
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 :       List[Any]							,       _A :       List							,       _A :       bool							,       _A :       int = None							,       _A :       int = None							,       _A :       int = None							,       _A :       str = "longest"							,       _A :       bool = True							,       _A :       int = 5_1_2							,       _A :       int = None							,       _A :       bool = True							,       _A :       bool = True							,       _A :       bool = True							,       **_A :       Optional[int]							,       )		->		Optional[int]:
         super().__init__()
         if use_fast_bert_tokenizer:
             snake_case						=   FastBertTokenizer(
                 _A							,       token_out_type=tf.intaa							,       lower_case_nfd_strip_accents=_A							,       **_A  )
         else:
             snake_case						=   tf.lookup.StaticVocabularyTable(
                 tf.lookup.KeyValueTensorInitializer(
                     keys=_A							,       key_dtype=tf.string							,       values=tf.range(tf.size(_A							,       out_type=tf.intaa  )							,       dtype=tf.intaa  )							,       value_dtype=tf.intaa							,       )							,       num_oov_buckets=1							,       )
             snake_case						=   BertTokenizerLayer(
                 _A							,       token_out_type=tf.intaa							,       lower_case=_A							,       **_A  )

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





     @classmethod
     def 						UpperCAmelCase(cls :       Optional[Any]							,       _A :       "PreTrainedTokenizerBase"							,       **_A :       Tuple  )		->		Union[str, Any]:  # noqa: F821
         snake_case						=   kwargs.pop("do_lower_case"							,       _A  )
         snake_case						=   tokenizer.do_lower_case if do_lower_case is None else do_lower_case
         snake_case						=   kwargs.pop("cls_token_id"							,       _A  )
         snake_case						=   tokenizer.cls_token_id if cls_token_id is None else cls_token_id
         snake_case						=   kwargs.pop("sep_token_id"							,       _A  )
         snake_case						=   tokenizer.sep_token_id if sep_token_id is None else sep_token_id
         snake_case						=   kwargs.pop("pad_token_id"							,       _A  )
         snake_case						=   tokenizer.pad_token_id if pad_token_id is None else pad_token_id

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





     @classmethod
     def 						UpperCAmelCase(cls :       int							,       _A :       Union[str, os.PathLike]							,       *_A :       List[str]							,       **_A :       Optional[Any]  )		->		Tuple:
         try:
             snake_case						=   BertTokenizer.from_pretrained(_A							,       *_A							,       **_A  )
         except:  # noqa: E722
             from .tokenization_bert_fast import BertTokenizerFast

             snake_case						=   BertTokenizerFast.from_pretrained(_A							,       *_A							,       **_A  )
         return cls.from_tokenizer(_A							,       **_A  )





     def 						UpperCAmelCase(self :       List[Any]							,       _A :       str  )		->		int:
         if self.do_lower_case:
             snake_case						=   case_fold_utfa(_A  )
         snake_case						=   self.tf_tokenizer.tokenize(_A  )
         return tokens.merge_dims(1							,       -1  )





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

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





     def 						UpperCAmelCase(self :       int  )		->		List[str]:
         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 warnings

from ...utils import logging
from .image_processing_levit import LevitImageProcessor


_A       =					logging.get_logger(__name__)







class 	lowerCamelCase						(    A_    ):





     def __init__(self :       Any							,       *_A :       Optional[Any]							,       **_A :       List[Any]  )		->		None:
         warnings.warn(
             "The class LevitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
             " use LevitImageProcessor instead."							,       _A							,       )
         super().__init__(*_A							,       **_A  )

