


'''simple docstring'''




import pickle

import numpy as np
from matplotlib import pyplot as plt



class  lowerCAmelCase				:



	def __init__(					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__=0.2 ,					snake_case__=0.2    ):
		lowerCAmelCase					:    Optional[Any]      							= bp_numa
		lowerCAmelCase					:    List[str]      							= bp_numa
		lowerCAmelCase					:    List[str]      							= bp_numa
		lowerCAmelCase					:    List[Any]      							= conva_get[:2]
		lowerCAmelCase					:    Union[str, Any]      							= conva_get[2]
		lowerCAmelCase					:    Dict      							= size_pa
		lowerCAmelCase					:    Dict      							= rate_w
		lowerCAmelCase					:    List[Any]      							= rate_t
		lowerCAmelCase					:    Union[str, Any]      							= [
		    np.mat(-1 * np.random.rand(self.conva[0] ,					self.conva[0]    ) + 0.5    )
		    for i in range(self.conva[1]    )
		]
		lowerCAmelCase					:    int      							= np.mat(-1 * np.random.rand(self.num_bpa ,					self.num_bpa    ) + 0.5    )
		lowerCAmelCase					:    Union[str, Any]      							= np.mat(-1 * np.random.rand(self.num_bpa ,					self.num_bpa    ) + 0.5    )
		lowerCAmelCase					:    Union[str, Any]      							= -2 * np.random.rand(self.conva[1]    ) + 1
		lowerCAmelCase					:    List[str]      							= -2 * np.random.rand(self.num_bpa    ) + 1
		lowerCAmelCase					:    str      							= -2 * np.random.rand(self.num_bpa    ) + 1



	def 				lowercase     (					self ,					snake_case__    ):
		# save model dict with pickle
		lowerCAmelCase					:    Union[str, 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(snake_case__ ,					'wb'    ) as f:
			pickle.dump(snake_case__ ,					snake_case__    )

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



	@classmethod
	def 				lowercase     (					cls ,					snake_case__    ):
		# read saved model
		with open(snake_case__ ,					'rb'    ) as f:
			lowerCAmelCase					:    Tuple      							= pickle.load(snake_case__    )  # noqa: S301

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



	def 				lowercase     (					self ,					snake_case__    ):
		return 1 / (1 + np.exp(-1 * x    ))



	def 				lowercase     (					self ,					snake_case__    ):
		return round(snake_case__ ,					3    )



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

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



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__="average_pool"    ):
		# pooling process
		lowerCAmelCase					:    Optional[Any]      							= len(featuremaps[0]    )
		lowerCAmelCase					:    List[str]      							= int(size_map / size_pooling    )
		lowerCAmelCase					:    int      							= []
		for i_map in range(len(snake_case__    )    ):
			lowerCAmelCase					:    List[Any]      							= featuremaps[i_map]
			lowerCAmelCase					:    Union[str, Any]      							= []
			for i_focus in range(0 ,					snake_case__ ,					snake_case__    ):
				for j_focus in range(0 ,					snake_case__ ,					snake_case__    ):
					lowerCAmelCase					:    Optional[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(snake_case__    )    )
					elif pooling_type == "max_pooling":
						# max pooling
						map_pooled.append(np.max(snake_case__    )    )
			lowerCAmelCase					:    Union[str, Any]      							= np.asmatrix(snake_case__    ).reshape(snake_case__ ,					snake_case__    )
			featuremap_pooled.append(snake_case__    )
		return featuremap_pooled



	def 				lowercase     (					self ,					snake_case__    ):
		# expanding three dimension data to one dimension list
		lowerCAmelCase					:    Optional[Any]      							= []
		for i in range(len(snake_case__    )    ):
			lowerCAmelCase					:    Optional[Any]      							= np.shape(data[i]    )
			lowerCAmelCase					:    Any      							= data[i].reshape(1 ,					shapes[0] * shapes[1]    )
			lowerCAmelCase					:    Tuple      							= data_listed.getA().tolist()[0]
			data_expanded.extend(snake_case__    )
		lowerCAmelCase					:    Optional[Any]      							= np.asarray(snake_case__    )
		return data_expanded



	def 				lowercase     (					self ,					snake_case__    ):
		# expanding matrix to one dimension list
		lowerCAmelCase					:    List[str]      							= np.asarray(snake_case__    )
		lowerCAmelCase					:    int      							= np.shape(snake_case__    )
		lowerCAmelCase					:    str      							= data_mat.reshape(1 ,					shapes[0] * shapes[1]    )
		return data_expanded



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    Tuple      							= []
		lowerCAmelCase					:    Tuple      							= 0
		for i_map in range(snake_case__    ):
			lowerCAmelCase					:    Dict      							= np.ones((size_map, size_map)    )
			for i in range(0 ,					snake_case__ ,					snake_case__    ):
				for j in range(0 ,					snake_case__ ,					snake_case__    ):
					lowerCAmelCase					:    List[str]      							= pd_pool[
					    i_pool
					]
					lowerCAmelCase					:    Union[str, Any]      							= i_pool + 1
			lowerCAmelCase					:    Tuple      							= np.multiply(
			    snake_case__ ,					np.multiply(out_map[i_map] ,					(1 - out_map[i_map])    )    )
			pd_all.append(snake_case__    )
		return pd_all



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

				lowerCAmelCase					:    Union[str, Any]      							= np.dot(snake_case__ ,					self.vji.T    ) - self.thre_bpa
				lowerCAmelCase					:    str      							= self.sig(snake_case__    )
				lowerCAmelCase					:    Union[str, Any]      							= np.dot(snake_case__ ,					self.wkj.T    ) - self.thre_bpa
				lowerCAmelCase					:    Union[str, Any]      							= self.sig(snake_case__    )

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

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

					lowerCAmelCase					:    Dict      							= 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					:    Any      							= self.wkj + pd_k_all.T * bp_outa * self.rate_weight
				lowerCAmelCase					:    str      							= self.vji + pd_j_all.T * bp_outa * self.rate_weight
				lowerCAmelCase					:    str      							= self.thre_bpa - pd_k_all * self.rate_thre
				lowerCAmelCase					:    Any      							= self.thre_bpa - pd_j_all * self.rate_thre
				# calculate the sum error of all single image
				lowerCAmelCase					:    int      							= np.sum(abs(data_teach - bp_outa    )    )
				error_count += errors
				# print('   ----Teach      ',data_teach)
				# print('   ----BP_output  ',bp_out3)
			lowerCAmelCase					:    int      							= rp + 1
			lowerCAmelCase					:    int      							= error_count / patterns
			all_mse.append(snake_case__    )

		def draw_error():
			lowerCAmelCase					:    Union[str, Any]      							= [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 				lowercase     (					self ,					snake_case__    ):
		# model predict
		lowerCAmelCase					:    Union[str, Any]      							= []
		print('-------------------Start Testing-------------------------'    )
		print((' - - Shape: Test_Data  ', np.shape(snake_case__    ))    )
		for p in range(len(snake_case__    )    ):
			lowerCAmelCase					:    Dict      							= np.asmatrix(datas_test[p]    )
			lowerCAmelCase					:    str      							= self.convolute(
			    snake_case__ ,					self.conva ,					self.w_conva ,					self.thre_conva ,					conv_step=self.step_conva ,					)
			lowerCAmelCase					:    Any      							= self.pooling(snake_case__ ,					self.size_poolinga    )
			lowerCAmelCase					:    Union[str, Any]      							= self._expand(snake_case__    )

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



	def 				lowercase     (					self ,					snake_case__    ):
		# return the data of image after convoluting process so we can check it out
		lowerCAmelCase					:    Optional[Any]      							= np.asmatrix(snake_case__    )
		lowerCAmelCase					:    List[Any]      							= self.convolute(
		    snake_case__ ,					self.conva ,					self.w_conva ,					self.thre_conva ,					conv_step=self.step_conva ,					)
		lowerCAmelCase					:    int      							= self.pooling(snake_case__ ,					self.size_poolinga    )

		return data_conveda, data_pooleda


if __name__ == "__main__":
					pass




'''simple docstring'''




import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


_lowerCAmelCase  :		List[Any]		     =	logging.getLogger(__name__)

def 			__UpperCamelCase    (    )    ->      Any:




	"""simple docstring"""





	lowerCAmelCase					:    str      							= argparse.ArgumentParser(
	    description='Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.'				)
	parser.add_argument(
	    '--dataset_name'					,   type=_A					,   default='wikitext'					,   help='Name of the training. Explore datasets at: hf.co/datasets.'					,   )
	parser.add_argument(
	    '--dataset_config'					,   type=_A					,   default='wikitext-103-raw-v1'					,   help='Configuration name of the dataset.'				)
	parser.add_argument(
	    '--tokenizer_name_or_path'					,   type=_A					,   default='sayakpaul/unigram-tokenizer-wikitext'					,   help='Tokenizer identifier. Can be a local filepath or a Hub identifier.'					,   )
	parser.add_argument(
	    '--shard_size'					,   type=_A					,   default=10_00					,   help='Number of entries to go in a single shard.'					,   )
	parser.add_argument('--split'					,   type=_A					,   default='train'					,   choices=['train', 'test', 'validation']				)
	parser.add_argument(
	    '--limit'					,   default=_A					,   type=_A					,   help='Limit the number of shards (used for debugging).'					,   )
	parser.add_argument(
	    '--max_length'					,   type=_A					,   default=5_12					,   help='Maximum sequence length. For training on TPUs, it helps to have a maximum'
	    ' sequence length that is a multiple of 8.'					,   )
	parser.add_argument(
	    '--output_dir'					,   default='tf-tpu'					,   type=_A					,   help='Output directory where the TFRecord shards will be saved. If the'
	    ' path is appended with `gs://` (\'gs://tf-tpu\', for example) then the TFRecord'
	    ' shards will be directly saved to a Google Cloud Storage bucket.'					,   )

	lowerCAmelCase					:    Any      							= parser.parse_args()
	return args

def 			__UpperCamelCase    (    _A    :				Optional[int]				)    ->      int:




	"""simple docstring"""





	def fn(_A    :				Tuple				):
		return tokenizer(examples['text']				)

	return fn

def 			__UpperCamelCase    (    _A    :				int				)    ->      int:




	"""simple docstring"""





	lowerCAmelCase					:    Tuple      							= []
	for i in range(len(tokenized_data['input_ids']				)				):
		lowerCAmelCase					:    Optional[Any]      							= {
		    'input_ids': tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data['input_ids'][i]				)				),
		    'attention_mask': tf.train.Feature(
		        intaa_list=tf.train.IntaaList(value=tokenized_data['attention_mask'][i]				)				),
		}
		lowerCAmelCase					:    Any      							= tf.train.Features(feature=_A				)
		lowerCAmelCase					:    List[str]      							= tf.train.Example(features=_A				)
		lowerCAmelCase					:    Tuple      							= example.SerializeToString()
		records.append(_A				)
	return records

def 			__UpperCamelCase    (    _A    :				int				)    ->      Union[str, Any]:




	"""simple docstring"""





	lowerCAmelCase					:    Union[str, Any]      							= datasets.load_dataset(args.dataset_name					,   args.dataset_config					,   split=args.split				)

	if args.limit is not None:
		lowerCAmelCase					:    Optional[Any]      							= min(len(_A				)					,   args.limit				)
		lowerCAmelCase					:    Dict      							= dataset.select(range(_A				)				)
		print(F"Limiting the dataset to {args.limit} entries."				)

	lowerCAmelCase					:    str      							= AutoTokenizer.from_pretrained(args.tokenizer_name_or_path				)

	# Handle output directory creation.
	# For serializing into a Google Cloud Storage Bucket, one needs to first
	# create a bucket.
	if "gs" not in args.output_dir:
		if not os.path.exists(args.output_dir				):
			os.makedirs(args.output_dir				)
		lowerCAmelCase					:    Any      							= os.path.join(args.output_dir					,   args.split				)
		if not os.path.exists(_A				):
			os.makedirs(_A				)
	else:
		lowerCAmelCase					:    List[Any]      							= os.path.join(args.output_dir					,   args.split				)

	# Tokenize the whole dataset at once.
	lowerCAmelCase					:    Any      							= tokenize_function(_A				)
	lowerCAmelCase					:    Optional[int]      							= dataset.map(_A					,   batched=_A					,   num_proc=4					,   remove_columns=['text']				)

	# We need to concatenate all our texts together, and then split the result
	# into chunks of a fixed size, which we will call block_size. To do this, we
	# will use the map method again, with the option batched=True. When we use batched=True,
	# the function we pass to map() will be passed multiple inputs at once, allowing us
	# to group them into more or fewer examples than we had in the input.
	# This allows us to create our new fixed-length samples. The advantage of this
	# method is that we don't lose a whole lot of content from the dataset compared to the
	# case where we simply tokenize with a pre-defined max_length.

	def group_texts(_A    :				str				):
		# Concatenate all texts.
		lowerCAmelCase					:    Optional[int]      							= {k: sum(examples[k]					,   []				) for k in examples.keys()}
		lowerCAmelCase					:    str      							= len(concatenated_examples[list(examples.keys()				)[0]]				)
		# We drop the small remainder, though you could add padding instead if the model supports it
		# In this, as in all things, we advise you to follow your heart 🫀
		lowerCAmelCase					:    List[Any]      							= (total_length // args.max_length) * args.max_length
		# Split by chunks of max_len.
		lowerCAmelCase					:    str      							= {
		    k: [t[i : i + args.max_length] for i in range(0					,   _A					,   args.max_length				)]
		    for k, t in concatenated_examples.items()
		}
		return result

	lowerCAmelCase					:    List[Any]      							= dataset_tokenized.map(_A					,   batched=_A					,   batch_size=10_00					,   num_proc=4				)

	lowerCAmelCase					:    Union[str, Any]      							= 0
	lowerCAmelCase					:    Tuple      							= 0
	for shard in range(0					,   len(_A				)					,   args.shard_size				):
		lowerCAmelCase					:    Optional[Any]      							= grouped_dataset[shard : shard + args.shard_size]
		lowerCAmelCase					:    List[str]      							= len(dataset_snapshot['input_ids']				)
		lowerCAmelCase					:    Union[str, Any]      							= os.path.join(_A					,   F"dataset-{shard_count}-{records_containing}.tfrecord"				)
		lowerCAmelCase					:    List[Any]      							= get_serialized_examples(_A				)

		with tf.io.TFRecordWriter(_A				) as out_file:
			for i in range(len(_A				)				):
				lowerCAmelCase					:    Union[str, Any]      							= serialized_examples[i]
				out_file.write(_A				)
			print('Wrote file {} containing {} records'.format(_A					,   _A				)				)

		shard_count += 1
		total_records += records_containing

	with open(F"split-{args.split}-records-count.txt"					,   'w'				) as f:
		print(F"Total {args.split} records: {total_records}"					,   file=_A				)


if __name__ == "__main__":
				_lowerCAmelCase  :		List[Any]		     =	parse_args()
				main(args)
