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import csv
from collections import deque
from typing import Any, Dict, Generator, List, Mapping, Optional, Tuple, Union

import pandas as pd  # type: ignore
from elasticsearch import Elasticsearch
from elasticsearch.helpers import parallel_bulk

from eland import DataFrame
from eland.common import DEFAULT_CHUNK_SIZE, PANDAS_VERSION, ensure_es_client
from eland.field_mappings import FieldMappings, verify_mapping_compatibility

try:
    from pandas.io.parsers import _c_parser_defaults  # type: ignore
except ImportError:
    from pandas.io.parsers.readers import _c_parser_defaults  # type: ignore

_DEFAULT_LOW_MEMORY: bool = _c_parser_defaults["low_memory"]


def pandas_to_eland(
    pd_df: pd.DataFrame,
    es_client: Union[str, List[str], Tuple[str, ...], Elasticsearch],
    es_dest_index: str,
    es_if_exists: str = "fail",
    es_refresh: bool = False,
    es_dropna: bool = False,
    es_type_overrides: Optional[Mapping[str, str]] = None,
    es_verify_mapping_compatibility: bool = True,
    thread_count: int = 4,
    chunksize: Optional[int] = None,
    use_pandas_index_for_es_ids: bool = True,
) -> DataFrame:
    """
    Append a pandas DataFrame to an Elasticsearch index.
    Mainly used in testing.
    Modifies the elasticsearch destination index

    Parameters
    ----------
    es_client: Elasticsearch client argument(s)
        - elasticsearch-py parameters or
        - elasticsearch-py instance
    es_dest_index: str
        Name of Elasticsearch index to be appended to
    es_if_exists : {'fail', 'replace', 'append'}, default 'fail'
        How to behave if the index already exists.

        - fail: Raise a ValueError.
        - replace: Delete the index before inserting new values.
        - append: Insert new values to the existing index. Create if does not exist.
    es_refresh: bool, default 'False'
        Refresh es_dest_index after bulk index
    es_dropna: bool, default 'False'
        * True: Remove missing values (see pandas.Series.dropna)
        * False: Include missing values - may cause bulk to fail
    es_type_overrides: dict, default None
        Dict of field_name: es_data_type that overrides default es data types
    es_verify_mapping_compatibility: bool, default 'True'
        * True: Verify that the dataframe schema matches the Elasticsearch index schema
        * False: Do not verify schema
    thread_count: int
        number of the threads to use for the bulk requests
    chunksize: int, default None
        Number of pandas.DataFrame rows to read before bulk index into Elasticsearch
    use_pandas_index_for_es_ids: bool, default 'True'
        * True: pandas.DataFrame.index fields will be used to populate Elasticsearch '_id' fields.
        * False: Ignore pandas.DataFrame.index when indexing into Elasticsearch

    Returns
    -------
    eland.Dataframe
        eland.DataFrame referencing data in destination_index

    Examples
    --------

    >>> pd_df = pd.DataFrame(data={'A': 3.141,
    ...                            'B': 1,
    ...                            'C': 'foo',
    ...                            'D': pd.Timestamp('20190102'),
    ...                            'E': [1.0, 2.0, 3.0],
    ...                            'F': False,
    ...                            'G': [1, 2, 3],
    ...                            'H': 'Long text - to be indexed as es type text'},
    ...                      index=['0', '1', '2'])
    >>> type(pd_df)
    <class 'pandas.core.frame.DataFrame'>
    >>> pd_df
           A  B  ...  G                                          H
    0  3.141  1  ...  1  Long text - to be indexed as es type text
    1  3.141  1  ...  2  Long text - to be indexed as es type text
    2  3.141  1  ...  3  Long text - to be indexed as es type text
    <BLANKLINE>
    [3 rows x 8 columns]
    >>> pd_df.dtypes
    A           float64
    B             int64
    C            object
    D    datetime64[ns]
    E           float64
    F              bool
    G             int64
    H            object
    dtype: object

    Convert `pandas.DataFrame` to `eland.DataFrame` - this creates an Elasticsearch index called `pandas_to_eland`.
    Overwrite existing Elasticsearch index if it exists `if_exists="replace"`, and sync index so it is
    readable on return `refresh=True`


    >>> ed_df = ed.pandas_to_eland(pd_df,
    ...                            'http://localhost:9200',
    ...                            'pandas_to_eland',
    ...                            es_if_exists="replace",
    ...                            es_refresh=True,
    ...                            es_type_overrides={'H':'text'}) # index field 'H' as text not keyword
    >>> type(ed_df)
    <class 'eland.dataframe.DataFrame'>
    >>> ed_df
           A  B  ...  G                                          H
    0  3.141  1  ...  1  Long text - to be indexed as es type text
    1  3.141  1  ...  2  Long text - to be indexed as es type text
    2  3.141  1  ...  3  Long text - to be indexed as es type text
    <BLANKLINE>
    [3 rows x 8 columns]
    >>> ed_df.dtypes
    A           float64
    B             int64
    C            object
    D    datetime64[ns]
    E           float64
    F              bool
    G             int64
    H            object
    dtype: object

    See Also
    --------
    eland.eland_to_pandas: Create a pandas.Dataframe from eland.DataFrame
    """
    if chunksize is None:
        chunksize = DEFAULT_CHUNK_SIZE

    mapping = FieldMappings._generate_es_mappings(pd_df, es_type_overrides)
    es_client = ensure_es_client(es_client)

    # If table exists, check if_exists parameter
    if es_client.indices.exists(index=es_dest_index):
        if es_if_exists == "fail":
            raise ValueError(
                f"Could not create the index [{es_dest_index}] because it "
                f"already exists. "
                f"Change the 'es_if_exists' parameter to "
                f"'append' or 'replace' data."
            )

        elif es_if_exists == "replace":
            es_client.indices.delete(index=es_dest_index)
            es_client.indices.create(index=es_dest_index, mappings=mapping["mappings"])

        elif es_if_exists == "append" and es_verify_mapping_compatibility:
            dest_mapping = es_client.indices.get_mapping(index=es_dest_index)[
                es_dest_index
            ]
            verify_mapping_compatibility(
                ed_mapping=mapping,
                es_mapping=dest_mapping,
                es_type_overrides=es_type_overrides,
            )
    else:
        es_client.indices.create(index=es_dest_index, mappings=mapping["mappings"])

    def action_generator(
        pd_df: pd.DataFrame,
        es_dropna: bool,
        use_pandas_index_for_es_ids: bool,
        es_dest_index: str,
    ) -> Generator[Dict[str, Any], None, None]:
        for row in pd_df.iterrows():
            if es_dropna:
                values = row[1].dropna().to_dict()
            else:
                values = row[1].to_dict()

            if use_pandas_index_for_es_ids:
                # Use index as _id
                id = row[0]

                action = {"_index": es_dest_index, "_source": values, "_id": str(id)}
            else:
                action = {"_index": es_dest_index, "_source": values}

            yield action

    # parallel_bulk is lazy generator so use deque to consume them immediately
    # maxlen = 0 because don't need results of parallel_bulk
    deque(
        parallel_bulk(
            client=es_client,
            actions=action_generator(
                pd_df, es_dropna, use_pandas_index_for_es_ids, es_dest_index
            ),
            thread_count=thread_count,
            chunk_size=int(chunksize / thread_count),
        ),
        maxlen=0,
    )

    if es_refresh:
        es_client.indices.refresh(index=es_dest_index)

    return DataFrame(es_client, es_dest_index)


def eland_to_pandas(ed_df: DataFrame, show_progress: bool = False) -> pd.DataFrame:
    """
    Convert an eland.Dataframe to a pandas.DataFrame

    **Note: this loads the entire Elasticsearch index into in core pandas.DataFrame structures. For large
    indices this can create significant load on the Elasticsearch cluster and require signficant memory**

    Parameters
    ----------
    ed_df: eland.DataFrame
        The source eland.Dataframe referencing the Elasticsearch index
    show_progress: bool
        Output progress of option to stdout? By default False.

    Returns
    -------
    pandas.Dataframe
        pandas.DataFrame contains all rows and columns in eland.DataFrame

    Examples
    --------
    >>> ed_df = ed.DataFrame('http://localhost:9200', 'flights').head()
    >>> type(ed_df)
    <class 'eland.dataframe.DataFrame'>
    >>> ed_df
       AvgTicketPrice  Cancelled  ... dayOfWeek           timestamp
    0      841.265642      False  ...         0 2018-01-01 00:00:00
    1      882.982662      False  ...         0 2018-01-01 18:27:00
    2      190.636904      False  ...         0 2018-01-01 17:11:14
    3      181.694216       True  ...         0 2018-01-01 10:33:28
    4      730.041778      False  ...         0 2018-01-01 05:13:00
    <BLANKLINE>
    [5 rows x 27 columns]

    Convert `eland.DataFrame` to `pandas.DataFrame` (Note: this loads entire Elasticsearch index into core memory)

    >>> pd_df = ed.eland_to_pandas(ed_df)
    >>> type(pd_df)
    <class 'pandas.core.frame.DataFrame'>
    >>> pd_df
       AvgTicketPrice  Cancelled  ... dayOfWeek           timestamp
    0      841.265642      False  ...         0 2018-01-01 00:00:00
    1      882.982662      False  ...         0 2018-01-01 18:27:00
    2      190.636904      False  ...         0 2018-01-01 17:11:14
    3      181.694216       True  ...         0 2018-01-01 10:33:28
    4      730.041778      False  ...         0 2018-01-01 05:13:00
    <BLANKLINE>
    [5 rows x 27 columns]

    Convert `eland.DataFrame` to `pandas.DataFrame` and show progress every 10000 rows

    >>> pd_df = ed.eland_to_pandas(ed.DataFrame('http://localhost:9200', 'flights'), show_progress=True) # doctest: +SKIP
    2020-01-29 12:43:36.572395: read 10000 rows
    2020-01-29 12:43:37.309031: read 13059 rows

    See Also
    --------
    eland.pandas_to_eland: Create an eland.Dataframe from pandas.DataFrame
    """
    return ed_df.to_pandas(show_progress=show_progress)


def csv_to_eland(  # type: ignore
    filepath_or_buffer,
    es_client: Union[str, List[str], Tuple[str, ...], Elasticsearch],
    es_dest_index: str,
    es_if_exists: str = "fail",
    es_refresh: bool = False,
    es_dropna: bool = False,
    es_type_overrides: Optional[Mapping[str, str]] = None,
    sep=",",
    delimiter=None,
    # Column and Index Locations and Names
    header="infer",
    names=None,
    index_col=None,
    usecols=None,
    squeeze=False,
    prefix=None,
    mangle_dupe_cols=True,
    # General Parsing Configuration
    dtype=None,
    engine=None,
    converters=None,
    true_values=None,
    false_values=None,
    skipinitialspace=False,
    skiprows=None,
    skipfooter=0,
    nrows=None,
    # Iteration
    # iterator=False,
    chunksize=None,
    # NA and Missing Data Handling
    na_values=None,
    keep_default_na=True,
    na_filter=True,
    verbose=False,
    skip_blank_lines=True,
    # Datetime Handling
    parse_dates=False,
    infer_datetime_format=False,
    keep_date_col=False,
    date_parser=None,
    dayfirst=False,
    cache_dates=True,
    # Quoting, Compression, and File Format
    compression="infer",
    thousands=None,
    decimal=b".",
    lineterminator=None,
    quotechar='"',
    quoting=csv.QUOTE_MINIMAL,
    doublequote=True,
    escapechar=None,
    comment=None,
    encoding=None,
    dialect=None,
    # Error Handling
    warn_bad_lines: bool = True,
    error_bad_lines: bool = True,
    on_bad_lines: str = "error",
    # Internal
    delim_whitespace=False,
    low_memory: bool = _DEFAULT_LOW_MEMORY,
    memory_map=False,
    float_precision=None,
) -> "DataFrame":
    """
    Read a comma-separated values (csv) file into eland.DataFrame (i.e. an Elasticsearch index).

    **Modifies an Elasticsearch index**

    **Note pandas iteration options not supported**

    Parameters
    ----------
    es_client: Elasticsearch client argument(s)
        - elasticsearch-py parameters or
        - elasticsearch-py instance
    es_dest_index: str
        Name of Elasticsearch index to be appended to
    es_if_exists : {'fail', 'replace', 'append'}, default 'fail'
        How to behave if the index already exists.

        - fail: Raise a ValueError.
        - replace: Delete the index before inserting new values.
        - append: Insert new values to the existing index. Create if does not exist.
    es_dropna: bool, default 'False'
        * True: Remove missing values (see pandas.Series.dropna)
        * False: Include missing values - may cause bulk to fail
    es_type_overrides: dict, default None
        Dict of columns: es_type to override default es datatype mappings
    chunksize
        number of csv rows to read before bulk index into Elasticsearch

    Other Parameters
    ----------------
    Parameters derived from :pandas_api_docs:`pandas.read_csv`.

    See Also
    --------
    :pandas_api_docs:`pandas.read_csv`

    Notes
    -----
    iterator not supported

    Examples
    --------

    See if 'churn' index exists in Elasticsearch

    >>> from elasticsearch import Elasticsearch # doctest: +SKIP
    >>> es = Elasticsearch() # doctest: +SKIP
    >>> es.indices.exists(index="churn") # doctest: +SKIP
    False

    Read 'churn.csv' and use first column as _id (and eland.DataFrame index)
    ::

        # churn.csv
        ,state,account length,area code,phone number,international plan,voice mail plan,number vmail messages,total day minutes,total day calls,total day charge,total eve minutes,total eve calls,total eve charge,total night minutes,total night calls,total night charge,total intl minutes,total intl calls,total intl charge,customer service calls,churn
        0,KS,128,415,382-4657,no,yes,25,265.1,110,45.07,197.4,99,16.78,244.7,91,11.01,10.0,3,2.7,1,0
        1,OH,107,415,371-7191,no,yes,26,161.6,123,27.47,195.5,103,16.62,254.4,103,11.45,13.7,3,3.7,1,0
        ...

    >>>  ed.csv_to_eland(
    ...      "churn.csv",
    ...      es_client='http://localhost:9200',
    ...      es_dest_index='churn',
    ...      es_refresh=True,
    ...      index_col=0
    ... ) # doctest: +SKIP
              account length  area code  churn  customer service calls  ... total night calls  total night charge total night minutes voice mail plan
    0                128        415      0                       1  ...                91               11.01               244.7             yes
    1                107        415      0                       1  ...               103               11.45               254.4             yes
    2                137        415      0                       0  ...               104                7.32               162.6              no
    3                 84        408      0                       2  ...                89                8.86               196.9              no
    4                 75        415      0                       3  ...               121                8.41               186.9              no
    ...              ...        ...    ...                     ...  ...               ...                 ...                 ...             ...
    3328             192        415      0                       2  ...                83               12.56               279.1             yes
    3329              68        415      0                       3  ...               123                8.61               191.3              no
    3330              28        510      0                       2  ...                91                8.64               191.9              no
    3331             184        510      0                       2  ...               137                6.26               139.2              no
    3332              74        415      0                       0  ...                77               10.86               241.4             yes
    <BLANKLINE>
    [3333 rows x 21 columns]

    Validate data now exists in 'churn' index:

    >>> es.search(index="churn", size=1) # doctest: +SKIP
    {'took': 1, 'timed_out': False, '_shards': {'total': 1, 'successful': 1, 'skipped': 0, 'failed': 0}, 'hits': {'total': {'value': 3333, 'relation': 'eq'}, 'max_score': 1.0, 'hits': [{'_index': 'churn', '_id': '0', '_score': 1.0, '_source': {'state': 'KS', 'account length': 128, 'area code': 415, 'phone number': '382-4657', 'international plan': 'no', 'voice mail plan': 'yes', 'number vmail messages': 25, 'total day minutes': 265.1, 'total day calls': 110, 'total day charge': 45.07, 'total eve minutes': 197.4, 'total eve calls': 99, 'total eve charge': 16.78, 'total night minutes': 244.7, 'total night calls': 91, 'total night charge': 11.01, 'total intl minutes': 10.0, 'total intl calls': 3, 'total intl charge': 2.7, 'customer service calls': 1, 'churn': 0}}]}}

    TODO - currently the eland.DataFrame may not retain the order of the data in the csv.
    """
    kwargs: Dict[str, Any] = {
        "sep": sep,
        "delimiter": delimiter,
        "engine": engine,
        "dialect": dialect,
        "compression": compression,
        # "engine_specified": engine_specified,
        "doublequote": doublequote,
        "escapechar": escapechar,
        "quotechar": quotechar,
        "quoting": quoting,
        "skipinitialspace": skipinitialspace,
        "lineterminator": lineterminator,
        "header": header,
        "index_col": index_col,
        "names": names,
        "prefix": prefix,
        "skiprows": skiprows,
        "skipfooter": skipfooter,
        "na_values": na_values,
        "true_values": true_values,
        "false_values": false_values,
        "keep_default_na": keep_default_na,
        "thousands": thousands,
        "comment": comment,
        "decimal": decimal,
        "parse_dates": parse_dates,
        "keep_date_col": keep_date_col,
        "dayfirst": dayfirst,
        "date_parser": date_parser,
        "cache_dates": cache_dates,
        "nrows": nrows,
        # "iterator": iterator,
        "chunksize": chunksize,
        "converters": converters,
        "dtype": dtype,
        "usecols": usecols,
        "verbose": verbose,
        "encoding": encoding,
        "squeeze": squeeze,
        "memory_map": memory_map,
        "float_precision": float_precision,
        "na_filter": na_filter,
        "delim_whitespace": delim_whitespace,
        "warn_bad_lines": warn_bad_lines,
        "error_bad_lines": error_bad_lines,
        "on_bad_lines": on_bad_lines,
        "low_memory": low_memory,
        "mangle_dupe_cols": mangle_dupe_cols,
        "infer_datetime_format": infer_datetime_format,
        "skip_blank_lines": skip_blank_lines,
    }

    if chunksize is None:
        kwargs["chunksize"] = DEFAULT_CHUNK_SIZE

    if PANDAS_VERSION >= (1, 3):
        # Bug in Pandas v1.3.0
        # If names and prefix both passed as None, it's considering them as specified values and throwing ValueError
        # Ref: https://github.com/pandas-dev/pandas/issues/42387
        if kwargs["names"] is None and kwargs["prefix"] is None:
            kwargs.pop("prefix")

        if kwargs["warn_bad_lines"] is True:
            kwargs["on_bad_lines"] = "warn"
        if kwargs["error_bad_lines"] is True:
            kwargs["on_bad_lines"] = "error"

        kwargs.pop("warn_bad_lines")
        kwargs.pop("error_bad_lines")

    else:
        if on_bad_lines == "warn":
            kwargs["warn_bad_lines"] = True
        if on_bad_lines == "error":
            kwargs["error_bad_lines"] = True

        kwargs.pop("on_bad_lines")

    # read csv in chunks to pandas DataFrame and dump to eland DataFrame (and Elasticsearch)
    reader = pd.read_csv(filepath_or_buffer, **kwargs)

    first_write = True
    for chunk in reader:
        pandas_to_eland(
            chunk,
            es_client,
            es_dest_index,
            chunksize=chunksize,
            es_refresh=es_refresh,
            es_dropna=es_dropna,
            es_type_overrides=es_type_overrides,
            # es_if_exists should be 'append' except on the first call to pandas_to_eland()
            es_if_exists=(es_if_exists if first_write else "append"),
        )
        first_write = False

    # Now create an eland.DataFrame that references the new index
    return DataFrame(es_client, es_index_pattern=es_dest_index)
