
import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer


lowercase  :					Optional[Any]						    =							logging.get_logger(__name__)

lowercase  :					str						    =							{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

lowercase  :					Optional[int]						    =							{
    """vocab_file""": {
        """google/realm-cc-news-pretrained-embedder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt"""
        ),
        """google/realm-cc-news-pretrained-encoder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt"""
        ),
        """google/realm-cc-news-pretrained-scorer""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt"""
        ),
        """google/realm-cc-news-pretrained-openqa""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt"""
        ),
        """google/realm-orqa-nq-openqa""": """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt""",
        """google/realm-orqa-nq-reader""": """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt""",
        """google/realm-orqa-wq-openqa""": """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt""",
        """google/realm-orqa-wq-reader""": """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt""",
    },
    """tokenizer_file""": {
        """google/realm-cc-news-pretrained-embedder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont"""
        ),
        """google/realm-cc-news-pretrained-encoder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json"""
        ),
        """google/realm-cc-news-pretrained-scorer""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json"""
        ),
        """google/realm-cc-news-pretrained-openqa""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-nq-openqa""": (
            """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-nq-reader""": (
            """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-wq-openqa""": (
            """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-wq-reader""": (
            """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json"""
        ),
    },
}

lowercase  :					List[Any]						    =							{
    """google/realm-cc-news-pretrained-embedder""": 512,
    """google/realm-cc-news-pretrained-encoder""": 512,
    """google/realm-cc-news-pretrained-scorer""": 512,
    """google/realm-cc-news-pretrained-openqa""": 512,
    """google/realm-orqa-nq-openqa""": 512,
    """google/realm-orqa-nq-reader""": 512,
    """google/realm-orqa-wq-openqa""": 512,
    """google/realm-orqa-wq-reader""": 512,
}

lowercase  :					Tuple						    =							{
    """google/realm-cc-news-pretrained-embedder""": {"""do_lower_case""": True},
    """google/realm-cc-news-pretrained-encoder""": {"""do_lower_case""": True},
    """google/realm-cc-news-pretrained-scorer""": {"""do_lower_case""": True},
    """google/realm-cc-news-pretrained-openqa""": {"""do_lower_case""": True},
    """google/realm-orqa-nq-openqa""": {"""do_lower_case""": True},
    """google/realm-orqa-nq-reader""": {"""do_lower_case""": True},
    """google/realm-orqa-wq-openqa""": {"""do_lower_case""": True},
    """google/realm-orqa-wq-reader""": {"""do_lower_case""": True},
}

class __snake_case		(      lowerCAmelCase     ):
    _a		:						str=				VOCAB_FILES_NAMES
    _a		:						Union[str, Any]=				PRETRAINED_VOCAB_FILES_MAP
    _a		:						Tuple=				PRETRAINED_INIT_CONFIGURATION
    _a		:						Optional[int]=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _a		:						List[Any]=				RealmTokenizer
    def __init__(  self		,snake_case=None		,snake_case=None		,snake_case=True		,snake_case="[UNK]"		,snake_case="[SEP]"		,snake_case="[PAD]"		,snake_case="[CLS]"		,snake_case="[MASK]"		,snake_case=True		,snake_case=None		,**snake_case		,):



           '''simple docstring'''







           super().__init__(
               snake_case		,tokenizer_file=snake_case		,do_lower_case=snake_case		,unk_token=snake_case		,sep_token=snake_case		,pad_token=snake_case		,cls_token=snake_case		,mask_token=snake_case		,tokenize_chinese_chars=snake_case		,strip_accents=snake_case		,**snake_case		,)

           lowercase							:     int       	=      json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
           if (
               normalizer_state.get("""lowercase"""		,snake_case      ) != do_lower_case
               or normalizer_state.get("""strip_accents"""		,snake_case      ) != strip_accents
               or normalizer_state.get("""handle_chinese_chars"""		,snake_case      ) != tokenize_chinese_chars
           ):
                  lowercase							:     Union[str, Any]       	=      getattr(snake_case		,normalizer_state.pop("""type"""      )      )
                  lowercase							:     str       	=      do_lower_case
                  lowercase							:     str       	=      strip_accents
                  lowercase							:     Optional[Any]       	=      tokenize_chinese_chars
                  lowercase							:     Tuple       	=      normalizer_class(**snake_case      )

           lowercase							:     List[str]       	=      do_lower_case
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,**snake_case      ):



           '''simple docstring'''







           lowercase							:     Dict       	=      PaddingStrategy.MAX_LENGTH

           lowercase							:     int       	=      text
           lowercase							:     int       	=      kwargs.pop("""text_pair"""		,snake_case      )
           lowercase							:     List[str]       	=      kwargs.pop("""return_tensors"""		,snake_case      )

           lowercase							:     Optional[int]       	=      {
               """input_ids""": [],
               """attention_mask""": [],
               """token_type_ids""": [],
           }

           for idx, candidate_text in enumerate(snake_case      ):
                  if batch_text_pair is not None:
                         lowercase							:     str       	=      batch_text_pair[idx]
                  else:
                         lowercase							:     Optional[Any]       	=      None

                  lowercase							:     Any       	=      super().__call__(snake_case		,snake_case		,return_tensors=snake_case		,**snake_case      )

                  lowercase							:     Tuple       	=      encoded_candidates.get("""input_ids"""      )
                  lowercase							:     Optional[int]       	=      encoded_candidates.get("""attention_mask"""      )
                  lowercase							:     Tuple       	=      encoded_candidates.get("""token_type_ids"""      )

                  if encoded_input_ids is not None:
                         output_data["input_ids"].append(snake_case      )
                  if encoded_attention_mask is not None:
                         output_data["attention_mask"].append(snake_case      )
                  if encoded_token_type_ids is not None:
                         output_data["token_type_ids"].append(snake_case      )

           lowercase							:     Tuple       	=      {key: item for key, item in output_data.items() if len(snake_case      ) != 0}

           return BatchEncoding(snake_case		,tensor_type=snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case=None      ):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      [self.cls_token_id] + token_ids_a + [self.sep_token_id]

           if token_ids_a:
                  output += token_ids_a + [self.sep_token_id]

           return output
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = None      ):



           '''simple docstring'''







           lowercase							:     str       	=      [self.sep_token_id]
           lowercase							:     List[Any]       	=      [self.cls_token_id]
           if token_ids_a is None:
                  return len(cls + token_ids_a + sep      ) * [0]
           return len(cls + token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = None      ):



           '''simple docstring'''







           lowercase							:     Any       	=      self._tokenizer.model.save(snake_case		,name=snake_case      )
           return tuple(snake_case      )







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



def A_	(		A__				)  ->		float:
       return np.dot(A__		,      A__				)





class  A__    :




   """simple docstring"""



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







          '''simple docstring'''

          a__						:	Tuple =				regularization
          a__						:	Optional[Any] =				gamma
          if kernel == "linear":
                 a__						:	Optional[Any] =				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')
                 a__						:	str =				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:
                 a__						:	Optional[int] =				F'Unknown kernel: {kernel}'
                 raise ValueError(lowercase)

   def 							__lowercase		(					self					,  lowercase					,  lowercase)			->						float:







          '''simple docstring'''

          return np.dot(lowercase					,  lowercase)

   def 							__lowercase		(					self					,  lowercase					,  lowercase)			->						float:







          '''simple docstring'''

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

   def 							__lowercase		(					self					,  lowercase					,  lowercase)			->						None:







          '''simple docstring'''

          a__						:	List[str] =				observations
          a__						:	Dict =				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

          ((a__) ,							)						:	Optional[int] =				np.shape(lowercase)

          def to_minimize(lowercase) -> float:
                 a__						:	Tuple =				0
                 ((a__) ,							)						:	Optional[int] =				np.shape(lowercase)
                 for i in range(lowercase):
                        for j in range(lowercase):
                               s += (
                                   candidate[i]
                                   * candidate[j]
                                   * classes[i]
                                   * classes[j]
                                   * self.kernel(observations[i]					,  observations[j])
                               )
                 return 1 / 2 * s - sum(lowercase)

          a__						:	Optional[Any] =				LinearConstraint(lowercase					,  0					,  0)
          a__						:	str =				Bounds(0					,  self.regularization)

          a__						:	List[str] =				minimize(
              lowercase					,  np.ones(lowercase)					,  bounds=lowercase					,  constraints=[ly_contraint]).x
          a__						:	Dict =				l_star

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

   def 							__lowercase		(					self					,  lowercase)			->						int:







          '''simple docstring'''

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


if __name__ == "__main__":
    import doctest

    doctest.testmod()


