fix: class migration from sphinx to mkdocs

Signed-off-by: Zerohertz <[email protected]>

Author: Zerohertz

vllm/inputs/data.py

@@ -80,22 +80,24 @@ class EmbedsPrompt(TypedDict):
 """
 Set of possible schemas for a single prompt:
 
-- A text prompt ({class}`str` or {class}`TextPrompt`)
-- A tokenized prompt ({class}`TokensPrompt`)
-- An embeddings prompt ({class}`EmbedsPrompt`)
+- A text prompt ([`str`][] or [`TextPrompt`][vllm.inputs.data.TextPrompt])
+- A tokenized prompt ([`TokensPrompt`][vllm.inputs.data.TokensPrompt])
+- An embeddings prompt ([`EmbedsPrompt`][vllm.inputs.data.EmbedsPrompt])
 
 Note that "singleton" is as opposed to a data structure
 which encapsulates multiple prompts, i.e. of the sort
 which may be utilized for encoder/decoder models when
 the user desires to express both the encoder & decoder
-prompts explicitly, i.e. {class}`ExplicitEncoderDecoderPrompt`
+prompts explicitly, i.e. 
+[`ExplicitEncoderDecoderPrompt`][vllm.inputs.data.ExplicitEncoderDecoderPrompt]
 
-A prompt of type {class}`SingletonPrompt` may be employed
-as (1) input to a decoder-only model, (2) input to
+A prompt of type [`SingletonPrompt`][vllm.inputs.data.SingletonPrompt] may be 
+employed as (1) input to a decoder-only model, (2) input to
 the encoder of an encoder/decoder model, in the scenario
 where the decoder-prompt is not specified explicitly, or
 (3) as a member of a larger data structure encapsulating
-more than one prompt, i.e. {class}`ExplicitEncoderDecoderPrompt`
+more than one prompt, i.e. 
+[`ExplicitEncoderDecoderPrompt`][vllm.inputs.data.ExplicitEncoderDecoderPrompt]
 """
 
 
@@ -126,18 +128,20 @@ class ExplicitEncoderDecoderPrompt(TypedDict, Generic[_T1_co, _T2_co]):
     comprising an explicit encoder prompt and a decoder prompt.
 
     The encoder and decoder prompts, respectively, may be formatted
-    according to any of the {class}`SingletonPrompt` schemas,
+    according to any of the 
+    [`SingletonPrompt`][vllm.inputs.data.SingletonPrompt] schemas,
     and are not required to have the same schema.
 
     Only the encoder prompt may have multi-modal data. mm_processor_kwargs
     should be at the top-level, and should not be set in the encoder/decoder
     prompts, since they are agnostic to the encoder/decoder.
 
-    Note that an {class}`ExplicitEncoderDecoderPrompt` may not
-    be used as an input to a decoder-only model,
+    Note that an 
+    [`ExplicitEncoderDecoderPrompt`][vllm.inputs.data.ExplicitEncoderDecoderPrompt] 
+    may not be used as an input to a decoder-only model,
     and that the `encoder_prompt` and `decoder_prompt`
     fields of this data structure themselves must be
-    {class}`SingletonPrompt` instances.
+    [`SingletonPrompt`][vllm.inputs.data.SingletonPrompt] instances.
     """
 
     encoder_prompt: _T1_co
@@ -152,11 +156,11 @@ class ExplicitEncoderDecoderPrompt(TypedDict, Generic[_T1_co, _T2_co]):
 Set of possible schemas for an LLM input, including
 both decoder-only and encoder/decoder input types:
 
-- A text prompt ({class}`str` or {class}`TextPrompt`)
-- A tokenized prompt ({class}`TokensPrompt`)
-- An embeddings prompt ({class}`EmbedsPrompt`)
+- A text prompt ([`str`][] or [`TextPrompt`][vllm.inputs.data.TextPrompt])
+- A tokenized prompt ([`TokensPrompt`][vllm.inputs.data.TokensPrompt])
+- An embeddings prompt ([`EmbedsPrompt`][vllm.inputs.data.EmbedsPrompt])
 - A single data structure containing both an encoder and a decoder prompt
-  ({class}`ExplicitEncoderDecoderPrompt`)
+  ([`ExplicitEncoderDecoderPrompt`][vllm.inputs.data.ExplicitEncoderDecoderPrompt])
 """
 
 
@@ -189,7 +193,8 @@ def token_inputs(
     prompt: Optional[str] = None,
     cache_salt: Optional[str] = None,
 ) -> TokenInputs:
-    """Construct {class}`TokenInputs` from optional values."""
+    """Construct [`TokenInputs`][vllm.inputs.data.TokenInputs] from optional 
+    values."""
     inputs = TokenInputs(type="token", prompt_token_ids=prompt_token_ids)
 
     if prompt is not None:
@@ -221,7 +226,8 @@ def embeds_inputs(
     prompt_embeds: torch.Tensor,
     cache_salt: Optional[str] = None,
 ) -> EmbedsInputs:
-    """Construct :class:`EmbedsInputs` from optional values."""
+    """Construct [`EmbedsInputs`][vllm.inputs.data.EmbedsInputs] from optional 
+    values."""
     inputs = EmbedsInputs(type="embeds", prompt_embeds=prompt_embeds)
 
     if cache_salt is not None:
@@ -232,19 +238,20 @@ def embeds_inputs(
 
 DecoderOnlyInputs = Union[TokenInputs, EmbedsInputs, "MultiModalInputs"]
 """
-The inputs in {class}`~vllm.LLMEngine` before they are
+The inputs in [`LLMEngine`][vllm.engine.llm_engine.LLMEngine] before they are
 passed to the model executor.
 This specifies the data required for decoder-only models.
 """
 
 
 class EncoderDecoderInputs(TypedDict):
     """
-    The inputs in {class}`~vllm.LLMEngine` before they are
-    passed to the model executor.
+    The inputs in [`LLMEngine`][vllm.engine.llm_engine.LLMEngine] before they 
+    are passed to the model executor.
 
     This specifies the required data for encoder-decoder models.
     """
+
     encoder: Union[TokenInputs, "MultiModalInputs"]
     """The inputs for the encoder portion."""
 
@@ -254,13 +261,13 @@ class EncoderDecoderInputs(TypedDict):
 
 SingletonInputs = Union[TokenInputs, EmbedsInputs, "MultiModalInputs"]
 """
-A processed {class}`SingletonPrompt` which can be passed to
-{class}`vllm.sequence.Sequence`.
+A processed [`SingletonPrompt`][vllm.inputs.data.SingletonPrompt] which can be 
+passed to [`vllm.sequence.Sequence`][vllm.sequence.Sequence].
 """
 
 ProcessorInputs = Union[DecoderOnlyInputs, EncoderDecoderInputs]
 """
-The inputs to {data}`vllm.inputs.InputProcessor`.
+The inputs to [`vllm.inputs.InputProcessor`][vllm.inputs.InputProcessor].
 """
 
 _T1 = TypeVar("_T1", bound=SingletonPrompt, default=SingletonPrompt)
@@ -277,7 +284,8 @@ def build_explicit_enc_dec_prompt(
     return ExplicitEncoderDecoderPrompt(
         encoder_prompt=encoder_prompt,
         decoder_prompt=decoder_prompt,
-        mm_processor_kwargs=mm_processor_kwargs)
+        mm_processor_kwargs=mm_processor_kwargs,
+    )
 
 
 def zip_enc_dec_prompts(
@@ -288,7 +296,8 @@ def zip_enc_dec_prompts(
 ) -> list[ExplicitEncoderDecoderPrompt[_T1, _T2]]:
     """
     Zip encoder and decoder prompts together into a list of
-    {class}`ExplicitEncoderDecoderPrompt` instances.
+    [`ExplicitEncoderDecoderPrompt`][vllm.inputs.data.ExplicitEncoderDecoderPrompt] 
+    instances.
 
     ``mm_processor_kwargs`` may also be provided; if a dict is passed, the same
     dictionary will be used for every encoder/decoder prompt. If an iterable is
@@ -299,10 +308,11 @@ def zip_enc_dec_prompts(
     if isinstance(mm_processor_kwargs, dict):
         return [
             build_explicit_enc_dec_prompt(
-                encoder_prompt, decoder_prompt,
-                cast(dict[str, Any], mm_processor_kwargs))
-            for (encoder_prompt,
-                 decoder_prompt) in zip(enc_prompts, dec_prompts)
+                encoder_prompt,
+                decoder_prompt,
+                cast(dict[str, Any], mm_processor_kwargs),
+            ) for (encoder_prompt,
+                   decoder_prompt) in zip(enc_prompts, dec_prompts)
         ]
     return [
         build_explicit_enc_dec_prompt(encoder_prompt, decoder_prompt,