id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
185,239 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import logging
import glob
import json
import argparse
import math
import string
from multiprocessing import Pool, cpu_count
from tqdm import tqdm, trange
from pathlib import Path
import numpy as np
im... | null |
185,241 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import logging
import glob
import json
import argparse
import math
import string
from multiprocessing import Pool, cpu_count
from tqdm import tqdm, trange
from pathlib import Path
import numpy as np
im... | null |
185,242 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import logging
import glob
import json
import argparse
import math
import string
from multiprocessing import Pool, cpu_count
from tqdm import tqdm, trange
from pathlib import Path
import numpy as np
im... | null |
185,243 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import logging
import glob
import json
import argparse
import math
import string
from multiprocessing import Pool, cpu_count
from tqdm import tqdm, trange
from pathlib import Path
import numpy as np
im... | null |
185,244 | import task
import deit
import trocr_models
import torch
import fairseq
from fairseq import utils
from fairseq_cli import generate
from PIL import Image
import torchvision.transforms as transforms
def init(model_path, beam=5):
model, cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[model... | null |
185,245 | import task
import deit
import trocr_models
import torch
import fairseq
from fairseq import utils
from fairseq_cli import generate
from PIL import Image
import torchvision.transforms as transforms
def preprocess(img_path, img_transform):
im = Image.open(img_path).convert('RGB').resize((384, 384))
im = img_tran... | null |
185,246 | import task
import deit
import trocr_models
import torch
import fairseq
from fairseq import utils
from fairseq_cli import generate
from PIL import Image
import torchvision.transforms as transforms
def get_text(cfg, generator, model, sample, bpe):
decoder_output = task.inference_step(generator, model, sample, prefi... | null |
185,247 | import cv2
import numpy as np
from wand.image import Image as WandImage
from scipy.ndimage import zoom as scizoom
from wand.api import library as wandlibrary
def clipped_zoom(img, zoom_factor):
h = img.shape[1]
# ceil crop height(= crop width)
ch = int(np.ceil(h / float(zoom_factor)))
top = (h - ch) /... | null |
185,248 | import cv2
import numpy as np
from wand.image import Image as WandImage
from scipy.ndimage import zoom as scizoom
from wand.api import library as wandlibrary
def disk(radius, alias_blur=0.1, dtype=np.float32):
if radius <= 8:
L = np.arange(-8, 8 + 1)
ksize = (3, 3)
else:
L = np.arange(-... | null |
185,249 | import cv2
import numpy as np
from wand.image import Image as WandImage
from scipy.ndimage import zoom as scizoom
from wand.api import library as wandlibrary
The provided code snippet includes necessary dependencies for implementing the `plasma_fractal` function. Write a Python function `def plasma_fractal(mapsize=256... | Generate a heightmap using diamond-square algorithm. Return square 2d array, side length 'mapsize', of floats in range 0-255. 'mapsize' must be a power of two. |
185,250 | import torchvision.transforms as transforms
from PIL import Image, ImageFilter
import random
import torch
import numpy as np
import logging
from enum import Enum
from .augmentation.warp import Curve, Distort, Stretch
from .augmentation.geometry import Rotate, Perspective, Shrink, TranslateX, TranslateY
from .augmentati... | null |
185,251 | import torchvision.transforms as transforms
from PIL import Image, ImageFilter
import random
import torch
import numpy as np
import logging
from enum import Enum
from .augmentation.warp import Curve, Distort, Stretch
from .augmentation.geometry import Rotate, Perspective, Shrink, TranslateX, TranslateY
from .augmentati... | null |
185,252 | import glob
import logging
import os
import random
import torch
from fairseq.data import FairseqDataset, data_utils
from natsort import natsorted
from PIL import Image
from tqdm import tqdm
def default_collater(target_dict, samples, dataset=None):
if not samples:
return None
if any([sample is None for ... | null |
185,253 | import glob
import logging
import os
import random
import torch
from fairseq.data import FairseqDataset, data_utils
from natsort import natsorted
from PIL import Image
from tqdm import tqdm
logger = logging.getLogger(__name__)
def read_txt_and_tokenize(txt_path: str, bpe, target_dict):
annotations = []
with ope... | null |
185,254 | import glob
import logging
import os
import random
import torch
from fairseq.data import FairseqDataset, data_utils
from natsort import natsorted
from PIL import Image
from tqdm import tqdm
def STR(gt_path, bpe_parser):
root_dir = os.path.dirname(gt_path)
data = []
img_id = 0
with open(gt_path, 'r') as... | null |
185,255 | import glob
import logging
import os
import random
import torch
from fairseq.data import FairseqDataset, data_utils
from natsort import natsorted
from PIL import Image
from tqdm import tqdm
def Receipt53K(gt_path):
root_dir = os.path.dirname(gt_path)
data = []
with open(gt_path, 'r', encoding='utf8') as fp... | null |
185,256 | from fairseq.models import FairseqEncoder, register_model, FairseqEncoderDecoderModel, register_model_architecture
from fairseq.models.transformer import TransformerDecoder, Embedding, TransformerModel
from fairseq.models.transformer import base_architecture as base_transformer
from fairseq.models.fairseq_encoder impor... | null |
185,257 | from fairseq.models import FairseqEncoder, register_model, FairseqEncoderDecoderModel, register_model_architecture
from fairseq.models.transformer import TransformerDecoder, Embedding, TransformerModel
from fairseq.models.transformer import base_architecture as base_transformer
from fairseq.models.fairseq_encoder impor... | null |
185,258 | from fairseq.models import FairseqEncoder, register_model, FairseqEncoderDecoderModel, register_model_architecture
from fairseq.models.transformer import TransformerDecoder, Embedding, TransformerModel
from fairseq.models.transformer import base_architecture as base_transformer
from fairseq.models.fairseq_encoder impor... | null |
185,259 | from fairseq.models import FairseqEncoder, register_model, FairseqEncoderDecoderModel, register_model_architecture
from fairseq.models.transformer import TransformerDecoder, Embedding, TransformerModel
from fairseq.models.transformer import base_architecture as base_transformer
from fairseq.models.fairseq_encoder impor... | null |
185,260 | from fairseq.models import FairseqEncoder, register_model, FairseqEncoderDecoderModel, register_model_architecture
from fairseq.models.transformer import TransformerDecoder, Embedding, TransformerModel
from fairseq.models.transformer import base_architecture as base_transformer
from fairseq.models.fairseq_encoder impor... | null |
185,261 | from fairseq.models import FairseqEncoder, register_model, FairseqEncoderDecoderModel, register_model_architecture
from fairseq.models.transformer import TransformerDecoder, Embedding, TransformerModel
from fairseq.models.transformer import base_architecture as base_transformer
from fairseq.models.fairseq_encoder impor... | null |
185,262 | from fairseq.models import FairseqEncoder, register_model, FairseqEncoderDecoderModel, register_model_architecture
from fairseq.models.transformer import TransformerDecoder, Embedding, TransformerModel
from fairseq.models.transformer import base_architecture as base_transformer
from fairseq.models.fairseq_encoder impor... | null |
185,263 | import os
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from timm.models.vision_transformer import VisionTransformer, _cfg
from timm.models.vision_transformer import Attention, Block
from timm.models.registry import register_model
from timm.models.layers... | null |
185,264 | import os
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from timm.models.vision_transformer import VisionTransformer, _cfg
from timm.models.vision_transformer import Attention, Block
from timm.models.registry import register_model
from timm.models.layers... | null |
185,265 | import os
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from timm.models.vision_transformer import VisionTransformer, _cfg
from timm.models.vision_transformer import Attention, Block
from timm.models.registry import register_model
from timm.models.layers... | null |
185,266 | import os
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from timm.models.vision_transformer import VisionTransformer, _cfg
from timm.models.vision_transformer import Attention, Block
from timm.models.registry import register_model
from timm.models.layers... | null |
185,267 | import os
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from timm.models.vision_transformer import VisionTransformer, _cfg
from timm.models.vision_transformer import Attention, Block
from timm.models.registry import register_model
from timm.models.layers... | null |
185,268 | import os
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from timm.models.vision_transformer import VisionTransformer, _cfg
from timm.models.vision_transformer import Attention, Block
from timm.models.registry import register_model
from timm.models.layers... | null |
185,269 | import os
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from timm.models.vision_transformer import VisionTransformer, _cfg
from timm.models.vision_transformer import Attention, Block
from timm.models.registry import register_model
from timm.models.layers... | null |
185,270 | import os
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from timm.models.vision_transformer import VisionTransformer, _cfg
from timm.models.vision_transformer import Attention, Block
from timm.models.registry import register_model
from timm.models.layers... | null |
185,271 | import os
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from timm.models.vision_transformer import VisionTransformer, _cfg
from timm.models.vision_transformer import Attention, Block
from timm.models.registry import register_model
from timm.models.layers... | null |
185,272 | import os
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from timm.models.vision_transformer import VisionTransformer, _cfg
from timm.models.vision_transformer import Attention, Block
from timm.models.registry import register_model
from timm.models.layers... | null |
185,273 | import os
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from timm.models.vision_transformer import VisionTransformer, _cfg
from timm.models.vision_transformer import Attention, Block
from timm.models.registry import register_model
from timm.models.layers... | null |
185,274 | import os
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from timm.models.vision_transformer import VisionTransformer, _cfg
from timm.models.vision_transformer import Attention, Block
from timm.models.registry import register_model
from timm.models.layers... | null |
185,275 | import torch.nn as nn
from fairseq.models import FairseqEncoder, register_model, FairseqEncoderDecoderModel, register_model_architecture
from fairseq.models.transformer import TransformerDecoder, Embedding, TransformerModel
from fairseq.models.fairseq_encoder import EncoderOut
from fairseq import utils
from timm.models... | null |
185,276 | import torch.nn as nn
from fairseq.models import FairseqEncoder, register_model, FairseqEncoderDecoderModel, register_model_architecture
from fairseq.models.transformer import TransformerDecoder, Embedding, TransformerModel
from fairseq.models.fairseq_encoder import EncoderOut
from fairseq import utils
from timm.models... | null |
185,277 | import torch
import logging
from torch import Tensor
from transformers import PreTrainedTokenizerFast, BatchEncoding
from typing import Mapping, Dict, List
logger = _setup_logger()
def _setup_logger():
log_format = logging.Formatter("[%(asctime)s %(levelname)s] %(message)s")
logger = logging.getLogger()
lo... | null |
185,278 | import torch
import logging
from torch import Tensor
from transformers import PreTrainedTokenizerFast, BatchEncoding
from typing import Mapping, Dict, List
def move_to_cuda(sample):
if len(sample) == 0:
return {}
def _move_to_cuda(maybe_tensor):
if torch.is_tensor(maybe_tensor):
re... | null |
185,279 | import torch
import logging
from torch import Tensor
from transformers import PreTrainedTokenizerFast, BatchEncoding
from typing import Mapping, Dict, List
def pool(last_hidden_states: Tensor,
attention_mask: Tensor,
pool_type: str) -> Tensor:
last_hidden = last_hidden_states.masked_fill(~attenti... | null |
185,280 | import torch
import logging
from torch import Tensor
from transformers import PreTrainedTokenizerFast, BatchEncoding
from typing import Mapping, Dict, List
def create_batch_dict(tokenizer: PreTrainedTokenizerFast, input_texts: List[str], always_add_eos: bool, max_length: int = 512) -> BatchEncoding:
if not always_... | null |
185,281 | import torch
import logging
from torch import Tensor
from transformers import PreTrainedTokenizerFast, BatchEncoding
from typing import Mapping, Dict, List
def get_task_def_by_task_name_and_type(task_name: str, task_type: str) -> str:
if task_type in ['STS']:
return "Retrieve semantically similar text."
... | null |
185,282 | import torch
import logging
from torch import Tensor
from transformers import PreTrainedTokenizerFast, BatchEncoding
from typing import Mapping, Dict, List
def get_detailed_instruct(task_description: str) -> str:
if not task_description:
return ''
return 'Instruct: {}\nQuery: '.format(task_description... | null |
185,283 | import torch
from fairseq import utils
def varsize_tensor_all_gather(tensor):
# cuda_device = f'cuda:{torch.distributed.get_rank()}
cuda_device = 'cuda'
if tensor is None:
size_tens = torch.tensor([0], dtype=torch.int64, device=cuda_device)
else:
size_tens = torch.tensor([tensor.shape[0]], dtype=torch.i... | Performs all_gather operation on the provided tensors. *** Warning ***: torch.distributed.all_gather has no gradient. |
185,284 | import torch
from fairseq import utils
def _get_logging_loss(loss, reduce=True):
if loss is None: return 0
return utils.item(loss.data) if reduce else loss.data | null |
185,285 | import torch
from fairseq import utils
def construct_idx_tensor_from_list(idx_list2d, lens, pad_idx, device=None):
max_len = max(lens)
padded_list = [list_i + [pad_idx] * (max_len - lens[i]) for i, list_i in enumerate(idx_list2d)]
tensor = torch.LongTensor(padded_list)
if device is not None:
tensor = tenso... | null |
185,286 | import torch
from fairseq import utils
def move_to_device(sample, device):
def _move_to_device(tensor):
return tensor.to(device=device)
return utils.apply_to_sample(_move_to_device, sample) | null |
185,287 | import numpy as np
import torch
from fairseq.data import data_utils, FairseqDataset, MaskTokensDataset, TruncateDataset, BaseWrapperDataset
from infoxlm.data.dict_dataset import DictDataset
class XlcoDataset(FairseqDataset):
def __init__(self, dataset, vocab, remove_bos_of_item2=True, seed=1):
def set_epoch(s... | null |
185,288 | import torch
from fairseq.data import (data_utils,
TokenBlockDataset, PrependTokenDataset, PadDataset, TruncateDataset,
NumelDataset, NumSamplesDataset, NestedDictionaryDataset,
MaskTokensDataset, AppendTokenDataset, )
from fairseq.data.encoders.utils import get_whole_word_mask
def get_prepended_token_block_data... | null |
185,289 | import torch
from fairseq.data import (data_utils,
TokenBlockDataset, PrependTokenDataset, PadDataset, TruncateDataset,
NumelDataset, NumSamplesDataset, NestedDictionaryDataset,
MaskTokensDataset, AppendTokenDataset, )
from fairseq.data.encoders.utils import get_whole_word_mask
def add_mlm_args(parser):
parse... | null |
185,290 | import torch
from fairseq.data import (data_utils,
TokenBlockDataset, PrependTokenDataset, PadDataset, TruncateDataset,
NumelDataset, NumSamplesDataset, NestedDictionaryDataset,
MaskTokensDataset, AppendTokenDataset, )
from fairseq.data.encoders.utils import get_whole_word_mask
def get_preprocessed_ptb_dataset(... | null |
185,291 | import torch
from fairseq.data import BaseWrapperDataset
from fairseq.data import (data_utils,
TokenBlockDataset, PrependTokenDataset, PadDataset, TruncateDataset,
NumelDataset, NumSamplesDataset, NestedDictionaryDataset,
MaskTokensDataset, AppendTokenDataset, )
from infoxlm.data.mlm_utils import get_mlm_dataset... | null |
185,292 | import torch
from fairseq.data import (data_utils,
TokenBlockDataset, PrependTokenDataset, PadDataset, TruncateDataset,
NumelDataset, NumSamplesDataset, NestedDictionaryDataset,
MaskTokensDataset, AppendTokenDataset, )
from fairseq.data.encoders.utils import get_whole_word_mask
from infoxlm.data.mlm_utils import... | null |
185,293 | import os
import torch
from functools import lru_cache
from fairseq.tasks import register_task, FairseqTask
from fairseq.data.dictionary import Dictionary
from fairseq.data import FairseqDataset
from fairseq import utils
from infoxlm.data import mlm_utils
from infoxlm.data.dict_dataset import DictDataset
from infoxlm.d... | null |
185,294 | import os
from functools import lru_cache
import numpy as np
import torch
from fairseq import utils
from fairseq.data.data_utils import process_bpe_symbol
from fairseq.data.dictionary import Dictionary
from fairseq.tasks import FairseqTask, register_task
from infoxlm.data import mlm_utils
from infoxlm.data.dict_dataset... | null |
185,295 | import os
from functools import lru_cache
import numpy as np
import torch
from fairseq import utils
from fairseq.data.data_utils import process_bpe_symbol
from fairseq.data.dictionary import Dictionary
from fairseq.tasks import FairseqTask, register_task
from infoxlm.data import mlm_utils
from infoxlm.data.dict_dataset... | null |
185,296 | import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import checkpoint_utils
from fairseq import utils
from fairseq.models import (
BaseFairseqModel,
register_model,
register_model_architecture,
)
from fairseq.models.roberta import (
RobertaModel,
roberta_base_archite... | null |
185,297 | import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import checkpoint_utils
from fairseq import utils
from fairseq.models import (
BaseFairseqModel,
register_model,
register_model_architecture,
)
from fairseq.models.roberta import (
RobertaModel,
roberta_base_archite... | null |
185,298 | import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import checkpoint_utils
from fairseq import utils
from fairseq.models import (
BaseFairseqModel,
register_model,
register_model_architecture,
)
from fairseq.models.roberta import (
RobertaModel,
roberta_base_archite... | null |
185,299 | import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import checkpoint_utils
from fairseq import utils
from fairseq.models import (
BaseFairseqModel,
register_model,
register_model_architecture,
)
from fairseq.models.roberta import (
RobertaModel,
RobertaEncoder,
ro... | null |
185,300 | import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import checkpoint_utils
from fairseq import utils
from fairseq.models import (
BaseFairseqModel,
register_model,
register_model_architecture,
)
from fairseq.models.roberta import (
RobertaModel,
RobertaEncoder,
ro... | null |
185,301 | import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import checkpoint_utils
from fairseq import utils
from fairseq.models import (
BaseFairseqModel,
register_model,
register_model_architecture,
)
from fairseq.models.roberta import (
RobertaModel,
roberta_base_architecture,
robert... | null |
185,302 | import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import checkpoint_utils
from fairseq import utils
from fairseq.models import (
BaseFairseqModel,
register_model,
register_model_architecture,
)
from fairseq.models.roberta import (
RobertaModel,
roberta_base_architecture,
robert... | null |
185,303 | import collections
import math
import random
import numpy as np
import torch
from fairseq import checkpoint_utils, distributed_utils, options, progress_bar, tasks, utils
from fairseq.data import iterators
from fairseq.trainer import Trainer
from fairseq.meters import AverageMeter, StopwatchMeter
def get_training_stats(... | Train the model for one epoch. |
185,304 | import collections
import math
import random
import numpy as np
import torch
from fairseq import checkpoint_utils, distributed_utils, options, progress_bar, tasks, utils
from fairseq.data import iterators
from fairseq.trainer import Trainer
from fairseq.meters import AverageMeter, StopwatchMeter
def main(args, init_dis... | null |
185,305 | from collections import namedtuple
import fileinput
import torch
from fairseq import checkpoint_utils, options, tasks, utils
from fairseq.data import encoders
def buffered_read(input, buffer_size):
buffer = []
with fileinput.input(files=[input], openhook=fileinput.hook_encoded("utf-8")) as h:
for src_s... | null |
185,306 | from collections import namedtuple
import fileinput
import torch
from fairseq import checkpoint_utils, options, tasks, utils
from fairseq.data import encoders
Batch = namedtuple('Batch', 'ids src_tokens src_lengths')
def make_batches(lines, args, task, max_positions, encode_fn):
tokens = [
task.source_dict... | null |
185,307 | from collections import namedtuple
import fileinput
import torch
from fairseq import checkpoint_utils, options, tasks, utils
from fairseq.data import encoders
def main(args):
utils.import_user_module(args)
if args.buffer_size < 1:
args.buffer_size = 1
if args.max_tokens is None and args.max_sentence... | null |
185,308 | import torch
def calc_mean_invstddev(feature):
if len(feature.size()) != 2:
raise ValueError("We expect the input feature to be 2-D tensor")
mean = feature.mean(0)
var = feature.var(0)
# avoid division by ~zero
eps = 1e-8
if (var < eps).any():
return mean, 1.0 / (torch.sqrt(var) ... | null |
185,313 | import json
import os
import re
import torch
from fairseq.data import Dictionary
from fairseq.tasks import FairseqTask, register_task
from examples.speech_recognition.data import AsrDataset
from examples.speech_recognition.data.replabels import replabel_symbol
The provided code snippet includes necessary dependencies ... | Parse data json and create dataset. See scripts/asr_prep_json.py which pack json from raw files Json example: { "utts": { "4771-29403-0025": { "input": { "length_ms": 170, "path": "/tmp/file1.flac" }, "output": { "text": "HELLO \n", "token": "HE LLO", "tokenid": "4815, 861" } }, "1564-142299-0096": { ... } } |
185,315 | import logging
import math
import os
import sentencepiece as spm
import torch
from fairseq import checkpoint_utils, options, progress_bar, utils, tasks
from fairseq.meters import StopwatchMeter, TimeMeter
from fairseq.utils import import_user_module
def check_args(args):
assert args.path is not None, "--path requi... | null |
185,316 | import logging
import math
import os
import sentencepiece as spm
import torch
from fairseq import checkpoint_utils, options, progress_bar, utils, tasks
from fairseq.meters import StopwatchMeter, TimeMeter
from fairseq.utils import import_user_module
def get_dataset_itr(args, task):
return task.get_batch_iterator(
... | null |
185,317 | import logging
import math
import os
import sentencepiece as spm
import torch
from fairseq import checkpoint_utils, options, progress_bar, utils, tasks
from fairseq.meters import StopwatchMeter, TimeMeter
from fairseq.utils import import_user_module
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
de... | null |
185,318 | import logging
import math
import os
import sentencepiece as spm
import torch
from fairseq import checkpoint_utils, options, progress_bar, utils, tasks
from fairseq.meters import StopwatchMeter, TimeMeter
from fairseq.utils import import_user_module
def prepare_result_files(args):
def get_res_file(file_prefix):
... | null |
185,319 | import logging
import math
import os
import sentencepiece as spm
import torch
from fairseq import checkpoint_utils, options, progress_bar, utils, tasks
from fairseq.meters import StopwatchMeter, TimeMeter
from fairseq.utils import import_user_module
def load_models_and_criterions(filenames, arg_overrides=None, task=No... | null |
185,320 | import logging
import math
import os
import sentencepiece as spm
import torch
from fairseq import checkpoint_utils, options, progress_bar, utils, tasks
from fairseq.meters import StopwatchMeter, TimeMeter
from fairseq.utils import import_user_module
The provided code snippet includes necessary dependencies for impleme... | Optimize ensemble for generation |
185,321 | import logging
import math
import os
import sentencepiece as spm
import torch
from fairseq import checkpoint_utils, options, progress_bar, utils, tasks
from fairseq.meters import StopwatchMeter, TimeMeter
from fairseq.utils import import_user_module
def add_asr_eval_argument(parser):
parser.add_argument("--kspmodel... | null |
185,322 | from __future__ import absolute_import, division, print_function, unicode_literals
from collections import namedtuple
import concurrent.futures
from itertools import chain
import argparse
import os
import json
import sentencepiece as spm
import multiprocessing
import torchaudio
from fairseq.data import Dictionary
MILLI... | null |
185,345 | from functools import lru_cache
import json
def find_token(sentence, start_pos):
found_tok = None
for tok in sentence:
if tok.idx == start_pos:
found_tok = tok
break
return found_tok
def find_span(sentence, search_text, start=0):
search_text = search_text.lower()
for ... | null |
185,349 | import argparse
from itertools import chain
import sys
import random
import numpy as np
from sacrebleu import compute_bleu, corpus_bleu as _corpus_bleu
def dictolist(d):
def load_sys(paths):
src, tgt, hypos, log_probs = {}, {}, {}, {}
for path in paths:
with open(path) as f:
for line in f:
... | null |
185,354 | from fairseq import options
def add_reranking_args(parser):
group = parser.add_argument_group("Reranking")
# fmt: off
group.add_argument('--score-model1', '-s1', type=str, metavar='FILE', required=True,
help='path to first model or ensemble of models for rescoring')
group.add_argu... | null |
185,355 | from fairseq import options
def add_reranking_args(parser):
group = parser.add_argument_group("Reranking")
# fmt: off
group.add_argument('--score-model1', '-s1', type=str, metavar='FILE', required=True,
help='path to first model or ensemble of models for rescoring')
group.add_argu... | null |
185,356 | import subprocess
import os
import re
from fairseq import options
import eval_lm
import preprocess
from contextlib import redirect_stdout
import math
The provided code snippet includes necessary dependencies for implementing the `reprocess` function. Write a Python function `def reprocess(fle)` to solve the following ... | reprocess generate.py output |
185,357 | import subprocess
import os
import re
from fairseq import options
import eval_lm
import preprocess
from contextlib import redirect_stdout
import math
The provided code snippet includes necessary dependencies for implementing the `reprocess_nbest` function. Write a Python function `def reprocess_nbest(fle)` to solve th... | reprocess interactive.py output |
185,358 | import subprocess
import os
import re
from fairseq import options
import eval_lm
import preprocess
from contextlib import redirect_stdout
import math
def remove_bpe(line, bpe_symbol):
line = line.replace("\n", '')
line = (line + ' ').replace(bpe_symbol, '').rstrip()
return line+("\n")
def remove_bpe_dict(p... | null |
185,359 | import subprocess
import os
import re
from fairseq import options
import eval_lm
import preprocess
from contextlib import redirect_stdout
import math
def parse_bleu_scoring(line):
p = re.compile(r'(BLEU4 = )\d+[.]\d+')
res = re.search(p, line)
assert res is not None, line
return float(res.group()[8:]) | null |
185,360 | import subprocess
import os
import re
from fairseq import options
import eval_lm
import preprocess
from contextlib import redirect_stdout
import math
The provided code snippet includes necessary dependencies for implementing the `get_full_from_prefix` function. Write a Python function `def get_full_from_prefix(hypo_pr... | given a hypo prefix, recover the first hypo from the list of complete hypos beginning with that prefix |
185,361 | import subprocess
import os
import re
from fairseq import options
import eval_lm
import preprocess
from contextlib import redirect_stdout
import math
def get_score(a, b, c, target_len, bitext_score1, bitext_score2=None, lm_score=None,
lenpen=None, src_len=None, tgt_len=None, bitext1_backwards=False,
... | null |
185,362 | import subprocess
import os
import re
from fairseq import options
import eval_lm
import preprocess
from contextlib import redirect_stdout
import math
def calc_length_from_frac(bpe_sentence, prefix_frac, bpe_symbol):
# return number of words, (not bpe tokens) that we want
no_bpe_sen = remove_bpe(bpe_sentence, bp... | null |
185,363 | import subprocess
import os
import re
from fairseq import options
import eval_lm
import preprocess
from contextlib import redirect_stdout
import math
def calc_length_from_frac(bpe_sentence, prefix_frac, bpe_symbol):
# return number of words, (not bpe tokens) that we want
no_bpe_sen = remove_bpe(bpe_sentence, bp... | parse output of eval_lm |
185,364 | import subprocess
import os
import re
from fairseq import options
import eval_lm
import preprocess
from contextlib import redirect_stdout
import math
def get_directories(data_dir_name, num_rescore, gen_subset,
fw_name, shard_id, num_shards,
sampling=False, prefix_len=None,
... | null |
185,365 | import subprocess
import os
import re
from fairseq import options
import eval_lm
import preprocess
from contextlib import redirect_stdout
import math
def write_reprocessed(sources, hypos, targets, source_outfile,
hypo_outfile, target_outfile, right_to_left=False,
prefix_len=N... | null |
185,366 | import subprocess
import os
import re
from fairseq import options
import eval_lm
import preprocess
from contextlib import redirect_stdout
import math
def rescore_file_name(nbest_dir, prefix_len, scorer_name, lm_file=False,
target_prefix_frac=None, source_prefix_frac=None, backwards=None):
if ... | null |
185,367 | import rerank_utils
import os
from fairseq import options
from examples.noisychannel import rerank_options
from contextlib import redirect_stdout
import generate
def score_bw(args):
if args.backwards1:
scorer1_src = args.target_lang
scorer1_tgt = args.source_lang
else:
... | null |
185,368 | import rerank_utils
import rerank_generate
import rerank_score_bw
import rerank_score_lm
from fairseq import bleu, options
from fairseq.data import dictionary
from examples.noisychannel import rerank_options
from multiprocessing import Pool
import math
import numpy as np
def rerank(args):
if type(args.lenpen) is no... | null |
185,369 | import rerank
import argparse
import numpy as np
import random
from examples.noisychannel import rerank_options
from fairseq import options
def random_search(args):
param_values = []
tuneable_parameters = ['lenpen', 'weight1', 'weight2', 'weight3']
initial_params = [args.lenpen, args.weight1, args.weight2, ... | null |
185,370 | import rerank_utils
import os
from fairseq import options
from examples.noisychannel import rerank_options
def score_lm(args):
using_nbest = args.nbest_list is not None
pre_gen, left_to_right_preprocessed_dir, right_to_left_preprocessed_dir, \
backwards_preprocessed_dir, lm_preprocessed_dir = \
... | null |
185,371 | from contextlib import redirect_stdout
import os
import subprocess
import rerank_utils
from examples.noisychannel import rerank_options
from fairseq import options
import generate
import preprocess
def gen_and_reprocess_nbest(args):
if args.score_dict_dir is None:
args.score_dict_dir = args.data
if args... | null |
185,372 | import torch
from fairseq import checkpoint_utils, options, progress_bar, utils
def main(args, override_args=None):
def cli_main():
parser = options.get_validation_parser()
args = options.parse_args_and_arch(parser)
# only override args that are explicitly given on the command line
override_parser = o... | null |
185,373 | from collections import Counter
from itertools import zip_longest
from fairseq import options, tasks, utils
from fairseq.data import indexed_dataset
from fairseq.binarizer import Binarizer
from multiprocessing import Pool
import os
import shutil
def dataset_dest_file(args, output_prefix, lang, extension):
base = da... | null |
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