
"""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 							A (    self      )   ->   Optional[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     '<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(' '      )
									SCREAMING_SNAKE_CASE           =     dict(zip(lowercase__		,     range(len(lowercase__      )      )      )      )

									SCREAMING_SNAKE_CASE           =     {
									    'pad_token': '<pad>',
									    'unk_token': '<unk>',
									    'bos_token': '<s>',
									    'eos_token': '</s>',
									}
									SCREAMING_SNAKE_CASE           =     {
									    'feature_size': 1,
									    'padding_value': 0.0,
									    'sampling_rate': 16000,
									    'return_attention_mask': False,
									    'do_normalize': True,
									}

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

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


				def 							A (    self		,     **lowercase__      )   ->   str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self.add_kwargs_tokens_map.copy()
									kwargs.update(lowercase__      )
									return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname		,     **lowercase__      )


				def 							A (    self		,     **lowercase__      )   ->   Tuple:
									"""simple docstring"""







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


				def 							A (    self      )   ->   List[str]:
									"""simple docstring"""







									shutil.rmtree(self.tmpdirname      )


				def 							A (    self      )   ->   str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self.get_tokenizer()
									SCREAMING_SNAKE_CASE           =     self.get_feature_extractor()

									SCREAMING_SNAKE_CASE           =     WavaVecaProcessor(tokenizer=lowercase__		,     feature_extractor=lowercase__      )

									processor.save_pretrained(self.tmpdirname      )
									SCREAMING_SNAKE_CASE           =     WavaVecaProcessor.from_pretrained(self.tmpdirname      )

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

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


				def 							A (    self      )   ->   Tuple:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     WavaVecaProcessor(tokenizer=self.get_tokenizer()		,     feature_extractor=self.get_feature_extractor()      )
									processor.save_pretrained(self.tmpdirname      )

									SCREAMING_SNAKE_CASE           =     self.get_tokenizer(bos_token='(BOS)'		,     eos_token='(EOS)'      )
									SCREAMING_SNAKE_CASE           =     self.get_feature_extractor(do_normalize=lowercase__		,     padding_value=1.0      )

									SCREAMING_SNAKE_CASE           =     WavaVecaProcessor.from_pretrained(
									    self.tmpdirname		,     bos_token='(BOS)'		,     eos_token='(EOS)'		,     do_normalize=lowercase__		,     padding_value=1.0      )

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

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


				def 							A (    self      )   ->   Tuple:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self.get_feature_extractor()
									SCREAMING_SNAKE_CASE           =     self.get_tokenizer()

									SCREAMING_SNAKE_CASE           =     WavaVecaProcessor(tokenizer=lowercase__		,     feature_extractor=lowercase__      )

									SCREAMING_SNAKE_CASE           =     floats_list((3, 1000)      )

									SCREAMING_SNAKE_CASE           =     feature_extractor(lowercase__		,     return_tensors='np'      )
									SCREAMING_SNAKE_CASE           =     processor(lowercase__		,     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"""







									SCREAMING_SNAKE_CASE           =     self.get_feature_extractor()
									SCREAMING_SNAKE_CASE           =     self.get_tokenizer()

									SCREAMING_SNAKE_CASE           =     WavaVecaProcessor(tokenizer=lowercase__		,     feature_extractor=lowercase__      )

									SCREAMING_SNAKE_CASE           =     'This is a test string'

									SCREAMING_SNAKE_CASE           =     processor(text=lowercase__      )

									SCREAMING_SNAKE_CASE           =     tokenizer(lowercase__      )

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


				def 							A (    self      )   ->   Union[str, Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self.get_feature_extractor()
									SCREAMING_SNAKE_CASE           =     self.get_tokenizer()

									SCREAMING_SNAKE_CASE           =     WavaVecaProcessor(tokenizer=lowercase__		,     feature_extractor=lowercase__      )

									SCREAMING_SNAKE_CASE           =     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									SCREAMING_SNAKE_CASE           =     processor.batch_decode(lowercase__      )
									SCREAMING_SNAKE_CASE           =     tokenizer.batch_decode(lowercase__      )

									self.assertListEqual(lowercase__		,     lowercase__      )




				def 							A (    self      )   ->   int:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self.get_feature_extractor()
									SCREAMING_SNAKE_CASE           =     self.get_tokenizer()

									SCREAMING_SNAKE_CASE           =     WavaVecaProcessor(tokenizer=lowercase__		,     feature_extractor=lowercase__      )

									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 abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


snake_case			       =		TypeVar('T', bound='TaskTemplate')




@dataclass(frozen=__magic_name__				)
class 					UpperCamelCase     (	abc.ABC				):

				"""simple docstring"""



				UpperCAmelCase_    :				str
				UpperCAmelCase_    :				ClassVar[Features]
				UpperCAmelCase_    :				ClassVar[Features]


				def 							A (    self		,     lowercase__      )   ->   T:
									"""simple docstring"""







									return copy.deepcopy(self      )


				@property
				def 							A (    self      )   ->   Features:
									"""simple docstring"""







									return Features(**self.input_schema		,     **self.label_schema      )


				@property
				@abc.abstractmethod
				def 							A (    self      )   ->   Dict[str, str]:
									"""simple docstring"""







									raise NotImplementedError




				@classmethod
				def 							A (    cls		,     lowercase__      )   ->   T:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     {f.name for f in dataclasses.fields(cls      )}
									return cls(**{k: v for k, v in template_dict.items() if k in field_names}      )
