'''simple docstring'''






import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer







class lowercase_					(		tf.keras.layers.Layer ):







   """simple docstring"""

   def __init__(			self			: str,   UpperCamelCase__			: List,   UpperCamelCase__			: bool,   UpperCamelCase__			: int = None,   UpperCamelCase__			: int = None,   UpperCamelCase__			: int = None,   UpperCamelCase__			: str = "longest",   UpperCamelCase__			: bool = True,   UpperCamelCase__			: int = 5_12,   UpperCamelCase__			: int = None,   UpperCamelCase__			: bool = True,   UpperCamelCase__			: bool = True,   UpperCamelCase__			: bool = True,   **UpperCamelCase__			: Tuple,   )     -> List[str]:
        super().__init__()
        if use_fast_bert_tokenizer:
             _A								=     FastBertTokenizer(
                 UpperCamelCase__,   token_out_type=tf.intaa,   lower_case_nfd_strip_accents=UpperCamelCase__,   **UpperCamelCase__    )
        else:
             _A								=     tf.lookup.StaticVocabularyTable(
                 tf.lookup.KeyValueTensorInitializer(
                     keys=UpperCamelCase__,   key_dtype=tf.string,   values=tf.range(tf.size(UpperCamelCase__,   out_type=tf.intaa    ),   dtype=tf.intaa    ),   value_dtype=tf.intaa,   ),   num_oov_buckets=1,   )
             _A								=     BertTokenizerLayer(
                 UpperCamelCase__,   token_out_type=tf.intaa,   lower_case=UpperCamelCase__,   **UpperCamelCase__    )

        _A								=     vocab_list
        _A								=     do_lower_case
        _A								=     cls_token_id or vocab_list.index('[CLS]'    )
        _A								=     sep_token_id or vocab_list.index('[SEP]'    )
        _A								=     pad_token_id or vocab_list.index('[PAD]'    )
        _A								=     ShrinkLongestTrimmer(max_length - 3,   axis=1    )  # Allow room for special tokens
        _A								=     max_length
        _A								=     padding
        _A								=     truncation
        _A								=     pad_to_multiple_of
        _A								=     return_token_type_ids
        _A								=     return_attention_mask
   @classmethod
   def   __UpperCAmelCase      (			cls			: List[str],   UpperCamelCase__			: "PreTrainedTokenizerBase",   **UpperCamelCase__			: str    )     -> List[str]:  # noqa: F821
        _A								=     kwargs.pop('do_lower_case',   UpperCamelCase__    )
        _A								=     tokenizer.do_lower_case if do_lower_case is None else do_lower_case
        _A								=     kwargs.pop('cls_token_id',   UpperCamelCase__    )
        _A								=     tokenizer.cls_token_id if cls_token_id is None else cls_token_id
        _A								=     kwargs.pop('sep_token_id',   UpperCamelCase__    )
        _A								=     tokenizer.sep_token_id if sep_token_id is None else sep_token_id
        _A								=     kwargs.pop('pad_token_id',   UpperCamelCase__    )
        _A								=     tokenizer.pad_token_id if pad_token_id is None else pad_token_id

        _A								=     tokenizer.get_vocab()
        _A								=     sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()],   key=lambda UpperCamelCase__    : x[1]    )
        _A								=     [entry[0] for entry in vocab]
        return cls(
            vocab_list=UpperCamelCase__,   do_lower_case=UpperCamelCase__,   cls_token_id=UpperCamelCase__,   sep_token_id=UpperCamelCase__,   pad_token_id=UpperCamelCase__,   **UpperCamelCase__,   )
   @classmethod
   def   __UpperCAmelCase      (			cls			: Optional[Any],   UpperCamelCase__			: Union[str, os.PathLike],   *UpperCamelCase__			: Union[str, Any],   **UpperCamelCase__			: int    )     -> int:
        try:
             _A								=     BertTokenizer.from_pretrained(UpperCamelCase__,   *UpperCamelCase__,   **UpperCamelCase__    )
        except:  # noqa: E722
             from .tokenization_bert_fast import BertTokenizerFast

             _A								=     BertTokenizerFast.from_pretrained(UpperCamelCase__,   *UpperCamelCase__,   **UpperCamelCase__    )
        return cls.from_tokenizer(UpperCamelCase__,   **UpperCamelCase__    )
   def   __UpperCAmelCase      (			self			: List[str],   UpperCamelCase__			: List[Any]    )     -> Optional[int]:
        if self.do_lower_case:
             _A								=     case_fold_utfa(UpperCamelCase__    )
        _A								=     self.tf_tokenizer.tokenize(UpperCamelCase__    )
        return tokens.merge_dims(1,   -1    )
   def   __UpperCAmelCase      (			self			: Union[str, Any],   UpperCamelCase__			: Dict,   UpperCamelCase__			: Any=None,   UpperCamelCase__			: int=None,   UpperCamelCase__			: Dict=None,   UpperCamelCase__			: Any=None,   UpperCamelCase__			: List[Any]=None,   UpperCamelCase__			: Any=None,   UpperCamelCase__			: List[Any]=None,   )     -> Dict:
        if padding is None:
             _A								=     self.padding
        if padding not in ("longest", "max_length"):
             raise ValueError('Padding must be either \'longest\' or \'max_length\'!'    )
        if max_length is not None and text_pair is not None:
             # Because we have to instantiate a Trimmer to do it properly
             raise ValueError('max_length cannot be overridden at call time when truncating paired texts!'    )
        if max_length is None:
             _A								=     self.max_length
        if truncation is None:
             _A								=     self.truncation
        if pad_to_multiple_of is None:
             _A								=     self.pad_to_multiple_of
        if return_token_type_ids is None:
             _A								=     self.return_token_type_ids
        if return_attention_mask is None:
             _A								=     self.return_attention_mask
        if not isinstance(UpperCamelCase__,   tf.Tensor    ):
             _A								=     tf.convert_to_tensor(UpperCamelCase__    )
        if text_pair is not None and not isinstance(UpperCamelCase__,   tf.Tensor    ):
             _A								=     tf.convert_to_tensor(UpperCamelCase__    )
        if text_pair is not None:
             if text.shape.rank > 1:
                  raise ValueError('text argument should not be multidimensional when a text pair is supplied!'    )
             if text_pair.shape.rank > 1:
                  raise ValueError('text_pair should not be multidimensional!'    )
        if text.shape.rank == 2:
             _A		,					_A								=     text[:, 0], text[:, 1]
        _A								=     self.unpaired_tokenize(UpperCamelCase__    )
        if text_pair is None:  # Unpaired text
             if truncation:
                  _A								=     text[:, : max_length - 2]  # Allow room for special tokens
             _A		,					_A								=     combine_segments(
                 (text,),   start_of_sequence_id=self.cls_token_id,   end_of_segment_id=self.sep_token_id    )
        else:  # Paired text
             _A								=     self.unpaired_tokenize(UpperCamelCase__    )
             if truncation:
                  _A		,					_A								=     self.paired_trimmer.trim([text, text_pair]    )
             _A		,					_A								=     combine_segments(
                 (text, text_pair),   start_of_sequence_id=self.cls_token_id,   end_of_segment_id=self.sep_token_id    )
        if padding == "longest":
             _A								=     input_ids.bounding_shape(axis=1    )
             if pad_to_multiple_of is not None:
                  # No ceiling division in tensorflow, so we negate floordiv instead
                  _A								=     pad_to_multiple_of * (-tf.math.floordiv(-pad_length,   UpperCamelCase__    ))
        else:
             _A								=     max_length

        _A		,					_A								=     pad_model_inputs(UpperCamelCase__,   max_seq_length=UpperCamelCase__,   pad_value=self.pad_token_id    )
        _A								=     {'input_ids': input_ids}
        if return_attention_mask:
             _A								=     attention_mask
        if return_token_type_ids:
             _A		,					_A								=     pad_model_inputs(
                 UpperCamelCase__,   max_seq_length=UpperCamelCase__,   pad_value=self.pad_token_id    )
             _A								=     token_type_ids
        return output

   def   __UpperCAmelCase      (			self			: List[str]    )     -> Optional[int]:
        return {
            "vocab_list": self.vocab_list,
            "do_lower_case": self.do_lower_case,
            "cls_token_id": self.cls_token_id,
            "sep_token_id": self.sep_token_id,
            "pad_token_id": self.pad_token_id,
        }




'''simple docstring'''






from sklearn.neural_network import MLPClassifier

_UpperCAmelCase						:    str     					=					[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
_UpperCAmelCase						:    Any     					=					[0, 1, 0, 0]


_UpperCAmelCase						:    List[Any]     					=					MLPClassifier(
    solver='''lbfgs''', alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


_UpperCAmelCase						:    Optional[Any]     					=					[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
_UpperCAmelCase						:    Tuple     					=					clf.predict(test)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[Any]							):
     return list(__snake_case							)


if __name__ == "__main__":
   import doctest

   doctest.testmod()



