fix imports and typing

src/huggingface_hub/hf_api.py

@@ -728,6 +728,8 @@ class ModelInfo:
             Status of the model on the inference API.
             Warm models are available for immediate use. Cold models will be loaded on first inference call.
             Frozen models are not available in Inference API.
+        inference_provider_mapping (`Dict`, *optional*):
+            Model's inference provider mapping.
         likes (`int`):
             Number of likes of the model.
         library_name (`str`, *optional*):
@@ -759,6 +761,7 @@ class ModelInfo:
             Model's safetensors information.
         security_repo_status (`Dict`, *optional*):
             Model's security scan status.
+
     """
 
     id: str
@@ -773,6 +776,7 @@ class ModelInfo:
     gated: Optional[Literal["auto", "manual", False]]
     gguf: Optional[Dict]
     inference: Optional[Literal["warm", "cold", "frozen"]]
+    inference_provider_mapping: Optional[Dict]
     likes: Optional[int]
     library_name: Optional[str]
     tags: Optional[List[str]]
@@ -806,6 +810,7 @@ def __init__(self, **kwargs):
         self.library_name = kwargs.pop("library_name", None)
         self.gguf = kwargs.pop("gguf", None)
         self.inference = kwargs.pop("inference", None)
+        self.inference_provider_mapping = kwargs.pop("inferenceProviderMapping", None)
         self.tags = kwargs.pop("tags", None)
         self.pipeline_tag = kwargs.pop("pipeline_tag", None)
         self.mask_token = kwargs.pop("mask_token", None)

src/huggingface_hub/inference/_client.py

@@ -263,7 +263,8 @@ def post(
                 "`InferenceClient.post` is deprecated and should not be used directly anymore."
             )
         provider_helper = HFInferenceTask(task or "unknown")
-        url = provider_helper.build_url(provider_helper.map_model(model))
+        mapped_model = provider_helper.map_model(model, provider="hf-inference", task=task)
+        url = provider_helper.build_url(mapped_model)
         headers = provider_helper.prepare_headers(headers=self.headers, api_key=self.token)
         return self._inner_post(
             request_parameters=RequestParameters(

src/huggingface_hub/inference/_common.py

@@ -49,7 +49,6 @@
     UnknownError,
     ValidationError,
 )
-from huggingface_hub.hf_api import HfApi
 
 from ..utils import (
     get_session,
@@ -64,6 +63,7 @@
     from aiohttp import ClientResponse, ClientSession
     from PIL.Image import Image
 
+    from ..hf_api import model_info
 # TYPES
 UrlT = str
 PathT = Union[str, Path]
@@ -104,11 +104,13 @@ def prepare_request(
     @abstractmethod
     def get_response(self, response: Union[bytes, Dict]) -> Any: ...
 
-    def map_model(self, model: Optional[str], chat_completion: bool = False) -> str:
+    def map_model(
+        self, model: Optional[str], provider: str, task: Optional[str] = None, conversational: bool = False
+    ) -> str:
         """Default implementation for mapping model IDs to provider-specific IDs."""
         if model is None:
-            raise ValueError(f"Please provide a model available on {self.provider}.")
-        return _get_provider_model_id(model, self.provider, self.task, chat_completion)
+            raise ValueError(f"Please provide a model available on {provider}.")
+        return _get_provider_model_id(model, provider, task, conversational)
 
 
 #### Fetching Inference Providers model mapping
@@ -119,11 +121,11 @@ def _fetch_provider_mappings(model: str) -> Dict:
     """
     Fetch provider mappings for a model from the Hub.
     """
-    try:
-        info = HfApi().model_info(model, expand=["inferenceProviderMapping"])
-        return info.get("inferenceProviderMapping", {})
-    except Exception as e:
-        raise ValueError(f"Failed to get provider mapping for model {model}: {e}")
+    info = model_info(model, expand=["inferenceProviderMapping"])
+    provider_mapping = info.inference_provider_mapping
+    if provider_mapping is None:
+        raise ValueError(f"No provider mapping found for model {model}")
+    return provider_mapping
 
 
 def _get_provider_model_id(

src/huggingface_hub/inference/_generated/_async_client.py

@@ -258,7 +258,8 @@ async def post(
                 "`InferenceClient.post` is deprecated and should not be used directly anymore."
             )
         provider_helper = HFInferenceTask(task or "unknown")
-        url = provider_helper.build_url(provider_helper.map_model(model))
+        mapped_model = provider_helper.map_model(model, provider="hf-inference", task=task)
+        url = provider_helper.build_url(mapped_model)
         headers = provider_helper.prepare_headers(headers=self.headers, api_key=self.token)
         return await self._inner_post(
             request_parameters=RequestParameters(

src/huggingface_hub/inference/_providers/fal_ai.py

@@ -36,7 +36,7 @@ def prepare_request(
             raise ValueError(
                 "You must provide an api_key to work with fal.ai API or log in with `huggingface-cli login`."
             )
-        mapped_model = self.map_model(model, conversational=conversational)
+        mapped_model = self.map_model(model, provider="fal-ai", task=self.task, conversational=conversational)
         headers = {
             **build_hf_headers(token=api_key),
             **headers,

src/huggingface_hub/inference/_providers/hf_inference.py

@@ -80,7 +80,7 @@ def prepare_request(
     ) -> RequestParameters:
         if extra_payload is None:
             extra_payload = {}
-        mapped_model = self.map_model(model, conversational=conversational)
+        mapped_model = self.map_model(model, provider="hf-inference", task=self.task, conversational=conversational)
         url = self.build_url(mapped_model)
         data, json = self._prepare_payload(
             inputs, parameters=parameters, model=mapped_model, extra_payload=extra_payload
@@ -165,7 +165,7 @@ def prepare_request(
         extra_payload: Optional[Dict[str, Any]] = None,
         conversational: bool = False,
     ) -> RequestParameters:
-        mapped_model = self.map_model(model, conversational=conversational)
+        mapped_model = self.map_model(model, provider="hf-inference", task=self.task, conversational=conversational)
         payload_model = parameters.get("model") or mapped_model
 
         if payload_model is None or payload_model.startswith(("http://", "https://")):

src/huggingface_hub/inference/_providers/replicate.py

@@ -47,7 +47,7 @@ def prepare_request(
             base_url = BASE_URL
             logger.info("Calling Replicate provider directly.")
 
-        mapped_model = self.map_model(model, conversational=conversational)
+        mapped_model = self.map_model(model, provider="replicate", task=self.task, conversational=conversational)
         url = _build_url(base_url, mapped_model)
 
         headers = {

src/huggingface_hub/inference/_providers/sambanova.py

@@ -43,7 +43,7 @@ def prepare_request(
             logger.info("Calling Sambanova provider directly.")
         headers = {**build_hf_headers(token=api_key), **headers}
 
-        mapped_model = self.map_model(model, conversational=conversational)
+        mapped_model = self.map_model(model, provider="sambanova", task=self.task, conversational=conversational)
         payload = {
             "messages": inputs,
             **{k: v for k, v in parameters.items() if v is not None},

src/huggingface_hub/inference/_providers/together.py

@@ -52,7 +52,7 @@ def prepare_request(
         else:
             base_url = BASE_URL
             logger.info("Calling Together provider directly.")
-        mapped_model = self.map_model(model, conversational=conversational)
+        mapped_model = self.map_model(model, provider="together", task=self.task, conversational=conversational)
         if "model" in parameters:
             parameters["model"] = mapped_model
         payload = self._prepare_payload(inputs, parameters=parameters)