



"""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 			A__   (  a__  ,		unittest.TestCase							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	BioGptTokenizer
	SCREAMING_SNAKE_CASE          =	False

	def 							_SCREAMING_SNAKE_CASE   (     self:		List[Any])				->       Optional[Any]:

				"""simple docstring"""

				super().setUp()

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

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

	def 							_SCREAMING_SNAKE_CASE   (     self:		int       ,			_SCREAMING_SNAKE_CASE:		Tuple)				->       Any:

				"""simple docstring"""

				__lowerCAmelCase      :  List[str]												=       'lower newer'
				__lowerCAmelCase      :  Optional[Any]												=       'lower newer'
				return input_text, output_text

	def 							_SCREAMING_SNAKE_CASE   (     self:		List[str])				->       List[str]:

				"""simple docstring"""

				__lowerCAmelCase      :  Union[str, Any]												=       BioGptTokenizer(self.vocab_file       ,			self.merges_file)

				__lowerCAmelCase      :  int												=       'lower'
				__lowerCAmelCase      :  Union[str, Any]												=       ['low', 'er</w>']
				__lowerCAmelCase      :  Union[str, Any]												=       tokenizer.tokenize(_UpperCAmelCase)
				self.assertListEqual(_UpperCAmelCase       ,			_UpperCAmelCase)

				__lowerCAmelCase      :  Optional[int]												=       tokens + ['<unk>']
				__lowerCAmelCase      :  Any												=       [14, 15, 20]
				self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase)       ,			_UpperCAmelCase)







	@slow
	def 							_SCREAMING_SNAKE_CASE   (     self:		Dict)				->       Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Any												=       BioGptTokenizer.from_pretrained("microsoft/biogpt")

				__lowerCAmelCase      :  Any												=       tokenizer.encode("sequence builders"       ,			add_special_tokens=_UpperCAmelCase)
				__lowerCAmelCase      :  List[Any]												=       tokenizer.encode("multi-sequence build"       ,			add_special_tokens=_UpperCAmelCase)

				__lowerCAmelCase      :  Tuple												=       tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase)
				__lowerCAmelCase      :  Union[str, Any]												=       tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase       ,			_UpperCAmelCase)

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






'''simple docstring'''




import random






def        _lowerCAmelCase			(  __snake_case  : int ,							__snake_case  : float ,							__snake_case  : bool = False			)     ->     dict:
     __A						: dict      =       {i: [] for i in range(__snake_case			)}

     # if probability is greater or equal than 1, then generate a complete graph
     if probability >= 1:
          return complete_graph(__snake_case			)
     # if probability is lower or equal than 0, then return a graph without edges
     if probability <= 0:
          return graph

     # for each couple of nodes, add an edge from u to v
     # if the number randomly generated is greater than probability probability
     for i in range(__snake_case			):
          for j in range(i + 1 ,							__snake_case			):
               if random.random() < probability:
                    graph[i].append(__snake_case			)
                    if not directed:
                         # if the graph is undirected, add an edge in from j to i, either
                         graph[j].append(__snake_case			)
     return graph






def        _lowerCAmelCase			(  __snake_case  : int			)     ->     dict:
     return {
         i: [j for j in range(__snake_case			) if i != j] for i in range(__snake_case			)
     }


if __name__ == "__main__":
  import doctest

  doctest.testmod()