
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
		import datasets



class    a_ (			_a    ):






			@slow
			@require_torch
			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny"""				,			"""prajjwal1/bert-tiny"""  )
									_lowercase					       =					BertTokenizer.from_pretrained("""bert-base-uncased"""  )

									_lowercase					       =					bertabert.config.encoder.vocab_size
									_lowercase					       =					tokenizer.sep_token_id
									_lowercase					       =					tokenizer.cls_token_id
									_lowercase					       =					128

									_lowercase					       =					datasets.load_dataset("""cnn_dailymail"""				,			"""3.0.0"""				,			split="""train[:1%]"""  )
									_lowercase					       =					datasets.load_dataset("""cnn_dailymail"""				,			"""3.0.0"""				,			split="""validation[:1%]"""  )

									_lowercase					       =					train_dataset.select(range(32  )  )
									_lowercase					       =					val_dataset.select(range(16  )  )

									_lowercase					       =					4

									def _map_to_encoder_decoder_inputs(__UpperCamelCase  ):
															# Tokenizer will automatically set [BOS] <text> [EOS]
															_lowercase					       =					tokenizer(batch["""article"""]				,			padding="""max_length"""				,			truncation=__UpperCamelCase				,			max_length=512  )
															_lowercase					       =					tokenizer(batch["""highlights"""]				,			padding="""max_length"""				,			truncation=__UpperCamelCase				,			max_length=128  )
															_lowercase					       =					inputs.input_ids
															_lowercase					       =					inputs.attention_mask

															_lowercase					       =					outputs.input_ids
															_lowercase					       =					outputs.input_ids.copy()
															_lowercase					       =					[
															    [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
															]
															_lowercase					       =					outputs.attention_mask

															assert all(len(__UpperCamelCase  ) == 512 for x in inputs.input_ids  )
															assert all(len(__UpperCamelCase  ) == 128 for x in outputs.input_ids  )

															return batch

									def _compute_metrics(__UpperCamelCase  ):
															_lowercase					       =					pred.label_ids
															_lowercase					       =					pred.predictions

															# all unnecessary tokens are removed
															_lowercase					       =					tokenizer.batch_decode(__UpperCamelCase				,			skip_special_tokens=__UpperCamelCase  )
															_lowercase					       =					tokenizer.batch_decode(__UpperCamelCase				,			skip_special_tokens=__UpperCamelCase  )

															_lowercase					       =					sum([int(pred_str[i] == label_str[i]  ) for i in range(len(__UpperCamelCase  )  )]  ) / len(__UpperCamelCase  )

															return {"accuracy": accuracy}

									# map train dataset
									_lowercase					       =					train_dataset.map(
									    _map_to_encoder_decoder_inputs				,			batched=__UpperCamelCase				,			batch_size=__UpperCamelCase				,			remove_columns=["""article""", """highlights"""]				,			)
									train_dataset.set_format(
									    type="""torch"""				,			columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""]				,			)

									# same for validation dataset
									_lowercase					       =					val_dataset.map(
									    _map_to_encoder_decoder_inputs				,			batched=__UpperCamelCase				,			batch_size=__UpperCamelCase				,			remove_columns=["""article""", """highlights"""]				,			)
									val_dataset.set_format(
									    type="""torch"""				,			columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""]				,			)

									_lowercase					       =					self.get_auto_remove_tmp_dir()

									_lowercase					       =					SeqaSeqTrainingArguments(
									    output_dir=__UpperCamelCase				,			per_device_train_batch_size=__UpperCamelCase				,			per_device_eval_batch_size=__UpperCamelCase				,			predict_with_generate=__UpperCamelCase				,			evaluation_strategy="""steps"""				,			do_train=__UpperCamelCase				,			do_eval=__UpperCamelCase				,			warmup_steps=0				,			eval_steps=2				,			logging_steps=2				,			)

									# instantiate trainer
									_lowercase					       =					SeqaSeqTrainer(
									    model=__UpperCamelCase				,			args=__UpperCamelCase				,			compute_metrics=_compute_metrics				,			train_dataset=__UpperCamelCase				,			eval_dataset=__UpperCamelCase				,			tokenizer=__UpperCamelCase				,			)

									# start training
									trainer.train()

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


A							,      A							,      A							,      A		: str								=     train_test_split(X, y, test_size=0.2, random_state=0)


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




def 	UpperCamelCase__  (     )  ->     Any:
						plt.scatter(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   color="""red"""       )
						plt.plot(SCREAMING_SNAKE_CASE_		,   pol_reg.predict(poly_reg.fit_transform(SCREAMING_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