

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					:			int,lowercase_					:			List,lowercase_					:			bool,lowercase_					:			int = None,lowercase_					:			int = None,lowercase_					:			int = None,lowercase_					:			str = "longest",lowercase_					:			bool = True,lowercase_					:			int = 5_1_2,lowercase_					:			int = None,lowercase_					:			bool = True,lowercase_					:			bool = True,lowercase_					:			bool = True,**lowercase_					:			Optional[Any],)->       List[str]:





        '''simple docstring'''







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

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


    @classmethod
    def 		snake_case__	( cls					:			List[str],lowercase_					:			"PreTrainedTokenizerBase",**lowercase_					:			Dict						)->       Dict:  # noqa: F821





        '''simple docstring'''







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

        A__        =  tokenizer.get_vocab()
        A__        =  sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()],key=lambda lowercase_						: x[1]						)
        A__        =  [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 		snake_case__	( cls					:			Any,lowercase_					:			Union[str, os.PathLike],*lowercase_					:			str,**lowercase_					:			List[str]						)->       Tuple:





        '''simple docstring'''







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

            A__        =  BertTokenizerFast.from_pretrained(lowercase_,*lowercase_,**lowercase_						)
        return cls.from_tokenizer(lowercase_,**lowercase_						)


    def 		snake_case__	( self					:			int,lowercase_					:			Optional[int]						)->       List[str]:





        '''simple docstring'''







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


    def 		snake_case__	( self					:			Optional[Any],lowercase_					:			Tuple,lowercase_					:			str=None,lowercase_					:			Tuple=None,lowercase_					:			Any=None,lowercase_					:			Optional[int]=None,lowercase_					:			Optional[Any]=None,lowercase_					:			Optional[Any]=None,lowercase_					:			Tuple=None,)->       str:





        '''simple docstring'''







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

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


    def 		snake_case__	( self					:			Tuple						)->       Tuple:





        '''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 Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


_lowerCAmelCase    : Any		= [
    "AutomaticSpeechRecognition",
    "AudioClassification",
    "ImageClassification",
    "LanguageModeling",
    "QuestionAnsweringExtractive",
    "Summarization",
    "TaskTemplate",
    "TextClassification",
]

_lowerCAmelCase    : Dict		= get_logger(__name__)


_lowerCAmelCase    : Optional[Any]		= {
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}



def     lowerCAmelCase		(		_lowerCAmelCase				: dict						):




 """simple docstring"""







 UpperCAmelCase__							=    task_template_dict.get("task"						)
 if task_name is None:
  logger.warning(F'''Couldn\'t find template for task \'{task_name}\'. Available templates: {list(_lowerCAmelCase						)}'''						)
  return None
 UpperCAmelCase__							=    NAMEaTEMPLATE.get(_lowerCAmelCase						)
 return template.from_dict(_lowerCAmelCase						)





