





"""simple docstring"""




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 			_UpperCAmelCase  (       tf.keras.layers.Layer		):



      '''simple docstring'''

      def __init__(					self ,				snake_case_ ,				snake_case_ ,				snake_case_ = None ,				snake_case_ = None ,				snake_case_ = None ,				snake_case_ = "longest" ,				snake_case_ = True ,				snake_case_ = 5_1_2 ,				snake_case_ = None ,				snake_case_ = True ,				snake_case_ = True ,				snake_case_ = True ,				**snake_case_ ,				):

        """simple docstring"""

        super().__init__()
        if use_fast_bert_tokenizer:
          A_  :	Optional[Any]    						=  FastBertTokenizer(
              snake_case_ ,				token_out_type=tf.intaa ,				lower_case_nfd_strip_accents=snake_case_ ,				**snake_case_  )
        else:
          A_  :	str    						=  tf.lookup.StaticVocabularyTable(
              tf.lookup.KeyValueTensorInitializer(
                  keys=snake_case_ ,				key_dtype=tf.string ,				values=tf.range(tf.size(snake_case_ ,				out_type=tf.intaa  ) ,				dtype=tf.intaa  ) ,				value_dtype=tf.intaa ,				) ,				num_oov_buckets=1 ,				)
          A_  :	List[str]    						=  BertTokenizerLayer(
              snake_case_ ,				token_out_type=tf.intaa ,				lower_case=snake_case_ ,				**snake_case_  )

        A_  :	Union[str, Any]    						=  vocab_list
        A_  :	Optional[Any]    						=  do_lower_case
        A_  :	int    						=  cls_token_id or vocab_list.index('[CLS]'  )
        A_  :	Tuple    						=  sep_token_id or vocab_list.index('[SEP]'  )
        A_  :	Any    						=  pad_token_id or vocab_list.index('[PAD]'  )
        A_  :	List[str]    						=  ShrinkLongestTrimmer(max_length - 3 ,				axis=1  )  # Allow room for special tokens
        A_  :	Optional[int]    						=  max_length
        A_  :	Optional[Any]    						=  padding
        A_  :	int    						=  truncation
        A_  :	Any    						=  pad_to_multiple_of
        A_  :	Dict    						=  return_token_type_ids
        A_  :	str    						=  return_attention_mask

      @classmethod
      def  lowerCamelCase_      (					cls ,				snake_case_ ,				**snake_case_  ):  # noqa: F821

        """simple docstring"""

        A_  :	str    						=  kwargs.pop('do_lower_case' ,				snake_case_  )
        A_  :	Any    						=  tokenizer.do_lower_case if do_lower_case is None else do_lower_case
        A_  :	Optional[int]    						=  kwargs.pop('cls_token_id' ,				snake_case_  )
        A_  :	Optional[Any]    						=  tokenizer.cls_token_id if cls_token_id is None else cls_token_id
        A_  :	List[Any]    						=  kwargs.pop('sep_token_id' ,				snake_case_  )
        A_  :	Optional[Any]    						=  tokenizer.sep_token_id if sep_token_id is None else sep_token_id
        A_  :	Optional[int]    						=  kwargs.pop('pad_token_id' ,				snake_case_  )
        A_  :	str    						=  tokenizer.pad_token_id if pad_token_id is None else pad_token_id

        A_  :	Union[str, Any]    						=  tokenizer.get_vocab()
        A_  :	Optional[int]    						=  sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()] ,				key=lambda snake_case_  : x[1]  )
        A_  :	Union[str, Any]    						=  [entry[0] for entry in vocab]
        return cls(
            vocab_list=snake_case_ ,				do_lower_case=snake_case_ ,				cls_token_id=snake_case_ ,				sep_token_id=snake_case_ ,				pad_token_id=snake_case_ ,				**snake_case_ ,				)

      @classmethod
      def  lowerCamelCase_      (					cls ,				snake_case_ ,				*snake_case_ ,				**snake_case_  ):

        """simple docstring"""

        try:
          A_  :	int    						=  BertTokenizer.from_pretrained(snake_case_ ,				*snake_case_ ,				**snake_case_  )
        except:  # noqa: E722
          from .tokenization_bert_fast import BertTokenizerFast

          A_  :	List[str]    						=  BertTokenizerFast.from_pretrained(snake_case_ ,				*snake_case_ ,				**snake_case_  )
        return cls.from_tokenizer(snake_case_ ,				**snake_case_  )

      def  lowerCamelCase_      (					self ,				snake_case_  ):

        """simple docstring"""

        if self.do_lower_case:
          A_  :	Dict    						=  case_fold_utfa(snake_case_  )
        A_  :	List[Any]    						=  self.tf_tokenizer.tokenize(snake_case_  )
        return tokens.merge_dims(1 ,				-1  )

      def  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_=None ,				snake_case_=None ,				snake_case_=None ,				snake_case_=None ,				snake_case_=None ,				snake_case_=None ,				snake_case_=None ,				):

        """simple docstring"""

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

        A_  :	Tuple    						=  pad_model_inputs(snake_case_ ,				max_seq_length=snake_case_ ,				pad_value=self.pad_token_id  )
        A_  :	Optional[Any]    						=  {'input_ids': input_ids}
        if return_attention_mask:
          A_  :	List[Any]    						=  attention_mask
        if return_token_type_ids:
          A_  :	Optional[Any]    						=  pad_model_inputs(
              snake_case_ ,				max_seq_length=snake_case_ ,				pad_value=self.pad_token_id  )
          A_  :	Optional[int]    						=  token_type_ids
        return output

      def  lowerCamelCase_      (					self  ):

        """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,
        }











"""simple docstring"""




from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  if not inplace:
    A_  :	Optional[int]    						=  ma + ma
  else:
    ma += ma

  return ma





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  A_  :	Tuple    						=  -1 * len(_UpperCAmelCase )
  A_  :	List[str]    						=  list(range(len(tensor.shape[:zero_index] ) ) )
  return tensor.permute(first_inds + [zero_index + i for i in inds] )





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  return t.reshape(t.shape[:-no_dims] + (-1,) )





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase = 1E-4 ):






  """simple docstring"""





  A_  :	Optional[Any]    						=  mask.expand(*value.shape )
  return torch.sum(mask * value			, dim=_UpperCAmelCase ) / (eps + torch.sum(_UpperCAmelCase			, dim=_UpperCAmelCase ))





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase = 2.3_125			, _UpperCAmelCase = 21.6_875			, _UpperCAmelCase = 64 ):






  """simple docstring"""





  A_  :	List[str]    						=  torch.linspace(_UpperCAmelCase			, _UpperCAmelCase			, no_bins - 1			, device=pts.device )
  A_  :	Optional[Any]    						=  torch.sqrt(torch.sum((pts.unsqueeze(-2 ) - pts.unsqueeze(-3 )) ** 2			, dim=-1 ) )
  return torch.bucketize(_UpperCAmelCase			, _UpperCAmelCase )





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  A_  :	Any    						=  dicts[0]
  A_  :	int    						=  {}
  for k, v in first.items():
    A_  :	Dict    						=  [d[k] for d in dicts]
    if isinstance(_UpperCAmelCase			, _UpperCAmelCase ):
      A_  :	List[Any]    						=  dict_multimap(_UpperCAmelCase			, _UpperCAmelCase )
    else:
      A_  :	Union[str, Any]    						=  fn(_UpperCAmelCase )

  return new_dict





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  A_  :	Tuple    						=  v_bins.view(((1,) * len(x.shape )) + (len(_UpperCAmelCase ),) )
  A_  :	Any    						=  x[..., None] - reshaped_bins
  A_  :	List[str]    						=  torch.argmin(torch.abs(_UpperCAmelCase )			, dim=-1 )
  return nn.functional.one_hot(_UpperCAmelCase			, num_classes=len(_UpperCAmelCase ) ).float()





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase = 0			, _UpperCAmelCase = 0 ):






  """simple docstring"""





  A_  :	List[Union[slice, torch.Tensor]]    						=  []
  for i, s in enumerate(data.shape[:no_batch_dims] ):
    A_  :	List[Any]    						=  torch.arange(_UpperCAmelCase )
    A_  :	Dict    						=  r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape ) - i - 1))) )
    ranges.append(_UpperCAmelCase )

  A_  :	List[Union[slice, torch.Tensor]]    						=  [slice(_UpperCAmelCase ) for _ in range(len(data.shape ) - no_batch_dims )]
  A_  :	Optional[Any]    						=  inds
  ranges.extend(_UpperCAmelCase )
  # Matt note: Editing this to get around the behaviour of using a list as an array index changing
  # in recent Numpy versions
  return data[tuple(_UpperCAmelCase )]


lowerCamelCase_					:						Union[str, Any]      = TypeVar('T')





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  A_  :	Dict[Any, Union[dict, list, tuple, Any]]    						=  {}
  for k, v in dic.items():
    if isinstance(_UpperCAmelCase			, _UpperCAmelCase ):
      A_  :	int    						=  dict_map(_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase )
    else:
      A_  :	Any    						=  tree_map(_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase )

  return new_dict





@overload
def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  ...





@overload
def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  ...





@overload
def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  ...





@overload
def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  ...





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  if isinstance(_UpperCAmelCase			, _UpperCAmelCase ):
    return dict_map(_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase )
  elif isinstance(_UpperCAmelCase			, _UpperCAmelCase ):
    return [tree_map(_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ) for x in tree]
  elif isinstance(_UpperCAmelCase			, _UpperCAmelCase ):
    return tuple(tree_map(_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ) for x in tree )
  elif isinstance(_UpperCAmelCase			, _UpperCAmelCase ):
    return fn(_UpperCAmelCase )
  else:
    print(type(_UpperCAmelCase ) )
    raise ValueError('Not supported' )





lowerCamelCase_					:						Optional[int]      = partial(tree_map, leaf_type=torch.Tensor)