Apply isort and black reformatting

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

nemo/collections/llm/evaluation/base.py

@@ -63,7 +63,10 @@ def _generate_tokens_logits(self, payload, return_text: bool = False, return_log
         if return_text:
             return response["choices"][0]["text"]  # shape[batch_size, 1]
         if return_logits:
-            return response["choices"][0]["context_logits"], response["choices"][0]["generation_logits"]  # shape[batch_size, 1, num_tokens, vocab_size]
+            return (
+                response["choices"][0]["context_logits"],
+                response["choices"][0]["generation_logits"],
+            )  # shape[batch_size, 1, num_tokens, vocab_size]
 
     def tokenizer_type(self, tokenizer):
         """
@@ -120,10 +123,11 @@ def loglikelihood(self, requests: list[Instance]):
             # Get the logits from the model
             context_logits, generation_logits = self._generate_tokens_logits(payload, return_logits=True)
             import numpy as np
-            are_equal=np.array_equal(context_logits[0][len(context_logits[0])-1], generation_logits[0][0][0])
-            logits = context_logits[:,-num_cont_tokens:,:]
+
+            are_equal = np.array_equal(context_logits[0][len(context_logits[0]) - 1], generation_logits[0][0][0])
+            logits = context_logits[:, -num_cont_tokens:, :]
             # Convert generation_logits to torch tensor to easily get logprobs wo manual implementation of log_softmax
-            #multi_logits = F.log_softmax(torch.tensor(generation_logits[0]), dim=-1)
+            # multi_logits = F.log_softmax(torch.tensor(generation_logits[0]), dim=-1)
             multi_logits = F.log_softmax(torch.tensor(logits), dim=-1)
             # Convert encoded continuation tokens to torch tensor
             cont_toks = torch.tensor(continuation_enc, dtype=torch.long).unsqueeze(0)
@@ -172,6 +176,7 @@ def generate_until(self, inputs: list[Instance]):
 
         return results
 
+
 def wait_for_server_ready(url, model_name, max_retries=600, retry_interval=2):
     """
     Wait for the Triton server and model to be ready, with retry logic.
@@ -187,13 +192,14 @@ def wait_for_server_ready(url, model_name, max_retries=600, retry_interval=2):
     """
 
     import time
+
     import requests
     from pytriton.client import ModelClient
-    from pytriton.client.exceptions import PyTritonClientTimeoutError, PyTritonClientModelUnavailableError
+    from pytriton.client.exceptions import PyTritonClientModelUnavailableError, PyTritonClientTimeoutError
 
     # If gRPC URL, extract HTTP URL from gRPC URL for health checks
     if url.startswith("grpc://"):
-        #TODO use triton port and grpc port instaed of harcoding
+        # TODO use triton port and grpc port instaed of harcoding
         url = url.replace("grpc://", "http://").replace(":8001", ":8000")
     health_url = f"{url}/v2/health/ready"
 
@@ -225,4 +231,4 @@ def wait_for_server_ready(url, model_name, max_retries=600, retry_interval=2):
         time.sleep(retry_interval)
 
     logging.error(f"Server or model '{model_name}' not ready after {max_retries} attempts.")
-    return False
+    return False