



"""simple docstring"""






import numpy as np
from matplotlib import pyplot as plt


def 							A__ (     UpperCamelCase__    ):


  '''simple docstring'''


  return 1 / (1 + np.exp(-1 * x    ))




class    __snake_case:




    def __init__(   self    ,							A_    ,							A_=None    ,							A_=None    ,							A_=False			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			units
      _SCREAMING_SNAKE_CASE        =			None
      _SCREAMING_SNAKE_CASE        =			None
      _SCREAMING_SNAKE_CASE        =			activation
      if learning_rate is None:
        _SCREAMING_SNAKE_CASE        =			0.3
      _SCREAMING_SNAKE_CASE        =			learning_rate
      _SCREAMING_SNAKE_CASE        =			is_input_layer




    def   A   (   self    ,							A_			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			np.asmatrix(np.random.normal(0    ,							0.5    ,							(self.units, back_units)			)			)
      _SCREAMING_SNAKE_CASE        =			np.asmatrix(np.random.normal(0    ,							0.5    ,							self.units			)			).T
      if self.activation is None:
        _SCREAMING_SNAKE_CASE        =			sigmoid




    def   A   (   self			):





      '''simple docstring'''



      if self.activation == sigmoid:
        _SCREAMING_SNAKE_CASE        =			np.dot(self.output    ,							(1 - self.output).T			)
        _SCREAMING_SNAKE_CASE        =			np.diag(np.diag(lowercase_			)			)
      else:
        _SCREAMING_SNAKE_CASE        =			1
      return gradient_activation




    def   A   (   self    ,							A_			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			xdata
      if self.is_input_layer:
        # input layer
        _SCREAMING_SNAKE_CASE        =			xdata
        _SCREAMING_SNAKE_CASE        =			xdata
        return xdata
      else:
        _SCREAMING_SNAKE_CASE        =			np.dot(self.weight    ,							self.xdata			) - self.bias
        _SCREAMING_SNAKE_CASE        =			self.activation(self.wx_plus_b			)
        return self.output




    def   A   (   self    ,							A_			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			self.cal_gradient()  # i * i 维
      _SCREAMING_SNAKE_CASE        =			np.asmatrix(np.dot(gradient.T    ,							lowercase_			)			)

      _SCREAMING_SNAKE_CASE        =			np.asmatrix(self.xdata			)
      _SCREAMING_SNAKE_CASE        =			-1
      _SCREAMING_SNAKE_CASE        =			self.weight

      _SCREAMING_SNAKE_CASE        =			np.dot(gradient.T    ,							self._gradient_weight.T			)
      _SCREAMING_SNAKE_CASE        =			gradient * self._gradient_bias
      _SCREAMING_SNAKE_CASE        =			np.dot(lowercase_    ,							self._gradient_x			).T
      # upgrade: the Negative gradient direction
      _SCREAMING_SNAKE_CASE        =			self.weight - self.learn_rate * self.gradient_weight
      _SCREAMING_SNAKE_CASE        =			self.bias - self.learn_rate * self.gradient_bias.T
      # updates the weights and bias according to learning rate (0.3 if undefined)
      return self.gradient




class    __snake_case:




    def __init__(   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			[]
      _SCREAMING_SNAKE_CASE        =			[]
      _SCREAMING_SNAKE_CASE        =			plt.figure()
      _SCREAMING_SNAKE_CASE        =			self.fig_loss.add_subplot(1    ,							1    ,							1			)




    def   A   (   self    ,							A_			):





      '''simple docstring'''



      self.layers.append(lowercase_			)




    def   A   (   self			):





      '''simple docstring'''



      for i, layer in enumerate(self.layers[:]			):
        if i < 1:
          _SCREAMING_SNAKE_CASE        =			True
        else:
          layer.initializer(self.layers[i - 1].units			)




    def   A   (   self			):





      '''simple docstring'''



      for i, layer in enumerate(self.layers[:]			):
        print(F'''------- layer {i} -------'''			)
        print('''weight.shape '''    ,							np.shape(layer.weight			)			)
        print('''bias.shape '''    ,							np.shape(layer.bias			)			)




    def   A   (   self    ,							A_    ,							A_    ,							A_    ,							A_			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			train_round
      _SCREAMING_SNAKE_CASE        =			accuracy

      self.ax_loss.hlines(self.accuracy    ,							0    ,							self.train_round * 1.1			)

      _SCREAMING_SNAKE_CASE        =			np.shape(lowercase_			)
      for _ in range(lowercase_			):
        _SCREAMING_SNAKE_CASE        =			0
        for row in range(x_shape[0]			):
          _SCREAMING_SNAKE_CASE        =			np.asmatrix(xdata[row, :]			).T
          _SCREAMING_SNAKE_CASE        =			np.asmatrix(ydata[row, :]			).T

          # forward propagation
          for layer in self.layers:
            _SCREAMING_SNAKE_CASE        =			layer.forward_propagation(_xdata			)

          _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			self.cal_loss(_ydata    ,							_xdata			)
          _SCREAMING_SNAKE_CASE        =			all_loss + loss

          # back propagation: the input_layer does not upgrade
          for layer in self.layers[:0:-1]:
            _SCREAMING_SNAKE_CASE        =			layer.back_propagation(lowercase_			)

        _SCREAMING_SNAKE_CASE        =			all_loss / x_shape[0]
        self.train_mse.append(lowercase_			)

        self.plot_loss()

        if mse < self.accuracy:
          print('''----达到精度----'''			)
          return mse
      return None




    def   A   (   self    ,							A_    ,							A_			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			np.sum(np.power((ydata - ydata_)    ,							2			)			)
      _SCREAMING_SNAKE_CASE        =			2 * (ydata_ - ydata)
      # vector (shape is the same as _ydata.shape)
      return self.loss, self.loss_gradient




    def   A   (   self			):





      '''simple docstring'''



      if self.ax_loss.lines:
        self.ax_loss.lines.remove(self.ax_loss.lines[0]			)
      self.ax_loss.plot(self.train_mse    ,							'''r-'''			)
      plt.ion()
      plt.xlabel('''step'''			)
      plt.ylabel('''loss'''			)
      plt.show()
      plt.pause(0.1			)



def 							A__ (     ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			np.random.randn(10       ,							10    )
  _SCREAMING_SNAKE_CASE        =			np.asarray(
      [
          [0.8, 0.4],
          [0.4, 0.3],
          [0.34, 0.45],
          [0.67, 0.32],
          [0.88, 0.67],
          [0.78, 0.77],
          [0.55, 0.66],
          [0.55, 0.43],
          [0.54, 0.1],
          [0.1, 0.5],
      ]    )
  _SCREAMING_SNAKE_CASE        =			BPNN()
  for i in (10, 20, 30, 2):
    model.add_layer(DenseLayer(SCREAMING_SNAKE_CASE__    )    )
  model.build()
  model.summary()
  model.train(xdata=SCREAMING_SNAKE_CASE__       ,							ydata=SCREAMING_SNAKE_CASE__       ,							train_round=100       ,							accuracy=0.01    )


if __name__ == "__main__":
    example()



import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
     import torch


lowercase_									=    random.Random()
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      Optional[int]       ,     SCREAMING_SNAKE_CASE__     :      List[str]=1.0       ,     SCREAMING_SNAKE_CASE__     :      List[Any]=None       ,     SCREAMING_SNAKE_CASE__     :      List[Any]=None )				-> List[str]:





    '''simple docstring'''






    if rng is None:
        A__        =  global_rng

    A__        =  []
    for batch_idx in range(shape[0] ):
        values.append([] )
        for _ in range(shape[1] ):
            values[-1].append(rng.random() * scale )

    return values


@require_torch
class 					A		(				unittest.TestCase					):







    """simple docstring"""





    def __init__( self					:			Any,lowercase_					:			Any,lowercase_					:			Tuple=7,lowercase_					:			Union[str, Any]=4_0_0,lowercase_					:			Union[str, Any]=2_0_0_0,lowercase_					:			int=1,lowercase_					:			Optional[Any]=0.0,lowercase_					:			Optional[int]=2_4_0_0_0,lowercase_					:			List[str]=True,)->       Any:





        '''simple docstring'''







        A__        =  parent
        A__        =  batch_size
        A__        =  min_seq_length
        A__        =  max_seq_length
        A__        =  (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        A__        =  feature_size
        A__        =  padding_value
        A__        =  sampling_rate
        A__        =  return_attention_mask


    def 		snake_case__	( self					:			str						)->       List[str]:





        '''simple docstring'''







        return {
            "feature_size": self.feature_size,
            "padding_value": self.padding_value,
            "sampling_rate": self.sampling_rate,
            "return_attention_mask": self.return_attention_mask,
        }


    def 		snake_case__	( self					:			Optional[Any],lowercase_					:			str=False,lowercase_					:			Tuple=False						)->       List[str]:





        '''simple docstring'''







        def _flatten(lowercase_					:			Any						):
            return list(itertools.chain(*lowercase_						)						)

        if equal_length:
            A__        =  floats_list((self.batch_size, self.max_seq_length)						)
        else:
            # make sure that inputs increase in size
            A__        =  [
                _flatten(floats_list((x, self.feature_size)						)						)
                for x in range(self.min_seq_length,self.max_seq_length,self.seq_length_diff						)
            ]

        if numpify:
            A__        =  [np.asarray(lowercase_						) for x in audio_inputs]

        return audio_inputs




@require_torch
class 					A		(				_UpperCAmelCase       ,				unittest.TestCase					):







    """simple docstring"""





    lowerCamelCase						      =				EncodecFeatureExtractor


    def 		snake_case__	( self					:			List[str]						)->       List[Any]:





        '''simple docstring'''







        A__        =  EnCodecFeatureExtractionTester(self						)


    def 		snake_case__	( self					:			Optional[Any]						)->       List[str]:





        '''simple docstring'''







        A__        =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
        # create three inputs of length 800, 1000, and 1200
        A__        =  [floats_list((1, x)						)[0] for x in range(8_0_0,1_4_0_0,2_0_0						)]
        A__        =  [np.asarray(lowercase_						) for audio_input in audio_inputs]

        # Test not batched input
        A__        =  feat_extract(audio_inputs[0],return_tensors='np'						).input_values
        A__        =  feat_extract(np_audio_inputs[0],return_tensors='np'						).input_values
        self.assertTrue(np.allclose(lowercase_,lowercase_,atol=1E-3						)						)

        # Test batched
        A__        =  feat_extract(lowercase_,padding=lowercase_,return_tensors='np'						).input_values
        A__        =  feat_extract(lowercase_,padding=lowercase_,return_tensors='np'						).input_values
        for enc_seq_a, enc_seq_a in zip(lowercase_,lowercase_						):
            self.assertTrue(np.allclose(lowercase_,lowercase_,atol=1E-3						)						)


    def 		snake_case__	( self					:			Optional[Any]						)->       Optional[Any]:





        '''simple docstring'''







        A__        =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
        A__        =  np.random.rand(1_0_0						).astype(np.floataa						)
        A__        =  np_audio_inputs.tolist()

        for inputs in [py_audio_inputs, np_audio_inputs]:
            A__        =  feature_extractor.pad([{'input_values': inputs}],return_tensors='np'						)
            self.assertTrue(np_processed.input_values.dtype == np.floataa						)
            A__        =  feature_extractor.pad([{'input_values': inputs}],return_tensors='pt'						)
            self.assertTrue(pt_processed.input_values.dtype == torch.floataa						)


    def 		snake_case__	( self					:			int,lowercase_					:			Tuple						)->       Dict:





        '''simple docstring'''







        from datasets import load_dataset

        A__        =  load_dataset('hf-internal-testing/librispeech_asr_dummy','clean',split='validation'						)
        # automatic decoding with librispeech
        A__        =  ds.sort('id'						).select(range(lowercase_						)						)[:num_samples]['audio']

        return [x["array"] for x in audio_samples]


    def 		snake_case__	( self					:			int						)->       List[str]:





        '''simple docstring'''







        A__        =  torch.tensor(
            [2.3_804E-03, 2.0_752E-03, 1.9_836E-03, 2.1_057E-03, 1.6_174E-03,
             3.0_518E-04, 9.1_553E-05, 3.3_569E-04, 9.7_656E-04, 1.8_311E-03,
             2.0_142E-03, 2.1_057E-03, 1.7_395E-03, 4.5_776E-04, -3.9_673E-04,
             4.5_776E-04, 1.0_071E-03, 9.1_553E-05, 4.8_828E-04, 1.1_597E-03,
             7.3_242E-04, 9.4_604E-04, 1.8_005E-03, 1.8_311E-03, 8.8_501E-04,
             4.2_725E-04, 4.8_828E-04, 7.3_242E-04, 1.0_986E-03, 2.1_057E-03]						)
        # fmt: on
        A__        =  self._load_datasamples(1						)
        A__        =  EncodecFeatureExtractor()
        A__        =  feature_extractor(lowercase_,return_tensors='pt'						).input_values
        self.assertEquals(input_values.shape,(1, 1, 9_3_6_8_0)						)
        self.assertTrue(torch.allclose(input_values[0, 0, :3_0],lowercase_,atol=1E-6						)						)


    def 		snake_case__	( self					:			int						)->       Optional[Any]:





        '''simple docstring'''







        A__        =  torch.tensor(
            [2.3_804E-03, 2.0_752E-03, 1.9_836E-03, 2.1_057E-03, 1.6_174E-03,
             3.0_518E-04, 9.1_553E-05, 3.3_569E-04, 9.7_656E-04, 1.8_311E-03,
             2.0_142E-03, 2.1_057E-03, 1.7_395E-03, 4.5_776E-04, -3.9_673E-04,
             4.5_776E-04, 1.0_071E-03, 9.1_553E-05, 4.8_828E-04, 1.1_597E-03,
             7.3_242E-04, 9.4_604E-04, 1.8_005E-03, 1.8_311E-03, 8.8_501E-04,
             4.2_725E-04, 4.8_828E-04, 7.3_242E-04, 1.0_986E-03, 2.1_057E-03]						)
        # fmt: on
        A__        =  self._load_datasamples(1						)
        A__        =  [np.tile(input_audio[0][None],reps=(2, 1)						)]
        input_audio[0][1] *= 0.5
        A__        =  EncodecFeatureExtractor(feature_size=2						)
        A__        =  feature_extractor(lowercase_,return_tensors='pt'						).input_values
        self.assertEquals(input_values.shape,(1, 2, 9_3_6_8_0)						)
        self.assertTrue(torch.allclose(input_values[0, 0, :3_0],lowercase_,atol=1E-6						)						)
        self.assertTrue(torch.allclose(input_values[0, 1, :3_0],EXPECTED_INPUT_VALUES * 0.5,atol=1E-6						)						)


    def 		snake_case__	( self					:			Any						)->       int:





        '''simple docstring'''







        A__        =  self._load_datasamples(2						)
        # would be easier if the stride was like
        A__        =  EncodecFeatureExtractor(feature_size=1,chunk_length_s=1,overlap=0.01						)

        # pad and trunc raise an error ?
        with self.assertRaisesRegex(
            lowercase_,'^Both padding and truncation were set. Make sure you only set one.$',):
            A__        =  feature_extractor(
                lowercase_,padding='max_length',truncation=lowercase_,return_tensors='pt'						).input_values

        # truncate to chunk
        A__        =  feature_extractor(lowercase_,truncation=lowercase_,return_tensors='pt'						).input_values
        self.assertEquals(truncated_outputs.shape,(2, 1, 7_1_5_2_0)						)  # 2 chunks

        # force truncate to max_length
        A__        =  feature_extractor(
            lowercase_,truncation=lowercase_,max_length=4_8_0_0_0,return_tensors='pt'						).input_values
        self.assertEquals(truncated_outputs.shape,(2, 1, 4_8_0_0_0)						)

        # pad to chunk
        A__        =  feature_extractor(lowercase_,padding=lowercase_,return_tensors='pt'						).input_values
        self.assertEquals(padded_outputs.shape,(2, 1, 9_5_2_8_0)						)

        # pad to chunk
        A__        =  feature_extractor(lowercase_,return_tensors='pt'						).input_values
        self.assertEquals(truncated_outputs.shape,(2, 1, 9_5_2_8_0)						)

        # force pad to max length
        A__        =  feature_extractor(
            lowercase_,padding='max_length',max_length=1_0_0_0_0_0,return_tensors='pt'						).input_values
        self.assertEquals(truncated_outputs.shape,(2, 1, 1_0_0_0_0_0)						)

        # force no pad
        with self.assertRaisesRegex(
            lowercase_,'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$',):
            A__        =  feature_extractor(lowercase_,padding=lowercase_,return_tensors='pt'						).input_values

        A__        =  feature_extractor(input_audio[0],padding=lowercase_,return_tensors='pt'						).input_values
        self.assertEquals(truncated_outputs.shape,(1, 1, 9_3_6_8_0)						)

        # no pad if no chunk_length_s
        A__        =  None
        with self.assertRaisesRegex(
            lowercase_,'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$',):
            A__        =  feature_extractor(lowercase_,padding=lowercase_,return_tensors='pt'						).input_values

        A__        =  feature_extractor(input_audio[0],padding=lowercase_,return_tensors='pt'						).input_values
        self.assertEquals(truncated_outputs.shape,(1, 1, 9_3_6_8_0)						)

        # no pad if no overlap
        A__        =  2
        A__        =  None
        with self.assertRaisesRegex(
            lowercase_,'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$',):
            A__        =  feature_extractor(lowercase_,padding=lowercase_,return_tensors='pt'						).input_values

        A__        =  feature_extractor(input_audio[0],padding=lowercase_,return_tensors='pt'						).input_values
        self.assertEquals(truncated_outputs.shape,(1, 1, 9_3_6_8_0)						)

