
"""simple docstring"""


import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
							from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


lowerCAmelCase			=		get_tests_dir("""fixtures/test_sentencepiece.model""")

@require_torch
@require_torchaudio
@require_sentencepiece
class 					A_			(  unittest.TestCase     ):



							"""simple docstring"""






							def    UpperCAmelCase__							(						self						:Optional[int] ):





										"""simple docstring"""

										lowerCamelCase__			:  str  =tempfile.mkdtemp()

										lowerCamelCase__			:  Any  =['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
										lowerCamelCase__			:  Dict  =dict(zip(lowerCamelCase_  ,							range(len(lowerCamelCase_ ) ) ) )
										lowerCamelCase__			:  Union[str, Any]  =Path(self.tmpdirname )
										save_json(lowerCamelCase_  ,							save_dir / VOCAB_FILES_NAMES['vocab_file'] )
										if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
													copyfile(lowerCamelCase_  ,							save_dir / VOCAB_FILES_NAMES['spm_file'] )

										lowerCamelCase__			:  int  =SpeechaTextTokenizer.from_pretrained(self.tmpdirname )
										tokenizer.save_pretrained(self.tmpdirname )

										lowerCamelCase__			:  List[str]  ={
										    'feature_size': 24,
										    'num_mel_bins': 24,
										    'padding_value': 0.0,
										    'sampling_rate': 16_000,
										    'return_attention_mask': False,
										    'do_normalize': True,
										}
										save_json(lowerCamelCase_  ,							save_dir / FEATURE_EXTRACTOR_NAME )





							def    UpperCAmelCase__							(						self						:Any  ,							**lowerCamelCase_						:List[Any] ):





										"""simple docstring"""

										return SpeechaTextTokenizer.from_pretrained(self.tmpdirname  ,							**lowerCamelCase_ )





							def    UpperCAmelCase__							(						self						:Optional[int]  ,							**lowerCamelCase_						:Optional[Any] ):





										"""simple docstring"""

										return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname  ,							**lowerCamelCase_ )





							def    UpperCAmelCase__							(						self						:Optional[int] ):





										"""simple docstring"""

										shutil.rmtree(self.tmpdirname )





							def    UpperCAmelCase__							(						self						:Any ):





										"""simple docstring"""

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

										lowerCamelCase__			:  Union[str, Any]  =SpeechaTextProcessor(tokenizer=lowerCamelCase_  ,							feature_extractor=lowerCamelCase_ )

										processor.save_pretrained(self.tmpdirname )
										lowerCamelCase__			:  Optional[int]  =SpeechaTextProcessor.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    UpperCAmelCase__							(						self						:Tuple ):





										"""simple docstring"""

										lowerCamelCase__			:  str  =SpeechaTextProcessor(
										    tokenizer=self.get_tokenizer()  ,							feature_extractor=self.get_feature_extractor() )
										processor.save_pretrained(self.tmpdirname )

										lowerCamelCase__			:  Optional[int]  =self.get_tokenizer(bos_token='(BOS)'  ,							eos_token='(EOS)' )
										lowerCamelCase__			:  Tuple  =self.get_feature_extractor(do_normalize=lowerCamelCase_  ,							padding_value=1.0 )

										lowerCamelCase__			:  Tuple  =SpeechaTextProcessor.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    UpperCAmelCase__							(						self						:str ):





										"""simple docstring"""

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

										lowerCamelCase__			:  Tuple  =SpeechaTextProcessor(tokenizer=lowerCamelCase_  ,							feature_extractor=lowerCamelCase_ )

										lowerCamelCase__			:  int  =floats_list((3, 1_000) )

										lowerCamelCase__			:  Union[str, Any]  =feature_extractor(lowerCamelCase_  ,							return_tensors='np' )
										lowerCamelCase__			:  str  =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    UpperCAmelCase__							(						self						:Dict ):





										"""simple docstring"""

										lowerCamelCase__			:  int  =self.get_feature_extractor()
										lowerCamelCase__			:  Tuple  =self.get_tokenizer()

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

										lowerCamelCase__			:  List[str]  ='This is a test string'

										lowerCamelCase__			:  Tuple  =processor(text=lowerCamelCase_ )

										lowerCamelCase__			:  Union[str, Any]  =tokenizer(lowerCamelCase_ )

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





							def    UpperCAmelCase__							(						self						:Tuple ):





										"""simple docstring"""

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

										lowerCamelCase__			:  List[Any]  =SpeechaTextProcessor(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__			:  str  =tokenizer.batch_decode(lowerCamelCase_ )

										self.assertListEqual(lowerCamelCase_  ,							lowerCamelCase_ )





							def    UpperCAmelCase__							(						self						:List[str] ):





										"""simple docstring"""

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

										lowerCamelCase__			:  Any  =SpeechaTextProcessor(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			=		[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
lowerCAmelCase			=		[0, 1, 0, 0]


lowerCAmelCase			=		MLPClassifier(
    solver="""lbfgs""", alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


lowerCAmelCase			=		[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
lowerCAmelCase			=		clf.predict(test)






def      lowerCAmelCase_     (	snake_case_	:    Tuple    )     ->List[Any]:
			return list(snake_case_    )


if __name__ == "__main__":
							import doctest

							doctest.testmod()