"""
MIT License

Copyright (c) 2023 hysts

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""

import json
import tempfile
import uuid
from pathlib import Path
from typing import Any, Dict, List, Optional, Union

import pyarrow as pa
import pyarrow.parquet as pq
from huggingface_hub import CommitScheduler, HfApi


class ParquetScheduler(CommitScheduler):
    """
    Usage: configure the scheduler with a repo id. Once started, you can add data to be uploaded to the Hub. 1 `.append`
    call will result in 1 row in your final dataset.

    ```py
    # Start scheduler
    >>> scheduler = ParquetScheduler(repo_id="my-parquet-dataset")

    # Append some data to be uploaded
    >>> scheduler.append({...})
    >>> scheduler.append({...})
    >>> scheduler.append({...})
    ```

    The scheduler will automatically infer the schema from the data it pushes.
    Optionally, you can manually set the schema yourself:

    ```py
    >>> scheduler = ParquetScheduler(
    ...     repo_id="my-parquet-dataset",
    ...     schema={
    ...         "prompt": {"_type": "Value", "dtype": "string"},
    ...         "negative_prompt": {"_type": "Value", "dtype": "string"},
    ...         "guidance_scale": {"_type": "Value", "dtype": "int64"},
    ...         "image": {"_type": "Image"},
    ...     },
    ... )

    See https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.Value for the list of
    possible values.
    """

    def __init__(
        self,
        *,
        repo_id: str,
        schema: Optional[Dict[str, Dict[str, str]]] = None,
        every: Union[int, float] = 5,
        revision: Optional[str] = None,
        private: bool = False,
        token: Optional[str] = None,
        allow_patterns: Union[List[str], str, None] = None,
        ignore_patterns: Union[List[str], str, None] = None,
        hf_api: Optional[HfApi] = None,
    ) -> None:
        super().__init__(
            repo_id=repo_id,
            folder_path=tempfile.tempdir,  # not used by the scheduler
            every=every,
            repo_type="dataset",
            revision=revision,
            private=private,
            token=token,
            allow_patterns=allow_patterns,
            ignore_patterns=ignore_patterns,
            hf_api=hf_api,
        )

        self._rows: List[Dict[str, Any]] = []
        self._schema = schema

    def append(self, row: Dict[str, Any]) -> None:
        """Add a new item to be uploaded."""
        with self.lock:
            self._rows.append(row)

    def push_to_hub(self):
        # Check for new rows to push
        with self.lock:
            rows = self._rows
            self._rows = []
        if not rows:
            return
        print(f"Got {len(rows)} item(s) to commit.")

        # Load images + create 'features' config for datasets library
        schema: Dict[str, Dict] = self._schema or {}
        path_to_cleanup: List[Path] = []
        for row in rows:
            for key, value in row.items():
                # Infer schema (for `datasets` library)
                if key not in schema:
                    schema[key] = _infer_schema(key, value)

                # Load binary files if necessary
                if schema[key]["_type"] in ("Image", "Audio"):
                    if isinstance(value, bytes):
                        row[key] = {
                            "path": "",
                            "bytes": value,
                        }
                    else:
                        # It's an image or audio: we load the bytes and remember to cleanup the file
                        file_path = Path(value)
                        if file_path.is_file():
                            row[key] = {
                                "path": file_path.name,
                                "bytes": file_path.read_bytes(),
                            }
                            path_to_cleanup.append(file_path)

        # Complete rows if needed
        for row in rows:
            for feature in schema:
                if feature not in row:
                    row[feature] = None

        # Export items to Arrow format
        table = pa.Table.from_pylist(rows)

        # Add metadata (used by datasets library)
        table = table.replace_schema_metadata(
            {"huggingface": json.dumps({"info": {"features": schema}})}
        )

        # Write to parquet file
        archive_file = tempfile.NamedTemporaryFile()
        pq.write_table(table, archive_file.name)

        # Upload
        self.api.upload_file(
            repo_id=self.repo_id,
            repo_type=self.repo_type,
            revision=self.revision,
            path_in_repo=f"{uuid.uuid4()}.parquet",
            path_or_fileobj=archive_file.name,
        )
        print("Commit completed.")

        # Cleanup
        archive_file.close()
        for path in path_to_cleanup:
            path.unlink(missing_ok=True)


def _infer_schema(key: str, value: Any) -> Dict[str, str]:
    """Infer schema for the `datasets` library.

    See
    https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.Value.
    """
    if "image" in key:
        return {"_type": "Image"}
    if "audio" in key:
        return {"_type": "Audio"}
    if isinstance(value, int):
        return {"_type": "Value", "dtype": "int64"}
    if isinstance(value, float):
        return {"_type": "Value", "dtype": "float64"}
    if isinstance(value, bool):
        return {"_type": "Value", "dtype": "bool"}
    if isinstance(value, bytes):
        return {"_type": "Value", "dtype": "binary"}
    # Otherwise in last resort => convert it to a string
    return {"_type": "Value", "dtype": "string"}