


'''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     lowercase__  (						_snake_case     ,     unittest.TestCase					):





							'''simple docstring'''


							A_							:      Any      	=  BioGptTokenizer
							A_							:      List[str]      	=  False






							def UpperCAmelCase_	(    self  ):
											super().setUp()

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

											_SCREAMING_SNAKE_CASE :       List[str]     		=      os.path.join(self.tmpdirname    ,					VOCAB_FILES_NAMES["""vocab_file"""]  )
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      os.path.join(self.tmpdirname    ,					VOCAB_FILES_NAMES["""merges_file"""]  )
											with open(self.vocab_file    ,					"""w"""  ) as fp:
															fp.write(json.dumps(__snake_case  )  )
											with open(self.merges_file    ,					"""w"""  ) as fp:
															fp.write("""\n""".join(__snake_case  )  )






							def UpperCAmelCase_	(    self    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       Tuple     		=      """lower newer"""
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      """lower newer"""
											return input_text, output_text






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      BioGptTokenizer(self.vocab_file    ,					self.merges_file  )

											_SCREAMING_SNAKE_CASE :       Any     		=      """lower"""
											_SCREAMING_SNAKE_CASE :       str     		=      ["""low""", """er</w>"""]
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      tokenizer.tokenize(__snake_case  )
											self.assertListEqual(__snake_case    ,					__snake_case  )

											_SCREAMING_SNAKE_CASE :       str     		=      tokens + ["""<unk>"""]
											_SCREAMING_SNAKE_CASE :       Dict     		=      [14, 15, 20]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case  )    ,					__snake_case  )






							@slow
							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      BioGptTokenizer.from_pretrained("""microsoft/biogpt"""  )

											_SCREAMING_SNAKE_CASE :       List[str]     		=      tokenizer.encode("""sequence builders"""    ,					add_special_tokens=__snake_case  )
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      tokenizer.encode("""multi-sequence build"""    ,					add_special_tokens=__snake_case  )

											_SCREAMING_SNAKE_CASE :       int     		=      tokenizer.build_inputs_with_special_tokens(__snake_case  )
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      tokenizer.build_inputs_with_special_tokens(__snake_case    ,					__snake_case  )

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








'''simple docstring'''


UpperCAmelCase_		:   Union[str, Any]							   =		'\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

UpperCAmelCase_		:   Dict							   =		[{'type': 'code', 'content': INSTALL_CONTENT}]
UpperCAmelCase_		:   Any							   =		{
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}




