

"""simple docstring"""


import argparse
import random

import joblib
import numpy as np
import torch
from igf.igf import (
    SecondaryLearner,
    collect_objective_set,
    compute_perplexity,
    generate_datasets,
    load_gpta,
    recopy_gpta,
    set_seed,
    train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler

from transformers import GPTaLMHeadModel






def     a__    (    __SCREAMING_SNAKE_CASE=3_2					, __SCREAMING_SNAKE_CASE=1_0					, __SCREAMING_SNAKE_CASE=1_0_0					, __SCREAMING_SNAKE_CASE=1_0_2_6					, __SCREAMING_SNAKE_CASE=True					, __SCREAMING_SNAKE_CASE="data/tokenized_stories_train_wikitext103.jbl"					, __SCREAMING_SNAKE_CASE="igf_context_pairs.jbl"					, )	->	List[str]:
		set_seed(3 )
		# generate train_data and objective_set
		__lowerCAmelCase      , __lowerCAmelCase:       str          =			generate_datasets(
		    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, number=__SCREAMING_SNAKE_CASE					, min_len=1_0_2_6					, trim=__SCREAMING_SNAKE_CASE )
		# keeps model same across runs
		set_seed(4 )
		# model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
		# can we train on GPU?
		__lowerCAmelCase:       List[str]          =			torch.device("cuda:0" if torch.cuda.is_available() else "cpu" )

		# load pretrained model
		__lowerCAmelCase:       str          =			load_gpta("gpt2" ).to(__SCREAMING_SNAKE_CASE )
		print("computing perplexity on objective set" )
		__lowerCAmelCase:       List[str]          =			compute_perplexity(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE ).item()
		print("perplexity on objective set:"					, __SCREAMING_SNAKE_CASE )

		# collect igf pairs and save to file demo.jbl
		collect_objective_set(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )

		# clean up, delete model and data we don't need anymore
		del model, train_data, objective_set
		torch.cuda.empty_cache()






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE=1_5					, __SCREAMING_SNAKE_CASE=1_2_8					, __SCREAMING_SNAKE_CASE=1_0_0					, __SCREAMING_SNAKE_CASE="igf_model.pt"					, )	->	Union[str, Any]:
		set_seed(4_2 )

		# Load pre-trained model
		__lowerCAmelCase:       Any          =			GPTaLMHeadModel.from_pretrained("gpt2" )

		# Initialize secondary learner to use embedding weights of model
		__lowerCAmelCase:       Optional[Any]          =			SecondaryLearner(__SCREAMING_SNAKE_CASE )

		# Train secondary learner
		__lowerCAmelCase:       List[Any]          =			train_secondary_learner(
		    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, max_epochs=__SCREAMING_SNAKE_CASE					, batch_size=__SCREAMING_SNAKE_CASE					, eval_freq=1_0_0					, igf_model_path=__SCREAMING_SNAKE_CASE					, )

		del model, secondary_learner_train_data
		torch.cuda.empty_cache()

		return secondary_learner






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE=3_2					, __SCREAMING_SNAKE_CASE=1_0_0_0					, __SCREAMING_SNAKE_CASE=1_6					, __SCREAMING_SNAKE_CASE=1.0					, __SCREAMING_SNAKE_CASE=recopy_gpta					, __SCREAMING_SNAKE_CASE=None					, __SCREAMING_SNAKE_CASE=1_0					, __SCREAMING_SNAKE_CASE="gpt2_finetuned.pt"					, )	->	Optional[int]:
		__lowerCAmelCase:       str          =			torch.device("cuda:0" if torch.cuda.is_available() else "cpu" )
		__lowerCAmelCase:       List[Any]          =			RandomSampler(__SCREAMING_SNAKE_CASE )
		__lowerCAmelCase:       Optional[int]          =			DataLoader(__SCREAMING_SNAKE_CASE					, sampler=__SCREAMING_SNAKE_CASE )

		__lowerCAmelCase:       List[str]          =			max_steps // (len(__SCREAMING_SNAKE_CASE )) + 1
		__lowerCAmelCase:       List[str]          =			0
		__lowerCAmelCase:       Optional[int]          =			torch.zeros((1, context_len)					, dtype=torch.long					, device=__SCREAMING_SNAKE_CASE )
		__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase:       List[Any]          =			recopy_model(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )

		model.train()
		if secondary_learner is not None:
				secondary_learner.to(__SCREAMING_SNAKE_CASE )
				secondary_learner.eval()
		__lowerCAmelCase:       str          =			[]
		__lowerCAmelCase:       Dict          =			0

		__lowerCAmelCase:       Union[str, Any]          =			[]
		__lowerCAmelCase:       List[str]          =			[]

		# Compute the performance of the transformer model at the beginning
		__lowerCAmelCase:       List[Any]          =			compute_perplexity(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )
		test_perps.append(__SCREAMING_SNAKE_CASE )
		print("Test perplexity, step"					, __SCREAMING_SNAKE_CASE					, ":"					, __SCREAMING_SNAKE_CASE )
		for epoch in range(int(__SCREAMING_SNAKE_CASE ) ):
				for step, example in enumerate(__SCREAMING_SNAKE_CASE ):
						torch.cuda.empty_cache()
						__lowerCAmelCase:       Any          =			random.randint(0					, example.size(2 ) - context_len - 1 )
						__lowerCAmelCase:       Optional[Any]          =			example[0, 0, start : start + context_len]
						lm_optimizer.zero_grad()
						__lowerCAmelCase:       Dict          =			model(__SCREAMING_SNAKE_CASE					, labels=__SCREAMING_SNAKE_CASE )
						__lowerCAmelCase:       Any          =			True

						if secondary_learner is not None:
								__lowerCAmelCase:       Optional[Any]          =			secondary_learner.forward(
								    torch.tensor(__SCREAMING_SNAKE_CASE					, dtype=torch.long					, device=__SCREAMING_SNAKE_CASE ).unsqueeze(0 ) )[0].item()
								observed_qs.append(float(__SCREAMING_SNAKE_CASE ) )

								# Here we implement the simple non-constant threshold for the predicted IG(X) value
								# We will decay the selectivity of our secondary learner filter from
								# 1 standard deviation above average to 1 below average after 10 batches.

								if global_step == 1_0:
										__lowerCAmelCase:       Optional[int]          =			-1
								if predicted_q < threshold:
										__lowerCAmelCase:       List[Any]          =			False

            # If we passed the filter, add the context to the batch!
						if do_backprop:
								contexts.append(np.array(context.cpu() ) )
								__lowerCAmelCase:       List[str]          =			outputs[0]
								lm_loss.backward()
								examples += 1

						del outputs

						# Once the batch is filled with enough contexts, backprop on the batch.
						if examples == batch_size:
								torch.cuda.empty_cache()
								__lowerCAmelCase:       Optional[Any]          =			0
								# Do LM backprop
								torch.nn.utils.clip_grad_norm_(model.parameters()					, 3.0 )
								lm_optimizer.step()
								lm_scheduler.step()  # Update learning rate schedule
								global_step += 1
								# Compute the performance of the transformer model at this batch
								if global_step % eval_interval == 0:
										__lowerCAmelCase:       str          =			compute_perplexity(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )
										test_perps.append(__SCREAMING_SNAKE_CASE )

										print("Test perplexity, step"					, __SCREAMING_SNAKE_CASE					, ":"					, __SCREAMING_SNAKE_CASE )
            # Break out of the loop after 60 batches
						if max_steps > 0 and global_step > 6_0:
								break
				if max_steps > 0 and global_step > 6_0:
						break

    # save finetuned transformer model
		torch.save(model.state_dict()					, __SCREAMING_SNAKE_CASE )
		torch.cuda.empty_cache()
		# Do some cleaning up so we can reinitialize for the next run of this function
		del lm_optimizer
		del lm_scheduler
		return model






def     a__    (    )	->	str:
		__lowerCAmelCase:       Optional[Any]          =			argparse.ArgumentParser(description="Fine-tune a transformer model with IGF on a language modeling task" )

		# Required parameters
		parser.add_argument(
		    "--data_dir"					, default=__SCREAMING_SNAKE_CASE					, type=__SCREAMING_SNAKE_CASE					, required=__SCREAMING_SNAKE_CASE					, help="The input data dir. Should contain data files for WikiText."					, )
		parser.add_argument(
		    "--model_name_or_path"					, default=__SCREAMING_SNAKE_CASE					, type=__SCREAMING_SNAKE_CASE					, required=__SCREAMING_SNAKE_CASE					, help="Path to pretrained model or model identifier from huggingface.co/models"					, )
		parser.add_argument(
		    "--data_file"					, type=__SCREAMING_SNAKE_CASE					, default=__SCREAMING_SNAKE_CASE					, help=(
		        "A jbl file containing tokenized data which can be split as objective dataset, "
		        "train_dataset and test_dataset."
		    )					, )

		parser.add_argument(
		    "--igf_data_file"					, type=__SCREAMING_SNAKE_CASE					, default=__SCREAMING_SNAKE_CASE					, help="A jbl file containing the context and information gain pairs to train secondary learner."					, )

		parser.add_argument(
		    "--output_dir"					, default=__SCREAMING_SNAKE_CASE					, type=__SCREAMING_SNAKE_CASE					, required=__SCREAMING_SNAKE_CASE					, help="The output directory where the final fine-tuned model is stored."					, )

		parser.add_argument(
		    "--tokenizer_name"					, default=__SCREAMING_SNAKE_CASE					, type=__SCREAMING_SNAKE_CASE					, help="Pretrained tokenizer name or path if not the same as model_name"					, )
		parser.add_argument("--seed"					, type=__SCREAMING_SNAKE_CASE					, default=__SCREAMING_SNAKE_CASE					, help="A seed for reproducible training." )

		parser.add_argument(
		    "--context_len"					, default=3_2					, type=__SCREAMING_SNAKE_CASE					, help=(
		        "The maximum total input sequence length after tokenization. Sequences longer "
		        "than this will be truncated, sequences shorter will be padded."
		    )					, )

		parser.add_argument(
		    "--size_objective_set"					, default=1_0_0					, type=__SCREAMING_SNAKE_CASE					, help="number of articles that are long enough to be used as our objective set"					, )
		parser.add_argument(
		    "--eval_freq"					, default=1_0_0					, type=__SCREAMING_SNAKE_CASE					, help="secondary model evaluation is triggered at eval_freq" )

		parser.add_argument("--max_steps"					, default=1_0_0_0					, type=__SCREAMING_SNAKE_CASE					, help="To calculate training epochs" )

		parser.add_argument(
		    "--secondary_learner_batch_size"					, default=1_2_8					, type=__SCREAMING_SNAKE_CASE					, help="batch size of training data for secondary learner"					, )

		parser.add_argument(
		    "--batch_size"					, default=1_6					, type=__SCREAMING_SNAKE_CASE					, help="batch size of training data of language model(gpt2) " )

		parser.add_argument(
		    "--eval_interval"					, default=1_0					, type=__SCREAMING_SNAKE_CASE					, help=(
		        "decay the selectivity of our secondary learner filter from"
		        "1 standard deviation above average to 1 below average after 10 batches"
		    )					, )

		parser.add_argument(
		    "--number"					, default=1_0_0					, type=__SCREAMING_SNAKE_CASE					, help="The number of examples split to be used as objective_set/test_data" )

		parser.add_argument(
		    "--min_len"					, default=1_0_2_6					, type=__SCREAMING_SNAKE_CASE					, help="The minimum length of the article to be used as objective set" )

		parser.add_argument(
		    "--secondary_learner_max_epochs"					, default=1_5					, type=__SCREAMING_SNAKE_CASE					, help="number of epochs to train secondary learner" )

		parser.add_argument("--trim"					, default=__SCREAMING_SNAKE_CASE					, type=__SCREAMING_SNAKE_CASE					, help="truncate the example if it exceeds context length" )

		parser.add_argument(
		    "--threshold"					, default=1.0					, type=__SCREAMING_SNAKE_CASE					, help=(
		        "The threshold value used by secondary learner to filter the train_data and allow only"
		        " informative data as input to the model"
		    )					, )

		parser.add_argument("--finetuned_model_name"					, default="gpt2_finetuned.pt"					, type=__SCREAMING_SNAKE_CASE					, help="finetuned_model_name" )

		parser.add_argument(
		    "--recopy_model"					, default=__SCREAMING_SNAKE_CASE					, type=__SCREAMING_SNAKE_CASE					, help="Reset the model to the original pretrained GPT-2 weights after each iteration"					, )

		# function calls
		# Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
		generate_n_pairs(
		    context_len=3_2					, max_steps=1_0					, size_objective_set=1_0_0					, min_len=1_0_2_6					, trim=__SCREAMING_SNAKE_CASE					, data_file="data/tokenized_stories_train_wikitext103.jbl"					, igf_data_file="igf_context_pairs.jbl"					, )

		# Load train data for secondary learner
		__lowerCAmelCase:       Union[str, Any]          =			joblib.load("data/IGF_values.jbl" )

		# Train secondary learner
		__lowerCAmelCase:       str          =			training_secondary_learner(
		    __SCREAMING_SNAKE_CASE					, secondary_learner_max_epochs=1_5					, secondary_learner_batch_size=1_2_8					, eval_freq=1_0_0					, igf_model_path="igf_model.pt"					, )

		# load pretrained gpt2 model
		__lowerCAmelCase:       Union[str, Any]          =			GPTaLMHeadModel.from_pretrained("gpt2" )
		set_seed(4_2 )

		# Generate train and test data to train and evaluate gpt2 model
		__lowerCAmelCase      , __lowerCAmelCase:       Optional[int]          =			generate_datasets(
		    context_len=3_2					, file="data/tokenized_stories_train_wikitext103.jbl"					, number=1_0_0					, min_len=1_0_2_6					, trim=__SCREAMING_SNAKE_CASE )

		# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
		finetune(
		    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, context_len=3_2					, max_steps=1_0_0_0					, batch_size=1_6					, threshold=1.0					, recopy_model=__SCREAMING_SNAKE_CASE					, secondary_learner=__SCREAMING_SNAKE_CASE					, eval_interval=1_0					, finetuned_model_name="gpt2_finetuned.pt"					, )


if __name__ == "__main__":
			main()





"""simple docstring"""


import pickle

import numpy as np
from matplotlib import pyplot as plt





class     snake_case	:

				def __init__(	self							:				int     ,  UpperCamelCase__							:				Optional[Any]     ,  UpperCamelCase__							:				Optional[int]     ,  UpperCamelCase__							:				List[str]     ,  UpperCamelCase__							:				List[str]     ,  UpperCamelCase__							:				Dict     ,  UpperCamelCase__							:				Union[str, Any]=0.2     ,  UpperCamelCase__							:				Any=0.2)->   Optional[int]:



						'''simple docstring'''


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

				def 							lowercase_     (	self							:				Optional[int]     ,  UpperCamelCase__							:				int)->   List[str]:



						'''simple docstring'''


						__lowerCAmelCase:       Any          =			{
						    "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(UpperCamelCase__     ,  "wb") as f:
								pickle.dump(UpperCamelCase__     ,  UpperCamelCase__)

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

				@classmethod
				def 							lowercase_     (	cls							:				Dict     ,  UpperCamelCase__							:				Union[str, Any])->   List[Any]:



						'''simple docstring'''


						with open(UpperCamelCase__     ,  "rb") as f:
								__lowerCAmelCase:       Dict          =			pickle.load(UpperCamelCase__)  # noqa: S301

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

				def 							lowercase_     (	self							:				Dict     ,  UpperCamelCase__							:				List[Any])->   List[Any]:



						'''simple docstring'''


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

				def 							lowercase_     (	self							:				Dict     ,  UpperCamelCase__							:				List[Any])->   Optional[Any]:



						'''simple docstring'''


						return round(UpperCamelCase__     ,  3)

				def 							lowercase_     (	self							:				Optional[int]     ,  UpperCamelCase__							:				Optional[int]     ,  UpperCamelCase__							:				Any     ,  UpperCamelCase__							:				List[str]     ,  UpperCamelCase__							:				Tuple     ,  UpperCamelCase__							:				int)->   Dict:



						'''simple docstring'''


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

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

				def 							lowercase_     (	self							:				Any     ,  UpperCamelCase__							:				List[str]     ,  UpperCamelCase__							:				List[Any]     ,  UpperCamelCase__							:				Optional[Any]="average_pool")->   str:



						'''simple docstring'''


						__lowerCAmelCase:       Tuple          =			len(featuremaps[0])
						__lowerCAmelCase:       List[Any]          =			int(size_map / size_pooling)
						__lowerCAmelCase:       int          =			[]
						for i_map in range(len(UpperCamelCase__)):
								__lowerCAmelCase:       str          =			featuremaps[i_map]
								__lowerCAmelCase:       List[Any]          =			[]
								for i_focus in range(0     ,  UpperCamelCase__     ,  UpperCamelCase__):
										for j_focus in range(0     ,  UpperCamelCase__     ,  UpperCamelCase__):
												__lowerCAmelCase:       Any          =			feature_map[
												    i_focus : i_focus + size_pooling,
												    j_focus : j_focus + size_pooling,
												]
												if pooling_type == "average_pool":
														# average pooling
														map_pooled.append(np.average(UpperCamelCase__))
												elif pooling_type == "max_pooling":
														# max pooling
														map_pooled.append(np.max(UpperCamelCase__))
								__lowerCAmelCase:       Optional[int]          =			np.asmatrix(UpperCamelCase__).reshape(UpperCamelCase__     ,  UpperCamelCase__)
								featuremap_pooled.append(UpperCamelCase__)
						return featuremap_pooled

				def 							lowercase_     (	self							:				Union[str, Any]     ,  UpperCamelCase__							:				str)->   int:



						'''simple docstring'''


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

				def 							lowercase_     (	self							:				Union[str, Any]     ,  UpperCamelCase__							:				Union[str, Any])->   Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Dict          =			np.asarray(UpperCamelCase__)
						__lowerCAmelCase:       Optional[int]          =			np.shape(UpperCamelCase__)
						__lowerCAmelCase:       Optional[int]          =			data_mat.reshape(1     ,  shapes[0] * shapes[1])
						return data_expanded

				def 							lowercase_     (	self							:				Dict     ,  UpperCamelCase__							:				Union[str, Any]     ,  UpperCamelCase__							:				Dict     ,  UpperCamelCase__							:				Optional[Any]     ,  UpperCamelCase__							:				List[Any]     ,  UpperCamelCase__							:				Dict)->   List[Any]:



						'''simple docstring'''


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

				def 							lowercase_     (	self							:				str     ,  UpperCamelCase__							:				Union[str, Any]     ,  UpperCamelCase__							:				int     ,  UpperCamelCase__							:				Dict     ,  UpperCamelCase__							:				List[Any]     ,  UpperCamelCase__							:				Any     ,  UpperCamelCase__							:				str=bool)->   List[str]:



						'''simple docstring'''


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

										__lowerCAmelCase:       int          =			np.dot(UpperCamelCase__     ,  self.vji.T) - self.thre_bpa
										__lowerCAmelCase:       int          =			self.sig(UpperCamelCase__)
										__lowerCAmelCase:       Optional[Any]          =			np.dot(UpperCamelCase__     ,  self.wkj.T) - self.thre_bpa
										__lowerCAmelCase:       str          =			self.sig(UpperCamelCase__)

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

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

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

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

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

						print("------------------Training Complished---------------------")
						print((" - - Training epoch: ", rp, f"     - - Mse: {mse:.6f}"))
						if draw_e:
								draw_error()
						return mse

				def 							lowercase_     (	self							:				Union[str, Any]     ,  UpperCamelCase__							:				Tuple)->   List[str]:



						'''simple docstring'''


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

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




				def 							lowercase_     (	self							:				int     ,  UpperCamelCase__							:				Any)->   Any:



						'''simple docstring'''


						__lowerCAmelCase:       Union[str, Any]          =			np.asmatrix(UpperCamelCase__)
						__lowerCAmelCase      , __lowerCAmelCase:       Optional[Any]          =			self.convolute(
						    UpperCamelCase__     ,  self.conva     ,  self.w_conva     ,  self.thre_conva     ,  conv_step=self.step_conva     ,  )
						__lowerCAmelCase:       Any          =			self.pooling(UpperCamelCase__     ,  self.size_poolinga)

						return data_conveda, data_pooleda


if __name__ == "__main__":
			pass


