





import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class        snake_case_		(			__A						,					unittest.TestCase):
		lowerCamelCase    :Union[str, Any]						=     OpenAIGPTTokenizer
		lowerCamelCase    :str						=     OpenAIGPTTokenizerFast
		lowerCamelCase    :Optional[int]						=     True
		lowerCamelCase    :Union[str, Any]						=     False







		def 			__lowercase   (		self   )				->   Optional[Any]:
				super().setUp()

				# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
				lowerCamelCase   : int								=[
				    '''l''',
				    '''o''',
				    '''w''',
				    '''e''',
				    '''r''',
				    '''s''',
				    '''t''',
				    '''i''',
				    '''d''',
				    '''n''',
				    '''w</w>''',
				    '''r</w>''',
				    '''t</w>''',
				    '''lo''',
				    '''low''',
				    '''er</w>''',
				    '''low</w>''',
				    '''lowest</w>''',
				    '''newer</w>''',
				    '''wider</w>''',
				    '''<unk>''',
				]
				lowerCamelCase   : List[str]								=dict(zip(__lowercase			,     range(len(__lowercase   )   )   )   )
				lowerCamelCase   : int								=['''#version: 0.2''', '''l o''', '''lo w''', '''e r</w>''', '''''']

				lowerCamelCase   : Dict								=os.path.join(self.tmpdirname			,     VOCAB_FILES_NAMES['''vocab_file''']   )
				lowerCamelCase   : List[str]								=os.path.join(self.tmpdirname			,     VOCAB_FILES_NAMES['''merges_file''']   )
				with open(self.vocab_file			,     '''w'''   ) as fp:
						fp.write(json.dumps(__lowercase   )   )
				with open(self.merges_file			,     '''w'''   ) as fp:
						fp.write('''\n'''.join(__lowercase   )   )







		def 			__lowercase   (		self			,     __lowercase   )				->   Any:
				return "lower newer", "lower newer"







		def 			__lowercase   (		self   )				->   Dict:
				lowerCamelCase   : str								=OpenAIGPTTokenizer(self.vocab_file			,     self.merges_file   )

				lowerCamelCase   : Any								='''lower'''
				lowerCamelCase   : Dict								=['''low''', '''er</w>''']
				lowerCamelCase   : int								=tokenizer.tokenize(__lowercase   )
				self.assertListEqual(__lowercase			,     __lowercase   )

				lowerCamelCase   : Any								=tokens + ['''<unk>''']
				lowerCamelCase   : Tuple								=[1_4, 1_5, 2_0]
				self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase   )			,     __lowercase   )







		def 			__lowercase   (		self			,     __lowercase=1_5   )				->   Optional[int]:

				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
						with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"   ):
								lowerCamelCase   : List[Any]								=self.rust_tokenizer_class.from_pretrained(__lowercase			,     **__lowercase   )

								# Simple input
								lowerCamelCase   : str								='''This is a simple input'''
								lowerCamelCase   : int								=['''This is a simple input 1''', '''This is a simple input 2''']
								lowerCamelCase   : Optional[int]								=('''This is a simple input''', '''This is a pair''')
								lowerCamelCase   : List[str]								=[
								    ('''This is a simple input 1''', '''This is a simple input 2'''),
								    ('''This is a simple pair 1''', '''This is a simple pair 2'''),
								]

								# Simple input tests
								self.assertRaises(__lowercase			,     tokenizer_r.encode			,     __lowercase			,     max_length=__lowercase			,     padding='''max_length'''   )

								# Simple input
								self.assertRaises(__lowercase			,     tokenizer_r.encode_plus			,     __lowercase			,     max_length=__lowercase			,     padding='''max_length'''   )

								# Simple input
								self.assertRaises(
								    __lowercase			,     tokenizer_r.batch_encode_plus			,     __lowercase			,     max_length=__lowercase			,     padding='''max_length'''			,     )

								# Pair input
								self.assertRaises(__lowercase			,     tokenizer_r.encode			,     __lowercase			,     max_length=__lowercase			,     padding='''max_length'''   )

								# Pair input
								self.assertRaises(__lowercase			,     tokenizer_r.encode_plus			,     __lowercase			,     max_length=__lowercase			,     padding='''max_length'''   )

								# Pair input
								self.assertRaises(
								    __lowercase			,     tokenizer_r.batch_encode_plus			,     __lowercase			,     max_length=__lowercase			,     padding='''max_length'''			,     )







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

@require_ftfy
@require_spacy
@require_tokenizers
class        snake_case_		(			__A):
		pass








import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     tuple:
		return (data["data"], data["target"])


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     np.ndarray:
		lowerCamelCase   : Dict								=XGBRegressor(verbosity=0							,       random_state=4_2		)
		xgb.fit(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)
		# Predict target for test data
		lowerCamelCase   : List[str]								=xgb.predict(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase   : List[Any]								=predictions.reshape(len(SCREAMING_SNAKE_CASE_		)							,       1		)
		return predictions


def 					A__	(       )   ->     None:
		lowerCamelCase   : Union[str, Any]								=fetch_california_housing()
		lowerCamelCase		,   lowerCamelCase   : Optional[Any]								=data_handling(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase		,   lowerCamelCase		,   lowerCamelCase		,   lowerCamelCase   : str								=train_test_split(
		    SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       test_size=0.2_5							,       random_state=1		)
		lowerCamelCase   : str								=xgboost(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)
		# Error printing
		print(F"Mean Absolute Error : {mean_absolute_error(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)}"		)
		print(F"Mean Square Error  : {mean_squared_error(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)}"		)


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)
				main()

