






'''simple docstring'''




import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


lowerCAmelCase__          =						logging.get_logger(__name__)

lowerCAmelCase__          =						'▁'

lowerCAmelCase__          =						{
    'vocab_file': 'vocab.json',
    'spm_file': 'sentencepiece.bpe.model',
}

lowerCAmelCase__          =						{
    'vocab_file': {
        'facebook/s2t-small-librispeech-asr': (
            'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'
        ),
    },
    'spm_file': {
        'facebook/s2t-small-librispeech-asr': (
            'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'
        )
    },
}

lowerCAmelCase__          =						{
    'facebook/s2t-small-librispeech-asr': 1024,
}

lowerCAmelCase__          =						['pt', 'fr', 'ru', 'nl', 'ro', 'it', 'es', 'de']

lowerCAmelCase__          =						{'mustc': MUSTC_LANGS}







class       __lowercase					(__lowerCamelCase    ):
	_lowerCamelCase              =  VOCAB_FILES_NAMES
	_lowerCamelCase              =  PRETRAINED_VOCAB_FILES_MAP
	_lowerCamelCase              =  MAX_MODEL_INPUT_SIZES
	_lowerCamelCase              =  ['''input_ids''', '''attention_mask''']

	_lowerCamelCase              =  []

	def __init__(      self   :						Dict  ,					UpperCAmelCase_   :						str  ,					UpperCAmelCase_   :						Dict  ,					UpperCAmelCase_   :						Any="<s>"  ,					UpperCAmelCase_   :						str="</s>"  ,					UpperCAmelCase_   :						Optional[int]="<pad>"  ,					UpperCAmelCase_   :						str="<unk>"  ,					UpperCAmelCase_   :						Optional[Any]=False  ,					UpperCAmelCase_   :						str=False  ,					UpperCAmelCase_   :						Union[str, Any]=None  ,					UpperCAmelCase_   :						Optional[int]=None  ,					UpperCAmelCase_   :						Optional[Dict[str, Any]] = None  ,					**UpperCAmelCase_   :						Tuple  ,					):
			UpperCamelCase__				:					List[str]					=					{} if sp_model_kwargs is None else sp_model_kwargs

			super().__init__(
			    bos_token=UpperCAmelCase_  ,					eos_token=UpperCAmelCase_  ,					unk_token=UpperCAmelCase_  ,					pad_token=UpperCAmelCase_  ,					do_upper_case=UpperCAmelCase_  ,					do_lower_case=UpperCAmelCase_  ,					tgt_lang=UpperCAmelCase_  ,					lang_codes=UpperCAmelCase_  ,					sp_model_kwargs=self.sp_model_kwargs  ,					**UpperCAmelCase_  ,					)
			UpperCamelCase__				:					List[str]					=					do_upper_case
			UpperCamelCase__				:					Any					=					do_lower_case

			UpperCamelCase__				:					Dict					=					load_json(UpperCAmelCase_)
			UpperCamelCase__				:					Union[str, Any]					=					{v: k for k, v in self.encoder.items()}
			UpperCamelCase__				:					int					=					spm_file
			UpperCamelCase__				:					List[str]					=					load_spm(UpperCAmelCase_  ,					self.sp_model_kwargs)

			if lang_codes is not None:
					UpperCamelCase__				:					Union[str, Any]					=					lang_codes
					UpperCamelCase__				:					List[Any]					=					LANGUAGES[lang_codes]
					UpperCamelCase__				:					List[Any]					=					[F'<lang:{lang}>' for lang in self.langs]
					UpperCamelCase__				:					Union[str, Any]					=					{lang: self.sp_model.PieceToId(F'<lang:{lang}>') for lang in self.langs}

					UpperCamelCase__				:					int					=					self.lang_tokens
					UpperCamelCase__				:					List[Any]					=					tgt_lang if tgt_lang is not None else self.langs[0]

					self.set_tgt_lang_special_tokens(self._tgt_lang)
			else:
					UpperCamelCase__				:					int					=					{}

	@property
	def      __UpperCamelCase							(      self   :						List[Any]):
			return len(self.encoder)

	@property
	def      __UpperCamelCase							(      self   :						Any):
			return self._tgt_lang

	@tgt_lang.setter
	def      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						Any):
			UpperCamelCase__				:					Optional[Any]					=					new_tgt_lang
			self.set_tgt_lang_special_tokens(UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						str):
			UpperCamelCase__				:					List[Any]					=					self.lang_code_to_id[tgt_lang]
			UpperCamelCase__				:					Optional[int]					=					[lang_code_id]

	def      __UpperCamelCase							(      self   :						List[Any]  ,					UpperCAmelCase_   :						str):
			return self.sp_model.encode(UpperCAmelCase_  ,					out_type=UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Tuple  ,					UpperCAmelCase_   :						Optional[Any]):
			return self.encoder.get(UpperCAmelCase_  ,					self.encoder[self.unk_token])

	def      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						int):
			return self.decoder.get(UpperCAmelCase_  ,					self.unk_token)

	def      __UpperCamelCase							(      self   :						Optional[int]  ,					UpperCAmelCase_   :						List[str]):
			UpperCamelCase__				:					str					=					[]
			UpperCamelCase__				:					int					=					''
			for token in tokens:
					# make sure that special tokens are not decoded using sentencepiece model
					if token in self.all_special_tokens:
							UpperCamelCase__				:					int					=					self.sp_model.decode(UpperCAmelCase_)
							out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
							UpperCamelCase__				:					Optional[Any]					=					[]
					else:
							current_sub_tokens.append(UpperCAmelCase_)
			UpperCamelCase__				:					Tuple					=					self.sp_model.decode(UpperCAmelCase_)
			out_string += decoded.upper() if self.do_upper_case else decoded
			return out_string.strip()

	def      __UpperCamelCase							(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						Any  ,					UpperCAmelCase_   :						Optional[int]=None):
			if token_ids_a is None:
					return self.prefix_tokens + token_ids_a + [self.eos_token_id]
			# We don't expect to process pairs, but leave the pair logic for API consistency
			return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]

	def      __UpperCamelCase							(      self   :						Dict  ,					UpperCAmelCase_   :						List[int]  ,					UpperCAmelCase_   :						Optional[List[int]] = None  ,					UpperCAmelCase_   :						bool = False):

			if already_has_special_tokens:
					return super().get_special_tokens_mask(
					    token_ids_a=UpperCAmelCase_  ,					token_ids_a=UpperCAmelCase_  ,					already_has_special_tokens=UpperCAmelCase_)

			UpperCamelCase__				:					Union[str, Any]					=					[1] * len(self.prefix_tokens)
			UpperCamelCase__				:					str					=					[1]
			if token_ids_a is None:
					return prefix_ones + ([0] * len(UpperCAmelCase_)) + suffix_ones
			return prefix_ones + ([0] * len(UpperCAmelCase_)) + ([0] * len(UpperCAmelCase_)) + suffix_ones

	def      __UpperCamelCase							(      self   :						Optional[int]):
			UpperCamelCase__				:					List[Any]					=					self.encoder.copy()
			vocab.update(self.added_tokens_encoder)
			return vocab

	def __getstate__(      self   :						str):
			UpperCamelCase__				:					int					=					self.__dict__.copy()
			UpperCamelCase__				:					Tuple					=					None
			return state

	def __setstate__(      self   :						int  ,					UpperCAmelCase_   :						Dict):
			UpperCamelCase__				:					Optional[int]					=					d

			# for backward compatibility
			if not hasattr(self  ,					'sp_model_kwargs'):
					UpperCamelCase__				:					Union[str, Any]					=					{}

			UpperCamelCase__				:					Any					=					load_spm(self.spm_file  ,					self.sp_model_kwargs)





	def      __UpperCamelCase							(      self   :						Optional[int]  ,					UpperCAmelCase_   :						str  ,					UpperCAmelCase_   :						Optional[str] = None):
			UpperCamelCase__				:					List[Any]					=					Path(UpperCAmelCase_)
			assert save_dir.is_dir(), F'{save_directory} should be a directory'
			UpperCamelCase__				:					Dict					=					save_dir / (
			    (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
			)
			UpperCamelCase__				:					str					=					save_dir / (
			    (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
			)

			save_json(self.encoder  ,					UpperCAmelCase_)

			if os.path.abspath(self.spm_file) != os.path.abspath(UpperCAmelCase_) and os.path.isfile(self.spm_file):
					copyfile(self.spm_file  ,					UpperCAmelCase_)
			elif not os.path.isfile(self.spm_file):
					with open(UpperCAmelCase_  ,					'wb') as fi:
							UpperCamelCase__				:					Optional[Any]					=					self.sp_model.serialized_model_proto()
							fi.write(UpperCAmelCase_)

			return (str(UpperCAmelCase_), str(UpperCAmelCase_))







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_)							->     sentencepiece.SentencePieceProcessor:
		UpperCamelCase__				:					Any					=					sentencepiece.SentencePieceProcessor(**lowerCamelCase_)
		spm.Load(str(lowerCamelCase_))
		return spm







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Union[Dict, List]:
		with open(lowerCamelCase_     ,      'r') as f:
				return json.load(lowerCamelCase_)







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_)							->     None:
		with open(lowerCamelCase_     ,      'w') as f:
				json.dump(lowerCamelCase_     ,      lowerCamelCase_     ,      indent=2)












'''simple docstring'''




import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize







def       __UpperCAmelCase						(  lowerCamelCase_)							->     float:
		return np.dot(lowerCamelCase_     ,      lowerCamelCase_)







class       __lowercase					:

	def __init__(      self   :						Tuple  ,					*,
	    UpperCAmelCase_   :						float = np.inf  ,					UpperCAmelCase_   :						str = "linear"  ,					UpperCAmelCase_   :						float = 0.0  ,					):
			UpperCamelCase__				:					Union[str, Any]					=					regularization
			UpperCamelCase__				:					Optional[int]					=					gamma
			if kernel == "linear":
					UpperCamelCase__				:					List[str]					=					self.__linear
			elif kernel == "rbf":
					if self.gamma == 0:
							raise ValueError('rbf kernel requires gamma')
					if not isinstance(self.gamma  ,					(float, int)):
							raise ValueError('gamma must be float or int')
					if not self.gamma > 0:
							raise ValueError('gamma must be > 0')
					UpperCamelCase__				:					Union[str, Any]					=					self.__rbf
					# in the future, there could be a default value like in sklearn
					# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
					# previously it was 1/(n_features)
			else:
					UpperCamelCase__				:					Optional[int]					=					F'Unknown kernel: {kernel}'
					raise ValueError(UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Any  ,					UpperCAmelCase_   :						ndarray  ,					UpperCAmelCase_   :						ndarray):
			return np.dot(UpperCAmelCase_  ,					UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						ndarray  ,					UpperCAmelCase_   :						ndarray):
			return np.exp(-(self.gamma * norm_squared(vectora - vectora)))

	def      __UpperCamelCase							(      self   :						Any  ,					UpperCAmelCase_   :						list[ndarray]  ,					UpperCAmelCase_   :						ndarray):
			UpperCamelCase__				:					Any					=					observations
			UpperCamelCase__				:					Tuple					=					classes

			# using Wolfe's Dual to calculate w.
			# Primal problem: minimize 1/2*norm_squared(w)
			#   constraint: yn(w . xn + b) >= 1
			#
			# With l a vector
			# Dual problem: maximize sum_n(ln) -
			#       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
			#   constraint: self.C >= ln >= 0
			#           and sum_n(ln*yn) = 0
			# Then we get w using w = sum_n(ln*yn*xn)
			# At the end we can get b ~= mean(yn - w . xn)
			#
			# Since we use kernels, we only need l_star to calculate b
			# and to classify observations

			((UpperCamelCase__),	)				:					Optional[Any]					=					np.shape(UpperCAmelCase_)

			def to_minimize(UpperCAmelCase_   :						ndarray) -> float:
					UpperCamelCase__				:					Union[str, Any]					=					0
					((UpperCamelCase__),	)				:					int					=					np.shape(UpperCAmelCase_)
					for i in range(UpperCAmelCase_):
							for j in range(UpperCAmelCase_):
									s += (
									    candidate[i]
									    * candidate[j]
									    * classes[i]
									    * classes[j]
									    * self.kernel(observations[i]  ,					observations[j])
									)
					return 1 / 2 * s - sum(UpperCAmelCase_)

			UpperCamelCase__				:					List[str]					=					LinearConstraint(UpperCAmelCase_  ,					0  ,					0)
			UpperCamelCase__				:					Dict					=					Bounds(0  ,					self.regularization)

			UpperCamelCase__				:					Any					=					minimize(
			    UpperCAmelCase_  ,					np.ones(UpperCAmelCase_)  ,					bounds=UpperCAmelCase_  ,					constraints=[ly_contraint]).x
			UpperCamelCase__				:					str					=					l_star

			# calculating mean offset of separation plane to points
			UpperCamelCase__				:					Any					=					0
			for i in range(UpperCAmelCase_):
					for j in range(UpperCAmelCase_):
							s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
							    observations[i]  ,					observations[j])
			UpperCamelCase__				:					List[str]					=					s / n





	def      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						ndarray):
			UpperCamelCase__				:					Optional[int]					=					sum(
			    self.optimum[n]
			    * self.classes[n]
			    * self.kernel(self.observations[n]  ,					UpperCAmelCase_)
			    for n in range(len(self.classes)))
			return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
		import doctest

		doctest.testmod()




