import{s as Nt,o as Rt,n as bt}from"../chunks/scheduler.9bc65507.js";import{S as Et,i as qt,g as s,s as a,r as p,A as Bt,h as i,f as o,c as r,j as P,u as f,x as m,k as J,y as l,a as n,v as h,d as g,t as _,w as y}from"../chunks/index.707bf1b6.js";import{T as Xt}from"../chunks/Tip.c2ecdbf4.js";import{D as fe}from"../chunks/Docstring.17db21ae.js";import{C as ze}from"../chunks/CodeBlock.54a9f38d.js";import{E as St}from"../chunks/ExampleCodeBlock.4f515aa9.js";import{H as xe}from"../chunks/Heading.342b1fa6.js";function At(I){let d,v=`The <code>Fuyu</code> models were trained using <code>bfloat16</code>, but the original inference uses <code>float16</code> The checkpoints uploaded on the hub use <code>torch_dtype = &#39;float16&#39;</code> which will be
used by the <code>AutoModel</code> API to cast the checkpoints from <code>torch.float32</code> to <code>torch.float16</code>.`,c,b,C="The <code>dtype</code> of the online weights is mostly irrelevant, unless you are using <code>torch_dtype=&quot;auto&quot;</code> when initializing a model using <code>model = AutoModelForCausalLM.from_pretrained(&quot;path&quot;, torch_dtype = &quot;auto&quot;)</code>. The reason is that the model will first be downloaded ( using the <code>dtype</code> of the checkpoints online) then it will be cast to the default <code>dtype</code> of <code>torch</code> (becomes <code>torch.float32</code>). Users should specify the <code>torch_dtype</code> they want, and if they don’t it will be <code>torch.float32</code>.",L,w,R="Finetuning the model in <code>float16</code> is not recommended and known to produce <code>nan</code>, as such the model should be fine-tuned in <code>bfloat16</code>.";return{c(){d=s("p"),d.innerHTML=v,c=a(),b=s("p"),b.innerHTML=C,L=a(),w=s("p"),w.innerHTML=R},l(u){d=i(u,"P",{"data-svelte-h":!0}),m(d)!=="svelte-1005mfc"&&(d.innerHTML=v),c=r(u),b=i(u,"P",{"data-svelte-h":!0}),m(b)!=="svelte-1mgue3n"&&(b.innerHTML=C),L=r(u),w=i(u,"P",{"data-svelte-h":!0}),m(w)!=="svelte-1p3drsu"&&(w.innerHTML=R)},m(u,U){n(u,d,U),n(u,c,U),n(u,b,U),n(u,L,U),n(u,w,U)},p:bt,d(u){u&&(o(d),o(c),o(b),o(L),o(w))}}}function Yt(I){let d,v;return d=new ze({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEZ1eXVDb25maWclMEElMEElMjMlMjBJbml0aWFsaXppbmclMjBhJTIwRnV5dSUyMGZ1eXUtN2IlMjBzdHlsZSUyMGNvbmZpZ3VyYXRpb24lMEFjb25maWd1cmF0aW9uJTIwJTNEJTIwRnV5dUNvbmZpZygp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> FuyuConfig

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a Fuyu fuyu-7b style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = FuyuConfig()`,wrap:!1}}),{c(){p(d.$$.fragment)},l(c){f(d.$$.fragment,c)},m(c,b){h(d,c,b),v=!0},p:bt,i(c){v||(g(d.$$.fragment,c),v=!0)},o(c){_(d.$$.fragment,c),v=!1},d(c){y(d,c)}}}function Dt(I){let d,v=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){d=s("p"),d.innerHTML=v},l(c){d=i(c,"P",{"data-svelte-h":!0}),m(d)!=="svelte-fincs2"&&(d.innerHTML=v)},m(c,b){n(c,d,b)},p:bt,d(c){c&&o(d)}}}function Qt(I){let d,v,c,b,C,L,w,R,u,U='The Fuyu model was created by <a href="https://www.adept.ai/blog/fuyu-8b" rel="nofollow">ADEPT</a>, and authored by Rohan Bavishi, Erich Elsen, Curtis Hawthorne, Maxwell Nye, Augustus Odena, Arushi Somani, Sağnak Taşırlar.',Le,E,vt="The authors introduced Fuyu-8B, a decoder-only multimodal model based on the classic transformers architecture, with query and key normalization. A linear encoder is added to create multimodal embeddings from image inputs.",Ze,q,wt="By treating image tokens like text tokens and using a special image-newline character, the model knows when an image line ends. Image positional embeddings are removed. This avoids the need for different training phases for various image resolutions. With 8 billion parameters and licensed under CC-BY-NC, Fuyu-8B is notable for its ability to handle both text and images, its impressive context size of 16K, and its overall performance.",Pe,G,Je,B,Tt="Tips:",Ie,S,Ft="<li>To convert the model, you need to clone the original repository using <code>git clone https://github.com/persimmon-ai-labs/adept-inference</code>, then get the checkpoints:</li>",Ue,A,Ge,Y,Mt="For the chat model:",je,D,Ve,Q,$t="Then, model can be loaded via:",We,O,He,K,kt=`Inputs need to be passed through a specific Processor to have the correct formats.
A processor requires an image_processor and a tokenizer. Hence, inputs can be loaded via:`,Xe,ee,Ne,te,xt=`This model was contributed by <a href="https://huggingface.co/Molbap" rel="nofollow">Molbap</a>.
The original code can be found <a href="https://github.com/persimmon-ai-labs/adept-inference" rel="nofollow">here</a>.`,Re,oe,Ct=`<li><p>Fuyu uses a <code>sentencepiece</code> based tokenizer, with a <code>Unigram</code> model. It supports bytefallback, which is only available in <code>tokenizers==0.14.0</code> for the fast tokenizer.
The <code>LlamaTokenizer</code> is used as it is a standard wrapper around sentencepiece.</p></li> <li><p>The authors suggest to use the following prompt for image captioning: <code>f&quot;Generate a coco-style caption.\\\\n&quot;</code></p></li>`,Ee,ne,qe,F,ae,ot,he,zt=`This is the configuration class to store the configuration of a <a href="/docs/transformers/main/en/model_doc/fuyu#transformers.FuyuForCausalLM">FuyuForCausalLM</a>. It is used to instantiate an
Fuyu model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the
<a href="https://huggingface.co/adept/fuyu-8b-base" rel="nofollow">adept/fuyu-8b-base</a>.`,nt,ge,Lt=`Configuration objects inherit from <a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> and can be used to control the model outputs. Read the
documentation from <a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> for more information.`,at,j,Be,re,Se,T,se,rt,_e,Zt=`The bare Fuyu Model outputting raw hidden-states without any specific head on top.
This model inherits from <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,st,ye,Pt=`This model is also a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`,it,be,Jt="Transformer decoder consisting of <em>config.num_hidden_layers</em> layers. Each layer is a <code>FuyuDecoderLayer</code>",dt,z,ie,lt,ve,It='The <a href="/docs/transformers/main/en/model_doc/fuyu#transformers.FuyuForCausalLM">FuyuForCausalLM</a> forward method, overrides the <code>__call__</code> special method.',ct,V,Ae,de,Ye,M,le,mt,we,Ut=`This class should handle the image processing part before the main FuyuForCausalLM. In particular, it should
handle:`,ut,Te,Gt=`<li><p>Processing Images:
Taking a batch of images as input. If the images are variable-sized, it resizes them based on the desired patch
dimensions. The image output is always img_h … 1080 img_w
… 1920 Then, it patches up these images using the patchify_image
function.</p></li> <li><p>Creating Image Input IDs:
For each patch, a placeholder ID is given to identify where these patches belong in a token sequence. For
variable-sized images, each line of patches is terminated with a newline ID.</p></li> <li><p>Image Patch Indices:
For each image patch, the code maintains an index where these patches should be inserted in a token stream.</p></li>`,pt,W,ce,ft,Fe,jt="Preprocess an image or a batch of images.",De,me,Qe,$,ue,ht,Me,Vt="Constructs a Fuyu processor which wraps a Fuyu image processor and a Llama tokenizer into a single processor.",gt,$e,Wt=`<a href="/docs/transformers/main/en/model_doc/fuyu#transformers.FuyuProcessor">FuyuProcessor</a> offers all the functionalities of <a href="/docs/transformers/main/en/model_doc/fuyu#transformers.FuyuImageProcessor">FuyuImageProcessor</a> and <a href="/docs/transformers/main/en/model_doc/llama#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a>. See the
<a href="/docs/transformers/main/en/model_doc/fuyu#transformers.FuyuProcessor.__call__"><strong>call</strong>()</a> and <code>decode()</code> for more information.`,_t,H,pe,yt,ke,Ht=`Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the <code>text</code>
and <code>kwargs</code> arguments to LlamaTokenizerFast’s <a href="/docs/transformers/main/en/model_doc/speecht5#transformers.SpeechT5Tokenizer.__call__"><strong>call</strong>()</a> if <code>text</code> is not <code>None</code> to
encode the text. To prepare the image(s), this method forwards the <code>images</code> and <code>kwrags</code> arguments to
FuyuImageProcessor’s <a href="/docs/transformers/main/en/model_doc/layoutlmv2#transformers.LayoutLMv2FeatureExtractor.__call__"><strong>call</strong>()</a> if <code>images</code> is not <code>None</code>. Please refer to the doctsring
of the above two methods for more information.`,Oe,Ce,Ke;return C=new xe({props:{title:"Fuyu",local:"fuyu",headingTag:"h1"}}),w=new xe({props:{title:"Overview",local:"overview",headingTag:"h2"}}),G=new Xt({props:{warning:!0,$$slots:{default:[At]},$$scope:{ctx:I}}}),A=new ze({props:{code:"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",highlighted:`git <span class="hljs-built_in">clone</span> https://github.com/persimmon-ai-labs/adept-inference
wget path/to/fuyu-8b-model-weights.tar
tar -xvf fuyu-8b-model-weights.tar
python src/transformers/models/fuyu/convert_fuyu_weights_to_hf.py  --input_dir /path/to/downloaded/fuyu/weights/ --output_dir /output/path \\
    --pt_model_path /path/to/fuyu_8b_release/iter_0001251/mp_rank_00/model_optim_rng.pt
    --ada_lib_path /path/to/adept-inference`,wrap:!1}}),D=new ze({props:{code:"d2dldCUyMGh0dHBzJTNBJTJGJTJGYXh0a240eGw1Y2lwLm9iamVjdHN0b3JhZ2UudXMtcGhvZW5peC0xLm9jaS5jdXN0b21lci1vY2kuY29tJTJGbiUyRmF4dGtuNHhsNWNpcCUyRmIlMkZhZGVwdC1wdWJsaWMtZGF0YSUyRm8lMkY4Yl9jaGF0X21vZGVsX3JlbGVhc2UudGFyJTBBdGFyJTIwLXh2ZiUyMDhiX2Jhc2VfbW9kZWxfcmVsZWFzZS50YXI=",highlighted:`wget https://axtkn4xl5cip.objectstorage.us-phoenix-1.oci.customer-oci.com/n/axtkn4xl5cip/b/adept-public-data/o/8b_chat_model_release.tar
tar -xvf 8b_base_model_release.tar`,wrap:!1}}),O=new ze({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEZ1eXVDb25maWclMkMlMjBGdXl1Rm9yQ2F1c2FsTE0lMEFtb2RlbF9jb25maWclMjAlM0QlMjBGdXl1Q29uZmlnKCklMEFtb2RlbCUyMCUzRCUyMEZ1eXVGb3JDYXVzYWxMTShtb2RlbF9jb25maWcpLmZyb21fcHJldHJhaW5lZCgnJTJGb3V0cHV0JTJGcGF0aCcp",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> FuyuConfig, FuyuForCausalLM
model_config = FuyuConfig()
model = FuyuForCausalLM(model_config).from_pretrained(<span class="hljs-string">&#x27;/output/path&#x27;</span>)`,wrap:!1}}),ee=new ze({props:{code:"ZnJvbSUyMFBJTCUyMGltcG9ydCUyMEltYWdlJTBBZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMEFmcm9tJTIwdHJhbnNmb3JtZXJzLm1vZGVscy5mdXl1LnByb2Nlc3NpbmdfZnV5dSUyMGltcG9ydCUyMEZ1eXVQcm9jZXNzb3IlMEFmcm9tJTIwdHJhbnNmb3JtZXJzLm1vZGVscy5mdXl1LmltYWdlX3Byb2Nlc3NpbmdfZnV5dSUyMGltcG9ydCUyMEZ1eXVJbWFnZVByb2Nlc3NvciUwQSUwQSUwQXRva2VuaXplciUyMCUzRCUyMEF1dG9Ub2tlbml6ZXIuZnJvbV9wcmV0cmFpbmVkKCdhZGVwdC1oZi1jb2xsYWIlMkZmdXl1LThiJyklMEFpbWFnZV9wcm9jZXNzb3IlMjAlM0QlMjBGdXl1SW1hZ2VQcm9jZXNzb3IoKSUwQSUwQSUwQXByb2Nlc3NvciUyMCUzRCUyMEZ1eXVQcm9jZXNzb3IoaW1hZ2VfcHJvY2Vzc29yJTNEaW1hZ2VfcHJvY2Vzc29yJTJDJTIwdG9rZW5pemVyJTNEdG9rZW5pemVyKSUwQXRleHRfcHJvbXB0JTIwJTNEJTIwJTIyR2VuZXJhdGUlMjBhJTIwY29jby1zdHlsZSUyMGNhcHRpb24uJTVDJTVDbiUyMiUwQSUwQWJ1c19pbWFnZV91cmwlMjAlM0QlMjAlMjJodHRwcyUzQSUyRiUyRmh1Z2dpbmdmYWNlLmNvJTJGZGF0YXNldHMlMkZoZi1pbnRlcm5hbC10ZXN0aW5nJTJGZml4dHVyZXMtY2FwdGlvbmluZyUyRnJlc29sdmUlMkZtYWluJTJGYnVzLnBuZyUyMiUwQWJ1c19pbWFnZV9waWwlMjAlM0QlMjBJbWFnZS5vcGVuKGlvLkJ5dGVzSU8ocmVxdWVzdHMuZ2V0KGJ1c19pbWFnZV91cmwpLmNvbnRlbnQpKSUwQWlucHV0c190b19tb2RlbCUyMCUzRCUyMHByb2Nlc3Nvcih0ZXh0JTNEdGV4dF9wcm9tcHQlMkMlMjBpbWFnZXMlM0RpbWFnZV9waWwpJTBBJTBB",highlighted:`<span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
<span class="hljs-keyword">from</span> transformers.models.fuyu.processing_fuyu <span class="hljs-keyword">import</span> FuyuProcessor
<span class="hljs-keyword">from</span> transformers.models.fuyu.image_processing_fuyu <span class="hljs-keyword">import</span> FuyuImageProcessor


tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&#x27;adept-hf-collab/fuyu-8b&#x27;</span>)
image_processor = FuyuImageProcessor()


processor = FuyuProcessor(image_processor=image_processor, tokenizer=tokenizer)
text_prompt = <span class="hljs-string">&quot;Generate a coco-style caption.\\\\n&quot;</span>

bus_image_url = <span class="hljs-string">&quot;https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/bus.png&quot;</span>
bus_image_pil = Image.<span class="hljs-built_in">open</span>(io.BytesIO(requests.get(bus_image_url).content))
inputs_to_model = processor(text=text_prompt, images=image_pil)

`,wrap:!1}}),ne=new xe({props:{title:"FuyuConfig",local:"transformers.FuyuConfig",headingTag:"h2"}}),ae=new fe({props:{name:"class transformers.FuyuConfig",anchor:"transformers.FuyuConfig",parameters:[{name:"vocab_size",val:" = 262144"},{name:"hidden_size",val:" = 4096"},{name:"intermediate_size",val:" = 16384"},{name:"num_hidden_layers",val:" = 36"},{name:"num_attention_heads",val:" = 64"},{name:"hidden_act",val:" = 'relu2'"},{name:"max_position_embeddings",val:" = 16384"},{name:"image_size",val:" = 300"},{name:"patch_size",val:" = 30"},{name:"num_channels",val:" = 3"},{name:"initializer_range",val:" = 0.02"},{name:"layer_norm_eps",val:" = 1e-05"},{name:"use_cache",val:" = True"},{name:"tie_word_embeddings",val:" = False"},{name:"rope_theta",val:" = 25000.0"},{name:"rope_scaling",val:" = None"},{name:"qk_layernorm",val:" = True"},{name:"hidden_dropout",val:" = 0.0"},{name:"attention_dropout",val:" = 0.0"},{name:"partial_rotary_factor",val:" = 0.5"},{name:"pad_token_id",val:" = None"},{name:"bos_token_id",val:" = 1"},{name:"eos_token_id",val:" = 2"},{name:"text_config",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FuyuConfig.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 262144) &#x2014;
Vocabulary size of the Fuyu model. Defines the number of different tokens that can be represented by the
<code>inputs_ids</code> passed when calling <a href="/docs/transformers/main/en/model_doc/fuyu#transformers.FuyuForCausalLM">FuyuForCausalLM</a>`,name:"vocab_size"},{anchor:"transformers.FuyuConfig.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 4096) &#x2014;
Dimension of the hidden representations.`,name:"hidden_size"},{anchor:"transformers.FuyuConfig.intermediate_size",description:`<strong>intermediate_size</strong> (<code>int</code>, <em>optional</em>, defaults to 16384) &#x2014;
Dimension of the MLP representations.`,name:"intermediate_size"},{anchor:"transformers.FuyuConfig.num_hidden_layers",description:`<strong>num_hidden_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 36) &#x2014;
Number of hidden layers in the Transformer encoder.`,name:"num_hidden_layers"},{anchor:"transformers.FuyuConfig.num_attention_heads",description:`<strong>num_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 64) &#x2014;
Number of attention heads for each attention layer in the Transformer encoder.`,name:"num_attention_heads"},{anchor:"transformers.FuyuConfig.hidden_act",description:`<strong>hidden_act</strong> (<code>str</code> or <code>function</code>, <em>optional</em>, defaults to <code>&quot;relu2&quot;</code>) &#x2014;
The non-linear activation function (function or string) in the decoder.`,name:"hidden_act"},{anchor:"transformers.FuyuConfig.max_position_embeddings",description:`<strong>max_position_embeddings</strong> (<code>int</code>, <em>optional</em>, defaults to 16384) &#x2014;
The maximum sequence length that this model might ever be used with.`,name:"max_position_embeddings"},{anchor:"transformers.FuyuConfig.image_size",description:`<strong>image_size</strong> (<code>int</code>, <em>optional</em>, defaults to 300) &#x2014;
The input image size.`,name:"image_size"},{anchor:"transformers.FuyuConfig.patch_size",description:`<strong>patch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 30) &#x2014;
The input vision transformer encoding patch size.`,name:"patch_size"},{anchor:"transformers.FuyuConfig.num_channels",description:`<strong>num_channels</strong> (<code>int</code>, <em>optional</em>, defaults to 3) &#x2014;
The input image number of channels.`,name:"num_channels"},{anchor:"transformers.FuyuConfig.initializer_range",description:`<strong>initializer_range</strong> (<code>float</code>, <em>optional</em>, defaults to 0.02) &#x2014;
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.`,name:"initializer_range"},{anchor:"transformers.FuyuConfig.layer_norm_eps",description:`<strong>layer_norm_eps</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-05) &#x2014;
The epsilon used by the rms normalization layers.`,name:"layer_norm_eps"},{anchor:"transformers.FuyuConfig.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if <code>config.is_decoder=True</code>. Whether to tie weight embeddings`,name:"use_cache"},{anchor:"transformers.FuyuConfig.tie_word_embeddings",description:`<strong>tie_word_embeddings</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to tie input and output embeddings.`,name:"tie_word_embeddings"},{anchor:"transformers.FuyuConfig.rope_theta",description:`<strong>rope_theta</strong> (<code>float</code>, <em>optional</em>, defaults to 25000.0) &#x2014;
The base period of the RoPE embeddings.`,name:"rope_theta"},{anchor:"transformers.FuyuConfig.rope_scaling",description:`<strong>rope_scaling</strong> (<code>Dict</code>, <em>optional</em>) &#x2014;
Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
<code>{&quot;type&quot;: strategy name, &quot;factor&quot;: scaling factor}</code>. When using this flag, don&#x2019;t update
<code>max_position_embeddings</code> to the expected new maximum. See the following thread for more information on how
these scaling strategies behave:
<a href="https://www.reddit.com/r/LocalFuyu/comments/14mrgpr/dynamically_scaled_rope_further_increases/" rel="nofollow">https://www.reddit.com/r/LocalFuyu/comments/14mrgpr/dynamically_scaled_rope_further_increases/</a>. This is an
experimental feature, subject to breaking API changes in future versions.`,name:"rope_scaling"},{anchor:"transformers.FuyuConfig.qk_layernorm",description:`<strong>qk_layernorm</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to normalize the Queries and Keys after projecting the hidden states`,name:"qk_layernorm"},{anchor:"transformers.FuyuConfig.hidden_dropout",description:`<strong>hidden_dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout ratio after applying the MLP to the hidden states.`,name:"hidden_dropout"},{anchor:"transformers.FuyuConfig.attention_dropout",description:`<strong>attention_dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout ratio after computing the attention scores.`,name:"attention_dropout"},{anchor:"transformers.FuyuConfig.partial_rotary_factor",description:`<strong>partial_rotary_factor</strong> (<code>float</code>, <em>optional</em>, defaults to 0.5) &#x2014;
Percentage of the query and keys which will have rotary embedding.`,name:"partial_rotary_factor"},{anchor:"transformers.FuyuConfig.pad_token_id",description:`<strong>pad_token_id</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The id of the <em>padding</em> token.`,name:"pad_token_id"},{anchor:"transformers.FuyuConfig.bos_token_id",description:`<strong>bos_token_id</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
The id of the <em>beginning-of-sequence</em> token.`,name:"bos_token_id"},{anchor:"transformers.FuyuConfig.eos_token_id",description:`<strong>eos_token_id</strong> (<code>Union[int, List[int]]</code>, <em>optional</em>, defaults to 2) &#x2014;
The id of the <em>end-of-sequence</em> token. Optionally, use a list to set multiple <em>end-of-sequence</em> tokens.`,name:"eos_token_id"},{anchor:"transformers.FuyuConfig.text_config",description:"<strong>text_config</strong> (<code>dict</code>, <em>optional</em>) &#x2014;\nDictionary of configuration options used to initialize the <code>language```Aut</code>.",name:"text_config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/fuyu/configuration_fuyu.py#L29"}}),j=new St({props:{anchor:"transformers.FuyuConfig.example",$$slots:{default:[Yt]},$$scope:{ctx:I}}}),re=new xe({props:{title:"FuyuForCausalLM",local:"transformers.FuyuForCausalLM",headingTag:"h2"}}),se=new fe({props:{name:"class transformers.FuyuForCausalLM",anchor:"transformers.FuyuForCausalLM",parameters:[{name:"config",val:": FuyuConfig"}],parametersDescription:[{anchor:"transformers.FuyuForCausalLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/fuyu#transformers.FuyuConfig">FuyuConfig</a>) &#x2014;
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.
config &#x2014; FuyuConfig`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/fuyu/modeling_fuyu.py#L142"}}),ie=new fe({props:{name:"forward",anchor:"transformers.FuyuForCausalLM.forward",parameters:[{name:"input_ids",val:": LongTensor = None"},{name:"image_patches",val:": Tensor = None"},{name:"image_patches_indices",val:": Tensor = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"past_key_values",val:": typing.Optional[typing.List[torch.FloatTensor]] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"use_cache",val:": typing.Optional[bool] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.FuyuForCausalLM.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/model_doc/speecht5#transformers.SpeechT5Tokenizer.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.FuyuForCausalLM.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a></p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/model_doc/speecht5#transformers.SpeechT5Tokenizer.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p>If <code>past_key_values</code> is used, optionally only the last <code>decoder_input_ids</code> have to be input (see
<code>past_key_values</code>).</p>
<p>If you want to change padding behavior, you should read <code>modeling_opt._prepare_decoder_attention_mask</code>
and modify to your needs. See diagram 1 in <a href="https://arxiv.org/abs/1910.13461" rel="nofollow">the paper</a> for more
information on the default strategy.</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"attention_mask"},{anchor:"transformers.FuyuForCausalLM.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.n_positions - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.FuyuForCausalLM.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) &#x2014;
Tuple of <code>tuple(torch.FloatTensor)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>) and 2 additional tensors of shape
<code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see <code>past_key_values</code> input) to speed up sequential decoding.</p>
<p>If <code>past_key_values</code> are used, the user can optionally input only the last <code>decoder_input_ids</code> (those that
don&#x2019;t have their past key value states given to this model) of shape <code>(batch_size, 1)</code> instead of all
<code>decoder_input_ids</code> of shape <code>(batch_size, sequence_length)</code>.`,name:"past_key_values"},{anchor:"transformers.FuyuForCausalLM.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.FuyuForCausalLM.forward.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
<code>past_key_values</code>).`,name:"use_cache"},{anchor:"transformers.FuyuForCausalLM.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.FuyuForCausalLM.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.FuyuForCausalLM.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/fuyu/modeling_fuyu.py#L210"}}),V=new Xt({props:{$$slots:{default:[Dt]},$$scope:{ctx:I}}}),de=new xe({props:{title:"FuyuImageProcessor",local:"transformers.FuyuImageProcessor",headingTag:"h2"}}),le=new fe({props:{name:"class transformers.FuyuImageProcessor",anchor:"transformers.FuyuImageProcessor",parameters:[{name:"target_height",val:" = 1080"},{name:"target_width",val:" = 1920"},{name:"padding_value",val:" = 1.0"},{name:"padding_mode",val:": str = 'constant'"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/fuyu/image_processing_fuyu.py#L25"}}),ce=new fe({props:{name:"__call__",anchor:"transformers.FuyuImageProcessor.__call__",parameters:[{name:"images",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/image_processing_utils.py#L547"}}),me=new xe({props:{title:"FuyuProcessor",local:"transformers.FuyuProcessor",headingTag:"h2"}}),ue=new fe({props:{name:"class transformers.FuyuProcessor",anchor:"transformers.FuyuProcessor",parameters:[{name:"image_processor",val:""},{name:"tokenizer",val:""}],parametersDescription:[{anchor:"transformers.FuyuProcessor.image_processor",description:`<strong>image_processor</strong> (<a href="/docs/transformers/main/en/model_doc/fuyu#transformers.FuyuImageProcessor">FuyuImageProcessor</a>) &#x2014;
The image processor is a required input.`,name:"image_processor"},{anchor:"transformers.FuyuProcessor.tokenizer",description:`<strong>tokenizer</strong> (<a href="/docs/transformers/main/en/model_doc/llama#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a>) &#x2014;
The tokenizer is a required input.`,name:"tokenizer"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/fuyu/processing_fuyu.py#L364"}}),pe=new fe({props:{name:"__call__",anchor:"transformers.FuyuProcessor.__call__",parameters:[{name:"text",val:" = None"},{name:"images",val:" = None"},{name:"return_tensors",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FuyuProcessor.__call__.text",description:`<strong>text</strong> (<code>str</code>, <code>List[str]</code>) &#x2014;
The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
<code>is_split_into_words=True</code> (to lift the ambiguity with a batch of sequences).`,name:"text"},{anchor:"transformers.FuyuProcessor.__call__.images",description:`<strong>images</strong> (<code>PIL.Image.Image</code>, <code>List[PIL.Image.Image]</code>) &#x2014;
The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
number of channels, H and W are image height and width.`,name:"images"},{anchor:"transformers.FuyuProcessor.__call__.return_tensors",description:`<strong>return_tensors</strong> (<code>str</code> or <a href="/docs/transformers/main/en/internal/file_utils#transformers.TensorType">TensorType</a>, <em>optional</em>) &#x2014;
If set, will return tensors of a particular framework. Acceptable values are:</p>
<ul>
<li><code>&apos;tf&apos;</code>: Return TensorFlow <code>tf.constant</code> objects.</li>
<li><code>&apos;pt&apos;</code>: Return PyTorch <code>torch.Tensor</code> objects.</li>
<li><code>&apos;np&apos;</code>: Return NumPy <code>np.ndarray</code> objects.</li>
<li><code>&apos;jax&apos;</code>: Return JAX <code>jnp.ndarray</code> objects.</li>
</ul>`,name:"return_tensors"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/fuyu/processing_fuyu.py#L418",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/main/en/main_classes/tokenizer#transformers.BatchEncoding"
>BatchEncoding</a> with the following fields:</p>
<ul>
<li><strong>input_ids</strong> — List of token ids to be fed to a model. Returned when <code>text</code> is not <code>None</code>.</li>
<li><strong>attention_mask</strong> — List of indices specifying which tokens should be attended to by the model (when
<code>return_attention_mask=True</code> or if <em>“attention_mask”</em> is in <code>self.model_input_names</code> and if <code>text</code> is not
<code>None</code>).</li>
<li><strong>pixel_values</strong> — Pixel values to be fed to a model. Returned when <code>images</code> is not <code>None</code>.</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/main/en/main_classes/tokenizer#transformers.BatchEncoding"
>BatchEncoding</a></p>
`}}),{c(){d=s("meta"),v=a(),c=s("p"),b=a(),p(C.$$.fragment),L=a(),p(w.$$.fragment),R=a(),u=s("p"),u.innerHTML=U,Le=a(),E=s("p"),E.textContent=vt,Ze=a(),q=s("p"),q.textContent=wt,Pe=a(),p(G.$$.fragment),Je=a(),B=s("p"),B.textContent=Tt,Ie=a(),S=s("ul"),S.innerHTML=Ft,Ue=a(),p(A.$$.fragment),Ge=a(),Y=s("p"),Y.textContent=Mt,je=a(),p(D.$$.fragment),Ve=a(),Q=s("p"),Q.textContent=$t,We=a(),p(O.$$.fragment),He=a(),K=s("p"),K.textContent=kt,Xe=a(),p(ee.$$.fragment),Ne=a(),te=s("p"),te.innerHTML=xt,Re=a(),oe=s("ul"),oe.innerHTML=Ct,Ee=a(),p(ne.$$.fragment),qe=a(),F=s("div"),p(ae.$$.fragment),ot=a(),he=s("p"),he.innerHTML=zt,nt=a(),ge=s("p"),ge.innerHTML=Lt,at=a(),p(j.$$.fragment),Be=a(),p(re.$$.fragment),Se=a(),T=s("div"),p(se.$$.fragment),rt=a(),_e=s("p"),_e.innerHTML=Zt,st=a(),ye=s("p"),ye.innerHTML=Pt,it=a(),be=s("p"),be.innerHTML=Jt,dt=a(),z=s("div"),p(ie.$$.fragment),lt=a(),ve=s("p"),ve.innerHTML=It,ct=a(),p(V.$$.fragment),Ae=a(),p(de.$$.fragment),Ye=a(),M=s("div"),p(le.$$.fragment),mt=a(),we=s("p"),we.textContent=Ut,ut=a(),Te=s("ul"),Te.innerHTML=Gt,pt=a(),W=s("div"),p(ce.$$.fragment),ft=a(),Fe=s("p"),Fe.textContent=jt,De=a(),p(me.$$.fragment),Qe=a(),$=s("div"),p(ue.$$.fragment),ht=a(),Me=s("p"),Me.textContent=Vt,gt=a(),$e=s("p"),$e.innerHTML=Wt,_t=a(),H=s("div"),p(pe.$$.fragment),yt=a(),ke=s("p"),ke.innerHTML=Ht,Oe=a(),Ce=s("p"),this.h()},l(e){const t=Bt("svelte-u9bgzb",document.head);d=i(t,"META",{name:!0,content:!0}),t.forEach(o),v=r(e),c=i(e,"P",{}),P(c).forEach(o),b=r(e),f(C.$$.fragment,e),L=r(e),f(w.$$.fragment,e),R=r(e),u=i(e,"P",{"data-svelte-h":!0}),m(u)!=="svelte-59lh8e"&&(u.innerHTML=U),Le=r(e),E=i(e,"P",{"data-svelte-h":!0}),m(E)!=="svelte-1g2inon"&&(E.textContent=vt),Ze=r(e),q=i(e,"P",{"data-svelte-h":!0}),m(q)!=="svelte-1xp7ehr"&&(q.textContent=wt),Pe=r(e),f(G.$$.fragment,e),Je=r(e),B=i(e,"P",{"data-svelte-h":!0}),m(B)!=="svelte-axv494"&&(B.textContent=Tt),Ie=r(e),S=i(e,"UL",{"data-svelte-h":!0}),m(S)!=="svelte-g9ue9k"&&(S.innerHTML=Ft),Ue=r(e),f(A.$$.fragment,e),Ge=r(e),Y=i(e,"P",{"data-svelte-h":!0}),m(Y)!=="svelte-1cs7acv"&&(Y.textContent=Mt),je=r(e),f(D.$$.fragment,e),Ve=r(e),Q=i(e,"P",{"data-svelte-h":!0}),m(Q)!=="svelte-1c7gnsu"&&(Q.textContent=$t),We=r(e),f(O.$$.fragment,e),He=r(e),K=i(e,"P",{"data-svelte-h":!0}),m(K)!=="svelte-60q0vi"&&(K.textContent=kt),Xe=r(e),f(ee.$$.fragment,e),Ne=r(e),te=i(e,"P",{"data-svelte-h":!0}),m(te)!=="svelte-1ius34j"&&(te.innerHTML=xt),Re=r(e),oe=i(e,"UL",{"data-svelte-h":!0}),m(oe)!=="svelte-qodqh0"&&(oe.innerHTML=Ct),Ee=r(e),f(ne.$$.fragment,e),qe=r(e),F=i(e,"DIV",{class:!0});var x=P(F);f(ae.$$.fragment,x),ot=r(x),he=i(x,"P",{"data-svelte-h":!0}),m(he)!=="svelte-9urb74"&&(he.innerHTML=zt),nt=r(x),ge=i(x,"P",{"data-svelte-h":!0}),m(ge)!=="svelte-o55m63"&&(ge.innerHTML=Lt),at=r(x),f(j.$$.fragment,x),x.forEach(o),Be=r(e),f(re.$$.fragment,e),Se=r(e),T=i(e,"DIV",{class:!0});var k=P(T);f(se.$$.fragment,k),rt=r(k),_e=i(k,"P",{"data-svelte-h":!0}),m(_e)!=="svelte-eelrsh"&&(_e.innerHTML=Zt),st=r(k),ye=i(k,"P",{"data-svelte-h":!0}),m(ye)!=="svelte-hswkmf"&&(ye.innerHTML=Pt),it=r(k),be=i(k,"P",{"data-svelte-h":!0}),m(be)!=="svelte-9yiusy"&&(be.innerHTML=Jt),dt=r(k),z=i(k,"DIV",{class:!0});var Z=P(z);f(ie.$$.fragment,Z),lt=r(Z),ve=i(Z,"P",{"data-svelte-h":!0}),m(ve)!=="svelte-15glyuy"&&(ve.innerHTML=It),ct=r(Z),f(V.$$.fragment,Z),Z.forEach(o),k.forEach(o),Ae=r(e),f(de.$$.fragment,e),Ye=r(e),M=i(e,"DIV",{class:!0});var X=P(M);f(le.$$.fragment,X),mt=r(X),we=i(X,"P",{"data-svelte-h":!0}),m(we)!=="svelte-psqr0m"&&(we.textContent=Ut),ut=r(X),Te=i(X,"UL",{"data-svelte-h":!0}),m(Te)!=="svelte-1ynv44l"&&(Te.innerHTML=Gt),pt=r(X),W=i(X,"DIV",{class:!0});var et=P(W);f(ce.$$.fragment,et),ft=r(et),Fe=i(et,"P",{"data-svelte-h":!0}),m(Fe)!=="svelte-khengj"&&(Fe.textContent=jt),et.forEach(o),X.forEach(o),De=r(e),f(me.$$.fragment,e),Qe=r(e),$=i(e,"DIV",{class:!0});var N=P($);f(ue.$$.fragment,N),ht=r(N),Me=i(N,"P",{"data-svelte-h":!0}),m(Me)!=="svelte-1wc4ris"&&(Me.textContent=Vt),gt=r(N),$e=i(N,"P",{"data-svelte-h":!0}),m($e)!=="svelte-m4qk42"&&($e.innerHTML=Wt),_t=r(N),H=i(N,"DIV",{class:!0});var tt=P(H);f(pe.$$.fragment,tt),yt=r(tt),ke=i(tt,"P",{"data-svelte-h":!0}),m(ke)!=="svelte-msa2x8"&&(ke.innerHTML=Ht),tt.forEach(o),N.forEach(o),Oe=r(e),Ce=i(e,"P",{}),P(Ce).forEach(o),this.h()},h(){J(d,"name","hf:doc:metadata"),J(d,"content",Ot),J(F,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),J(z,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),J(T,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),J(W,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),J(M,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),J(H,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),J($,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8")},m(e,t){l(document.head,d),n(e,v,t),n(e,c,t),n(e,b,t),h(C,e,t),n(e,L,t),h(w,e,t),n(e,R,t),n(e,u,t),n(e,Le,t),n(e,E,t),n(e,Ze,t),n(e,q,t),n(e,Pe,t),h(G,e,t),n(e,Je,t),n(e,B,t),n(e,Ie,t),n(e,S,t),n(e,Ue,t),h(A,e,t),n(e,Ge,t),n(e,Y,t),n(e,je,t),h(D,e,t),n(e,Ve,t),n(e,Q,t),n(e,We,t),h(O,e,t),n(e,He,t),n(e,K,t),n(e,Xe,t),h(ee,e,t),n(e,Ne,t),n(e,te,t),n(e,Re,t),n(e,oe,t),n(e,Ee,t),h(ne,e,t),n(e,qe,t),n(e,F,t),h(ae,F,null),l(F,ot),l(F,he),l(F,nt),l(F,ge),l(F,at),h(j,F,null),n(e,Be,t),h(re,e,t),n(e,Se,t),n(e,T,t),h(se,T,null),l(T,rt),l(T,_e),l(T,st),l(T,ye),l(T,it),l(T,be),l(T,dt),l(T,z),h(ie,z,null),l(z,lt),l(z,ve),l(z,ct),h(V,z,null),n(e,Ae,t),h(de,e,t),n(e,Ye,t),n(e,M,t),h(le,M,null),l(M,mt),l(M,we),l(M,ut),l(M,Te),l(M,pt),l(M,W),h(ce,W,null),l(W,ft),l(W,Fe),n(e,De,t),h(me,e,t),n(e,Qe,t),n(e,$,t),h(ue,$,null),l($,ht),l($,Me),l($,gt),l($,$e),l($,_t),l($,H),h(pe,H,null),l(H,yt),l(H,ke),n(e,Oe,t),n(e,Ce,t),Ke=!0},p(e,[t]){const x={};t&2&&(x.$$scope={dirty:t,ctx:e}),G.$set(x);const k={};t&2&&(k.$$scope={dirty:t,ctx:e}),j.$set(k);const Z={};t&2&&(Z.$$scope={dirty:t,ctx:e}),V.$set(Z)},i(e){Ke||(g(C.$$.fragment,e),g(w.$$.fragment,e),g(G.$$.fragment,e),g(A.$$.fragment,e),g(D.$$.fragment,e),g(O.$$.fragment,e),g(ee.$$.fragment,e),g(ne.$$.fragment,e),g(ae.$$.fragment,e),g(j.$$.fragment,e),g(re.$$.fragment,e),g(se.$$.fragment,e),g(ie.$$.fragment,e),g(V.$$.fragment,e),g(de.$$.fragment,e),g(le.$$.fragment,e),g(ce.$$.fragment,e),g(me.$$.fragment,e),g(ue.$$.fragment,e),g(pe.$$.fragment,e),Ke=!0)},o(e){_(C.$$.fragment,e),_(w.$$.fragment,e),_(G.$$.fragment,e),_(A.$$.fragment,e),_(D.$$.fragment,e),_(O.$$.fragment,e),_(ee.$$.fragment,e),_(ne.$$.fragment,e),_(ae.$$.fragment,e),_(j.$$.fragment,e),_(re.$$.fragment,e),_(se.$$.fragment,e),_(ie.$$.fragment,e),_(V.$$.fragment,e),_(de.$$.fragment,e),_(le.$$.fragment,e),_(ce.$$.fragment,e),_(me.$$.fragment,e),_(ue.$$.fragment,e),_(pe.$$.fragment,e),Ke=!1},d(e){e&&(o(v),o(c),o(b),o(L),o(R),o(u),o(Le),o(E),o(Ze),o(q),o(Pe),o(Je),o(B),o(Ie),o(S),o(Ue),o(Ge),o(Y),o(je),o(Ve),o(Q),o(We),o(He),o(K),o(Xe),o(Ne),o(te),o(Re),o(oe),o(Ee),o(qe),o(F),o(Be),o(Se),o(T),o(Ae),o(Ye),o(M),o(De),o(Qe),o($),o(Oe),o(Ce)),o(d),y(C,e),y(w,e),y(G,e),y(A,e),y(D,e),y(O,e),y(ee,e),y(ne,e),y(ae),y(j),y(re,e),y(se),y(ie),y(V),y(de,e),y(le),y(ce),y(me,e),y(ue),y(pe)}}}const Ot='{"title":"Fuyu","local":"fuyu","sections":[{"title":"Overview","local":"overview","sections":[],"depth":2},{"title":"FuyuConfig","local":"transformers.FuyuConfig","sections":[],"depth":2},{"title":"FuyuForCausalLM","local":"transformers.FuyuForCausalLM","sections":[],"depth":2},{"title":"FuyuImageProcessor","local":"transformers.FuyuImageProcessor","sections":[],"depth":2},{"title":"FuyuProcessor","local":"transformers.FuyuProcessor","sections":[],"depth":2}],"depth":1}';function Kt(I){return Rt(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class io extends Et{constructor(d){super(),qt(this,d,Kt,Qt,Nt,{})}}export{io as component};
