
"""simple docstring"""




from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser


@dataclass
class 			__magic_name__       :
       UpperCamelCase_   =		field(
           metadata={'''help''': '''The output directory where the model will be written.'''}   ,					)
       UpperCamelCase_   =		field(
           metadata={
               '''help''': (
                   '''The encoder model checkpoint for weights initialization.'''
                   '''Don\'t set if you want to train an encoder model from scratch.'''
               )
           }   ,					)
       UpperCamelCase_   =		field(
           metadata={
               '''help''': (
                   '''The decoder model checkpoint for weights initialization.'''
                   '''Don\'t set if you want to train a decoder model from scratch.'''
               )
           }   ,					)
       UpperCamelCase_   =		field(
           default=SCREAMING_SNAKE_CASE__   ,					metadata={'''help''': '''Pretrained encoder config name or path if not the same as encoder_model_name'''} )
       UpperCamelCase_   =		field(
           default=SCREAMING_SNAKE_CASE__   ,					metadata={'''help''': '''Pretrained decoder config name or path if not the same as decoder_model_name'''} )




def  _lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:			Union[str, Any]       =     HfArgumentParser((ModelArguments,)						)
  ((_lowercase) ,				):			str       =     parser.parse_args_into_dataclasses()

  # Load pretrained model and tokenizer

  # Use explicit specified encoder config
  if model_args.encoder_config_name:
    _lowercase:			Union[str, Any]       =     AutoConfig.from_pretrained(model_args.encoder_config_name						)
  # Use pretrained encoder model's config
  else:
    _lowercase:			int       =     AutoConfig.from_pretrained(model_args.encoder_model_name_or_path						)

  # Use explicit specified decoder config
  if model_args.decoder_config_name:
    _lowercase:			Tuple       =     AutoConfig.from_pretrained(model_args.decoder_config_name						)
  # Use pretrained decoder model's config
  else:
    _lowercase:			Dict       =     AutoConfig.from_pretrained(model_args.decoder_model_name_or_path						)

  # necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
  _lowercase:			Tuple       =     True
  _lowercase:			Any       =     True

  _lowercase:			List[Any]       =     FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
      encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path ,				decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path ,				encoder_config=_UpperCamelCase ,				decoder_config=_UpperCamelCase ,				)

  # GPT2 only has bos/eos tokens but not decoder_start/pad tokens
  _lowercase:			str       =     decoder_config.decoder_start_token_id
  _lowercase:			int       =     decoder_config.pad_token_id
  if decoder_start_token_id is None:
    _lowercase:			List[Any]       =     decoder_config.bos_token_id
  if pad_token_id is None:
    _lowercase:			List[Any]       =     decoder_config.eos_token_id

  # This is necessary to make Flax's generate() work
  _lowercase:			Union[str, Any]       =     decoder_config.eos_token_id
  _lowercase:			List[str]       =     decoder_start_token_id
  _lowercase:			str       =     pad_token_id

  _lowercase:			Dict       =     AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path						)

  _lowercase:			Optional[int]       =     AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path						)
  _lowercase:			Dict       =     tokenizer.convert_ids_to_tokens(model.config.pad_token_id						)

  model.save_pretrained(model_args.output_dir						)
  image_processor.save_pretrained(model_args.output_dir						)
  tokenizer.save_pretrained(model_args.output_dir						)


if __name__ == "__main__":
  main()


"""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
