import{s as zs,o as As,n as Ds}from"../chunks/scheduler.b108d059.js";import{S as Ps,i as Ks,g as p,s as a,r as o,A as st,h as c,f as l,c as n,j as x,u as r,x as m,k as Ss,y as g,a as e,v as d,d as y,t as M,w as h}from"../chunks/index.008de539.js";import{T as tt}from"../chunks/Tip.aeb15ab7.js";import{C as P}from"../chunks/CodeBlock.7b00c886.js";import{H as Ns,E as lt}from"../chunks/index.c4599453.js";function et(ts){let i,j="<code>HfFileSystem</code> 提供了 fsspec 兼容性，这对于需要它的库（例如，直接使用 <code>pandas</code> 读取 Hugging Face 数据集）非常有用。然而，由于这种兼容性层，会引入额外的开销。为了更好的性能和可靠性，建议尽可能使用 <code>HfApi</code> 方法。";return{c(){i=p("p"),i.innerHTML=j},l(U){i=c(U,"P",{"data-svelte-h":!0}),m(i)!=="svelte-1y5y408"&&(i.innerHTML=j)},m(U,L){e(U,i,L)},p:Ds,d(U){U&&l(i)}}}function at(ts){let i,j,U,L,J,ls,w,Gs='除了 <code>HfApi</code>，<code>huggingface_hub</code> 库还提供了 <code>HfFileSystem</code>，这是一个符合 <a href="https://filesystem-spec.readthedocs.io/en/latest/" rel="nofollow">fsspec</a> 规范的 Python 文件接口，用于与 Hugging Face Hub 交互。<code>HfFileSystem</code> 基于 <code>HfApi</code> 构建，提供了典型的文件系统操作，如 <code>cp</code>、<code>mv</code>、<code>ls</code>、<code>du</code>、<code>glob</code>、<code>get_file</code> 和 <code>put_file</code>。',es,u,as,k,ns,R,ps,V,Ys="可以传递可选的 <code>revision</code> 参数，以从特定提交（如分支、标签名或提交哈希）运行操作。",cs,C,vs="与 Python 内置的 <code>open</code> 不同，<code>fsspec</code> 的 <code>open</code> 默认是二进制模式 <code>&quot;rb&quot;</code>。这意味着您必须明确设置模式为 <code>&quot;r&quot;</code> 以读取文本模式，或 <code>&quot;w&quot;</code> 以写入文本模式。目前不支持追加到文件（模式 <code>&quot;a&quot;</code> 和 <code>&quot;ab&quot;</code>）",is,b,ms,N,Xs="<code>HfFileSystem</code> 可以与任何集成了 <code>fsspec</code> 的库一起使用，前提是 URL 遵循以下格式：",os,Q,rs,f,Es='<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/huggingface_hub/hf_urls.png"/>',ds,F,Is="对于数据集，<code>repo_type_prefix</code> 为 <code>datasets/</code>，对于Space，<code>repo_type_prefix</code>为 <code>spaces/</code>，模型不需要在 URL 中使用这样的前缀。",ys,$,_s="以下是一些 <code>HfFileSystem</code> 简化与 Hub 交互的有趣集成：",Ms,W,Z,z,Hs='从 Hub 仓库读取/写入 <a href="https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#reading-writing-remote-files" rel="nofollow">Pandas</a> DataFrame ：',Qs,S,hs,G,Os='同样的工作流程也适用于 <a href="https://docs.dask.org/en/stable/how-to/connect-to-remote-data.html" rel="nofollow">Dask</a> 和 <a href="https://pola-rs.github.io/polars/py-polars/html/reference/io.html" rel="nofollow">Polars</a> DataFrames.',Us,T,Y,A,qs='使用 <a href="https://duckdb.org/docs/guides/python/filesystems" rel="nofollow">DuckDB</a> 查询(远程)Hub文件：',Fs,v,$s,X,D,Bs='使用 <a href="https://zarr.readthedocs.io/en/stable/tutorial.html#io-with-fsspec" rel="nofollow">Zarr</a> 将 Hub 作为数组存储：',Zs,E,gs,I,us,_,xs='在许多情况下，您必须登录 Hugging Face 账户才能与 Hub 交互。请参阅文档的<a href="../quick-start#authentication">认证</a> 部分，了解有关 Hub 上认证方法的更多信息。',fs,H,Ls="也可以通过将您的 token 作为参数传递给 <code>HfFileSystem</code> 以编程方式登录：",Ts,O,js,q,Ws="如果您以这种方式登录，请注意在共享源代码时不要意外泄露令牌！",Js,B,ws,K,ks;return J=new Ns({props:{title:"通过文件系统 API 与 Hub 交互",local:"通过文件系统-api-与-hub-交互",headingTag:"h1"}}),u=new tt({props:{warning:!0,$$slots:{default:[et]},$$scope:{ctx:ts}}}),k=new Ns({props:{title:"使用方法",local:"使用方法",headingTag:"h2"}}),R=new P({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEhmRmlsZVN5c3RlbSUwQWZzJTIwJTNEJTIwSGZGaWxlU3lzdGVtKCklMEElMEElMjMlMjAlRTUlODglOTclRTUlODclQkElRTclOUIlQUUlRTUlQkQlOTUlRTQlQjglQUQlRTclOUElODQlRTYlODklODAlRTYlOUMlODklRTYlOTYlODclRTQlQkIlQjYlMEFmcy5scyglMjJkYXRhc2V0cyUyRm15LXVzZXJuYW1lJTJGbXktZGF0YXNldC1yZXBvJTJGZGF0YSUyMiUyQyUyMGRldGFpbCUzREZhbHNlKSUwQSUwQSUyMyUyMCVFNSU4OCU5NyVFNSU4NyVCQSVFNCVCQiU5MyVFNSVCQSU5MyVFNCVCOCVBRCVFNyU5QSU4NCVFNiU4OSU4MCVFNiU5QyU4OSUyMCUyMi5jc3YlMjIlMjAlRTYlOTYlODclRTQlQkIlQjYlMEFmcy5nbG9iKCUyMmRhdGFzZXRzJTJGbXktdXNlcm5hbWUlMkZteS1kYXRhc2V0LXJlcG8lMkYqKiUyRiouY3N2JTIyKSUwQSUwQSUyMyUyMCVFOCVBRiVCQiVFNSU4RiU5NiVFOCVCRiU5QyVFNyVBOCU4QiVFNiU5NiU4NyVFNCVCQiVCNiUwQXdpdGglMjBmcy5vcGVuKCUyMmRhdGFzZXRzJTJGbXktdXNlcm5hbWUlMkZteS1kYXRhc2V0LXJlcG8lMkZkYXRhJTJGdHJhaW4uY3N2JTIyJTJDJTIwJTIyciUyMiklMjBhcyUyMGYlM0ElMEElMjAlMjAlMjAlMjB0cmFpbl9kYXRhJTIwJTNEJTIwZi5yZWFkbGluZXMoKSUwQSUwQSUyMyUyMCVFOCVCRiU5QyVFNyVBOCU4QiVFNiU5NiU4NyVFNCVCQiVCNiVFNSU4NiU4NSVFNSVBRSVCOSVFOCVBRiVCQiVFNSU4RiU5NiVFNCVCOCVCQSVFNSVBRCU5NyVFNyVBQyVBNiVFNCVCOCVCMiUwQXRyYWluX2RhdGElMjAlM0QlMjBmcy5yZWFkX3RleHQoJTIyZGF0YXNldHMlMkZteS11c2VybmFtZSUyRm15LWRhdGFzZXQtcmVwbyUyRmRhdGElMkZ0cmFpbi5jc3YlMjIlMkMlMjByZXZpc2lvbiUzRCUyMmRldiUyMiklMEElMEElMjMlMjAlRTUlODYlOTklRTUlODUlQTUlRTglQkYlOUMlRTclQTglOEIlRTYlOTYlODclRTQlQkIlQjYlMEF3aXRoJTIwZnMub3BlbiglMjJkYXRhc2V0cyUyRm15LXVzZXJuYW1lJTJGbXktZGF0YXNldC1yZXBvJTJGZGF0YSUyRnZhbGlkYXRpb24uY3N2JTIyJTJDJTIwJTIydyUyMiklMjBhcyUyMGYlM0ElMEElMjAlMjAlMjAlMjBmLndyaXRlKCUyMnRleHQlMkNsYWJlbCUyMiklMEElMjAlMjAlMjAlMjBmLndyaXRlKCUyMkZhbnRhc3RpYyUyMG1vdmllISUyQ2dvb2QlMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> HfFileSystem
<span class="hljs-meta">&gt;&gt;&gt; </span>fs = HfFileSystem()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># 列出目录中的所有文件</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>fs.ls(<span class="hljs-string">&quot;datasets/my-username/my-dataset-repo/data&quot;</span>, detail=<span class="hljs-literal">False</span>)
[<span class="hljs-string">&#x27;datasets/my-username/my-dataset-repo/data/train.csv&#x27;</span>, <span class="hljs-string">&#x27;datasets/my-username/my-dataset-repo/data/test.csv&#x27;</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># 列出仓库中的所有 &quot;.csv&quot; 文件</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>fs.glob(<span class="hljs-string">&quot;datasets/my-username/my-dataset-repo/**/*.csv&quot;</span>)
[<span class="hljs-string">&#x27;datasets/my-username/my-dataset-repo/data/train.csv&#x27;</span>, <span class="hljs-string">&#x27;datasets/my-username/my-dataset-repo/data/test.csv&#x27;</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># 读取远程文件</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> fs.<span class="hljs-built_in">open</span>(<span class="hljs-string">&quot;datasets/my-username/my-dataset-repo/data/train.csv&quot;</span>, <span class="hljs-string">&quot;r&quot;</span>) <span class="hljs-keyword">as</span> f:
<span class="hljs-meta">... </span>    train_data = f.readlines()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># 远程文件内容读取为字符串</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>train_data = fs.read_text(<span class="hljs-string">&quot;datasets/my-username/my-dataset-repo/data/train.csv&quot;</span>, revision=<span class="hljs-string">&quot;dev&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># 写入远程文件</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> fs.<span class="hljs-built_in">open</span>(<span class="hljs-string">&quot;datasets/my-username/my-dataset-repo/data/validation.csv&quot;</span>, <span class="hljs-string">&quot;w&quot;</span>) <span class="hljs-keyword">as</span> f:
<span class="hljs-meta">... </span>    f.write(<span class="hljs-string">&quot;text,label&quot;</span>)
<span class="hljs-meta">... </span>    f.write(<span class="hljs-string">&quot;Fantastic movie!,good&quot;</span>)`,wrap:!1}}),b=new Ns({props:{title:"集成",local:"集成",headingTag:"h2"}}),Q=new P({props:{code:"aGYlM0ElMkYlMkYlNUIlM0NyZXBvX3R5cGVfcHJlZml4JTNFJTVEJTNDcmVwb19pZCUzRSU1QiU0MCUzQ3JldmlzaW9uJTNFJTVEJTJGJTNDcGF0aCUyRmluJTJGcmVwbyUzRQ==",highlighted:'hf:<span class="hljs-regexp">//</span>[&lt;repo_type_prefix&gt;]&lt;repo_id&gt;[@&lt;revision&gt;]<span class="hljs-regexp">/&lt;path/i</span>n/repo&gt;',wrap:!1}}),S=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> pandas <span class="hljs-keyword">as</span> pd

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># 将远程 CSV 文件读取到 DataFrame</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>df = pd.read_csv(<span class="hljs-string">&quot;hf://datasets/my-username/my-dataset-repo/train.csv&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># 将 DataFrame 写入远程 CSV 文件</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>df.to_csv(<span class="hljs-string">&quot;hf://datasets/my-username/my-dataset-repo/test.csv&quot;</span>)`,wrap:!1}}),v=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> HfFileSystem
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> duckdb

<span class="hljs-meta">&gt;&gt;&gt; </span>fs = HfFileSystem()
<span class="hljs-meta">&gt;&gt;&gt; </span>duckdb.register_filesystem(fs)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># 查询远程文件并将结果返回为 DataFrame</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>fs_query_file = <span class="hljs-string">&quot;hf://datasets/my-username/my-dataset-repo/data_dir/data.parquet&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>df = duckdb.query(<span class="hljs-string">f&quot;SELECT * FROM &#x27;<span class="hljs-subst">{fs_query_file}</span>&#x27; LIMIT 10&quot;</span>).df()`,wrap:!1}}),E=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> zarr

<span class="hljs-meta">&gt;&gt;&gt; </span>embeddings = np.random.randn(<span class="hljs-number">50000</span>, <span class="hljs-number">1000</span>).astype(<span class="hljs-string">&quot;float32&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># 将数组写入仓库</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> zarr.open_group(<span class="hljs-string">&quot;hf://my-username/my-model-repo/array-store&quot;</span>, mode=<span class="hljs-string">&quot;w&quot;</span>) <span class="hljs-keyword">as</span> root:
<span class="hljs-meta">... </span>   foo = root.create_group(<span class="hljs-string">&quot;embeddings&quot;</span>)
<span class="hljs-meta">... </span>   foobar = foo.zeros(<span class="hljs-string">&#x27;experiment_0&#x27;</span>, shape=(<span class="hljs-number">50000</span>, <span class="hljs-number">1000</span>), chunks=(<span class="hljs-number">10000</span>, <span class="hljs-number">1000</span>), dtype=<span class="hljs-string">&#x27;f4&#x27;</span>)
<span class="hljs-meta">... </span>   foobar[:] = embeddings

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># 从仓库读取数组</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> zarr.open_group(<span class="hljs-string">&quot;hf://my-username/my-model-repo/array-store&quot;</span>, mode=<span class="hljs-string">&quot;r&quot;</span>) <span class="hljs-keyword">as</span> root:
<span class="hljs-meta">... </span>   first_row = root[<span class="hljs-string">&quot;embeddings/experiment_0&quot;</span>][<span class="hljs-number">0</span>]`,wrap:!1}}),I=new Ns({props:{title:"认证",local:"认证",headingTag:"h2"}}),O=new P({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEhmRmlsZVN5c3RlbSUwQWZzJTIwJTNEJTIwSGZGaWxlU3lzdGVtKHRva2VuJTNEdG9rZW4p",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> HfFileSystem
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