



import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 		UpperCAmelCase__			(			__UpperCamelCase						,unittest.TestCase   ):




			'''simple docstring'''






			UpperCamelCase									=       RobertaTokenizer
			UpperCamelCase									=       RobertaTokenizerFast
			UpperCamelCase									=       True
			UpperCamelCase									=       {"""cls_token""": """<s>"""}




			def  snake_case__     (   self							: Optional[int] ):
					'''simple docstring'''





					super().setUp()

					# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
					__UpperCAmelCase    :     int													=	[
					    '''l''',
					    '''o''',
					    '''w''',
					    '''e''',
					    '''r''',
					    '''s''',
					    '''t''',
					    '''i''',
					    '''d''',
					    '''n''',
					    '''\u0120''',
					    '''\u0120l''',
					    '''\u0120n''',
					    '''\u0120lo''',
					    '''\u0120low''',
					    '''er''',
					    '''\u0120lowest''',
					    '''\u0120newer''',
					    '''\u0120wider''',
					    '''<unk>''',
					]
					__UpperCAmelCase    :     Union[str, Any]													=	dict(zip(a_       ,       range(len(a_ ) ) ) )
					__UpperCAmelCase    :     Tuple													=	['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
					__UpperCAmelCase    :     Tuple													=	{'''unk_token''': '''<unk>'''}

					__UpperCAmelCase    :     Optional[Any]													=	os.path.join(self.tmpdirname       ,       VOCAB_FILES_NAMES['''vocab_file'''] )
					__UpperCAmelCase    :     Optional[Any]													=	os.path.join(self.tmpdirname       ,       VOCAB_FILES_NAMES['''merges_file'''] )
					with open(self.vocab_file       ,       '''w'''       ,       encoding='''utf-8''' ) as fp:
							fp.write(json.dumps(a_ ) + '''\n''' )
					with open(self.merges_file       ,       '''w'''       ,       encoding='''utf-8''' ) as fp:
							fp.write('''\n'''.join(a_ ) )




			def  snake_case__     (   self							: int       ,       **a_							: Union[str, Any] ):
					'''simple docstring'''





					kwargs.update(self.special_tokens_map )
					return self.tokenizer_class.from_pretrained(self.tmpdirname       ,       **a_ )




			def  snake_case__     (   self							: Union[str, Any]       ,       **a_							: Any ):
					'''simple docstring'''





					kwargs.update(self.special_tokens_map )
					return RobertaTokenizerFast.from_pretrained(self.tmpdirname       ,       **a_ )




			def  snake_case__     (   self							: Optional[Any]       ,       a_							: Optional[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Union[str, Any]													=	'''lower newer'''
					__UpperCAmelCase    :     Any													=	'''lower newer'''
					return input_text, output_text




			def  snake_case__     (   self							: List[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     List[str]													=	self.tokenizer_class(self.vocab_file       ,       self.merges_file       ,       **self.special_tokens_map )
					__UpperCAmelCase    :     Optional[Any]													=	'''lower newer'''
					__UpperCAmelCase    :     Dict													=	['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
					__UpperCAmelCase    :     Any													=	tokenizer.tokenize(a_ )  # , add_prefix_space=True)
					self.assertListEqual(a_       ,       a_ )

					__UpperCAmelCase    :     Optional[Any]													=	tokens + [tokenizer.unk_token]
					__UpperCAmelCase    :     Tuple													=	[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
					self.assertListEqual(tokenizer.convert_tokens_to_ids(a_ )       ,       a_ )




			def  snake_case__     (   self							: List[str] ):
					'''simple docstring'''





					__UpperCAmelCase    :     List[str]													=	self.get_tokenizer()

					self.assertListEqual(tokenizer.encode('''Hello world!'''       ,       add_special_tokens=a_ )       ,       [0, 3_14_14, 2_32, 3_28, 2] )
					self.assertListEqual(
					    tokenizer.encode('''Hello world! cécé herlolip 418'''       ,       add_special_tokens=a_ )       ,       [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]       ,       )




			@slow
			def  snake_case__     (   self							: Any ):
					'''simple docstring'''





					__UpperCAmelCase    :     Dict													=	self.tokenizer_class.from_pretrained('''roberta-base''' )

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

					__UpperCAmelCase    :     Optional[int]													=	tokenizer.encode(
					    '''sequence builders'''       ,       add_special_tokens=a_       ,       add_prefix_space=a_ )
					__UpperCAmelCase    :     Optional[int]													=	tokenizer.encode(
					    '''sequence builders'''       ,       '''multi-sequence build'''       ,       add_special_tokens=a_       ,       add_prefix_space=a_ )

					__UpperCAmelCase    :     Optional[Any]													=	tokenizer.build_inputs_with_special_tokens(a_ )
					__UpperCAmelCase    :     List[str]													=	tokenizer.build_inputs_with_special_tokens(a_       ,       a_ )

					assert encoded_sentence == encoded_text_from_decode
					assert encoded_pair == encoded_pair_from_decode




			def  snake_case__     (   self							: Optional[int] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Any													=	self.get_tokenizer()

					__UpperCAmelCase    :     List[Any]													=	'''Encode this sequence.'''
					__UpperCAmelCase    :     Dict													=	tokenizer.byte_encoder[''' '''.encode('''utf-8''' )[0]]

					# Testing encoder arguments
					__UpperCAmelCase    :     List[str]													=	tokenizer.encode(a_       ,       add_special_tokens=a_       ,       add_prefix_space=a_ )
					__UpperCAmelCase    :     Any													=	tokenizer.convert_ids_to_tokens(encoded[0] )[0]
					self.assertNotEqual(a_       ,       a_ )

					__UpperCAmelCase    :     int													=	tokenizer.encode(a_       ,       add_special_tokens=a_       ,       add_prefix_space=a_ )
					__UpperCAmelCase    :     List[Any]													=	tokenizer.convert_ids_to_tokens(encoded[0] )[0]
					self.assertEqual(a_       ,       a_ )

					tokenizer.add_special_tokens({'''bos_token''': '''<s>'''} )
					__UpperCAmelCase    :     Union[str, Any]													=	tokenizer.encode(a_       ,       add_special_tokens=a_ )
					__UpperCAmelCase    :     Tuple													=	tokenizer.convert_ids_to_tokens(encoded[1] )[0]
					self.assertNotEqual(a_       ,       a_ )

					# Testing spaces after special tokens
					__UpperCAmelCase    :     Union[str, Any]													=	'''<mask>'''
					tokenizer.add_special_tokens(
					    {'''mask_token''': AddedToken(a_       ,       lstrip=a_       ,       rstrip=a_ )} )  # mask token has a left space
					__UpperCAmelCase    :     Optional[int]													=	tokenizer.convert_tokens_to_ids(a_ )

					__UpperCAmelCase    :     int													=	'''Encode <mask> sequence'''
					__UpperCAmelCase    :     List[str]													=	'''Encode <mask>sequence'''

					__UpperCAmelCase    :     str													=	tokenizer.encode(a_ )
					__UpperCAmelCase    :     Optional[Any]													=	encoded.index(a_ )
					__UpperCAmelCase    :     Union[str, Any]													=	tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
					self.assertEqual(a_       ,       a_ )

					__UpperCAmelCase    :     Dict													=	tokenizer.encode(a_ )
					__UpperCAmelCase    :     Optional[int]													=	encoded.index(a_ )
					__UpperCAmelCase    :     List[str]													=	tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
					self.assertNotEqual(a_       ,       a_ )




			def  snake_case__     (   self							: Optional[Any] ):
					'''simple docstring'''





					pass




			def  snake_case__     (   self							: List[str] ):
					'''simple docstring'''





					for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})' ):
									__UpperCAmelCase    :     int													=	self.rust_tokenizer_class.from_pretrained(a_       ,       **a_ )
									__UpperCAmelCase    :     Optional[int]													=	self.tokenizer_class.from_pretrained(a_       ,       **a_ )
									__UpperCAmelCase    :     Optional[int]													=	'''A, <mask> AllenNLP sentence.'''
									__UpperCAmelCase    :     Union[str, Any]													=	tokenizer_r.encode_plus(a_       ,       add_special_tokens=a_       ,       return_token_type_ids=a_ )
									__UpperCAmelCase    :     List[str]													=	tokenizer_p.encode_plus(a_       ,       add_special_tokens=a_       ,       return_token_type_ids=a_ )

									# token_type_ids should put 0 everywhere
									self.assertEqual(sum(tokens_r['''token_type_ids'''] )       ,       sum(tokens_p['''token_type_ids'''] ) )

									# attention_mask should put 1 everywhere, so sum over length should be 1
									self.assertEqual(
									    sum(tokens_r['''attention_mask'''] ) / len(tokens_r['''attention_mask'''] )       ,       sum(tokens_p['''attention_mask'''] ) / len(tokens_p['''attention_mask'''] )       ,       )

									__UpperCAmelCase    :     List[Any]													=	tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids'''] )
									__UpperCAmelCase    :     Tuple													=	tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids'''] )

									# Rust correctly handles the space before the mask while python doesnt
									self.assertSequenceEqual(tokens_p['''input_ids''']       ,       [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
									self.assertSequenceEqual(tokens_r['''input_ids''']       ,       [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )

									self.assertSequenceEqual(
									    a_       ,       ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''] )
									self.assertSequenceEqual(
									    a_       ,       ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''] )




			def  snake_case__     (   self							: Any ):
					'''simple docstring'''





					for trim_offsets, add_prefix_space in itertools.product([True, False]       ,       repeat=2 ):
							__UpperCAmelCase    :     Tuple													=	self.rust_tokenizer_class.from_pretrained(
							    self.tmpdirname       ,       use_fast=a_       ,       add_prefix_space=a_       ,       trim_offsets=a_ )

							__UpperCAmelCase    :     Optional[Any]													=	json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
							__UpperCAmelCase    :     Any													=	json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() )

							self.assertEqual(pre_tokenizer_state['''add_prefix_space''']       ,       a_ )

							self.assertEqual(post_processor_state['''add_prefix_space''']       ,       a_ )
							self.assertEqual(post_processor_state['''trim_offsets''']       ,       a_ )




			def  snake_case__     (   self							: Optional[Any] ):
					'''simple docstring'''





					for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})' ):
									__UpperCAmelCase    :     Optional[Any]													=	'''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
									__UpperCAmelCase    :     Optional[Any]													=	F'{text_of_1_token} {text_of_1_token}'

									__UpperCAmelCase    :     str													=	self.rust_tokenizer_class.from_pretrained(
									    a_       ,       use_fast=a_       ,       add_prefix_space=a_       ,       trim_offsets=a_ )
									__UpperCAmelCase    :     Tuple													=	tokenizer_r(a_       ,       return_offsets_mapping=a_       ,       add_special_tokens=a_ )
									self.assertEqual(encoding.offset_mapping[0]       ,       (0, len(a_ )) )
									self.assertEqual(
									    encoding.offset_mapping[1]       ,       (len(a_ ) + 1, len(a_ ) + 1 + len(a_ ))       ,       )

									__UpperCAmelCase    :     Tuple													=	self.rust_tokenizer_class.from_pretrained(
									    a_       ,       use_fast=a_       ,       add_prefix_space=a_       ,       trim_offsets=a_ )
									__UpperCAmelCase    :     List[str]													=	tokenizer_r(a_       ,       return_offsets_mapping=a_       ,       add_special_tokens=a_ )
									self.assertEqual(encoding.offset_mapping[0]       ,       (0, len(a_ )) )
									self.assertEqual(
									    encoding.offset_mapping[1]       ,       (len(a_ ) + 1, len(a_ ) + 1 + len(a_ ))       ,       )

									__UpperCAmelCase    :     List[Any]													=	self.rust_tokenizer_class.from_pretrained(
									    a_       ,       use_fast=a_       ,       add_prefix_space=a_       ,       trim_offsets=a_ )
									__UpperCAmelCase    :     Dict													=	tokenizer_r(a_       ,       return_offsets_mapping=a_       ,       add_special_tokens=a_ )
									self.assertEqual(encoding.offset_mapping[0]       ,       (0, len(a_ )) )
									self.assertEqual(
									    encoding.offset_mapping[1]       ,       (len(a_ ), len(a_ ) + 1 + len(a_ ))       ,       )

									__UpperCAmelCase    :     Dict													=	self.rust_tokenizer_class.from_pretrained(
									    a_       ,       use_fast=a_       ,       add_prefix_space=a_       ,       trim_offsets=a_ )
									__UpperCAmelCase    :     Union[str, Any]													=	tokenizer_r(a_       ,       return_offsets_mapping=a_       ,       add_special_tokens=a_ )
									self.assertEqual(encoding.offset_mapping[0]       ,       (0, len(a_ )) )
									self.assertEqual(
									    encoding.offset_mapping[1]       ,       (len(a_ ), len(a_ ) + 1 + len(a_ ))       ,       )

									__UpperCAmelCase    :     Tuple													=	F' {text}'

									# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
									#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
									# )
									# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
									# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
									# self.assertEqual(
									#     encoding.offset_mapping[1],
									#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
									# )

									__UpperCAmelCase    :     Tuple													=	self.rust_tokenizer_class.from_pretrained(
									    a_       ,       use_fast=a_       ,       add_prefix_space=a_       ,       trim_offsets=a_ )
									__UpperCAmelCase    :     List[Any]													=	tokenizer_r(a_       ,       return_offsets_mapping=a_       ,       add_special_tokens=a_ )
									self.assertEqual(encoding.offset_mapping[0]       ,       (1, 1 + len(a_ )) )
									self.assertEqual(
									    encoding.offset_mapping[1]       ,       (1 + len(a_ ) + 1, 1 + len(a_ ) + 1 + len(a_ ))       ,       )

									__UpperCAmelCase    :     Any													=	self.rust_tokenizer_class.from_pretrained(
									    a_       ,       use_fast=a_       ,       add_prefix_space=a_       ,       trim_offsets=a_ )
									__UpperCAmelCase    :     Optional[int]													=	tokenizer_r(a_       ,       return_offsets_mapping=a_       ,       add_special_tokens=a_ )
									self.assertEqual(encoding.offset_mapping[0]       ,       (0, 1 + len(a_ )) )
									self.assertEqual(
									    encoding.offset_mapping[1]       ,       (1 + len(a_ ), 1 + len(a_ ) + 1 + len(a_ ))       ,       )

									__UpperCAmelCase    :     str													=	self.rust_tokenizer_class.from_pretrained(
									    a_       ,       use_fast=a_       ,       add_prefix_space=a_       ,       trim_offsets=a_ )
									__UpperCAmelCase    :     Dict													=	tokenizer_r(a_       ,       return_offsets_mapping=a_       ,       add_special_tokens=a_ )
									self.assertEqual(encoding.offset_mapping[0]       ,       (0, 1 + len(a_ )) )
									self.assertEqual(
									    encoding.offset_mapping[1]       ,       (1 + len(a_ ), 1 + len(a_ ) + 1 + len(a_ ))       ,       )










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





