id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
181,599 | import argparse
from pathlib import Path
from typing import Callable, List, Optional, Union
import torch
from fairseq import utils
from fairseq.data.indexed_dataset import get_available_dataset_impl
from fairseq.dataclass.configs import (
CheckpointConfig,
CommonConfig,
CommonEvalConfig,
DatasetConfig,
... | null |
181,600 | import argparse
from pathlib import Path
from typing import Callable, List, Optional, Union
import torch
from fairseq import utils
from fairseq.data.indexed_dataset import get_available_dataset_impl
from fairseq.dataclass.configs import (
CheckpointConfig,
CommonConfig,
CommonEvalConfig,
DatasetConfig,
... | null |
181,605 | import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.model_parallel.models.transformer import ModelParallelTransformerEncoder
from fairseq.models import register_model, register_model_architecture
from fairseq.models.roberta import (
roberta_base_a... | null |
181,610 | import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.model_parallel.models.pipeline_parallel_transformer.layers import (
Embedding,
TransformerDecoderEmbedding,
TransformerDecoderLayer,
TransformerDecoderOutputLayer,
TransformerEnco... | null |
181,611 | import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.model_parallel.models.pipeline_parallel_transformer.layers import (
Embedding,
TransformerDecoderEmbedding,
TransformerDecoderLayer,
TransformerDecoderOutputLayer,
TransformerEnco... | null |
181,615 | import logging
from hydra.core.config_store import ConfigStore
from fairseq.dataclass.configs import FairseqConfig
from omegaconf import DictConfig, OmegaConf
class FairseqConfig(FairseqDataclass):
common: CommonConfig = CommonConfig()
common_eval: CommonEvalConfig = CommonEvalConfig()
distributed_training... | This function adds default values that are stored in dataclasses that hydra doesn't know about |
181,617 | import ast
import inspect
import logging
import os
import re
from argparse import ArgumentError, ArgumentParser, Namespace
from dataclasses import _MISSING_TYPE, MISSING, is_dataclass
from enum import Enum
from typing import Any, Dict, List, Optional, Tuple, Type
from fairseq.dataclass import FairseqDataclass
from fair... | Convert a flat argparse.Namespace to a structured DictConfig. |
181,618 | import ast
import inspect
import logging
import os
import re
from argparse import ArgumentError, ArgumentParser, Namespace
from dataclasses import _MISSING_TYPE, MISSING, is_dataclass
from enum import Enum
from typing import Any, Dict, List, Optional, Tuple, Type
from fairseq.dataclass import FairseqDataclass
from fair... | null |
181,621 | import multiprocessing
import os
import pdb
import sys
class MultiprocessingPdb(pdb.Pdb):
"""A Pdb wrapper that works in a multiprocessing environment.
Usage: `from fairseq import pdb; pdb.set_trace()`
"""
def __init__(self):
pdb.Pdb.__init__(self, nosigint=True)
def _cmdloop(self):
... | null |
181,626 | import io
import logging
import os
import pickle
import random
import socket
import struct
import subprocess
import warnings
from argparse import Namespace
from collections import OrderedDict
from dataclasses import dataclass
from typing import Any, Dict, List, Mapping, Optional
import torch
import torch.distributed as... | null |
181,627 | import io
import logging
import os
import pickle
import random
import socket
import struct
import subprocess
import warnings
from argparse import Namespace
from collections import OrderedDict
from dataclasses import dataclass
from typing import Any, Dict, List, Mapping, Optional
import torch
import torch.distributed as... | null |
181,636 | import contextlib
from typing import Optional
import torch
from fairseq.dataclass.configs import DistributedTrainingConfig
from fairseq.distributed import utils as dist_utils
try:
from fairscale.nn.data_parallel import FullyShardedDataParallel as FSDP
has_FSDP = True
except ImportError:
FSDP = torch.nn.Modu... | null |
181,651 | import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import (
FairseqEncoder,
FairseqEncoderModel,
register_model,
register_model_architecture,
)
from fairseq.models.transformer import DEFAULT_MIN_PARAMS_TO_WRAP, TransformerEncod... | null |
181,652 | import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import (
FairseqEncoder,
FairseqEncoderModel,
register_model,
register_model_architecture,
)
from fairseq.models.transformer import DEFAULT_MIN_PARAMS_TO_WRAP, TransformerEncod... | null |
181,660 | from typing import Optional
import logging
import torch
import torch.nn as nn
from fairseq import utils
from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import TransformerModel
from fairseq.modules.transformer_sentence_encoder import init_bert_params
from .hub_inter... | null |
181,661 | from dataclasses import dataclass, field
from typing import Optional
from fairseq import options, utils
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
from fairseq.models import (
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.models.transformer import (
DE... | null |
181,662 | from dataclasses import dataclass, field
from typing import Optional
from fairseq import options, utils
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
from fairseq.models import (
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.models.transformer import (
DE... | null |
181,663 | from dataclasses import dataclass, field
from typing import Optional
from fairseq import options, utils
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
from fairseq.models import (
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.models.transformer import (
DE... | null |
181,664 | from dataclasses import dataclass, field
from typing import Optional
from fairseq import options, utils
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
from fairseq.models import (
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.models.transformer import (
DE... | null |
181,665 | from dataclasses import dataclass, field
from typing import Optional
from fairseq import options, utils
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
from fairseq.models import (
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.models.transformer import (
DE... | null |
181,666 | from dataclasses import dataclass, field
from typing import Optional
from fairseq import options, utils
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
from fairseq.models import (
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.models.transformer import (
DE... | null |
181,667 | from dataclasses import dataclass, field
from typing import Optional
from fairseq import options, utils
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
from fairseq.models import (
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.models.transformer import (
DE... | null |
181,668 | from dataclasses import dataclass, field
from typing import Optional
from fairseq import options, utils
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
from fairseq.models import (
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.models.transformer import (
DE... | null |
181,669 | from dataclasses import dataclass, field
from typing import Optional
from fairseq import options, utils
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
from fairseq.models import (
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.models.transformer import (
DE... | null |
181,670 | from dataclasses import dataclass, field
from typing import Optional
from fairseq import options, utils
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
from fairseq.models import (
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.models.transformer import (
DE... | null |
181,671 | from dataclasses import dataclass, field
from typing import Optional
from fairseq import options, utils
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
from fairseq.models import (
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.models.transformer import (
DE... | null |
181,672 | from dataclasses import dataclass, field
from typing import Optional
from fairseq import options, utils
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
from fairseq.models import (
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.models.transformer import (
DE... | null |
181,673 | from dataclasses import dataclass, field
from typing import Optional
from fairseq import options, utils
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
from fairseq.models import (
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.models.transformer import (
DE... | null |
181,674 | from dataclasses import dataclass, field
from typing import Optional
from fairseq import options, utils
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
from fairseq.models import (
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.models.transformer import (
DE... | null |
181,675 | from dataclasses import dataclass, field
from typing import Optional
from fairseq import options, utils
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
from fairseq.models import (
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.models.transformer import (
DE... | null |
181,678 | from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import (
TransformerModel,
base_architecture,
transformer_wmt_en_de_big,
)
def base_architecture(args):
args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
args.encoder_embed_dim =... | null |
181,679 | from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import (
TransformerModel,
base_architecture,
transformer_wmt_en_de_big,
)
def transformer_wmt_en_de_big(args):
args.attention_dropout = getattr(args, "attention_dropout", 0.1)
transformer_vaswani... | null |
181,689 | import torch
from fairseq.utils import new_arange
def load_libnat():
def _get_ins_targets(in_tokens, out_tokens, padding_idx, unk_idx):
libnat, use_cuda = load_libnat()
def _get_ins_targets_cuda(in_tokens, out_tokens, padding_idx, unk_idx):
in_masks = in_tokens.ne(padding_idx)
out_masks = out_... | null |
181,693 | import torch
from fairseq.utils import new_arange
def new_arange(x, *size):
"""
Return a Tensor of `size` filled with a range function on the device of x.
If size is empty, using the size of the variable x.
"""
if len(size) == 0:
size = x.size()
return torch.arange(size[-1], device=x.de... | null |
181,703 | import torch
import torch.nn.functional as F
from fairseq import utils
from fairseq.iterative_refinement_generator import DecoderOut
from fairseq.models import register_model, register_model_architecture
from fairseq.models.nat import FairseqNATDecoder, FairseqNATModel, ensemble_decoder
from fairseq.models.transformer ... | null |
181,707 | import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq.iterative_refinement_generator import DecoderOut
from fairseq.models import register_model, register_model_architecture
from fairseq.models.nat import FairseqNATDecoder, FairseqNATModel, ensemble_decoder
from fairseq.models.transformer impo... | null |
181,732 | from fairseq import utils
from fairseq.models import (
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.models.fconv import FConvDecoder
from fairseq.utils import safe_hasattr
def base_lm_architecture(args):
def fconv_lm_dauphin_gbw(args):
layers = "[(512, 5)]"
laye... | null |
181,736 | import logging
import math
from pathlib import Path
from typing import Dict, List, Optional, Tuple
import torch
import torch.nn as nn
from torch import Tensor
from fairseq import checkpoint_utils, utils
from fairseq.data.data_utils import lengths_to_padding_mask
from fairseq.models import (
FairseqEncoder,
Fair... | null |
181,739 | import logging
import math
from pathlib import Path
from typing import Dict, List, Optional, Tuple
import torch
import torch.nn as nn
from torch import Tensor
from fairseq import checkpoint_utils, utils
from fairseq.data.data_utils import lengths_to_padding_mask
from fairseq.models import (
FairseqEncoder,
Fair... | null |
181,774 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import (
FairseqEncoder,
FairseqEncoderDecoderModel,
FairseqIncrementalDecoder,
register_model,
register_model_architecture,
)
from fairseq.modules import (
AdaptiveSoftma... | null |
181,784 | import logging
import json
from typing import Dict
import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
from fairseq.data.audio.audio_utils import (
get_window,
get_fourier_basis,
get_mel_filters,
TTSSpectrogram,
)
from fairseq.data.audio.speech_to_text_dataset import S2T... | null |
181,809 | import argparse
import collections
import os
import re
import torch
from fairseq.file_io import PathManager
class PathManager:
"""
Wrapper for insulating OSS I/O (using Python builtin operations) from
iopath's PathManager abstraction (for transparently handling various
internal backends).
"""
... | Loads checkpoints from inputs and returns a model with averaged weights. Args: inputs: An iterable of string paths of checkpoints to load from. Returns: A dict of string keys mapping to various values. The 'model' key from the returned dict should correspond to an OrderedDict mapping string parameter names to torch Ten... |
181,810 | import argparse
import collections
import os
import re
import torch
from fairseq.file_io import PathManager
class PathManager:
"""
Wrapper for insulating OSS I/O (using Python builtin operations) from
iopath's PathManager abstraction (for transparently handling various
internal backends).
"""
... | null |
181,811 | import argparse
import logging
import math
import os
import sys
from typing import Any, Callable, Dict, List, Optional, Tuple
logger = logging.getLogger("fairseq_cli.train")
import numpy as np
import torch
from omegaconf import DictConfig, OmegaConf
from fairseq import checkpoint_utils, options, quantization_utils, tas... | Train the model for one epoch and return validation losses. |
181,812 | import argparse
import logging
import math
import os
import sys
from typing import Any, Callable, Dict, List, Optional, Tuple
logger = logging.getLogger("fairseq_cli.train")
import numpy as np
import torch
from omegaconf import DictConfig, OmegaConf
from fairseq import checkpoint_utils, options, quantization_utils, tas... | null |
181,814 | import ast
import fileinput
import logging
import math
import os
import sys
import time
from argparse import Namespace
from collections import namedtuple
import numpy as np
import torch
from fairseq import checkpoint_utils, distributed_utils, options, tasks, utils
from fairseq.dataclass.configs import FairseqConfig
fro... | null |
181,815 | import ast
import fileinput
import logging
import math
import os
import sys
import time
from argparse import Namespace
from collections import namedtuple
import numpy as np
import torch
from fairseq import checkpoint_utils, distributed_utils, options, tasks, utils
from fairseq.dataclass.configs import FairseqConfig
fro... | null |
181,816 | import logging
import os
import sys
from argparse import Namespace
from itertools import chain
import torch
from omegaconf import DictConfig
from fairseq import checkpoint_utils, distributed_utils, options, utils
from fairseq.dataclass.utils import convert_namespace_to_omegaconf
from fairseq.logging import metrics, pro... | null |
181,825 | import logging
import os
import hydra
import torch
from hydra.core.hydra_config import HydraConfig
from omegaconf import OmegaConf, open_dict
from fairseq import distributed_utils, metrics
from fairseq.dataclass.configs import FairseqConfig
from fairseq.dataclass.initialize import add_defaults, hydra_init
from fairseq.... | null |
181,826 | import logging
import math
import os
import sys
from argparse import Namespace
from typing import Iterable, List, Optional
import torch
import fairseq
from fairseq import checkpoint_utils, distributed_utils, options, tasks, utils
from fairseq.dataclass.utils import convert_namespace_to_omegaconf
from fairseq.logging im... | null |
181,827 | import ast
import logging
import math
import os
import sys
from argparse import Namespace
from itertools import chain
import numpy as np
import torch
from omegaconf import DictConfig
from fairseq import checkpoint_utils, options, scoring, tasks, utils
from fairseq.dataclass.utils import convert_namespace_to_omegaconf
f... | null |
181,829 | import os
import gzip
from sre_parse import SPECIAL_CHARS
import numpy as np
from random import Random
from typing import Any, Callable, Dict, Generator, Iterable, Iterator, List, Optional, Tuple, Union
import collections
from infinibatch import iterators
def apply_to_sample(f, sample):
if hasattr(sample, "__len__... | null |
181,830 | import copy
import logging
from dataclasses import dataclass, field
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.mod... | null |
181,831 | import copy
import logging
from dataclasses import dataclass, field
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.mod... | null |
181,832 | import torch
import torch.nn as nn
from fairseq.modules import MultiheadAttention
from fairseq import utils
class SimpleConnector(nn.Module):
"""Connector model of GPT and MLM."""
def __init__(self, input_dim, output_dim):
super().__init__()
self.dense = nn.Linear(input_dim, output_dim)
def ... | null |
181,833 | from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
from torch import Tensor
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import distributed_utils, utils
from fairseq import che... | null |
181,834 | from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
from torch import Tensor
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import distributed_utils, utils
from fairseq import che... | null |
181,835 | from fairseq.dataclass import FairseqDataclass
from fairseq.models import (
BaseFairseqModel,
)
from fairseq.models import (
register_model,
register_model_architecture,
)
from fairseq.utils import safe_getattr
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from diffusers import ... | null |
181,836 | from fairseq.dataclass import FairseqDataclass
from fairseq.models import (
BaseFairseqModel,
)
from fairseq.models import (
register_model,
register_model_architecture,
)
from fairseq.utils import safe_getattr
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from diffusers import ... | null |
181,837 | import logging
import os
import torch
from copy import deepcopy
from typing import Tuple, Union, Callable, Optional
from torch import nn
from torch.nn import functional as F
from open_clip.model import CLIP, CLIPVisionCfg, QuickGELU, TimmModel, ModifiedResNet, VisualTransformer, to_2tuple, LayerNorm, Transformer
from o... | null |
181,838 | from app_utils import *
def create_demo_segmentation(generation_fn):
with gr.Blocks() as demo:
with gr.Row():
with gr.Column(scale=1):
image = gr.Image(label="Control image")
prompt = gr.Textbox(label="Prompt", max_lines=1,
pla... | null |
181,839 | from app_utils import *
def create_demo_softedge(generation_fn):
with gr.Blocks() as demo:
with gr.Row():
with gr.Column(scale=1):
image = gr.Image(label="Control image")
prompt = gr.Textbox(label="Prompt", max_lines=1,
placeho... | null |
181,840 | from app_utils import *
def create_demo_normal(generation_fn):
with gr.Blocks() as demo:
with gr.Row():
with gr.Column(scale=1):
image = gr.Image(label="Control image")
prompt = gr.Textbox(label="Prompt", max_lines=1,
placehold... | null |
181,841 | from app_utils import *
def create_canvas(w, h):
return np.zeros(shape=(h, w, 3), dtype=np.uint8) + 255
def create_demo_scribble_interactive(generation_fn):
with gr.Blocks() as demo:
with gr.Row():
with gr.Column(scale=1):
canvas_width = gr.Slider(label='Canvas width',
... | null |
181,842 | from app_utils import *
def create_demo_canny(generation_fn):
with gr.Blocks() as demo:
with gr.Row():
with gr.Column(scale=1):
image = gr.Image(label="Control image")
prompt = gr.Textbox(label="Prompt", max_lines=1,
placeholde... | null |
181,843 | import cv2
import numpy as np
def resize_image(input_image, resolution, interpolation=None):
H, W, C = input_image.shape
H = float(H)
W = float(W)
k = float(resolution) / max(H, W)
H *= k
W *= k
H = int(np.round(H / 64.0)) * 64
W = int(np.round(W / 64.0)) * 64
if interpolation is No... | null |
181,844 | from app_utils import *
def create_demo_openpose(generation_fn):
with gr.Blocks() as demo:
with gr.Row():
with gr.Column(scale=1):
image = gr.Image(label="Control image")
prompt = gr.Textbox(label="Prompt", max_lines=1,
placeho... | null |
181,845 | from app_utils import *
def create_demo_mlsd(generation_fn):
with gr.Blocks() as demo:
with gr.Row():
with gr.Column(scale=1):
image = gr.Image(label="Control image")
prompt = gr.Textbox(label="Prompt", max_lines=1,
placeholder... | null |
181,846 | from app_utils import *
def create_demo_depth(generation_fn):
with gr.Blocks() as demo:
with gr.Row():
with gr.Column(scale=1):
image = gr.Image(label="Control image")
prompt = gr.Textbox(label="Prompt", max_lines=1,
placeholde... | null |
181,847 | from app_utils import *
def create_demo_ip2p(generation_fn):
with gr.Blocks() as demo:
with gr.Row():
with gr.Column(scale=1):
image = gr.Image(label="Control image")
prompt = gr.Textbox(label="Prompt", max_lines=1,
placeholder... | null |
181,848 | from app_utils import *
def create_demo_scribble(generation_fn):
with gr.Blocks() as demo:
with gr.Row():
with gr.Column(scale=1):
image = gr.Image(label="Control image")
prompt = gr.Textbox(label="Prompt", max_lines=1,
placeho... | null |
181,849 | from app_utils import *
def create_demo_shuffle(generation_fn):
with gr.Blocks() as demo:
with gr.Row():
with gr.Column(scale=1):
image = gr.Image(label="Control image")
prompt = gr.Textbox(label="Prompt", max_lines=1,
placehol... | null |
181,850 | from app_utils import *
def create_demo_lineart(generation_fn):
with gr.Blocks() as demo:
with gr.Row():
with gr.Column(scale=1):
image = gr.Image(label="Control image")
prompt = gr.Textbox(label="Prompt", max_lines=1,
placehol... | null |
181,851 | import base64
import io
import json
import multiprocessing
import os
import random
from argparse import ArgumentParser
from multiprocessing import Process
import numpy as np
import requests
import torch
import torch.nn.functional as F
from PIL import Image
from scipy.ndimage import label, find_objects, grey_dilation
fr... | null |
181,852 | import base64
import io
import json
import multiprocessing
import os
import random
from argparse import ArgumentParser
from multiprocessing import Process
import numpy as np
import requests
import torch
import torch.nn.functional as F
from PIL import Image
from scipy.ndimage import label, find_objects, grey_dilation
fr... | null |
181,853 | import argparse
import base64
import io
import json
import os
from multiprocessing import Process
from PIL import Image
from tqdm import tqdm
def save_tsv(args, i, sub_seeds_list):
with open(os.path.join(args.output_dir, 'data', f'{str(i).zfill(4)}.tsv'), 'w') as f:
for name, seeds in tqdm(sub_seeds_list, ... | null |
181,854 | import random
import gradio as gr
import numpy as np
import torch
MAX_SEED = np.iinfo(np.int32).max
def randomize_seed_fn(seed, randomize_seed):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(se... | null |
181,855 | import random
import gradio as gr
import numpy as np
import torch
MAX_INPUT_IMAGES = 10
def variable_images(k):
k = int(k)
return [gr.Textbox.update(visible=True)] * k + [gr.Textbox.update(visible=False)] * (MAX_INPUT_IMAGES - k) | null |
181,857 | import collections
from random import Random
from typing import Dict, Iterable, Optional
import numpy as np
from infinibatch import iterators
The provided code snippet includes necessary dependencies for implementing the `safe_getattr` function. Write a Python function `def safe_getattr(obj, k, default=None)` to solve... | Returns obj[k] if it exists and is not None, otherwise returns default. |
181,858 | import collections
from random import Random
from typing import Dict, Iterable, Optional
import numpy as np
from infinibatch import iterators
The provided code snippet includes necessary dependencies for implementing the `safe_hasattr` function. Write a Python function `def safe_hasattr(obj, k)` to solve the following... | Returns True if the given key exists and is not None. |
181,859 | import collections
from random import Random
from typing import Dict, Iterable, Optional
import numpy as np
from infinibatch import iterators
def image_code_to_token(code):
return "<image{}>".format(code) | null |
181,860 | import logging
from typing import Dict, List, Optional, Tuple
import torch
from fairseq import distributed_utils, utils
from fairseq.distributed import utils as fsdp_wrap
from fairseq.models import (
FairseqEncoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.... | null |
181,861 | import logging
from dataclasses import dataclass, field
from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from apex.normalization import FusedLayerNorm as LayerNorm
from fairseq import utils
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
from fairseq.models impor... | null |
181,862 | import logging
from dataclasses import dataclass, field
from typing import Optional
import torch
from fairseq import distributed_utils, utils
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
from fairseq.models import (
FairseqIncrementalDecoder,
FairseqLanguageModel,
register_model,
register_... | null |
181,863 | import math
import warnings
import torch
import torch.distributed as dist
from fairseq.utils import multi_tensor_l2norm_available, multi_tensor_total_norm
def multi_tensor_total_norm(grads, chunk_size=2048 * 32) -> torch.Tensor:
def clip_grad_norm_(
params, max_norm, moe_expert_count, aggregate_norm_fn=None
) -> ... | null |
181,864 | import torch
import torch.nn as nn
import torch.nn.functional as F
from apex.normalization import FusedLayerNorm as LayerNorm
class set_torch_seed(object):
def __init__(self, seed):
assert isinstance(seed, int)
self.rng_state = self.get_rng_state()
torch.manual_seed(seed)
if torch.cu... | null |
181,865 | import torch
import torch.nn as nn
import torch.nn.functional as F
from apex.normalization import FusedLayerNorm as LayerNorm
def get_activation_fn(activation):
if activation == "relu":
return F.relu
elif activation == "gelu":
return F.gelu
else:
raise NotImplementedError | null |
181,866 | import math
import torch
import torch.nn.functional as F
from apex.normalization import FusedLayerNorm as LayerNorm
from torch import nn
from diffusers.models.attention_processor import LoRALinearLayer
from .multiway_network import MultiwayWrapper
from xformers.ops import memory_efficient_attention, LowerTriangularMask... | null |
181,867 | import logging
import time
from typing import Any, Tuple, cast
import torch
import torch.distributed as dist
from torch import Tensor
from torch.nn import Module, ModuleList
def _find_my_group_index(grouped_ranks):
my_rank = dist.get_rank()
for i, group in enumerate(grouped_ranks):
if my_rank in group:
... | null |
181,868 | import logging
import time
from typing import Any, Tuple, cast
import torch
import torch.distributed as dist
from torch import Tensor
from torch.nn import Module, ModuleList
def _find_my_group_index(grouped_ranks):
def get_all2all_group(moe_expert_count):
if dist.is_initialized():
if not hasattr(get_all2al... | null |
181,871 | import copy
import torch
import torch.nn as nn
class MultiwayNetwork(nn.Module):
def __init__(self, module, dim=0):
super().__init__()
self.dim = dim
self.A = module
self.B = copy.deepcopy(module)
self.B.reset_parameters()
self.split_position = -1
def forward(self... | null |
181,873 | import numpy as np
from scipy.optimize import minimize
import torch
import torch.nn as nn
def fixed_pos_embedding(x):
seq_len, dim = x.shape
inv_freq = 1.0 / (10000 ** (torch.arange(0, dim) / dim))
sinusoid_inp = (
torch.einsum("i , j -> i j", torch.arange(0, seq_len, dtype=torch.float), inv_freq).... | null |
181,874 | import torch.nn as nn
from torchscale.component.multihead_attention import MultiheadAttention
from torchscale.component.multiway_network import MultiwayNetwork
def init_bert_params(module):
def normal_(data):
data.copy_(data.cpu().normal_(mean=0.0, std=0.02).to(data.device))
if isinstance(module, nn.L... | null |
181,875 | from typing import Any, List, Optional, Sequence, Union, Tuple
import torch
from PIL import Image
from torch import Tensor
from torchmetrics import Metric
from torchmetrics.functional.multimodal.clip_score import _get_model_and_processor
from torchmetrics.utilities.checks import _SKIP_SLOW_DOCTEST, _try_proceed_with_ti... | null |
181,876 | import os
import torch
from PIL import Image
from accelerate import Accelerator
from omegaconf import OmegaConf
from torch.nn.utils.rnn import pad_sequence
from torchmetrics.multimodal.clip_score import CLIPScore as CLIP_TScore
from tqdm import tqdm
from app_model import AppModel
from app_utils import randomize_seed_fn... | null |
181,877 | import os
import torch
from PIL import Image
from accelerate import Accelerator
from omegaconf import OmegaConf
from torch.nn.utils.rnn import pad_sequence
from torchmetrics.multimodal.clip_score import CLIPScore as CLIP_TScore
from tqdm import tqdm
from app_model import AppModel
from app_utils import randomize_seed_fn... | null |
181,878 | import os
import subprocess
import sys
from setuptools import setup, find_packages, Extension
from setuptools import Extension, find_packages, setup
import site
version = write_version_py()
with open("README.md") as f:
readme = f.read()
if "READTHEDOCS" in os.environ:
# don't build extensions when generating do... | null |
181,879 | import os
import subprocess
import sys
from setuptools import setup, find_packages, Extension
from setuptools import Extension, find_packages, setup
import site
version = write_version_py()
extensions = [
Extension(
"fairseq.libbleu",
sources=[
"fairseq/clib/libbleu/libbleu.cpp",
... | null |
181,880 | import os
import subprocess
import sys
from setuptools import setup, find_packages, Extension
from setuptools import Extension, find_packages, setup
import site
if "READTHEDOCS" in os.environ:
# don't build extensions when generating docs
extensions = []
if "build_ext" in cmdclass:
del cmdclass["bui... | null |
181,938 | 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 |
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