






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 							lowerCAmelCase__  (	UpperCamelCase					,							unittest.TestCase						):
					__A     :    List[Any]           =						BioGptTokenizer
					__A     :    Optional[Any]           =						False







					def  _lowercase			(       self      :     str):
											super().setUp()

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

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







					def  _lowercase			(       self      :     Optional[Any]			,		_A      :     Dict):
											A__	:							Dict						    = "lower newer"
											A__	:							Dict						    = "lower newer"
											return input_text, output_text







					def  _lowercase			(       self      :     Optional[Any]):
											A__	:							Dict						    = BioGptTokenizer(self.vocab_file			,		self.merges_file)

											A__	:							Any						    = "lower"
											A__	:							List[Any]						    = ["low", "er</w>"]
											A__	:							Tuple						    = tokenizer.tokenize(_A)
											self.assertListEqual(_A			,		_A)

											A__	:							List[Any]						    = tokens + ["<unk>"]
											A__	:							Optional[Any]						    = [14, 15, 20]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(_A)			,		_A)







					@slow
					def  _lowercase			(       self      :     Optional[int]):
											A__	:							Tuple						    = BioGptTokenizer.from_pretrained("microsoft/biogpt")

											A__	:							Optional[int]						    = tokenizer.encode("sequence builders"			,		add_special_tokens=_A)
											A__	:							Optional[Any]						    = tokenizer.encode("multi-sequence build"			,		add_special_tokens=_A)

											A__	:							Tuple						    = tokenizer.build_inputs_with_special_tokens(_A)
											A__	:							Union[str, Any]						    = tokenizer.build_inputs_with_special_tokens(_A			,		_A)

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




from collections.abc import Callable

import numpy as np
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase       )		->		np.array:







						"""simple docstring"""

						A__	:							Any						    = int(np.ceil((x_end - xa) / step_size       )       )
						A__	:							Union[str, Any]						    = np.zeros((n + 1,)       )
						A__	:							Any						    = ya
						A__	:							Union[str, Any]						    = xa

						for k in range(__lowercase       ):
												A__	:							Any						    = y[k] + step_size * ode_func(__lowercase							,     y[k]       )
												A__	:							Any						    = y[k] + (
												    (step_size / 2) * (ode_func(__lowercase							,     y[k]       ) + ode_func(x + step_size							,     __lowercase       ))
												)
												x += step_size

						return y


if __name__ == "__main__":
		import doctest

		doctest.testmod()