



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        snake_case__    (__UpperCamelCase ,     unittest.TestCase		):







						"""simple docstring"""




						__lowerCAmelCase					:Optional[int]      =					BioGptTokenizer
						__lowerCAmelCase					:Optional[int]      =					False

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[int]:
									"""simple docstring"""




									super().setUp()

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

									a__       :						str							=  os.path.join(self.tmpdirname ,       VOCAB_FILES_NAMES["""vocab_file"""]  )
									a__       :						Optional[int]							=  os.path.join(self.tmpdirname ,       VOCAB_FILES_NAMES["""merges_file"""]  )
									with open(self.vocab_file ,       """w"""  ) as fp:
												fp.write(json.dumps(__lowercase  )  )
									with open(self.merges_file ,       """w"""  ) as fp:
												fp.write("""\n""".join(__lowercase  )  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				str:
									"""simple docstring"""




									a__       :						List[Any]							=  'lower newer'
									a__       :						Dict							=  'lower newer'
									return input_text, output_text

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Tuple:
									"""simple docstring"""




									a__       :						Dict							=  BioGptTokenizer(self.vocab_file ,       self.merges_file  )

									a__       :						int							=  'lower'
									a__       :						Dict							=  ['low', 'er</w>']
									a__       :						Optional[int]							=  tokenizer.tokenize(__lowercase  )
									self.assertListEqual(__lowercase ,       __lowercase  )

									a__       :						List[str]							=  tokens + ['<unk>']
									a__       :						Tuple							=  [1_4, 1_5, 2_0]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase  ) ,       __lowercase  )

						@slow
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				str:
									"""simple docstring"""




									a__       :						Optional[Any]							=  BioGptTokenizer.from_pretrained("""microsoft/biogpt"""  )

									a__       :						Union[str, Any]							=  tokenizer.encode("""sequence builders""" ,       add_special_tokens=__lowercase  )
									a__       :						int							=  tokenizer.encode("""multi-sequence build""" ,       add_special_tokens=__lowercase  )

									a__       :						Optional[Any]							=  tokenizer.build_inputs_with_special_tokens(__lowercase  )
									a__       :						Tuple							=  tokenizer.build_inputs_with_special_tokens(__lowercase ,       __lowercase  )

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









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


_lowercase		:				List[Any]      		={}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:				int      		=["BartphoTokenizer"]

if TYPE_CHECKING:
		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_bartpho import BartphoTokenizer

else:
		import sys

		_lowercase		:				Tuple      		=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




