



'''simple docstring'''





import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin






class __A     (    A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			Tuple		=			BlenderbotSmallTokenizer
 __lowerCamelCase       :			List[str]		=			False



 def    a__    (self			)       ->	Tuple:


      """simple docstring"""



      super().setUp()

      _a           =    ['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
      _a           =    dict(zip(A					,  range(len(A			)			)			)			)

      _a           =    ['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
      _a           =    {'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}

      _a           =    os.path.join(self.tmpdirname					,  VOCAB_FILES_NAMES['''vocab_file''']			)
      _a           =    os.path.join(self.tmpdirname					,  VOCAB_FILES_NAMES['''merges_file''']			)
      with open(self.vocab_file					,  '''w'''					,  encoding='''utf-8'''			) as fp:
           fp.write(json.dumps(A			) + '''\n'''			)
      with open(self.merges_file					,  '''w'''					,  encoding='''utf-8'''			) as fp:
           fp.write('''\n'''.join(A			)			)



 def    a__    (self					,  **A			)       ->	Optional[int]:


      """simple docstring"""



      kwargs.update(self.special_tokens_map			)
      return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname					,  **A			)



 def    a__    (self					,  A			)       ->	Union[str, Any]:


      """simple docstring"""



      _a           =    '''adapt act apte'''
      _a           =    '''adapt act apte'''
      return input_text, output_text



 def    a__    (self			)       ->	Any:


      """simple docstring"""



      _a           =    BlenderbotSmallTokenizer(self.vocab_file					,  self.merges_file					,  **self.special_tokens_map			)
      _a           =    '''adapt act apte'''
      _a           =    ['''adapt''', '''act''', '''ap@@''', '''te''']
      _a           =    tokenizer.tokenize(A			)
      self.assertListEqual(A					,  A			)

      _a           =    [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

      _a           =    [0, 1, 2, 3, 4, 5]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(A			)					,  A			)



 def    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



      _a           =    BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''			)
      assert tok('''sam'''			).input_ids == [1_384]
      _a           =    '''I am a small frog.'''
      _a           =    tok([src_text]					,  padding=A					,  truncation=A			)['''input_ids''']
      _a           =    tok.batch_decode(A					,  skip_special_tokens=A					,  clean_up_tokenization_spaces=A			)[0]
      assert src_text != decoded  # I wish it did!
      assert decoded == "i am a small frog ."



 def    a__    (self			)       ->	int:


      """simple docstring"""



      _a           =    BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''			)
      _a           =    '''I am a small frog .'''
      _a           =    '''.'''
      _a           =    tok(A			)['''input_ids''']
      _a           =    tok(A			)['''input_ids''']

      assert encoded[-1] == encoded_dot[0]








"""simple docstring"""






import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
__snake_case				:  Optional[Any] =       pd.read_csv(
    'https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'
    'position_salaries.csv'
)
__snake_case				:  int =       dataset.iloc[:, 1:2].values
__snake_case				:  Tuple =       dataset.iloc[:, 2].values


__snake_case ,						__snake_case ,						__snake_case ,						__snake_case				:  int =       train_test_split(X, y, test_size=0.2, random_state=0)


__snake_case				:  Tuple =       PolynomialFeatures(degree=4)
__snake_case				:  Any =       poly_reg.fit_transform(X)
__snake_case				:  List[str] =       LinearRegression()
pol_reg.fit(X_poly, y)


def      _lowercase							(    )		->  List[Any]:
			plt.scatter(__snake_case   ,__snake_case   ,color="red"      )
			plt.plot(__snake_case   ,pol_reg.predict(poly_reg.fit_transform(__snake_case      )      )   ,color="blue"      )
			plt.title("Truth or Bluff (Linear Regression)"      )
			plt.xlabel("Position level"      )
			plt.ylabel("Salary"      )
			plt.show()


if __name__ == "__main__":
	viz_polymonial()

	# Predicting a new result with Polymonial Regression
	pol_reg.predict(poly_reg.fit_transform([[5.5]]))
	# output should be 132148.43750003