






"""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					):







      '''simple docstring'''

      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     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(' '   )
       lowerCamelCase__	:     List[str]								=		dict(zip(lowerCamelCase__,	range(len(lowerCamelCase__   )   )   )   )

       lowerCamelCase__	:     str								=		{
           'pad_token': '<pad>',
           'unk_token': '<unk>',
           'bos_token': '<s>',
           'eos_token': '</s>',
       }
       lowerCamelCase__	:     Union[str, Any]								=		{
           'feature_size': 1,
           'padding_value': 0.0,
           'sampling_rate': 1_6_0_0_0,
           'return_attention_mask': False,
           'do_normalize': True,
       }

       lowerCamelCase__	:     str								=		tempfile.mkdtemp()
       lowerCamelCase__	:     int								=		os.path.join(self.tmpdirname,	VOCAB_FILES_NAMES['vocab_file']   )
       lowerCamelCase__	:     List[str]								=		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_   ):

       '''simple docstring'''



       lowerCamelCase__	:     Optional[Any]								=		self.add_kwargs_tokens_map.copy()
       kwargs.update(lowerCamelCase__   )
       return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname,	**lowerCamelCase__   )




      def      a__				(self,	**lowerCamelCase_   ):

       '''simple docstring'''



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




      def      a__				(self   ):

       '''simple docstring'''



       shutil.rmtree(self.tmpdirname   )




      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Optional[Any]								=		self.get_tokenizer()
       lowerCamelCase__	:     Optional[Any]								=		self.get_feature_extractor()

       lowerCamelCase__	:     int								=		WavaVecaProcessor(tokenizer=lowerCamelCase__,	feature_extractor=lowerCamelCase__   )

       processor.save_pretrained(self.tmpdirname   )
       lowerCamelCase__	:     Union[str, Any]								=		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   ):

       '''simple docstring'''



       lowerCamelCase__	:     Tuple								=		WavaVecaProcessor(tokenizer=self.get_tokenizer(),	feature_extractor=self.get_feature_extractor()   )
       processor.save_pretrained(self.tmpdirname   )

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

       lowerCamelCase__	:     int								=		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   ):

       '''simple docstring'''



       lowerCamelCase__	:     Optional[Any]								=		self.get_feature_extractor()
       lowerCamelCase__	:     Union[str, Any]								=		self.get_tokenizer()

       lowerCamelCase__	:     Optional[int]								=		WavaVecaProcessor(tokenizer=lowerCamelCase__,	feature_extractor=lowerCamelCase__   )

       lowerCamelCase__	:     Union[str, Any]								=		floats_list((3, 1_0_0_0)   )

       lowerCamelCase__	:     List[Any]								=		feature_extractor(lowerCamelCase__,	return_tensors='np'   )
       lowerCamelCase__	:     Union[str, Any]								=		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   ):

       '''simple docstring'''



       lowerCamelCase__	:     str								=		self.get_feature_extractor()
       lowerCamelCase__	:     Optional[int]								=		self.get_tokenizer()

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

       lowerCamelCase__	:     str								=		'This is a test string'

       lowerCamelCase__	:     Dict								=		processor(text=lowerCamelCase__   )

       lowerCamelCase__	:     List[str]								=		tokenizer(lowerCamelCase__   )

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




      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Any								=		self.get_feature_extractor()
       lowerCamelCase__	:     Any								=		self.get_tokenizer()

       lowerCamelCase__	:     int								=		WavaVecaProcessor(tokenizer=lowerCamelCase__,	feature_extractor=lowerCamelCase__   )

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

       lowerCamelCase__	:     Tuple								=		processor.batch_decode(lowerCamelCase__   )
       lowerCamelCase__	:     List[Any]								=		tokenizer.batch_decode(lowerCamelCase__   )

       self.assertListEqual(lowerCamelCase__,	lowerCamelCase__   )





      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Optional[int]								=		self.get_feature_extractor()
       lowerCamelCase__	:     Optional[Any]								=		self.get_tokenizer()

       lowerCamelCase__	:     Tuple								=		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',	)


"""simple docstring"""






import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

lowerCAmelCase__    :    Union[str, Any]			=      [(1,)]



def     a_		(   ):
				UpperCAmelCase__			 =				bool(os.getenv('RUN_SLOW'   ,     0			)			)
				if run_slow:
								assert True
				else:
								assert False, "should have been skipped"



def     a_		(   ):
				UpperCAmelCase__			 =				bool(os.getenv('RUN_SLOW'   ,     0			)			)
				if run_slow and torch_device == "cuda":
								assert True
				else:
								assert False, "should have been skipped"
@require_torch
class 							snake_case			(					unittest.TestCase							):




					"""simple docstring"""

					@slow
					@require_torch_gpu
					def 						__lowerCAmelCase		(							self  :      Optional[Any]     ):
									check_slow_torch_cuda()







					@require_torch_gpu
					@slow
					def 						__lowerCAmelCase		(							self  :      Any     ):
									check_slow_torch_cuda()







					@parameterized.expand(lowerCamelCase__     )
					@slow
					def 						__lowerCAmelCase		(							self  :      Union[str, Any]       ,lowerCamelCase__  :      str=None     ):
									check_slow()






@slow
@require_torch_gpu
def     a_		(   ):
				check_slow_torch_cuda()



@require_torch_gpu
@slow
def     a_		(   ):
				check_slow_torch_cuda()



@slow
@pytest.mark.parametrize('param'   ,     [1]			)
def     a_		(   lowerCamelCase			):
				check_slow()







@pytest.mark.parametrize('param'   ,     [1]			)
@slow
def     a_		(   lowerCamelCase			):
				check_slow()



