# /// script
# requires-python = ">=3.12"
# dependencies = [
#     "numpy",
#     "einops",
#     "pandas",
#     "matplotlib",
#     "protobuf",
#     "torch",
#     "sentencepiece",
#     "torchvision",
#     "transformers",
#     "timm",
#     "diffusers",
#     "sentence-transformers",
#     "accelerate",
#     "peft",
#     "slack-sdk",
# ]
# ///

try:
    import pandas as pd
    from chronos import BaseChronosPipeline
    
    pipeline = BaseChronosPipeline.from_pretrained("amazon/chronos-2", device_map="cuda")
    
    # Load historical data
    context_df = pd.read_csv("https://autogluon.s3.us-west-2.amazonaws.com/datasets/timeseries/misc/AirPassengers.csv")
    
    # Generate predictions
    pred_df = pipeline.predict_df(
        context_df,
        prediction_length=36,  # Number of steps to forecast
        quantile_levels=[0.1, 0.5, 0.9],  # Quantiles for probabilistic forecast
        id_column="item_id",  # Column identifying different time series
        timestamp_column="Month",  # Column with datetime information
        target="#Passengers",  # Column(s) with time series values to predict
    )
    with open('amazon_chronos-2_0.txt', 'w', encoding='utf-8') as f:
        f.write('Everything was good in amazon_chronos-2_0.txt')
except Exception as e:
    import os
    from slack_sdk import WebClient
    client = WebClient(token=os.environ['SLACK_TOKEN'])
    client.chat_postMessage(
        channel='#hub-model-metadata-snippets-sprint',
        text='Problem in <https://huggingface.co/datasets/model-metadata/code_execution_files/blob/main/amazon_chronos-2_0.txt|amazon_chronos-2_0.txt>',
    )

    with open('amazon_chronos-2_0.txt', 'a', encoding='utf-8') as f:
        import traceback
        f.write('''```CODE: 
import pandas as pd
from chronos import BaseChronosPipeline

pipeline = BaseChronosPipeline.from_pretrained("amazon/chronos-2", device_map="cuda")

# Load historical data
context_df = pd.read_csv("https://autogluon.s3.us-west-2.amazonaws.com/datasets/timeseries/misc/AirPassengers.csv")

# Generate predictions
pred_df = pipeline.predict_df(
    context_df,
    prediction_length=36,  # Number of steps to forecast
    quantile_levels=[0.1, 0.5, 0.9],  # Quantiles for probabilistic forecast
    id_column="item_id",  # Column identifying different time series
    timestamp_column="Month",  # Column with datetime information
    target="#Passengers",  # Column(s) with time series values to predict
)
```

ERROR: 
''')
        traceback.print_exc(file=f)
    
finally:
    from huggingface_hub import upload_file
    upload_file(
        path_or_fileobj='amazon_chronos-2_0.txt',
        repo_id='model-metadata/code_execution_files',
        path_in_repo='amazon_chronos-2_0.txt',
        repo_type='dataset',
    )