

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    ):






		def __init__(	self	:							Dict							,    SCREAMING_SNAKE_CASE__	:							List							,    SCREAMING_SNAKE_CASE__	:							bool							,    SCREAMING_SNAKE_CASE__	:							int = None							,    SCREAMING_SNAKE_CASE__	:							int = None							,    SCREAMING_SNAKE_CASE__	:							int = None							,    SCREAMING_SNAKE_CASE__	:							str = "longest"							,    SCREAMING_SNAKE_CASE__	:							bool = True							,    SCREAMING_SNAKE_CASE__	:							int = 5_12							,    SCREAMING_SNAKE_CASE__	:							int = None							,    SCREAMING_SNAKE_CASE__	:							bool = True							,    SCREAMING_SNAKE_CASE__	:							bool = True							,    SCREAMING_SNAKE_CASE__	:							bool = True							,    **SCREAMING_SNAKE_CASE__	:							int							,    )  ->							Dict:
						super().__init__()
						if use_fast_bert_tokenizer:
										__lowerCamelCase					      =			FastBertTokenizer(
										    SCREAMING_SNAKE_CASE__							,    token_out_type=tf.intaa							,    lower_case_nfd_strip_accents=SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)
						else:
										__lowerCamelCase					      =			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							,    )
										__lowerCamelCase					      =			BertTokenizerLayer(
										    SCREAMING_SNAKE_CASE__							,    token_out_type=tf.intaa							,    lower_case=SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)

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






		@classmethod
		def 				__A (	cls	:							List[Any]							,    SCREAMING_SNAKE_CASE__	:							"PreTrainedTokenizerBase"							,    **SCREAMING_SNAKE_CASE__	:							Optional[int]						)  ->							int:  # noqa: F821
						__lowerCamelCase					      =			kwargs.pop('''do_lower_case'''							,    SCREAMING_SNAKE_CASE__						)
						__lowerCamelCase					      =			tokenizer.do_lower_case if do_lower_case is None else do_lower_case
						__lowerCamelCase					      =			kwargs.pop('''cls_token_id'''							,    SCREAMING_SNAKE_CASE__						)
						__lowerCamelCase					      =			tokenizer.cls_token_id if cls_token_id is None else cls_token_id
						__lowerCamelCase					      =			kwargs.pop('''sep_token_id'''							,    SCREAMING_SNAKE_CASE__						)
						__lowerCamelCase					      =			tokenizer.sep_token_id if sep_token_id is None else sep_token_id
						__lowerCamelCase					      =			kwargs.pop('''pad_token_id'''							,    SCREAMING_SNAKE_CASE__						)
						__lowerCamelCase					      =			tokenizer.pad_token_id if pad_token_id is None else pad_token_id

						__lowerCamelCase					      =			tokenizer.get_vocab()
						__lowerCamelCase					      =			sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]							,    key=lambda SCREAMING_SNAKE_CASE__						: x[1]						)
						__lowerCamelCase					      =			[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 				__A (	cls	:							Tuple							,    SCREAMING_SNAKE_CASE__	:							Union[str, os.PathLike]							,    *SCREAMING_SNAKE_CASE__	:							List[Any]							,    **SCREAMING_SNAKE_CASE__	:							Tuple						)  ->							str:
						try:
										__lowerCamelCase					      =			BertTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__							,    *SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)
						except:  # noqa: E722
										from .tokenization_bert_fast import BertTokenizerFast

										__lowerCamelCase					      =			BertTokenizerFast.from_pretrained(SCREAMING_SNAKE_CASE__							,    *SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)
						return cls.from_tokenizer(SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)






		def 				__A (	self	:							Dict							,    SCREAMING_SNAKE_CASE__	:							int						)  ->							Optional[Any]:
						if self.do_lower_case:
										__lowerCamelCase					      =			case_fold_utfa(SCREAMING_SNAKE_CASE__						)
						__lowerCamelCase					      =			self.tf_tokenizer.tokenize(SCREAMING_SNAKE_CASE__						)
						return tokens.merge_dims(1							,    -1						)






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

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







		def 				__A (	self	:							List[str]						)  ->							Optional[Any]:
						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 ..utils import requires_backends


SCREAMING_SNAKE_CASE__     :    Union[str, Any]      							=					None

class      lowerCAmelCase__  :






		def __init__(	self	:							int							,    *SCREAMING_SNAKE_CASE__	:							Dict							,    **SCREAMING_SNAKE_CASE__	:							Tuple						)  ->							Optional[Any]:
						requires_backends(self							,    ['''detectron2''']						)







		@classmethod
		def 				__A (	cls	:							Dict							,    *SCREAMING_SNAKE_CASE__	:							Dict							,    **SCREAMING_SNAKE_CASE__	:							Tuple						)  ->							Tuple:
						requires_backends(cls							,    ['''detectron2''']						)



