

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     __magic_name__     (							tf.keras.layers.Layer     ):


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

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


 @classmethod
 def _UpperCamelCase						(    cls		:			List[Any]      ,    lowerCAmelCase__		:			"PreTrainedTokenizerBase"      ,    **lowerCAmelCase__		:			Any     )     ->   List[Any]:  # noqa: F821
   UpperCAmelCase          =	kwargs.pop("do_lower_case"      ,    lowerCAmelCase__     )
   UpperCAmelCase          =	tokenizer.do_lower_case if do_lower_case is None else do_lower_case
   UpperCAmelCase          =	kwargs.pop("cls_token_id"      ,    lowerCAmelCase__     )
   UpperCAmelCase          =	tokenizer.cls_token_id if cls_token_id is None else cls_token_id
   UpperCAmelCase          =	kwargs.pop("sep_token_id"      ,    lowerCAmelCase__     )
   UpperCAmelCase          =	tokenizer.sep_token_id if sep_token_id is None else sep_token_id
   UpperCAmelCase          =	kwargs.pop("pad_token_id"      ,    lowerCAmelCase__     )
   UpperCAmelCase          =	tokenizer.pad_token_id if pad_token_id is None else pad_token_id

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


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

     UpperCAmelCase          =	BertTokenizerFast.from_pretrained(lowerCAmelCase__      ,    *lowerCAmelCase__      ,    **lowerCAmelCase__     )
   return cls.from_tokenizer(lowerCAmelCase__      ,    **lowerCAmelCase__     )


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


 def _UpperCamelCase						(    self		:			str      ,    lowerCAmelCase__		:			Optional[Any]      ,    lowerCAmelCase__		:			List[str]=None      ,    lowerCAmelCase__		:			str=None      ,    lowerCAmelCase__		:			str=None      ,    lowerCAmelCase__		:			Optional[int]=None      ,    lowerCAmelCase__		:			Dict=None      ,    lowerCAmelCase__		:			List[Any]=None      ,    lowerCAmelCase__		:			Optional[int]=None      ,    )     ->   Dict:
   if padding is None:
     UpperCAmelCase          =	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          =	self.max_length
   if truncation is None:
     UpperCAmelCase          =	self.truncation
   if pad_to_multiple_of is None:
     UpperCAmelCase          =	self.pad_to_multiple_of
   if return_token_type_ids is None:
     UpperCAmelCase          =	self.return_token_type_ids
   if return_attention_mask is None:
     UpperCAmelCase          =	self.return_attention_mask
   if not isinstance(lowerCAmelCase__      ,    tf.Tensor     ):
     UpperCAmelCase          =	tf.convert_to_tensor(lowerCAmelCase__     )
   if text_pair is not None and not isinstance(lowerCAmelCase__      ,    tf.Tensor     ):
     UpperCAmelCase          =	tf.convert_to_tensor(lowerCAmelCase__     )
   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   ,						UpperCAmelCase          =	text[:, 0], text[:, 1]
   UpperCAmelCase          =	self.unpaired_tokenize(lowerCAmelCase__     )
   if text_pair is None:  # Unpaired text
     if truncation:
       UpperCAmelCase          =	text[:, : max_length - 2]  # Allow room for special tokens
     UpperCAmelCase   ,						UpperCAmelCase          =	combine_segments(
         (text,)      ,    start_of_sequence_id=self.cls_token_id      ,    end_of_segment_id=self.sep_token_id     )
   else:  # Paired text
     UpperCAmelCase          =	self.unpaired_tokenize(lowerCAmelCase__     )
     if truncation:
       UpperCAmelCase   ,						UpperCAmelCase          =	self.paired_trimmer.trim([text, text_pair]     )
     UpperCAmelCase   ,						UpperCAmelCase          =	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          =	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          =	pad_to_multiple_of * (-tf.math.floordiv(-pad_length      ,    lowerCAmelCase__     ))
   else:
     UpperCAmelCase          =	max_length

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


 def _UpperCamelCase						(    self		:			Tuple     )     ->   int:
   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 Dict, Optional, Tuple

import torch
def   _lowerCAmelCase(						__A      ):
  UpperCAmelCase          =	boundaries[1] - boundaries[0]
  UpperCAmelCase          =	boundaries + step / 2
  UpperCAmelCase          =	torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1      )] ,							dim=0      )
  return bin_centers
def   _lowerCAmelCase(						__A ,							__A ,							):
  UpperCAmelCase          =	_calculate_bin_centers(__A      )
  return (
      torch.sum(aligned_distance_error_probs * bin_centers ,							dim=-1      ),
      bin_centers[-1],
  )
def   _lowerCAmelCase(						__A ,							__A = 31 ,							__A = 64 ,							**__A ,							):
  UpperCAmelCase          =	torch.linspace(0 ,							__A ,							steps=(no_bins - 1) ,							device=logits.device      )

  UpperCAmelCase          =	torch.nn.functional.softmax(__A ,							dim=-1      )
  UpperCAmelCase   ,						UpperCAmelCase          =	_calculate_expected_aligned_error(
      alignment_confidence_breaks=__A ,							aligned_distance_error_probs=__A ,							)

  return {
      "aligned_confidence_probs": aligned_confidence_probs,
      "predicted_aligned_error": predicted_aligned_error,
      "max_predicted_aligned_error": max_predicted_aligned_error,
  }





def   _lowerCAmelCase(						__A ,							__A = None ,							__A = 31 ,							__A = 64 ,							__A = 1E-8 ,							**__A ,							):
  if residue_weights is None:
    UpperCAmelCase          =	logits.new_ones(logits.shape[-2]      )

  UpperCAmelCase          =	torch.linspace(0 ,							__A ,							steps=(no_bins - 1) ,							device=logits.device      )

  UpperCAmelCase          =	_calculate_bin_centers(__A      )
  torch.sum(__A      )
  UpperCAmelCase          =	logits.shape[-2]
  UpperCAmelCase          =	max(__A ,							19      )

  UpperCAmelCase          =	1.24 * (clipped_n - 15) ** (1.0 / 3) - 1.8

  UpperCAmelCase          =	torch.nn.functional.softmax(__A ,							dim=-1      )

  UpperCAmelCase          =	1.0 / (1 + (bin_centers**2) / (da**2))
  UpperCAmelCase          =	torch.sum(probs * tm_per_bin ,							dim=-1      )

  UpperCAmelCase          =	residue_weights / (eps + residue_weights.sum())
  UpperCAmelCase          =	torch.sum(predicted_tm_term * normed_residue_mask ,							dim=-1      )

  UpperCAmelCase          =	per_alignment * residue_weights

  UpperCAmelCase          =	(weighted == torch.max(__A      )).nonzero()[0]
  return per_alignment[tuple(__A      )]



