
"""simple docstring"""





import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__magic_name__       =			"▁"

__magic_name__       =			get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
class 						SCREAMING_SNAKE_CASE_ (						snake_case_     ,				unittest.TestCase ):




		"""simple docstring"""





		__lowercase							:  str						   =		BertGenerationTokenizer
		__lowercase							:  Any						   =		False
		__lowercase							:  str						   =		True




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

				__SCREAMING_SNAKE_CASE            = BertGenerationTokenizer(lowerCAmelCase__							,  keep_accents=lowerCAmelCase__)
				tokenizer.save_pretrained(self.tmpdirname)




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = """<s>"""
				__SCREAMING_SNAKE_CASE            = 1

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




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = list(self.get_tokenizer().get_vocab().keys())

				self.assertEqual(vocab_keys[0]							,  """<unk>""")
				self.assertEqual(vocab_keys[1]							,  """<s>""")
				self.assertEqual(vocab_keys[-1]							,  """<pad>""")
				self.assertEqual(len(lowerCAmelCase__)							,  1_0_0_2)




		def 	snake_case_     (     self):
				self.assertEqual(self.get_tokenizer().vocab_size							,  1_0_0_0)




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = BertGenerationTokenizer(lowerCAmelCase__							,  keep_accents=lowerCAmelCase__)

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

				self.assertListEqual(
				    tokenizer.convert_tokens_to_ids(lowerCAmelCase__)							,  [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]							,  )

				__SCREAMING_SNAKE_CASE            = tokenizer.tokenize("""I was born in 92000, and this is falsé.""")
				self.assertListEqual(
				    lowerCAmelCase__							,  [
				        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""",
				        """.""",
				    ]							,  )
				__SCREAMING_SNAKE_CASE            = tokenizer.convert_tokens_to_ids(lowerCAmelCase__)
				self.assertListEqual(
				    lowerCAmelCase__							,  [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4]							,  )

				__SCREAMING_SNAKE_CASE            = tokenizer.convert_ids_to_tokens(lowerCAmelCase__)
				self.assertListEqual(
				    lowerCAmelCase__							,  [
				        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>""",
				        """.""",
				    ]							,  )




		@cached_property
		def 	snake_case_     (     self):
				return BertGenerationTokenizer.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder""")




		@slow
		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = """Hello World!"""
				__SCREAMING_SNAKE_CASE            = [1_8_5_3_6, 2_2_6_0, 1_0_1]

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




		@slow
		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = (
				    """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"""
				)
				__SCREAMING_SNAKE_CASE            = [
				    8_7_1,
				    4_1_9,
				    3_5_8,
				    9_4_6,
				    9_9_1,
				    2_5_2_1,
				    4_5_2,
				    3_5_8,
				    1_3_5_7,
				    3_8_7,
				    7_7_5_1,
				    3_5_3_6,
				    1_1_2,
				    9_8_5,
				    4_5_6,
				    1_2_6,
				    8_6_5,
				    9_3_8,
				    5_4_0_0,
				    5_7_3_4,
				    4_5_8,
				    1_3_6_8,
				    4_6_7,
				    7_8_6,
				    2_4_6_2,
				    5_2_4_6,
				    1_1_5_9,
				    6_3_3,
				    8_6_5,
				    4_5_1_9,
				    4_5_7,
				    5_8_2,
				    8_5_2,
				    2_5_5_7,
				    4_2_7,
				    9_1_6,
				    5_0_8,
				    4_0_5,
				    3_4_3_2_4,
				    4_9_7,
				    3_9_1,
				    4_0_8,
				    1_1_3_4_2,
				    1_2_4_4,
				    3_8_5,
				    1_0_0,
				    9_3_8,
				    9_8_5,
				    4_5_6,
				    5_7_4,
				    3_6_2,
				    1_2_5_9_7,
				    3_2_0_0,
				    3_1_2_9,
				    1_1_7_2,
				]

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




		@require_torch
		@slow
		def 	snake_case_     (     self):
				import torch

				from transformers import BertGenerationConfig, BertGenerationEncoder

				# Build sequence
				__SCREAMING_SNAKE_CASE            = list(self.big_tokenizer.get_vocab().keys())[:1_0]
				__SCREAMING_SNAKE_CASE            = """ """.join(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = self.big_tokenizer.encode_plus(lowerCAmelCase__							,  return_tensors="""pt"""							,  return_token_type_ids=lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = self.big_tokenizer.batch_encode_plus(
				    [sequence + """ """ + sequence]							,  return_tensors="""pt"""							,  return_token_type_ids=lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = BertGenerationConfig()
				__SCREAMING_SNAKE_CASE            = BertGenerationEncoder(lowerCAmelCase__)

				assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

				with torch.no_grad():
						model(**lowerCAmelCase__)
						model(**lowerCAmelCase__)




		@slow
		def 	snake_case_     (     self):
				# fmt: off
				__SCREAMING_SNAKE_CASE            = {"""input_ids""": [[3_9_2_8_6, 4_5_8, 3_6_3_3_5, 2_0_0_1, 4_5_6, 1_3_0_7_3, 1_3_2_6_6, 4_5_5, 1_1_3, 7_7_4_6, 1_7_4_1, 1_1_1_5_7, 3_9_1, 1_3_0_7_3, 1_3_2_6_6, 4_5_5, 1_1_3, 3_9_6_7, 3_5_4_1_2, 1_1_3, 4_9_3_6, 1_0_9, 3_8_7_0, 2_3_7_7, 1_1_3, 3_0_0_8_4, 4_5_7_2_0, 4_5_8, 1_3_4, 1_7_4_9_6, 1_1_2, 5_0_3, 1_1_6_7_2, 1_1_3, 1_1_8, 1_1_2, 5_6_6_5, 1_3_3_4_7, 3_8_6_8_7, 1_1_2, 1_4_9_6, 3_1_3_8_9, 1_1_2, 3_2_6_8, 4_7_2_6_4, 1_3_4, 9_6_2, 1_1_2, 1_6_3_7_7, 8_0_3_5, 2_3_1_3_0, 4_3_0, 1_2_1_6_9, 1_5_5_1_8, 2_8_5_9_2, 4_5_8, 1_4_6, 4_1_6_9_7, 1_0_9, 3_9_1, 1_2_1_6_9, 1_5_5_1_8, 1_6_6_8_9, 4_5_8, 1_4_6, 4_1_3_5_8, 1_0_9, 4_5_2, 7_2_6, 4_0_3_4, 1_1_1, 7_6_3, 3_5_4_1_2, 5_0_8_2, 3_8_8, 1_9_0_3, 1_1_1, 9_0_5_1, 3_9_1, 2_8_7_0, 4_8_9_1_8, 1_9_0_0, 1_1_2_3, 5_5_0, 9_9_8, 1_1_2, 9_5_8_6, 1_5_9_8_5, 4_5_5, 3_9_1, 4_1_0, 2_2_9_5_5, 3_7_6_3_6, 1_1_4], [4_4_8, 1_7_4_9_6, 4_1_9, 3_6_6_3, 3_8_5, 7_6_3, 1_1_3, 2_7_5_3_3, 2_8_7_0, 3_2_8_3, 1_3_0_4_3, 1_6_3_9, 2_4_7_1_3, 5_2_3, 6_5_6, 2_4_0_1_3, 1_8_5_5_0, 2_5_2_1, 5_1_7, 2_7_0_1_4, 2_1_2_4_4, 4_2_0, 1_2_1_2, 1_4_6_5, 3_9_1, 9_2_7, 4_8_3_3, 3_8_8, 5_7_8, 1_1_7_8_6, 1_1_4, 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], [4_8_4, 2_1_6_9, 7_6_8_7, 2_1_9_3_2, 1_8_1_4_6, 7_2_6, 3_6_3, 1_7_0_3_2, 3_3_9_1, 1_1_4, 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]], """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, 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, 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=lowerCAmelCase__							,  model_name="""google/bert_for_seq_generation_L-24_bbc_encoder"""							,  revision="""c817d1fd1be2ffa69431227a1fe320544943d4db"""							,  )












import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX





def 						__UpperCAmelCase			(     a_							,     a_							,     a_							,     a_							,     a_):
							snake_case_				=							np.array([[1, item, train_mtch[i]] for i, item in enumerate(a_)])
							snake_case_				=							np.array(a_)
							snake_case_				=							np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()							,     a_))							,     x.transpose())							,     a_)
							return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2])





def 						__UpperCAmelCase			(     a_							,     a_							,     a_):
							snake_case_				=							(1, 2, 1)
							snake_case_				=							(1, 1, 0, 7)
							snake_case_				=							SARIMAX(
							    a_							,     exog=a_							,     order=a_							,     seasonal_order=a_)
							snake_case_				=							model.fit(disp=a_							,     maxiter=6_00							,     method='nm')
							snake_case_				=							model_fit.predict(1							,     len(a_)							,     exog=[test_match])
							return result[0]





def 						__UpperCAmelCase			(     a_							,     a_							,     a_):
							snake_case_				=							SVR(kernel='rbf'							,     C=1							,     gamma=0.1							,     epsilon=0.1)
							regressor.fit(a_							,     a_)
							snake_case_				=							regressor.predict(a_)
							return y_pred[0]





def 						__UpperCAmelCase			(     a_):
							train_user.sort()
							snake_case_				=							np.percentile(a_							,     25)
							snake_case_				=							np.percentile(a_							,     75)
							snake_case_				=							qa - qa
							snake_case_				=							qa - (iqr * 0.1)
							return low_lim





def 						__UpperCAmelCase			(     a_							,     a_):
							snake_case_				=							0
							snake_case_				=							0
							for i in list_vote:
														if i > actual_result:
																					snake_case_				=							not_safe + 1
														else:
																					if abs(abs(a_) - abs(a_)) <= 0.1:
																												safe += 1
																					else:
																												not_safe += 1
							return safe > not_safe


if __name__ == "__main__":
	# data_input_df = pd.read_csv("ex_data.csv", header=None)
	lowercase  							=					[[18231, 0.0, 1], [22621, 1.0, 2], [15675, 0.0, 3], [23583, 1.0, 4]]
	lowercase  							=					pd.DataFrame(
	    data_input, columns=["total_user", "total_even", "days"]
	)
	lowercase  							=					Normalizer().fit_transform(data_input_df.values)
	# split data
	lowercase  							=					normalize_df[:, 2].tolist()
	lowercase  							=					normalize_df[:, 0].tolist()
	lowercase  							=					normalize_df[:, 1].tolist()

	# for svr (input variable = total date and total match)
	lowercase  							=					normalize_df[:, [1, 2]].tolist()
	lowercase  							=					x[: len(x) - 1]
	lowercase  							=					x[len(x) - 1 :]

	# for linear regression & sarimax
	lowercase  							=					total_date[: len(total_date) - 1]
	lowercase  							=					total_user[: len(total_user) - 1]
	lowercase  							=					total_match[: len(total_match) - 1]

	lowercase  							=					total_date[len(total_date) - 1 :]
	lowercase  							=					total_user[len(total_user) - 1 :]
	lowercase  							=					total_match[len(total_match) - 1 :]

	# voting system with forecasting
	lowercase  							=					[
	    linear_regression_prediction(
	        trn_date, trn_user, trn_match, tst_date, tst_match
	    ),
	    sarimax_predictor(trn_user, trn_match, tst_match),
	    support_vector_regressor(x_train, x_test, trn_user),
	]

	# check the safety of today's data
	lowercase  							=					"" if data_safety_checker(res_vote, tst_user) else "not "
	print("Today's data is {not_str}safe.")





