





'''simple docstring'''


import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple

import torch
from audiocraft.models import MusicGen

from transformers import (
    AutoFeatureExtractor,
    AutoTokenizer,
    EncodecModel,
    MusicgenDecoderConfig,
    MusicgenForConditionalGeneration,
    MusicgenProcessor,
    TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging


logging.set_verbosity_info()
a__			:					Optional[int]       						=						logging.get_logger(__name__)


a__			:					int       						=						['model.decoder.embed_positions.weights']
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			List[Any] )							->     Tuple:

       """simple docstring"""


       if "emb" in name:
              UpperCAmelCase  =						name.replace('''emb'''			,	'''model.decoder.embed_tokens''' )
       if "transformer" in name:
              UpperCAmelCase  =						name.replace('''transformer'''			,	'''model.decoder''' )
       if "cross_attention" in name:
              UpperCAmelCase  =						name.replace('''cross_attention'''			,	'''encoder_attn''' )
       if "linear1" in name:
              UpperCAmelCase  =						name.replace('''linear1'''			,	'''fc1''' )
       if "linear2" in name:
              UpperCAmelCase  =						name.replace('''linear2'''			,	'''fc2''' )
       if "norm1" in name:
              UpperCAmelCase  =						name.replace('''norm1'''			,	'''self_attn_layer_norm''' )
       if "norm_cross" in name:
              UpperCAmelCase  =						name.replace('''norm_cross'''			,	'''encoder_attn_layer_norm''' )
       if "norm2" in name:
              UpperCAmelCase  =						name.replace('''norm2'''			,	'''final_layer_norm''' )
       if "out_norm" in name:
              UpperCAmelCase  =						name.replace('''out_norm'''			,	'''model.decoder.layer_norm''' )
       if "linears" in name:
              UpperCAmelCase  =						name.replace('''linears'''			,	'''lm_heads''' )
       if "condition_provider.conditioners.description.output_proj" in name:
              UpperCAmelCase  =						name.replace('''condition_provider.conditioners.description.output_proj'''			,	'''enc_to_dec_proj''' )
       return name
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			OrderedDict			,	SCREAMING_SNAKE_CASE_ :			int )							->     Tuple[Dict, Dict]:

       """simple docstring"""


       UpperCAmelCase  =						list(state_dict.keys() )
       UpperCAmelCase  =						{}
       for key in keys:
              UpperCAmelCase  =						state_dict.pop(SCREAMING_SNAKE_CASE_ )
              UpperCAmelCase  =						rename_keys(SCREAMING_SNAKE_CASE_ )
              if "in_proj_weight" in key:
                     # split fused qkv proj
                     UpperCAmelCase  =						val[:hidden_size, :]
                     UpperCAmelCase  =						val[hidden_size : 2 * hidden_size, :]
                     UpperCAmelCase  =						val[-hidden_size:, :]
              elif "enc_to_dec_proj" in key:
                     UpperCAmelCase  =						val
              else:
                     UpperCAmelCase  =						val
       return state_dict, enc_dec_proj_state_dict
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			str )							->     MusicgenDecoderConfig:

       """simple docstring"""


       if checkpoint == "small":
              # default config values
              UpperCAmelCase  =						1_024
              UpperCAmelCase  =						24
              UpperCAmelCase  =						16
       elif checkpoint == "medium":
              UpperCAmelCase  =						1_536
              UpperCAmelCase  =						48
              UpperCAmelCase  =						24
       elif checkpoint == "large":
              UpperCAmelCase  =						2_048
              UpperCAmelCase  =						48
              UpperCAmelCase  =						32
       else:
              raise ValueError(f"Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}." )
       UpperCAmelCase  =						MusicgenDecoderConfig(
           hidden_size=SCREAMING_SNAKE_CASE_			,	ffn_dim=hidden_size * 4			,	num_hidden_layers=SCREAMING_SNAKE_CASE_			,	num_attention_heads=SCREAMING_SNAKE_CASE_			,	)
       return config
@torch.no_grad()
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			str			,	SCREAMING_SNAKE_CASE_ :			int=None			,	SCREAMING_SNAKE_CASE_ :			List[str]=None			,	SCREAMING_SNAKE_CASE_ :			Union[str, Any]="cpu" )							->     List[str]:

       """simple docstring"""


       UpperCAmelCase  =						MusicGen.get_pretrained(SCREAMING_SNAKE_CASE_			,	device=SCREAMING_SNAKE_CASE_ )
       UpperCAmelCase  =						decoder_config_from_checkpoint(SCREAMING_SNAKE_CASE_ )

       UpperCAmelCase  =						fairseq_model.lm.state_dict()
       UpperCAmelCase,					UpperCAmelCase  =						rename_state_dict(
           SCREAMING_SNAKE_CASE_			,	hidden_size=decoder_config.hidden_size )

       UpperCAmelCase  =						TaEncoderModel.from_pretrained('''t5-base''' )
       UpperCAmelCase  =						EncodecModel.from_pretrained('''facebook/encodec_32khz''' )
       UpperCAmelCase  =						MusicgenForCausalLM(SCREAMING_SNAKE_CASE_ ).eval()

       # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
       UpperCAmelCase,					UpperCAmelCase  =						decoder.load_state_dict(SCREAMING_SNAKE_CASE_			,	strict=SCREAMING_SNAKE_CASE_ )

       for key in missing_keys.copy():
              if key.startswith(('''text_encoder''', '''audio_encoder''') ) or key in EXPECTED_MISSING_KEYS:
                     missing_keys.remove(SCREAMING_SNAKE_CASE_ )

       if len(SCREAMING_SNAKE_CASE_ ) > 0:
              raise ValueError(f"Missing key(s) in state_dict: {missing_keys}" )

       if len(SCREAMING_SNAKE_CASE_ ) > 0:
              raise ValueError(f"Unexpected key(s) in state_dict: {unexpected_keys}" )

       # init the composite model
       UpperCAmelCase  =						MusicgenForConditionalGeneration(text_encoder=SCREAMING_SNAKE_CASE_			,	audio_encoder=SCREAMING_SNAKE_CASE_			,	decoder=SCREAMING_SNAKE_CASE_ )

       # load the pre-trained enc-dec projection (from the decoder state dict)
       model.enc_to_dec_proj.load_state_dict(SCREAMING_SNAKE_CASE_ )

       # check we can do a forward pass
       UpperCAmelCase  =						torch.arange(0			,	8			,	dtype=torch.long ).reshape(2			,	-1 )
       UpperCAmelCase  =						input_ids.reshape(2 * 4			,	-1 )

       with torch.no_grad():
              UpperCAmelCase  =						model(input_ids=SCREAMING_SNAKE_CASE_			,	decoder_input_ids=SCREAMING_SNAKE_CASE_ ).logits

       if logits.shape != (8, 1, 2_048):
              raise ValueError('''Incorrect shape for logits''' )

       # now construct the processor
       UpperCAmelCase  =						AutoTokenizer.from_pretrained('''t5-base''' )
       UpperCAmelCase  =						AutoFeatureExtractor.from_pretrained('''facebook/encodec_32khz'''			,	padding_side='''left''' )

       UpperCAmelCase  =						MusicgenProcessor(feature_extractor=SCREAMING_SNAKE_CASE_			,	tokenizer=SCREAMING_SNAKE_CASE_ )

       # set the appropriate bos/pad token ids
       UpperCAmelCase  =						2_048
       UpperCAmelCase  =						2_048

       # set other default generation config params
       UpperCAmelCase  =						int(30 * audio_encoder.config.frame_rate )
       UpperCAmelCase  =						True
       UpperCAmelCase  =						3.0

       if pytorch_dump_folder is not None:
              Path(SCREAMING_SNAKE_CASE_ ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_ )
              logger.info(f"Saving model {checkpoint} to {pytorch_dump_folder}" )
              model.save_pretrained(SCREAMING_SNAKE_CASE_ )
              processor.save_pretrained(SCREAMING_SNAKE_CASE_ )

       if repo_id:
              logger.info(f"Pushing model {checkpoint} to {repo_id}" )
              model.push_to_hub(SCREAMING_SNAKE_CASE_ )
              processor.push_to_hub(SCREAMING_SNAKE_CASE_ )


if __name__ == "__main__":
    a__			:					Tuple       						=						argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--checkpoint',
        default='small',
        type=str,
        help='Checkpoint size of the MusicGen model you\'d like to convert. Can be one of: `[\'small\', \'medium\', \'large\']`.',
    )
    parser.add_argument(
        '--pytorch_dump_folder',
        required=True,
        default=None,
        type=str,
        help='Path to the output PyTorch model directory.',
    )
    parser.add_argument(
        '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
    )
    parser.add_argument(
        '--device', default='cpu', type=str, help='Torch device to run the conversion, either cpu or cuda.'
    )

    a__			:					List[str]       						=						parser.parse_args()
    convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)







'''simple docstring'''





import pickle

import numpy as np
from matplotlib import pyplot as plt



class  snake_case__						:
 def __init__(   self						:							List[Any]      ,     __a						:							str      ,     __a						:							Dict      ,     __a						:							List[Any]      ,     __a						:							str      ,     __a						:							str      ,     __a						:							List[str]=0.2      ,     __a						:							Any=0.2  )      ->   Any:







       '''simple docstring'''
       __snake_case :   Any													=    bp_numa
       __snake_case :   str													=    bp_numa
       __snake_case :   Optional[Any]													=    bp_numa
       __snake_case :   Any													=    conva_get[:2]
       __snake_case :   Dict													=    conva_get[2]
       __snake_case :   Optional[int]													=    size_pa
       __snake_case :   str													=    rate_w
       __snake_case :   Optional[Any]													=    rate_t
       __snake_case :   Optional[Any]													=    [
           np.mat(-1 * np.random.rand(self.conva[0]      ,     self.conva[0]  ) + 0.5  )
           for i in range(self.conva[1]  )
       ]
       __snake_case :   List[Any]													=    np.mat(-1 * np.random.rand(self.num_bpa      ,     self.num_bpa  ) + 0.5  )
       __snake_case :   Any													=    np.mat(-1 * np.random.rand(self.num_bpa      ,     self.num_bpa  ) + 0.5  )
       __snake_case :   Optional[int]													=    -2 * np.random.rand(self.conva[1]  ) + 1
       __snake_case :   Optional[Any]													=    -2 * np.random.rand(self.num_bpa  ) + 1
       __snake_case :   str													=    -2 * np.random.rand(self.num_bpa  ) + 1
 def A_   (   self						:							int      ,     __a						:							Dict  )      ->   Optional[Any]:







       '''simple docstring'''
       # save model dict with pickle
       __snake_case :   int													=    {
           '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 A_   (   cls						:							List[Any]      ,     __a						:							Union[str, Any]  )      ->   int:







       '''simple docstring'''
       # read saved model
       with open(__a      ,     'rb'  ) as f:
             __snake_case :   Dict													=    pickle.load(__a  )  # noqa: S301

       __snake_case :   Tuple													=    model_dic.get('conv1'  )
       conv_get.append(model_dic.get('step_conv1'  )  )
       __snake_case :   Optional[Any]													=    model_dic.get('size_pooling1'  )
       __snake_case :   int													=    model_dic.get('num_bp1'  )
       __snake_case :   Optional[Any]													=    model_dic.get('num_bp2'  )
       __snake_case :   Optional[Any]													=    model_dic.get('num_bp3'  )
       __snake_case :   Any													=    model_dic.get('rate_weight'  )
       __snake_case :   Optional[int]													=    model_dic.get('rate_thre'  )
       # create model instance
       __snake_case :   Any													=    CNN(__a      ,     __a      ,     __a      ,     __a      ,     __a      ,     __a      ,     __a  )
       # modify model parameter
       __snake_case :   Dict													=    model_dic.get('w_conv1'  )
       __snake_case :   Any													=    model_dic.get('wkj'  )
       __snake_case :   List[str]													=    model_dic.get('vji'  )
       __snake_case :   int													=    model_dic.get('thre_conv1'  )
       __snake_case :   Optional[int]													=    model_dic.get('thre_bp2'  )
       __snake_case :   List[Any]													=    model_dic.get('thre_bp3'  )
       return conv_ins
 def A_   (   self						:							List[Any]      ,     __a						:							Tuple  )      ->   Optional[int]:







       '''simple docstring'''
       return 1 / (1 + np.exp(-1 * x  ))
 def A_   (   self						:							Any      ,     __a						:							Union[str, Any]  )      ->   List[Any]:







       '''simple docstring'''
       return round(__a      ,     3  )
 def A_   (   self						:							Optional[Any]      ,     __a						:							Tuple      ,     __a						:							List[str]      ,     __a						:							Dict      ,     __a						:							Optional[int]      ,     __a						:							List[str]  )      ->   str:







       '''simple docstring'''
       # convolution process
       __snake_case :   int													=    convs[0]
       __snake_case :   List[str]													=    convs[1]
       __snake_case :   Optional[Any]													=    np.shape(__a  )[0]
       # get the data slice of original image data, data_focus
       __snake_case :   str													=    []
       for i_focus in range(0      ,     size_data - size_conv + 1      ,     __a  ):
             for j_focus in range(0      ,     size_data - size_conv + 1      ,     __a  ):
                   __snake_case :   Any													=    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
       __snake_case :   Optional[int]													=    []
       __snake_case :   Dict													=    int((size_data - size_conv) / conv_step + 1  )
       for i_map in range(__a  ):
             __snake_case :   Optional[int]													=    []
             for i_focus in range(len(__a  )  ):
                   __snake_case :   List[Any]													=    (
                       np.sum(np.multiply(data_focus[i_focus]      ,     w_convs[i_map]  )  )
                       - thre_convs[i_map]
                   )
                   featuremap.append(self.sig(__a  )  )
             __snake_case :   List[Any]													=    np.asmatrix(__a  ).reshape(
                 __a      ,     __a  )
             data_featuremap.append(__a  )

       # expanding the data slice to One dimenssion
       __snake_case :   Union[str, Any]													=    []
       for each_focus in data_focus:
             focusa_list.extend(self.Expand_Mat(__a  )  )
       __snake_case :   List[Any]													=    np.asarray(__a  )
       return focus_list, data_featuremap
 def A_   (   self						:							Any      ,     __a						:							int      ,     __a						:							Tuple      ,     __a						:							List[Any]="average_pool"  )      ->   Dict:







       '''simple docstring'''
       # pooling process
       __snake_case :   List[str]													=    len(featuremaps[0]  )
       __snake_case :   Tuple													=    int(size_map / size_pooling  )
       __snake_case :   int													=    []
       for i_map in range(len(__a  )  ):
             __snake_case :   str													=    featuremaps[i_map]
             __snake_case :   Optional[Any]													=    []
             for i_focus in range(0      ,     __a      ,     __a  ):
                   for j_focus in range(0      ,     __a      ,     __a  ):
                         __snake_case :   Dict													=    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  )  )
             __snake_case :   List[str]													=    np.asmatrix(__a  ).reshape(__a      ,     __a  )
             featuremap_pooled.append(__a  )
       return featuremap_pooled
 def A_   (   self						:							List[str]      ,     __a						:							Union[str, Any]  )      ->   int:







       '''simple docstring'''
       # expanding three dimension data to one dimension list
       __snake_case :   Tuple													=    []
       for i in range(len(__a  )  ):
             __snake_case :   Optional[int]													=    np.shape(data[i]  )
             __snake_case :   List[str]													=    data[i].reshape(1      ,     shapes[0] * shapes[1]  )
             __snake_case :   List[Any]													=    data_listed.getA().tolist()[0]
             data_expanded.extend(__a  )
       __snake_case :   Optional[int]													=    np.asarray(__a  )
       return data_expanded
 def A_   (   self						:							Union[str, Any]      ,     __a						:							int  )      ->   Any:







       '''simple docstring'''
       # expanding matrix to one dimension list
       __snake_case :   int													=    np.asarray(__a  )
       __snake_case :   str													=    np.shape(__a  )
       __snake_case :   Any													=    data_mat.reshape(1      ,     shapes[0] * shapes[1]  )
       return data_expanded
 def A_   (   self						:							List[Any]      ,     __a						:							str      ,     __a						:							Optional[int]      ,     __a						:							List[str]      ,     __a						:							int      ,     __a						:							List[str]  )      ->   Dict:







       '''simple docstring'''
       __snake_case :   Union[str, Any]													=    []
       __snake_case :   Tuple													=    0
       for i_map in range(__a  ):
             __snake_case :   Union[str, Any]													=    np.ones((size_map, size_map)  )
             for i in range(0      ,     __a      ,     __a  ):
                   for j in range(0      ,     __a      ,     __a  ):
                         __snake_case :   Any													=    pd_pool[
                             i_pool
                         ]
                         __snake_case :   List[Any]													=    i_pool + 1
             __snake_case :   List[Any]													=    np.multiply(
                 __a      ,     np.multiply(out_map[i_map]      ,     (1 - out_map[i_map])  )  )
             pd_all.append(__a  )
       return pd_all
 def A_   (   self						:							Tuple      ,     __a						:							List[str]      ,     __a						:							Optional[int]      ,     __a						:							Union[str, Any]      ,     __a						:							Dict      ,     __a						:							Tuple      ,     __a						:							Optional[int]=bool  )      ->   List[Any]:







       '''simple docstring'''
       # model traning
       print('----------------------Start Training-------------------------'  )
       print((' - - Shape: Train_Data  ', np.shape(__a  ))  )
       print((' - - Shape: Teach_Data  ', np.shape(__a  ))  )
       __snake_case :   str													=    0
       __snake_case :   List[str]													=    []
       __snake_case :   List[Any]													=    10000
       while rp < n_repeat and mse >= error_accuracy:
             __snake_case :   int													=    0
             print(f'''-------------Learning Time {rp}--------------'''  )
             for p in range(len(__a  )  ):
                   # print('------------Learning Image: %d--------------'%p)
                   __snake_case :   List[Any]													=    np.asmatrix(datas_train[p]  )
                   __snake_case :   Optional[Any]													=    np.asarray(datas_teach[p]  )
                   __snake_case							,     __snake_case :   List[Any]													=    self.convolute(
                       __a      ,     self.conva      ,     self.w_conva      ,     self.thre_conva      ,     conv_step=self.step_conva      ,     )
                   __snake_case :   Tuple													=    self.pooling(__a      ,     self.size_poolinga  )
                   __snake_case :   Dict													=    np.shape(__a  )
                   __snake_case :   Tuple													=    self._expand(__a  )
                   __snake_case :   str													=    data_bp_input

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

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

                   __snake_case :   Optional[int]													=    pd_i_all / (self.size_poolinga * self.size_poolinga)
                   __snake_case :   Tuple													=    pd_conva_pooled.T.getA().tolist()
                   __snake_case :   Optional[Any]													=    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]  ):
                         __snake_case :   int													=    self._expand_mat(pd_conva_all[k_conv]  )
                         __snake_case :   Optional[int]													=    self.rate_weight * np.dot(__a      ,     __a  )

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

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

       def draw_error():
             __snake_case :   Union[str, Any]													=    [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 A_   (   self						:							Tuple      ,     __a						:							Union[str, Any]  )      ->   List[Any]:







       '''simple docstring'''
       # model predict
       __snake_case :   str													=    []
       print('-------------------Start Testing-------------------------'  )
       print((' - - Shape: Test_Data  ', np.shape(__a  ))  )
       for p in range(len(__a  )  ):
             __snake_case :   int													=    np.asmatrix(datas_test[p]  )
             __snake_case							,     __snake_case :   str													=    self.convolute(
                 __a      ,     self.conva      ,     self.w_conva      ,     self.thre_conva      ,     conv_step=self.step_conva      ,     )
             __snake_case :   List[str]													=    self.pooling(__a      ,     self.size_poolinga  )
             __snake_case :   List[Any]													=    self._expand(__a  )

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





 def A_   (   self						:							Optional[Any]      ,     __a						:							Optional[int]  )      ->   Tuple:







       '''simple docstring'''
       # return the data of image after convoluting process so we can check it out
       __snake_case :   int													=    np.asmatrix(__a  )
       __snake_case							,     __snake_case :   int													=    self.convolute(
           __a      ,     self.conva      ,     self.w_conva      ,     self.thre_conva      ,     conv_step=self.step_conva      ,     )
       __snake_case :   Dict													=    self.pooling(__a      ,     self.size_poolinga  )

       return data_conveda, data_pooleda


if __name__ == "__main__":
    pass



