--- /nfs/site/disks/swip_dla_1/resources/inference_engine/2023.3.0_with_dev_tools/1/linux64/suse12/python/openvino/tools/benchmark/benchmark.py	2024-03-01 14:01:50.443877000 -0500
+++ benchmark.py	2024-04-01 10:06:18.751566000 -0400
@@ -1,14 +1,15 @@
-# Copyright (C) 2018-2023 Intel Corporation
+# Copyright (C) 2018-2022 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
 import os
 from datetime import datetime
 from math import ceil
+import warnings
 from openvino.runtime import Core, get_version, AsyncInferQueue
 
-from .utils.constants import GPU_DEVICE_NAME, XML_EXTENSION, BIN_EXTENSION
-from .utils.logging import logger
-from .utils.utils import get_duration_seconds
+from openvino.tools.benchmark.utils.constants import GPU_DEVICE_NAME, XML_EXTENSION, BIN_EXTENSION
+from openvino.tools.benchmark.utils.logging import logger
+from openvino.tools.benchmark.utils.utils import get_duration_seconds
 
 def percentile(values, percent):
     return values[ceil(len(values) * percent / 100) - 1]
@@ -17,7 +18,17 @@
     def __init__(self, device: str, number_infer_requests: int = 0, number_iterations: int = None,
                  duration_seconds: int = None, api_type: str = 'async', inference_only = None):
         self.device = device
-        self.core = Core()
+        dla_plugins = os.environ.get('DLA_PLUGINS', default='')
+        if dla_plugins == '':
+            # Backwards compatability for old DLA_PLUGINS_XML_FILE
+            warnings.warn("DLA_PLUGINS_XML_FILE option is deprecated as of 2024.1, Please use DLA_PLUGINS") 
+            dla_plugins = os.environ.get('DLA_PLUGINS_XML_FILE', default='')
+        self.core = Core(dla_plugins)
+        if "FPGA" in self.device:
+            dla_arch_file = os.environ.get('DLA_ARCH_FILE')
+            if dla_arch_file is None:
+                raise Exception(f"To use FPGA, you need to specify the path to an arch_file!")
+            self.core.set_property(device_name="FPGA", properties={"ARCH_PATH": dla_arch_file})
         self.nireq = number_infer_requests if api_type == 'async' else 1
         self.niter = number_iterations
         self.duration_seconds = get_duration_seconds(duration_seconds, self.niter, self.device)
@@ -59,6 +70,9 @@
     def set_cache_dir(self, cache_dir: str):
         self.core.set_property({'CACHE_DIR': cache_dir})
 
+    def set_allow_auto_batching(self, flag: bool):
+        self.core.set_property({'ALLOW_AUTO_BATCHING': flag})
+
     def read_model(self, path_to_model: str):
         model_filename = os.path.abspath(path_to_model)
         head, ext = os.path.splitext(model_filename)
@@ -110,7 +124,7 @@
               (self.duration_seconds and exec_time < self.duration_seconds) or \
               (iteration % self.nireq):
             idle_id = infer_queue.get_idle_request_id()
-            if idle_id in in_fly:
+            if idle_id in in_fly:       # Is this check neccessary?
                 times.append(infer_queue[idle_id].latency)
             else:
                 in_fly.add(idle_id)
@@ -162,7 +176,6 @@
     def main_loop(self, requests, data_queue, batch_size, latency_percentile, pcseq):
         if self.api_type == 'sync':
             times, total_duration_sec, iteration = self.sync_inference(requests[0], data_queue)
-            fps = len(batch_size) * iteration / total_duration_sec
         elif self.inference_only:
             times, total_duration_sec, iteration = self.async_inference_only(requests)
             fps = len(batch_size) * iteration / total_duration_sec
@@ -175,6 +188,9 @@
         min_latency_ms = times[0]
         max_latency_ms = times[-1]
 
+        if self.api_type == 'sync':
+            fps = len(batch_size) * 1000 / median_latency_ms
+
         if pcseq:
             for group in self.latency_groups:
                 if group.times: