"""Interface for implementing a source."""

import abc
from typing import ClassVar, Iterable, Optional

import numpy as np
import pandas as pd
import pyarrow as pa
from pydantic import BaseModel, validator

from ...schema import (
  Field,
  ImageInfo,
  Item,
  Schema,
  arrow_dtype_to_dtype,
  arrow_schema_to_schema,
  field,
)


class SourceSchema(BaseModel):
  """The schema of a source."""
  fields: dict[str, Field]
  num_items: Optional[int]


class SourceProcessResult(BaseModel):
  """The result after processing all the shards of a source dataset."""
  filepaths: list[str]
  data_schema: Schema
  images: Optional[list[ImageInfo]]
  num_items: int


class Source(abc.ABC, BaseModel):
  """Interface for sources to implement. A source processes a set of shards and writes files."""
  # ClassVars do not get serialized with pydantic.
  name: ClassVar[str]

  # The source_name will get populated in init automatically from the class name so it gets
  # serialized and the source author doesn't have to define both the static property and the field.
  source_name: Optional[str]

  class Config:
    underscore_attrs_are_private = True

  @validator('source_name', always=True)
  def validate_source_name(cls, source_name: str) -> str:
    """Return the static name when the source_name name hasn't yet been set."""
    # When it's already been set from JSON, just return it.
    if source_name:
      return source_name

    if 'name' not in cls.__dict__:
      raise ValueError('Source attribute "name" must be defined.')

    return cls.name

  @abc.abstractmethod
  def source_schema(self) -> SourceSchema:
    """Return the source schema for this source.

    Returns
      A SourceSchema with
        fields: mapping top-level columns to fields that describes the schema of the source.
        num_items: the number of items in the source, used for progress.

    """
    pass

  def setup(self) -> None:
    """Prepare the source for processing.

    This allows the source to do setup outside the constructor, but before its processed. This
    avoids potentially expensive computation the pydantic model is deserialized.
    """
    pass

  def teardown(self) -> None:
    """Tears down the source after processing."""
    pass

  @abc.abstractmethod
  def process(self) -> Iterable[Item]:
    """Process the source upload request.

    Args:
      task_step_id: The TaskManager `task_step_id` for this process run. This is used to update the
        progress of the task.
    """
    pass


def schema_from_df(df: pd.DataFrame, index_colname: str) -> SourceSchema:
  """Create a source schema from a dataframe."""
  index_np_dtype = df.index.dtype
  # String index dtypes are stored as objects.
  if index_np_dtype == np.dtype(object):
    index_np_dtype = np.dtype(str)
  index_dtype = arrow_dtype_to_dtype(pa.from_numpy_dtype(index_np_dtype))

  schema = arrow_schema_to_schema(pa.Schema.from_pandas(df, preserve_index=False))
  return SourceSchema(
    fields={
      **schema.fields, index_colname: field(dtype=index_dtype)
    }, num_items=len(df))


def normalize_column_name(name: str) -> str:
  """Normalize a column name."""
  return name
  #return name.replace(' ', '_').replace(':', '_').replace('.', '_')