"""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 							snake_case			(					unittest.TestCase							):




					"""simple docstring"""

					def 						__lowerCAmelCase		(							self  :      List[Any]     ):
									UpperCAmelCase__			 =				'<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__			 =				dict(zip(lowerCamelCase__       ,range(len(lowerCamelCase__     )     )     )     )

									UpperCAmelCase__			 =				{
									    'pad_token': '<pad>',
									    'unk_token': '<unk>',
									    'bos_token': '<s>',
									    'eos_token': '</s>',
									}
									UpperCAmelCase__			 =				{
									    'feature_size': 1,
									    'padding_value': 0.0,
									    'sampling_rate': 16_000,
									    'return_attention_mask': False,
									    'do_normalize': True,
									}

									UpperCAmelCase__			 =				tempfile.mkdtemp()
									UpperCAmelCase__			 =				os.path.join(self.tmpdirname       ,VOCAB_FILES_NAMES['vocab_file']     )
									UpperCAmelCase__			 =				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 						__lowerCAmelCase		(							self  :      Optional[int]       ,**lowerCamelCase__  :      List[str]     ):
									UpperCAmelCase__			 =				self.add_kwargs_tokens_map.copy()
									kwargs.update(lowerCamelCase__     )
									return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname       ,**lowerCamelCase__     )







					def 						__lowerCAmelCase		(							self  :      Union[str, Any]       ,**lowerCamelCase__  :      Optional[Any]     ):
									return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname       ,**lowerCamelCase__     )







					def 						__lowerCAmelCase		(							self  :      Any     ):
									shutil.rmtree(self.tmpdirname     )







					def 						__lowerCAmelCase		(							self  :      List[str]     ):
									UpperCAmelCase__			 =				self.get_tokenizer()
									UpperCAmelCase__			 =				self.get_feature_extractor()

									UpperCAmelCase__			 =				WavaVecaProcessor(tokenizer=lowerCamelCase__       ,feature_extractor=lowerCamelCase__     )

									processor.save_pretrained(self.tmpdirname     )
									UpperCAmelCase__			 =				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 						__lowerCAmelCase		(							self  :      Any     ):
									UpperCAmelCase__			 =				WavaVecaProcessor(tokenizer=self.get_tokenizer()       ,feature_extractor=self.get_feature_extractor()     )
									processor.save_pretrained(self.tmpdirname     )

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

									UpperCAmelCase__			 =				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 						__lowerCAmelCase		(							self  :      List[Any]     ):
									UpperCAmelCase__			 =				self.get_feature_extractor()
									UpperCAmelCase__			 =				self.get_tokenizer()

									UpperCAmelCase__			 =				WavaVecaProcessor(tokenizer=lowerCamelCase__       ,feature_extractor=lowerCamelCase__     )

									UpperCAmelCase__			 =				floats_list((3, 1_000)     )

									UpperCAmelCase__			 =				feature_extractor(lowerCamelCase__       ,return_tensors='np'     )
									UpperCAmelCase__			 =				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 						__lowerCAmelCase		(							self  :      int     ):
									UpperCAmelCase__			 =				self.get_feature_extractor()
									UpperCAmelCase__			 =				self.get_tokenizer()

									UpperCAmelCase__			 =				WavaVecaProcessor(tokenizer=lowerCamelCase__       ,feature_extractor=lowerCamelCase__     )

									UpperCAmelCase__			 =				'This is a test string'

									UpperCAmelCase__			 =				processor(text=lowerCamelCase__     )

									UpperCAmelCase__			 =				tokenizer(lowerCamelCase__     )

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







					def 						__lowerCAmelCase		(							self  :      str     ):
									UpperCAmelCase__			 =				self.get_feature_extractor()
									UpperCAmelCase__			 =				self.get_tokenizer()

									UpperCAmelCase__			 =				WavaVecaProcessor(tokenizer=lowerCamelCase__       ,feature_extractor=lowerCamelCase__     )

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

									UpperCAmelCase__			 =				processor.batch_decode(lowerCamelCase__     )
									UpperCAmelCase__			 =				tokenizer.batch_decode(lowerCamelCase__     )

									self.assertListEqual(lowerCamelCase__       ,lowerCamelCase__     )







					def 						__lowerCAmelCase		(							self  :      Union[str, Any]     ):
									UpperCAmelCase__			 =				self.get_feature_extractor()
									UpperCAmelCase__			 =				self.get_tokenizer()

									UpperCAmelCase__			 =				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"""






from sklearn.neural_network import MLPClassifier

lowerCAmelCase__    :    Any			=      [[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
lowerCAmelCase__    :    Optional[int]			=      [0, 1, 0, 0]


lowerCAmelCase__    :    Dict			=      MLPClassifier(
    solver='lbfgs', alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


lowerCAmelCase__    :    Optional[int]			=      [[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
lowerCAmelCase__    :    Union[str, Any]			=      clf.predict(test)



def     a_		(   lowerCamelCase			):
				return list(lowerCamelCase			)


if __name__ == "__main__":
						import doctest

						doctest.testmod()



