





import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
_lowerCamelCase 					=      logging.get_logger(__name__)

_lowerCamelCase 					=      {
    '''post_extract_proj''': '''feature_projection.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.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
_lowerCamelCase 					=      [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Any       , UpperCamelCase__:				List[str]       , UpperCamelCase__:				str       , UpperCamelCase__:				Tuple       , UpperCamelCase__:				Dict     ):
		for attribute in key.split("""."""     ):
				SCREAMING_SNAKE_CASE__								=   getattr(_UpperCAmelCase       , _UpperCAmelCase     )

		if weight_type is not None:
				SCREAMING_SNAKE_CASE__								=   getattr(_UpperCAmelCase       , _UpperCAmelCase     ).shape
		else:
				SCREAMING_SNAKE_CASE__								=   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":
				SCREAMING_SNAKE_CASE__								=   value
		elif weight_type == "weight_g":
				SCREAMING_SNAKE_CASE__								=   value
		elif weight_type == "weight_v":
				SCREAMING_SNAKE_CASE__								=   value
		elif weight_type == "bias":
				SCREAMING_SNAKE_CASE__								=   value
		else:
				SCREAMING_SNAKE_CASE__								=   value

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






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				int       , UpperCamelCase__:				int     ):
		SCREAMING_SNAKE_CASE__								=   []
		SCREAMING_SNAKE_CASE__								=   fairseq_model.state_dict()

		SCREAMING_SNAKE_CASE__								=   hf_model.feature_extractor

		# if encoder has different dim to decoder -> use proj_weight
		SCREAMING_SNAKE_CASE__								=   None

		for name, value in fairseq_dict.items():
				SCREAMING_SNAKE_CASE__								=   False
				if "conv_layers" in name:
						load_conv_layer(
						    _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase       , hf_model.config.feat_extract_norm == """group"""       , )
						SCREAMING_SNAKE_CASE__								=   True
				elif name.split("""."""     )[0] == "proj":
						SCREAMING_SNAKE_CASE__								=   fairseq_model.proj
						SCREAMING_SNAKE_CASE__								=   True
				else:
						for key, mapped_key in MAPPING.items():
								if key in name or key.split("""w2v_model."""     )[-1] == name.split("""."""     )[0]:
										SCREAMING_SNAKE_CASE__								=   True
										if "*" in mapped_key:
												SCREAMING_SNAKE_CASE__								=   name.split(_UpperCAmelCase     )[0].split("""."""     )[-2]
												SCREAMING_SNAKE_CASE__								=   mapped_key.replace("""*"""       , _UpperCAmelCase     )
										if "weight_g" in name:
												SCREAMING_SNAKE_CASE__								=   'weight_g'
										elif "weight_v" in name:
												SCREAMING_SNAKE_CASE__								=   'weight_v'
										elif "bias" in name:
												SCREAMING_SNAKE_CASE__								=   'bias'
										elif "weight" in name:
												SCREAMING_SNAKE_CASE__								=   'weight'
										else:
												SCREAMING_SNAKE_CASE__								=   None
										set_recursively(_UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase     )
								continue
				if not is_used:
						unused_weights.append(_UpperCAmelCase     )

		logger.warning(f'''Unused weights: {unused_weights}'''     )

		return proj_weight






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Tuple       , UpperCamelCase__:				Optional[int]       , UpperCamelCase__:				Optional[Any]       , UpperCamelCase__:				Dict       , UpperCamelCase__:				int     ):
		SCREAMING_SNAKE_CASE__								=   full_name.split("""conv_layers."""     )[-1]
		SCREAMING_SNAKE_CASE__								=   name.split("""."""     )
		SCREAMING_SNAKE_CASE__								=   int(items[0]     )
		SCREAMING_SNAKE_CASE__								=   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.'''
						)
						SCREAMING_SNAKE_CASE__								=   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.'''
						)
						SCREAMING_SNAKE_CASE__								=   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."
						)
						SCREAMING_SNAKE_CASE__								=   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.'''
						)
						SCREAMING_SNAKE_CASE__								=   value
						logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''     )
		else:
				unused_weights.append(_UpperCAmelCase     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Optional[Any]     ):
		SCREAMING_SNAKE_CASE__								=   emb.weight.shape
		SCREAMING_SNAKE_CASE__								=   nn.Linear(_UpperCAmelCase       , _UpperCAmelCase       , bias=_UpperCAmelCase     )
		SCREAMING_SNAKE_CASE__								=   emb.weight.data
		return lin_layer






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Optional[int]     ):
		with open(_UpperCAmelCase       , """r"""       , encoding="""utf-8"""     ) as f:
				SCREAMING_SNAKE_CASE__								=   f.readlines()
				SCREAMING_SNAKE_CASE__								=   [line.split(""" """     )[0] for line in lines]

		SCREAMING_SNAKE_CASE__								=   len(_UpperCAmelCase     )

		SCREAMING_SNAKE_CASE__								=   {
		    '<s>': 0,
		    '<pad>': 1,
		    '</s>': 2,
		    '<unk>': 3,
		}

		vocab_dict.update(dict(zip(_UpperCAmelCase       , range(4       , num_words + 4     )     )     )     )
		return vocab_dict






@torch.no_grad()
def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Any       , UpperCamelCase__:				List[Any]       , UpperCamelCase__:				int       , UpperCamelCase__:				Any       , UpperCamelCase__:				List[str]       , UpperCamelCase__:				Any       , UpperCamelCase__:				Optional[Any]       , ):
		SCREAMING_SNAKE_CASE__								=   WavaVecaConfig.from_pretrained(_UpperCAmelCase     )
		SCREAMING_SNAKE_CASE__								=   SpeechaTextaConfig.from_pretrained(
		    _UpperCAmelCase       , vocab_size=_UpperCAmelCase       , decoder_layers=_UpperCAmelCase       , do_stable_layer_norm=_UpperCAmelCase     )

		SCREAMING_SNAKE_CASE__								=   WavaVecaFeatureExtractor(
		    feature_size=1       , sampling_rate=16_000       , padding_value=0       , do_normalize=_UpperCAmelCase       , return_attention_mask=_UpperCAmelCase       , )

		SCREAMING_SNAKE_CASE__								=   fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]       , arg_overrides={"""data""": """/""".join(dict_path.split("""/"""     )[:-1]     )}     )
		SCREAMING_SNAKE_CASE__								=   model[0].eval()

		# set weights for wav2vec2 encoder
		SCREAMING_SNAKE_CASE__								=   WavaVecaModel(_UpperCAmelCase     )
		SCREAMING_SNAKE_CASE__								=   recursively_load_weights_wavaveca(model.encoder       , _UpperCAmelCase     )

		SCREAMING_SNAKE_CASE__								=   SpeechaTextaForCausalLM(_UpperCAmelCase     )
		SCREAMING_SNAKE_CASE__								=   hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()       , strict=_UpperCAmelCase     )

		# set output linear layer
		unexpected_keys.remove("""embed_out"""     )
		SCREAMING_SNAKE_CASE__								=   nn.Parameter(model.decoder.embed_out.detach()     )

		# layer norm is init to identity matrix so leaving it is fine
		logger.warning(f'''The following keys are missing when loading the decoder weights: {missing_keys}'''     )
		logger.warning(f'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}'''     )

		SCREAMING_SNAKE_CASE__								=   SpeechEncoderDecoderModel(encoder=_UpperCAmelCase       , decoder=_UpperCAmelCase     )
		SCREAMING_SNAKE_CASE__								=   False

		# add projection layer
		SCREAMING_SNAKE_CASE__								=   nn.Parameter(projection_layer.weight     )
		SCREAMING_SNAKE_CASE__								=   nn.Parameter(projection_layer.bias     )

		SCREAMING_SNAKE_CASE__								=   create_vocab_dict(_UpperCAmelCase     )

		with open(os.path.join(_UpperCAmelCase       , """vocab.json"""     )       , """w"""     ) as fp:
				json.dump(_UpperCAmelCase       , _UpperCAmelCase     )

		SCREAMING_SNAKE_CASE__								=   SpeechaTextaTokenizer(os.path.join(_UpperCAmelCase       , """vocab.json"""     )     )
		tokenizer.save_pretrained(_UpperCAmelCase     )

		SCREAMING_SNAKE_CASE__								=   hf_wavavec.config.to_dict()
		SCREAMING_SNAKE_CASE__								=   tokenizer.pad_token_id
		SCREAMING_SNAKE_CASE__								=   tokenizer.bos_token_id
		SCREAMING_SNAKE_CASE__								=   tokenizer.eos_token_id
		SCREAMING_SNAKE_CASE__								=   'speech_to_text_2'
		SCREAMING_SNAKE_CASE__								=   'wav2vec2'

		SCREAMING_SNAKE_CASE__								=   SpeechEncoderDecoderConfig.from_dict(_UpperCAmelCase     )

		hf_wavavec.save_pretrained(_UpperCAmelCase     )
		feature_extractor.save_pretrained(_UpperCAmelCase     )


if __name__ == "__main__":
	_lowerCamelCase 					=      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(
	    '--encoder_config_path',
	    default='facebook/wav2vec2-large-lv60',
	    type=str,
	    help='Path to hf encoder wav2vec2 checkpoint config',
	)
	parser.add_argument(
	    '--decoder_config_path',
	    default='facebook/s2t-small-mustc-en-fr-st',
	    type=str,
	    help='Path to hf decoder s2t checkpoint config',
	)
	parser.add_argument('--vocab_size', default=10224, type=int, help='Vocab size of decoder')
	parser.add_argument('--num_decoder_layers', default=7, type=int, help='Number of decoder layers')

	_lowerCamelCase 					=      parser.parse_args()
	convert_wavaveca_checkpoint(
	    args.checkpoint_path,
	    args.pytorch_dump_folder_path,
	    args.dict_path,
	    encoder_config_path=args.encoder_config_path,
	    decoder_config_path=args.decoder_config_path,
	    vocab_size=args.vocab_size,
	    num_decoder_layers=args.num_decoder_layers,
	)




'''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



