





'''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 							__SCREAMING_SNAKE_CASE			(       unittest.TestCase      ):
      '''simple docstring'''



      def   _lowerCamelCase		(							self    : Union[str, Any]  )		->  List[str]:
           _lowercase							:      List[str]  						=							'<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							:      int  						=							dict(zip(UpperCamelCase   ,range(len(UpperCamelCase  )  )  )  )

           _lowercase							:      str  						=							{
               'pad_token': '<pad>',
               'unk_token': '<unk>',
               'bos_token': '<s>',
               'eos_token': '</s>',
           }
           _lowercase							:      Tuple  						=							{
               'feature_size': 1,
               'padding_value': 0.0,
               'sampling_rate': 1_6000,
               'return_attention_mask': False,
               'do_normalize': True,
           }

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

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

      def   _lowerCamelCase		(							self    : Union[str, Any]   ,**UpperCamelCase    : Dict  )		->  Optional[Any]:
           _lowercase							:      Tuple  						=							self.add_kwargs_tokens_map.copy()
           kwargs.update(UpperCamelCase  )
           return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname   ,**UpperCamelCase  )

      def   _lowerCamelCase		(							self    : Optional[int]   ,**UpperCamelCase    : Tuple  )		->  int:
           return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname   ,**UpperCamelCase  )

      def   _lowerCamelCase		(							self    : Dict  )		->  Any:
           shutil.rmtree(self.tmpdirname  )

      def   _lowerCamelCase		(							self    : List[str]  )		->  Optional[Any]:
           _lowercase							:      Optional[Any]  						=							self.get_tokenizer()
           _lowercase							:      Any  						=							self.get_feature_extractor()

           _lowercase							:      Tuple  						=							WavaVecaProcessor(tokenizer=UpperCamelCase   ,feature_extractor=UpperCamelCase  )

           processor.save_pretrained(self.tmpdirname  )
           _lowercase							:      Tuple  						=							WavaVecaProcessor.from_pretrained(self.tmpdirname  )

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

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

      def   _lowerCamelCase		(							self    : Tuple  )		->  Optional[int]:
           _lowercase							:      Dict  						=							WavaVecaProcessor(tokenizer=self.get_tokenizer()   ,feature_extractor=self.get_feature_extractor()  )
           processor.save_pretrained(self.tmpdirname  )

           _lowercase							:      List[Any]  						=							self.get_tokenizer(bos_token='(BOS)'   ,eos_token='(EOS)'  )
           _lowercase							:      List[str]  						=							self.get_feature_extractor(do_normalize=UpperCamelCase   ,padding_value=1.0  )

           _lowercase							:      Dict  						=							WavaVecaProcessor.from_pretrained(
               self.tmpdirname   ,bos_token='(BOS)'   ,eos_token='(EOS)'   ,do_normalize=UpperCamelCase   ,padding_value=1.0  )

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

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

      def   _lowerCamelCase		(							self    : Optional[Any]  )		->  Optional[Any]:
           _lowercase							:      int  						=							self.get_feature_extractor()
           _lowercase							:      List[Any]  						=							self.get_tokenizer()

           _lowercase							:      str  						=							WavaVecaProcessor(tokenizer=UpperCamelCase   ,feature_extractor=UpperCamelCase  )

           _lowercase							:      str  						=							floats_list((3, 1000)  )

           _lowercase							:      str  						=							feature_extractor(UpperCamelCase   ,return_tensors='np'  )
           _lowercase							:      Any  						=							processor(UpperCamelCase   ,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   _lowerCamelCase		(							self    : Optional[Any]  )		->  List[Any]:
           _lowercase							:      Tuple  						=							self.get_feature_extractor()
           _lowercase							:      Optional[Any]  						=							self.get_tokenizer()

           _lowercase							:      Union[str, Any]  						=							WavaVecaProcessor(tokenizer=UpperCamelCase   ,feature_extractor=UpperCamelCase  )

           _lowercase							:      Any  						=							'This is a test string'

           _lowercase							:      int  						=							processor(text=UpperCamelCase  )

           _lowercase							:      List[Any]  						=							tokenizer(UpperCamelCase  )

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

      def   _lowerCamelCase		(							self    : int  )		->  int:
           _lowercase							:      Dict  						=							self.get_feature_extractor()
           _lowercase							:      List[Any]  						=							self.get_tokenizer()

           _lowercase							:      Optional[Any]  						=							WavaVecaProcessor(tokenizer=UpperCamelCase   ,feature_extractor=UpperCamelCase  )

           _lowercase							:      Tuple  						=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           _lowercase							:      Optional[int]  						=							processor.batch_decode(UpperCamelCase  )
           _lowercase							:      Optional[Any]  						=							tokenizer.batch_decode(UpperCamelCase  )

           self.assertListEqual(UpperCamelCase   ,UpperCamelCase  )



      def   _lowerCamelCase		(							self    : Dict  )		->  List[Any]:
           _lowercase							:      Tuple  						=							self.get_feature_extractor()
           _lowercase							:      Optional[Any]  						=							self.get_tokenizer()

           _lowercase							:      Dict  						=							WavaVecaProcessor(tokenizer=UpperCamelCase   ,feature_extractor=UpperCamelCase  )

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






'''simple docstring'''







import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


A						    =							logging.get_logger(__name__)



class 							__SCREAMING_SNAKE_CASE			(       lowerCAmelCase__      ):
      '''simple docstring'''



      def __init__(							self    : Tuple   ,*UpperCamelCase    : str   ,**UpperCamelCase    : List[Any]  )		->  None:
           warnings.warn(
               'The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
               ' Please use MobileNetV2ImageProcessor instead.'   ,UpperCamelCase   ,)
           super().__init__(*UpperCamelCase   ,**UpperCamelCase  )