# models.py

import numpy as np # type: ignore

class LanguageModel$($object$)$:

def get$\_$next$\_$char$\_$log$\_$probs$($self$,$ context$)->$ np$.$ndarray:

$"""$

Returns a log probability distribution over the next characters given a context.

The log should be base e

NOTE: You should make sure you call model.eval$()$ to determinize inference here $($turns off dropout

layers in TransformerEncoder$).$

:param context: the string context that the LM conditions on

:return: A numpy vector log P$($y $|$ context$)$ where y ranges over the output vocabulary.

$"""$

raise Exception$("$Only implemented in subclasses"$)$

def get$\_$log$\_$prob$\_$sequence$($self$,$ next$\_$chars, context$)->$ float:

$"""$

Scores a bunch of characters following context. That is$,$ returns

log P$($nc$1,$ nc$2,$ nc$3,...|$ context$)=$ log P$($nc$1|$ context$)+$ log P$($nc$2|$ context, nc$1),...$

The log should be base e

NOTE: You should make sure you call model.eval$()$ to determinize inference here $($turns off dropout

layers in TransformerEncoder$).$

:param next$\_$chars:

:param context:

:return: The float probability

$"""$

raise Exception$("$Only implemented in subclasses"$)$

class UniformLanguageModel$($LanguageModel$)$:

def $\_\_$init$\_\_($self$,$ voc$\_$size$)$:

self.voc$\_$size $=$ voc$\_$size

def get$\_$next$\_$char$\_$log$\_$probs$($self$,$ context$)$:

return np$.$ones$([$self$.$voc$\_$size$])*$ np$.$log$(1\mathrm{.}0/$self$.$voc$\_$size$)$

def get$\_$log$\_$prob$\_$sequence$($self$,$ next$\_$chars, context$)$:

return np$.$log$(1\mathrm{.}0/$self$.$voc$\_$size$)*$ len$($next$\_$chars$)$

class NeuralLanguageModel$($LanguageModel$)$:

def $\_\_$init$\_\_($self$)$:

raise Exception$("$Implement me$")$

def get$\_$next$\_$char$\_$log$\_$probs$($self$,$ context$)$:

raise Exception$("$Implement me$")$

def get$\_$log$\_$prob$\_$sequence$($self$,$ next$\_$chars, context$)$:

raise Exception$("$Implement me$")$

def train$\_$lm$($args$,$ train$\_$text, dev$\_$text, vocab$\_$index$)$:

$"""$

:param args: command$-$line args, passed through here for your convenience

:param train$\_$text: train text as a sequence of characters

:param dev$\_$text: dev text as a sequence of characters

:param vocab$\_$index: an Indexer of the character vocabulary $(27$ characters$)$

:return: a NeuralLanguageModel instance trained on the given data

$"""$

raise Exception$("$Implement me$")-$ please help me to implement this code