chore: initial snapshot for gitea/github upload

llm-gateway-competitors/litellm-wheel-src/litellm/llms/azure/cost_calculation.py

@@ -0,0 +1,50 @@
+"""
+Helper util for handling azure openai-specific cost calculation
+- e.g.: prompt caching, audio tokens
+"""
+
+from typing import Optional, Tuple
+
+from litellm._logging import verbose_logger
+from litellm.litellm_core_utils.llm_cost_calc.utils import generic_cost_per_token
+from litellm.types.utils import Usage
+from litellm.utils import get_model_info
+
+
+def cost_per_token(
+    model: str, usage: Usage, response_time_ms: Optional[float] = 0.0
+) -> Tuple[float, float]:
+    """
+    Calculates the cost per token for a given model, prompt tokens, and completion tokens.
+
+    Input:
+        - model: str, the model name without provider prefix
+        - usage: LiteLLM Usage block, containing caching and audio token information
+
+    Returns:
+        Tuple[float, float] - prompt_cost_in_usd, completion_cost_in_usd
+    """
+    ## GET MODEL INFO
+    model_info = get_model_info(model=model, custom_llm_provider="azure")
+
+    ## Speech / Audio cost calculation (cost per second for TTS models)
+    if (
+        "output_cost_per_second" in model_info
+        and model_info["output_cost_per_second"] is not None
+        and response_time_ms is not None
+    ):
+        verbose_logger.debug(
+            f"For model={model} - output_cost_per_second: {model_info.get('output_cost_per_second')}; response time: {response_time_ms}"
+        )
+        ## COST PER SECOND ##
+        prompt_cost = 0.0
+        completion_cost = model_info["output_cost_per_second"] * response_time_ms / 1000
+        return prompt_cost, completion_cost
+
+    ## Use generic cost calculator for all other cases
+    ## This properly handles: text tokens, audio tokens, cached tokens, reasoning tokens, etc.
+    return generic_cost_per_token(
+        model=model,
+        usage=usage,
+        custom_llm_provider="azure",
+    )