

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





import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin





class 		__SCREAMING_SNAKE_CASE       ( lowercase__	,       unittest.TestCase			):
				lowerCamelCase_							=						BioGptTokenizer
				lowerCamelCase_							=						False







				def    lowerCamelCase_  (					self    :		Optional[Any]      ):
										'''simple docstring'''
										super().setUp()

										# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
										lowercase   :       List[str]								=[
										    '''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>''',
										]
										lowercase   :       Any								=dict(zip(UpperCAmelCase__		,				range(len(UpperCAmelCase__      )      )      )      )
										lowercase   :       Union[str, Any]								=['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

										lowercase   :       Optional[int]								=os.path.join(self.tmpdirname		,				VOCAB_FILES_NAMES['''vocab_file''']      )
										lowercase   :       Union[str, Any]								=os.path.join(self.tmpdirname		,				VOCAB_FILES_NAMES['''merges_file''']      )
										with open(self.vocab_file		,				'''w'''      ) as fp:
																fp.write(json.dumps(UpperCAmelCase__      )      )
										with open(self.merges_file		,				'''w'''      ) as fp:
																fp.write('''\n'''.join(UpperCAmelCase__      )      )







				def    lowerCamelCase_  (					self    :		List[Any]		,				UpperCAmelCase__    :		Union[str, Any]      ):
										'''simple docstring'''
										lowercase   :       Dict								='''lower newer'''
										lowercase   :       str								='''lower newer'''
										return input_text, output_text







				def    lowerCamelCase_  (					self    :		List[Any]      ):
										'''simple docstring'''
										lowercase   :       List[Any]								=BioGptTokenizer(self.vocab_file		,				self.merges_file      )

										lowercase   :       Any								='''lower'''
										lowercase   :       int								=['''low''', '''er</w>''']
										lowercase   :       Optional[Any]								=tokenizer.tokenize(UpperCAmelCase__      )
										self.assertListEqual(UpperCAmelCase__		,				UpperCAmelCase__      )

										lowercase   :       Optional[int]								=tokens + ['''<unk>''']
										lowercase   :       Any								=[14, 15, 20]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase__      )		,				UpperCAmelCase__      )







				@slow
				def    lowerCamelCase_  (					self    :		int      ):
										'''simple docstring'''
										lowercase   :       Dict								=BioGptTokenizer.from_pretrained('''microsoft/biogpt'''      )

										lowercase   :       List[str]								=tokenizer.encode('''sequence builders'''		,				add_special_tokens=UpperCAmelCase__      )
										lowercase   :       Optional[int]								=tokenizer.encode('''multi-sequence build'''		,				add_special_tokens=UpperCAmelCase__      )

										lowercase   :       str								=tokenizer.build_inputs_with_special_tokens(UpperCAmelCase__      )
										lowercase   :       Optional[Any]								=tokenizer.build_inputs_with_special_tokens(UpperCAmelCase__		,				UpperCAmelCase__      )

										self.assertTrue(encoded_sentence == [2] + text      )
										self.assertTrue(encoded_pair == [2] + text + [2] + text_a      )





