






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    a   (			tf.keras.layers.Layer       ):



      """simple docstring"""


      def __init__(				self :      Dict		,				__lowercase :      List		,				__lowercase :      bool		,				__lowercase :      int = None		,				__lowercase :      int = None		,				__lowercase :      int = None		,				__lowercase :      str = "longest"		,				__lowercase :      bool = True		,				__lowercase :      int = 512		,				__lowercase :      int = None		,				__lowercase :      bool = True		,				__lowercase :      bool = True		,				__lowercase :      bool = True		,				**__lowercase :      Dict		,				)							->				Tuple:
        super().__init__()
        if use_fast_bert_tokenizer:
          __UpperCAmelCase							:     Tuple									=  FastBertTokenizer(
              __lowercase		,				token_out_type=tf.intaa		,				lower_case_nfd_strip_accents=__lowercase		,				**__lowercase )
        else:
          __UpperCAmelCase							:     Optional[int]									=  tf.lookup.StaticVocabularyTable(
              tf.lookup.KeyValueTensorInitializer(
                  keys=__lowercase		,				key_dtype=tf.string		,				values=tf.range(tf.size(__lowercase		,				out_type=tf.intaa )		,				dtype=tf.intaa )		,				value_dtype=tf.intaa		,				)		,				num_oov_buckets=1		,				)
          __UpperCAmelCase							:     str									=  BertTokenizerLayer(
              __lowercase		,				token_out_type=tf.intaa		,				lower_case=__lowercase		,				**__lowercase )

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

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

          __UpperCAmelCase							:     Dict									=  BertTokenizerFast.from_pretrained(__lowercase		,				*__lowercase		,				**__lowercase )
        return cls.from_tokenizer(__lowercase		,				**__lowercase )
      def      UpperCAmelCase				(				self :      Optional[Any]		,				__lowercase :      Dict )							->				Dict:
        if self.do_lower_case:
          __UpperCAmelCase							:     Dict									=  case_fold_utfa(__lowercase )
        __UpperCAmelCase							:     Optional[Any]									=  self.tf_tokenizer.tokenize(__lowercase )
        return tokens.merge_dims(1		,				-1 )
      def      UpperCAmelCase				(				self :      Optional[Any]		,				__lowercase :      List[str]		,				__lowercase :      int=None		,				__lowercase :      Optional[int]=None		,				__lowercase :      List[Any]=None		,				__lowercase :      int=None		,				__lowercase :      List[str]=None		,				__lowercase :      Union[str, Any]=None		,				__lowercase :      List[str]=None		,				)							->				List[str]:
        if padding is None:
          __UpperCAmelCase							:     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							:     Optional[Any]									=  self.max_length
        if truncation is None:
          __UpperCAmelCase							:     Optional[int]									=  self.truncation
        if pad_to_multiple_of is None:
          __UpperCAmelCase							:     Optional[int]									=  self.pad_to_multiple_of
        if return_token_type_ids is None:
          __UpperCAmelCase							:     int									=  self.return_token_type_ids
        if return_attention_mask is None:
          __UpperCAmelCase							:     Union[str, Any]									=  self.return_attention_mask
        if not isinstance(__lowercase		,				tf.Tensor ):
          __UpperCAmelCase							:     str									=  tf.convert_to_tensor(__lowercase )
        if text_pair is not None and not isinstance(__lowercase		,				tf.Tensor ):
          __UpperCAmelCase							:     int									=  tf.convert_to_tensor(__lowercase )
        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							:     Optional[int]									=  text[:, 0], text[:, 1]
        __UpperCAmelCase							:     str									=  self.unpaired_tokenize(__lowercase )
        if text_pair is None:  # Unpaired text
          if truncation:
            __UpperCAmelCase							:     Optional[int]									=  text[:, : max_length - 2]  # Allow room for special tokens
          __UpperCAmelCase							:     Dict									=  combine_segments(
              (text,)		,				start_of_sequence_id=self.cls_token_id		,				end_of_segment_id=self.sep_token_id )
        else:  # Paired text
          __UpperCAmelCase							:     List[str]									=  self.unpaired_tokenize(__lowercase )
          if truncation:
            __UpperCAmelCase							:     Dict									=  self.paired_trimmer.trim([text, text_pair] )
          __UpperCAmelCase							:     List[str]									=  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							:     Tuple									=  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							:     Tuple									=  pad_to_multiple_of * (-tf.math.floordiv(-pad_length		,				__lowercase ))
        else:
          __UpperCAmelCase							:     Union[str, Any]									=  max_length

        __UpperCAmelCase							:     List[str]									=  pad_model_inputs(__lowercase		,				max_seq_length=__lowercase		,				pad_value=self.pad_token_id )
        __UpperCAmelCase							:     str									=  {"""input_ids""": input_ids}
        if return_attention_mask:
          __UpperCAmelCase							:     Optional[Any]									=  attention_mask
        if return_token_type_ids:
          __UpperCAmelCase							:     List[Any]									=  pad_model_inputs(
              __lowercase		,				max_seq_length=__lowercase		,				pad_value=self.pad_token_id )
          __UpperCAmelCase							:     int									=  token_type_ids
        return output




      def      UpperCAmelCase				(				self :      Optional[int] )							->				Dict:
        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 TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a		:	Any      					=			{
    "configuration_deta": ["DETA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetaConfig"],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a		:	Optional[int]      					=			["DetaImageProcessor"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a		:	Tuple      					=			[
           "DETA_PRETRAINED_MODEL_ARCHIVE_LIST",
           "DetaForObjectDetection",
           "DetaModel",
           "DetaPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .image_processing_deta import DetaImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_deta import (
                  DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DetaForObjectDetection,
                  DetaModel,
                  DetaPreTrainedModel,
              )

else:
       import sys

       a		:	Any      					=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


