






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    ,    lowercase_    ,    lowercase_    ,    lowercase_ = None    ,    lowercase_ = None    ,    lowercase_ = None    ,    lowercase_ = "longest"    ,    lowercase_ = True    ,    lowercase_ = 5_1_2    ,    lowercase_ = None    ,    lowercase_ = True    ,    lowercase_ = True    ,    lowercase_ = True    ,    **lowercase_    ,    )      ->      str:
              '''simple docstring'''

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

              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   _lowercase (	cls    ,    lowercase_    ,    **lowercase_     )      ->      Optional[Any]:  # noqa: F821
              '''simple docstring'''

              lowerCAmelCase_       		=  kwargs.pop('do_lower_case'    ,    lowercase_     )
              lowerCAmelCase_       		=  tokenizer.do_lower_case if do_lower_case is None else do_lower_case
              lowerCAmelCase_       		=  kwargs.pop('cls_token_id'    ,    lowercase_     )
              lowerCAmelCase_       		=  tokenizer.cls_token_id if cls_token_id is None else cls_token_id
              lowerCAmelCase_       		=  kwargs.pop('sep_token_id'    ,    lowercase_     )
              lowerCAmelCase_       		=  tokenizer.sep_token_id if sep_token_id is None else sep_token_id
              lowerCAmelCase_       		=  kwargs.pop('pad_token_id'    ,    lowercase_     )
              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 lowercase_     : x[1]     )
              lowerCAmelCase_       		=  [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   _lowercase (	cls    ,    lowercase_    ,    *lowercase_    ,    **lowercase_     )      ->      Dict:
              '''simple docstring'''

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

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

       def   _lowercase (	self    ,    lowercase_     )      ->      List[str]:
              '''simple docstring'''

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

       def   _lowercase (	self    ,    lowercase_    ,    lowercase_=None    ,    lowercase_=None    ,    lowercase_=None    ,    lowercase_=None    ,    lowercase_=None    ,    lowercase_=None    ,    lowercase_=None    ,    )      ->      Tuple:
              '''simple docstring'''

              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(lowercase_    ,    tf.Tensor     ):
                     lowerCAmelCase_       		=  tf.convert_to_tensor(lowercase_     )
              if text_pair is not None and not isinstance(lowercase_    ,    tf.Tensor     ):
                     lowerCAmelCase_       		=  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:
                     lowerCAmelCase_		,     lowerCAmelCase_       		=  text[:, 0], text[:, 1]
              lowerCAmelCase_       		=  self.unpaired_tokenize(lowercase_     )
              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(lowercase_     )
                     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    ,    lowercase_     ))
              else:
                     lowerCAmelCase_       		=  max_length

              lowerCAmelCase_		,     lowerCAmelCase_       		=  pad_model_inputs(lowercase_    ,    max_seq_length=lowercase_    ,    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(
                         lowercase_    ,    max_seq_length=lowercase_    ,    pad_value=self.pad_token_id     )
                     lowerCAmelCase_       		=  token_type_ids
              return output






       def   _lowercase (	self     )      ->      str:
              '''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 TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase_		  =						{
    """configuration_prophetnet""": ["""PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ProphetNetConfig"""],
    """tokenization_prophetnet""": ["""ProphetNetTokenizer"""],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase_		  =						[
		    """PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """ProphetNetDecoder""",
		    """ProphetNetEncoder""",
		    """ProphetNetForCausalLM""",
		    """ProphetNetForConditionalGeneration""",
		    """ProphetNetModel""",
		    """ProphetNetPreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
		from .tokenization_prophetnet import ProphetNetTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_prophetnet import (
				    PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ProphetNetDecoder,
				    ProphetNetEncoder,
				    ProphetNetForCausalLM,
				    ProphetNetForConditionalGeneration,
				    ProphetNetModel,
				    ProphetNetPreTrainedModel,
				)

else:
		import sys

		lowerCamelCase_		  =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



