






"""simple docstring"""



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
    from .tokenization_xglm import XGLMTokenizer
else:
    _UpperCamelCase						:    Optional[int]		=						None


_UpperCamelCase						:    Optional[int]		=						logging.get_logger(__name__)

_UpperCamelCase						:    Optional[int]		=						{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

_UpperCamelCase						:    List[str]		=						{
    'vocab_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json',
    },
}

_UpperCamelCase						:    List[str]		=						{
    'facebook/xglm-564M': 2_0_4_8,
}

class 			a	(  a_      ):
    UpperCAmelCase_					:	Dict      	=VOCAB_FILES_NAMES
    UpperCAmelCase_					:	Any      	=PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase_					:	List[str]      	=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase_					:	int      	=["input_ids", "attention_mask"]
    UpperCAmelCase_					:	Dict      	=XGLMTokenizer







    def __init__(     self				,						_lowerCamelCase=None				,						_lowerCamelCase=None				,						_lowerCamelCase="<s>"				,						_lowerCamelCase="</s>"				,						_lowerCamelCase="</s>"				,						_lowerCamelCase="<s>"				,						_lowerCamelCase="<unk>"				,						_lowerCamelCase="<pad>"				,						**_lowerCamelCase				,						):
      # Compatibility with the original tokenizer
      lowercase				       =			7
      lowercase				       =			[F'<madeupword{i}>' for i in range(self.num_madeup_words			)]

      lowercase				       =			kwargs.get('additional_special_tokens'				,						[]			)
      kwargs["additional_special_tokens"] += [
          word for word in madeup_words if word not in kwargs["additional_special_tokens"]
      ]

      super().__init__(
          _lowerCamelCase				,						tokenizer_file=_lowerCamelCase				,						bos_token=_lowerCamelCase				,						eos_token=_lowerCamelCase				,						sep_token=_lowerCamelCase				,						cls_token=_lowerCamelCase				,						unk_token=_lowerCamelCase				,						pad_token=_lowerCamelCase				,						**_lowerCamelCase				,						)

      lowercase				       =			vocab_file
      lowercase				       =			False if not self.vocab_file else True







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase = None			):

      if token_ids_a is None:
        return [self.sep_token_id] + token_ids_a
      lowercase				       =			[self.sep_token_id]
      return sep + token_ids_a + sep + sep + token_ids_a







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase = None			):
      lowercase				       =			[self.sep_token_id]

      if token_ids_a is None:
        return len(sep + token_ids_a			) * [0]
      return len(sep + token_ids_a + sep + sep + token_ids_a			) * [0]







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase = None			):
      if not self.can_save_slow_tokenizer:
        raise ValueError(
            'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
            'tokenizer.'			)

      if not os.path.isdir(_lowerCamelCase			):
        logger.error(F'Vocabulary path ({save_directory}) should be a directory.'			)
        return
      lowercase				       =			os.path.join(
          _lowerCamelCase				,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)

      if os.path.abspath(self.vocab_file			) != os.path.abspath(_lowerCamelCase			):
        copyfile(self.vocab_file				,						_lowerCamelCase			)

      return (out_vocab_file,)










"""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'				,						)


