


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  UpperCAmelCase_						(				tf.keras.layers.Layer):
       '''simple docstring'''






       def __init__(  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE = None			,       __SCREAMING_SNAKE_CASE = None			,       __SCREAMING_SNAKE_CASE = None			,       __SCREAMING_SNAKE_CASE = "longest"			,       __SCREAMING_SNAKE_CASE = True			,       __SCREAMING_SNAKE_CASE = 512			,       __SCREAMING_SNAKE_CASE = None			,       __SCREAMING_SNAKE_CASE = True			,       __SCREAMING_SNAKE_CASE = True			,       __SCREAMING_SNAKE_CASE = True			,       **__SCREAMING_SNAKE_CASE			,       ):




              """simple docstring"""




              super().__init__()
              if use_fast_bert_tokenizer:
                     UpperCamelCase     :						Dict 						=		FastBertTokenizer(
                         __SCREAMING_SNAKE_CASE			,       token_out_type=tf.intaa			,       lower_case_nfd_strip_accents=__SCREAMING_SNAKE_CASE			,       **__SCREAMING_SNAKE_CASE    )
              else:
                     UpperCamelCase     :						List[Any] 						=		tf.lookup.StaticVocabularyTable(
                         tf.lookup.KeyValueTensorInitializer(
                             keys=__SCREAMING_SNAKE_CASE			,       key_dtype=tf.string			,       values=tf.range(tf.size(__SCREAMING_SNAKE_CASE			,       out_type=tf.intaa    )			,       dtype=tf.intaa    )			,       value_dtype=tf.intaa			,       )			,       num_oov_buckets=1			,       )
                     UpperCamelCase     :						Optional[Any] 						=		BertTokenizerLayer(
                         __SCREAMING_SNAKE_CASE			,       token_out_type=tf.intaa			,       lower_case=__SCREAMING_SNAKE_CASE			,       **__SCREAMING_SNAKE_CASE    )

              UpperCamelCase     :						Optional[int] 						=		vocab_list
              UpperCamelCase     :						Union[str, Any] 						=		do_lower_case
              UpperCamelCase     :						List[str] 						=		cls_token_id or vocab_list.index('''[CLS]'''    )
              UpperCamelCase     :						Optional[int] 						=		sep_token_id or vocab_list.index('''[SEP]'''    )
              UpperCamelCase     :						Dict 						=		pad_token_id or vocab_list.index('''[PAD]'''    )
              UpperCamelCase     :						Union[str, Any] 						=		ShrinkLongestTrimmer(max_length - 3			,       axis=1    )  # Allow room for special tokens
              UpperCamelCase     :						Union[str, Any] 						=		max_length
              UpperCamelCase     :						Tuple 						=		padding
              UpperCamelCase     :						int 						=		truncation
              UpperCamelCase     :						Any 						=		pad_to_multiple_of
              UpperCamelCase     :						List[str] 						=		return_token_type_ids
              UpperCamelCase     :						int 						=		return_attention_mask






       @classmethod
       def 			_lowercase (  cls			,       __SCREAMING_SNAKE_CASE			,       **__SCREAMING_SNAKE_CASE    ):  # noqa: F821




              """simple docstring"""




              UpperCamelCase     :						int 						=		kwargs.pop('''do_lower_case'''			,       __SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Union[str, Any] 						=		tokenizer.do_lower_case if do_lower_case is None else do_lower_case
              UpperCamelCase     :						List[str] 						=		kwargs.pop('''cls_token_id'''			,       __SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Dict 						=		tokenizer.cls_token_id if cls_token_id is None else cls_token_id
              UpperCamelCase     :						str 						=		kwargs.pop('''sep_token_id'''			,       __SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						int 						=		tokenizer.sep_token_id if sep_token_id is None else sep_token_id
              UpperCamelCase     :						Union[str, Any] 						=		kwargs.pop('''pad_token_id'''			,       __SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Optional[int] 						=		tokenizer.pad_token_id if pad_token_id is None else pad_token_id

              UpperCamelCase     :						Tuple 						=		tokenizer.get_vocab()
              UpperCamelCase     :						List[str] 						=		sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]			,       key=lambda __SCREAMING_SNAKE_CASE    : x[1]    )
              UpperCamelCase     :						List[str] 						=		[entry[0] for entry in vocab]
              return cls(
                  vocab_list=__SCREAMING_SNAKE_CASE			,       do_lower_case=__SCREAMING_SNAKE_CASE			,       cls_token_id=__SCREAMING_SNAKE_CASE			,       sep_token_id=__SCREAMING_SNAKE_CASE			,       pad_token_id=__SCREAMING_SNAKE_CASE			,       **__SCREAMING_SNAKE_CASE			,       )






       @classmethod
       def 			_lowercase (  cls			,       __SCREAMING_SNAKE_CASE			,       *__SCREAMING_SNAKE_CASE			,       **__SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              try:
                     UpperCamelCase     :						Union[str, Any] 						=		BertTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE			,       *__SCREAMING_SNAKE_CASE			,       **__SCREAMING_SNAKE_CASE    )
              except:  # noqa: E722
                     from .tokenization_bert_fast import BertTokenizerFast

                     UpperCamelCase     :						Optional[Any] 						=		BertTokenizerFast.from_pretrained(__SCREAMING_SNAKE_CASE			,       *__SCREAMING_SNAKE_CASE			,       **__SCREAMING_SNAKE_CASE    )
              return cls.from_tokenizer(__SCREAMING_SNAKE_CASE			,       **__SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              if self.do_lower_case:
                     UpperCamelCase     :						List[str] 						=		case_fold_utfa(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						str 						=		self.tf_tokenizer.tokenize(__SCREAMING_SNAKE_CASE    )
              return tokens.merge_dims(1			,       -1    )






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE=None			,       __SCREAMING_SNAKE_CASE=None			,       __SCREAMING_SNAKE_CASE=None			,       __SCREAMING_SNAKE_CASE=None			,       __SCREAMING_SNAKE_CASE=None			,       __SCREAMING_SNAKE_CASE=None			,       __SCREAMING_SNAKE_CASE=None			,       ):




              """simple docstring"""




              if padding is None:
                     UpperCamelCase     :						Optional[int] 						=		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:
                     UpperCamelCase     :						Dict 						=		self.max_length
              if truncation is None:
                     UpperCamelCase     :						Optional[Any] 						=		self.truncation
              if pad_to_multiple_of is None:
                     UpperCamelCase     :						List[str] 						=		self.pad_to_multiple_of
              if return_token_type_ids is None:
                     UpperCamelCase     :						str 						=		self.return_token_type_ids
              if return_attention_mask is None:
                     UpperCamelCase     :						List[str] 						=		self.return_attention_mask
              if not isinstance(__SCREAMING_SNAKE_CASE			,       tf.Tensor    ):
                     UpperCamelCase     :						Optional[int] 						=		tf.convert_to_tensor(__SCREAMING_SNAKE_CASE    )
              if text_pair is not None and not isinstance(__SCREAMING_SNAKE_CASE			,       tf.Tensor    ):
                     UpperCamelCase     :						Tuple 						=		tf.convert_to_tensor(__SCREAMING_SNAKE_CASE    )
              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:
                     UpperCamelCase						,			UpperCamelCase     :						Union[str, Any] 						=		text[:, 0], text[:, 1]
              UpperCamelCase     :						List[Any] 						=		self.unpaired_tokenize(__SCREAMING_SNAKE_CASE    )
              if text_pair is None:  # Unpaired text
                     if truncation:
                            UpperCamelCase     :						str 						=		text[:, : max_length - 2]  # Allow room for special tokens
                     UpperCamelCase						,			UpperCamelCase     :						int 						=		combine_segments(
                         (text,)			,       start_of_sequence_id=self.cls_token_id			,       end_of_segment_id=self.sep_token_id    )
              else:  # Paired text
                     UpperCamelCase     :						int 						=		self.unpaired_tokenize(__SCREAMING_SNAKE_CASE    )
                     if truncation:
                            UpperCamelCase						,			UpperCamelCase     :						Dict 						=		self.paired_trimmer.trim([text, text_pair]    )
                     UpperCamelCase						,			UpperCamelCase     :						List[Any] 						=		combine_segments(
                         (text, text_pair)			,       start_of_sequence_id=self.cls_token_id			,       end_of_segment_id=self.sep_token_id    )
              if padding == "longest":
                     UpperCamelCase     :						Dict 						=		input_ids.bounding_shape(axis=1    )
                     if pad_to_multiple_of is not None:
                            # No ceiling division in tensorflow, so we negate floordiv instead
                            UpperCamelCase     :						Dict 						=		pad_to_multiple_of * (-tf.math.floordiv(-pad_length			,       __SCREAMING_SNAKE_CASE    ))
              else:
                     UpperCamelCase     :						Union[str, Any] 						=		max_length

              UpperCamelCase						,			UpperCamelCase     :						str 						=		pad_model_inputs(__SCREAMING_SNAKE_CASE			,       max_seq_length=__SCREAMING_SNAKE_CASE			,       pad_value=self.pad_token_id    )
              UpperCamelCase     :						Optional[Any] 						=		{'''input_ids''': input_ids}
              if return_attention_mask:
                     UpperCamelCase     :						List[Any] 						=		attention_mask
              if return_token_type_ids:
                     UpperCamelCase						,			UpperCamelCase     :						Optional[Any] 						=		pad_model_inputs(
                         __SCREAMING_SNAKE_CASE			,       max_seq_length=__SCREAMING_SNAKE_CASE			,       pad_value=self.pad_token_id    )
                     UpperCamelCase     :						Optional[Any] 						=		token_type_ids
              return output






       def 			_lowercase (  self    ):




              """simple docstring"""




              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,
              }





import sys


def   a						(				SCREAMING_SNAKE_CASE_   :		int				,      SCREAMING_SNAKE_CASE_   :		list[int]							):






       """simple docstring"""





       if not numbers:
              return 0
       if target == 0 and target in numbers:
              return 0
       if not isinstance(SCREAMING_SNAKE_CASE_				,      (list, tuple)							) or not all(
           isinstance(SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_							) for number in numbers							):
              raise ValueError('''numbers must be an iterable of integers'''							)

       UpperCamelCase     :						Dict 						=		0
       UpperCamelCase     :						Tuple 						=		sys.maxsize

       while right < len(SCREAMING_SNAKE_CASE_							):
              curr_sum += numbers[right]
              while curr_sum >= target and left <= right:
                     UpperCamelCase     :						int 						=		min(SCREAMING_SNAKE_CASE_				,      right - left + 1							)
                     curr_sum -= numbers[left]
                     left += 1
              right += 1

       return 0 if min_len == sys.maxsize else min_len

