



import numpy as np
from matplotlib import pyplot as plt





def 						__lowerCamelCase       (      __magic_name__	:  Union[str, Any]						):
							return 1 / (1 + np.exp(-1 * x						))


class 	lowerCamelCase__ :


						def __init__( self						:  Tuple      ,       _a						:  List[str]      ,       _a						:  Union[str, Any]=None      ,       _a						:  Union[str, Any]=None      ,       _a						:  Dict=False			):
													a__:       Optional[Any]											=units
													a__:       Tuple											=None
													a__:       Optional[Any]											=None
													a__:       Union[str, Any]											=activation
													if learning_rate is None:
																				a__:       Any											=0.3
													a__:       List[Any]											=learning_rate
													a__:       Dict											=is_input_layer


						def   _lowerCamelCase			( self						:  Optional[int]      ,       _a						:  str			):
													a__:       int											=np.asmatrix(np.random.normal(0      ,       0.5      ,       (self.units, back_units)			)			)
													a__:       int											=np.asmatrix(np.random.normal(0      ,       0.5      ,       self.units			)			).T
													if self.activation is None:
																				a__:       Any											=sigmoid


						def   _lowerCamelCase			( self						:  int			):
													# activation function may be sigmoid or linear
													if self.activation == sigmoid:
																				a__:       List[str]											=np.dot(self.output      ,       (1 - self.output).T			)
																				a__:       Tuple											=np.diag(np.diag(_a			)			)
													else:
																				a__:       Any											=1
													return gradient_activation


						def   _lowerCamelCase			( self						:  int      ,       _a						:  Tuple			):
													a__:       Union[str, Any]											=xdata
													if self.is_input_layer:
																				# input layer
																				a__:       Optional[Any]											=xdata
																				a__:       Any											=xdata
																				return xdata
													else:
																				a__:       str											=np.dot(self.weight      ,       self.xdata			) - self.bias
																				a__:       Any											=self.activation(self.wx_plus_b			)
																				return self.output







						def   _lowerCamelCase			( self						:  Tuple      ,       _a						:  List[str]			):
													a__:       str											=self.cal_gradient()  # i * i 维
													a__:       Optional[Any]											=np.asmatrix(np.dot(gradient.T      ,       _a			)			)

													a__:       List[str]											=np.asmatrix(self.xdata			)
													a__:       Optional[int]											=-1
													a__:       Union[str, Any]											=self.weight

													a__:       int											=np.dot(gradient.T      ,       self._gradient_weight.T			)
													a__:       Optional[int]											=gradient * self._gradient_bias
													a__:       List[str]											=np.dot(_a      ,       self._gradient_x			).T
													# upgrade: the Negative gradient direction
													a__:       Any											=self.weight - self.learn_rate * self.gradient_weight
													a__:       str											=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 	lowerCamelCase__ :


						def __init__( self						:  Any			):
													a__:       str											=[]
													a__:       Optional[Any]											=[]
													a__:       List[str]											=plt.figure()
													a__:       List[Any]											=self.fig_loss.add_subplot(1      ,       1      ,       1			)


						def   _lowerCamelCase			( self						:  Tuple      ,       _a						:  Dict			):
													self.layers.append(_a			)


						def   _lowerCamelCase			( self						:  Optional[Any]			):
													for i, layer in enumerate(self.layers[:]			):
																				if i < 1:
																											a__:       Dict											=True
																				else:
																											layer.initializer(self.layers[i - 1].units			)


						def   _lowerCamelCase			( self						:  Union[str, Any]			):
													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   _lowerCamelCase			( self						:  Dict      ,       _a						:  List[str]      ,       _a						:  List[Any]      ,       _a						:  List[Any]      ,       _a						:  str			):
													a__:       List[Any]											=train_round
													a__:       str											=accuracy

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

													a__:       Any											=np.shape(_a			)
													for _ in range(_a			):
																				a__:       Optional[Any]											=0
																				for row in range(x_shape[0]			):
																											a__:       Optional[Any]											=np.asmatrix(xdata[row, :]			).T
																											a__:       Optional[int]											=np.asmatrix(ydata[row, :]			).T

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

																											a__							,			a__:       List[Any]											=self.cal_loss(_ydata      ,       _xdata			)
																											a__:       List[str]											=all_loss + loss

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

																				a__:       Tuple											=all_loss / x_shape[0]
																				self.train_mse.append(_a			)

																				self.plot_loss()

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


						def   _lowerCamelCase			( self						:  List[str]      ,       _a						:  Union[str, Any]      ,       _a						:  int			):
													a__:       Dict											=np.sum(np.power((ydata - ydata_)      ,       2			)			)
													a__:       Union[str, Any]											=2 * (ydata_ - ydata)
													# vector (shape is the same as _ydata.shape)
													return self.loss, self.loss_gradient







						def   _lowerCamelCase			( self						:  Union[str, Any]			):
													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 						__lowerCamelCase       (      ):
							a__:       List[str]											=np.random.randn(10     , 10						)
							a__:       Optional[int]											=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],
							    ]						)
							a__:       Optional[Any]											=BPNN()
							for i in (10, 20, 30, 2):
														model.add_layer(DenseLayer(__magic_name__						)						)
							model.build()
							model.summary()
							model.train(xdata=__magic_name__     , ydata=__magic_name__     , train_round=100     , accuracy=0.01						)


if __name__ == "__main__":
		example()










import subprocess
from typing import Union

import numpy as np
import requests

from ..utils import add_end_docstrings, is_torch_available, is_torchaudio_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_torch_available():
		from ..models.auto.modeling_auto import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING

__UpperCAmelCase						  =     logging.get_logger(__name__)





def 						__lowerCamelCase       (      __magic_name__	:  bytes     , __magic_name__	:  int						):
							a__:       Optional[int]											=F"{sampling_rate}"
							a__:       Union[str, Any]											="1"
							a__:       List[str]											="f32le"
							a__:       Dict											=[
							    "ffmpeg",
							    "-i",
							    "pipe:0",
							    "-ac",
							    ac,
							    "-ar",
							    ar,
							    "-f",
							    format_for_conversion,
							    "-hide_banner",
							    "-loglevel",
							    "quiet",
							    "pipe:1",
							]

							try:
														a__:       List[Any]											=subprocess.Popen(__magic_name__     , stdin=subprocess.PIPE     , stdout=subprocess.PIPE						)
							except FileNotFoundError:
														raise ValueError("ffmpeg was not found but is required to load audio files from filename"						)
							a__:       int											=ffmpeg_process.communicate(__magic_name__						)
							a__:       List[str]											=output_stream[0]

							a__:       str											=np.frombuffer(__magic_name__     , np.floataa						)
							if audio.shape[0] == 0:
														raise ValueError("Malformed soundfile"						)
							return audio







@add_end_docstrings(_a   )
class 	lowerCamelCase__ (		_a   ):


						def __init__( self						:  Union[str, Any]      ,       *_a						:  Dict      ,       **_a						:  Tuple			):
													# Default, might be overriden by the model.config.
													a__:       List[Any]											=5
													super().__init__(*_a      ,       **_a			)

													if self.framework != "pt":
																				raise ValueError(F"The {self.__class__} is only available in PyTorch."			)

													self.check_model_type(_a			)


						def __call__( self						:  Tuple      ,       _a						:  Union[np.ndarray, bytes, str]      ,       **_a						:  Union[str, Any]      ,       ):
													return super().__call__(_a      ,       **_a			)


						def   _lowerCamelCase			( self						:  int      ,       _a						:  Tuple=None      ,       **_a						:  int			):
													# No parameters on this pipeline right now
													a__:       Optional[int]											={}
													if top_k is not None:
																				if top_k > self.model.config.num_labels:
																											a__:       Union[str, Any]											=self.model.config.num_labels
																				a__:       Union[str, Any]											=top_k
													return {}, {}, postprocess_params


						def   _lowerCamelCase			( self						:  Any      ,       _a						:  Union[str, Any]			):
													if isinstance(_a      ,       _a			):
																				if inputs.startswith("http://"			) or inputs.startswith("https://"			):
																											# We need to actually check for a real protocol, otherwise it's impossible to use a local file
																											# like http_huggingface_co.png
																											a__:       List[str]											=requests.get(_a			).content
																				else:
																											with open(_a      ,       "rb"			) as f:
																																		a__:       Dict											=f.read()

													if isinstance(_a      ,       _a			):
																				a__:       Tuple											=ffmpeg_read(_a      ,       self.feature_extractor.sampling_rate			)

													if isinstance(_a      ,       _a			):
																				# Accepting `"array"` which is the key defined in `datasets` for
																				# better integration
																				if not ("sampling_rate" in inputs and ("raw" in inputs or "array" in inputs)):
																											raise ValueError(
																											    "When passing a dictionary to AudioClassificationPipeline, the dict needs to contain a "
																											    "\"raw\" key containing the numpy array representing the audio and a \"sampling_rate\" key, "
																											    "containing the sampling_rate associated with that array"			)

																				a__:       Tuple											=inputs.pop("raw"      ,       _a			)
																				if _inputs is None:
																											# Remove path which will not be used from `datasets`.
																											inputs.pop("path"      ,       _a			)
																											a__:       int											=inputs.pop("array"      ,       _a			)
																				a__:       Optional[Any]											=inputs.pop("sampling_rate"			)
																				a__:       Optional[int]											=_inputs
																				if in_sampling_rate != self.feature_extractor.sampling_rate:
																											import torch

																											if is_torchaudio_available():
																																		from torchaudio import functional as F
																											else:
																																		raise ImportError(
																																		    "torchaudio is required to resample audio samples in AudioClassificationPipeline. "
																																		    "The torchaudio package can be installed through: `pip install torchaudio`."			)

																											a__:       Union[str, Any]											=F.resample(
																											    torch.from_numpy(_a			)      ,       _a      ,       self.feature_extractor.sampling_rate			).numpy()

													if not isinstance(_a      ,       np.ndarray			):
																				raise ValueError("We expect a numpy ndarray as input"			)
													if len(inputs.shape			) != 1:
																				raise ValueError("We expect a single channel audio input for AudioClassificationPipeline"			)

													a__:       Union[str, Any]											=self.feature_extractor(
													    _a      ,       sampling_rate=self.feature_extractor.sampling_rate      ,       return_tensors="pt"			)
													return processed


						def   _lowerCamelCase			( self						:  Dict      ,       _a						:  Tuple			):
													a__:       str											=self.model(**_a			)
													return model_outputs







						def   _lowerCamelCase			( self						:  Optional[Any]      ,       _a						:  Tuple      ,       _a						:  Tuple=5			):
													a__:       Tuple											=model_outputs.logits[0].softmax(-1			)
													a__							,			a__:       Any											=probs.topk(_a			)

													a__:       Union[str, Any]											=scores.tolist()
													a__:       Dict											=ids.tolist()

													a__:       Optional[int]											=[{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(_a      ,       _a			)]

													return labels





