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Fixups (google#135)
* Fixups * fix json * Update tuning_quickstart_python.ipynb
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‎site/en/tutorials/tuning_quickstart_python.ipynb‎

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@@ -113,7 +113,7 @@
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"If you want to run this notebook in Colab start by uploading your\n",
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"`client_secret*.json` file using the \"File > Upload\" option.\n",
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"\n",
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"![Show colab's File > Upload option](https://developers.generativeai.google/tutorials/images/colab_upload.png)"
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"<img width=400 src=\"https://developers.generativeai.google/tutorials/images/colab_upload.png\">"
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]
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},
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{
@@ -133,7 +133,7 @@
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],
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"source": [
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"!cp client_secret*.json client_secret.json\n",
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"!ls"
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"!ls client_secret.json"
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]
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},
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{
@@ -174,26 +174,13 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 7,
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"metadata": {
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"id": "VYetBMbknUVp"
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"id": "cbcf72bcb56d"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/122.9 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r",
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"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m71.7/122.9 kB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m122.9/122.9 kB\u001b[0m \u001b[31m2.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25h\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/113.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m113.3/113.3 kB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25h"
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]
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}
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],
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"outputs": [],
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"source": [
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"!pip install google-generativeai"
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"!pip install -q google-generativeai"
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]
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},
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{
@@ -213,10 +200,7 @@
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},
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"outputs": [],
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"source": [
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"import google.generativeai as palm\n",
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"\n",
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"import google.ai.generativelanguage as glm\n",
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"import pprint"
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"import google.generativeai as palm\n"
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]
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},
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{
@@ -239,16 +223,17 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"['tunedModels/testnumbergenerator-fvitocr834l6',\n",
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" 'tunedModels/my-display-name-81-9wpmc1m920vq',\n",
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" 'tunedModels/number-generator-model-kctlevca1g3q',\n",
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" 'tunedModels/my-display-name-81-r9wcuda14lyy']\n"
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"tunedModels/my-model-8527\n",
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"tunedModels/my-model-7092\n",
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"tunedModels/my-model-2778\n",
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"tunedModels/my-model-1298\n",
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"tunedModels/my-model-3883\n"
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]
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}
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],
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"source": [
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"tuned_models = list(palm.list_tuned_models())\n",
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"pprint.pprint([m.name for m in tuned_models])"
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"for i, m in zip(range(5), palm.list_tuned_models()):\n",
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" print(m.name)"
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]
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},
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{
@@ -311,6 +296,7 @@
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"\n",
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"name = f'generate-num-{random.randint(0,10000)}'\n",
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"operation = palm.create_tuned_model(\n",
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" # You can use a tuned model here too. Set `source_model=\"tunedModels/...\"` \n",
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" source_model=base_model.name,\n",
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" training_data=[\n",
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" {\n",
@@ -387,18 +373,18 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"TunedModel(name='tunedModels/generate-num-4668',\n",
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" source_model=None,\n",
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"TunedModel(name='tunedModels/generate-num-9028',\n",
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" source_model='tunedModels/generate-num-4110',\n",
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" base_model='models/text-bison-001',\n",
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" display_name='',\n",
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" description='',\n",
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" temperature=0.7,\n",
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" top_p=0.95,\n",
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" top_k=40,\n",
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" state=<State.CREATING: 1>,\n",
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" create_time=datetime.datetime(2023, 9, 19, 19, 3, 38, 22249, tzinfo=<UTC>),\n",
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" update_time=datetime.datetime(2023, 9, 19, 19, 3, 38, 22249, tzinfo=<UTC>),\n",
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" tuning_task=TuningTask(start_time=datetime.datetime(2023, 9, 19, 19, 3, 38, 562798, tzinfo=<UTC>),\n",
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" create_time=datetime.datetime(2023, 9, 29, 21, 37, 32, 188028, tzinfo=datetime.timezone.utc),\n",
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" update_time=datetime.datetime(2023, 9, 29, 21, 37, 32, 188028, tzinfo=datetime.timezone.utc),\n",
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" tuning_task=TuningTask(start_time=datetime.datetime(2023, 9, 29, 21, 37, 32, 734118, tzinfo=datetime.timezone.utc),\n",
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" complete_time=None,\n",
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" snapshots=[],\n",
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" hyperparameters=Hyperparameters(epoch_count=100,\n",
@@ -410,12 +396,12 @@
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"source": [
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"model = palm.get_tuned_model(f'tunedModels/{name}')\n",
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"\n",
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"pprint.pprint(model)"
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"model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 40,
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"metadata": {
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"id": "EUodUwZkKPi-"
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},
@@ -463,11 +449,11 @@
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{
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"data": {
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"text/plain": [
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"total_steps: 375\n",
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"tuned_model: \"tunedModels/generate-num-4668\""
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"tuned_model: \"tunedModels/generate-num-9028\"\n",
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"total_steps: 375"
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]
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},
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"execution_count": 15,
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"execution_count": 41,
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"metadata": {},
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"output_type": "execute_result"
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}
@@ -487,15 +473,15 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 42,
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"metadata": {
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"id": "SOUowIv1HgSE"
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},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "c07d8bb1884d460cb7e7e40aa459c2ea",
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"model_id": "98e4b6958bfc43c98e6e77354f7bf315",
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"version_major": 2,
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"version_minor": 0
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},
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],
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"source": [
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"import time\n",
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"\n",
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"for status in operation.wait_bar():\n",
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" time.sleep(30)"
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]
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"source": [
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"model = palm.get_tuned_model(f'tunedModels/{name}')\n",
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"\n",
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"pprint.pprint(model)"
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"model"
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]
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},
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{
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"\n",
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"try:\n",
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" m = palm.get_tuned_model(f'tunedModels/{name}')\n",
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" pprint.pprint(m)\n",
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" print(m)\n",
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"except Exception as e:\n",
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" print(f\"{type(e)}: {e}\")"
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]

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