Mixail Petrochuk PetrochukM

## subword_text_tokenizer.py
# coding=utf-8
# Copyright 2017 The Tensor2Tensor Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software

## optimizer_1.py
import torch

from torch.optim import Optimizer


class Optimizer_1(Optimizer):
    """Implements Optimizer_1 algorithm.

    It was been proposed in `http://proceedings.mlr.press/v70/bello17a/bello17a.pdf`.

## top_k_viterbi.py
import torch

# Credits to AllenNLP for the base implementation and base tests:
# https://github.com/allenai/allennlp/blob/master/allennlp/nn/util.py#L174

# Modified AllenNLP `viterbi_decode` to support `top_k` sequences efficiently.
def viterbi_decode(tag_sequence: torch.Tensor, transition_matrix: torch.Tensor, top_k: int=5):
    """
    Perform Viterbi decoding in log space over a sequence given a transition matrix
    specifying pairwise (transition) potentials between tags and a matrix of shape

## hyperband.py
"""
We implement additional hyperparameter optimization methods not present in
https://scikit-optimize.github.io/.

Gist: https://gist.github.com/Deepblue129/2c5fae9daf0529ed589018c6353c9f7b
"""

import math
import logging
import random
	# coding=utf-8
	# Copyright 2017 The Tensor2Tensor Authors.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	import torch

	from torch.optim import Optimizer


	class Optimizer_1(Optimizer):
	"""Implements Optimizer_1 algorithm.

	It was been proposed in `http://proceedings.mlr.press/v70/bello17a/bello17a.pdf`.
	import torch

	# Credits to AllenNLP for the base implementation and base tests:
	# https://github.com/allenai/allennlp/blob/master/allennlp/nn/util.py#L174

	# Modified AllenNLP `viterbi_decode` to support `top_k` sequences efficiently.
	def viterbi_decode(tag_sequence: torch.Tensor, transition_matrix: torch.Tensor, top_k: int=5):
	"""
	Perform Viterbi decoding in log space over a sequence given a transition matrix
	specifying pairwise (transition) potentials between tags and a matrix of shape
	"""
	We implement additional hyperparameter optimization methods not present in
	https://scikit-optimize.github.io/.

	Gist: https://gist.github.com/Deepblue129/2c5fae9daf0529ed589018c6353c9f7b
	"""

	import math
	import logging
	import random