

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__				(      UpperCAmelCase		,	unittest.TestCase				):







  """simple docstring"""




  UpperCAmelCase__					:       List[Any]					=BioGptTokenizer
  UpperCAmelCase__					:       Optional[int]					=False


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







        super().setUp()

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

        SCREAMING_SNAKE_CASE       :					Dict        =      os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""]						)
        SCREAMING_SNAKE_CASE       :					int        =      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			:       List[Any] , UpperCAmelCase__			:       Tuple						)    ->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE       :					Dict        =      "lower newer"
        SCREAMING_SNAKE_CASE       :					Union[str, Any]        =      "lower newer"
        return input_text, output_text


  def        _lowercase					(	self			:       Optional[Any]						)    ->Any:
        """simple docstring"""







        SCREAMING_SNAKE_CASE       :					List[str]        =      BioGptTokenizer(self.vocab_file , self.merges_file						)

        SCREAMING_SNAKE_CASE       :					Optional[int]        =      "lower"
        SCREAMING_SNAKE_CASE       :					Any        =      ["low", "er</w>"]
        SCREAMING_SNAKE_CASE       :					List[str]        =      tokenizer.tokenize(__A						)
        self.assertListEqual(__A , __A						)

        SCREAMING_SNAKE_CASE       :					Union[str, Any]        =      tokens + ["<unk>"]
        SCREAMING_SNAKE_CASE       :					List[str]        =      [1_4, 1_5, 2_0]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__A						) , __A						)




  @slow
  def        _lowercase					(	self			:       int						)    ->int:
        """simple docstring"""







        SCREAMING_SNAKE_CASE       :					List[str]        =      BioGptTokenizer.from_pretrained("""microsoft/biogpt"""						)

        SCREAMING_SNAKE_CASE       :					Optional[int]        =      tokenizer.encode("""sequence builders""" , add_special_tokens=__A						)
        SCREAMING_SNAKE_CASE       :					Tuple        =      tokenizer.encode("""multi-sequence build""" , add_special_tokens=__A						)

        SCREAMING_SNAKE_CASE       :					Tuple        =      tokenizer.build_inputs_with_special_tokens(__A						)
        SCREAMING_SNAKE_CASE       :					List[str]        =      tokenizer.build_inputs_with_special_tokens(__A , __A						)

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








from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
_snake_case             =     2_99_79_24_58

# Symbols
_snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case             =     symbols('''ct x y z''')






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  if velocity > c:
    raise ValueError("Speed must not exceed light speed 299,792,458 [m/s]!"	)
  elif velocity < 1:
    # Usually the speed should be much higher than 1 (c order of magnitude)
    raise ValueError("Speed must be greater than or equal to 1!"	)

  return velocity / c






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  return 1 / sqrt(1 - beta(SCREAMING_SNAKE_CASE_	) ** 2	)






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  return np.array(
      [
          [gamma(SCREAMING_SNAKE_CASE_	), -gamma(SCREAMING_SNAKE_CASE_	) * beta(SCREAMING_SNAKE_CASE_	), 0, 0],
          [-gamma(SCREAMING_SNAKE_CASE_	) * beta(SCREAMING_SNAKE_CASE_	), gamma(SCREAMING_SNAKE_CASE_	), 0, 0],
          [0, 0, 1, 0],
          [0, 0, 0, 1],
      ]	)






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_ = None	):


  '''simple docstring'''

  if event is None:
    lowerCamelCase :					Tuple    =				np.array([ct, x, y, z]	)  # Symbolic four vector
  else:
    event[0] *= c  # x0 is ct (speed of light * time)

  return transformation_matrix(SCREAMING_SNAKE_CASE_	) @ event


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   # Example of symbolic vector:
   _snake_case             =     transform(29_97_92_45)
   print('''Example of four vector: ''')
   print(f'''ct\' = {four_vector[0]}''')
   print(f'''x\' = {four_vector[1]}''')
   print(f'''y\' = {four_vector[2]}''')
   print(f'''z\' = {four_vector[3]}''')

   # Substitute symbols with numerical values
   _snake_case             =     {ct: c, x: 1, y: 1, z: 1}
   _snake_case             =     [four_vector[i].subs(sub_dict) for i in range(4)]

   print(f'''\n{numerical_vector}''')



