import pickle
import boto3
import csv
import pandas as pd
from io import BytesIO, StringIO
from utils.logger import setup_logger
from botocore.exceptions import NoCredentialsError, PartialCredentialsError

logger = setup_logger()


def read_from_s3(bucket_name, s3_file_name):
    """
    Read an object from s3. Decoding of the data is left for outside of this function

    :param bucket_name: The name of the S3 bucket
    :param s3_file_name: The file name to use for the saved data in S3
    """
    # Initialize a session using Amazon S3
    s3 = boto3.resource('s3')

    # Get the MessagePack data from S3
    obj = s3.Object(bucket_name, s3_file_name)
    data = obj.get()['Body'].read()

    return data


def save_data_to_s3(data, bucket_name, s3_file_name):
    """
    Save an object to an S3 bucket

    :param data: The data to save
    :param bucket_name: The name of the S3 bucket
    :param s3_file_name: The file name to use for the saved data in S3
    """
    # Ensure you have AWS credentials set up - either via environment variables, AWS CLI, or IAM roles
    try:
        s3 = boto3.client('s3')
    except NoCredentialsError:
        print("Credentials not available.")
        return
    except PartialCredentialsError:
        print("Incomplete credentials provided.")
        return

    try:
        s3.put_object(Bucket=bucket_name, Key=s3_file_name, Body=data)
        print(f'Successfully uploaded data to {bucket_name}/{s3_file_name}')
    except Exception as e:
        print(f'Failed to upload data to {bucket_name}/{s3_file_name}: {str(e)}')


def read_io_from_s3(bucket_name, file_key):
    """
    Read a file from S3 into a BytesIO object. This can be used by other methods to parse the response

    Because we use

    :param bucket_name: The name of the S3 bucket
    :param file_key: The file name of the shapefile in S3
    :return: Io file to be parsed by another method
    """
    client = boto3.client('s3')

    # Get the Parquet file from S3
    response = client.get_object(Bucket=bucket_name, Key=file_key)

    # Read the file into an io object
    buffer = BytesIO(response['Body'].read())

    return buffer


def save_dataframe_to_s3_parquet(df, bucket_name, file_key):
    """
    Save a pandas DataFrame to S3 as a Parquet file.

    :param df: The pandas DataFrame.
    :param bucket_name: Name of the S3 bucket.
    :param file_key: Key of the file (including directory path within the bucket).
    """

    # Convert the DataFrame to a Parquet format in memory
    parquet_buffer = BytesIO()
    df.to_parquet(parquet_buffer)

    # Create the boto3 client
    client = boto3.client('s3')

    # Upload the Parquet file to S3
    client.put_object(Bucket=bucket_name, Key=file_key, Body=parquet_buffer.getvalue())


def read_dataframe_from_s3_parquet(bucket_name, file_key):
    """
    Read a pandas DataFrame from a Parquet file stored in S3.

    :param bucket_name: Name of the S3 bucket.
    :param file_key: Key of the file (including directory path within the bucket).
    :return: A pandas DataFrame.
    """

    if bucket_name is None:
        raise ValueError("Bucket name is None when trying to read dataframe from parquet")

    if not file_key.endswith(".parquet"):
        raise ValueError("This file doesn't look like a parquet file")

    parquet_buffer = read_io_from_s3(
        bucket_name=bucket_name,
        file_key=file_key
    )

    df = pd.read_parquet(parquet_buffer)

    return df


def save_csv_to_s3(dataframe, bucket_name, file_name):
    """
    Save a Pandas DataFrame to a CSV file in an S3 bucket.

    Parameters:
        dataframe (pd.DataFrame): The Pandas DataFrame to save.
        bucket_name (str): The name of the S3 bucket.
        file_name (str): The name of the file to save in the S3 bucket.

    Returns:
        bool: True if the file was successfully saved, False otherwise.
    """
    # Initialize S3 client
    s3 = boto3.client('s3')

    # Create an in-memory text stream
    csv_buffer = StringIO()

    # Save DataFrame to buffer
    dataframe.to_csv(csv_buffer, index=False)

    # Upload buffer contents to S3
    try:
        s3.put_object(Body=csv_buffer.getvalue(), Bucket=bucket_name, Key=file_name)
        return True
    except Exception as e:
        logger.error(f"An error occurred: {e}")
        return False


def save_pickle_to_s3(data, bucket_name, s3_file_name):
    """
    Save an object to an S3 bucket as a pickle file.

    :param data: The data to save
    :param bucket_name: The name of the S3 bucket
    :param s3_file_name: The file name to use for the saved data in S3 (should end in .pkl)
    """
    # Serialize data to a pickle format
    try:
        serialized_data = pickle.dumps(data)
    except Exception as e:
        print(f'Failed to serialize data: {str(e)}')
        return

    # Use save_data_to_s3 function to upload the serialized data to S3
    save_data_to_s3(serialized_data, bucket_name, s3_file_name)


def read_pickle_from_s3(bucket_name, s3_file_name):
    """
    Read a pickle file from an S3 bucket and return the data.

    :param bucket_name: The name of the S3 bucket
    :param s3_file_name: The file name of the pickle file in S3
    :return: The data read from the pickle file
    """
    try:
        s3 = boto3.client('s3')
        s3_response = s3.get_object(Bucket=bucket_name, Key=s3_file_name)
        serialized_data = s3_response['Body'].read()
    except NoCredentialsError:
        logger.errpr("Credentials not available.")
        return None
    except PartialCredentialsError:
        logger.errpr("Incomplete credentials provided.")
        return None
    except Exception as e:
        logger.error(f'Failed to download data from {bucket_name}/{s3_file_name}: {str(e)}')
        return None

    # Deserialize data from pickle format
    try:
        data = pickle.loads(serialized_data)
    except Exception as e:
        logger.errpr(f'Failed to deserialize data: {str(e)}')
        return None

    return data


def read_excel_from_s3(bucket_name, file_key, header_row):
    """
    Read an Excel file from an S3 bucket and return it as a pandas DataFrame.

    :param bucket_name: Name of the S3 bucket.
    :param file_key: Key of the file (including directory path within the bucket).
    :param header_row: The row number to use as the header (0-indexed).
    :return: A pandas DataFrame containing the data from the Excel file.
    """

    # Ensure the file_key is an Excel file
    if not file_key.endswith((".xls", ".xlsx")):
        raise ValueError("The specified file does not appear to be an Excel file.")

    # Use the read_io_from_s3 function to get the data as a BytesIO object
    excel_buffer = read_io_from_s3(bucket_name, file_key)

    # Read the Excel file into a pandas DataFrame
    df = pd.read_excel(excel_buffer, header=header_row)

    # Drop columns where all values are NaN
    df.dropna(axis=1, how='all', inplace=True)

    # Reset index if the first column is just an index or entirely NaN
    df.reset_index(drop=True, inplace=True)

    return df


def read_csv_from_s3(bucket_name, filepath):
    s3 = boto3.client('s3')

    # Get the object from s3
    s3_object = s3.get_object(Bucket=bucket_name, Key=filepath)

    # Read the CSV body from the s3 object
    body = s3_object['Body'].read()

    # Use StringIO to create a file-like object from the string
    csv_data = StringIO(body.decode('utf-8'))

    # Use csv library to read it into a list of dictionaries
    reader = csv.DictReader(csv_data)
    data = list(reader)

    return data