




import numpy as np
from matplotlib import pyplot as plt






def  snake_case (							A      )							->							Union[str, Any]:




	'''simple docstring'''


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


class     __SCREAMING_SNAKE_CASE							:

					def __init__(    self			,	__lowercase			,	__lowercase=None			,	__lowercase=None			,	__lowercase=False ):







						'''simple docstring'''




						lowerCamelCase_							:		str							       =							units
						lowerCamelCase_							:		List[str]							       =							None
						lowerCamelCase_							:		Dict							       =							None
						lowerCamelCase_							:		str							       =							activation
						if learning_rate is None:
							lowerCamelCase_							:		Tuple							       =							0.3
						lowerCamelCase_							:		List[str]							       =							learning_rate
						lowerCamelCase_							:		Optional[int]							       =							is_input_layer

					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		List[Any]							       =							np.asmatrix(np.random.normal(0			,	0.5			,	(self.units, back_units) ) )
						lowerCamelCase_							:		Any							       =							np.asmatrix(np.random.normal(0			,	0.5			,	self.units ) ).T
						if self.activation is None:
							lowerCamelCase_							:		Any							       =							sigmoid

					def      lowercase_							(    self ):







						'''simple docstring'''




						if self.activation == sigmoid:
							lowerCamelCase_							:		List[str]							       =							np.dot(self.output			,	(1 - self.output).T )
							lowerCamelCase_							:		Any							       =							np.diag(np.diag(__lowercase ) )
						else:
							lowerCamelCase_							:		List[str]							       =							1
						return gradient_activation

					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							       =							xdata
						if self.is_input_layer:
							# input layer
							lowerCamelCase_							:		Tuple							       =							xdata
							lowerCamelCase_							:		str							       =							xdata
							return xdata
						else:
							lowerCamelCase_							:		str							       =							np.dot(self.weight			,	self.xdata ) - self.bias
							lowerCamelCase_							:		Tuple							       =							self.activation(self.wx_plus_b )
							return self.output





					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Tuple							       =							self.cal_gradient()  # i * i 维
						lowerCamelCase_							:		Tuple							       =							np.asmatrix(np.dot(gradient.T			,	__lowercase ) )

						lowerCamelCase_							:		Any							       =							np.asmatrix(self.xdata )
						lowerCamelCase_							:		Optional[int]							       =							-1
						lowerCamelCase_							:		Tuple							       =							self.weight

						lowerCamelCase_							:		Any							       =							np.dot(gradient.T			,	self._gradient_weight.T )
						lowerCamelCase_							:		Optional[int]							       =							gradient * self._gradient_bias
						lowerCamelCase_							:		Optional[Any]							       =							np.dot(__lowercase			,	self._gradient_x ).T
						# upgrade: the Negative gradient direction
						lowerCamelCase_							:		List[Any]							       =							self.weight - self.learn_rate * self.gradient_weight
						lowerCamelCase_							:		Optional[Any]							       =							self.bias - self.learn_rate * self.gradient_bias.T
						# updates the weights and bias according to learning rate (0.3 if undefined)
						return self.gradient


class     __SCREAMING_SNAKE_CASE							:

					def __init__(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		int							       =							[]
						lowerCamelCase_							:		Dict							       =							[]
						lowerCamelCase_							:		int							       =							plt.figure()
						lowerCamelCase_							:		Tuple							       =							self.fig_loss.add_subplot(1			,	1			,	1 )

					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						self.layers.append(__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						for i, layer in enumerate(self.layers[:] ):
							if i < 1:
								lowerCamelCase_							:		str							       =							True
							else:
								layer.initializer(self.layers[i - 1].units )

					def      lowercase_							(    self ):







						'''simple docstring'''




						for i, layer in enumerate(self.layers[:] ):
							print(f"------- layer {i} -------" )
							print('weight.shape '			,	np.shape(layer.weight ) )
							print('bias.shape '			,	np.shape(layer.bias ) )

					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[int]							       =							train_round
						lowerCamelCase_							:		Optional[int]							       =							accuracy

						self.ax_loss.hlines(self.accuracy			,	0			,	self.train_round * 1.1 )

						lowerCamelCase_							:		str							       =							np.shape(__lowercase )
						for _ in range(__lowercase ):
							lowerCamelCase_							:		Any							       =							0
							for row in range(x_shape[0] ):
								lowerCamelCase_							:		Tuple							       =							np.asmatrix(xdata[row, :] ).T
								lowerCamelCase_							:		Tuple							       =							np.asmatrix(ydata[row, :] ).T

								# forward propagation
								for layer in self.layers:
									lowerCamelCase_							:		List[str]							       =							layer.forward_propagation(_xdata )

								lowerCamelCase_       ,      lowerCamelCase_							:		Union[str, Any]							       =							self.cal_loss(_ydata			,	_xdata )
								lowerCamelCase_							:		Any							       =							all_loss + loss

								# back propagation: the input_layer does not upgrade
								for layer in self.layers[:0:-1]:
									lowerCamelCase_							:		Dict							       =							layer.back_propagation(__lowercase )

							lowerCamelCase_							:		Optional[int]							       =							all_loss / x_shape[0]
							self.train_mse.append(__lowercase )

							self.plot_loss()

							if mse < self.accuracy:
								print('----达到精度----' )
								return mse
						return None

					def      lowercase_							(    self			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		str							       =							np.sum(np.power((ydata - ydata_)			,	2 ) )
						lowerCamelCase_							:		int							       =							2 * (ydata_ - ydata)
						# vector (shape is the same as _ydata.shape)
						return self.loss, self.loss_gradient





					def      lowercase_							(    self ):







						'''simple docstring'''




						if self.ax_loss.lines:
							self.ax_loss.lines.remove(self.ax_loss.lines[0] )
						self.ax_loss.plot(self.train_mse			,	'r-' )
						plt.ion()
						plt.xlabel('step' )
						plt.ylabel('loss' )
						plt.show()
						plt.pause(0.1 )






def  snake_case (							)							->							str:




	'''simple docstring'''


	lowerCamelCase_							:		str							       =							np.random.randn(10						,       10      )
	lowerCamelCase_							:		Optional[Any]							       =							np.asarray(
	    [
	        [0.8, 0.4],
	        [0.4, 0.3],
	        [0.34, 0.45],
	        [0.67, 0.32],
	        [0.88, 0.67],
	        [0.78, 0.77],
	        [0.55, 0.66],
	        [0.55, 0.43],
	        [0.54, 0.1],
	        [0.1, 0.5],
	    ]      )
	lowerCamelCase_							:		Any							       =							BPNN()
	for i in (10, 20, 30, 2):
		model.add_layer(DenseLayer(A      )      )
	model.build()
	model.summary()
	model.train(xdata=A						,       ydata=A						,       train_round=100						,       accuracy=0.01      )


if __name__ == "__main__":
							example()






import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCamelCase__		=						logging.get_logger(__name__)

UpperCamelCase__		=						{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

UpperCamelCase__		=						{
    'vocab_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/vocab.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/vocab.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/vocab.json',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/merges.txt',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/merges.txt',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/merges.txt',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'
        ),
    },
}

UpperCamelCase__		=						{
    'roberta-base': 5_1_2,
    'roberta-large': 5_1_2,
    'roberta-large-mnli': 5_1_2,
    'distilroberta-base': 5_1_2,
    'roberta-base-openai-detector': 5_1_2,
    'roberta-large-openai-detector': 5_1_2,
}






@lru_cache()
def  snake_case (							)							->							List[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		Tuple							       =							(
	    list(range(ord('!'      )						,       ord('~'      ) + 1      )      ) + list(range(ord('¡'      )						,       ord('¬'      ) + 1      )      ) + list(range(ord('®'      )						,       ord('ÿ'      ) + 1      )      )
	)
	lowerCamelCase_							:		Dict							       =							bs[:]
	lowerCamelCase_							:		Dict							       =							0
	for b in range(2**8      ):
		if b not in bs:
			bs.append(A      )
			cs.append(2**8 + n      )
			n += 1
	lowerCamelCase_							:		List[str]							       =							[chr(A      ) for n in cs]
	return dict(zip(A						,       A      )      )






def  snake_case (							A      )							->							int:




	'''simple docstring'''


	lowerCamelCase_							:		Union[str, Any]							       =							set()
	lowerCamelCase_							:		Optional[int]							       =							word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)      )
		lowerCamelCase_							:		Optional[Any]							       =							char
	return pairs



class     __SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):
					_lowerCamelCase		  =			VOCAB_FILES_NAMES
					_lowerCamelCase		  =			PRETRAINED_VOCAB_FILES_MAP
					_lowerCamelCase		  =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_lowerCamelCase		  =			["""input_ids""", """attention_mask"""]

					def __init__(    self			,	__lowercase			,	__lowercase			,	__lowercase="replace"			,	__lowercase="<s>"			,	__lowercase="</s>"			,	__lowercase="</s>"			,	__lowercase="<s>"			,	__lowercase="<unk>"			,	__lowercase="<pad>"			,	__lowercase="<mask>"			,	__lowercase=False			,	**__lowercase			,	):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[int]							       =							AddedToken(__lowercase			,	lstrip=__lowercase			,	rstrip=__lowercase ) if isinstance(__lowercase			,	__lowercase ) else bos_token
						lowerCamelCase_							:		Dict							       =							AddedToken(__lowercase			,	lstrip=__lowercase			,	rstrip=__lowercase ) if isinstance(__lowercase			,	__lowercase ) else eos_token
						lowerCamelCase_							:		Union[str, Any]							       =							AddedToken(__lowercase			,	lstrip=__lowercase			,	rstrip=__lowercase ) if isinstance(__lowercase			,	__lowercase ) else sep_token
						lowerCamelCase_							:		Union[str, Any]							       =							AddedToken(__lowercase			,	lstrip=__lowercase			,	rstrip=__lowercase ) if isinstance(__lowercase			,	__lowercase ) else cls_token
						lowerCamelCase_							:		Any							       =							AddedToken(__lowercase			,	lstrip=__lowercase			,	rstrip=__lowercase ) if isinstance(__lowercase			,	__lowercase ) else unk_token
						lowerCamelCase_							:		Tuple							       =							AddedToken(__lowercase			,	lstrip=__lowercase			,	rstrip=__lowercase ) if isinstance(__lowercase			,	__lowercase ) else pad_token

						# Mask token behave like a normal word, i.e. include the space before it
						lowerCamelCase_							:		Dict							       =							AddedToken(__lowercase			,	lstrip=__lowercase			,	rstrip=__lowercase ) if isinstance(__lowercase			,	__lowercase ) else mask_token

						super().__init__(
						    errors=__lowercase			,	bos_token=__lowercase			,	eos_token=__lowercase			,	unk_token=__lowercase			,	sep_token=__lowercase			,	cls_token=__lowercase			,	pad_token=__lowercase			,	mask_token=__lowercase			,	add_prefix_space=__lowercase			,	**__lowercase			,	)

						with open(__lowercase			,	encoding='utf-8' ) as vocab_handle:
							lowerCamelCase_							:		Tuple							       =							json.load(__lowercase )
						lowerCamelCase_							:		Dict							       =							{v: k for k, v in self.encoder.items()}
						lowerCamelCase_							:		Any							       =							errors  # how to handle errors in decoding
						lowerCamelCase_							:		Dict							       =							bytes_to_unicode()
						lowerCamelCase_							:		Any							       =							{v: k for k, v in self.byte_encoder.items()}
						with open(__lowercase			,	encoding='utf-8' ) as merges_handle:
							lowerCamelCase_							:		Dict							       =							merges_handle.read().split('\n' )[1:-1]
						lowerCamelCase_							:		Tuple							       =							[tuple(merge.split() ) for merge in bpe_merges]
						lowerCamelCase_							:		Tuple							       =							dict(zip(__lowercase			,	range(len(__lowercase ) ) ) )
						lowerCamelCase_							:		List[str]							       =							{}
						lowerCamelCase_							:		Union[str, Any]							       =							add_prefix_space

						# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
						lowerCamelCase_							:		Dict							       =							re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+' )

					@property
					def      lowercase_							(    self ):







						'''simple docstring'''




						return len(self.encoder )

					def      lowercase_							(    self ):







						'''simple docstring'''




						return dict(self.encoder			,	**self.added_tokens_encoder )

					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						if token in self.cache:
							return self.cache[token]
						lowerCamelCase_							:		Any							       =							tuple(__lowercase )
						lowerCamelCase_							:		Tuple							       =							get_pairs(__lowercase )

						if not pairs:
							return token

						while True:
							lowerCamelCase_							:		str							       =							min(__lowercase			,	key=lambda __lowercase : self.bpe_ranks.get(__lowercase			,	float('inf' ) ) )
							if bigram not in self.bpe_ranks:
								break
							lowerCamelCase_       ,      lowerCamelCase_							:		Dict							       =							bigram
							lowerCamelCase_							:		str							       =							[]
							lowerCamelCase_							:		int							       =							0
							while i < len(__lowercase ):
								try:
									lowerCamelCase_							:		Tuple							       =							word.index(__lowercase			,	__lowercase )
								except ValueError:
									new_word.extend(word[i:] )
									break
								else:
									new_word.extend(word[i:j] )
									lowerCamelCase_							:		Any							       =							j

								if word[i] == first and i < len(__lowercase ) - 1 and word[i + 1] == second:
									new_word.append(first + second )
									i += 2
								else:
									new_word.append(word[i] )
									i += 1
							lowerCamelCase_							:		List[Any]							       =							tuple(__lowercase )
							lowerCamelCase_							:		Optional[Any]							       =							new_word
							if len(__lowercase ) == 1:
								break
							else:
								lowerCamelCase_							:		Optional[Any]							       =							get_pairs(__lowercase )
						lowerCamelCase_							:		Optional[int]							       =							' '.join(__lowercase )
						lowerCamelCase_							:		Union[str, Any]							       =							word
						return word

					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							       =							[]
						for token in re.findall(self.pat			,	__lowercase ):
							lowerCamelCase_							:		int							       =							''.join(
							    self.byte_encoder[b] for b in token.encode('utf-8' ) )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
							bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__lowercase ).split(' ' ) )
						return bpe_tokens

					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						return self.encoder.get(__lowercase			,	self.encoder.get(self.unk_token ) )

					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						return self.decoder.get(__lowercase )

					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		int							       =							''.join(__lowercase )
						lowerCamelCase_							:		Tuple							       =							bytearray([self.byte_decoder[c] for c in text] ).decode('utf-8'			,	errors=self.errors )
						return text

					def      lowercase_							(    self			,	__lowercase			,	__lowercase = None ):







						'''simple docstring'''




						if not os.path.isdir(__lowercase ):
							logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
							return
						lowerCamelCase_							:		List[str]							       =							os.path.join(
						    __lowercase			,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
						lowerCamelCase_							:		Union[str, Any]							       =							os.path.join(
						    __lowercase			,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )

						with open(__lowercase			,	'w'			,	encoding='utf-8' ) as f:
							f.write(json.dumps(self.encoder			,	indent=2			,	sort_keys=__lowercase			,	ensure_ascii=__lowercase ) + '\n' )

						lowerCamelCase_							:		Dict							       =							0
						with open(__lowercase			,	'w'			,	encoding='utf-8' ) as writer:
							writer.write('#version: 0.2\n' )
							for bpe_tokens, token_index in sorted(self.bpe_ranks.items()			,	key=lambda __lowercase : kv[1] ):
								if index != token_index:
									logger.warning(
									    f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
									    ' Please check that the tokenizer is not corrupted!' )
									lowerCamelCase_							:		Tuple							       =							token_index
								writer.write(' '.join(__lowercase ) + '\n' )
								index += 1

						return vocab_file, merge_file

					def      lowercase_							(    self			,	__lowercase			,	__lowercase = None ):







						'''simple docstring'''




						if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						lowerCamelCase_							:		Optional[Any]							       =							[self.cls_token_id]
						lowerCamelCase_							:		Optional[Any]							       =							[self.sep_token_id]
						return cls + token_ids_a + sep + sep + token_ids_a + sep

					def      lowercase_							(    self			,	__lowercase			,	__lowercase = None			,	__lowercase = False ):







						'''simple docstring'''




						if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=__lowercase			,	token_ids_a=__lowercase			,	already_has_special_tokens=__lowercase )

						if token_ids_a is None:
							return [1] + ([0] * len(__lowercase )) + [1]
						return [1] + ([0] * len(__lowercase )) + [1, 1] + ([0] * len(__lowercase )) + [1]

					def      lowercase_							(    self			,	__lowercase			,	__lowercase = None ):







						'''simple docstring'''




						lowerCamelCase_							:		List[Any]							       =							[self.sep_token_id]
						lowerCamelCase_							:		Any							       =							[self.cls_token_id]

						if token_ids_a is None:
							return len(cls + token_ids_a + sep ) * [0]
						return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]





					def      lowercase_							(    self			,	__lowercase			,	__lowercase=False			,	**__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Any							       =							kwargs.pop('add_prefix_space'			,	self.add_prefix_space )
						if (is_split_into_words or add_prefix_space) and (len(__lowercase ) > 0 and not text[0].isspace()):
							lowerCamelCase_							:		List[Any]							       =							' ' + text
						return (text, kwargs)
