

import pickle

import numpy as np
from matplotlib import pyplot as plt





class        lowerCAmelCase__  :



       '''simple docstring'''







       def __init__(							self ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__=0.2 ,   lowercase__=0.2     ):







             '''simple docstring'''



             __A    =bp_numa
             __A    =bp_numa
             __A    =bp_numa
             __A    =conva_get[:2]
             __A    =conva_get[2]
             __A    =size_pa
             __A    =rate_w
             __A    =rate_t
             __A    =[
                 np.mat(-1 * np.random.rand(self.conva[0] ,   self.conva[0]     ) + 0.5     )
                 for i in range(self.conva[1]     )
             ]
             __A    =np.mat(-1 * np.random.rand(self.num_bpa ,   self.num_bpa     ) + 0.5     )
             __A    =np.mat(-1 * np.random.rand(self.num_bpa ,   self.num_bpa     ) + 0.5     )
             __A    =-2 * np.random.rand(self.conva[1]     ) + 1
             __A    =-2 * np.random.rand(self.num_bpa     ) + 1
             __A    =-2 * np.random.rand(self.num_bpa     ) + 1

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             __A    ={
                 '''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(snake_case_ ,   '''wb'''     ) as f:
                   pickle.dump(snake_case_ ,   snake_case_     )

             print(f'''Model saved： {save_path}'''     )

       @classmethod
       def     __UpperCamelCase     (							cls ,   lowercase__     ):







             '''simple docstring'''



             with open(snake_case_ ,   '''rb'''     ) as f:
                   __A    =pickle.load(snake_case_     )  # noqa: S301

             __A    =model_dic.get('''conv1'''     )
             conv_get.append(model_dic.get('''step_conv1'''     )     )
             __A    =model_dic.get('''size_pooling1'''     )
             __A    =model_dic.get('''num_bp1'''     )
             __A    =model_dic.get('''num_bp2'''     )
             __A    =model_dic.get('''num_bp3'''     )
             __A    =model_dic.get('''rate_weight'''     )
             __A    =model_dic.get('''rate_thre'''     )
             # create model instance
             __A    =CNN(snake_case_ ,   snake_case_ ,   snake_case_ ,   snake_case_ ,   snake_case_ ,   snake_case_ ,   snake_case_     )
             # modify model parameter
             __A    =model_dic.get('''w_conv1'''     )
             __A    =model_dic.get('''wkj'''     )
             __A    =model_dic.get('''vji'''     )
             __A    =model_dic.get('''thre_conv1'''     )
             __A    =model_dic.get('''thre_bp2'''     )
             __A    =model_dic.get('''thre_bp3'''     )
             return conv_ins

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



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

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             return round(snake_case_ ,   3     )

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__     ):







             '''simple docstring'''



             __A    =convs[0]
             __A    =convs[1]
             __A    =np.shape(snake_case_     )[0]
             # get the data slice of original image data, data_focus
             __A    =[]
             for i_focus in range(0 ,   size_data - size_conv + 1 ,   snake_case_     ):
                   for j_focus in range(0 ,   size_data - size_conv + 1 ,   snake_case_     ):
                         __A    =data[
                             i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
                         ]
                         data_focus.append(snake_case_     )
        # calculate the feature map of every single kernel, and saved as list of matrix
             __A    =[]
             __A    =int((size_data - size_conv) / conv_step + 1     )
             for i_map in range(snake_case_     ):
                   __A    =[]
                   for i_focus in range(len(snake_case_     )     ):
                         __A    =(
                             np.sum(np.multiply(data_focus[i_focus] ,   w_convs[i_map]     )     )
                             - thre_convs[i_map]
                         )
                         featuremap.append(self.sig(snake_case_     )     )
                   __A    =np.asmatrix(snake_case_     ).reshape(
                       snake_case_ ,   snake_case_     )
                   data_featuremap.append(snake_case_     )

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

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__="average_pool"     ):







             '''simple docstring'''



             __A    =len(featuremaps[0]     )
             __A    =int(size_map / size_pooling     )
             __A    =[]
             for i_map in range(len(snake_case_     )     ):
                   __A    =featuremaps[i_map]
                   __A    =[]
                   for i_focus in range(0 ,   snake_case_ ,   snake_case_     ):
                         for j_focus in range(0 ,   snake_case_ ,   snake_case_     ):
                               __A    =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(snake_case_     )     )
                               elif pooling_type == "max_pooling":
                                     # max pooling
                                     map_pooled.append(np.max(snake_case_     )     )
                   __A    =np.asmatrix(snake_case_     ).reshape(snake_case_ ,   snake_case_     )
                   featuremap_pooled.append(snake_case_     )
             return featuremap_pooled

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             __A    =[]
             for i in range(len(snake_case_     )     ):
                   __A    =np.shape(data[i]     )
                   __A    =data[i].reshape(1 ,   shapes[0] * shapes[1]     )
                   __A    =data_listed.getA().tolist()[0]
                   data_expanded.extend(snake_case_     )
             __A    =np.asarray(snake_case_     )
             return data_expanded

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             __A    =np.asarray(snake_case_     )
             __A    =np.shape(snake_case_     )
             __A    =data_mat.reshape(1 ,   shapes[0] * shapes[1]     )
             return data_expanded

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__     ):







             '''simple docstring'''



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

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__=bool     ):







             '''simple docstring'''



             print('''----------------------Start Training-------------------------'''     )
             print((''' - - Shape: Train_Data  ''', np.shape(snake_case_     ))     )
             print((''' - - Shape: Teach_Data  ''', np.shape(snake_case_     ))     )
             __A    =0
             __A    =[]
             __A    =1_0_0_0_0
             while rp < n_repeat and mse >= error_accuracy:
                   __A    =0
                   print(f'''-------------Learning Time {rp}--------------'''     )
                   for p in range(len(snake_case_     )     ):
                         # print('------------Learning Image: %d--------------'%p)
                         __A    =np.asmatrix(datas_train[p]     )
                         __A    =np.asarray(datas_teach[p]     )
                         __A    ,		__A    =self.convolute(
                             snake_case_ ,   self.conva ,   self.w_conva ,   self.thre_conva ,   conv_step=self.step_conva ,   )
                         __A    =self.pooling(snake_case_ ,   self.size_poolinga     )
                         __A    =np.shape(snake_case_     )
                         __A    =self._expand(snake_case_     )
                         __A    =data_bp_input

                         __A    =np.dot(snake_case_ ,   self.vji.T     ) - self.thre_bpa
                         __A    =self.sig(snake_case_     )
                         __A    =np.dot(snake_case_ ,   self.wkj.T     ) - self.thre_bpa
                         __A    =self.sig(snake_case_     )

                         # --------------Model Leaning ------------------------
                         # calculate error and gradient---------------
                         __A    =np.multiply(
                             (data_teach - bp_outa) ,   np.multiply(snake_case_ ,   (1 - bp_outa)     )     )
                         __A    =np.multiply(
                             np.dot(snake_case_ ,   self.wkj     ) ,   np.multiply(snake_case_ ,   (1 - bp_outa)     )     )
                         __A    =np.dot(snake_case_ ,   self.vji     )

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

                               __A    =self.w_conva[k_conv] + delta_w.reshape(
                                   (self.conva[0], self.conva[0])     )

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

             def draw_error():
                   __A    =[error_accuracy for i in range(int(n_repeat * 1.2     )     )]
                   plt.plot(snake_case_ ,   '''+-'''     )
                   plt.plot(snake_case_ ,   '''r--'''     )
                   plt.xlabel('''Learning Times'''     )
                   plt.ylabel('''All_mse'''     )
                   plt.grid(snake_case_ ,   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 ,   lowercase__     ):







             '''simple docstring'''



             __A    =[]
             print('''-------------------Start Testing-------------------------'''     )
             print((''' - - Shape: Test_Data  ''', np.shape(snake_case_     ))     )
             for p in range(len(snake_case_     )     ):
                   __A    =np.asmatrix(datas_test[p]     )
                   __A    ,		__A    =self.convolute(
                       snake_case_ ,   self.conva ,   self.w_conva ,   self.thre_conva ,   conv_step=self.step_conva ,   )
                   __A    =self.pooling(snake_case_ ,   self.size_poolinga     )
                   __A    =self._expand(snake_case_     )

                   __A    =data_bp_input
                   __A    =bp_outa * self.vji.T - self.thre_bpa
                   __A    =self.sig(snake_case_     )
                   __A    =bp_outa * self.wkj.T - self.thre_bpa
                   __A    =self.sig(snake_case_     )
                   produce_out.extend(bp_outa.getA().tolist()     )
             __A    =[list(map(self.do_round ,   snake_case_     )     ) for each in produce_out]
             return np.asarray(snake_case_     )


       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             __A    =np.asmatrix(snake_case_     )
             __A    ,		__A    =self.convolute(
                 snake_case_ ,   self.conva ,   self.w_conva ,   self.thre_conva ,   conv_step=self.step_conva ,   )
             __A    =self.pooling(snake_case_ ,   self.size_poolinga     )

             return data_conveda, data_pooleda


if __name__ == "__main__":
   pass






import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
a			:			List[str]												=					logging.get_logger(__name__)


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = torch.load(_UpperCamelCase							,      map_location='''cpu'''	)
			if "model" in sd.keys():
						__lowercase			       = torch.load(_UpperCamelCase							,      map_location='''cpu'''	)['''model''']

			# pop unnecessary weights
			__lowercase			       = [
			    '''decoder.version''',
			    '''decoder.output_projection.weight''',
			]
			for key in keys_to_delete:
						if key in sd:
									sd.pop(_UpperCamelCase	)

			__lowercase			       = {
			    '''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
			    '''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
			    '''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
			    '''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
			}
			for old_key, new_key in keys_to_rename.items():
						if old_key in sd:
									__lowercase			       = sd.pop(_UpperCamelCase	)

			__lowercase			       = list(sd.keys()	)
			for key in keys:
						if ".qkv_proj." in key:
									__lowercase			       = sd[key]
									# We split QKV in separate Q,K,V

									__lowercase			       = key.replace('''.qkv_proj.'''							,      '''.q_proj.'''	)
									__lowercase			       = key.replace('''.qkv_proj.'''							,      '''.k_proj.'''	)
									__lowercase			       = key.replace('''.qkv_proj.'''							,      '''.v_proj.'''	)

									__lowercase			       = value.shape[0]
									assert depth % 3 == 0
									# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
									# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
									__lowercase       ,			__lowercase       ,			__lowercase			       = torch.split(_UpperCamelCase							,      depth // 3							,      dim=0	)

									__lowercase			       = q
									__lowercase			       = k
									__lowercase			       = v
									del sd[key]

			return sd


@torch.no_grad()
def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase=None	):


			'''simple docstring'''







			__lowercase			       = load_checkpoint(_UpperCamelCase	)

			if config is not None:
						__lowercase			       = OPTConfig.from_pretrained(_UpperCamelCase	)
			else:
						__lowercase			       = OPTConfig()

			__lowercase			       = OPTModel(_UpperCamelCase	).half().eval()
			model.load_state_dict(_UpperCamelCase	)

			# Check results
			Path(_UpperCamelCase	).mkdir(exist_ok=_UpperCamelCase	)
			model.save_pretrained(_UpperCamelCase	)


if __name__ == "__main__":
	a			:			List[Any]												=					argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--fairseq_path''',
	    type=str,
	    help=(
	        '''path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'''
	        ''' https://huggingface.co/models?other=opt_metasq'''
	    ),
	)
	parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
	parser.add_argument('''--hf_config''', default=None, type=str, help='''Define HF config.''')
	a			:			Any												=					parser.parse_args()
	convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)


