




"""simple docstring"""







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


SCREAMING_SNAKE_CASE_      =					logging.getLogger(__name__)





def     __snake_case   (							):


    """simple docstring"""







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

    return parser.parse_args()





def     __snake_case   (							):


    """simple docstring"""







    UpperCamelCase										=  parse_args()
    UpperCamelCase										=  8888
    UpperCamelCase										=  int(os.environ['''SM_NUM_GPUS''']						)
    UpperCamelCase										=  json.loads(os.environ['''SM_HOSTS''']						)
    UpperCamelCase										=  len(_lowercase						)
    UpperCamelCase										=  os.environ['''SM_CURRENT_HOST''']
    UpperCamelCase										=  hosts.index(_lowercase						)
    UpperCamelCase										=  '''INFO'''

    if num_nodes > 1:
        UpperCamelCase										=  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										=  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(_lowercase ,shell=_lowercase						)
    except Exception as e:
        logger.info(_lowercase						)


if __name__ == "__main__":
  main()





"""simple docstring"""







import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE_      =					get_tests_dir('fixtures/spiece.model')

@require_sentencepiece
@require_tokenizers
class 							snake_case_ (				lowerCamelCase_  ,   unittest.TestCase ):







   """simple docstring"""




   A_										=					ErnieMTokenizer
   A_										=					False
   A_										=					True
   A_										=					False
   A_										=					False







   def      UpperCAmelCase__ (		self)							->      List[str]:
       super().setUp()

       # We have a SentencePiece fixture for testing
       UpperCamelCase										=  ErnieMTokenizer(lowerCamelCase_					,				unk_token='''<unk>'''					,				pad_token='''<pad>''')
       tokenizer.save_pretrained(self.tmpdirname)







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_)							->      Optional[Any]:
       UpperCamelCase										=  '''this is a test'''
       UpperCamelCase										=  '''this is a test'''
       return input_text, output_text







   def      UpperCAmelCase__ (		self)							->      Optional[Any]:
       UpperCamelCase										=  '''<pad>'''
       UpperCamelCase										=  0

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase_)					,				lowerCamelCase_)
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase_)					,				lowerCamelCase_)







   def      UpperCAmelCase__ (		self)							->      Optional[Any]:
       UpperCamelCase										=  list(self.get_tokenizer().get_vocab().keys())

       self.assertEqual(vocab_keys[0]					,				'''<pad>''')
       self.assertEqual(vocab_keys[1]					,				'''<unk>''')
       self.assertEqual(vocab_keys[-1]					,				'''▁eloquent''')
       self.assertEqual(len(lowerCamelCase_)					,				3_0_0_0_0)







   def      UpperCAmelCase__ (		self)							->      int:
       self.assertEqual(self.get_tokenizer().vocab_size					,				3_0_0_0_0)







   def      UpperCAmelCase__ (		self)							->      List[Any]:
       if not self.test_rust_tokenizer:
           return

       UpperCamelCase										=  self.get_tokenizer()
       UpperCamelCase										=  self.get_rust_tokenizer()

       UpperCamelCase										=  '''I was born in 92000, and this is falsé.'''

       UpperCamelCase										=  tokenizer.tokenize(lowerCamelCase_)
       UpperCamelCase										=  rust_tokenizer.tokenize(lowerCamelCase_)
       self.assertListEqual(lowerCamelCase_					,				lowerCamelCase_)

       UpperCamelCase										=  tokenizer.encode(lowerCamelCase_					,				add_special_tokens=lowerCamelCase_)
       UpperCamelCase										=  rust_tokenizer.encode(lowerCamelCase_					,				add_special_tokens=lowerCamelCase_)
       self.assertListEqual(lowerCamelCase_					,				lowerCamelCase_)

       UpperCamelCase										=  self.get_rust_tokenizer()
       UpperCamelCase										=  tokenizer.encode(lowerCamelCase_)
       UpperCamelCase										=  rust_tokenizer.encode(lowerCamelCase_)
       self.assertListEqual(lowerCamelCase_					,				lowerCamelCase_)







   def      UpperCAmelCase__ (		self)							->      List[Any]:
       UpperCamelCase										=  ErnieMTokenizer(lowerCamelCase_					,				do_lower_case=lowerCamelCase_					,				unk_token='''<unk>'''					,				pad_token='''<pad>''')

       UpperCamelCase										=  tokenizer.tokenize('''This is a test''')
       self.assertListEqual(lowerCamelCase_					,				['''▁this''', '''▁is''', '''▁a''', '''▁test'''])

       self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_)					,				[4_8, 2_5, 2_1, 1_2_8_9])

       UpperCamelCase										=  tokenizer.tokenize('''I was born in 92000, and this is falsé.''')
       # ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
       self.assertListEqual(
           lowerCamelCase_					,				['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''é''', '''.'''])
       UpperCamelCase										=  tokenizer.convert_tokens_to_ids(lowerCamelCase_)
       self.assertListEqual(lowerCamelCase_					,				[3_1, 2_3, 3_8_6, 1_9, 5_1_8, 3_0_5_0, 1_5, 1_7, 4_8, 2_5, 8_2_5_6, 1_8, 1, 9])

       UpperCamelCase										=  tokenizer.convert_ids_to_tokens(lowerCamelCase_)
       self.assertListEqual(
           lowerCamelCase_					,				['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''']					,				)







   def      UpperCAmelCase__ (		self)							->      Dict:
       UpperCamelCase										=  ErnieMTokenizer(lowerCamelCase_					,				unk_token='''<unk>'''					,				pad_token='''<pad>''')

       UpperCamelCase										=  tokenizer.encode('''sequence builders''')
       UpperCamelCase										=  tokenizer.encode('''multi-sequence build''')

       UpperCamelCase										=  tokenizer.build_inputs_with_special_tokens(lowerCamelCase_)
       UpperCamelCase										=  tokenizer.build_inputs_with_special_tokens(lowerCamelCase_					,				lowerCamelCase_)

       assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
       assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
           tokenizer.sep_token_id
       ] + text_a + [tokenizer.sep_token_id]







   @slow
   def      UpperCAmelCase__ (		self)							->      List[str]:
       # fmt: off
       UpperCamelCase										=  {'''input_ids''': [[0, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 9, 3_0_4, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 5, 5, 5, 1_6, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 6_4_6_0, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 3_5_5_9, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [0, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=lowerCamelCase_					,				model_name='''susnato/ernie-m-base_pytorch'''					,				sequences=[
               '''Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '''
               '''general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural '''
               '''Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained '''
               '''models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.''',
               '''BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '''
               '''conditioning on both left and right context in all layers.''',
               '''The quick brown fox jumps over the lazy dog.''',
           ]					,				)