






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 __lowerCamelCase				(   tf.keras.layers.Layer):





	"""simple docstring"""






	def __init__(							self ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase = None ,					UpperCAmelCase = None ,					UpperCAmelCase = None ,					UpperCAmelCase = "longest" ,					UpperCAmelCase = True ,					UpperCAmelCase = 512 ,					UpperCAmelCase = None ,					UpperCAmelCase = True ,					UpperCAmelCase = True ,					UpperCAmelCase = True ,					**UpperCAmelCase ,					):
							"""simple docstring"""







							super().__init__()
							if use_fast_bert_tokenizer:
													_UpperCAmelCase             =    FastBertTokenizer(
													    UpperCAmelCase ,					token_out_type=tf.intaa ,					lower_case_nfd_strip_accents=UpperCAmelCase ,					**UpperCAmelCase					)
							else:
													_UpperCAmelCase             =    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 ,					)
													_UpperCAmelCase             =    BertTokenizerLayer(
													    UpperCAmelCase ,					token_out_type=tf.intaa ,					lower_case=UpperCAmelCase ,					**UpperCAmelCase					)

							_UpperCAmelCase             =    vocab_list
							_UpperCAmelCase             =    do_lower_case
							_UpperCAmelCase             =    cls_token_id or vocab_list.index('[CLS]'					)
							_UpperCAmelCase             =    sep_token_id or vocab_list.index('[SEP]'					)
							_UpperCAmelCase             =    pad_token_id or vocab_list.index('[PAD]'					)
							_UpperCAmelCase             =    ShrinkLongestTrimmer(max_length - 3 ,					axis=1					)  # Allow room for special tokens
							_UpperCAmelCase             =    max_length
							_UpperCAmelCase             =    padding
							_UpperCAmelCase             =    truncation
							_UpperCAmelCase             =    pad_to_multiple_of
							_UpperCAmelCase             =    return_token_type_ids
							_UpperCAmelCase             =    return_attention_mask

	@classmethod
	def  UpperCamelCase					(							cls ,					UpperCAmelCase ,					**UpperCAmelCase					):  # noqa: F821
							"""simple docstring"""







							_UpperCAmelCase             =    kwargs.pop('do_lower_case' ,					UpperCAmelCase					)
							_UpperCAmelCase             =    tokenizer.do_lower_case if do_lower_case is None else do_lower_case
							_UpperCAmelCase             =    kwargs.pop('cls_token_id' ,					UpperCAmelCase					)
							_UpperCAmelCase             =    tokenizer.cls_token_id if cls_token_id is None else cls_token_id
							_UpperCAmelCase             =    kwargs.pop('sep_token_id' ,					UpperCAmelCase					)
							_UpperCAmelCase             =    tokenizer.sep_token_id if sep_token_id is None else sep_token_id
							_UpperCAmelCase             =    kwargs.pop('pad_token_id' ,					UpperCAmelCase					)
							_UpperCAmelCase             =    tokenizer.pad_token_id if pad_token_id is None else pad_token_id

							_UpperCAmelCase             =    tokenizer.get_vocab()
							_UpperCAmelCase             =    sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()] ,					key=lambda UpperCAmelCase					: x[1]					)
							_UpperCAmelCase             =    [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 ,					UpperCAmelCase ,					*UpperCAmelCase ,					**UpperCAmelCase					):
							"""simple docstring"""







							try:
													_UpperCAmelCase             =    BertTokenizer.from_pretrained(UpperCAmelCase ,					*UpperCAmelCase ,					**UpperCAmelCase					)
							except:  # noqa: E722
													from .tokenization_bert_fast import BertTokenizerFast

													_UpperCAmelCase             =    BertTokenizerFast.from_pretrained(UpperCAmelCase ,					*UpperCAmelCase ,					**UpperCAmelCase					)
							return cls.from_tokenizer(UpperCAmelCase ,					**UpperCAmelCase					)

	def  UpperCamelCase					(							self ,					UpperCAmelCase					):
							"""simple docstring"""







							if self.do_lower_case:
													_UpperCAmelCase             =    case_fold_utfa(UpperCAmelCase					)
							_UpperCAmelCase             =    self.tf_tokenizer.tokenize(UpperCAmelCase					)
							return tokens.merge_dims(1 ,					-1					)

	def  UpperCamelCase					(							self ,					UpperCAmelCase ,					UpperCAmelCase=None ,					UpperCAmelCase=None ,					UpperCAmelCase=None ,					UpperCAmelCase=None ,					UpperCAmelCase=None ,					UpperCAmelCase=None ,					UpperCAmelCase=None ,					):
							"""simple docstring"""







							if padding is None:
													_UpperCAmelCase             =    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             =    self.max_length
							if truncation is None:
													_UpperCAmelCase             =    self.truncation
							if pad_to_multiple_of is None:
													_UpperCAmelCase             =    self.pad_to_multiple_of
							if return_token_type_ids is None:
													_UpperCAmelCase             =    self.return_token_type_ids
							if return_attention_mask is None:
													_UpperCAmelCase             =    self.return_attention_mask
							if not isinstance(UpperCAmelCase ,					tf.Tensor					):
													_UpperCAmelCase             =    tf.convert_to_tensor(UpperCAmelCase					)
							if text_pair is not None and not isinstance(UpperCAmelCase ,					tf.Tensor					):
													_UpperCAmelCase             =    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:
													_UpperCAmelCase             =    text[:, 0], text[:, 1]
							_UpperCAmelCase             =    self.unpaired_tokenize(UpperCAmelCase					)
							if text_pair is None:  # Unpaired text
													if truncation:
																			_UpperCAmelCase             =    text[:, : max_length - 2]  # Allow room for special tokens
													_UpperCAmelCase             =    combine_segments(
													    (text,) ,					start_of_sequence_id=self.cls_token_id ,					end_of_segment_id=self.sep_token_id					)
							else:  # Paired text
													_UpperCAmelCase             =    self.unpaired_tokenize(UpperCAmelCase					)
													if truncation:
																			_UpperCAmelCase             =    self.paired_trimmer.trim([text, text_pair]					)
													_UpperCAmelCase             =    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             =    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             =    pad_to_multiple_of * (-tf.math.floordiv(-pad_length ,					UpperCAmelCase					))
							else:
													_UpperCAmelCase             =    max_length

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



	def  UpperCamelCase					(							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,
							}








from typing import Any





class __lowerCamelCase				:





	"""simple docstring"""






	def __init__(							self					):
							"""simple docstring"""







							_UpperCAmelCase             =    []
							_UpperCAmelCase             =    0

	def __str__(							self					):
							"""simple docstring"""







							_UpperCAmelCase             =    '<' + str(self.stack					)[1:-1] + '>'
							return printed

	def  UpperCamelCase					(							self ,					UpperCAmelCase					):
							"""simple docstring"""







							self.stack.append(UpperCAmelCase					)
							_UpperCAmelCase             =    self.length + 1

	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							self.rotate(1					)
							_UpperCAmelCase             =    self.stack[self.length - 1]
							_UpperCAmelCase             =    self.stack[:-1]
							self.rotate(self.length - 1					)
							_UpperCAmelCase             =    self.length - 1
							return dequeued

	def  UpperCamelCase					(							self ,					UpperCAmelCase					):
							"""simple docstring"""







							for _ in range(UpperCAmelCase					):
													_UpperCAmelCase             =    self.stack[0]
													_UpperCAmelCase             =    self.stack[1:]
													self.put(UpperCAmelCase					)
													_UpperCAmelCase             =    self.length - 1

	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							_UpperCAmelCase             =    self.get()
							self.put(UpperCAmelCase					)
							self.rotate(self.length - 1					)
							return front

	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							return self.length
