


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    lowerCamelCase__     ( tf.keras.layers.Layer):





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

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





     @classmethod
     def   __A      (cls	,  UpperCAmelCase	,  **UpperCAmelCase )  ->     Union[str, Any]:  # noqa: F821
      _lowercase							   =kwargs.pop('''do_lower_case'''	,  UpperCAmelCase )
      _lowercase							   =tokenizer.do_lower_case if do_lower_case is None else do_lower_case
      _lowercase							   =kwargs.pop('''cls_token_id'''	,  UpperCAmelCase )
      _lowercase							   =tokenizer.cls_token_id if cls_token_id is None else cls_token_id
      _lowercase							   =kwargs.pop('''sep_token_id'''	,  UpperCAmelCase )
      _lowercase							   =tokenizer.sep_token_id if sep_token_id is None else sep_token_id
      _lowercase							   =kwargs.pop('''pad_token_id'''	,  UpperCAmelCase )
      _lowercase							   =tokenizer.pad_token_id if pad_token_id is None else pad_token_id

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





     @classmethod
     def   __A      (cls	,  UpperCAmelCase	,  *UpperCAmelCase	,  **UpperCAmelCase )  ->     Optional[int]:
      try:
       _lowercase							   =BertTokenizer.from_pretrained(UpperCAmelCase	,  *UpperCAmelCase	,  **UpperCAmelCase )
      except:  # noqa: E722
       from .tokenization_bert_fast import BertTokenizerFast

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





     def   __A      (self	,  UpperCAmelCase )  ->     Optional[int]:
      if self.do_lower_case:
       _lowercase							   =case_fold_utfa(UpperCAmelCase )
      _lowercase							   =self.tf_tokenizer.tokenize(UpperCAmelCase )
      return tokens.merge_dims(1	,  -1 )





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

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





     def   __A      (self )  ->     Optional[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,
      }





import unittest

import numpy as np

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
  import tensorflow as tf

  from transformers.activations_tf import get_tf_activation







@require_tf
class    lowerCamelCase__     ( unittest.TestCase):





     def   __A      (self )  ->     Optional[Any]:
      _lowercase							   =tf.constant([-1_0_0, -1.0, -0.1, 0, 0.1, 1.0, 100.0] )
      _lowercase							   =get_tf_activation('''gelu''' )
      _lowercase							   =get_tf_activation('''gelu_10''' )

      _lowercase							   =gelu(UpperCAmelCase )
      _lowercase							   =geluaa(UpperCAmelCase )

      _lowercase							   =tf.where(y_gelu_aa < 10.0	,  1.0	,  0.0 )

      self.assertEqual(tf.math.reduce_max(UpperCAmelCase ).numpy().item()	,  10.0 )
      self.assertTrue(np.allclose(y_gelu * clipped_mask	,  y_gelu_aa * clipped_mask ) )





     def   __A      (self )  ->     Optional[Any]:
      get_tf_activation('''gelu''' )
      get_tf_activation('''gelu_10''' )
      get_tf_activation('''gelu_fast''' )
      get_tf_activation('''gelu_new''' )
      get_tf_activation('''glu''' )
      get_tf_activation('''mish''' )
      get_tf_activation('''quick_gelu''' )
      get_tf_activation('''relu''' )
      get_tf_activation('''sigmoid''' )
      get_tf_activation('''silu''' )
      get_tf_activation('''swish''' )
      get_tf_activation('''tanh''' )
      with self.assertRaises(UpperCAmelCase ):
       get_tf_activation('''bogus''' )
      with self.assertRaises(UpperCAmelCase ):
       get_tf_activation(UpperCAmelCase )

