


import random


class 	a__						:

       def __init__( self   :  Tuple   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  List[Any]   ,				lowerCamelCase_   :  Union[str, Any] = 0.0_1   ,				lowerCamelCase_   :  List[Any] = 1_0_0_0   ,				lowerCamelCase_   :  Dict = -1   ,				):
            a_     :  Optional[Any]				      =     sample
            if len(self.sample     ) == 0:
                 raise ValueError("""Sample data can not be empty"""     )
            a_     :  List[Any]				      =     target
            if len(self.target     ) == 0:
                 raise ValueError("""Target data can not be empty"""     )
            if len(self.sample     ) != len(self.target     ):
                 raise ValueError("""Sample data and Target data do not have matching lengths"""     )
            a_     :  int				      =     learning_rate
            a_     :  int				      =     epoch_number
            a_     :  Dict				      =     bias
            a_     :  str				      =     len(__SCREAMING_SNAKE_CASE     )
            a_     :  List[Any]				      =     len(sample[0]     )  # number of columns in dataset
            a_     :  list				      =     []

       def     UpperCAmelCase( self   :  Tuple     ):




            for sample in self.sample:
                 sample.insert(0   ,				self.bias     )

            for _ in range(self.col_sample     ):
                 self.weight.append(random.random()     )

            self.weight.insert(0   ,				self.bias     )

            a_     :  Optional[int]				      =     0

            while True:
                 a_     :  int				      =     False
                 for i in range(self.number_sample     ):
                      a_     :  Optional[Any]				      =     0
                      for j in range(self.col_sample + 1     ):
                           a_     :  Optional[int]				      =     u + self.weight[j] * self.sample[i][j]
                      a_     :  List[Any]				      =     self.sign(__SCREAMING_SNAKE_CASE     )
                      if y != self.target[i]:
                           for j in range(self.col_sample + 1     ):
                                a_     :  Optional[Any]				      =     (
                                    self.weight[j]
                                    + self.learning_rate
                                    * (self.target[i] - y)
                                    * self.sample[i][j]
                                )
                           a_     :  Optional[int]				      =     True
            # print('Epoch: \n',epoch_count)
                 a_     :  int				      =     epoch_count + 1
                 # if you want control the epoch or just by error
                 if not has_misclassified:
                      print(("""\nEpoch:\n""", epoch_count)     )
                      print("""------------------------\n"""     )
                      # if epoch_count > self.epoch_number or not error:
                      break

       def     UpperCAmelCase( self   :  str   ,				lowerCamelCase_   :  Optional[int]     ):




            if len(self.sample     ) == 0:
                 raise ValueError("""Sample data can not be empty"""     )
            sample.insert(0   ,				self.bias     )
            a_     :  Union[str, Any]				      =     0
            for i in range(self.col_sample + 1     ):
                 a_     :  Dict				      =     u + self.weight[i] * sample[i]

            a_     :  str				      =     self.sign(__SCREAMING_SNAKE_CASE     )

            if y == -1:
                 print(("""Sample: """, sample)     )
                 print("""classification: P1"""     )
            else:
                 print(("""Sample: """, sample)     )
                 print("""classification: P2"""     )

       def     UpperCAmelCase( self   :  Dict   ,				lowerCamelCase_   :  Optional[Any]     ):
            return 1 if u >= 0 else -1

__lowerCamelCase   	=  [
    [-0.6508, 0.1097, 4.0009],
    [-1.4492, 0.8896, 4.4005],
    [2.0850, 0.6876, 12.0710],
    [0.2626, 1.1476, 7.7985],
    [0.6418, 1.0234, 7.0427],
    [0.2569, 0.6730, 8.3265],
    [1.1155, 0.6043, 7.4446],
    [0.0914, 0.3399, 7.0677],
    [0.0121, 0.5256, 4.6316],
    [-0.0429, 0.4660, 5.4323],
    [0.4340, 0.6870, 8.2287],
    [0.2735, 1.0287, 7.1934],
    [0.4839, 0.4851, 7.4850],
    [0.4089, -0.1267, 5.5019],
    [1.4391, 0.1614, 8.5843],
    [-0.9115, -0.1973, 2.1962],
    [0.3654, 1.0475, 7.4858],
    [0.2144, 0.7515, 7.1699],
    [0.2013, 1.0014, 6.5489],
    [0.6483, 0.2183, 5.8991],
    [-0.1147, 0.2242, 7.2435],
    [-0.7970, 0.8795, 3.8762],
    [-1.0625, 0.6366, 2.4707],
    [0.5307, 0.1285, 5.6883],
    [-1.2200, 0.7777, 1.7252],
    [0.3957, 0.1076, 5.6623],
    [-0.1013, 0.5989, 7.1812],
    [2.4482, 0.9455, 11.2095],
    [2.0149, 0.6192, 10.9263],
    [0.2012, 0.2611, 5.4631],
]

__lowerCamelCase   	=  [
    -1,
    -1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    -1,
    -1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    -1,
    1,
    -1,
    1,
]


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       __lowerCamelCase   	=  Perceptron(
           sample=samples, target=target, learning_rate=0.01, epoch_number=1000, bias=-1
       )
       network.training()
       print('''Finished training perceptron''')
       print('''Enter values to predict or q to exit''')
       while True:
              __lowerCamelCase   	=  []
              for i in range(len(samples[0])):
                     __lowerCamelCase   	=  input('''value: ''').strip()
                     if user_input == "q":
                            break
                     __lowerCamelCase   	=  float(user_input)
                     sample.insert(i, observation)
              network.sort(sample)







import random
import shutil
import tempfile
import unittest

import numpy as np

from transformers import TaTokenizer, TaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torch
from transformers.utils.import_utils import is_speech_available, is_torch_available


if is_torch_available():
  pass

if is_speech_available():
  from transformers import EncodecFeatureExtractor, MusicgenProcessor


__UpperCAmelCase			:			Union[str, Any]			      =					random.Random()


def   a						(				SCREAMING_SNAKE_CASE_   :		Optional[Any]				,      SCREAMING_SNAKE_CASE_   :		Optional[Any]=1.0				,      SCREAMING_SNAKE_CASE_   :		Optional[Any]=None				,      SCREAMING_SNAKE_CASE_   :		Union[str, Any]=None							):






       """simple docstring"""





       if rng is None:
              UpperCamelCase     :						str 						=		global_rng

       UpperCamelCase     :						Union[str, Any] 						=		[]
       for batch_idx in range(shape[0]							):
              values.append([]							)
              for _ in range(shape[1]							):
                     values[-1].append(rng.random() * scale							)

       return values



@require_torch
@require_sentencepiece
class  UpperCAmelCase_						(				unittest.TestCase):
       '''simple docstring'''






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						int 						=		'''facebook/musicgen-small'''
              UpperCamelCase     :						str 						=		tempfile.mkdtemp()






       def 			_lowercase (  self			,       **__SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              return TaTokenizer.from_pretrained(self.checkpoint			,       **__SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self			,       **__SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              return EncodecFeatureExtractor.from_pretrained(self.checkpoint			,       **__SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              shutil.rmtree(self.tmpdirname    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Union[str, Any] 						=		self.get_tokenizer()
              UpperCamelCase     :						Any 						=		self.get_feature_extractor()

              UpperCamelCase     :						Optional[int] 						=		MusicgenProcessor(tokenizer=__SCREAMING_SNAKE_CASE			,       feature_extractor=__SCREAMING_SNAKE_CASE    )

              processor.save_pretrained(self.tmpdirname    )
              UpperCamelCase     :						int 						=		MusicgenProcessor.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 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						str 						=		MusicgenProcessor(tokenizer=self.get_tokenizer()			,       feature_extractor=self.get_feature_extractor()    )
              processor.save_pretrained(self.tmpdirname    )

              UpperCamelCase     :						Any 						=		self.get_tokenizer(bos_token='''(BOS)'''			,       eos_token='''(EOS)'''    )
              UpperCamelCase     :						Union[str, Any] 						=		self.get_feature_extractor(do_normalize=__SCREAMING_SNAKE_CASE			,       padding_value=1.0    )

              UpperCamelCase     :						Union[str, Any] 						=		MusicgenProcessor.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 			_lowercase (  self    ):




              """simple docstring"""




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

              UpperCamelCase     :						Any 						=		MusicgenProcessor(tokenizer=__SCREAMING_SNAKE_CASE			,       feature_extractor=__SCREAMING_SNAKE_CASE    )

              UpperCamelCase     :						Union[str, Any] 						=		floats_list((3, 1_000)    )

              UpperCamelCase     :						Tuple 						=		feature_extractor(__SCREAMING_SNAKE_CASE			,       return_tensors='''np'''    )
              UpperCamelCase     :						Optional[Any] 						=		processor(__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 			_lowercase (  self    ):




              """simple docstring"""




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

              UpperCamelCase     :						Optional[int] 						=		MusicgenProcessor(tokenizer=__SCREAMING_SNAKE_CASE			,       feature_extractor=__SCREAMING_SNAKE_CASE    )

              UpperCamelCase     :						Tuple 						=		'''This is a test string'''

              UpperCamelCase     :						Dict 						=		processor(text=__SCREAMING_SNAKE_CASE    )

              UpperCamelCase     :						Union[str, Any] 						=		tokenizer(__SCREAMING_SNAKE_CASE    )

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






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Any 						=		self.get_feature_extractor()
              UpperCamelCase     :						int 						=		self.get_tokenizer()

              UpperCamelCase     :						Optional[int] 						=		MusicgenProcessor(tokenizer=__SCREAMING_SNAKE_CASE			,       feature_extractor=__SCREAMING_SNAKE_CASE    )

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

              UpperCamelCase     :						int 						=		processor.batch_decode(sequences=__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Union[str, Any] 						=		tokenizer.batch_decode(__SCREAMING_SNAKE_CASE    )

              self.assertListEqual(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Dict 						=		self.get_feature_extractor()
              UpperCamelCase     :						Union[str, Any] 						=		self.get_tokenizer()

              UpperCamelCase     :						str 						=		MusicgenProcessor(tokenizer=__SCREAMING_SNAKE_CASE			,       feature_extractor=__SCREAMING_SNAKE_CASE    )

              self.assertListEqual(
                  processor.model_input_names			,       feature_extractor.model_input_names			,       msg='''`processor` and `feature_extractor` model input names do not match'''			,       )






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						str 						=		self.get_feature_extractor(padding_side='''left'''    )
              UpperCamelCase     :						Optional[Any] 						=		self.get_tokenizer()

              UpperCamelCase     :						List[Any] 						=		MusicgenProcessor(tokenizer=__SCREAMING_SNAKE_CASE			,       feature_extractor=__SCREAMING_SNAKE_CASE    )

              UpperCamelCase     :						List[str] 						=		[floats_list((1, x)    )[0] for x in range(5			,       20			,       5    )]
              UpperCamelCase     :						Tuple 						=		processor(__SCREAMING_SNAKE_CASE    ).padding_mask

              UpperCamelCase     :						List[str] 						=		np.asarray(floats_list((3, 20)    )    )[:, None, :]
              UpperCamelCase     :						Optional[int] 						=		processor.batch_decode(__SCREAMING_SNAKE_CASE			,       padding_mask=__SCREAMING_SNAKE_CASE    )

              self.assertIsInstance(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )

              for audio in decoded_audios:
                     self.assertIsInstance(__SCREAMING_SNAKE_CASE			,       np.ndarray    )

              self.assertTrue(decoded_audios[0].shape == (1, 10)    )
              self.assertTrue(decoded_audios[1].shape == (1, 15)    )
              self.assertTrue(decoded_audios[2].shape == (1, 20)    )

