

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_     =       logging.get_logger(__name__)

lowercase_     =       {
    'uw-madison/yoso-4096': 'https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json',
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}





class 			__lowerCAmelCase					( SCREAMING_SNAKE_CASE							):
  _a						      =  """yoso"""




  def __init__(    self							,       lowerCAmelCase=50_265							,       lowerCAmelCase=768							,       lowerCAmelCase=12							,       lowerCAmelCase=12							,       lowerCAmelCase=3_072							,       lowerCAmelCase="gelu"							,       lowerCAmelCase=0.1							,       lowerCAmelCase=0.1							,       lowerCAmelCase=4_096							,       lowerCAmelCase=1							,       lowerCAmelCase=0.02							,       lowerCAmelCase=1e-12							,       lowerCAmelCase="absolute"							,       lowerCAmelCase=True							,       lowerCAmelCase=9							,       lowerCAmelCase=64							,       lowerCAmelCase=None							,       lowerCAmelCase=True							,       lowerCAmelCase=True							,       lowerCAmelCase=1							,       lowerCAmelCase=0							,       lowerCAmelCase=2							,       **lowerCAmelCase							,       )  ->			Tuple:
       '''simple docstring'''






       super().__init__(pad_token_id=lowerCAmelCase							,       bos_token_id=lowerCAmelCase							,       eos_token_id=lowerCAmelCase							,       **lowerCAmelCase )

       _lowercase     	=vocab_size
       _lowercase     	=max_position_embeddings
       _lowercase     	=hidden_size
       _lowercase     	=num_hidden_layers
       _lowercase     	=num_attention_heads
       _lowercase     	=intermediate_size
       _lowercase     	=hidden_act
       _lowercase     	=hidden_dropout_prob
       _lowercase     	=attention_probs_dropout_prob
       _lowercase     	=initializer_range
       _lowercase     	=type_vocab_size
       _lowercase     	=layer_norm_eps
       _lowercase     	=position_embedding_type
       _lowercase     	=use_expectation
       _lowercase     	=hash_code_len
       _lowercase     	=num_hash
       _lowercase     	=conv_window
       _lowercase     	=use_fast_hash
       _lowercase     	=lsh_backward





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 			__lowerCAmelCase					( unittest.TestCase							):




  def   A__					(    self )  ->			Tuple:
        '''simple docstring'''






        _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': 16_000,
            '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   A__					(    self							,       **lowerCAmelCase )  ->			Optional[Any]:
        '''simple docstring'''






        _lowercase     	=self.add_kwargs_tokens_map.copy()
        kwargs.update(lowerCAmelCase )
        return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname							,       **lowerCAmelCase )




  def   A__					(    self							,       **lowerCAmelCase )  ->			List[str]:
        '''simple docstring'''






        return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname							,       **lowerCAmelCase )




  def   A__					(    self )  ->			Union[str, Any]:
        '''simple docstring'''






        shutil.rmtree(self.tmpdirname )




  def   A__					(    self )  ->			Optional[int]:
        '''simple docstring'''






        _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   A__					(    self )  ->			str:
        '''simple docstring'''






        _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   A__					(    self )  ->			List[Any]:
        '''simple docstring'''






        _lowercase     	=self.get_feature_extractor()
        _lowercase     	=self.get_tokenizer()

        _lowercase     	=WavaVecaProcessor(tokenizer=lowerCAmelCase							,       feature_extractor=lowerCAmelCase )

        _lowercase     	=floats_list((3, 1_000) )

        _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   A__					(    self )  ->			int:
        '''simple docstring'''






        _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   A__					(    self )  ->			str:
        '''simple docstring'''






        _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   A__					(    self )  ->			str:
        '''simple docstring'''






        _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'							,       )


