
"""simple docstring"""




import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
      import torch

      from transformers import AutoModelForCausalLM


@require_torch
class 			__magic_name__       (       unittest.TestCase ):
       def 							lowercase_							(     self       )     ->	Union[str, Any]:





         """simple docstring"""




         _lowercase:			Optional[int]       =     AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''       )
         _lowercase:			int       =     AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''       ).to(lowerCAmelCase_       )
         _lowercase:			str       =     -1

         _lowercase:			int       =     ids_tensor((1, 5) ,				vocab_size=model.config.vocab_size       ).to(lowerCAmelCase_       )
         _lowercase:			Optional[int]       =     model.generate(lowerCAmelCase_ ,				max_new_tokens=10 ,				do_sample=lowerCAmelCase_       )
         _lowercase:			Union[str, Any]       =     tokenizer.decode(greedy_ids[0]       )

         with CaptureStdout() as cs:
           _lowercase:			Optional[Any]       =     TextStreamer(lowerCAmelCase_       )
           model.generate(lowerCAmelCase_ ,				max_new_tokens=10 ,				do_sample=lowerCAmelCase_ ,				streamer=lowerCAmelCase_       )
         # The greedy text should be printed to stdout, except for the final "\n" in the streamer
         _lowercase:			str       =     cs.out[:-1]

         self.assertEqual(lowerCAmelCase_ ,				lowerCAmelCase_       )
       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         _lowercase:			int       =     AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''       )
         _lowercase:			Dict       =     AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''       ).to(lowerCAmelCase_       )
         _lowercase:			str       =     -1

         _lowercase:			Tuple       =     ids_tensor((1, 5) ,				vocab_size=model.config.vocab_size       ).to(lowerCAmelCase_       )
         _lowercase:			Dict       =     model.generate(lowerCAmelCase_ ,				max_new_tokens=10 ,				do_sample=lowerCAmelCase_       )
         _lowercase:			Union[str, Any]       =     tokenizer.decode(greedy_ids[0]       )

         _lowercase:			List[Any]       =     TextIteratorStreamer(lowerCAmelCase_       )
         _lowercase:			List[Any]       =     {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
         _lowercase:			Dict       =     Thread(target=model.generate ,				kwargs=lowerCAmelCase_       )
         thread.start()
         _lowercase:			List[str]       =     ''''''
         for new_text in streamer:
           streamer_text += new_text

         self.assertEqual(lowerCAmelCase_ ,				lowerCAmelCase_       )
       def 							lowercase_							(     self       )     ->	Optional[int]:





         """simple docstring"""




         _lowercase:			Any       =     AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''       )
         _lowercase:			Tuple       =     AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''       ).to(lowerCAmelCase_       )
         _lowercase:			List[Any]       =     -1

         _lowercase:			Union[str, Any]       =     ids_tensor((1, 5) ,				vocab_size=model.config.vocab_size       ).to(lowerCAmelCase_       )
         _lowercase:			Optional[int]       =     model.generate(lowerCAmelCase_ ,				max_new_tokens=10 ,				do_sample=lowerCAmelCase_       )
         _lowercase:			Dict       =     greedy_ids[:, input_ids.shape[1] :]
         _lowercase:			Dict       =     tokenizer.decode(new_greedy_ids[0]       )

         with CaptureStdout() as cs:
           _lowercase:			Optional[Any]       =     TextStreamer(lowerCAmelCase_ ,				skip_prompt=lowerCAmelCase_       )
           model.generate(lowerCAmelCase_ ,				max_new_tokens=10 ,				do_sample=lowerCAmelCase_ ,				streamer=lowerCAmelCase_       )
         # The greedy text should be printed to stdout, except for the final "\n" in the streamer
         _lowercase:			Dict       =     cs.out[:-1]

         self.assertEqual(lowerCAmelCase_ ,				lowerCAmelCase_       )
       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         _lowercase:			List[Any]       =     AutoTokenizer.from_pretrained('''distilgpt2'''       )
         _lowercase:			List[str]       =     AutoModelForCausalLM.from_pretrained('''distilgpt2'''       ).to(lowerCAmelCase_       )
         _lowercase:			List[str]       =     -1

         _lowercase:			Optional[Any]       =     torch.ones((1, 5) ,				device=lowerCAmelCase_       ).long() * model.config.bos_token_id
         with CaptureStdout() as cs:
           _lowercase:			List[str]       =     TextStreamer(lowerCAmelCase_ ,				skip_special_tokens=lowerCAmelCase_       )
           model.generate(lowerCAmelCase_ ,				max_new_tokens=1 ,				do_sample=lowerCAmelCase_ ,				streamer=lowerCAmelCase_       )

         # The prompt contains a special token, so the streamer should not print it. As such, the output text, when
         # re-tokenized, must only contain one token
         _lowercase:			List[str]       =     cs.out[:-1]  # Remove the final "\n"
         _lowercase:			Tuple       =     tokenizer(lowerCAmelCase_ ,				return_tensors='''pt'''       )
         self.assertEqual(streamer_text_tokenized.input_ids.shape ,				(1, 1)       )



       def 							lowercase_							(     self       )     ->	Optional[int]:





         """simple docstring"""




         _lowercase:			Union[str, Any]       =     AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''       )
         _lowercase:			Union[str, Any]       =     AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''       ).to(lowerCAmelCase_       )
         _lowercase:			str       =     -1

         _lowercase:			Tuple       =     ids_tensor((1, 5) ,				vocab_size=model.config.vocab_size       ).to(lowerCAmelCase_       )
         _lowercase:			int       =     TextIteratorStreamer(lowerCAmelCase_ ,				timeout=0.0_01       )
         _lowercase:			List[Any]       =     {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
         _lowercase:			Optional[int]       =     Thread(target=model.generate ,				kwargs=lowerCAmelCase_       )
         thread.start()

         # The streamer will timeout after 0.001 seconds, so an exception will be raised
         with self.assertRaises(lowerCAmelCase_       ):
           _lowercase:			int       =     ''''''
           for new_text in streamer:
             streamer_text += new_text


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


A__							, A__							, A__							, A__   :					Dict							=   train_test_split(X, y, test_size=0.2, random_state=0)


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




def  _lowerCAmelCase		(		):






  """simple docstring"""



  plt.scatter(_UpperCamelCase ,				_UpperCamelCase ,				color='''red'''						)
  plt.plot(_UpperCamelCase ,				pol_reg.predict(poly_reg.fit_transform(_UpperCamelCase						)						) ,				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
