
import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


A		: Optional[Any]								=     get_tests_dir('''fixtures/test_sentencepiece.model''')



@require_sentencepiece
@require_tokenizers
class    a_ (			_a					,			unittest.TestCase    ):
			a	:			Tuple					   =							XLMRobertaTokenizer
			a	:			int					   =							XLMRobertaTokenizerFast
			a	:			str					   =							True
			a	:			Union[str, Any]					   =							True






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

									# We have a SentencePiece fixture for testing
									_lowercase					       =					XLMRobertaTokenizer(__UpperCamelCase				,			keep_accents=__UpperCamelCase  )
									tokenizer.save_pretrained(self.tmpdirname  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					"""<pad>"""
									_lowercase					       =					1

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCamelCase  )				,			__UpperCamelCase  )
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCamelCase  )				,			__UpperCamelCase  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					list(self.get_tokenizer().get_vocab().keys()  )

									self.assertEqual(vocab_keys[0]				,			"""<s>"""  )
									self.assertEqual(vocab_keys[1]				,			"""<pad>"""  )
									self.assertEqual(vocab_keys[-1]				,			"""<mask>"""  )
									self.assertEqual(len(__UpperCamelCase  )				,			1_002  )






			def    UpperCamelCase_				(      self  ):
									self.assertEqual(self.get_tokenizer().vocab_size				,			1_002  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					XLMRobertaTokenizer(__UpperCamelCase				,			keep_accents=__UpperCamelCase  )

									_lowercase					       =					tokenizer.tokenize("""This is a test"""  )
									self.assertListEqual(__UpperCamelCase				,			["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]  )

									self.assertListEqual(
									    tokenizer.convert_tokens_to_ids(__UpperCamelCase  )				,			[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]				,			)

									_lowercase					       =					tokenizer.tokenize("""I was born in 92000, and this is falsé."""  )
									self.assertListEqual(
									    __UpperCamelCase				,			[
									        SPIECE_UNDERLINE + """I""",
									        SPIECE_UNDERLINE + """was""",
									        SPIECE_UNDERLINE + """b""",
									        """or""",
									        """n""",
									        SPIECE_UNDERLINE + """in""",
									        SPIECE_UNDERLINE + """""",
									        """9""",
									        """2""",
									        """0""",
									        """0""",
									        """0""",
									        """,""",
									        SPIECE_UNDERLINE + """and""",
									        SPIECE_UNDERLINE + """this""",
									        SPIECE_UNDERLINE + """is""",
									        SPIECE_UNDERLINE + """f""",
									        """al""",
									        """s""",
									        """é""",
									        """.""",
									    ]				,			)
									_lowercase					       =					tokenizer.convert_tokens_to_ids(__UpperCamelCase  )
									self.assertListEqual(
									    __UpperCamelCase				,			[
									        value + tokenizer.fairseq_offset
									        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
									        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
									    ]				,			)

									_lowercase					       =					tokenizer.convert_ids_to_tokens(__UpperCamelCase  )
									self.assertListEqual(
									    __UpperCamelCase				,			[
									        SPIECE_UNDERLINE + """I""",
									        SPIECE_UNDERLINE + """was""",
									        SPIECE_UNDERLINE + """b""",
									        """or""",
									        """n""",
									        SPIECE_UNDERLINE + """in""",
									        SPIECE_UNDERLINE + """""",
									        """<unk>""",
									        """2""",
									        """0""",
									        """0""",
									        """0""",
									        """,""",
									        SPIECE_UNDERLINE + """and""",
									        SPIECE_UNDERLINE + """this""",
									        SPIECE_UNDERLINE + """is""",
									        SPIECE_UNDERLINE + """f""",
									        """al""",
									        """s""",
									        """<unk>""",
									        """.""",
									    ]				,			)






			def    UpperCamelCase_				(      self  ):
									if not self.test_slow_tokenizer:
															# as we don't have a slow version, we can't compare the outputs between slow and fast versions
															return

									_lowercase					       =					(self.rust_tokenizer_class, """hf-internal-testing/tiny-xlm-roberta""", {})
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""  ):
																					_lowercase					       =					self.rust_tokenizer_class.from_pretrained(__UpperCamelCase				,			**__UpperCamelCase  )
																					_lowercase					       =					self.tokenizer_class.from_pretrained(__UpperCamelCase				,			**__UpperCamelCase  )

																					_lowercase					       =					tempfile.mkdtemp()

																					_lowercase					       =					tokenizer_r.save_pretrained(__UpperCamelCase  )
																					_lowercase					       =					tokenizer_p.save_pretrained(__UpperCamelCase  )

																					# Checks it save with the same files + the tokenizer.json file for the fast one
																					self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files  )  )
																					_lowercase					       =					tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f  )
																					self.assertSequenceEqual(__UpperCamelCase				,			__UpperCamelCase  )

																					# Checks everything loads correctly in the same way
																					_lowercase					       =					tokenizer_r.from_pretrained(__UpperCamelCase  )
																					_lowercase					       =					tokenizer_p.from_pretrained(__UpperCamelCase  )

																					# Check special tokens are set accordingly on Rust and Python
																					for key in tokenizer_pp.special_tokens_map:
																											self.assertTrue(hasattr(__UpperCamelCase				,			__UpperCamelCase  )  )
																											# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																											# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																					shutil.rmtree(__UpperCamelCase  )

																					# Save tokenizer rust, legacy_format=True
																					_lowercase					       =					tempfile.mkdtemp()

																					_lowercase					       =					tokenizer_r.save_pretrained(__UpperCamelCase				,			legacy_format=__UpperCamelCase  )
																					_lowercase					       =					tokenizer_p.save_pretrained(__UpperCamelCase  )

																					# Checks it save with the same files
																					self.assertSequenceEqual(__UpperCamelCase				,			__UpperCamelCase  )

																					# Checks everything loads correctly in the same way
																					_lowercase					       =					tokenizer_r.from_pretrained(__UpperCamelCase  )
																					_lowercase					       =					tokenizer_p.from_pretrained(__UpperCamelCase  )

																					# Check special tokens are set accordingly on Rust and Python
																					for key in tokenizer_pp.special_tokens_map:
																											self.assertTrue(hasattr(__UpperCamelCase				,			__UpperCamelCase  )  )

																					shutil.rmtree(__UpperCamelCase  )

																					# Save tokenizer rust, legacy_format=False
																					_lowercase					       =					tempfile.mkdtemp()

																					_lowercase					       =					tokenizer_r.save_pretrained(__UpperCamelCase				,			legacy_format=__UpperCamelCase  )
																					_lowercase					       =					tokenizer_p.save_pretrained(__UpperCamelCase  )

																					# Checks it saved the tokenizer.json file
																					self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files  )  )

																					# Checks everything loads correctly in the same way
																					_lowercase					       =					tokenizer_r.from_pretrained(__UpperCamelCase  )
																					_lowercase					       =					tokenizer_p.from_pretrained(__UpperCamelCase  )

																					# Check special tokens are set accordingly on Rust and Python
																					for key in tokenizer_pp.special_tokens_map:
																											self.assertTrue(hasattr(__UpperCamelCase				,			__UpperCamelCase  )  )

																					shutil.rmtree(__UpperCamelCase  )






			@cached_property
			def    UpperCamelCase_				(      self  ):
									return XLMRobertaTokenizer.from_pretrained("""xlm-roberta-base"""  )






			def    UpperCamelCase_				(      self  ):
									with tempfile.NamedTemporaryFile() as f:
															shutil.copyfile(__UpperCamelCase				,			f.name  )
															_lowercase					       =					XLMRobertaTokenizer(f.name				,			keep_accents=__UpperCamelCase  )
															_lowercase					       =					pickle.dumps(__UpperCamelCase  )
									pickle.loads(__UpperCamelCase  )






			def    UpperCamelCase_				(      self  ):
									if not self.test_rust_tokenizer:
															return

									_lowercase					       =					self.get_tokenizer()
									_lowercase					       =					self.get_rust_tokenizer()

									_lowercase					       =					"""I was born in 92000, and this is falsé."""

									_lowercase					       =					tokenizer.tokenize(__UpperCamelCase  )
									_lowercase					       =					rust_tokenizer.tokenize(__UpperCamelCase  )
									self.assertListEqual(__UpperCamelCase				,			__UpperCamelCase  )

									_lowercase					       =					tokenizer.encode(__UpperCamelCase				,			add_special_tokens=__UpperCamelCase  )
									_lowercase					       =					rust_tokenizer.encode(__UpperCamelCase				,			add_special_tokens=__UpperCamelCase  )
									self.assertListEqual(__UpperCamelCase				,			__UpperCamelCase  )

									_lowercase					       =					self.get_rust_tokenizer()
									_lowercase					       =					tokenizer.encode(__UpperCamelCase  )
									_lowercase					       =					rust_tokenizer.encode(__UpperCamelCase  )
									self.assertListEqual(__UpperCamelCase				,			__UpperCamelCase  )






			@slow
			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					"""Hello World!"""
									_lowercase					       =					[0, 35_378, 6_661, 38, 2]
									# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
									# xlmr.eval()
									# xlmr.encode(symbols)

									self.assertListEqual(__UpperCamelCase				,			self.big_tokenizer.encode(__UpperCamelCase  )  )






			@slow
			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					(
									    """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
									    """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
									)
									_lowercase					       =					[
									    0,
									    3_293,
									    83,
									    10,
									    4_552,
									    4_989,
									    7_986,
									    678,
									    10,
									    5_915,
									    111,
									    179_459,
									    124_850,
									    4,
									    6_044,
									    237,
									    12,
									    6,
									    5,
									    6,
									    4,
									    6_780,
									    705,
									    15,
									    1_388,
									    44,
									    378,
									    10_114,
									    711,
									    152,
									    20,
									    6,
									    5,
									    22_376,
									    642,
									    1_221,
									    15_190,
									    34_153,
									    450,
									    5_608,
									    959,
									    1_119,
									    57_702,
									    136,
									    186,
									    47,
									    1_098,
									    29_367,
									    47,
									    # 4426, # What fairseq tokenizes from "<unk>": "_<"
									    # 3678, # What fairseq tokenizes from "<unk>": "unk"
									    # 2740, # What fairseq tokenizes from "<unk>": ">"
									    3,  # What we tokenize from "<unk>": "<unk>"
									    6,  # Residue from the tokenization: an extra sentencepiece underline
									    4,
									    6_044,
									    237,
									    6_284,
									    50_901,
									    528,
									    31,
									    90,
									    34,
									    927,
									    2,
									]
									# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
									# xlmr.eval()
									# xlmr.encode(symbols)

									self.assertListEqual(__UpperCamelCase				,			self.big_tokenizer.encode(__UpperCamelCase  )  )






			@slow
			def    UpperCamelCase_				(      self  ):
									# fmt: off
									_lowercase					       =					{"""input_ids""": [[0, 11_062, 82_772, 7, 15, 82_772, 538, 51_529, 237, 17_198, 1_290, 206, 9, 215_175, 1_314, 136, 17_198, 1_290, 206, 9, 56_359, 42, 122_009, 9, 16_466, 16, 87_344, 4_537, 9, 4_717, 78_381, 6, 159_958, 7, 15, 24_480, 618, 4, 527, 22_693, 5_428, 4, 2_777, 24_480, 9_874, 4, 43_523, 594, 4, 803, 18_392, 33_189, 18, 4, 43_523, 24_447, 12_399, 100, 24_955, 83_658, 9_626, 144_057, 15, 839, 22_335, 16, 136, 24_955, 83_658, 83_479, 15, 39_102, 724, 16, 678, 645, 2_789, 1_328, 4_589, 42, 122_009, 115_774, 23, 805, 1_328, 46_876, 7, 136, 53_894, 1_940, 42_227, 41_159, 17_721, 823, 425, 4, 27_512, 98_722, 206, 136, 5_531, 4_970, 919, 17_336, 5, 2], [0, 20_080, 618, 83, 82_775, 47, 479, 9, 1_517, 73, 53_894, 333, 80_581, 110_117, 18_811, 5_256, 1_295, 51, 152_526, 297, 7_986, 390, 124_416, 538, 35_431, 214, 98, 15_044, 25_737, 136, 7_108, 43_701, 23, 756, 135_355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 63_773, 119_455, 6, 147_797, 88_203, 7, 645, 70, 21, 3_285, 10_269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
									# fmt: on

									self.tokenizer_integration_test_util(
									    expected_encoding=__UpperCamelCase				,			model_name="""xlm-roberta-base"""				,			revision="""d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3"""				,			)

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