
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
		import tensorflow as tf

if is_tensorflow_text_available():
		from transformers.models.bert import TFBertTokenizer


A		: Tuple								=     ['''bert-base-uncased''', '''bert-base-cased''']
A		: Tuple								=     '''hf-internal-testing/tiny-bert-tf-only'''

if is_tf_available():



		class    a_ (			tf.keras.Model    ):






					def __init__(      self				,			__UpperCamelCase  ):
											super().__init__()
											_lowercase					       =					tokenizer
											_lowercase					       =					AutoConfig.from_pretrained(__UpperCamelCase  )
											_lowercase					       =					TFAutoModel.from_config(__UpperCamelCase  )






					def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
											_lowercase					       =					self.tokenizer(__UpperCamelCase  )
											_lowercase					       =					self.bert(**__UpperCamelCase  )
											return out["pooler_output"]



@require_tf
@require_tensorflow_text
class    a_ (			unittest.TestCase    ):






			def    UpperCamelCase_				(      self  ):
									super().setUp()

									_lowercase					       =					[
									    BertTokenizer.from_pretrained(__UpperCamelCase  ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
									]  # repeat for when fast_bert_tokenizer=false
									_lowercase					       =					[TFBertTokenizer.from_pretrained(__UpperCamelCase  ) for checkpoint in TOKENIZER_CHECKPOINTS] + [
									    TFBertTokenizer.from_pretrained(__UpperCamelCase				,			use_fast_bert_tokenizer=__UpperCamelCase  )
									    for checkpoint in TOKENIZER_CHECKPOINTS
									]
									assert len(self.tokenizers  ) == len(self.tf_tokenizers  )

									_lowercase					       =					[
									    """This is a straightforward English test sentence.""",
									    """This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.""",
									    """Now we're going to add some Chinese: 一 二 三 一二三""",
									    """And some much more rare Chinese: 齉 堃 齉堃""",
									    """Je vais aussi écrire en français pour tester les accents""",
									    """Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ""",
									]
									_lowercase					       =					list(zip(self.test_sentences				,			self.test_sentences[::-1]  )  )






			def    UpperCamelCase_				(      self  ):
									for tokenizer, tf_tokenizer in zip(self.tokenizers				,			self.tf_tokenizers  ):
															for test_inputs in (self.test_sentences, self.paired_sentences):
																					_lowercase					       =					tokenizer(__UpperCamelCase				,			return_tensors="""tf"""				,			padding="""longest"""  )
																					_lowercase					       =					tf_tokenizer(__UpperCamelCase  )

																					for key in python_outputs.keys():
																											self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape  )  )
																											self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]				,			tf.intaa  ) == tf_outputs[key]  )  )






			@slow
			def    UpperCamelCase_				(      self  ):
									for tf_tokenizer in self.tf_tokenizers:
															_lowercase					       =					tf_tokenizer(self.paired_sentences  )
															_lowercase					       =					tf_tokenizer(
															    text=[sentence[0] for sentence in self.paired_sentences]				,			text_pair=[sentence[1] for sentence in self.paired_sentences]				,			)
															for key in merged_outputs.keys():
																					self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]				,			tf.intaa  ) == separated_outputs[key]  )  )






			@slow
			def    UpperCamelCase_				(      self  ):
									for tf_tokenizer in self.tf_tokenizers:
															_lowercase					       =					tf.function(__UpperCamelCase  )
															for test_inputs in (self.test_sentences, self.paired_sentences):
																					_lowercase					       =					tf.constant(__UpperCamelCase  )
																					_lowercase					       =					compiled_tokenizer(__UpperCamelCase  )
																					_lowercase					       =					tf_tokenizer(__UpperCamelCase  )

																					for key in eager_outputs.keys():
																											self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]  )  )






			@slow
			def    UpperCamelCase_				(      self  ):
									for tf_tokenizer in self.tf_tokenizers:
															_lowercase					       =					ModelToSave(tokenizer=__UpperCamelCase  )
															_lowercase					       =					tf.convert_to_tensor(self.test_sentences  )
															_lowercase					       =					model(__UpperCamelCase  )  # Build model with some sample inputs
															with TemporaryDirectory() as tempdir:
																					_lowercase					       =					Path(__UpperCamelCase  ) / """saved.model"""
																					model.save(__UpperCamelCase  )
																					_lowercase					       =					tf.keras.models.load_model(__UpperCamelCase  )
															_lowercase					       =					loaded_model(__UpperCamelCase  )
															# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
															self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output  )  )				,			1E-5  )

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