text stringlengths 7 318k | id stringlengths 14 166 | metadata dict | __index_level_0__ int64 0 439 |
|---|---|---|---|
use crate::{Error, Result, Shape};
#[derive(Debug, PartialEq, Eq, Clone)]
pub struct Layout {
shape: Shape,
// The strides are given in number of elements and not in bytes.
stride: Vec<usize>,
start_offset: usize,
}
impl Layout {
pub fn new(shape: Shape, stride: Vec<usize>, start_offset: usize) ->... | candle/candle-core/src/layout.rs/0 | {
"file_path": "candle/candle-core/src/layout.rs",
"repo_id": "candle",
"token_count": 4361
} | 18 |
use crate::{DType, Device, Error, Result, Tensor, WithDType};
use safetensors::tensor as st;
use safetensors::tensor::SafeTensors;
use std::borrow::Cow;
use std::collections::HashMap;
use std::path::Path;
impl From<DType> for st::Dtype {
fn from(value: DType) -> Self {
match value {
DType::U8 =... | candle/candle-core/src/safetensors.rs/0 | {
"file_path": "candle/candle-core/src/safetensors.rs",
"repo_id": "candle",
"token_count": 7743
} | 19 |
use candle_core::{test_device, test_utils, Device, IndexOp, Result, Tensor};
// https://github.com/huggingface/candle/issues/364
fn avg_pool2d(dev: &Device) -> Result<()> {
let data: Vec<f32> = vec![
1., 1., 1., 1., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
];
let t = Tensor::from_vec(data, (... | candle/candle-core/tests/pool_tests.rs/0 | {
"file_path": "candle/candle-core/tests/pool_tests.rs",
"repo_id": "candle",
"token_count": 2083
} | 20 |
[package]
name = "candle-examples"
version.workspace = true
edition.workspace = true
description.workspace = true
repository.workspace = true
keywords.workspace = true
categories.workspace = true
license.workspace = true
readme = "README.md"
[dependencies]
accelerate-src = { workspace = true, optional = true }
candle ... | candle/candle-examples/Cargo.toml/0 | {
"file_path": "candle/candle-examples/Cargo.toml",
"repo_id": "candle",
"token_count": 882
} | 21 |
# candle-distilbert
DistilBert is a distiled version of the Bert model.
## Sentence embeddings
DistilBert is used to compute the sentence embeddings for a prompt. The model weights
are downloaded from the hub on the first run.
```bash
cargo run --example distilbert --release -- --prompt "Here is a test sentence"
>... | candle/candle-examples/examples/distilbert/README.md/0 | {
"file_path": "candle/candle-examples/examples/distilbert/README.md",
"repo_id": "candle",
"token_count": 367
} | 22 |
# candle-phi: 1.3b and 2.7b LLM with state of the art performance for <10b models.
[Phi-1.5](https://huggingface.co/microsoft/phi-1_5) and
[Phi-2](https://huggingface.co/microsoft/phi-2) are language models using
only 1.3 and 2.7 billion parameters but with state of the art performance compared to
models with up to 10... | candle/candle-examples/examples/phi/README.md/0 | {
"file_path": "candle/candle-examples/examples/phi/README.md",
"repo_id": "candle",
"token_count": 1011
} | 23 |
# candle-repvgg
[RepVGG: Making VGG-style ConvNets Great Again](https://arxiv.org/abs/2101.03697).
This candle implementation uses a pre-trained RepVGG network for inference. The
classification head has been trained on the ImageNet dataset and returns the
probabilities for the top-5 classes.
## Running an example
`... | candle/candle-examples/examples/repvgg/README.md/0 | {
"file_path": "candle/candle-examples/examples/repvgg/README.md",
"repo_id": "candle",
"token_count": 254
} | 24 |
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use std::io::Write;
use std::path::PathBuf;
use candle_transformers::models::t5;
use anyhow::{Error as E, Result};
use candle::{DType, Device, Tensor};
use candle_nn::VarBuilder;
use candle_transformers::g... | candle/candle-examples/examples/t5/main.rs/0 | {
"file_path": "candle/candle-examples/examples/t5/main.rs",
"repo_id": "candle",
"token_count": 5920
} | 25 |
pub mod coco_classes;
pub mod imagenet;
pub mod token_output_stream;
use candle::utils::{cuda_is_available, metal_is_available};
use candle::{Device, Result, Tensor};
pub fn device(cpu: bool) -> Result<Device> {
if cpu {
Ok(Device::Cpu)
} else if cuda_is_available() {
Ok(Device::new_cuda(0)?)
... | candle/candle-examples/src/lib.rs/0 | {
"file_path": "candle/candle-examples/src/lib.rs",
"repo_id": "candle",
"token_count": 2436
} | 26 |
/******************************************************************************
* Copyright (c) 2023, Tri Dao.
******************************************************************************/
#pragma once
#include <assert.h>
#include <stdint.h>
#include <stdlib.h>
#include <cuda_fp16.h>
#if defined(__CUDA_ARCH__) ... | candle/candle-flash-attn/kernels/utils.h/0 | {
"file_path": "candle/candle-flash-attn/kernels/utils.h",
"repo_id": "candle",
"token_count": 6965
} | 27 |
pub const AFFINE: &str = include_str!(concat!(env!("OUT_DIR"), "/affine.ptx"));
pub const BINARY: &str = include_str!(concat!(env!("OUT_DIR"), "/binary.ptx"));
pub const CAST: &str = include_str!(concat!(env!("OUT_DIR"), "/cast.ptx"));
pub const CONV: &str = include_str!(concat!(env!("OUT_DIR"), "/conv.ptx"));
pub cons... | candle/candle-kernels/src/lib.rs/0 | {
"file_path": "candle/candle-kernels/src/lib.rs",
"repo_id": "candle",
"token_count": 298
} | 28 |
#include <metal_stdlib>
#
using namespace metal;
METAL_FUNC uint get_strided_index(
uint idx,
constant size_t &num_dims,
constant size_t *dims,
constant size_t *strides
) {
uint strided_i = 0;
for (uint d = 0; d < num_dims; d++) {
uint dim_idx = num_dims - 1 - d;
strided_i += (i... | candle/candle-metal-kernels/src/ternary.metal/0 | {
"file_path": "candle/candle-metal-kernels/src/ternary.metal",
"repo_id": "candle",
"token_count": 2209
} | 29 |
//! Layers defined by closures.
use candle::{Result, Tensor};
use std::sync::Arc;
/// A layer defined by a simple closure.
#[derive(Clone)]
pub struct Func<'a> {
#[allow(clippy::type_complexity)]
f: Arc<dyn 'a + Fn(&Tensor) -> Result<Tensor> + Send + Sync>,
}
impl<'a> std::fmt::Debug for Func<'a> {
fn fmt... | candle/candle-nn/src/func.rs/0 | {
"file_path": "candle/candle-nn/src/func.rs",
"repo_id": "candle",
"token_count": 804
} | 30 |
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use candle::test_utils::to_vec0_round;
use candle::{Device, Result, Tensor};
/* Equivalent python code:
import torch
import torch.nn.functional as F
input = torch.tensor([
[ 1.1050, 0.3013, -1.5394, -... | candle/candle-nn/tests/loss.rs/0 | {
"file_path": "candle/candle-nn/tests/loss.rs",
"repo_id": "candle",
"token_count": 1344
} | 31 |
from typing import Union, Sequence
class Tensor:
"""
This contains the type hints for the magic methodes of the `candle.Tensor` class.
"""
def __add__(self, rhs: Union["Tensor", "Scalar"]) -> "Tensor":
"""
Add a scalar to a tensor or two tensors together.
"""
pass
... | candle/candle-pyo3/_additional_typing/__init__.py/0 | {
"file_path": "candle/candle-pyo3/_additional_typing/__init__.py",
"repo_id": "candle",
"token_count": 1174
} | 32 |
# Generated content DO NOT EDIT
from typing import Any, Callable, Dict, List, Optional, Tuple, Union, Sequence
from os import PathLike
from candle.typing import _ArrayLike, Device, Scalar, Index, Shape
from candle import Tensor, DType, QTensor
class ONNXModel:
"""
A wrapper around an ONNX model.
"""
d... | candle/candle-pyo3/py_src/candle/onnx/__init__.pyi/0 | {
"file_path": "candle/candle-pyo3/py_src/candle/onnx/__init__.pyi",
"repo_id": "candle",
"token_count": 939
} | 33 |
import candle
from candle import Tensor, QTensor
from candle.nn import Module, Linear
from candle.utils import cuda_is_available
import pytest
def test_module_can_be_constructed():
class A(Module):
pass
a = A()
assert a is not None
assert len(list(a.buffers())) == 0
def test_module_registe... | candle/candle-pyo3/tests/bindings/test_module.py/0 | {
"file_path": "candle/candle-pyo3/tests/bindings/test_module.py",
"repo_id": "candle",
"token_count": 1853
} | 34 |
use candle::{Result, Tensor, D};
use candle_nn as nn;
use nn::{Module, VarBuilder};
// Based on the Python version from torchvision.
// https://github.com/pytorch/vision/blob/0d75d9e5516f446c9c0ef93bd4ed9fea13992d06/torchvision/models/efficientnet.py#L47
#[derive(Debug, Clone, Copy)]
pub struct MBConvConfig {
expa... | candle/candle-transformers/src/models/efficientnet.rs/0 | {
"file_path": "candle/candle-transformers/src/models/efficientnet.rs",
"repo_id": "candle",
"token_count": 5123
} | 35 |
use candle::{Result, Tensor};
use candle_nn::{layer_norm, LayerNorm, Linear, Module, VarBuilder};
#[derive(Debug)]
struct Attention {
q_proj: Linear,
k_proj: Linear,
v_proj: Linear,
out_proj: Linear,
num_heads: usize,
}
impl Attention {
fn new(
embedding_dim: usize,
num_heads: ... | candle/candle-transformers/src/models/segment_anything/transformer.rs/0 | {
"file_path": "candle/candle-transformers/src/models/segment_anything/transformer.rs",
"repo_id": "candle",
"token_count": 3597
} | 36 |
use crate::models::vit::{Config, Embeddings, Encoder};
use candle::{Result, Tensor};
use candle_nn::{
embedding, layer_norm, linear_no_bias, Embedding, LayerNorm, Linear, Module, VarBuilder,
};
use serde::Deserialize;
#[derive(Debug, Clone, PartialEq, Deserialize)]
pub struct TrOCRConfig {
pub vocab_size: usiz... | candle/candle-transformers/src/models/trocr.rs/0 | {
"file_path": "candle/candle-transformers/src/models/trocr.rs",
"repo_id": "candle",
"token_count": 7391
} | 37 |
/// A bounding box around an object.
#[derive(Debug, Clone)]
pub struct Bbox<D> {
pub xmin: f32,
pub ymin: f32,
pub xmax: f32,
pub ymax: f32,
pub confidence: f32,
pub data: D,
}
#[derive(Debug, Clone, Copy, PartialEq)]
pub struct KeyPoint {
pub x: f32,
pub y: f32,
pub mask: f32,
}
... | candle/candle-transformers/src/object_detection.rs/0 | {
"file_path": "candle/candle-transformers/src/object_detection.rs",
"repo_id": "candle",
"token_count": 894
} | 38 |
use yew_agent::PublicWorker;
fn main() {
console_error_panic_hook::set_once();
candle_wasm_example_llama2::Worker::register();
}
| candle/candle-wasm-examples/llama2-c/src/bin/worker.rs/0 | {
"file_path": "candle/candle-wasm-examples/llama2-c/src/bin/worker.rs",
"repo_id": "candle",
"token_count": 54
} | 39 |
use candle::{DType, Device, Tensor};
use candle_nn::VarBuilder;
use candle_wasm_example_sam as sam;
use wasm_bindgen::prelude::*;
struct Embeddings {
original_width: u32,
original_height: u32,
width: u32,
height: u32,
data: Tensor,
}
#[wasm_bindgen]
pub struct Model {
sam: sam::Sam,
embedd... | candle/candle-wasm-examples/segment-anything/src/bin/m.rs/0 | {
"file_path": "candle/candle-wasm-examples/segment-anything/src/bin/m.rs",
"repo_id": "candle",
"token_count": 2400
} | 40 |
<html>
<head>
<meta content="text/html;charset=utf-8" http-equiv="Content-Type" />
<title>Candle Whisper Rust/WASM</title>
</head>
<body></body>
</html>
<!DOCTYPE html>
<html>
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<style>
... | candle/candle-wasm-examples/whisper/lib-example.html/0 | {
"file_path": "candle/candle-wasm-examples/whisper/lib-example.html",
"repo_id": "candle",
"token_count": 6488
} | 41 |
use crate::console_log;
use crate::worker::{ModelData, RunData, Worker, WorkerInput, WorkerOutput};
use wasm_bindgen::prelude::*;
use wasm_bindgen_futures::JsFuture;
use yew::{html, Component, Context, Html};
use yew_agent::{Bridge, Bridged};
async fn fetch_url(url: &str) -> Result<Vec<u8>, JsValue> {
use web_sys:... | candle/candle-wasm-examples/yolo/src/app.rs/0 | {
"file_path": "candle/candle-wasm-examples/yolo/src/app.rs",
"repo_id": "candle",
"token_count": 5971
} | 42 |
# Use .env.local to change these variables
# DO NOT EDIT THIS FILE WITH SENSITIVE DATA
MONGODB_URL=#your mongodb URL here
MONGODB_DB_NAME=chat-ui
MONGODB_DIRECT_CONNECTION=false
COOKIE_NAME=hf-chat
HF_TOKEN=#hf_<token> from from https://huggingface.co/settings/token
HF_API_ROOT=https://api-inference.huggingface.co/mo... | chat-ui/.env/0 | {
"file_path": "chat-ui/.env",
"repo_id": "chat-ui",
"token_count": 1922
} | 43 |
export default {
plugins: {
tailwindcss: {},
autoprefixer: {},
},
};
| chat-ui/postcss.config.js/0 | {
"file_path": "chat-ui/postcss.config.js",
"repo_id": "chat-ui",
"token_count": 34
} | 44 |
<script lang="ts">
import { base } from "$app/paths";
import { page } from "$app/stores";
import { PUBLIC_APP_DESCRIPTION, PUBLIC_APP_NAME } from "$env/static/public";
import LogoHuggingFaceBorderless from "$lib/components/icons/LogoHuggingFaceBorderless.svelte";
import Modal from "$lib/components/Modal.svelte";
... | chat-ui/src/lib/components/LoginModal.svelte/0 | {
"file_path": "chat-ui/src/lib/components/LoginModal.svelte",
"repo_id": "chat-ui",
"token_count": 917
} | 45 |
<script lang="ts">
import { webSearchParameters } from "$lib/stores/webSearchParameters";
import CarbonInformation from "~icons/carbon/information";
import Switch from "./Switch.svelte";
const toggle = () => ($webSearchParameters.useSearch = !$webSearchParameters.useSearch);
</script>
<div
class="flex h-8 cursor... | chat-ui/src/lib/components/WebSearchToggle.svelte/0 | {
"file_path": "chat-ui/src/lib/components/WebSearchToggle.svelte",
"repo_id": "chat-ui",
"token_count": 447
} | 46 |
export const PUBLIC_SEP_TOKEN = "</s>";
| chat-ui/src/lib/constants/publicSepToken.ts/0 | {
"file_path": "chat-ui/src/lib/constants/publicSepToken.ts",
"repo_id": "chat-ui",
"token_count": 16
} | 47 |
import { error } from "@sveltejs/kit";
import { collections } from "../database";
import type { Conversation } from "$lib/types/Conversation";
import type { SharedConversation } from "$lib/types/SharedConversation";
export async function downloadFile(
sha256: string,
convId: Conversation["_id"] | SharedConversation[... | chat-ui/src/lib/server/files/downloadFile.ts/0 | {
"file_path": "chat-ui/src/lib/server/files/downloadFile.ts",
"repo_id": "chat-ui",
"token_count": 383
} | 48 |
import { writable } from "svelte/store";
export interface TitleUpdate {
convId: string;
title: string;
}
export default writable<TitleUpdate | null>(null);
| chat-ui/src/lib/stores/titleUpdate.ts/0 | {
"file_path": "chat-ui/src/lib/stores/titleUpdate.ts",
"repo_id": "chat-ui",
"token_count": 50
} | 49 |
export interface Timestamps {
createdAt: Date;
updatedAt: Date;
}
| chat-ui/src/lib/types/Timestamps.ts/0 | {
"file_path": "chat-ui/src/lib/types/Timestamps.ts",
"repo_id": "chat-ui",
"token_count": 23
} | 50 |
export async function sha256(input: string): Promise<string> {
const utf8 = new TextEncoder().encode(input);
const hashBuffer = await crypto.subtle.digest("SHA-256", utf8);
const hashArray = Array.from(new Uint8Array(hashBuffer));
const hashHex = hashArray.map((bytes) => bytes.toString(16).padStart(2, "0")).join(""... | chat-ui/src/lib/utils/sha256.ts/0 | {
"file_path": "chat-ui/src/lib/utils/sha256.ts",
"repo_id": "chat-ui",
"token_count": 119
} | 51 |
<script lang="ts">
import { base } from "$app/paths";
import { clickOutside } from "$lib/actions/clickOutside";
import { afterNavigate, goto } from "$app/navigation";
import { useSettingsStore } from "$lib/stores/settings";
import type { PageData } from "./$types";
import { applyAction, enhance } from "$app/form... | chat-ui/src/routes/assistant/[assistantId]/+page.svelte/0 | {
"file_path": "chat-ui/src/routes/assistant/[assistantId]/+page.svelte",
"repo_id": "chat-ui",
"token_count": 1417
} | 52 |
import { redirect, error } from "@sveltejs/kit";
import { getOIDCUserData, validateAndParseCsrfToken } from "$lib/server/auth";
import { z } from "zod";
import { base } from "$app/paths";
import { updateUser } from "./updateUser";
export async function load({ url, locals, cookies, request, getClientAddress }) {
const... | chat-ui/src/routes/login/callback/+page.server.ts/0 | {
"file_path": "chat-ui/src/routes/login/callback/+page.server.ts",
"repo_id": "chat-ui",
"token_count": 449
} | 53 |
import { base } from "$app/paths";
import { requiresUser } from "$lib/server/auth";
import { collections } from "$lib/server/database";
import { fail, type Actions, redirect } from "@sveltejs/kit";
import { ObjectId } from "mongodb";
import { z } from "zod";
import { sha256 } from "$lib/utils/sha256";
import sharp fr... | chat-ui/src/routes/settings/assistants/[assistantId]/edit/+page.server.ts/0 | {
"file_path": "chat-ui/src/routes/settings/assistants/[assistantId]/edit/+page.server.ts",
"repo_id": "chat-ui",
"token_count": 1573
} | 54 |
<svg xmlns="http://www.w3.org/2000/svg" width="32" height="32" fill="none">
<path
fill="#FFD21E"
d="M4 15.55C4 9.72 8.72 5 14.55 5h4.11a9.34 9.34 0 1 1 0 18.68H7.58l-2.89 2.8a.41.41 0 0 1-.69-.3V15.55Z"
/>
<path
fill="#32343D"
d="M19.63 12.48c.37.14.52.9.9.7.71-.38.98-1.27.6-1.98a1.46 1.46 0 0 0-1.98-.61 1.4... | chat-ui/static/huggingchat/logo.svg/0 | {
"file_path": "chat-ui/static/huggingchat/logo.svg",
"repo_id": "chat-ui",
"token_count": 523
} | 55 |
import json
import os
import tempfile
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import Array2D
from utils import generate_examples, get_duration
SHAPE_TEST_1 = (30, 487)
SHAPE_TEST_2 = (36, 1024)
SPEED_TEST_SHAPE = (100, 100)
SPEED_TEST_N_EXAMPLES = 100
DEFAULT_FEATURES = ... | datasets/benchmarks/benchmark_array_xd.py/0 | {
"file_path": "datasets/benchmarks/benchmark_array_xd.py",
"repo_id": "datasets",
"token_count": 2176
} | 56 |
- sections:
- local: index
title: ๐ค Datasets
- local: quickstart
title: Quickstart
- local: installation
title: Installation
title: Get started
- sections:
- local: tutorial
title: Overview
- local: load_hub
title: Load a dataset from the Hub
- local: access
title: Know your data... | datasets/docs/source/_toctree.yml/0 | {
"file_path": "datasets/docs/source/_toctree.yml",
"repo_id": "datasets",
"token_count": 1247
} | 57 |
# Create a dataset loading script
<Tip>
The dataset loading script is likely not needed if your dataset is in one of the following formats: CSV, JSON, JSON lines, text, images, audio or Parquet.
With those formats, you should be able to load your dataset automatically with [`~datasets.load_dataset`],
as long as your... | datasets/docs/source/dataset_script.mdx/0 | {
"file_path": "datasets/docs/source/dataset_script.mdx",
"repo_id": "datasets",
"token_count": 5380
} | 58 |
# Evaluate predictions
<Tip warning={true}>
Metrics is deprecated in ๐ค Datasets. To learn more about how to use metrics, take a look at the library ๐ค [Evaluate](https://huggingface.co/docs/evaluate/index)! In addition to metrics, you can find more tools for evaluating models and datasets.
</Tip>
๐ค Datasets provi... | datasets/docs/source/metrics.mdx/0 | {
"file_path": "datasets/docs/source/metrics.mdx",
"repo_id": "datasets",
"token_count": 1193
} | 59 |
# Load tabular data
A tabular dataset is a generic dataset used to describe any data stored in rows and columns, where the rows represent an example and the columns represent a feature (can be continuous or categorical). These datasets are commonly stored in CSV files, Pandas DataFrames, and in database tables. This g... | datasets/docs/source/tabular_load.mdx/0 | {
"file_path": "datasets/docs/source/tabular_load.mdx",
"repo_id": "datasets",
"token_count": 1868
} | 60 |
# Copyright 2020 The HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | datasets/metrics/bleurt/bleurt.py/0 | {
"file_path": "datasets/metrics/bleurt/bleurt.py",
"repo_id": "datasets",
"token_count": 1982
} | 61 |
# Copyright 2020 The HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | datasets/metrics/cuad/cuad.py/0 | {
"file_path": "datasets/metrics/cuad/cuad.py",
"repo_id": "datasets",
"token_count": 2242
} | 62 |
# Metric Card for Mahalanobis Distance
## Metric Description
Mahalonobis distance is the distance between a point and a distribution (as opposed to the distance between two points), making it the multivariate equivalent of the Euclidean distance.
It is often used in multivariate anomaly detection, classification on h... | datasets/metrics/mahalanobis/README.md/0 | {
"file_path": "datasets/metrics/mahalanobis/README.md",
"repo_id": "datasets",
"token_count": 738
} | 63 |
# Metric Card for Precision
## Metric Description
Precision is the fraction of correctly labeled positive examples out of all of the examples that were labeled as positive. It is computed via the equation:
Precision = TP / (TP + FP)
where TP is the True positives (i.e. the examples correctly labeled as positive) and... | datasets/metrics/precision/README.md/0 | {
"file_path": "datasets/metrics/precision/README.md",
"repo_id": "datasets",
"token_count": 1878
} | 64 |
# Metric Card for SQuAD
## Metric description
This metric wraps the official scoring script for version 1 of the [Stanford Question Answering Dataset (SQuAD)](https://huggingface.co/datasets/squad).
SQuAD is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles... | datasets/metrics/squad/README.md/0 | {
"file_path": "datasets/metrics/squad/README.md",
"repo_id": "datasets",
"token_count": 1494
} | 65 |
# Copyright 2020 The HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | datasets/metrics/xnli/xnli.py/0 | {
"file_path": "datasets/metrics/xnli/xnli.py",
"repo_id": "datasets",
"token_count": 1107
} | 66 |
import fnmatch
import json
import os
import shutil
import tempfile
import xml.etree.ElementTree as ET
from argparse import ArgumentParser
from pathlib import Path
from typing import Optional
from datasets import config
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_config import D... | datasets/src/datasets/commands/dummy_data.py/0 | {
"file_path": "datasets/src/datasets/commands/dummy_data.py",
"repo_id": "datasets",
"token_count": 11107
} | 67 |
# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... | datasets/src/datasets/features/features.py/0 | {
"file_path": "datasets/src/datasets/features/features.py",
"repo_id": "datasets",
"token_count": 39514
} | 68 |
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class AbstractDatasetReader(ABC):
def __init__(
self,
path_or_paths: ... | datasets/src/datasets/io/abc.py/0 | {
"file_path": "datasets/src/datasets/io/abc.py",
"repo_id": "datasets",
"token_count": 721
} | 69 |
import copy
import os
import warnings
from functools import partial
from itertools import groupby
from typing import TYPE_CHECKING, Callable, Iterator, List, Optional, Tuple, TypeVar, Union
import numpy as np
import pyarrow as pa
import pyarrow.compute as pc
from . import config
from .utils.logging import get_logger
... | datasets/src/datasets/table.py/0 | {
"file_path": "datasets/src/datasets/table.py",
"repo_id": "datasets",
"token_count": 42695
} | 70 |
from typing import Callable
def is_documented_by(function_with_docstring: Callable):
"""Decorator to share docstrings across common functions.
Args:
function_with_docstring (`Callable`): Name of the function with the docstring.
"""
def wrapper(target_function):
target_function.__doc_... | datasets/src/datasets/utils/doc_utils.py/0 | {
"file_path": "datasets/src/datasets/utils/doc_utils.py",
"repo_id": "datasets",
"token_count": 137
} | 71 |
{
"monolingual": "contains a single language",
"multilingual": "contains multiple languages",
"translation": "contains translated or aligned text",
"other": "other type of language distribution"
}
| datasets/src/datasets/utils/resources/multilingualities.json/0 | {
"file_path": "datasets/src/datasets/utils/resources/multilingualities.json",
"repo_id": "datasets",
"token_count": 55
} | 72 |
# isort: skip_file
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: F401 - this is just for tests
import os as renamed_os # noqa: F401 - this is just for tests
from os import path # noqa: F401 - this is just for tests
from os import path as renamed_path # noqa: F401 - th... | datasets/tests/_test_patching.py/0 | {
"file_path": "datasets/tests/_test_patching.py",
"repo_id": "datasets",
"token_count": 175
} | 73 |
import datetime
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pandas as pd
import pyarrow as pa
import pytest
from datasets import Array2D
from datasets.arrow_dataset import Dataset
from datasets.features import Audio, ClassLabel, Features, Image, Sequence, Value
from dataset... | datasets/tests/features/test_features.py/0 | {
"file_path": "datasets/tests/features/test_features.py",
"repo_id": "datasets",
"token_count": 12404
} | 74 |
import shutil
import textwrap
import librosa
import numpy as np
import pytest
import soundfile as sf
from datasets import Audio, ClassLabel, Features, Value
from datasets.data_files import DataFilesDict, get_data_patterns
from datasets.download.streaming_download_manager import StreamingDownloadManager
from datasets.... | datasets/tests/packaged_modules/test_audiofolder.py/0 | {
"file_path": "datasets/tests/packaged_modules/test_audiofolder.py",
"repo_id": "datasets",
"token_count": 8594
} | 75 |
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class DatasetListTest(TestCase):
def _create_example_records(self):
return [
{"col_1": 3, "col_2": "a"},
{"col_1": 2, "col_2": "b"},
{"col_1": 1, "col_2": "c"}... | datasets/tests/test_dataset_list.py/0 | {
"file_path": "datasets/tests/test_dataset_list.py",
"repo_id": "datasets",
"token_count": 875
} | 76 |
import importlib
import os
import pickle
import shutil
import tempfile
import time
from hashlib import sha256
from multiprocessing import Pool
from pathlib import Path
from unittest import TestCase
from unittest.mock import patch
import dill
import pyarrow as pa
import pytest
import requests
import datasets
from data... | datasets/tests/test_load.py/0 | {
"file_path": "datasets/tests/test_load.py",
"repo_id": "datasets",
"token_count": 33517
} | 77 |
import fnmatch
import gc
import os
import shutil
import tempfile
import textwrap
import time
import unittest
from io import BytesIO
from pathlib import Path
from unittest.mock import patch
import numpy as np
import pytest
from huggingface_hub import DatasetCard, HfApi
from huggingface_hub.utils import RepositoryNotFou... | datasets/tests/test_upstream_hub.py/0 | {
"file_path": "datasets/tests/test_upstream_hub.py",
"repo_id": "datasets",
"token_count": 22617
} | 78 |
<jupyter_start><jupyter_text>Unit 4: Code your first Deep Reinforcement Learning Algorithm with PyTorch: Reinforce. And test its robustness ๐ชIn this notebook, you'll code your first Deep Reinforcement Learning algorithm from scratch: Reinforce (also called Monte Carlo Policy Gradient).Reinforce is a *Policy-based meth... | deep-rl-class/notebooks/unit4/unit4.ipynb/0 | {
"file_path": "deep-rl-class/notebooks/unit4/unit4.ipynb",
"repo_id": "deep-rl-class",
"token_count": 12740
} | 79 |
# The Exploration/Exploitation trade-off [[exp-exp-tradeoff]]
Finally, before looking at the different methods to solve Reinforcement Learning problems, we must cover one more very important topic:ย *the exploration/exploitation trade-off.*
- *Exploration* is exploring the environment by trying random actions in order... | deep-rl-class/units/en/unit1/exp-exp-tradeoff.mdx/0 | {
"file_path": "deep-rl-class/units/en/unit1/exp-exp-tradeoff.mdx",
"repo_id": "deep-rl-class",
"token_count": 699
} | 80 |
# Monte Carlo vs Temporal Difference Learning [[mc-vs-td]]
The last thing we need to discuss before diving into Q-Learning is the two learning strategies.
Remember that an RL agentย **learns by interacting with its environment.**ย The idea is thatย **given the experience and the received reward, the agent will update it... | deep-rl-class/units/en/unit2/mc-vs-td.mdx/0 | {
"file_path": "deep-rl-class/units/en/unit2/mc-vs-td.mdx",
"repo_id": "deep-rl-class",
"token_count": 2316
} | 81 |
# Deep Q-Learning [[deep-q-learning]]
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit4/thumbnail.jpg" alt="Unit 3 thumbnail" width="100%">
In the last unit, we learned our first reinforcement learning algorithm: Q-Learning,ย **implemented it from scratch**, and... | deep-rl-class/units/en/unit3/introduction.mdx/0 | {
"file_path": "deep-rl-class/units/en/unit3/introduction.mdx",
"repo_id": "deep-rl-class",
"token_count": 437
} | 82 |
# How do Unity ML-Agents work? [[how-mlagents-works]]
Before training our agent, we need to understand **what ML-Agents is and how it works**.
## What is Unity ML-Agents? [[what-is-mlagents]]
[Unity ML-Agents](https://github.com/Unity-Technologies/ml-agents) is a toolkit for the game engine Unity that **allows us to... | deep-rl-class/units/en/unit5/how-mlagents-works.mdx/0 | {
"file_path": "deep-rl-class/units/en/unit5/how-mlagents-works.mdx",
"repo_id": "deep-rl-class",
"token_count": 1276
} | 83 |
# Introduction [[introduction]]
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit0/thumbnail.png" alt="Thumbnail"/>
Since the beginning of this course, we learned to train agents in a *single-agent system* where our agent was alone in its environment: it was **no... | deep-rl-class/units/en/unit7/introduction.mdx/0 | {
"file_path": "deep-rl-class/units/en/unit7/introduction.mdx",
"repo_id": "deep-rl-class",
"token_count": 574
} | 84 |
# Introduction [[introduction]]
In this bonus unit, we'll reinforce what we learned in the first unit by teaching Huggy the Dog to fetch the stick and then [play with him directly in your browser](https://huggingface.co/spaces/ThomasSimonini/Huggy) ๐ถ
<img src="https://huggingface.co/datasets/huggingface-deep-rl-cour... | deep-rl-class/units/en/unitbonus1/introduction.mdx/0 | {
"file_path": "deep-rl-class/units/en/unitbonus1/introduction.mdx",
"repo_id": "deep-rl-class",
"token_count": 138
} | 85 |
# Brief introduction to RL documentation
In this advanced topic, we address the question: **how should we monitor and keep track of powerful reinforcement learning agents that we are training in the real world and
interfacing with humans?**
As machine learning systems have increasingly impacted modern life, the **cal... | deep-rl-class/units/en/unitbonus3/rl-documentation.mdx/0 | {
"file_path": "deep-rl-class/units/en/unitbonus3/rl-documentation.mdx",
"repo_id": "deep-rl-class",
"token_count": 886
} | 86 |
import os
import sys
import torch
from diffusers import (
AutoPipelineForImage2Image,
AutoPipelineForInpainting,
AutoPipelineForText2Image,
ControlNetModel,
LCMScheduler,
StableDiffusionAdapterPipeline,
StableDiffusionControlNetPipeline,
StableDiffusionXLAdapterPipeline,
StableDiff... | diffusers/benchmarks/base_classes.py/0 | {
"file_path": "diffusers/benchmarks/base_classes.py",
"repo_id": "diffusers",
"token_count": 5055
} | 87 |
<!--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 applicable law or agreed... | diffusers/docs/source/en/api/loaders/unet.md/0 | {
"file_path": "diffusers/docs/source/en/api/loaders/unet.md",
"repo_id": "diffusers",
"token_count": 403
} | 88 |
<!--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 applicable law or agreed... | diffusers/docs/source/en/optimization/fp16.md/0 | {
"file_path": "diffusers/docs/source/en/optimization/fp16.md",
"repo_id": "diffusers",
"token_count": 943
} | 89 |
<!--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 applicable law or agreed... | diffusers/docs/source/en/training/ddpo.md/0 | {
"file_path": "diffusers/docs/source/en/training/ddpo.md",
"repo_id": "diffusers",
"token_count": 322
} | 90 |
<!--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 applicable law or agreed... | diffusers/docs/source/en/tutorials/fast_diffusion.md/0 | {
"file_path": "diffusers/docs/source/en/tutorials/fast_diffusion.md",
"repo_id": "diffusers",
"token_count": 4864
} | 91 |
<!--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 applicable law or agreed... | diffusers/docs/source/en/using-diffusers/inference_with_lcm.md/0 | {
"file_path": "diffusers/docs/source/en/using-diffusers/inference_with_lcm.md",
"repo_id": "diffusers",
"token_count": 3678
} | 92 |
<!--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 applicable law or agreed... | diffusers/docs/source/en/using-diffusers/shap-e.md/0 | {
"file_path": "diffusers/docs/source/en/using-diffusers/shap-e.md",
"repo_id": "diffusers",
"token_count": 2476
} | 93 |
<!--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 applicable law or agreed... | diffusers/docs/source/ko/api/pipelines/stable_diffusion/stable_diffusion_xl.md/0 | {
"file_path": "diffusers/docs/source/ko/api/pipelines/stable_diffusion/stable_diffusion_xl.md",
"repo_id": "diffusers",
"token_count": 10988
} | 94 |
<!--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 applicable law or agreed... | diffusers/docs/source/ko/training/adapt_a_model.md/0 | {
"file_path": "diffusers/docs/source/ko/training/adapt_a_model.md",
"repo_id": "diffusers",
"token_count": 1827
} | 95 |
# ์ด๋ฏธ์ง ๋ฐ๊ธฐ ์กฐ์ ํ๊ธฐ
Stable Diffusion ํ์ดํ๋ผ์ธ์ [์ผ๋ฐ์ ์ธ ๋ํจ์ ๋
ธ์ด์ฆ ์ค์ผ์ค๊ณผ ์ํ ๋จ๊ณ์ ๊ฒฐํจ์ด ์์](https://huggingface.co/papers/2305.08891) ๋
ผ๋ฌธ์์ ์ค๋ช
ํ ๊ฒ์ฒ๋ผ ๋งค์ฐ ๋ฐ๊ฑฐ๋ ์ด๋์ด ์ด๋ฏธ์ง๋ฅผ ์์ฑํ๋ ๋ฐ๋ ์ฑ๋ฅ์ด ํ๋ฒํฉ๋๋ค. ์ด ๋
ผ๋ฌธ์์ ์ ์ํ ์๋ฃจ์
์ ํ์ฌ [`DDIMScheduler`]์ ๊ตฌํ๋์ด ์์ผ๋ฉฐ ์ด๋ฏธ์ง์ ๋ฐ๊ธฐ๋ฅผ ๊ฐ์ ํ๋ ๋ฐ ์ฌ์ฉํ ์ ์์ต๋๋ค.
<Tip>
๐ก ์ ์๋ ์๋ฃจ์
์ ๋ํ ์์ธํ ๋ด์ฉ์ ์์ ๋งํฌ๋ ๋
ผ๋ฌธ์ ์ฐธ๊ณ ํ์ธ์!
</Tip>
ํด๊ฒฐ์ฑ
์ค ํ๋๋ *v ์์ธก๊ฐ*๊ณผ *v ๋ก์ค... | diffusers/docs/source/ko/using-diffusers/control_brightness.md/0 | {
"file_path": "diffusers/docs/source/ko/using-diffusers/control_brightness.md",
"repo_id": "diffusers",
"token_count": 1435
} | 96 |
<!--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 applicable law or agreed... | diffusers/docs/source/ko/using-diffusers/unconditional_image_generation.md/0 | {
"file_path": "diffusers/docs/source/ko/using-diffusers/unconditional_image_generation.md",
"repo_id": "diffusers",
"token_count": 1742
} | 97 |
## Amused training
Amused can be finetuned on simple datasets relatively cheaply and quickly. Using 8bit optimizers, lora, and gradient accumulation, amused can be finetuned with as little as 5.5 GB. Here are a set of examples for finetuning amused on some relatively simple datasets. These training recipies are aggres... | diffusers/examples/amused/README.md/0 | {
"file_path": "diffusers/examples/amused/README.md",
"repo_id": "diffusers",
"token_count": 5921
} | 98 |
#!/usr/bin/env python3
import torch
from diffusers import DiffusionPipeline
class UnetSchedulerOneForwardPipeline(DiffusionPipeline):
def __init__(self, unet, scheduler):
super().__init__()
self.register_modules(unet=unet, scheduler=scheduler)
def __call__(self):
image = torch.randn... | diffusers/examples/community/one_step_unet.py/0 | {
"file_path": "diffusers/examples/community/one_step_unet.py",
"repo_id": "diffusers",
"token_count": 299
} | 99 |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2023 Custom Diffusion authors 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... | diffusers/examples/custom_diffusion/train_custom_diffusion.py/0 | {
"file_path": "diffusers/examples/custom_diffusion/train_custom_diffusion.py",
"repo_id": "diffusers",
"token_count": 26323
} | 100 |
# InstructPix2Pix SDXL training example
***This is based on the original InstructPix2Pix training example.***
[Stable Diffusion XL](https://huggingface.co/papers/2307.01952) (or SDXL) is the latest image generation model that is tailored towards more photorealistic outputs with more detailed imagery and composition c... | diffusers/examples/instruct_pix2pix/README_sdxl.md/0 | {
"file_path": "diffusers/examples/instruct_pix2pix/README_sdxl.md",
"repo_id": "diffusers",
"token_count": 3490
} | 101 |
# Stable Diffusion text-to-image fine-tuning
This extended LoRA training script was authored by [haofanwang](https://github.com/haofanwang).
This is an experimental LoRA extension of [this example](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_lora.py). We further support... | diffusers/examples/research_projects/lora/README.md/0 | {
"file_path": "diffusers/examples/research_projects/lora/README.md",
"repo_id": "diffusers",
"token_count": 1628
} | 102 |
# Stable Diffusion text-to-image fine-tuning
The `train_text_to_image.py` script shows how to fine-tune stable diffusion model on your own dataset.
___Note___:
___This script is experimental. The script fine-tunes the whole model and often times the model overfits and runs into issues like catastrophic forgetting. I... | diffusers/examples/research_projects/onnxruntime/text_to_image/README.md/0 | {
"file_path": "diffusers/examples/research_projects/onnxruntime/text_to_image/README.md",
"repo_id": "diffusers",
"token_count": 847
} | 103 |
# Stable Diffusion XL for JAX + TPUv5e
[TPU v5e](https://cloud.google.com/blog/products/compute/how-cloud-tpu-v5e-accelerates-large-scale-ai-inference) is a new generation of TPUs from Google Cloud. It is the most cost-effective, versatile, and scalable Cloud TPU to date. This makes them ideal for serving and scaling ... | diffusers/examples/research_projects/sdxl_flax/README.md/0 | {
"file_path": "diffusers/examples/research_projects/sdxl_flax/README.md",
"repo_id": "diffusers",
"token_count": 3342
} | 104 |
#!/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... | diffusers/examples/text_to_image/train_text_to_image.py/0 | {
"file_path": "diffusers/examples/text_to_image/train_text_to_image.py",
"repo_id": "diffusers",
"token_count": 19182
} | 105 |
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffusers/examples/unconditional_image_generation/test_unconditional.py/0 | {
"file_path": "diffusers/examples/unconditional_image_generation/test_unconditional.py",
"repo_id": "diffusers",
"token_count": 2493
} | 106 |
"""
This script requires you to build `LAVIS` from source, since the pip version doesn't have BLIP Diffusion. Follow instructions here: https://github.com/salesforce/LAVIS/tree/main.
"""
import argparse
import os
import tempfile
import torch
from lavis.models import load_model_and_preprocess
from transformers import ... | diffusers/scripts/convert_blipdiffusion_to_diffusers.py/0 | {
"file_path": "diffusers/scripts/convert_blipdiffusion_to_diffusers.py",
"repo_id": "diffusers",
"token_count": 5920
} | 107 |
# coding=utf-8
# Copyright 2023 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/scripts/convert_ldm_original_checkpoint_to_diffusers.py/0 | {
"file_path": "diffusers/scripts/convert_ldm_original_checkpoint_to_diffusers.py",
"repo_id": "diffusers",
"token_count": 6854
} | 108 |
import argparse
import sys
import tensorrt as trt
def convert_models(onnx_path: str, num_controlnet: int, output_path: str, fp16: bool = False, sd_xl: bool = False):
"""
Function to convert models in stable diffusion controlnet pipeline into TensorRT format
Example:
python convert_stable_diffusion_c... | diffusers/scripts/convert_stable_diffusion_controlnet_to_tensorrt.py/0 | {
"file_path": "diffusers/scripts/convert_stable_diffusion_controlnet_to_tensorrt.py",
"repo_id": "diffusers",
"token_count": 1860
} | 109 |
# 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... | diffusers/src/diffusers/commands/env.py/0 | {
"file_path": "diffusers/src/diffusers/commands/env.py",
"repo_id": "diffusers",
"token_count": 1070
} | 110 |
# coding=utf-8
# Copyright 2023 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/src/diffusers/loaders/peft.py/0 | {
"file_path": "diffusers/src/diffusers/loaders/peft.py",
"repo_id": "diffusers",
"token_count": 3290
} | 111 |
# 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... | diffusers/src/diffusers/models/autoencoders/autoencoder_kl_temporal_decoder.py/0 | {
"file_path": "diffusers/src/diffusers/models/autoencoders/autoencoder_kl_temporal_decoder.py",
"repo_id": "diffusers",
"token_count": 7180
} | 112 |
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffusers/src/diffusers/models/normalization.py/0 | {
"file_path": "diffusers/src/diffusers/models/normalization.py",
"repo_id": "diffusers",
"token_count": 4030
} | 113 |
# 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... | diffusers/src/diffusers/models/unet_2d_blocks.py/0 | {
"file_path": "diffusers/src/diffusers/models/unet_2d_blocks.py",
"repo_id": "diffusers",
"token_count": 7249
} | 114 |
# coding=utf-8
# Copyright 2023 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/src/diffusers/models/unets/uvit_2d.py/0 | {
"file_path": "diffusers/src/diffusers/models/unets/uvit_2d.py",
"repo_id": "diffusers",
"token_count": 8291
} | 115 |
# 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... | diffusers/src/diffusers/pipelines/audioldm/pipeline_audioldm.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/audioldm/pipeline_audioldm.py",
"repo_id": "diffusers",
"token_count": 11819
} | 116 |
# 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... | diffusers/src/diffusers/pipelines/deprecated/audio_diffusion/pipeline_audio_diffusion.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/deprecated/audio_diffusion/pipeline_audio_diffusion.py",
"repo_id": "diffusers",
"token_count": 6241
} | 117 |
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