"""Asynchronous Spark UDFs for the OpenAI and Azure OpenAI APIs.

This module provides functions (`responses_udf`, `task_udf`, `embeddings_udf`,
`count_tokens_udf`, `split_to_chunks_udf`)
for creating asynchronous Spark UDFs that communicate with either the public
OpenAI API or Azure OpenAI using the `openaivec.spark` subpackage.
It supports UDFs for generating responses and creating embeddings asynchronously.
The UDFs operate on Spark DataFrames and leverage asyncio for potentially
improved performance in I/O-bound operations.

**Performance Optimization**: All AI-powered UDFs (`responses_udf`, `task_udf`, `embeddings_udf`)
automatically cache duplicate inputs within each partition, significantly reducing
API calls and costs when processing datasets with overlapping content.

__all__ = [
    "count_tokens_udf",
    "embeddings_udf",
    "responses_udf",
    "similarity_udf",
    "split_to_chunks_udf",
    "task_udf",
]

## Setup

First, obtain a Spark session and configure authentication:

```python
import os
from pyspark.sql import SparkSession

spark = SparkSession.builder.getOrCreate()
sc = spark.sparkContext

# Configure authentication via SparkContext environment variables
# Option 1: Using OpenAI
sc.environment["OPENAI_API_KEY"] = "your-openai-api-key"

# Option 2: Using Azure OpenAI
# sc.environment["AZURE_OPENAI_API_KEY"] = "your-azure-openai-api-key"
# sc.environment["AZURE_OPENAI_BASE_URL"] = "https://YOUR-RESOURCE-NAME.services.ai.azure.com/openai/v1/"
# sc.environment["AZURE_OPENAI_API_VERSION"] = "preview"
```

Next, create UDFs and register them:

```python
from openaivec.spark import responses_udf, task_udf, embeddings_udf, count_tokens_udf, split_to_chunks_udf
from pydantic import BaseModel

# Define a Pydantic model for structured responses (optional)
class Translation(BaseModel):
    en: str
    fr: str
    # ... other languages

# Register the asynchronous responses UDF with performance tuning
spark.udf.register(
    "translate_async",
    responses_udf(
        instructions="Translate the text to multiple languages.",
        response_format=Translation,
        model_name="gpt-4.1-mini",  # For Azure: deployment name, for OpenAI: model name
        batch_size=64,              # Rows per API request within partition
        max_concurrency=8           # Concurrent requests PER EXECUTOR
    ),
)

# Or use a predefined task with task_udf
from openaivec.task import nlp
spark.udf.register(
    "sentiment_async",
    task_udf(nlp.SENTIMENT_ANALYSIS),
)

# Register the asynchronous embeddings UDF with performance tuning
spark.udf.register(
    "embed_async",
    embeddings_udf(
        model_name="text-embedding-3-small",  # For Azure: deployment name, for OpenAI: model name
        batch_size=128,                       # Larger batches for embeddings
        max_concurrency=8                     # Concurrent requests PER EXECUTOR
    ),
)

# Register token counting and text chunking UDFs
spark.udf.register("count_tokens", count_tokens_udf())
spark.udf.register("split_chunks", split_to_chunks_udf(max_tokens=512, sep=[".", "!", "?"]))
```

You can now invoke the UDFs from Spark SQL:

```sql
SELECT
    text,
    translate_async(text) AS translation,
    sentiment_async(text) AS sentiment,
    embed_async(text) AS embedding,
    count_tokens(text) AS token_count,
    split_chunks(text) AS chunks
FROM your_table;
```

## Performance Considerations

When using these UDFs in distributed Spark environments:

- **`batch_size`**: Controls rows processed per API request within each partition.
  Recommended: 32-128 for responses, 64-256 for embeddings.

- **`max_concurrency`**: Sets concurrent API requests **PER EXECUTOR**, not per cluster.
  Total cluster concurrency = max_concurrency × number_of_executors.
  Recommended: 4-12 per executor to avoid overwhelming OpenAI rate limits.

- **Rate Limit Management**: Monitor OpenAI API usage when scaling executors.
  Consider your OpenAI tier limits and adjust max_concurrency accordingly.

Example for a 5-executor cluster with max_concurrency=8:
Total concurrent requests = 8 × 5 = 40 simultaneous API calls.

Note: This module provides asynchronous support through the pandas extensions.
"""

import asyncio
import logging
from enum import Enum
from typing import Dict, Iterator, List, Type, Union, get_args, get_origin

import numpy as np
import pandas as pd
import tiktoken
from pydantic import BaseModel
from pyspark.sql.pandas.functions import pandas_udf
from pyspark.sql.types import ArrayType, BooleanType, FloatType, IntegerType, StringType, StructField, StructType
from pyspark.sql.udf import UserDefinedFunction
from typing_extensions import Literal

from openaivec import pandas_ext
from openaivec._model import PreparedTask, ResponseFormat
from openaivec._proxy import AsyncBatchingMapProxy
from openaivec._serialize import deserialize_base_model, serialize_base_model
from openaivec._util import TextChunker

__all__ = [
    "responses_udf",
    "task_udf",
    "embeddings_udf",
    "split_to_chunks_udf",
    "count_tokens_udf",
    "similarity_udf",
]


_LOGGER: logging.Logger = logging.getLogger(__name__)


def _python_type_to_spark(python_type):
    origin = get_origin(python_type)

    # For list types (e.g., List[int])
    if origin is list or origin is List:
        # Retrieve the inner type and recursively convert it
        inner_type = get_args(python_type)[0]
        return ArrayType(_python_type_to_spark(inner_type))

    # For Optional types (Union[..., None])
    elif origin is Union:
        non_none_args = [arg for arg in get_args(python_type) if arg is not type(None)]
        if len(non_none_args) == 1:
            return _python_type_to_spark(non_none_args[0])
        else:
            raise ValueError(f"Unsupported Union type with multiple non-None types: {python_type}")

    # For Literal types - treat as StringType since Spark doesn't have enum types
    elif origin is Literal:
        return StringType()

    # For Enum types - also treat as StringType since Spark doesn't have enum types
    elif hasattr(python_type, "__bases__") and Enum in python_type.__bases__:
        return StringType()

    # For nested Pydantic models (to be treated as Structs)
    elif isinstance(python_type, type) and issubclass(python_type, BaseModel):
        return _pydantic_to_spark_schema(python_type)

    # Basic type mapping
    elif python_type is int:
        return IntegerType()
    elif python_type is float:
        return FloatType()
    elif python_type is str:
        return StringType()
    elif python_type is bool:
        return BooleanType()
    else:
        raise ValueError(f"Unsupported type: {python_type}")


def _pydantic_to_spark_schema(model: Type[BaseModel]) -> StructType:
    fields = []
    for field_name, field in model.model_fields.items():
        field_type = field.annotation
        # Use outer_type_ to correctly handle types like Optional
        spark_type = _python_type_to_spark(field_type)
        # Set nullable to True (adjust logic as needed)
        fields.append(StructField(field_name, spark_type, nullable=True))
    return StructType(fields)


def _safe_cast_str(x: str | None) -> str | None:
    try:
        if x is None:
            return None

        return str(x)
    except Exception as e:
        _LOGGER.info(f"Error during casting to str: {e}")
        return None


def _safe_dump(x: BaseModel | None) -> Dict:
    try:
        if x is None:
            return {}

        return x.model_dump()
    except Exception as e:
        _LOGGER.info(f"Error during model_dump: {e}")
        return {}


def responses_udf(
    instructions: str,
    response_format: Type[ResponseFormat] = str,
    model_name: str = "gpt-4.1-mini",
    batch_size: int | None = None,
    temperature: float | None = 0.0,
    top_p: float = 1.0,
    max_concurrency: int = 8,
) -> UserDefinedFunction:
    """Create an asynchronous Spark pandas UDF for generating responses.

    Configures and builds UDFs that leverage `pandas_ext.aio.responses_with_cache`
    to generate text or structured responses from OpenAI models asynchronously.
    Each partition maintains its own cache to eliminate duplicate API calls within
    the partition, significantly reducing API usage and costs when processing
    datasets with overlapping content.

    Note:
        Authentication must be configured via SparkContext environment variables.
        Set the appropriate environment variables on the SparkContext:

        For OpenAI:
            sc.environment["OPENAI_API_KEY"] = "your-openai-api-key"

        For Azure OpenAI:
            sc.environment["AZURE_OPENAI_API_KEY"] = "your-azure-openai-api-key"
            sc.environment["AZURE_OPENAI_BASE_URL"] = "https://YOUR-RESOURCE-NAME.services.ai.azure.com/openai/v1/"
            sc.environment["AZURE_OPENAI_API_VERSION"] = "preview"

    Args:
        instructions (str): The system prompt or instructions for the model.
        response_format (Type[ResponseFormat]): The desired output format. Either `str` for plain text
            or a Pydantic `BaseModel` for structured JSON output. Defaults to `str`.
        model_name (str): For Azure OpenAI, use your deployment name (e.g., "my-gpt4-deployment").
            For OpenAI, use the model name (e.g., "gpt-4.1-mini"). Defaults to "gpt-4.1-mini".
        batch_size (int | None): Number of rows per async batch request within each partition.
            Larger values reduce API call overhead but increase memory usage.
            Defaults to None (automatic batch size optimization that dynamically
            adjusts based on execution time, targeting 30-60 seconds per batch).
            Set to a positive integer (e.g., 32-128) for fixed batch size.
        temperature (float): Sampling temperature (0.0 to 2.0). Defaults to 0.0.
        top_p (float): Nucleus sampling parameter. Defaults to 1.0.
        max_concurrency (int): Maximum number of concurrent API requests **PER EXECUTOR**.
            Total cluster concurrency = max_concurrency × number_of_executors.
            Higher values increase throughput but may hit OpenAI rate limits.
            Recommended: 4-12 per executor. Defaults to 8.

    Returns:
        UserDefinedFunction: A Spark pandas UDF configured to generate responses asynchronously.
            Output schema is `StringType` or a struct derived from `response_format`.

    Raises:
        ValueError: If `response_format` is not `str` or a Pydantic `BaseModel`.

    Note:
        For optimal performance in distributed environments:
        - **Automatic Caching**: Duplicate inputs within each partition are cached,
          reducing API calls and costs significantly on datasets with repeated content
        - Monitor OpenAI API rate limits when scaling executor count
        - Consider your OpenAI tier limits: total_requests = max_concurrency × executors
        - Use Spark UI to optimize partition sizes relative to batch_size
    """
    if issubclass(response_format, BaseModel):
        spark_schema = _pydantic_to_spark_schema(response_format)
        json_schema_string = serialize_base_model(response_format)

        @pandas_udf(returnType=spark_schema)  # type: ignore[call-overload]
        def structure_udf(col: Iterator[pd.Series]) -> Iterator[pd.DataFrame]:
            pandas_ext.responses_model(model_name)
            response_format = deserialize_base_model(json_schema_string)
            cache = AsyncBatchingMapProxy[str, response_format](
                batch_size=batch_size,
                max_concurrency=max_concurrency,
            )

            try:
                for part in col:
                    predictions: pd.Series = asyncio.run(
                        part.aio.responses_with_cache(
                            instructions=instructions,
                            response_format=response_format,
                            temperature=temperature,
                            top_p=top_p,
                            cache=cache,
                        )
                    )
                    yield pd.DataFrame(predictions.map(_safe_dump).tolist())
            finally:
                asyncio.run(cache.clear())

        return structure_udf  # type: ignore[return-value]

    elif issubclass(response_format, str):

        @pandas_udf(returnType=StringType())  # type: ignore[call-overload]
        def string_udf(col: Iterator[pd.Series]) -> Iterator[pd.Series]:
            pandas_ext.responses_model(model_name)
            cache = AsyncBatchingMapProxy[str, str](
                batch_size=batch_size,
                max_concurrency=max_concurrency,
            )

            try:
                for part in col:
                    predictions: pd.Series = asyncio.run(
                        part.aio.responses_with_cache(
                            instructions=instructions,
                            response_format=str,
                            temperature=temperature,
                            top_p=top_p,
                            cache=cache,
                        )
                    )
                    yield predictions.map(_safe_cast_str)
            finally:
                asyncio.run(cache.clear())

        return string_udf  # type: ignore[return-value]

    else:
        raise ValueError(f"Unsupported response_format: {response_format}")


def task_udf(
    task: PreparedTask[ResponseFormat],
    model_name: str = "gpt-4.1-mini",
    batch_size: int | None = None,
    max_concurrency: int = 8,
) -> UserDefinedFunction:
    """Create an asynchronous Spark pandas UDF from a predefined task.

    This function allows users to create UDFs from predefined tasks such as sentiment analysis,
    translation, or other common NLP operations defined in the openaivec.task module.
    Each partition maintains its own cache to eliminate duplicate API calls within
    the partition, significantly reducing API usage and costs when processing
    datasets with overlapping content.

    Args:
        task (PreparedTask): A predefined task configuration containing instructions,
            response format, temperature, and top_p settings.
        model_name (str): For Azure OpenAI, use your deployment name (e.g., "my-gpt4-deployment").
            For OpenAI, use the model name (e.g., "gpt-4.1-mini"). Defaults to "gpt-4.1-mini".
        batch_size (int | None): Number of rows per async batch request within each partition.
            Larger values reduce API call overhead but increase memory usage.
            Defaults to None (automatic batch size optimization that dynamically
            adjusts based on execution time, targeting 30-60 seconds per batch).
            Set to a positive integer (e.g., 32-128) for fixed batch size.
        max_concurrency (int): Maximum number of concurrent API requests **PER EXECUTOR**.
            Total cluster concurrency = max_concurrency × number_of_executors.
            Higher values increase throughput but may hit OpenAI rate limits.
            Recommended: 4-12 per executor. Defaults to 8.

    Returns:
        UserDefinedFunction: A Spark pandas UDF configured to execute the specified task
            asynchronously with automatic caching for duplicate inputs within each partition.
            Output schema is StringType for str response format or a struct derived from
            the task's response format for BaseModel.

    Example:
        ```python
        from openaivec.task import nlp

        sentiment_udf = task_udf(nlp.SENTIMENT_ANALYSIS)

        spark.udf.register("analyze_sentiment", sentiment_udf)
        ```

    Note:
        **Automatic Caching**: Duplicate inputs within each partition are cached,
        reducing API calls and costs significantly on datasets with repeated content.
    """
    # Serialize task parameters for Spark serialization compatibility
    task_instructions = task.instructions
    task_temperature = task.temperature
    task_top_p = task.top_p

    if issubclass(task.response_format, BaseModel):
        task_response_format_json = serialize_base_model(task.response_format)

        # Deserialize the response format from JSON
        response_format = deserialize_base_model(task_response_format_json)
        spark_schema = _pydantic_to_spark_schema(response_format)

        @pandas_udf(returnType=spark_schema)  # type: ignore[call-overload]
        def task_udf(col: Iterator[pd.Series]) -> Iterator[pd.DataFrame]:
            pandas_ext.responses_model(model_name)
            cache = AsyncBatchingMapProxy[str, response_format](
                batch_size=batch_size,
                max_concurrency=max_concurrency,
            )

            try:
                for part in col:
                    predictions: pd.Series = asyncio.run(
                        part.aio.responses_with_cache(
                            instructions=task_instructions,
                            response_format=response_format,
                            temperature=task_temperature,
                            top_p=task_top_p,
                            cache=cache,
                        )
                    )
                    yield pd.DataFrame(predictions.map(_safe_dump).tolist())
            finally:
                asyncio.run(cache.clear())

        return task_udf  # type: ignore[return-value]

    elif issubclass(task.response_format, str):

        @pandas_udf(returnType=StringType())  # type: ignore[call-overload]
        def task_string_udf(col: Iterator[pd.Series]) -> Iterator[pd.Series]:
            pandas_ext.responses_model(model_name)
            cache = AsyncBatchingMapProxy[str, str](
                batch_size=batch_size,
                max_concurrency=max_concurrency,
            )

            try:
                for part in col:
                    predictions: pd.Series = asyncio.run(
                        part.aio.responses_with_cache(
                            instructions=task_instructions,
                            response_format=str,
                            temperature=task_temperature,
                            top_p=task_top_p,
                            cache=cache,
                        )
                    )
                    yield predictions.map(_safe_cast_str)
            finally:
                asyncio.run(cache.clear())

        return task_string_udf  # type: ignore[return-value]

    else:
        raise ValueError(f"Unsupported response_format in task: {task.response_format}")


def embeddings_udf(
    model_name: str = "text-embedding-3-small", batch_size: int | None = None, max_concurrency: int = 8
) -> UserDefinedFunction:
    """Create an asynchronous Spark pandas UDF for generating embeddings.

    Configures and builds UDFs that leverage `pandas_ext.aio.embeddings_with_cache`
    to generate vector embeddings from OpenAI models asynchronously.
    Each partition maintains its own cache to eliminate duplicate API calls within
    the partition, significantly reducing API usage and costs when processing
    datasets with overlapping content.

    Note:
        Authentication must be configured via SparkContext environment variables.
        Set the appropriate environment variables on the SparkContext:

        For OpenAI:
            sc.environment["OPENAI_API_KEY"] = "your-openai-api-key"

        For Azure OpenAI:
            sc.environment["AZURE_OPENAI_API_KEY"] = "your-azure-openai-api-key"
            sc.environment["AZURE_OPENAI_BASE_URL"] = "https://YOUR-RESOURCE-NAME.services.ai.azure.com/openai/v1/"
            sc.environment["AZURE_OPENAI_API_VERSION"] = "preview"

    Args:
        model_name (str): For Azure OpenAI, use your deployment name (e.g., "my-embedding-deployment").
            For OpenAI, use the model name (e.g., "text-embedding-3-small"). Defaults to "text-embedding-3-small".
        batch_size (int | None): Number of rows per async batch request within each partition.
            Larger values reduce API call overhead but increase memory usage.
            Defaults to None (automatic batch size optimization that dynamically
            adjusts based on execution time, targeting 30-60 seconds per batch).
            Set to a positive integer (e.g., 64-256) for fixed batch size.
            Embeddings typically handle larger batches efficiently.
        max_concurrency (int): Maximum number of concurrent API requests **PER EXECUTOR**.
            Total cluster concurrency = max_concurrency × number_of_executors.
            Higher values increase throughput but may hit OpenAI rate limits.
            Recommended: 4-12 per executor. Defaults to 8.

    Returns:
        UserDefinedFunction: A Spark pandas UDF configured to generate embeddings asynchronously
            with automatic caching for duplicate inputs within each partition,
            returning an `ArrayType(FloatType())` column.

    Note:
        For optimal performance in distributed environments:
        - **Automatic Caching**: Duplicate inputs within each partition are cached,
          reducing API calls and costs significantly on datasets with repeated content
        - Monitor OpenAI API rate limits when scaling executor count
        - Consider your OpenAI tier limits: total_requests = max_concurrency × executors
        - Embeddings API typically has higher throughput than chat completions
        - Use larger batch_size for embeddings compared to response generation
    """

    @pandas_udf(returnType=ArrayType(FloatType()))  # type: ignore[call-overload,misc]
    def _embeddings_udf(col: Iterator[pd.Series]) -> Iterator[pd.Series]:
        pandas_ext.embeddings_model(model_name)
        cache = AsyncBatchingMapProxy[str, np.ndarray](
            batch_size=batch_size,
            max_concurrency=max_concurrency,
        )

        try:
            for part in col:
                embeddings: pd.Series = asyncio.run(part.aio.embeddings_with_cache(cache=cache))
                yield embeddings.map(lambda x: x.tolist())
        finally:
            asyncio.run(cache.clear())

    return _embeddings_udf  # type: ignore[return-value]


def split_to_chunks_udf(max_tokens: int, sep: List[str]) -> UserDefinedFunction:
    """Create a pandas‑UDF that splits text into token‑bounded chunks.

    Args:
        max_tokens (int): Maximum tokens allowed per chunk.
        sep (List[str]): Ordered list of separator strings used by ``TextChunker``.

    Returns:
        A pandas UDF producing an ``ArrayType(StringType())`` column whose
            values are lists of chunks respecting the ``max_tokens`` limit.
    """

    @pandas_udf(ArrayType(StringType()))  # type: ignore[call-overload,misc]
    def fn(col: Iterator[pd.Series]) -> Iterator[pd.Series]:
        encoding = tiktoken.get_encoding("o200k_base")
        chunker = TextChunker(encoding)

        for part in col:
            yield part.map(lambda x: chunker.split(x, max_tokens=max_tokens, sep=sep) if isinstance(x, str) else [])

    return fn  # type: ignore[return-value]


def count_tokens_udf() -> UserDefinedFunction:
    """Create a pandas‑UDF that counts tokens for every string cell.

    The UDF uses *tiktoken* to approximate tokenisation and caches the
    resulting ``Encoding`` object per executor.

    Returns:
        A pandas UDF producing an ``IntegerType`` column with token counts.
    """

    @pandas_udf(IntegerType())  # type: ignore[call-overload]
    def fn(col: Iterator[pd.Series]) -> Iterator[pd.Series]:
        encoding = tiktoken.get_encoding("o200k_base")

        for part in col:
            yield part.map(lambda x: len(encoding.encode(x)) if isinstance(x, str) else 0)

    return fn  # type: ignore[return-value]


def similarity_udf() -> UserDefinedFunction:
    @pandas_udf(FloatType())  # type: ignore[call-overload]
    def fn(a: pd.Series, b: pd.Series) -> pd.Series:
        """Compute cosine similarity between two vectors.

        Args:
            a: First vector.
            b: Second vector.

        Returns:
            Cosine similarity between the two vectors.
        """
        # Import pandas_ext to ensure .ai accessor is available in Spark workers
        from openaivec import pandas_ext  # noqa: F401

        return pd.DataFrame({"a": a, "b": b}).ai.similarity("a", "b")

    return fn  # type: ignore[return-value]
