






"""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 			a	(  tf.keras.layers.Layer      ):







    def __init__(     self				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase = None				,						_lowerCamelCase = None				,						_lowerCamelCase = None				,						_lowerCamelCase = "longest"				,						_lowerCamelCase = True				,						_lowerCamelCase = 5_1_2				,						_lowerCamelCase = None				,						_lowerCamelCase = True				,						_lowerCamelCase = True				,						_lowerCamelCase = True				,						**_lowerCamelCase				,						):
      super().__init__()
      if use_fast_bert_tokenizer:
        lowercase				       =			FastBertTokenizer(
            _lowerCamelCase				,						token_out_type=tf.intaa				,						lower_case_nfd_strip_accents=_lowerCamelCase				,						**_lowerCamelCase			)
      else:
        lowercase				       =			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				,						)
        lowercase				       =			BertTokenizerLayer(
            _lowerCamelCase				,						token_out_type=tf.intaa				,						lower_case=_lowerCamelCase				,						**_lowerCamelCase			)

      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      UpperCamelCase_ (     cls				,						_lowerCamelCase				,						**_lowerCamelCase			):  # noqa: F821
      lowercase				       =			kwargs.pop('do_lower_case'				,						_lowerCamelCase			)
      lowercase				       =			tokenizer.do_lower_case if do_lower_case is None else do_lower_case
      lowercase				       =			kwargs.pop('cls_token_id'				,						_lowerCamelCase			)
      lowercase				       =			tokenizer.cls_token_id if cls_token_id is None else cls_token_id
      lowercase				       =			kwargs.pop('sep_token_id'				,						_lowerCamelCase			)
      lowercase				       =			tokenizer.sep_token_id if sep_token_id is None else sep_token_id
      lowercase				       =			kwargs.pop('pad_token_id'				,						_lowerCamelCase			)
      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 _lowerCamelCase			: x[1]			)
      lowercase				       =			[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				,						_lowerCamelCase				,						*_lowerCamelCase				,						**_lowerCamelCase			):
      try:
        lowercase				       =			BertTokenizer.from_pretrained(_lowerCamelCase				,						*_lowerCamelCase				,						**_lowerCamelCase			)
      except:  # noqa: E722
        from .tokenization_bert_fast import BertTokenizerFast

        lowercase				       =			BertTokenizerFast.from_pretrained(_lowerCamelCase				,						*_lowerCamelCase				,						**_lowerCamelCase			)
      return cls.from_tokenizer(_lowerCamelCase				,						**_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      if self.do_lower_case:
        lowercase				       =			case_fold_utfa(_lowerCamelCase			)
      lowercase				       =			self.tf_tokenizer.tokenize(_lowerCamelCase			)
      return tokens.merge_dims(1				,						-1			)







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

      lowercase       ,	lowercase				       =			pad_model_inputs(_lowerCamelCase				,						max_seq_length=_lowerCamelCase				,						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(
            _lowerCamelCase				,						max_seq_length=_lowerCamelCase				,						pad_value=self.pad_token_id			)
        lowercase				       =			token_type_ids
      return output







    def      UpperCamelCase_ (     self			):
      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"""



import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list

class 			a	(  unittest.TestCase      ):







    def      UpperCamelCase_ (     self			):
      lowercase				       =			'<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K \' X J Q Z'.split(' '			)
      lowercase				       =			dict(zip(_lowerCamelCase				,						range(len(_lowerCamelCase			)			)			)			)

      lowercase				       =			{
          'pad_token': '<pad>',
          'unk_token': '<unk>',
          'bos_token': '<s>',
          'eos_token': '</s>',
      }
      lowercase				       =			{
          'feature_size': 1,
          'padding_value': 0.0,
          'sampling_rate': 1_6_0_0_0,
          'return_attention_mask': False,
          'do_normalize': True,
      }

      lowercase				       =			tempfile.mkdtemp()
      lowercase				       =			os.path.join(self.tmpdirname				,						VOCAB_FILES_NAMES['vocab_file']			)
      lowercase				       =			os.path.join(self.tmpdirname				,						_lowerCamelCase			)
      with open(self.vocab_file				,						'w'				,						encoding='utf-8'			) as fp:
        fp.write(json.dumps(_lowerCamelCase			) + '\n'			)

      with open(self.feature_extraction_file				,						'w'				,						encoding='utf-8'			) as fp:
        fp.write(json.dumps(_lowerCamelCase			) + '\n'			)







    def      UpperCamelCase_ (     self				,						**_lowerCamelCase			):
      lowercase				       =			self.add_kwargs_tokens_map.copy()
      kwargs.update(_lowerCamelCase			)
      return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname				,						**_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						**_lowerCamelCase			):
      return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname				,						**_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      shutil.rmtree(self.tmpdirname			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_tokenizer()
      lowercase				       =			self.get_feature_extractor()

      lowercase				       =			WavaVecaProcessor(tokenizer=_lowerCamelCase				,						feature_extractor=_lowerCamelCase			)

      processor.save_pretrained(self.tmpdirname			)
      lowercase				       =			WavaVecaProcessor.from_pretrained(self.tmpdirname			)

      self.assertEqual(processor.tokenizer.get_vocab()				,						tokenizer.get_vocab()			)
      self.assertIsInstance(processor.tokenizer				,						_lowerCamelCase			)

      self.assertEqual(processor.feature_extractor.to_json_string()				,						feature_extractor.to_json_string()			)
      self.assertIsInstance(processor.feature_extractor				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			WavaVecaProcessor(tokenizer=self.get_tokenizer()				,						feature_extractor=self.get_feature_extractor()			)
      processor.save_pretrained(self.tmpdirname			)

      lowercase				       =			self.get_tokenizer(bos_token='(BOS)'				,						eos_token='(EOS)'			)
      lowercase				       =			self.get_feature_extractor(do_normalize=_lowerCamelCase				,						padding_value=1.0			)

      lowercase				       =			WavaVecaProcessor.from_pretrained(
          self.tmpdirname				,						bos_token='(BOS)'				,						eos_token='(EOS)'				,						do_normalize=_lowerCamelCase				,						padding_value=1.0			)

      self.assertEqual(processor.tokenizer.get_vocab()				,						tokenizer_add_kwargs.get_vocab()			)
      self.assertIsInstance(processor.tokenizer				,						_lowerCamelCase			)

      self.assertEqual(processor.feature_extractor.to_json_string()				,						feature_extractor_add_kwargs.to_json_string()			)
      self.assertIsInstance(processor.feature_extractor				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_feature_extractor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			WavaVecaProcessor(tokenizer=_lowerCamelCase				,						feature_extractor=_lowerCamelCase			)

      lowercase				       =			floats_list((3, 1_0_0_0)			)

      lowercase				       =			feature_extractor(_lowerCamelCase				,						return_tensors='np'			)
      lowercase				       =			processor(_lowerCamelCase				,						return_tensors='np'			)

      for key in input_feat_extract.keys():
        self.assertAlmostEqual(input_feat_extract[key].sum()				,						input_processor[key].sum()				,						delta=1e-2			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_feature_extractor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			WavaVecaProcessor(tokenizer=_lowerCamelCase				,						feature_extractor=_lowerCamelCase			)

      lowercase				       =			'This is a test string'

      lowercase				       =			processor(text=_lowerCamelCase			)

      lowercase				       =			tokenizer(_lowerCamelCase			)

      for key in encoded_tok.keys():
        self.assertListEqual(encoded_tok[key]				,						encoded_processor[key]			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_feature_extractor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			WavaVecaProcessor(tokenizer=_lowerCamelCase				,						feature_extractor=_lowerCamelCase			)

      lowercase				       =			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      lowercase				       =			processor.batch_decode(_lowerCamelCase			)
      lowercase				       =			tokenizer.batch_decode(_lowerCamelCase			)

      self.assertListEqual(_lowerCamelCase				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_feature_extractor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			WavaVecaProcessor(tokenizer=_lowerCamelCase				,						feature_extractor=_lowerCamelCase			)

      self.assertListEqual(
          processor.model_input_names				,						feature_extractor.model_input_names				,						msg='`processor` and `feature_extractor` model input names do not match'				,						)


