python_code stringlengths 0 290k | repo_name stringclasses 30
values | file_path stringlengths 6 125 |
|---|---|---|
# flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all.
from .utils import is_inflect_available, is_transformers_available, is_unidecode_available
__version__ = "0.0.4"
from .modeling_utils import ModelM... | diffusers_all-main | src/diffusers/__init__.py |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# 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... | diffusers_all-main | src/diffusers/hub_utils.py |
import copy
import torch
class EMAModel:
"""
Exponential Moving Average of models weights
"""
def __init__(
self,
model,
update_after_step=0,
inv_gamma=1.0,
power=2 / 3,
min_value=0.0,
max_value=0.9999,
device=None,
):
"""
... | diffusers_all-main | src/diffusers/training_utils.py |
# THIS FILE HAS BEEN AUTOGENERATED. To update:
# 1. modify the `_deps` dict in setup.py
# 2. run `make deps_table_update``
deps = {
"Pillow": "Pillow",
"black": "black~=22.0,>=22.3",
"filelock": "filelock",
"flake8": "flake8>=3.8.3",
"huggingface-hub": "huggingface-hub",
"isort": "isort>=5.5.4",... | diffusers_all-main | src/diffusers/dependency_versions_table.py |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
# Copyright (c) 2022, 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.a... | diffusers_all-main | src/diffusers/modeling_utils.py |
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
#
# 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... | diffusers_all-main | src/diffusers/dynamic_modules_utils.py |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
# Copyright (c) 2022, 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.a... | diffusers_all-main | src/diffusers/pipeline_utils.py |
from ..utils import is_inflect_available, is_transformers_available, is_unidecode_available
from .bddm import BDDMPipeline
from .ddim import DDIMPipeline
from .ddpm import DDPMPipeline
from .latent_diffusion_uncond import LatentDiffusionUncondPipeline
from .pndm import PNDMPipeline
from .score_sde_ve import ScoreSdeVeP... | diffusers_all-main | src/diffusers/pipelines/__init__.py |
# Copyright 2022 The HuggingFace Team. 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 applicabl... | diffusers_all-main | src/diffusers/pipelines/ddim/pipeline_ddim.py |
from .pipeline_ddim import DDIMPipeline
| diffusers_all-main | src/diffusers/pipelines/ddim/__init__.py |
# coding=utf-8
# Copyright 2022 The OpenAI Team Authors and The HuggingFace Team. 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/L... | diffusers_all-main | src/diffusers/pipelines/glide/pipeline_glide.py |
from ...utils import is_transformers_available
if is_transformers_available():
from .pipeline_glide import CLIPTextModel, GlidePipeline
| diffusers_all-main | src/diffusers/pipelines/glide/__init__.py |
import torch
import tqdm
from ...pipeline_utils import DiffusionPipeline
class LatentDiffusionUncondPipeline(DiffusionPipeline):
def __init__(self, vqvae, unet, noise_scheduler):
super().__init__()
noise_scheduler = noise_scheduler.set_format("pt")
self.register_modules(vqvae=vqvae, unet... | diffusers_all-main | src/diffusers/pipelines/latent_diffusion_uncond/pipeline_latent_diffusion_uncond.py |
from .pipeline_latent_diffusion_uncond import LatentDiffusionUncondPipeline
| diffusers_all-main | src/diffusers/pipelines/latent_diffusion_uncond/__init__.py |
from ...utils import is_transformers_available
if is_transformers_available():
from .pipeline_latent_diffusion import LatentDiffusionPipeline, LDMBertModel
| diffusers_all-main | src/diffusers/pipelines/latent_diffusion/__init__.py |
from typing import Optional, Tuple, Union
import numpy as np
import torch
import torch.nn as nn
import torch.utils.checkpoint
import tqdm
from transformers.activations import ACT2FN
from transformers.configuration_utils import PretrainedConfig
from transformers.modeling_outputs import BaseModelOutput
from transformer... | diffusers_all-main | src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py |
from .pipeline_bddm import BDDMPipeline, DiffWave
| diffusers_all-main | src/diffusers/pipelines/bddm/__init__.py |
#!/bin/env python
# -*- coding: utf-8 -*-
########################################################################
#
# DiffWave: A Versatile Diffusion Model for Audio Synthesis
# (https://arxiv.org/abs/2009.09761)
# Modified from https://github.com/philsyn/DiffWave-Vocoder
#
# Author: Max W. Y. Lam (maxwylam@tencen... | diffusers_all-main | src/diffusers/pipelines/bddm/pipeline_bddm.py |
#!/usr/bin/env python3
import torch
from diffusers import DiffusionPipeline
# TODO(Patrick, Anton, Suraj) - rename `x` to better variable names
class ScoreSdeVpPipeline(DiffusionPipeline):
def __init__(self, model, scheduler):
super().__init__()
self.register_modules(model=model, scheduler=schedu... | diffusers_all-main | src/diffusers/pipelines/score_sde_vp/pipeline_score_sde_vp.py |
from .pipeline_score_sde_vp import ScoreSdeVpPipeline
| diffusers_all-main | src/diffusers/pipelines/score_sde_vp/__init__.py |
from .pipeline_score_sde_ve import ScoreSdeVePipeline
| diffusers_all-main | src/diffusers/pipelines/score_sde_ve/__init__.py |
#!/usr/bin/env python3
import torch
from diffusers import DiffusionPipeline
# TODO(Patrick, Anton, Suraj) - rename `x` to better variable names
class ScoreSdeVePipeline(DiffusionPipeline):
def __init__(self, model, scheduler):
super().__init__()
self.register_modules(model=model, scheduler=schedu... | diffusers_all-main | src/diffusers/pipelines/score_sde_ve/pipeline_score_sde_ve.py |
# Copyright 2022 The HuggingFace Team. 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 applicabl... | diffusers_all-main | src/diffusers/pipelines/pndm/pipeline_pndm.py |
from .pipeline_pndm import PNDMPipeline
| diffusers_all-main | src/diffusers/pipelines/pndm/__init__.py |
""" from https://github.com/jaywalnut310/glow-tts"""
import math
import torch
from torch import nn
import tqdm
from ...configuration_utils import ConfigMixin
from ...modeling_utils import ModelMixin
from ...pipeline_utils import DiffusionPipeline
from .grad_tts_utils import GradTTSTokenizer # flake8: noqa
def se... | diffusers_all-main | src/diffusers/pipelines/grad_tts/pipeline_grad_tts.py |
from ...utils import is_inflect_available, is_transformers_available, is_unidecode_available
if is_transformers_available() and is_unidecode_available() and is_inflect_available():
from .grad_tts_utils import GradTTSTokenizer
from .pipeline_grad_tts import GradTTSPipeline, TextEncoder
| diffusers_all-main | src/diffusers/pipelines/grad_tts/__init__.py |
# tokenizer
import os
import re
from shutil import copyfile
import torch
import inflect
from transformers import PreTrainedTokenizer
from unidecode import unidecode
valid_symbols = [
"AA",
"AA0",
"AA1",
"AA2",
"AE",
"AE0",
"AE1",
"AE2",
"AH",
"AH0",
"AH1",
"AH2",
... | diffusers_all-main | src/diffusers/pipelines/grad_tts/grad_tts_utils.py |
from .pipeline_ddpm import DDPMPipeline
| diffusers_all-main | src/diffusers/pipelines/ddpm/__init__.py |
# Copyright 2022 The HuggingFace Team. 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 applicabl... | diffusers_all-main | src/diffusers/pipelines/ddpm/pipeline_ddpm.py |
# coding=utf-8
# Copyright 2020 Optuna, Hugging Face
#
# 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 o... | diffusers_all-main | src/diffusers/utils/logging.py |
# This file is autogenerated by the command `make fix-copies`, do not edit.
# flake8: noqa
from ..utils import DummyObject, requires_backends
class GlideSuperResUNetModel(metaclass=DummyObject):
_backends = ["transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["transformers"]... | diffusers_all-main | src/diffusers/utils/dummy_transformers_objects.py |
# Copyright 2021 The HuggingFace Inc. team. 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 appl... | diffusers_all-main | src/diffusers/utils/__init__.py |
# This file is autogenerated by the command `make fix-copies`, do not edit.
# flake8: noqa
from ..utils import DummyObject, requires_backends
class GradTTSPipeline(metaclass=DummyObject):
_backends = ["transformers", "inflect", "unidecode"]
def __init__(self, *args, **kwargs):
requires_backends(self,... | diffusers_all-main | src/diffusers/utils/dummy_transformers_and_inflect_and_unidecode_objects.py |
import math
from inspect import isfunction
import torch
import torch.nn.functional as F
from torch import nn
# unet_grad_tts.py
# TODO(Patrick) - weird linear attention layer. Check with: https://github.com/huawei-noah/Speech-Backbones/issues/15
class LinearAttention(torch.nn.Module):
def __init__(self, dim, hea... | diffusers_all-main | src/diffusers/models/attention.py |
import torch
from ..configuration_utils import ConfigMixin
from ..modeling_utils import ModelMixin
from .attention import LinearAttention
from .embeddings import get_timestep_embedding
from .resnet import Downsample2D, ResnetBlock2D, Upsample2D
from .unet_new import UNetMidBlock2D
class Mish(torch.nn.Module):
de... | diffusers_all-main | src/diffusers/models/unet_grad_tts.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..configuration_utils import ConfigMixin
from ..modeling_utils import ModelMixin
from .attention import AttentionBlock
from .embeddings import get_timestep_embedding
from .resnet import Downsample2D, ResnetBlock2D, Upsample2D
from .unet_new import... | diffusers_all-main | src/diffusers/models/unet_glide.py |
# model adapted from diffuser https://github.com/jannerm/diffuser/blob/main/diffuser/models/temporal.py
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin
from ..modeling_utils import ModelMixin
from .embeddings import get_timestep_embedding
from .resnet import Downsample1D, ResidualTemp... | diffusers_all-main | src/diffusers/models/unet_rl.py |
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin
from ..modeling_utils import ModelMixin
from .attention import AttentionBlock
from .embeddings import get_timestep_embedding
from .resnet import Downsample2D, ResnetBlock2D, Upsample2D
from .unet_new import UNetMidBlock2D, get_down_block,... | diffusers_all-main | src/diffusers/models/unet_unconditional.py |
# Copyright 2022 The HuggingFace Team. 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 applicabl... | diffusers_all-main | src/diffusers/models/unet.py |
# flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all.
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use thi... | diffusers_all-main | src/diffusers/models/__init__.py |
# Copyright 2022 The HuggingFace Team. 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 applicabl... | diffusers_all-main | src/diffusers/models/embeddings.py |
from functools import partial
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class Upsample2D(nn.Module):
"""
An upsampling layer with an optional convolution.
:param channels: channels in the inputs and outputs. :param use_conv: a bool determining if a convolution... | diffusers_all-main | src/diffusers/models/resnet.py |
import math
from inspect import isfunction
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..configuration_utils import ConfigMixin
from ..modeling_utils import ModelMixin
from .attention import AttentionBlock
from .embeddings import get_timestep_embedding
from .resnet impor... | diffusers_all-main | src/diffusers/models/unet_ldm.py |
import numpy as np
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin
from ..modeling_utils import ModelMixin
from .attention import AttentionBlock
from .resnet import Downsample2D, ResnetBlock2D, Upsample2D
def nonlinearity(x):
# swish
return x * torch.sigmoid(x)
def Normaliz... | diffusers_all-main | src/diffusers/models/vae.py |
# Copyright 2022 The HuggingFace Team. 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 applicabl... | diffusers_all-main | src/diffusers/models/unet_sde_score_estimation.py |
# Copyright 2022 The HuggingFace Team. 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 applicabl... | diffusers_all-main | src/diffusers/models/unet_new.py |
# Copyright 2022 UC Berkely Team and The HuggingFace Team. 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 r... | diffusers_all-main | src/diffusers/schedulers/scheduling_ddpm.py |
# Copyright 2022 Google Brain and The HuggingFace Team. 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 requ... | diffusers_all-main | src/diffusers/schedulers/scheduling_sde_vp.py |
# Copyright 2022 The HuggingFace Team. 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 applicabl... | diffusers_all-main | src/diffusers/schedulers/scheduling_grad_tts.py |
# flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all.
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use thi... | diffusers_all-main | src/diffusers/schedulers/__init__.py |
# Copyright 2022 The HuggingFace Team. 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 applicabl... | diffusers_all-main | src/diffusers/schedulers/scheduling_utils.py |
# Copyright 2022 Google Brain and The HuggingFace Team. 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 requ... | diffusers_all-main | src/diffusers/schedulers/scheduling_sde_ve.py |
# Copyright 2022 Stanford University Team and The HuggingFace Team. 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
#
#... | diffusers_all-main | src/diffusers/schedulers/scheduling_ddim.py |
# Copyright 2022 Zhejiang University Team and The HuggingFace Team. 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
#
#... | diffusers_all-main | src/diffusers/schedulers/scheduling_pndm.py |
from setuptools import setup, find_packages
with open("requirements.txt", "r") as f:
requirements = f.read().splitlines()
setup(name="huggan", install_requires=requirements, packages=find_packages())
| community-events-main | setup.py |
from pathlib import Path
TEMPLATE_MODEL_CARD_PATH = Path(__file__).parent.absolute() / 'model_card_template.md' | community-events-main | huggan/__init__.py |
import argparse
from datasets import load_dataset
from tqdm import tqdm
# choose a dataset
available_datasets = ["apple2orange", "summer2winter_yosemite", "horse2zebra", "monet2photo", "cezanne2photo", "ukiyoe2photo", "vangogh2photo", "maps", "cityscapes", "facades", "iphone2dslr_flower", "ae_photos", "grumpifycat"]
... | community-events-main | huggan/utils/push_to_hub_example.py |
community-events-main | huggan/utils/__init__.py | |
from typing import Optional
from huggingface_hub import HfFolder, whoami
def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
if token is None:
token = HfFolder.get_token()
if organization is None:
username = whoami(token)["name"]
retu... | community-events-main | huggan/utils/hub.py |
community-events-main | huggan/tensorflow/dcgan/__init__.py | |
import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from pathlib import Path
import os
import PIL
from tqdm.auto import tqdm
import argparse
from tensorflow.keras import layers
from datasets import load_dataset
from transformers import DefaultDataCollator
from huggingfa... | community-events-main | huggan/tensorflow/dcgan/train.py |
community-events-main | huggan/pytorch/__init__.py | |
from pathlib import Path
from re import TEMPLATE
from typing import Optional, Union
import os
from huggingface_hub import PyTorchModelHubMixin, HfApi, HfFolder, Repository
from huggan import TEMPLATE_MODEL_CARD_PATH
class HugGANModelHubMixin(PyTorchModelHubMixin):
"""A mixin to push PyTorch Models to the Huggin... | community-events-main | huggan/pytorch/huggan_mixin.py |
community-events-main | huggan/pytorch/metrics/__init__.py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
try:
from torchvision.models.utils import load_state_dict_from_url
except ImportError:
from torch.utils.model_zoo import load_url as load_state_dict_from_url
# Inception weights ported to Pytorch from
# http://download.tenso... | community-events-main | huggan/pytorch/metrics/inception.py |
# sources:
# https://www.kaggle.com/code/ibtesama/gan-in-pytorch-with-fid/notebook
# https://github.com/mseitzer/pytorch-fid/blob/master/src/pytorch_fid/fid_score.py
import numpy as np
from scipy import linalg
from torch.nn.functional import adaptive_avg_pool2d
def calculate_activation_statistics(images, model, bat... | community-events-main | huggan/pytorch/metrics/fid_score.py |
community-events-main | huggan/pytorch/dcgan/__init__.py | |
#!/usr/bin/env python
# coding=utf-8
# Copyright (c) 2022 PyTorch contributors and The HuggingFace Inc. team. 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... | community-events-main | huggan/pytorch/dcgan/modeling_dcgan.py |
#!/usr/bin/env python
# coding=utf-8
# Copyright (c) 2022 PyTorch contributors and The HuggingFace Inc. team. 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... | community-events-main | huggan/pytorch/dcgan/train.py |
import random
import torch
import torch.nn.functional as F
def DiffAugment(x, types=[]):
for p in types:
for f in AUGMENT_FNS[p]:
x = f(x)
return x.contiguous()
# """
# Augmentation functions got images as `x`
# where `x` is tensor with this dimensions:
# 0 - count of images
# 1 - chann... | community-events-main | huggan/pytorch/lightweight_gan/diff_augment.py |
community-events-main | huggan/pytorch/lightweight_gan/__init__.py | |
import fire
import random
from retry.api import retry_call
from tqdm import tqdm
from datetime import datetime
from pathlib import Path
from lightweight_gan import Trainer, NanException
import torch
import torch.multiprocessing as mp
import numpy as np
def exists(val):
return val is not None
def default(val, d)... | community-events-main | huggan/pytorch/lightweight_gan/cli.py |
import os
import json
import tempfile
from random import random
import math
from math import log2, floor
from pathlib import Path
from functools import partial
from contextlib import contextmanager, ExitStack
from pathlib import Path
from shutil import rmtree
import torch
from torch.optim import Adam
from torch import... | community-events-main | huggan/pytorch/lightweight_gan/lightweight_gan.py |
community-events-main | huggan/pytorch/cyclegan/__init__.py | |
import random
import time
import datetime
import sys
from torch.autograd import Variable
import torch
import numpy as np
from torchvision.utils import save_image
class ReplayBuffer:
def __init__(self, max_size=50):
assert max_size > 0, "Empty buffer or trying to create a black hole. Be careful."
... | community-events-main | huggan/pytorch/cyclegan/utils.py |
import argparse
import os
import numpy as np
import itertools
from pathlib import Path
import datetime
import time
import sys
from PIL import Image
from torchvision.transforms import Compose, Resize, ToTensor, Normalize, RandomCrop, RandomHorizontalFlip
from torchvision.utils import save_image, make_grid
from torch.... | community-events-main | huggan/pytorch/cyclegan/train.py |
import torch.nn as nn
import torch.nn.functional as F
import torch
from huggan.pytorch.huggan_mixin import HugGANModelHubMixin
##############################
# RESNET
##############################
class ResidualBlock(nn.Module):
def __init__(self, in_features):
super(ResidualBlock, self).__i... | community-events-main | huggan/pytorch/cyclegan/modeling_cyclegan.py |
#!/usr/bin/env python
# coding=utf-8
# Copyright (c) 2022 Erik Linder-Norén and The HuggingFace Inc. team. 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://... | community-events-main | huggan/pytorch/pix2pix/modeling_pix2pix.py |
community-events-main | huggan/pytorch/pix2pix/__init__.py | |
#!/usr/bin/env python
# coding=utf-8
# Copyright (c) 2022 Erik Linder-Norén and The HuggingFace Inc. team. 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://... | community-events-main | huggan/pytorch/pix2pix/train.py |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2022 The HuggingFace Team. 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... | community-events-main | whisper-fine-tuning-event/run_speech_recognition_seq2seq_streaming.py |
import argparse
from transformers import pipeline
from transformers.models.whisper.english_normalizer import BasicTextNormalizer
from datasets import load_dataset, Audio
import evaluate
wer_metric = evaluate.load("wer")
def is_target_text_in_range(ref):
if ref.strip() == "ignore time segment in scoring":
... | community-events-main | whisper-fine-tuning-event/run_eval_whisper_streaming.py |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. 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/LI... | community-events-main | jax-controlnet-sprint/training_scripts/train_controlnet_flax.py |
import os
import datasets
import pandas as pd
_VERSION = datasets.Version("0.0.2")
_DESCRIPTION = "TODO"
_HOMEPAGE = "TODO"
_LICENSE = "TODO"
_CITATION = "TODO"
_FEATURES = datasets.Features(
{
"image": datasets.Image(),
"conditioning_image": datasets.Image(),
"text": datasets.Value("str... | community-events-main | jax-controlnet-sprint/dataset_tools/data.py |
import argparse
import logging
import random
import cv2
import jsonlines
import numpy as np
import requests
from datasets import load_dataset
from PIL import Image
logger = logging.getLogger(__name__)
def parse_args():
parser = argparse.ArgumentParser(
description="Example of a data preprocessing script... | community-events-main | jax-controlnet-sprint/dataset_tools/coyo_1m_dataset_preprocess.py |
# Copyright 2021 The HuggingFace Team. 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 applicabl... | accelerate-main | setup.py |
# Copyright 2022 The HuggingFace Team. 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 applicabl... | accelerate-main | tests/test_big_modeling.py |
# Copyright 2022 The HuggingFace Team. 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 applicabl... | accelerate-main | tests/test_optimizer.py |
# Copyright 2021 The HuggingFace Team. 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 applicabl... | accelerate-main | tests/test_data_loader.py |
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class MockLaunchConfig(SageMakerConfig):
compute_environment = ... | accelerate-main | tests/test_sagemaker.py |
# Copyright 2021 The HuggingFace Team. 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 applicabl... | accelerate-main | tests/test_utils.py |
# Copyright 2022 The HuggingFace Team. 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 applicabl... | accelerate-main | tests/test_cpu.py |
# Copyright 2022 The HuggingFace Team. 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 applicabl... | accelerate-main | tests/test_memory_utils.py |
# Copyright 2021 The HuggingFace Team. 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 applicabl... | accelerate-main | tests/test_metrics.py |
# Copyright 2021 The HuggingFace Team. 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 applicabl... | accelerate-main | tests/test_kwargs_handlers.py |
# Copyright 2021 The HuggingFace Team. 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 applicabl... | accelerate-main | tests/test_scheduler.py |
# Copyright 2022 The HuggingFace Team. 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 applicabl... | accelerate-main | tests/test_modeling_utils.py |
import json
import os
import pickle
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import Gradient... | accelerate-main | tests/test_accelerator.py |
# Copyright 2022 The HuggingFace Team. 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 applicabl... | accelerate-main | tests/test_tracking.py |
# Copyright 2023 The HuggingFace Team. 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 applicabl... | accelerate-main | tests/test_quantization.py |
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