fix linting error

litellm/model_prices_and_context_window_backup.json

@@ -22283,6 +22283,50 @@
         "supports_vision": true,
         "tool_use_system_prompt_tokens": 159
     },
+    "vertex_ai/mistralai/codestral-2@001": {
+        "input_cost_per_token": 3e-07,
+        "litellm_provider": "vertex_ai-mistral_models",
+        "max_input_tokens": 128000,
+        "max_output_tokens": 128000,
+        "max_tokens": 128000,
+        "mode": "chat",
+        "output_cost_per_token": 9e-07,
+        "supports_function_calling": true,
+        "supports_tool_choice": true
+    },
+    "vertex_ai/codestral-2": {
+        "input_cost_per_token": 3e-07,
+        "litellm_provider": "vertex_ai-mistral_models",
+        "max_input_tokens": 128000,
+        "max_output_tokens": 128000,
+        "max_tokens": 128000,
+        "mode": "chat",
+        "output_cost_per_token": 9e-07,
+        "supports_function_calling": true,
+        "supports_tool_choice": true
+    },
+    "vertex_ai/codestral-2@001": {
+        "input_cost_per_token": 3e-07,
+        "litellm_provider": "vertex_ai-mistral_models",
+        "max_input_tokens": 128000,
+        "max_output_tokens": 128000,
+        "max_tokens": 128000,
+        "mode": "chat",
+        "output_cost_per_token": 9e-07,
+        "supports_function_calling": true,
+        "supports_tool_choice": true
+    },
+    "vertex_ai/mistralai/codestral-2": {
+        "input_cost_per_token": 3e-07,
+        "litellm_provider": "vertex_ai-mistral_models",
+        "max_input_tokens": 128000,
+        "max_output_tokens": 128000,
+        "max_tokens": 128000,
+        "mode": "chat",
+        "output_cost_per_token": 9e-07,
+        "supports_function_calling": true,
+        "supports_tool_choice": true
+    },
     "vertex_ai/codestral-2501": {
         "input_cost_per_token": 2e-07,
         "litellm_provider": "vertex_ai-mistral_models",
@@ -22612,6 +22656,50 @@
         "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#partner-models",
         "supports_tool_choice": true
     },
+    "vertex_ai/mistral-medium-3": {
+        "input_cost_per_token": 4e-07,
+        "litellm_provider": "vertex_ai-mistral_models",
+        "max_input_tokens": 128000,
+        "max_output_tokens": 8191,
+        "max_tokens": 8191,
+        "mode": "chat",
+        "output_cost_per_token": 2e-06,
+        "supports_function_calling": true,
+        "supports_tool_choice": true
+    },
+    "vertex_ai/mistral-medium-3@001": {
+        "input_cost_per_token": 4e-07,
+        "litellm_provider": "vertex_ai-mistral_models",
+        "max_input_tokens": 128000,
+        "max_output_tokens": 8191,
+        "max_tokens": 8191,
+        "mode": "chat",
+        "output_cost_per_token": 2e-06,
+        "supports_function_calling": true,
+        "supports_tool_choice": true
+    },
+    "vertex_ai/mistralai/mistral-medium-3": {
+        "input_cost_per_token": 4e-07,
+        "litellm_provider": "vertex_ai-mistral_models",
+        "max_input_tokens": 128000,
+        "max_output_tokens": 8191,
+        "max_tokens": 8191,
+        "mode": "chat",
+        "output_cost_per_token": 2e-06,
+        "supports_function_calling": true,
+        "supports_tool_choice": true
+    },
+    "vertex_ai/mistralai/mistral-medium-3@001": {
+        "input_cost_per_token": 4e-07,
+        "litellm_provider": "vertex_ai-mistral_models",
+        "max_input_tokens": 128000,
+        "max_output_tokens": 8191,
+        "max_tokens": 8191,
+        "mode": "chat",
+        "output_cost_per_token": 2e-06,
+        "supports_function_calling": true,
+        "supports_tool_choice": true
+    },
     "vertex_ai/mistral-large-2411": {
         "input_cost_per_token": 2e-06,
         "litellm_provider": "vertex_ai-mistral_models",
@@ -23833,12 +23921,21 @@
         "supports_vision": true,
         "supports_web_search": true
     },
-    "sora-2": {
-        "input_cost_per_second": 0.0,
+    "openai/sora-2": {
         "litellm_provider": "openai",
         "mode": "video_generation",
-        "output_cost_per_second": 0.0,
-        "supports_video_generation": true
+        "output_cost_per_video_per_second": 0.10,
+        "source": "https://platform.openai.com/docs/api-reference/videos",
+        "supported_modalities": [
+            "text"
+        ],
+        "supported_output_modalities": [
+            "video"
+        ],
+        "supported_resolutions": [
+            "720x1280",
+            "1280x720"
+        ]
     },
     "azure/sora-2": {
         "litellm_provider": "azure",

litellm/proxy/guardrails/guardrail_hooks/bedrock_guardrails.py

@@ -23,7 +23,6 @@ import litellm
 from litellm._logging import verbose_proxy_logger
 from litellm.caching import DualCache
 from litellm.integrations.custom_guardrail import CustomGuardrail
-from litellm.types.llms.openai import ChatCompletionUserMessage
 from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM
 from litellm.llms.custom_httpx.http_handler import (
     get_async_httpx_client,
@@ -32,7 +31,7 @@ from litellm.llms.custom_httpx.http_handler import (
 from litellm.proxy._types import UserAPIKeyAuth
 from litellm.secret_managers.main import get_secret_str
 from litellm.types.guardrails import GuardrailEventHooks, PiiEntityType
-from litellm.types.llms.openai import AllMessageValues
+from litellm.types.llms.openai import AllMessageValues, ChatCompletionUserMessage
 from litellm.types.proxy.guardrails.guardrail_hooks.bedrock_guardrails import (
     BedrockContentItem,
     BedrockGuardrailOutput,
@@ -1103,7 +1102,7 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM):
             verbose_proxy_logger.debug(
                 "Bedrock Guardrail: Applying guardrail"
             )
-            mock_messages = [ChatCompletionUserMessage(role="user", content=text)]
+            mock_messages: List[AllMessageValues] = [ChatCompletionUserMessage(role="user", content=text)]
             bedrock_response = await self.make_bedrock_api_request(
                 source="INPUT",
                 messages=mock_messages,
@@ -1115,18 +1114,23 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM):
             
             # Apply any masking that was applied by the guardrail
             masked_text = text
-            if bedrock_response.get("output") and bedrock_response["output"]:
+            output_list = bedrock_response.get("output")
+            if output_list:
                 # If the guardrail returned modified content, use that
-                for output_item in bedrock_response["output"]:
-                    if output_item.get("text"):
-                        masked_text = str(output_item["text"])
-                        break
-            elif bedrock_response.get("content") and bedrock_response["content"]:
-                # Fallback to content field if output is not available
-                for content_item in bedrock_response["content"]:
-                    if content_item.get("text") and content_item["text"].get("text"):
-                        masked_text = str(content_item["text"]["text"])
+                for output_item in output_list:
+                    text_content = output_item.get("text")
+                    if text_content:
+                        masked_text = str(text_content)
                         break
+            else:
+                outputs_list = bedrock_response.get("outputs")
+                if outputs_list:
+                    # Fallback to outputs field if output is not available
+                    for output_item in outputs_list:
+                        text_content = output_item.get("text")
+                        if text_content:
+                            masked_text = str(text_content)
+                            break
             
             verbose_proxy_logger.debug(
                 "Bedrock Guardrail: Successfully applied guardrail"