



import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors

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




			'''simple docstring'''






			UpperCamelCase     		=			BartTokenizer
			UpperCamelCase     		=			BartTokenizerFast
			UpperCamelCase     		=			True
			UpperCamelCase     		=			filter_roberta_detectors




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





					super().setUp()
					__UpperCAmelCase       :	Dict						=      [
					    '''l''',
					    '''o''',
					    '''w''',
					    '''e''',
					    '''r''',
					    '''s''',
					    '''t''',
					    '''i''',
					    '''d''',
					    '''n''',
					    '''\u0120''',
					    '''\u0120l''',
					    '''\u0120n''',
					    '''\u0120lo''',
					    '''\u0120low''',
					    '''er''',
					    '''\u0120lowest''',
					    '''\u0120newer''',
					    '''\u0120wider''',
					    '''<unk>''',
					]
					__UpperCAmelCase       :	Any						=      dict(zip(a_			,			range(len(a_					)					)					)					)
					__UpperCAmelCase       :	Optional[Any]						=      ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
					__UpperCAmelCase       :	str						=      {'''unk_token''': '''<unk>'''}

					__UpperCAmelCase       :	Optional[int]						=      os.path.join(self.tmpdirname			,			VOCAB_FILES_NAMES['''vocab_file''']					)
					__UpperCAmelCase       :	List[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				:					Union[str, Any]			,			**a_				:					List[Any]					):
					'''simple docstring'''





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




			def 					snake_case__	(  self				:					Optional[int]			,			**a_				:					Dict					):
					'''simple docstring'''





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




			def 					snake_case__	(  self				:					str			,			a_				:					Any					):
					'''simple docstring'''





					return "lower newer", "lower newer"




			@cached_property
			def 					snake_case__	(  self				:					int					):
					'''simple docstring'''





					return BartTokenizer.from_pretrained('''facebook/bart-large'''					)




			@cached_property
			def 					snake_case__	(  self				:					Union[str, Any]					):
					'''simple docstring'''





					return BartTokenizerFast.from_pretrained('''facebook/bart-large'''					)




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





					__UpperCAmelCase       :	List[str]						=      ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
					__UpperCAmelCase       :	Any						=      [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							__UpperCAmelCase       :	List[str]						=      tokenizer(a_			,			max_length=len(a_					)			,			padding=a_			,			return_tensors='''pt'''					)
							self.assertIsInstance(a_			,			a_					)

							self.assertEqual((2, 9)			,			batch.input_ids.shape					)
							self.assertEqual((2, 9)			,			batch.attention_mask.shape					)
							__UpperCAmelCase       :	List[Any]						=      batch.input_ids.tolist()[0]
							self.assertListEqual(a_			,			a_					)
							# Test that special tokens are reset




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





					__UpperCAmelCase       :	Tuple						=      ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							__UpperCAmelCase       :	List[Any]						=      tokenizer(a_			,			padding=a_			,			return_tensors='''pt'''					)
							# check if input_ids are returned and no labels
							self.assertIn('''input_ids'''			,			a_					)
							self.assertIn('''attention_mask'''			,			a_					)
							self.assertNotIn('''labels'''			,			a_					)
							self.assertNotIn('''decoder_attention_mask'''			,			a_					)




			@require_torch
			def 					snake_case__	(  self				:					Tuple					):
					'''simple docstring'''





					__UpperCAmelCase       :	Dict						=      [
					    '''Summary of the text.''',
					    '''Another summary.''',
					]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							__UpperCAmelCase       :	str						=      tokenizer(text_target=a_			,			max_length=32			,			padding='''max_length'''			,			return_tensors='''pt'''					)
							self.assertEqual(32			,			targets['''input_ids'''].shape[1]					)




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





					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							__UpperCAmelCase       :	Any						=      tokenizer(
							    ['''I am a small frog''' * 10_24, '''I am a small frog''']			,			padding=a_			,			truncation=a_			,			return_tensors='''pt'''					)
							self.assertIsInstance(a_			,			a_					)
							self.assertEqual(batch.input_ids.shape			,			(2, 10_24)					)




			@require_torch
			def 					snake_case__	(  self				:					Tuple					):
					'''simple docstring'''





					__UpperCAmelCase       :	Optional[Any]						=      ['''A long paragraph for summarization.''']
					__UpperCAmelCase       :	str						=      [
					    '''Summary of the text.''',
					]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							__UpperCAmelCase       :	Optional[Any]						=      tokenizer(a_			,			return_tensors='''pt'''					)
							__UpperCAmelCase       :	str						=      tokenizer(text_target=a_			,			return_tensors='''pt'''					)
							__UpperCAmelCase       :	Dict						=      inputs['''input_ids''']
							__UpperCAmelCase       :	Tuple						=      targets['''input_ids''']
							self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()					)
							self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()					)
							self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()					)
							self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()					)




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





					pass




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





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










import json
import logging
import os
import subprocess
from argparse import ArgumentParser


__A       =logging.getLogger(__name__)

def 			a		( ):







		'''simple docstring'''





		__UpperCAmelCase       :	Dict						=      ArgumentParser()
		__UpperCAmelCase				,   __UpperCAmelCase       :	int						=      parser.parse_known_args()
		for arg in unknown:
				if arg.startswith(('''-''', '''--''')		):
						parser.add_argument(arg.split('''='''		)[0]		)

		return parser.parse_args()

def 			a		( ):







		'''simple docstring'''





		__UpperCAmelCase       :	List[Any]						=      parse_args()
		__UpperCAmelCase       :	Tuple						=      88_88
		__UpperCAmelCase       :	Any						=      int(os.environ['''SM_NUM_GPUS''']		)
		__UpperCAmelCase       :	Optional[int]						=      json.loads(os.environ['''SM_HOSTS''']		)
		__UpperCAmelCase       :	int						=      len(_UpperCAmelCase		)
		__UpperCAmelCase       :	Optional[Any]						=      os.environ['''SM_CURRENT_HOST''']
		__UpperCAmelCase       :	List[str]						=      hosts.index(_UpperCAmelCase		)
		__UpperCAmelCase       :	Dict						=      '''INFO'''

		if num_nodes > 1:
				__UpperCAmelCase       :	Tuple						=      f'python -m torch.distributed.launch \\n                --nnodes={num_nodes}  \\n                --node_rank={rank}  \\n                --nproc_per_node={num_gpus}  \\n                --master_addr={hosts[0]}  \\n                --master_port={port} \\n                ./run_glue.py \\n                {"".join(["nested fstring" for parameter,value in args.__dict__.items()]		)}'
		else:
				__UpperCAmelCase       :	Dict						=      f'python -m torch.distributed.launch \\n            --nproc_per_node={num_gpus}  \\n            ./run_glue.py \\n            {"".join(["nested fstring" for parameter,value in args.__dict__.items()]		)}'
		try:
				subprocess.run(_UpperCAmelCase					,  shell=_UpperCAmelCase		)
		except Exception as e:
				logger.info(_UpperCAmelCase		)


if __name__ == "__main__":
				main()





