


"""simple docstring"""
import json
import os
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE :    Any									=				logging.get_logger(__name__)


SCREAMING_SNAKE_CASE :    Optional[Any]									=				{
    """vocab_file""": """vocab.json""",
    """tokenizer_config_file""": """tokenizer_config.json""",
    """merges_file""": """merges.txt""",
}

SCREAMING_SNAKE_CASE :    Tuple									=				{
    """vocab_file""": {
        """facebook/s2t-wav2vec2-large-en-de""": (
            """https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json"""
        ),
    },
    """tokenizer_config_file""": {
        """facebook/s2t-wav2vec2-large-en-de""": (
            """https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json"""
        ),
    },
    """merges_file""": {
        """facebook/s2t-wav2vec2-large-en-de""": (
            """https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt"""
        ),
    },
}

SCREAMING_SNAKE_CASE :    Union[str, Any]									=				"""</w>"""
SCREAMING_SNAKE_CASE :    str									=				"""@@ """






def 		lowercase							(     _snake_case						:      Optional[Any]     )     ->List[str]:
	"""simple docstring"""





	__snake_case	:						List[Any]						=				set()
	__snake_case	:						Optional[int]						=				word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)     )
		__snake_case	:						Optional[int]						=				char
	return pairs


# Speech2Text2 has no max input length
SCREAMING_SNAKE_CASE :    Optional[int]									=				{"""facebook/s2t-wav2vec2-large-en-de""": 1024}



class 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		lowerCamelCase__				=VOCAB_FILES_NAMES
		lowerCamelCase__				=PRETRAINED_VOCAB_FILES_MAP
		lowerCamelCase__				=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCamelCase__				=['input_ids', 'attention_mask']



		def __init__(self				,       a_				,       a_="<s>"				,       a_="<pad>"				,       a_="</s>"				,       a_="<unk>"				,       a_=False				,       a_=None				,       **a_				,       ):





			'''simple docstring'''

			super().__init__(
			    unk_token=a_				,       bos_token=a_				,       eos_token=a_				,       pad_token=a_				,       do_lower_case=a_				,       **a_				,       )

			__snake_case	:						Union[str, Any]						=				do_lower_case

			with open(a_				,       encoding='''utf-8'''					) as vocab_handle:
				__snake_case	:						Tuple						=				json.load(a_					)
			__snake_case	:						List[str]						=				{v: k for k, v in self.encoder.items()}

			if merges_file is None:
				logger.info(f"""No merges files provided. {self.__class__.__name__} can only be used for decoding."""					)

				__snake_case	:						str						=				None
				__snake_case	:						Tuple						=				None
			else:
				with open(a_				,       encoding='''utf-8'''					) as merges_handle:
					__snake_case	:						List[str]						=				merges_handle.read().split('''\n'''					)[:-1]

				__snake_case	:						List[Any]						=				[tuple(merge.split()[:2]					) for merge in merges]
				__snake_case	:						str						=				dict(zip(a_				,       range(len(a_					)					)					)					)
				__snake_case	:						Dict						=				{}



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return len(self.decoder					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return dict(self.encoder				,       **self.added_tokens_encoder					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_					):





			'''simple docstring'''

			__snake_case	:						Union[str, Any]						=				tuple(token[:-1]					) + (token[-1] + BPE_TOKEN_MERGES,)
			if token in self.cache:
				return self.cache[token]
			__snake_case	:						Union[str, Any]						=				get_pairs(a_					)

			if not pairs:
				return token

			while True:
				__snake_case	:						int						=				min(a_				,       key=lambda a_					: self.bpe_ranks.get(a_				,       float('''inf'''					)					)					)
				if bigram not in self.bpe_ranks:
					break
				__snake_case	,							__snake_case	:						Optional[Any]						=				bigram
				__snake_case	:						Optional[Any]						=				[]
				__snake_case	:						List[Any]						=				0
				while i < len(a_					):
					try:
						__snake_case	:						Optional[int]						=				word.index(a_				,       a_					)
					except ValueError:
						new_word.extend(word[i:]					)
						break
					else:
						new_word.extend(word[i:j]					)
						__snake_case	:						str						=				j

					if word[i] == first and i < len(a_					) - 1 and word[i + 1] == second:
						new_word.append(first + second					)
						i += 2
					else:
						new_word.append(word[i]					)
						i += 1
				__snake_case	:						Optional[int]						=				tuple(a_					)
				__snake_case	:						List[str]						=				new_word
				if len(a_					) == 1:
					break
				else:
					__snake_case	:						int						=				get_pairs(a_					)
			__snake_case	:						Dict						=				''' '''.join(a_					)
			if word == "\n  " + BPE_TOKEN_MERGES:
				__snake_case	:						Union[str, Any]						=				'''\n''' + BPE_TOKEN_MERGES

			if word.endswith(a_					):
				__snake_case	:						Any						=				word.replace(a_				,       ''''''					)

			__snake_case	:						str						=				word.replace(''' '''				,       a_					)
			__snake_case	:						List[str]						=				word
			return word



		def 				SCREAMING_SNAKE_CASE  (self				,       a_					):





			'''simple docstring'''

			if self.bpe_ranks is None:
				raise ValueError(
				    '''This tokenizer was instantiated without a `merges.txt` file, so'''
				    ''' that it can only be used for decoding, not for encoding.'''
				    '''Make sure to provide `merges.txt` file at instantiation to enable '''
				    '''encoding.'''					)

			if self.do_lower_case:
				__snake_case	:						Optional[int]						=				text.lower()

			__snake_case	:						Union[str, Any]						=				text.split()

			__snake_case	:						Optional[Any]						=				[]
			for token in text:
				if token:
					split_tokens.extend(list(self.bpe(a_					).split(''' '''					)					)					)

			return split_tokens



		def 				SCREAMING_SNAKE_CASE  (self				,       a_					):





			'''simple docstring'''

			return self.encoder.get(a_				,       self.encoder.get(self.unk_token					)					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_					):





			'''simple docstring'''

			__snake_case	:						List[Any]						=				self.decoder.get(a_				,       self.unk_token					)
			return result



		def 				SCREAMING_SNAKE_CASE  (self				,       a_					):





			'''simple docstring'''

			__snake_case	:						Dict						=				''' '''.join(a_					)

			# make sure @@ tokens are concatenated
			__snake_case	:						int						=				''''''.join(string.split(a_					)					)

			return string



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_ = None					):





			'''simple docstring'''

			if not os.path.isdir(a_					):
				logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""					)
				return
			__snake_case	:						List[str]						=				os.path.join(
			    a_				,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)
			__snake_case	:						Optional[int]						=				os.path.join(
			    a_				,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']					)

			with open(a_				,       '''w'''				,       encoding='''utf-8'''					) as f:
				f.write(json.dumps(self.encoder				,       indent=2				,       sort_keys=a_				,       ensure_ascii=a_					) + '''\n'''					)

			__snake_case	:						List[Any]						=				0
			if self.bpe_ranks is None:
				return (vocab_file,)

			with open(a_				,       '''w'''				,       encoding='''utf-8'''					) as writer:
				for bpe_tokens, token_index in sorted(self.bpe_ranks.items()				,       key=lambda a_					: kv[1]					):
					if index != token_index:
						logger.warning(
						    f"""Saving vocabulary to {merges_file}: BPE merge indices are not consecutive."""
						    ''' Please check that the tokenizer is not corrupted!'''					)
						__snake_case	:						Optional[Any]						=				token_index
					writer.write(''' '''.join(a_					) + '''\n'''					)
					index += 1

			return (vocab_file, merges_file)













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.")





