Update Protobuf and add protos for CS2 (#176)

* Replace protobuf 2.6.1 with 3.21.8

* Update/add protobuf libs

* Add CS2 protos

* Remove old csgo/dota protos

* Add versioned protoc bin

* Comment out Valve's `schema` define for now

* Use ENetworkDisconnectionReason

* Fix-up `offsetof` to avoid errors on some Clang versions

thirdparty/protobuf-3.21.8/benchmarks/util/big_query_utils.py

@@ -0,0 +1,189 @@
+#!/usr/bin/env python2.7
+
+from __future__ import print_function
+import argparse
+import json
+import uuid
+import httplib2
+
+from apiclient import discovery
+from apiclient.errors import HttpError
+from oauth2client.client import GoogleCredentials
+
+# 30 days in milliseconds
+_EXPIRATION_MS = 30 * 24 * 60 * 60 * 1000
+NUM_RETRIES = 3
+
+
+def create_big_query():
+    """Authenticates with cloud platform and gets a BiqQuery service object
+  """
+    creds = GoogleCredentials.get_application_default()
+    return discovery.build(
+        'bigquery', 'v2', credentials=creds, cache_discovery=False)
+
+
+def create_dataset(biq_query, project_id, dataset_id):
+    is_success = True
+    body = {
+        'datasetReference': {
+            'projectId': project_id,
+            'datasetId': dataset_id
+        }
+    }
+
+    try:
+        dataset_req = biq_query.datasets().insert(
+            projectId=project_id, body=body)
+        dataset_req.execute(num_retries=NUM_RETRIES)
+    except HttpError as http_error:
+        if http_error.resp.status == 409:
+            print('Warning: The dataset %s already exists' % dataset_id)
+        else:
+            # Note: For more debugging info, print "http_error.content"
+            print('Error in creating dataset: %s. Err: %s' % (dataset_id,
+                                                              http_error))
+            is_success = False
+    return is_success
+
+
+def create_table(big_query, project_id, dataset_id, table_id, table_schema,
+                 description):
+    fields = [{
+        'name': field_name,
+        'type': field_type,
+        'description': field_description
+    } for (field_name, field_type, field_description) in table_schema]
+    return create_table2(big_query, project_id, dataset_id, table_id, fields,
+                         description)
+
+
+def create_partitioned_table(big_query,
+                             project_id,
+                             dataset_id,
+                             table_id,
+                             table_schema,
+                             description,
+                             partition_type='DAY',
+                             expiration_ms=_EXPIRATION_MS):
+    """Creates a partitioned table. By default, a date-paritioned table is created with
+  each partition lasting 30 days after it was last modified.
+  """
+    fields = [{
+        'name': field_name,
+        'type': field_type,
+        'description': field_description
+    } for (field_name, field_type, field_description) in table_schema]
+    return create_table2(big_query, project_id, dataset_id, table_id, fields,
+                         description, partition_type, expiration_ms)
+
+
+def create_table2(big_query,
+                  project_id,
+                  dataset_id,
+                  table_id,
+                  fields_schema,
+                  description,
+                  partition_type=None,
+                  expiration_ms=None):
+    is_success = True
+
+    body = {
+        'description': description,
+        'schema': {
+            'fields': fields_schema
+        },
+        'tableReference': {
+            'datasetId': dataset_id,
+            'projectId': project_id,
+            'tableId': table_id
+        }
+    }
+
+    if partition_type and expiration_ms:
+        body["timePartitioning"] = {
+            "type": partition_type,
+            "expirationMs": expiration_ms
+        }
+
+    try:
+        table_req = big_query.tables().insert(
+            projectId=project_id, datasetId=dataset_id, body=body)
+        res = table_req.execute(num_retries=NUM_RETRIES)
+        print('Successfully created %s "%s"' % (res['kind'], res['id']))
+    except HttpError as http_error:
+        if http_error.resp.status == 409:
+            print('Warning: Table %s already exists' % table_id)
+        else:
+            print('Error in creating table: %s. Err: %s' % (table_id,
+                                                            http_error))
+            is_success = False
+    return is_success
+
+
+def patch_table(big_query, project_id, dataset_id, table_id, fields_schema):
+    is_success = True
+
+    body = {
+        'schema': {
+            'fields': fields_schema
+        },
+        'tableReference': {
+            'datasetId': dataset_id,
+            'projectId': project_id,
+            'tableId': table_id
+        }
+    }
+
+    try:
+        table_req = big_query.tables().patch(
+            projectId=project_id,
+            datasetId=dataset_id,
+            tableId=table_id,
+            body=body)
+        res = table_req.execute(num_retries=NUM_RETRIES)
+        print('Successfully patched %s "%s"' % (res['kind'], res['id']))
+    except HttpError as http_error:
+        print('Error in creating table: %s. Err: %s' % (table_id, http_error))
+        is_success = False
+    return is_success
+
+
+def insert_rows(big_query, project_id, dataset_id, table_id, rows_list):
+    is_success = True
+    body = {'rows': rows_list}
+    try:
+        insert_req = big_query.tabledata().insertAll(
+            projectId=project_id,
+            datasetId=dataset_id,
+            tableId=table_id,
+            body=body)
+        res = insert_req.execute(num_retries=NUM_RETRIES)
+        if res.get('insertErrors', None):
+            print('Error inserting rows! Response: %s' % res)
+            is_success = False
+    except HttpError as http_error:
+        print('Error inserting rows to the table %s' % table_id)
+        is_success = False
+
+    return is_success
+
+
+def sync_query_job(big_query, project_id, query, timeout=5000):
+    query_data = {'query': query, 'timeoutMs': timeout}
+    query_job = None
+    try:
+        query_job = big_query.jobs().query(
+            projectId=project_id,
+            body=query_data).execute(num_retries=NUM_RETRIES)
+    except HttpError as http_error:
+        print('Query execute job failed with error: %s' % http_error)
+        print(http_error.content)
+    return query_job
+
+
+    # List of (column name, column type, description) tuples
+def make_row(unique_row_id, row_values_dict):
+    """row_values_dict is a dictionary of column name and column value.
+  """
+    return {'insertId': unique_row_id, 'json': row_values_dict}