
"""simple docstring"""




import pickle

import numpy as np
from matplotlib import pyplot as plt

class 						a__		:
   def __init__(     self      ,       _a      ,       _a      ,       _a      ,       _a      ,       _a      ,       _a=0.2      ,       _a=0.2							):
          lowercase       :							int									=				bp_numa
          lowercase       :							Optional[int]									=				bp_numa
          lowercase       :							Optional[int]									=				bp_numa
          lowercase       :							Optional[int]									=				conva_get[:2]
          lowercase       :							Dict									=				conva_get[2]
          lowercase       :							List[str]									=				size_pa
          lowercase       :							int									=				rate_w
          lowercase       :							List[Any]									=				rate_t
          lowercase       :							List[Any]									=				[
              np.mat(-1 * np.random.rand(self.conva[0]      ,       self.conva[0]							) + 0.5							)
              for i in range(self.conva[1]							)
          ]
          lowercase       :							Dict									=				np.mat(-1 * np.random.rand(self.num_bpa      ,       self.num_bpa							) + 0.5							)
          lowercase       :							Optional[int]									=				np.mat(-1 * np.random.rand(self.num_bpa      ,       self.num_bpa							) + 0.5							)
          lowercase       :							Optional[int]									=				-2 * np.random.rand(self.conva[1]							) + 1
          lowercase       :							List[str]									=				-2 * np.random.rand(self.num_bpa							) + 1
          lowercase       :							str									=				-2 * np.random.rand(self.num_bpa							) + 1
   def      __magic_name__     (     self      ,       _a							):
          # save model dict with pickle
          lowercase       :							Tuple									=				{
              "num_bp1": self.num_bpa,
              "num_bp2": self.num_bpa,
              "num_bp3": self.num_bpa,
              "conv1": self.conva,
              "step_conv1": self.step_conva,
              "size_pooling1": self.size_poolinga,
              "rate_weight": self.rate_weight,
              "rate_thre": self.rate_thre,
              "w_conv1": self.w_conva,
              "wkj": self.wkj,
              "vji": self.vji,
              "thre_conv1": self.thre_conva,
              "thre_bp2": self.thre_bpa,
              "thre_bp3": self.thre_bpa,
          }
          with open(__a      ,       "wb"							) as f:
                 pickle.dump(__a      ,       __a							)

          print(f"""Model saved： {save_path}"""							)
   @classmethod
   def      __magic_name__     (     cls      ,       _a							):
          # read saved model
          with open(__a      ,       "rb"							) as f:
                 lowercase       :							List[Any]									=				pickle.load(__a							)  # noqa: S301

          lowercase       :							Optional[int]									=				model_dic.get("conv1"							)
          conv_get.append(model_dic.get("step_conv1"							)							)
          lowercase       :							Tuple									=				model_dic.get("size_pooling1"							)
          lowercase       :							str									=				model_dic.get("num_bp1"							)
          lowercase       :							str									=				model_dic.get("num_bp2"							)
          lowercase       :							Union[str, Any]									=				model_dic.get("num_bp3"							)
          lowercase       :							Dict									=				model_dic.get("rate_weight"							)
          lowercase       :							Union[str, Any]									=				model_dic.get("rate_thre"							)
          # create model instance
          lowercase       :							str									=				CNN(__a      ,       __a      ,       __a      ,       __a      ,       __a      ,       __a      ,       __a							)
          # modify model parameter
          lowercase       :							List[Any]									=				model_dic.get("w_conv1"							)
          lowercase       :							Union[str, Any]									=				model_dic.get("wkj"							)
          lowercase       :							Optional[Any]									=				model_dic.get("vji"							)
          lowercase       :							List[str]									=				model_dic.get("thre_conv1"							)
          lowercase       :							Tuple									=				model_dic.get("thre_bp2"							)
          lowercase       :							Dict									=				model_dic.get("thre_bp3"							)
          return conv_ins
   def      __magic_name__     (     self      ,       _a							):
          return 1 / (1 + np.exp(-1 * x							))
   def      __magic_name__     (     self      ,       _a							):
          return round(__a      ,       3							)
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a      ,       _a      ,       _a							):
          # convolution process
          lowercase       :							List[Any]									=				convs[0]
          lowercase       :							Optional[Any]									=				convs[1]
          lowercase       :							List[Any]									=				np.shape(__a							)[0]
          # get the data slice of original image data, data_focus
          lowercase       :							Dict									=				[]
          for i_focus in range(0      ,       size_data - size_conv + 1      ,       __a							):
                 for j_focus in range(0      ,       size_data - size_conv + 1      ,       __a							):
                        lowercase       :							List[str]									=				data[
                            i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
                        ]
                        data_focus.append(__a							)
        # calculate the feature map of every single kernel, and saved as list of matrix
          lowercase       :							Optional[Any]									=				[]
          lowercase       :							Tuple									=				int((size_data - size_conv) / conv_step + 1							)
          for i_map in range(__a							):
                 lowercase       :							int									=				[]
                 for i_focus in range(len(__a							)							):
                        lowercase       :							Optional[int]									=				(
                            np.sum(np.multiply(data_focus[i_focus]      ,       w_convs[i_map]							)							)
                            - thre_convs[i_map]
                        )
                        featuremap.append(self.sig(__a							)							)
                 lowercase       :							List[Any]									=				np.asmatrix(__a							).reshape(
                     __a      ,       __a							)
                 data_featuremap.append(__a							)

          # expanding the data slice to One dimenssion
          lowercase       :							Optional[Any]									=				[]
          for each_focus in data_focus:
                 focusa_list.extend(self.Expand_Mat(__a							)							)
          lowercase       :							List[str]									=				np.asarray(__a							)
          return focus_list, data_featuremap
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a="average_pool"							):
          # pooling process
          lowercase       :							Dict									=				len(featuremaps[0]							)
          lowercase       :							Optional[Any]									=				int(size_map / size_pooling							)
          lowercase       :							List[Any]									=				[]
          for i_map in range(len(__a							)							):
                 lowercase       :							List[str]									=				featuremaps[i_map]
                 lowercase       :							Union[str, Any]									=				[]
                 for i_focus in range(0      ,       __a      ,       __a							):
                        for j_focus in range(0      ,       __a      ,       __a							):
                               lowercase       :							Any									=				feature_map[
                                   i_focus : i_focus + size_pooling,
                                   j_focus : j_focus + size_pooling,
                               ]
                               if pooling_type == "average_pool":
                                      # average pooling
                                      map_pooled.append(np.average(__a							)							)
                               elif pooling_type == "max_pooling":
                                      # max pooling
                                      map_pooled.append(np.max(__a							)							)
                 lowercase       :							Union[str, Any]									=				np.asmatrix(__a							).reshape(__a      ,       __a							)
                 featuremap_pooled.append(__a							)
          return featuremap_pooled
   def      __magic_name__     (     self      ,       _a							):
          # expanding three dimension data to one dimension list
          lowercase       :							List[Any]									=				[]
          for i in range(len(__a							)							):
                 lowercase       :							Optional[int]									=				np.shape(data[i]							)
                 lowercase       :							Optional[int]									=				data[i].reshape(1      ,       shapes[0] * shapes[1]							)
                 lowercase       :							Any									=				data_listed.getA().tolist()[0]
                 data_expanded.extend(__a							)
          lowercase       :							Any									=				np.asarray(__a							)
          return data_expanded
   def      __magic_name__     (     self      ,       _a							):
          # expanding matrix to one dimension list
          lowercase       :							List[str]									=				np.asarray(__a							)
          lowercase       :							Dict									=				np.shape(__a							)
          lowercase       :							Any									=				data_mat.reshape(1      ,       shapes[0] * shapes[1]							)
          return data_expanded
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a      ,       _a      ,       _a							):
          lowercase       :							List[Any]									=				[]
          lowercase       :							Optional[Any]									=				0
          for i_map in range(__a							):
                 lowercase       :							List[Any]									=				np.ones((size_map, size_map)							)
                 for i in range(0      ,       __a      ,       __a							):
                        for j in range(0      ,       __a      ,       __a							):
                               lowercase       :							Any									=				pd_pool[
                                   i_pool
                               ]
                               lowercase       :							int									=				i_pool + 1
                 lowercase       :							List[Any]									=				np.multiply(
                     __a      ,       np.multiply(out_map[i_map]      ,       (1 - out_map[i_map])							)							)
                 pd_all.append(__a							)
          return pd_all
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a      ,       _a      ,       _a      ,       _a=bool							):
          # model traning
          print("----------------------Start Training-------------------------"							)
          print((" - - Shape: Train_Data  ", np.shape(__a							))							)
          print((" - - Shape: Teach_Data  ", np.shape(__a							))							)
          lowercase       :							Tuple									=				0
          lowercase       :							int									=				[]
          lowercase       :							Optional[int]									=				10_000
          while rp < n_repeat and mse >= error_accuracy:
                 lowercase       :							Tuple									=				0
                 print(f"""-------------Learning Time {rp}--------------"""							)
                 for p in range(len(__a							)							):
                        # print('------------Learning Image: %d--------------'%p)
                        lowercase       :							str									=				np.asmatrix(datas_train[p]							)
                        lowercase       :							Dict									=				np.asarray(datas_teach[p]							)
                        lowercase						,       lowercase       :							int									=				self.convolute(
                            __a      ,       self.conva      ,       self.w_conva      ,       self.thre_conva      ,       conv_step=self.step_conva      ,       )
                        lowercase       :							Tuple									=				self.pooling(__a      ,       self.size_poolinga							)
                        lowercase       :							int									=				np.shape(__a							)
                        lowercase       :							Dict									=				self._expand(__a							)
                        lowercase       :							Tuple									=				data_bp_input

                        lowercase       :							str									=				np.dot(__a      ,       self.vji.T							) - self.thre_bpa
                        lowercase       :							str									=				self.sig(__a							)
                        lowercase       :							List[Any]									=				np.dot(__a      ,       self.wkj.T							) - self.thre_bpa
                        lowercase       :							Any									=				self.sig(__a							)

                        # --------------Model Leaning ------------------------
                        # calculate error and gradient---------------
                        lowercase       :							Optional[Any]									=				np.multiply(
                            (data_teach - bp_outa)      ,       np.multiply(__a      ,       (1 - bp_outa)							)							)
                        lowercase       :							List[str]									=				np.multiply(
                            np.dot(__a      ,       self.wkj							)      ,       np.multiply(__a      ,       (1 - bp_outa)							)							)
                        lowercase       :							Tuple									=				np.dot(__a      ,       self.vji							)

                        lowercase       :							List[str]									=				pd_i_all / (self.size_poolinga * self.size_poolinga)
                        lowercase       :							Union[str, Any]									=				pd_conva_pooled.T.getA().tolist()
                        lowercase       :							str									=				self._calculate_gradient_from_pool(
                            __a      ,       __a      ,       shape_featuremapa[0]      ,       shape_featuremapa[1]      ,       self.size_poolinga      ,       )
                        # weight and threshold learning process---------
                        # convolution layer
                        for k_conv in range(self.conva[1]							):
                               lowercase       :							Optional[int]									=				self._expand_mat(pd_conva_all[k_conv]							)
                               lowercase       :							List[Any]									=				self.rate_weight * np.dot(__a      ,       __a							)

                               lowercase       :							List[str]									=				self.w_conva[k_conv] + delta_w.reshape(
                                   (self.conva[0], self.conva[0])							)

                               lowercase       :							Optional[int]									=				(
                                   self.thre_conva[k_conv]
                                   - np.sum(pd_conva_all[k_conv]							) * self.rate_thre
                               )
                        # all connected layer
                        lowercase       :							Dict									=				self.wkj + pd_k_all.T * bp_outa * self.rate_weight
                        lowercase       :							int									=				self.vji + pd_j_all.T * bp_outa * self.rate_weight
                        lowercase       :							str									=				self.thre_bpa - pd_k_all * self.rate_thre
                        lowercase       :							List[str]									=				self.thre_bpa - pd_j_all * self.rate_thre
                        # calculate the sum error of all single image
                        lowercase       :							List[str]									=				np.sum(abs(data_teach - bp_outa							)							)
                        error_count += errors
                        # print('   ----Teach      ',data_teach)
                        # print('   ----BP_output  ',bp_out3)
                 lowercase       :							str									=				rp + 1
                 lowercase       :							Union[str, Any]									=				error_count / patterns
                 all_mse.append(__a							)

          def draw_error():
                 lowercase       :							int									=				[error_accuracy for i in range(int(n_repeat * 1.2							)							)]
                 plt.plot(__a      ,       "+-"							)
                 plt.plot(__a      ,       "r--"							)
                 plt.xlabel("Learning Times"							)
                 plt.ylabel("All_mse"							)
                 plt.grid(__a      ,       alpha=0.5							)
                 plt.show()

          print("------------------Training Complished---------------------"							)
          print((" - - Training epoch: ", rp, f"""     - - Mse: {mse:.6f}""")							)
          if draw_e:
                 draw_error()
          return mse
   def      __magic_name__     (     self      ,       _a							):
          # model predict
          lowercase       :							int									=				[]
          print("-------------------Start Testing-------------------------"							)
          print((" - - Shape: Test_Data  ", np.shape(__a							))							)
          for p in range(len(__a							)							):
                 lowercase       :							Dict									=				np.asmatrix(datas_test[p]							)
                 lowercase						,       lowercase       :							Tuple									=				self.convolute(
                     __a      ,       self.conva      ,       self.w_conva      ,       self.thre_conva      ,       conv_step=self.step_conva      ,       )
                 lowercase       :							Optional[int]									=				self.pooling(__a      ,       self.size_poolinga							)
                 lowercase       :							Dict									=				self._expand(__a							)

                 lowercase       :							Any									=				data_bp_input
                 lowercase       :							Any									=				bp_outa * self.vji.T - self.thre_bpa
                 lowercase       :							Union[str, Any]									=				self.sig(__a							)
                 lowercase       :							Dict									=				bp_outa * self.wkj.T - self.thre_bpa
                 lowercase       :							Tuple									=				self.sig(__a							)
                 produce_out.extend(bp_outa.getA().tolist()							)
          lowercase       :							Optional[Any]									=				[list(map(self.do_round      ,       __a							)							) for each in produce_out]
          return np.asarray(__a							)

   def      __magic_name__     (     self      ,       _a							):
          # return the data of image after convoluting process so we can check it out
          lowercase       :							Dict									=				np.asmatrix(__a							)
          lowercase						,       lowercase       :							int									=				self.convolute(
              __a      ,       self.conva      ,       self.w_conva      ,       self.thre_conva      ,       conv_step=self.step_conva      ,       )
          lowercase       :							Optional[Any]									=				self.pooling(__a      ,       self.size_poolinga							)

          return data_conveda, data_pooleda


if __name__ == "__main__":
   pass



'''simple docstring'''


from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


__lowerCAmelCase						:							List[Any]    							=						logging.get_logger(__name__)  # pylint: disable=invalid-name






class     A ( UpperCAmelCase ):





							def __init__(   self					:				Dict  ,		__a					:				List[Any]  ,		__a					:				Optional[Any]						)				->						Dict:
								super().__init__()
								self.register_modules(unet=__a  ,		scheduler=__a						)






							@torch.no_grad()
							def __call__(   self					:				Tuple  ,		__a					:				int = 1  ,		__a					:				int = 1_0_0  ,		__a					:				Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,		__a					:				Optional[float] = None  ,		__a					:				bool = True  ,		)				->						Union[AudioPipelineOutput, Tuple]:

								if audio_length_in_s is None:
									__UpperCAmelCase      					=      self.unet.config.sample_size / self.unet.config.sample_rate

								__UpperCAmelCase      					=      audio_length_in_s * self.unet.config.sample_rate

								__UpperCAmelCase      					=      2 ** len(self.unet.up_blocks						)
								if sample_size < 3 * down_scale_factor:
									raise ValueError(
									    f"""{audio_length_in_s} is too small. Make sure it's bigger or equal to"""
									    f""" {3 * down_scale_factor / self.unet.config.sample_rate}."""						)

								__UpperCAmelCase      					=      int(__a						)
								if sample_size % down_scale_factor != 0:
									__UpperCAmelCase      					=      (
									    (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
									) * down_scale_factor
									logger.info(
									    f"""{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"""
									    f""" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"""
									    ''' process.'''						)
								__UpperCAmelCase      					=      int(__a						)

								__UpperCAmelCase      					=      next(iter(self.unet.parameters()						)						).dtype
								__UpperCAmelCase      					=      (batch_size, self.unet.config.in_channels, sample_size)
								if isinstance(__a  ,		__a						) and len(__a						) != batch_size:
									raise ValueError(
									    f"""You have passed a list of generators of length {len(__a						)}, but requested an effective batch"""
									    f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""						)

								__UpperCAmelCase      					=      randn_tensor(__a  ,		generator=__a  ,		device=self.device  ,		dtype=__a						)

								# set step values
								self.scheduler.set_timesteps(__a  ,		device=audio.device						)
								__UpperCAmelCase      					=      self.scheduler.timesteps.to(__a						)

								for t in self.progress_bar(self.scheduler.timesteps						):
									# 1. predict noise model_output
									__UpperCAmelCase      					=      self.unet(__a  ,		__a						).sample

									# 2. compute previous image: x_t -> t_t-1
									__UpperCAmelCase      					=      self.scheduler.step(__a  ,		__a  ,		__a						).prev_sample

								__UpperCAmelCase      					=      audio.clamp(-1  ,		1						).float().cpu().numpy()

								__UpperCAmelCase      					=      audio[:, :, :original_sample_size]

								if not return_dict:
									return (audio,)

								return AudioPipelineOutput(audios=__a						)
