





"""simple docstring"""
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_snake_case     :		str       						= logging.get_logger(__name__)

_snake_case     :		Optional[Any]       						= {
    """post_extract_proj""": """feature_projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.upsample.0""": """encoder.upsample.projection""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}






def 					A__				(			UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase       ):
							for attribute in key.split("."       ):
														A		=  getattr(_lowerCAmelCase					,  _lowerCAmelCase       )

							if weight_type is not None:
														A		=  getattr(_lowerCAmelCase					,  _lowerCAmelCase       ).shape
							else:
														A		=  hf_pointer.shape

							assert hf_shape == value.shape, (
							    F"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
							    F" {value.shape} for {full_name}"
							)

							if weight_type == "weight":
														A		=  value
							elif weight_type == "weight_g":
														A		=  value
							elif weight_type == "weight_v":
														A		=  value
							elif weight_type == "bias":
														A		=  value
							else:
														A		=  value

							logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."       )






def 					A__				(			UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase       ):
							A		=  []
							A		=  fairseq_model.state_dict()

							A		=  hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

							for name, value in fairseq_dict.items():
														A		=  False
														if "conv_layers" in name:
																					load_conv_layer(
																					    _lowerCAmelCase					,  _lowerCAmelCase					,  _lowerCAmelCase					,  _lowerCAmelCase					,  hf_model.config.feat_extract_norm == "group"					,  )
																					A		=  True
														else:
																					for key, mapped_key in MAPPING.items():
																												A		=  "sew." + mapped_key if (is_finetuned and mapped_key != "lm_head") else mapped_key

																												if key in name or key.split("w2v_model."       )[-1] == name.split("."       )[0]:
																																			A		=  True
																																			if "*" in mapped_key:
																																										A		=  name.split(_lowerCAmelCase       )[0].split("."       )[-2]
																																										A		=  mapped_key.replace("*"					,  _lowerCAmelCase       )
																																			if "weight_g" in name:
																																										A		=  "weight_g"
																																			elif "weight_v" in name:
																																										A		=  "weight_v"
																																			elif "weight" in name:
																																										A		=  "weight"
																																			elif "bias" in name:
																																										A		=  "bias"
																																			else:
																																										A		=  None
																																			set_recursively(_lowerCAmelCase					,  _lowerCAmelCase					,  _lowerCAmelCase					,  _lowerCAmelCase					,  _lowerCAmelCase       )
																												continue
														if not is_used:
																					unused_weights.append(_lowerCAmelCase       )

							logger.warning(F"Unused weights: {unused_weights}"       )






def 					A__				(			UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase       ):
							A		=  full_name.split("conv_layers."       )[-1]
							A		=  name.split("."       )
							A		=  int(items[0]       )
							A		=  int(items[1]       )

							if type_id == 0:
														if "bias" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
																					    F"{full_name} has size {value.shape}, but"
																					    F" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
																					)
																					A		=  value
																					logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."       )
														elif "weight" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
																					    F"{full_name} has size {value.shape}, but"
																					    F" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
																					)
																					A		=  value
																					logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."       )
							elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
														if "bias" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
																					    F"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
																					    " found."
																					)
																					A		=  value
																					logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."       )
														elif "weight" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
																					    F"{full_name} has size {value.shape}, but"
																					    F" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
																					)
																					A		=  value
																					logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."       )
							else:
														unused_weights.append(_lowerCAmelCase       )






def 					A__				(			UpperCamelCase					,  UpperCamelCase       ):
							A		=  SEWConfig()
							if is_finetuned:
														A		=  model.wav_encoder.wav_model.cfg
							else:
														A		=  model.cfg

							A		=  fs_config.conv_bias
							A		=  eval(fs_config.conv_feature_layers       )
							A		=  [x[0] for x in conv_layers]
							A		=  [x[1] for x in conv_layers]
							A		=  [x[2] for x in conv_layers]
							A		=  "gelu"
							A		=  "layer" if fs_config.extractor_mode == "layer_norm" else "group"
							A		=  0.0
							A		=  fs_config.activation_fn.name
							A		=  fs_config.encoder_embed_dim
							A		=  0.02
							A		=  fs_config.encoder_ffn_embed_dim
							A		=  1E-5
							A		=  fs_config.encoder_layerdrop
							A		=  fs_config.encoder_attention_heads
							A		=  fs_config.conv_pos_groups
							A		=  fs_config.conv_pos
							A		=  len(_lowerCAmelCase       )
							A		=  fs_config.encoder_layers
							A		=  fs_config.squeeze_factor

							# take care of any params that are overridden by the Wav2VecCtc model
							if is_finetuned:
														A		=  model.cfg
														A		=  fs_config.final_dropout
														A		=  fs_config.layerdrop
							A		=  fs_config.activation_dropout
							A		=  fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
							A		=  fs_config.attention_dropout
							A		=  fs_config.dropout_input
							A		=  fs_config.dropout
							A		=  fs_config.mask_channel_length
							A		=  fs_config.mask_channel_prob
							A		=  fs_config.mask_length
							A		=  fs_config.mask_prob

							A		=  "Wav2Vec2FeatureExtractor"
							A		=  "Wav2Vec2CTCTokenizer"

							return config






@torch.no_grad()
def 					A__				(			UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase=None					,  UpperCamelCase=None					,  UpperCamelCase=True       ):

							if is_finetuned:
														A		=  fairseq.checkpoint_utils.load_model_ensemble_and_task(
														    [checkpoint_path]					,  arg_overrides={"data": "/".join(dict_path.split("/"       )[:-1]       )}       )
							else:
														A		=  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       )

							if config_path is not None:
														A		=  SEWConfig.from_pretrained(_lowerCAmelCase       )
							else:
														A		=  convert_config(model[0]					,  _lowerCAmelCase       )
							A		=  model[0].eval()

							A		=  True if config.feat_extract_norm == "layer" else False
							A		=  WavaVecaFeatureExtractor(
							    feature_size=1					,  sampling_rate=16_000					,  padding_value=0					,  do_normalize=_lowerCAmelCase					,  return_attention_mask=_lowerCAmelCase					,  )

							if is_finetuned:
														if dict_path:
																					A		=  Dictionary.load(_lowerCAmelCase       )

																					# important change bos & pad token id since CTC symbol is <pad> and
																					# not <s> as in fairseq
																					A		=  target_dict.pad_index
																					A		=  target_dict.bos_index
																					A		=  target_dict.pad_index
																					A		=  target_dict.bos_index
																					A		=  target_dict.eos_index
																					A		=  len(target_dict.symbols       )
																					A		=  os.path.join(_lowerCAmelCase					,  "vocab.json"       )
																					if not os.path.isdir(_lowerCAmelCase       ):
																												logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(_lowerCAmelCase       )       )
																												return
																					os.makedirs(_lowerCAmelCase					,  exist_ok=_lowerCAmelCase       )
																					with open(_lowerCAmelCase					,  "w"					,  encoding="utf-8"       ) as vocab_handle:
																												json.dump(target_dict.indices					,  _lowerCAmelCase       )
																					A		=  WavaVecaCTCTokenizer(
																					    _lowerCAmelCase					,  unk_token=target_dict.unk_word					,  pad_token=target_dict.pad_word					,  bos_token=target_dict.bos_word					,  eos_token=target_dict.eos_word					,  word_delimiter_token="|"					,  do_lower_case=_lowerCAmelCase					,  )
																					A		=  WavaVecaProcessor(feature_extractor=_lowerCAmelCase					,  tokenizer=_lowerCAmelCase       )
																					processor.save_pretrained(_lowerCAmelCase       )

														A		=  SEWForCTC(_lowerCAmelCase       )
							else:
														A		=  SEWModel(_lowerCAmelCase       )
														feature_extractor.save_pretrained(_lowerCAmelCase       )

							recursively_load_weights(_lowerCAmelCase					,  _lowerCAmelCase					,  _lowerCAmelCase       )

							hf_model.save_pretrained(_lowerCAmelCase       )


if __name__ == "__main__":
		_snake_case     :		Union[str, Any]       						= argparse.ArgumentParser()
		parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
		parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
		parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
		parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
		parser.add_argument(
		    '--is_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
		)
		_snake_case     :		Tuple       						= parser.parse_args()
		convert_sew_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
		)









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   ):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   bp_numa
       UpperCamelCase	:					int       	=   bp_numa
       UpperCamelCase	:					List[Any]       	=   bp_numa
       UpperCamelCase	:					Optional[int]       	=   conva_get[:2]
       UpperCamelCase	:					Optional[Any]       	=   conva_get[2]
       UpperCamelCase	:					Dict       	=   size_pa
       UpperCamelCase	:					Union[str, Any]       	=   rate_w
       UpperCamelCase	:					Dict       	=   rate_t
       UpperCamelCase	:					Union[str, Any]       	=   [
           np.mat(-1 * np.random.rand(self.conva[0]       ,							self.conva[0]   ) + 0.5   )
           for i in range(self.conva[1]   )
       ]
       UpperCamelCase	:					Any       	=   np.mat(-1 * np.random.rand(self.num_bpa       ,							self.num_bpa   ) + 0.5   )
       UpperCamelCase	:					List[Any]       	=   np.mat(-1 * np.random.rand(self.num_bpa       ,							self.num_bpa   ) + 0.5   )
       UpperCamelCase	:					Optional[Any]       	=   -2 * np.random.rand(self.conva[1]   ) + 1
       UpperCamelCase	:					Any       	=   -2 * np.random.rand(self.num_bpa   ) + 1
       UpperCamelCase	:					int       	=   -2 * np.random.rand(self.num_bpa   ) + 1





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[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        __UpperCamelCase( cls       ,							A_   ):
       '''simple docstring'''




       with open(A_       ,							"rb"   ) as f:
        UpperCamelCase	:					Optional[Any]       	=   pickle.load(A_   )  # noqa: S301

       UpperCamelCase	:					List[Any]       	=   model_dic.get("conv1"   )
       conv_get.append(model_dic.get("step_conv1"   )   )
       UpperCamelCase	:					Union[str, Any]       	=   model_dic.get("size_pooling1"   )
       UpperCamelCase	:					List[Any]       	=   model_dic.get("num_bp1"   )
       UpperCamelCase	:					Dict       	=   model_dic.get("num_bp2"   )
       UpperCamelCase	:					Dict       	=   model_dic.get("num_bp3"   )
       UpperCamelCase	:					Dict       	=   model_dic.get("rate_weight"   )
       UpperCamelCase	:					str       	=   model_dic.get("rate_thre"   )
       # create model instance
       UpperCamelCase	:					Any       	=   CNN(A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_   )
       # modify model parameter
       UpperCamelCase	:					str       	=   model_dic.get("w_conv1"   )
       UpperCamelCase	:					Optional[Any]       	=   model_dic.get("wkj"   )
       UpperCamelCase	:					int       	=   model_dic.get("vji"   )
       UpperCamelCase	:					Any       	=   model_dic.get("thre_conv1"   )
       UpperCamelCase	:					Optional[int]       	=   model_dic.get("thre_bp2"   )
       UpperCamelCase	:					Union[str, Any]       	=   model_dic.get("thre_bp3"   )
       return conv_ins





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




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





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       return round(A_       ,							3   )





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					str       	=   convs[0]
       UpperCamelCase	:					Optional[Any]       	=   convs[1]
       UpperCamelCase	:					Optional[Any]       	=   np.shape(A_   )[0]
       # get the data slice of original image data, data_focus
       UpperCamelCase	:					List[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_   ):
         UpperCamelCase	:					Union[str, 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
       UpperCamelCase	:					int       	=   []
       UpperCamelCase	:					Optional[Any]       	=   int((size_data - size_conv) / conv_step + 1   )
       for i_map in range(A_   ):
        UpperCamelCase	:					str       	=   []
        for i_focus in range(len(A_   )   ):
         UpperCamelCase	:					List[Any]       	=   (
             np.sum(np.multiply(data_focus[i_focus]       ,							w_convs[i_map]   )   )
             - thre_convs[i_map]
         )
         featuremap.append(self.sig(A_   )   )
        UpperCamelCase	:					Optional[int]       	=   np.asmatrix(A_   ).reshape(
            A_       ,							A_   )
        data_featuremap.append(A_   )

       # expanding the data slice to One dimenssion
       UpperCamelCase	:					List[Any]       	=   []
       for each_focus in data_focus:
        focusa_list.extend(self.Expand_Mat(A_   )   )
       UpperCamelCase	:					Tuple       	=   np.asarray(A_   )
       return focus_list, data_featuremap





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_="average_pool"   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   len(featuremaps[0]   )
       UpperCamelCase	:					str       	=   int(size_map / size_pooling   )
       UpperCamelCase	:					Optional[int]       	=   []
       for i_map in range(len(A_   )   ):
        UpperCamelCase	:					Tuple       	=   featuremaps[i_map]
        UpperCamelCase	:					Any       	=   []
        for i_focus in range(0       ,							A_       ,							A_   ):
         for j_focus in range(0       ,							A_       ,							A_   ):
          UpperCamelCase	:					int       	=   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_   )   )
        UpperCamelCase	:					Optional[Any]       	=   np.asmatrix(A_   ).reshape(A_       ,							A_   )
        featuremap_pooled.append(A_   )
       return featuremap_pooled





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					List[Any]       	=   []
       for i in range(len(A_   )   ):
        UpperCamelCase	:					List[Any]       	=   np.shape(data[i]   )
        UpperCamelCase	:					str       	=   data[i].reshape(1       ,							shapes[0] * shapes[1]   )
        UpperCamelCase	:					Optional[int]       	=   data_listed.getA().tolist()[0]
        data_expanded.extend(A_   )
       UpperCamelCase	:					Any       	=   np.asarray(A_   )
       return data_expanded





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					List[Any]       	=   np.asarray(A_   )
       UpperCamelCase	:					List[Any]       	=   np.shape(A_   )
       UpperCamelCase	:					Any       	=   data_mat.reshape(1       ,							shapes[0] * shapes[1]   )
       return data_expanded





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   []
       UpperCamelCase	:					Optional[int]       	=   0
       for i_map in range(A_   ):
        UpperCamelCase	:					int       	=   np.ones((size_map, size_map)   )
        for i in range(0       ,							A_       ,							A_   ):
         for j in range(0       ,							A_       ,							A_   ):
          UpperCamelCase	:					str       	=   pd_pool[
              i_pool
          ]
          UpperCamelCase	:					str       	=   i_pool + 1
        UpperCamelCase	:					str       	=   np.multiply(
            A_       ,							np.multiply(out_map[i_map]       ,							(1 - out_map[i_map])   )   )
        pd_all.append(A_   )
       return pd_all





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_=bool   ):
       '''simple docstring'''




       print("----------------------Start Training-------------------------"   )
       print((" - - Shape: Train_Data  ", np.shape(A_   ))   )
       print((" - - Shape: Teach_Data  ", np.shape(A_   ))   )
       UpperCamelCase	:					List[str]       	=   0
       UpperCamelCase	:					Union[str, Any]       	=   []
       UpperCamelCase	:					int       	=   1_0000
       while rp < n_repeat and mse >= error_accuracy:
        UpperCamelCase	:					Tuple       	=   0
        print(F"""-------------Learning Time {rp}--------------"""   )
        for p in range(len(A_   )   ):
         # print('------------Learning Image: %d--------------'%p)
         UpperCamelCase	:					Any       	=   np.asmatrix(datas_train[p]   )
         UpperCamelCase	:					List[str]       	=   np.asarray(datas_teach[p]   )
         UpperCamelCase	,    UpperCamelCase	:					Dict       	=   self.convolute(
             A_       ,							self.conva       ,							self.w_conva       ,							self.thre_conva       ,							conv_step=self.step_conva       ,							)
         UpperCamelCase	:					Tuple       	=   self.pooling(A_       ,							self.size_poolinga   )
         UpperCamelCase	:					int       	=   np.shape(A_   )
         UpperCamelCase	:					List[str]       	=   self._expand(A_   )
         UpperCamelCase	:					Optional[int]       	=   data_bp_input

         UpperCamelCase	:					str       	=   np.dot(A_       ,							self.vji.T   ) - self.thre_bpa
         UpperCamelCase	:					Optional[int]       	=   self.sig(A_   )
         UpperCamelCase	:					List[Any]       	=   np.dot(A_       ,							self.wkj.T   ) - self.thre_bpa
         UpperCamelCase	:					Dict       	=   self.sig(A_   )

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

         UpperCamelCase	:					Dict       	=   pd_i_all / (self.size_poolinga * self.size_poolinga)
         UpperCamelCase	:					List[Any]       	=   pd_conva_pooled.T.getA().tolist()
         UpperCamelCase	:					List[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]   ):
          UpperCamelCase	:					List[Any]       	=   self._expand_mat(pd_conva_all[k_conv]   )
          UpperCamelCase	:					List[Any]       	=   self.rate_weight * np.dot(A_       ,							A_   )

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

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

       def draw_error():
        UpperCamelCase	:					Tuple       	=   [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        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   []
       print("-------------------Start Testing-------------------------"   )
       print((" - - Shape: Test_Data  ", np.shape(A_   ))   )
       for p in range(len(A_   )   ):
        UpperCamelCase	:					int       	=   np.asmatrix(datas_test[p]   )
        UpperCamelCase	,    UpperCamelCase	:					Any       	=   self.convolute(
            A_       ,							self.conva       ,							self.w_conva       ,							self.thre_conva       ,							conv_step=self.step_conva       ,							)
        UpperCamelCase	:					List[str]       	=   self.pooling(A_       ,							self.size_poolinga   )
        UpperCamelCase	:					Dict       	=   self._expand(A_   )

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





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Union[str, Any]       	=   np.asmatrix(A_   )
       UpperCamelCase	,    UpperCamelCase	:					List[Any]       	=   self.convolute(
           A_       ,							self.conva       ,							self.w_conva       ,							self.thre_conva       ,							conv_step=self.step_conva       ,							)
       UpperCamelCase	:					str       	=   self.pooling(A_       ,							self.size_poolinga   )

       return data_conveda, data_pooleda


if __name__ == "__main__":
    pass
