assertRaises(__A							,				tokenizer_r.encode_plus							,				__A							,				max_length=__A							,				padding="""max_length"""		)

													# Pair input
													self.assertRaises(
													    __A							,				tokenizer_r.batch_encode_plus							,				__A							,				max_length=__A							,				padding="""max_length"""							,				)


							def      _snake_case  (							self    :Dict		)   ->   List[Any]:



									"""simple docstring"""



									pass




@require_ftfy
@require_spacy
@require_tokenizers
class   UpperCamelCase_       (     UpperCamelCase__    ):
							pass