
'''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 			UpperCAmelCase				(					unittest.TestCase ):





		"""simple docstring"""





		def _lowercase  (    self     )				->    Dict:
				_UpperCamelCase       : Optional[int]    =      '''<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(''' '''     )
				_UpperCamelCase       : List[Any]    =      dict(zip(_snake_case  ,					range(len(_snake_case     )     )     )     )

				_UpperCamelCase       : Optional[int]    =      {
				    '''pad_token''': '''<pad>''',
				    '''unk_token''': '''<unk>''',
				    '''bos_token''': '''<s>''',
				    '''eos_token''': '''</s>''',
				}
				_UpperCamelCase       : Optional[Any]    =      {
				    '''feature_size''': 1,
				    '''padding_value''': 0.0,
				    '''sampling_rate''': 16000,
				    '''return_attention_mask''': False,
				    '''do_normalize''': True,
				}

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

				with open(self.feature_extraction_file  ,					'''w'''  ,					encoding='''utf-8'''     ) as fp:
						fp.write(json.dumps(_snake_case     ) + '''\n'''     )
		def _lowercase  (    self  ,					**_snake_case     )				->    Optional[int]:
				_UpperCamelCase       : Tuple    =      self.add_kwargs_tokens_map.copy()
				kwargs.update(_snake_case     )
				return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname  ,					**_snake_case     )
		def _lowercase  (    self  ,					**_snake_case     )				->    List[str]:
				return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname  ,					**_snake_case     )
		def _lowercase  (    self     )				->    Optional[Any]:
				shutil.rmtree(self.tmpdirname     )
		def _lowercase  (    self     )				->    List[str]:
				_UpperCamelCase       : List[Any]    =      self.get_tokenizer()
				_UpperCamelCase       : Union[str, Any]    =      self.get_feature_extractor()

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

				processor.save_pretrained(self.tmpdirname     )
				_UpperCamelCase       : Optional[Any]    =      WavaVecaProcessor.from_pretrained(self.tmpdirname     )

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

				self.assertEqual(processor.feature_extractor.to_json_string()  ,					feature_extractor.to_json_string()     )
				self.assertIsInstance(processor.feature_extractor  ,					_snake_case     )
		def _lowercase  (    self     )				->    Optional[int]:
				_UpperCamelCase       : Tuple    =      WavaVecaProcessor(tokenizer=self.get_tokenizer()  ,					feature_extractor=self.get_feature_extractor()     )
				processor.save_pretrained(self.tmpdirname     )

				_UpperCamelCase       : str    =      self.get_tokenizer(bos_token='''(BOS)'''  ,					eos_token='''(EOS)'''     )
				_UpperCamelCase       : int    =      self.get_feature_extractor(do_normalize=_snake_case  ,					padding_value=1.0     )

				_UpperCamelCase       : List[str]    =      WavaVecaProcessor.from_pretrained(
				    self.tmpdirname  ,					bos_token='''(BOS)'''  ,					eos_token='''(EOS)'''  ,					do_normalize=_snake_case  ,					padding_value=1.0     )

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

				self.assertEqual(processor.feature_extractor.to_json_string()  ,					feature_extractor_add_kwargs.to_json_string()     )
				self.assertIsInstance(processor.feature_extractor  ,					_snake_case     )
		def _lowercase  (    self     )				->    List[Any]:
				_UpperCamelCase       : Optional[int]    =      self.get_feature_extractor()
				_UpperCamelCase       : List[str]    =      self.get_tokenizer()

				_UpperCamelCase       : str    =      WavaVecaProcessor(tokenizer=_snake_case  ,					feature_extractor=_snake_case     )

				_UpperCamelCase       : Optional[Any]    =      floats_list((3, 1000)     )

				_UpperCamelCase       : Tuple    =      feature_extractor(_snake_case  ,					return_tensors='''np'''     )
				_UpperCamelCase       : Optional[Any]    =      processor(_snake_case  ,					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 _lowercase  (    self     )				->    Optional[int]:
				_UpperCamelCase       : int    =      self.get_feature_extractor()
				_UpperCamelCase       : Union[str, Any]    =      self.get_tokenizer()

				_UpperCamelCase       : int    =      WavaVecaProcessor(tokenizer=_snake_case  ,					feature_extractor=_snake_case     )

				_UpperCamelCase       : Any    =      '''This is a test string'''

				_UpperCamelCase       : Tuple    =      processor(text=_snake_case     )

				_UpperCamelCase       : Optional[int]    =      tokenizer(_snake_case     )

				for key in encoded_tok.keys():
						self.assertListEqual(encoded_tok[key]  ,					encoded_processor[key]     )
		def _lowercase  (    self     )				->    Optional[Any]:
				_UpperCamelCase       : Tuple    =      self.get_feature_extractor()
				_UpperCamelCase       : int    =      self.get_tokenizer()

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

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

				_UpperCamelCase       : Tuple    =      processor.batch_decode(_snake_case     )
				_UpperCamelCase       : Optional[Any]    =      tokenizer.batch_decode(_snake_case     )

				self.assertListEqual(_snake_case  ,					_snake_case     )




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

				_UpperCamelCase       : Any    =      WavaVecaProcessor(tokenizer=_snake_case  ,					feature_extractor=_snake_case     )

				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


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

class 			UpperCAmelCase				(					a_ ):





		"""simple docstring"""





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





