




"""simple docstring"""







import numpy as np
from matplotlib import pyplot as plt





def 							lowercase   (snake_case__				:	int	)       ->					Any:


				'''simple docstring'''






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






class  SCREAMING_SNAKE_CASE__   :


						def __init__(							self			:     Dict  ,  lowerCAmelCase			:     Optional[Any]  ,  lowerCAmelCase			:     Tuple=None  ,  lowerCAmelCase			:     Optional[int]=None  ,  lowerCAmelCase			:     Optional[int]=False   ):
										lowerCAmelCase		=			units
										lowerCAmelCase		=			None
										lowerCAmelCase		=			None
										lowerCAmelCase		=			activation
										if learning_rate is None:
														lowerCAmelCase		=			0.3
										lowerCAmelCase		=			learning_rate
										lowerCAmelCase		=			is_input_layer


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


						def 					__lowercase	(							self			:     Optional[Any]   ):







										if self.activation == sigmoid:
														lowerCAmelCase		=			np.dot(self.output  ,  (1 - self.output).T   )
														lowerCAmelCase		=			np.diag(np.diag(lowerCAmelCase   )   )
										else:
														lowerCAmelCase		=			1
										return gradient_activation


						def 					__lowercase	(							self			:     Optional[Any]  ,  lowerCAmelCase			:     Dict   ):
										lowerCAmelCase		=			xdata
										if self.is_input_layer:
														# input layer
														lowerCAmelCase		=			xdata
														lowerCAmelCase		=			xdata
														return xdata
										else:
														lowerCAmelCase		=			np.dot(self.weight  ,  self.xdata   ) - self.bias
														lowerCAmelCase		=			self.activation(self.wx_plus_b   )
														return self.output




						def 					__lowercase	(							self			:     Union[str, Any]  ,  lowerCAmelCase			:     Dict   ):
										lowerCAmelCase		=			self.cal_gradient()  # i * i 维
										lowerCAmelCase		=			np.asmatrix(np.dot(gradient.T  ,  lowerCAmelCase   )   )

										lowerCAmelCase		=			np.asmatrix(self.xdata   )
										lowerCAmelCase		=			-1
										lowerCAmelCase		=			self.weight

										lowerCAmelCase		=			np.dot(gradient.T  ,  self._gradient_weight.T   )
										lowerCAmelCase		=			gradient * self._gradient_bias
										lowerCAmelCase		=			np.dot(lowerCAmelCase  ,  self._gradient_x   ).T
										# upgrade: the Negative gradient direction
										lowerCAmelCase		=			self.weight - self.learn_rate * self.gradient_weight
										lowerCAmelCase		=			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			:     Optional[int]   ):
										lowerCAmelCase		=			[]
										lowerCAmelCase		=			[]
										lowerCAmelCase		=			plt.figure()
										lowerCAmelCase		=			self.fig_loss.add_subplot(1  ,  1  ,  1   )


						def 					__lowercase	(							self			:     str  ,  lowerCAmelCase			:     int   ):
										self.layers.append(lowerCAmelCase   )


						def 					__lowercase	(							self			:     Optional[Any]   ):







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


						def 					__lowercase	(							self			:     Tuple   ):







										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			:     Dict  ,  lowerCAmelCase			:     Dict  ,  lowerCAmelCase			:     Dict  ,  lowerCAmelCase			:     str  ,  lowerCAmelCase			:     Optional[Any]   ):
										lowerCAmelCase		=			train_round
										lowerCAmelCase		=			accuracy

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

										lowerCAmelCase		=			np.shape(lowerCAmelCase   )
										for _ in range(lowerCAmelCase   ):
														lowerCAmelCase		=			0
														for row in range(x_shape[0]   ):
																		lowerCAmelCase		=			np.asmatrix(xdata[row, :]   ).T
																		lowerCAmelCase		=			np.asmatrix(ydata[row, :]   ).T

																		# forward propagation
																		for layer in self.layers:
																						lowerCAmelCase		=			layer.forward_propagation(_xdata   )

																		lowerCAmelCase	,  lowerCAmelCase		=			self.cal_loss(_ydata  ,  _xdata   )
																		lowerCAmelCase		=			all_loss + loss

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

														lowerCAmelCase		=			all_loss / x_shape[0]
														self.train_mse.append(lowerCAmelCase   )

														self.plot_loss()

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


						def 					__lowercase	(							self			:     Optional[Any]  ,  lowerCAmelCase			:     List[str]  ,  lowerCAmelCase			:     Optional[int]   ):
										lowerCAmelCase		=			np.sum(np.power((ydata - ydata_)  ,  2   )   )
										lowerCAmelCase		=			2 * (ydata_ - ydata)
										# vector (shape is the same as _ydata.shape)
										return self.loss, self.loss_gradient




						def 					__lowercase	(							self			:     List[Any]   ):







										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 							lowercase   ()       ->					Optional[Any]:


				'''simple docstring'''






				lowerCAmelCase		=			np.random.randn(10 ,    10	)
				lowerCAmelCase		=			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		=			BPNN()
				for i in (10, 20, 30, 2):
								model.add_layer(DenseLayer(snake_case__	)	)
				model.build()
				model.summary()
				model.train(xdata=snake_case__ ,    ydata=snake_case__ ,    train_round=100 ,    accuracy=0.01	)


if __name__ == "__main__":
		example()











"""simple docstring"""







import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


a								=							logging.get_logger(__name__)

a								=							{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

a								=							{
    'vocab_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
    },
    'tokenizer_file': {
        'allenai/longformer-base-4096': (
            'https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
    },
}

a								=							{
    'allenai/longformer-base-4096': 4_0_9_6,
    'allenai/longformer-large-4096': 4_0_9_6,
    'allenai/longformer-large-4096-finetuned-triviaqa': 4_0_9_6,
    'allenai/longformer-base-4096-extra.pos.embd.only': 4_0_9_6,
    'allenai/longformer-large-4096-extra.pos.embd.only': 4_0_9_6,
}






class  SCREAMING_SNAKE_CASE__   (   _a   ):
						_a        =  VOCAB_FILES_NAMES
						_a        =  PRETRAINED_VOCAB_FILES_MAP
						_a        =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_a        =  ['input_ids', 'attention_mask']
						_a        =  LongformerTokenizer


						def __init__(							self			:     Optional[Any]  ,  lowerCAmelCase			:     Tuple=None  ,  lowerCAmelCase			:     Any=None  ,  lowerCAmelCase			:     List[Any]=None  ,  lowerCAmelCase			:     Optional[Any]="replace"  ,  lowerCAmelCase			:     Union[str, Any]="<s>"  ,  lowerCAmelCase			:     int="</s>"  ,  lowerCAmelCase			:     Any="</s>"  ,  lowerCAmelCase			:     Union[str, Any]="<s>"  ,  lowerCAmelCase			:     List[str]="<unk>"  ,  lowerCAmelCase			:     Optional[Any]="<pad>"  ,  lowerCAmelCase			:     List[str]="<mask>"  ,  lowerCAmelCase			:     Tuple=False  ,  lowerCAmelCase			:     List[str]=True  ,  **lowerCAmelCase			:     Optional[int]  ,  ):
										super().__init__(
										    lowerCAmelCase  ,  lowerCAmelCase  ,  tokenizer_file=lowerCAmelCase  ,  errors=lowerCAmelCase  ,  bos_token=lowerCAmelCase  ,  eos_token=lowerCAmelCase  ,  sep_token=lowerCAmelCase  ,  cls_token=lowerCAmelCase  ,  unk_token=lowerCAmelCase  ,  pad_token=lowerCAmelCase  ,  mask_token=lowerCAmelCase  ,  add_prefix_space=lowerCAmelCase  ,  trim_offsets=lowerCAmelCase  ,  **lowerCAmelCase  ,  )

										lowerCAmelCase		=			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
										if pre_tok_state.get("""add_prefix_space"""  ,  lowerCAmelCase   ) != add_prefix_space:
														lowerCAmelCase		=			getattr(lowerCAmelCase  ,  pre_tok_state.pop("""type"""   )   )
														lowerCAmelCase		=			add_prefix_space
														lowerCAmelCase		=			pre_tok_class(**lowerCAmelCase   )

										lowerCAmelCase		=			add_prefix_space

										lowerCAmelCase		=			"""post_processor"""
										lowerCAmelCase		=			getattr(self.backend_tokenizer  ,  lowerCAmelCase  ,  lowerCAmelCase   )
										if tokenizer_component_instance:
														lowerCAmelCase		=			json.loads(tokenizer_component_instance.__getstate__()   )

														# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
														if "sep" in state:
																		lowerCAmelCase		=			tuple(state["""sep"""]   )
														if "cls" in state:
																		lowerCAmelCase		=			tuple(state["""cls"""]   )

														lowerCAmelCase		=			False

														if state.get("""add_prefix_space"""  ,  lowerCAmelCase   ) != add_prefix_space:
																		lowerCAmelCase		=			add_prefix_space
																		lowerCAmelCase		=			True

														if state.get("""trim_offsets"""  ,  lowerCAmelCase   ) != trim_offsets:
																		lowerCAmelCase		=			trim_offsets
																		lowerCAmelCase		=			True

														if changes_to_apply:
																		lowerCAmelCase		=			getattr(lowerCAmelCase  ,  state.pop("""type"""   )   )
																		lowerCAmelCase		=			component_class(**lowerCAmelCase   )
																		setattr(self.backend_tokenizer  ,  lowerCAmelCase  ,  lowerCAmelCase   )


						@property
						def 					__lowercase	(							self			:     List[Any]   ):
										if self._mask_token is None:
														if self.verbose:
																		logger.error("""Using mask_token, but it is not set yet."""   )
														return None
										return str(self._mask_token   )


						@mask_token.setter
						def 					__lowercase	(							self			:     List[Any]  ,  lowerCAmelCase			:     Dict   ):
										lowerCAmelCase		=			AddedToken(lowerCAmelCase  ,  lstrip=lowerCAmelCase  ,  rstrip=lowerCAmelCase   ) if isinstance(lowerCAmelCase  ,  lowerCAmelCase   ) else value
										lowerCAmelCase		=			value


						def 					__lowercase	(							self			:     Tuple  ,  *lowerCAmelCase			:     Optional[int]  ,  **lowerCAmelCase			:     Optional[Any]   ):
										lowerCAmelCase		=			kwargs.get("""is_split_into_words"""  ,  lowerCAmelCase   )
										assert self.add_prefix_space or not is_split_into_words, (
										    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
										    "to use it with pretokenized inputs."
										)

										return super()._batch_encode_plus(*lowerCAmelCase  ,  **lowerCAmelCase   )


						def 					__lowercase	(							self			:     str  ,  *lowerCAmelCase			:     str  ,  **lowerCAmelCase			:     str   ):
										lowerCAmelCase		=			kwargs.get("""is_split_into_words"""  ,  lowerCAmelCase   )

										assert self.add_prefix_space or not is_split_into_words, (
										    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
										    "to use it with pretokenized inputs."
										)

										return super()._encode_plus(*lowerCAmelCase  ,  **lowerCAmelCase   )


						def 					__lowercase	(							self			:     Optional[int]  ,  lowerCAmelCase			:     str  ,  lowerCAmelCase			:     Optional[str] = None   ):
										lowerCAmelCase		=			self._tokenizer.model.save(lowerCAmelCase  ,  name=lowerCAmelCase   )
										return tuple(lowerCAmelCase   )


						def 					__lowercase	(							self			:     Optional[Any]  ,  lowerCAmelCase			:     List[str]  ,  lowerCAmelCase			:     Optional[int]=None   ):
										lowerCAmelCase		=			[self.bos_token_id] + token_ids_a + [self.eos_token_id]
										if token_ids_a is None:
														return output

										return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




						def 					__lowercase	(							self			:     Optional[int]  ,  lowerCAmelCase			:     List[int]  ,  lowerCAmelCase			:     Optional[List[int]] = None   ):
										lowerCAmelCase		=			[self.sep_token_id]
										lowerCAmelCase		=			[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]





