import{S as zo,i as Bo,s as Io,e as n,k as f,w as y,t as l,M as Wo,c as r,d as o,m as h,a,x as b,h as c,b as i,G as t,g as m,y as M,q as w,o as j,B as $,v as Fo,L as xo}from"../../chunks/vendor-hf-doc-builder.js";import{T as Po}from"../../chunks/Tip-hf-doc-builder.js";import{D as Be}from"../../chunks/Docstring-hf-doc-builder.js";import{C as Ao}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as Ie}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as No}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function So(z){let p,v,g,u,T;return u=new Ao({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DecisionTransformerConfig, DecisionTransformerModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a DecisionTransformer configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = DecisionTransformerConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model (with random weights) from the configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = DecisionTransformerModel(configuration)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Accessing the model configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = model.config`}}),{c(){p=n("p"),v=l("Example:"),g=f(),y(u.$$.fragment)},l(s){p=r(s,"P",{});var _=a(p);v=c(_,"Example:"),_.forEach(o),g=h(s),b(u.$$.fragment,s)},m(s,_){m(s,p,_),t(p,v),m(s,g,_),M(u,s,_),T=!0},p:xo,i(s){T||(w(u.$$.fragment,s),T=!0)},o(s){j(u.$$.fragment,s),T=!1},d(s){s&&o(p),s&&o(g),$(u,s)}}}function Vo(z){let p,v,g,u,T;return{c(){p=n("p"),v=l("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=n("code"),u=l("Module"),T=l(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(s){p=r(s,"P",{});var _=a(p);v=c(_,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=r(_,"CODE",{});var B=a(g);u=c(B,"Module"),B.forEach(o),T=c(_,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),_.forEach(o)},m(s,_){m(s,p,_),t(p,v),t(p,g),t(g,u),t(p,T)},d(s){s&&o(p)}}}function Lo(z){let p,v,g,u,T;return u=new Ao({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DecisionTransformerModel
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>model = DecisionTransformerModel.from_pretrained(<span class="hljs-string">&quot;edbeeching/decision-transformer-gym-hopper-medium&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># evaluation</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = model.to(device)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.<span class="hljs-built_in">eval</span>()

<span class="hljs-meta">&gt;&gt;&gt; </span>env = gym.make(<span class="hljs-string">&quot;Hopper-v3&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>state_dim = env.observation_space.shape[<span class="hljs-number">0</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>act_dim = env.action_space.shape[<span class="hljs-number">0</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>state = env.reset()
<span class="hljs-meta">&gt;&gt;&gt; </span>states = torch.from_numpy(state).reshape(<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, state_dim).to(device=device, dtype=torch.float32)
<span class="hljs-meta">&gt;&gt;&gt; </span>actions = torch.zeros((<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, act_dim), device=device, dtype=torch.float32)
<span class="hljs-meta">&gt;&gt;&gt; </span>rewards = torch.zeros(<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, device=device, dtype=torch.float32)
<span class="hljs-meta">&gt;&gt;&gt; </span>target_return = torch.tensor(TARGET_RETURN, dtype=torch.float32).reshape(<span class="hljs-number">1</span>, <span class="hljs-number">1</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>timesteps = torch.tensor(<span class="hljs-number">0</span>, device=device, dtype=torch.long).reshape(<span class="hljs-number">1</span>, <span class="hljs-number">1</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>attention_mask = torch.zeros(<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, device=device, dtype=torch.float32)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># forward pass</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    state_preds, action_preds, return_preds = model(
<span class="hljs-meta">... </span>        states=states,
<span class="hljs-meta">... </span>        actions=actions,
<span class="hljs-meta">... </span>        rewards=rewards,
<span class="hljs-meta">... </span>        returns_to_go=target_return,
<span class="hljs-meta">... </span>        timesteps=timesteps,
<span class="hljs-meta">... </span>        attention_mask=attention_mask,
<span class="hljs-meta">... </span>        return_dict=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span>    )`}}),{c(){p=n("p"),v=l("Examples:"),g=f(),y(u.$$.fragment)},l(s){p=r(s,"P",{});var _=a(p);v=c(_,"Examples:"),_.forEach(o),g=h(s),b(u.$$.fragment,s)},m(s,_){m(s,p,_),t(p,v),m(s,g,_),M(u,s,_),T=!0},p:xo,i(s){T||(w(u.$$.fragment,s),T=!0)},o(s){j(u.$$.fragment,s),T=!1},d(s){s&&o(p),s&&o(g),$(u,s)}}}function Xo(z){let p,v,g,u,T,s,_,B,lt,We,R,I,De,q,ct,Je,dt,Fe,Z,mt,O,pt,ft,ht,Pe,fe,ut,Se,he,Ee,gt,Ve,ue,_t,Le,ge,Tt,Xe,k,vt,Q,yt,bt,K,Mt,wt,Ye,U,W,ke,ee,jt,Re,$t,He,D,te,Dt,oe,Jt,_e,Et,kt,Rt,C,Zt,Te,Ut,Ct,ve,Gt,Nt,xt,F,qe,G,P,Ze,ne,At,Ue,zt,Oe,N,re,Bt,ye,ae,Qe,x,S,Ce,se,It,Ge,Wt,Ke,J,ie,Ft,le,Pt,ce,St,Vt,Lt,be,Xt,de,Yt,Ht,E,me,qt,A,Ot,Me,Qt,Kt,Ne,eo,to,oo,V,no,L,et;return s=new Ie({}),q=new Ie({}),ee=new Ie({}),te=new Be({props:{name:"class transformers.DecisionTransformerConfig",anchor:"transformers.DecisionTransformerConfig",parameters:[{name:"state_dim",val:" = 17"},{name:"act_dim",val:" = 4"},{name:"hidden_size",val:" = 128"},{name:"max_ep_len",val:" = 4096"},{name:"action_tanh",val:" = True"},{name:"vocab_size",val:" = 1"},{name:"n_positions",val:" = 1024"},{name:"n_layer",val:" = 3"},{name:"n_head",val:" = 1"},{name:"n_inner",val:" = None"},{name:"activation_function",val:" = 'relu'"},{name:"resid_pdrop",val:" = 0.1"},{name:"embd_pdrop",val:" = 0.1"},{name:"attn_pdrop",val:" = 0.1"},{name:"layer_norm_epsilon",val:" = 1e-05"},{name:"initializer_range",val:" = 0.02"},{name:"scale_attn_weights",val:" = True"},{name:"use_cache",val:" = True"},{name:"bos_token_id",val:" = 50256"},{name:"eos_token_id",val:" = 50256"},{name:"scale_attn_by_inverse_layer_idx",val:" = False"},{name:"reorder_and_upcast_attn",val:" = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.DecisionTransformerConfig.state_dim",description:`<strong>state_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 17) &#x2014;
The state size for the RL environment`,name:"state_dim"},{anchor:"transformers.DecisionTransformerConfig.act_dim",description:`<strong>act_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 4) &#x2014;
The size of the output action space`,name:"act_dim"},{anchor:"transformers.DecisionTransformerConfig.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 128) &#x2014;
The size of the hidden layers`,name:"hidden_size"},{anchor:"transformers.DecisionTransformerConfig.max_ep_len",description:`<strong>max_ep_len</strong> (<code>int</code>, <em>optional</em>, defaults to 4096) &#x2014;
The maximum length of an episode in the environment`,name:"max_ep_len"},{anchor:"transformers.DecisionTransformerConfig.action_tanh",description:`<strong>action_tanh</strong> (<code>bool</code>, <em>optional</em>, defaults to True) &#x2014;
Whether to use a tanh activation on action prediction`,name:"action_tanh"},{anchor:"transformers.DecisionTransformerConfig.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 50257) &#x2014;
Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the
<code>inputs_ids</code> passed when calling <a href="/docs/transformers/v4.33.0/en/model_doc/decision_transformer#transformers.DecisionTransformerModel">DecisionTransformerModel</a>.`,name:"vocab_size"},{anchor:"transformers.DecisionTransformerConfig.n_positions",description:`<strong>n_positions</strong> (<code>int</code>, <em>optional</em>, defaults to 1024) &#x2014;
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).`,name:"n_positions"},{anchor:"transformers.DecisionTransformerConfig.n_layer",description:`<strong>n_layer</strong> (<code>int</code>, <em>optional</em>, defaults to 3) &#x2014;
Number of hidden layers in the Transformer encoder.`,name:"n_layer"},{anchor:"transformers.DecisionTransformerConfig.n_head",description:`<strong>n_head</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
Number of attention heads for each attention layer in the Transformer encoder.`,name:"n_head"},{anchor:"transformers.DecisionTransformerConfig.n_inner",description:`<strong>n_inner</strong> (<code>int</code>, <em>optional</em>) &#x2014;
Dimensionality of the inner feed-forward layers. If unset, will default to 4 times <code>n_embd</code>.`,name:"n_inner"},{anchor:"transformers.DecisionTransformerConfig.activation_function",description:`<strong>activation_function</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;gelu&quot;</code>) &#x2014;
Activation function, to be selected in the list <code>[&quot;relu&quot;, &quot;silu&quot;, &quot;gelu&quot;, &quot;tanh&quot;, &quot;gelu_new&quot;]</code>.`,name:"activation_function"},{anchor:"transformers.DecisionTransformerConfig.resid_pdrop",description:`<strong>resid_pdrop</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.`,name:"resid_pdrop"},{anchor:"transformers.DecisionTransformerConfig.embd_pdrop",description:`<strong>embd_pdrop</strong> (<code>int</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout ratio for the embeddings.`,name:"embd_pdrop"},{anchor:"transformers.DecisionTransformerConfig.attn_pdrop",description:`<strong>attn_pdrop</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout ratio for the attention.`,name:"attn_pdrop"},{anchor:"transformers.DecisionTransformerConfig.layer_norm_epsilon",description:`<strong>layer_norm_epsilon</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-5) &#x2014;
The epsilon to use in the layer normalization layers.`,name:"layer_norm_epsilon"},{anchor:"transformers.DecisionTransformerConfig.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.DecisionTransformerConfig.scale_attn_weights",description:`<strong>scale_attn_weights</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Scale attention weights by dividing by sqrt(hidden_size)..`,name:"scale_attn_weights"},{anchor:"transformers.DecisionTransformerConfig.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).`,name:"use_cache"},{anchor:"transformers.DecisionTransformerConfig.scale_attn_by_inverse_layer_idx",description:`<strong>scale_attn_by_inverse_layer_idx</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to additionally scale attention weights by <code>1 / layer_idx + 1</code>.`,name:"scale_attn_by_inverse_layer_idx"},{anchor:"transformers.DecisionTransformerConfig.reorder_and_upcast_attn",description:`<strong>reorder_and_upcast_attn</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to scale keys (K) prior to computing attention (dot-product) and upcast attention
dot-product/softmax to float() when training with mixed precision.`,name:"reorder_and_upcast_attn"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/decision_transformer/configuration_decision_transformer.py#L31"}}),F=new No({props:{anchor:"transformers.DecisionTransformerConfig.example",$$slots:{default:[So]},$$scope:{ctx:z}}}),ne=new Ie({}),re=new Be({props:{name:"class transformers.DecisionTransformerGPT2Model",anchor:"transformers.DecisionTransformerGPT2Model",parameters:[{name:"config",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/decision_transformer/modeling_decision_transformer.py#L477"}}),ae=new Be({props:{name:"forward",anchor:"transformers.DecisionTransformerGPT2Model.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"past_key_values",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"encoder_hidden_states",val:": typing.Optional[torch.Tensor] = None"},{name:"encoder_attention_mask",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"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/decision_transformer/modeling_decision_transformer.py#L507"}}),se=new Ie({}),ie=new Be({props:{name:"class transformers.DecisionTransformerModel",anchor:"transformers.DecisionTransformerModel",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.DecisionTransformerModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/model_doc/decision_transformer#transformers.DecisionTransformerConfig">~DecisionTransformerConfig</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/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/decision_transformer/modeling_decision_transformer.py#L796"}}),me=new Be({props:{name:"forward",anchor:"transformers.DecisionTransformerModel.forward",parameters:[{name:"states",val:": typing.Optional[torch.FloatTensor] = None"},{name:"actions",val:": typing.Optional[torch.FloatTensor] = None"},{name:"rewards",val:": typing.Optional[torch.FloatTensor] = None"},{name:"returns_to_go",val:": typing.Optional[torch.FloatTensor] = None"},{name:"timesteps",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.DecisionTransformerModel.forward.states",description:`<strong>states</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, episode_length, state_dim)</code>) &#x2014;
The states for each step in the trajectory`,name:"states"},{anchor:"transformers.DecisionTransformerModel.forward.actions",description:`<strong>actions</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, episode_length, act_dim)</code>) &#x2014;
The actions taken by the &#x201C;expert&#x201D; policy for the current state, these are masked for auto regressive
prediction`,name:"actions"},{anchor:"transformers.DecisionTransformerModel.forward.rewards",description:`<strong>rewards</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, episode_length, 1)</code>) &#x2014;
The rewards for each state, action`,name:"rewards"},{anchor:"transformers.DecisionTransformerModel.forward.returns_to_go",description:`<strong>returns_to_go</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, episode_length, 1)</code>) &#x2014;
The returns for each state in the trajectory`,name:"returns_to_go"},{anchor:"transformers.DecisionTransformerModel.forward.timesteps",description:`<strong>timesteps</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, episode_length)</code>) &#x2014;
The timestep for each step in the trajectory`,name:"timesteps"},{anchor:"transformers.DecisionTransformerModel.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, episode_length)</code>) &#x2014;
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<p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the model.</p>
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<p><strong>action_preds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, action_dim)</code>) \u2014 Model action predictions</p>
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<p><strong>return_preds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, 1)</code>) \u2014 Predicted returns for each state</p>
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