






'''simple docstring'''







import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list

@require_torchaudio
@require_sentencepiece
class _lowercase     (    unittest.TestCase		):




       def 		lowerCamelCase_    (							self:    Optional[int]					):
              lowerCamelCase__ :     Optional[Any]	  =	"""laion/clap-htsat-unfused"""
              lowerCamelCase__ :     Tuple	  =	tempfile.mkdtemp()




       def 		lowerCamelCase_    (							self:    Dict      ,							**UpperCamelCase__:    Any					):
              return RobertaTokenizer.from_pretrained(self.checkpoint      ,							**SCREAMING_SNAKE_CASE_					)




       def 		lowerCamelCase_    (							self:    Any      ,							**UpperCamelCase__:    Union[str, Any]					):
              return ClapFeatureExtractor.from_pretrained(self.checkpoint      ,							**SCREAMING_SNAKE_CASE_					)




       def 		lowerCamelCase_    (							self:    List[Any]					):
              shutil.rmtree(self.tmpdirname					)




       def 		lowerCamelCase_    (							self:    List[str]					):
              lowerCamelCase__ :     Any	  =	self.get_tokenizer()
              lowerCamelCase__ :     Any	  =	self.get_feature_extractor()

              lowerCamelCase__ :     List[Any]	  =	ClapProcessor(tokenizer=SCREAMING_SNAKE_CASE_      ,							feature_extractor=SCREAMING_SNAKE_CASE_					)

              processor.save_pretrained(self.tmpdirname					)
              lowerCamelCase__ :     Optional[Any]	  =	ClapProcessor.from_pretrained(self.tmpdirname					)

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

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




       def 		lowerCamelCase_    (							self:    Any					):
              lowerCamelCase__ :     Dict	  =	ClapProcessor(tokenizer=self.get_tokenizer()      ,							feature_extractor=self.get_feature_extractor()					)
              processor.save_pretrained(self.tmpdirname					)

              lowerCamelCase__ :     Union[str, Any]	  =	self.get_tokenizer(bos_token="""(BOS)"""      ,							eos_token="""(EOS)"""					)
              lowerCamelCase__ :     Any	  =	self.get_feature_extractor(do_normalize=SCREAMING_SNAKE_CASE_      ,							padding_value=1.0					)

              lowerCamelCase__ :     str	  =	ClapProcessor.from_pretrained(
                  self.tmpdirname      ,							bos_token="""(BOS)"""      ,							eos_token="""(EOS)"""      ,							do_normalize=SCREAMING_SNAKE_CASE_      ,							padding_value=1.0					)

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

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




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

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

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

              lowerCamelCase__ :     Optional[Any]	  =	feature_extractor(SCREAMING_SNAKE_CASE_      ,							return_tensors="""np"""					)
              lowerCamelCase__ :     Union[str, Any]	  =	processor(audios=SCREAMING_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 		lowerCamelCase_    (							self:    Union[str, Any]					):
              lowerCamelCase__ :     List[Any]	  =	self.get_feature_extractor()
              lowerCamelCase__ :     Optional[int]	  =	self.get_tokenizer()

              lowerCamelCase__ :     int	  =	ClapProcessor(tokenizer=SCREAMING_SNAKE_CASE_      ,							feature_extractor=SCREAMING_SNAKE_CASE_					)

              lowerCamelCase__ :     Union[str, Any]	  =	"""This is a test string"""

              lowerCamelCase__ :     Dict	  =	processor(text=SCREAMING_SNAKE_CASE_					)

              lowerCamelCase__ :     int	  =	tokenizer(SCREAMING_SNAKE_CASE_					)

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




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

              lowerCamelCase__ :     Any	  =	ClapProcessor(tokenizer=SCREAMING_SNAKE_CASE_      ,							feature_extractor=SCREAMING_SNAKE_CASE_					)

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

              lowerCamelCase__ :     List[str]	  =	processor.batch_decode(SCREAMING_SNAKE_CASE_					)
              lowerCamelCase__ :     Optional[int]	  =	tokenizer.batch_decode(SCREAMING_SNAKE_CASE_					)

              self.assertListEqual(SCREAMING_SNAKE_CASE_      ,							SCREAMING_SNAKE_CASE_					)




       def 		lowerCamelCase_    (							self:    List[str]					):
              lowerCamelCase__ :     Optional[int]	  =	self.get_feature_extractor()
              lowerCamelCase__ :     Dict	  =	self.get_tokenizer()

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

              self.assertListEqual(
                  processor.model_input_names[2:]      ,							feature_extractor.model_input_names      ,							msg="""`processor` and `feature_extractor` model input names do not match"""      ,							)









'''simple docstring'''







import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential

if __name__ == "__main__":
			_A     :      Optional[int]   					=pd.read_csv('''sample_data.csv''', header=None)
			_A     :      Any   					=df.shape[:1][0]
			# If you're using some other dataset input the target column
			_A     :      List[str]   					=df.iloc[:, 1:2]
			_A     :      int   					=actual_data.values.reshape(len_data, 1)
			_A     :      Union[str, Any]   					=MinMaxScaler().fit_transform(actual_data)
			_A     :      Optional[int]   					=10
			_A     :      Union[str, Any]   					=5
			_A     :      Union[str, Any]   					=20
			_A     :      str   					=len_data - periods * look_back
			_A     :      List[Any]   					=actual_data[:division]
			_A     :      Optional[Any]   					=actual_data[division - look_back :]
			_A					,				_A     :      Tuple   					=[], []
			_A					,				_A     :      List[str]   					=[], []

			for i in range(0, len(train_data) - forward_days - look_back + 1):
						train_x.append(train_data[i : i + look_back])
						train_y.append(train_data[i + look_back : i + look_back + forward_days])
			for i in range(0, len(test_data) - forward_days - look_back + 1):
						test_x.append(test_data[i : i + look_back])
						test_y.append(test_data[i + look_back : i + look_back + forward_days])
			_A     :      List[Any]   					=np.array(train_x)
			_A     :      str   					=np.array(test_x)
			_A     :      List[Any]   					=np.array([list(i.ravel()) for i in train_y])
			_A     :      Any   					=np.array([list(i.ravel()) for i in test_y])

			_A     :      Optional[Any]   					=Sequential()
			model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
			model.add(LSTM(64, input_shape=(128, 1)))
			model.add(Dense(forward_days))
			model.compile(loss='''mean_squared_error''', optimizer='''adam''')
			_A     :      Dict   					=model.fit(
			    x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
			)
			_A     :      List[str]   					=model.predict(x_test)

