text: # Copyright (c) 2017-present, Facebook, Inc.
  # All rights reserved.
  #
  # This source code is licensed under the license found in the LICENSE file in
  # the root directory of this source tree. An additional grant of patent rights
  # can be found in the PATENTS file in the same directory.
  #
  #-------------------------------------------------------------------------
  #
  # Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
  # 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
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import argparse
  
  import torch
  
  from fairseq.criterions import CRITERION_REGISTRY
  from fairseq.models import ARCH_MODEL_REGISTRY, ARCH_CONFIG_REGISTRY
  from fairseq.optim import OPTIMIZER_REGISTRY
  from fairseq.optim.lr_scheduler import LR_SCHEDULER_REGISTRY
  from fairseq.tasks import TASK_REGISTRY
  
  
  def get_training_parser(default_task='translation'):
      parser = get_parser('Trainer', default_task)
      add_dataset_args(parser, train=True, gen=True)
      add_distributed_training_args(parser)
      add_model_args(parser)
      add_optimization_args(parser)
      add_checkpoint_args(parser)
      add_generation_args(parser)
      add_perf_args(parser)
      return parser
  
  
  def get_generation_parser(interactive=False, default_task='translation'):
      parser = get_parser('Generation', default_task)
      add_dataset_args(parser, gen=True)
      add_generation_args(parser)
      add_perf_args(parser)
      if interactive:
          add_interactive_args(parser)
      return parser
  
  
  def get_eval_lm_parser(default_task='language_modeling'):
      parser = get_parser('Evaluate Language Model', default_task)
      add_dataset_args(parser, gen=True)
      add_eval_lm_args(parser)
      return parser
  
  
  def eval_str_list(x, type=float):
      if x is None:
          return None
      if isinstance(x, str):
          x = eval(x)
      try:
          return list(map(type, x))
      except TypeError:
          return [type(x)]
  
  
  def eval_bool(x, default=False):
      if x is None:
          return default
      try:
          return bool(eval(x))
      except TypeError:
          return default
  
  
  def parse_args_and_arch(parser, input_args=None, parse_known=False):
      # The parser doesn't know about model/criterion/optimizer-specific args, so
      # we parse twice. First we parse the model/criterion/optimizer, then we
      # parse a second time after adding the *-specific arguments.
      # If input_args is given, we will parse those args instead of sys.argv.
      args, _ = parser.parse_known_args(input_args)
  
  
      # Add model-specific args to parser.
      if hasattr(args, 'arch'):
          model_specific_group = parser.add_argument_group(
              'Model-specific configuration',
              # Only include attributes which are explicitly given as command-line
              # arguments or which have default values.
              argument_default=argparse.SUPPRESS,
          )
          ARCH_MODEL_REGISTRY[args.arch].add_args(model_specific_group)
  
      # Add *-specific args to parser.
      if hasattr(args, 'criterion'):
          CRITERION_REGISTRY[args.criterion].add_args(parser)
      if hasattr(args, 'optimizer'):
          OPTIMIZER_REGISTRY[args.optimizer].add_args(parser)
      if hasattr(args, 'lr_scheduler'):
          LR_SCHEDULER_REGISTRY[args.lr_scheduler].add_args(parser)
      if hasattr(args, 'task'):
          TASK_REGISTRY[args.task].add_args(parser)
  
      # Parse a second time.
      if parse_known:
          args, extra = parser.parse_known_args(input_args)
      else:
          args = parser.parse_args(input_args)
          extra = None
  
      # Post-process args.
      if hasattr(args, 'lr'):
          args.lr = eval_str_list(args.lr, type=float)
      if hasattr(args, 'update_freq'):
          args.update_freq = eval_str_list(args.update_freq, type=int)
      if hasattr(args, 'max_sentences_valid') and args.max_sentences_valid is None:
          args.max_sentences_valid = args.max_sentences
  
      # Apply architecture configuration.
      if hasattr(args, 'arch'):
          ARCH_CONFIG_REGISTRY[args.arch](args)
  
      if parse_known:
          return args, extra
      else:
          return args
  
  
  def get_parser(desc, default_task='translation'):
      parser = argparse.ArgumentParser(
          description='Facebook AI Research Sequence-to-Sequence Toolkit -- ' + desc)
      parser.add_argument('--no-progress-bar', action='store_true', help='disable progress bar')
      parser.add_argument('--log-interval', type=int, default=1000, metavar='N',
                          help='log progress every N batches (when progress bar is disabled)')
      parser.add_argument('--log-format', default=None, help='log format to use',
                          choices=['json', 'none', 'simple', 'tqdm'])
      parser.add_argument('--seed', default=1, type=int, metavar='N',
                          help='pseudo random number generator seed')
      parser.add_argument('--fp16', action='store_true', help='use FP16')
      parser.add_argument('--profile', type=int, default=None)
      # Task definitions can be found under fairseq/tasks/
      parser.add_argument(
          '--task', metavar='TASK', default=default_task, choices=TASK_REGISTRY.keys(),
          help='task: {} (default: {})'.format(', '.join(TASK_REGISTRY.keys()), default_task)
      )
  
      return parser
  
  
  def add_dataset_args(parser, train=False, gen=False):
      group = parser.add_argument_group('Dataset and data loading')
      group.add_argument('--skip-invalid-size-inputs-valid-test', action='store_true',
                         help='ignore too long or too short lines in valid and test set')
      group.add_argument('--max-tokens', type=int, metavar='N',
                         help='maximum number of tokens in a batch')
      group.add_argument('--max-sentences', '--batch-size', type=int, metavar='N',
                         help='maximum number of sentences in a batch')
      group.add_argument('--sentencepiece', action='store_true',
                          help='use when dataset uses sentencepiece encoding')
      if train:
          group.add_argument('--train-subset', default='train', metavar='SPLIT',
                             choices=['train', 'valid', 'test'],
                             help='data subset to use for training (train, valid, test)')
          group.add_argument('--valid-subset', default='valid', metavar='SPLIT',
                             help='comma separated list of data subsets to use for validation'
                                  ' (train, valid, valid1, test, test1)')
          group.add_argument('--max-sentences-valid', type=int, metavar='N',
                             help='maximum number of sentences in a validation batch'
                                  ' (defaults to --max-sentences)')
      if gen:
          group.add_argument('--gen-subset', default='test', metavar='SPLIT',
                             help='data subset to generate (train, valid, test)')
          group.add_argument('--num-shards', default=1, type=int, metavar='N',
                             help='shard generation over N shards')
          group.add_argument('--shard-id', default=0, type=int, metavar='ID',
                             help='id of the shard to generate (id < num_shards)')
      return group
  
  
  def add_distributed_training_args(parser):
      group = parser.add_argument_group('Distributed training')
      group.add_argument('--distributed-world-size', type=int, metavar='N',
                         default=torch.cuda.device_count(),
                         help='total number of GPUs across all nodes (default: all visible GPUs)')
      group.add_argument('--distributed-rank', default=0, type=int,
                         help='rank of the current worker')
      group.add_argument('--local_rank', default=0, type=int,
                         help='rank of the current worker')
      group.add_argument('--distributed-backend', default='nccl', type=str,
                         help='distributed backend')
      group.add_argument('--distributed-init-method', default=None, type=str,
                         help='typically tcp://hostname:port that will be used to '
                              'establish initial connetion')
      group.add_argument('--distributed-port', default=-1, type=int,
                         help='port number (not required if using --distributed-init-method)')
      group.add_argument('--device-id', default=0, type=int,
                         help='which GPU to use (usually configured automatically)')
      return group
  
  
  def add_optimization_args(parser):
      group = parser.add_argument_group('Optimization')
      group.add_argument('--max-epoch', '--me', default=0, type=int, metavar='N',
                         help='force stop training at specified epoch')
      group.add_argument('--max-update', '--mu', default=0, type=int, metavar='N',
                         help='force stop training at specified update')
      group.add_argument('--target-bleu', default=0.0, type=float, metavar='TARGET',
                         help='force stop training after reaching target bleu')
      group.add_argument('--clip-norm', default=25, type=float, metavar='NORM',
                         help='clip threshold of gradients')
      group.add_argument('--sentence-avg', action='store_true',
                         help='normalize gradients by the number of sentences in a batch'
                              ' (default is to normalize by number of tokens)')
      group.add_argument('--update-freq', default='1', metavar='N',
                         help='update parameters every N_i batches, when in epoch i')
  
      # Optimizer definitions can be found under fairseq/optim/
      group.add_argument('--optimizer', default='nag', metavar='OPT',
                         choices=OPTIMIZER_REGISTRY.keys(),
                         help='optimizer: {} (default: nag)'.format(', '.join(OPTIMIZER_REGISTRY.keys())))
      group.add_argument('--lr', '--learning-rate', default='0.25', metavar='LR_1,LR_2,...,LR_N',
                         help='learning rate for the first N epochs; all epochs >N using LR_N'
                              ' (note: this may be interpreted differently depending on --lr-scheduler)')
      group.add_argument('--momentum', default=0.99, type=float, metavar='M',
                         help='momentum factor')
      group.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD',
                         help='weight decay')
  
      # Learning rate schedulers can be found under fairseq/optim/lr_scheduler/
      group.add_argument('--lr-scheduler', default='reduce_lr_on_plateau',
                         help='learning rate scheduler: {} (default: reduce_lr_on_plateau)'.format(
                             ', '.join(LR_SCHEDULER_REGISTRY.keys())))
      group.add_argument('--lr-shrink', default=0.1, type=float, metavar='LS',
                         help='learning rate shrink factor for annealing, lr_new = (lr * lr_shrink)')
      group.add_argument('--min-lr', default=1e-5, type=float, metavar='LR',
                         help='minimum learning rate')
      group.add_argument('--min-loss-scale', default=1e-4, type=float, metavar='D',
                         help='minimum loss scale (for FP16 training)')
  
      # Parallel backward + all-reduce optimization
      group.add_argument('--enable-parallel-backward-allred-opt', action='store_true',
                         help='enable all reduce of w-gradients in parallel with backward propagation (only for FP16 training)')
      group.add_argument('--parallel-backward-allred-opt-threshold', type=int, default=0, metavar='N',
                         help='min num of contiguous gradient elements before all-reduce is triggered')
      group.add_argument('--enable-parallel-backward-allred-opt-correctness-check', action='store_true',
                         help='compare w-gradient values obtained doing all-reduce in parallel vs. at the end')
  
      return group
  
  
  def add_checkpoint_args(parser):
      group = parser.add_argument_group('Checkpointing')
      group.add_argument('--save-dir', metavar='DIR', default='checkpoints',
                         help='path to save checkpoints')
      group.add_argument('--restore-file', default='checkpoint_last.pt',
                         help='filename in save-dir from which to load checkpoint')
      group.add_argument('--save-interval', type=int, default=1, metavar='N',
                         help='save a checkpoint every N epochs')
      group.add_argument('--save-interval-updates', type=int, default=0, metavar='N',
                         help='save a checkpoint (and validate) every N updates')
      group.add_argument('--keep-interval-updates', type=int, default=-1, metavar='N',
                         help='keep last N checkpoints saved with --save-interval-updates')
      group.add_argument('--no-save', action='store_true',
                         help='don\'t save models or checkpoints')
      group.add_argument('--no-epoch-checkpoints', action='store_true',
                         help='only store last and best checkpoints')
      group.add_argument('--validate-interval', type=int, default=1, metavar='N',
                         help='validate every N epochs')
      return group
  
  
  def add_common_eval_args(group):
      group.add_argument('--path', metavar='FILE',
                         help='path(s) to model file(s), colon separated')
      group.add_argument('--remove-bpe', nargs='?', const='@@ ', default=None,
                         help='remove BPE tokens before scoring')
      group.add_argument('--cpu', action='store_true', help='generate on CPU')
      group.add_argument('--quiet', action='store_true',
                         help='only print final scores')
  
  
  def add_eval_lm_args(parser):
      group = parser.add_argument_group('LM Evaluation')
      add_common_eval_args(group)
      group.add_argument('--output-word-probs', action='store_true',
                         help='if set, outputs words and their predicted log probabilities to standard output')
  
  
  def add_generation_args(parser):
      group = parser.add_argument_group('Generation')
      add_common_eval_args(group)
      group.add_argument('--beam', default=4, type=int, metavar='N',
                         help='beam size')
      group.add_argument('--nbest', default=1, type=int, metavar='N',
                         help='number of hypotheses to output')
      group.add_argument('--max-len-a', default=0, type=float, metavar='N',
                         help=('generate sequences of maximum length ax + b, '
                               'where x is the source length'))
      group.add_argument('--max-len-b', default=200, type=int, metavar='N',
                         help=('generate sequences of maximum length ax + b, '
                               'where x is the source length'))
      group.add_argument('--min-len', default=1, type=float, metavar='N',
                         help=('minimum generation length'))
      group.add_argument('--no-early-stop', action='store_true',
                         help=('continue searching even after finalizing k=beam '
                               'hypotheses; this is more correct, but increases '
                               'generation time by 50%%'))
      group.add_argument('--unnormalized', action='store_true',
                         help='compare unnormalized hypothesis scores')
      group.add_argument('--no-beamable-mm', action='store_true',
                         help='don\'t use BeamableMM in attention layers')
      group.add_argument('--lenpen', default=1, type=float,
                         help='length penalty: <1.0 favors shorter, >1.0 favors longer sentences')
      group.add_argument('--unkpen', default=0, type=float,
                         help='unknown word penalty: <0 produces more unks, >0 produces fewer')
      group.add_argument('--replace-unk', nargs='?', const=True, default=None,
                         help='perform unknown replacement (optionally with alignment dictionary)')
      group.add_argument('--score-reference', action='store_true',
                         help='just score the reference translation')
      group.add_argument('--prefix-size', default=0, type=int, metavar='PS',
                         help='initialize generation by target prefix of given length')
      group.add_argument('--sampling', action='store_true',
                         help='sample hypotheses instead of using beam search')
      group.add_argument('--sampling-topk', default=-1, type=int, metavar='PS',
                         help='sample from top K likely next words instead of all words')
      group.add_argument('--sampling-temperature', default=1, type=float, metavar='N',
                         help='temperature for random sampling')
      group.add_argument('--print-alignment', action='store_true',
                         help='if set, uses attention feedback to compute and print alignment to source tokens')
      group.add_argument('--model-overrides', default="{}", type=str, metavar='DICT',
                         help='a dictionary used to override model args at generation that were used during model training')
      group.add_argument('--online-eval', action='store_true',
                         help='score model at the end of epoch')
      group.add_argument('--ignore-case', action='store_true',
                         help='ignore case druing online eval')
      group.add_argument('--bpe-codes', default=None, type=str, metavar='CODES',
                          help='file with bpe codes')
      return group
  
  
  def add_interactive_args(parser):
      group = parser.add_argument_group('Interactive')
      group.add_argument('--buffer-size', default=0, type=int, metavar='N',
                         help='read this many sentences into a buffer before processing them')
  
  
  def add_model_args(parser):
      group = parser.add_argument_group('Model configuration')
  
      # Model definitions can be found under fairseq/models/
      #
      # The model architecture can be specified in several ways.
      # In increasing order of priority:
      # 1) model defaults (lowest priority)
      # 2) --arch argument
      # 3) --encoder/decoder-* arguments (highest priority)
      group.add_argument(
          '--arch', '-a', default='fconv', metavar='ARCH', required=True,
          choices=ARCH_MODEL_REGISTRY.keys(),
          help='model architecture: {} (default: fconv)'.format(
              ', '.join(ARCH_MODEL_REGISTRY.keys())),
      )
  
      # Criterion definitions can be found under fairseq/criterions/
      group.add_argument(
          '--criterion', default='cross_entropy', metavar='CRIT',
          choices=CRITERION_REGISTRY.keys(),
          help='training criterion: {} (default: cross_entropy)'.format(
              ', '.join(CRITERION_REGISTRY.keys())),
      )
  
      return group
  
  def add_perf_args(parser):
      group = parser.add_argument_group('Performance')
      group.add_argument('--fuse-dropout-add', action='store_true',
                         help='Fuse dropout and residual adds.')
      group.add_argument('--fuse-relu-dropout', action='store_true',
                         help='Fuse Relu and Dropout.')
      return group
  