



import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_ta import TaTokenizer
else:
							__snake_case    :		List[str]  =None


__snake_case    :		int  =logging.get_logger(__name__)

__snake_case    :		Dict  ={'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

__snake_case    :		List[Any]  ={
    'vocab_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/spiece.model',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/spiece.model',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/spiece.model',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/spiece.model',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/tokenizer.json',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/tokenizer.json',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/tokenizer.json',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/tokenizer.json',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/tokenizer.json',
    },
}


# TODO(PVP) - this should be removed in Transformers v5
__snake_case    :		int  ={
    't5-small': 5_1_2,
    't5-base': 5_1_2,
    't5-large': 5_1_2,
    't5-3b': 5_1_2,
    't5-11b': 5_1_2,
}



class 	lowerCamelCase__      (							_lowercase):




		'''simple docstring'''



		snake_case_     =VOCAB_FILES_NAMES
		snake_case_     =PRETRAINED_VOCAB_FILES_MAP
		snake_case_     =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		snake_case_     =["""input_ids""", """attention_mask"""]
		snake_case_     =TaTokenizer

		snake_case_     =[]



		def __init__(self		,__lowerCamelCase=None		,__lowerCamelCase=None		,__lowerCamelCase="</s>"		,__lowerCamelCase="<unk>"		,__lowerCamelCase="<pad>"		,__lowerCamelCase=1_00		,__lowerCamelCase=None		,**__lowerCamelCase		,)					->      Optional[int]:

						"""simple docstring"""






						if extra_ids > 0 and additional_special_tokens is None:
										lowerCAmelCase__					:    str												=					[f"""<extra_id_{i}>""" for i in range(__UpperCamelCase			)]
						elif extra_ids > 0 and additional_special_tokens is not None:
										# Check that we have the right number of extra special tokens
										lowerCAmelCase__					:    Optional[Any]												=					len(set(filter(lambda __lowerCamelCase			: bool('''extra_id_''' in str(__UpperCamelCase			)			)		,__UpperCamelCase			)			)			)
										if extra_tokens != extra_ids:
														raise ValueError(
														    f"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
														    ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
														    ''' tokens'''			)

						super().__init__(
						    __UpperCamelCase		,tokenizer_file=__UpperCamelCase		,eos_token=__UpperCamelCase		,unk_token=__UpperCamelCase		,pad_token=__UpperCamelCase		,extra_ids=__UpperCamelCase		,additional_special_tokens=__UpperCamelCase		,**__UpperCamelCase		,)

						lowerCAmelCase__					:    Any												=					vocab_file
						lowerCAmelCase__					:    Optional[Any]												=					False if not self.vocab_file else True
						lowerCAmelCase__					:    Any												=					extra_ids



		@staticmethod
		def 		lowerCAmelCase__				(__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase			)					->      Tuple:

						"""simple docstring"""






						if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
										lowerCAmelCase__					:    str												=					TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
										if init_max_model_length is not None and init_max_model_length != max_model_length:
														return init_max_model_length
										elif init_max_model_length is None:
														warnings.warn(
														    '''This tokenizer was incorrectly instantiated with a model max length of'''
														    f""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
														    ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
														    ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
														    f""" {pretrained_model_name_or_path} automatically truncating your input to"""
														    f""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
														    f""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
														    ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
														    ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''		,__UpperCamelCase		,)

						return max_model_length



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase = None			)					->      Tuple[str]:

						"""simple docstring"""






						if not self.can_save_slow_tokenizer:
										raise ValueError(
										    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
										    '''tokenizer.'''			)

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

						if os.path.abspath(self.vocab_file			) != os.path.abspath(__UpperCamelCase			):
										copyfile(self.vocab_file		,__UpperCamelCase			)
										logger.info(f"""Copy vocab file to {out_vocab_file}"""			)

						return (out_vocab_file,)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase = None			)					->      List[int]:

						"""simple docstring"""






						lowerCAmelCase__					:    Any												=					token_ids_a + [self.eos_token_id]
						if token_ids_a is None:
										return self.prefix_tokens + token_ids_a
						else:
										lowerCAmelCase__					:    int												=					token_ids_a + [self.eos_token_id]
										return self.prefix_tokens + token_ids_a + token_ids_a



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase = None			)					->      List[int]:

						"""simple docstring"""






						lowerCAmelCase__					:    List[Any]												=					[self.eos_token_id]

						if token_ids_a is None:
										return len(token_ids_a + eos			) * [0]
						return len(token_ids_a + eos + token_ids_a + eos			) * [0]



		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						return list(
						    set(filter(lambda __lowerCamelCase			: bool(re.search(R'''<extra_id_\d+>'''		,__UpperCamelCase			)			) is not None		,self.additional_special_tokens			)			)			)



		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						return [self.convert_tokens_to_ids(__UpperCamelCase			) for token in self.get_sentinel_tokens()]










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






def      lowerCAmelCase__		(   lowerCamelCase_		:		ndarray):





				'''simple docstring'''

				return np.dot(lowerCamelCase_	,lowerCamelCase_)



class 	lowerCamelCase__      :




		'''simple docstring'''



		def __init__(self		,*,
		    __lowerCamelCase = np.inf		,__lowerCamelCase = "linear"		,__lowerCamelCase = 0.0		,)					->      None:

						"""simple docstring"""






						lowerCAmelCase__					:    Any												=					regularization
						lowerCAmelCase__					:    str												=					gamma
						if kernel == "linear":
										lowerCAmelCase__					:    Dict												=					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'''			)
										lowerCAmelCase__					:    Optional[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:
										lowerCAmelCase__					:    List[str]												=					f"""Unknown kernel: {kernel}"""
										raise ValueError(__lowerCamelCase			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase			)					->      float:

						"""simple docstring"""






						return np.dot(__lowerCamelCase		,__lowerCamelCase			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase			)					->      float:

						"""simple docstring"""






						return np.exp(-(self.gamma * norm_squared(vectora - vectora			))			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase			)					->      None:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					observations
						lowerCAmelCase__					:    Optional[int]												=					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

						((lowerCAmelCase__)						,		)					:    List[str]												=					np.shape(__lowerCamelCase			)

						def to_minimize(__lowerCamelCase			) -> float:
										lowerCAmelCase__					:    List[str]												=					0
										((lowerCAmelCase__)						,		)					:    str												=					np.shape(__lowerCamelCase			)
										for i in range(__lowerCamelCase			):
														for j in range(__lowerCamelCase			):
																		s += (
																		    candidate[i]
																		    * candidate[j]
																		    * classes[i]
																		    * classes[j]
																		    * self.kernel(observations[i]		,observations[j]			)
																		)
										return 1 / 2 * s - sum(__lowerCamelCase			)

						lowerCAmelCase__					:    List[str]												=					LinearConstraint(__lowerCamelCase		,0		,0			)
						lowerCAmelCase__					:    List[str]												=					Bounds(0		,self.regularization			)

						lowerCAmelCase__					:    int												=					minimize(
						    __lowerCamelCase		,np.ones(__lowerCamelCase			)		,bounds=__lowerCamelCase		,constraints=[ly_contraint]			).x
						lowerCAmelCase__					:    List[Any]												=					l_star

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



		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      int:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					sum(
						    self.optimum[n]
						    * self.classes[n]
						    * self.kernel(self.observations[n]		,__lowerCamelCase			)
						    for n in range(len(self.classes			)			)			)
						return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
							import doctest

							doctest.testmod()

