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321 lines
11 KiB
Python
321 lines
11 KiB
Python
# ========= Copyright 2023-2026 @ CAMEL-AI.org. All Rights Reserved. =========
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ========= Copyright 2023-2026 @ CAMEL-AI.org. All Rights Reserved. =========
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import abc
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import asyncio
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import json
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import random
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from pathlib import Path
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from typing import Any, Dict, List, Union
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from pydantic import ValidationError
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from torch.utils.data import IterableDataset
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from camel.logger import get_logger
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from .models import DataPoint
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logger = get_logger(__name__)
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class BaseGenerator(abc.ABC, IterableDataset):
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r"""Abstract base class for data generators.
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This class defines the interface for generating synthetic datapoints.
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Concrete implementations should provide specific generation strategies.
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"""
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def __init__(
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self,
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seed: int = 42,
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buffer: int = 20,
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cache: Union[str, Path, None] = None,
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data_path: Union[str, Path, None] = None,
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**kwargs,
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):
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r"""Initialize the base generator.
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Args:
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seed (int): Random seed for reproducibility. (default: :obj:`42`)
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buffer (int): Amount of DataPoints to be generated when the
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iterator runs out of DataPoints in data. (default: :obj:`20`)
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cache (Union[str, Path, None]): Optional path to save generated
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datapoints during iteration. If None is provided, datapoints
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will be discarded every 100 generations.
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data_path (Union[str, Path, None]): Optional path to a JSONL file
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to initialize the dataset from.
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**kwargs: Additional generator parameters.
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"""
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self._rng = random.Random(seed)
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self.cache = Path(cache) if cache else None
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self._buffer = buffer
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self._data: List[DataPoint] = []
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self._batch_to_save: List[DataPoint] = []
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self._iter_position: int = 0
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if data_path:
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file_path = Path(data_path)
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raw_data = self._init_from_jsonl(file_path)
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try:
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data_points = [DataPoint(**item) for item in raw_data]
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self._data.extend(data_points)
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except ValidationError as e:
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raise ValueError(
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f"Failed to create DataPoint from JSONL data: {e}"
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)
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@abc.abstractmethod
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async def generate_new(self, n: int, **kwargs) -> None:
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r"""Generate n new datapoints and append them to self._data.
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Subclass implementations must generate the specified number of
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datapoints and append them directly to the `self._data` list.
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This method should not return the datapoints; the iterator
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relies on `self._data` being populated.
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Args:
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n (int): Number of datapoints to generate and append.
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**kwargs: Additional generation parameters.
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Returns:
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None: This method should not return anything.
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Example:
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```python
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async def generate_new(self, n: int, **kwargs) -> None:
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new_points = [DataPoint(...) for _ in range(n)]
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self._data.extend(new_points)
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```
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"""
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pass
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def __aiter__(self):
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r"""Async iterator that yields datapoints dynamically.
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If a `data_path` was provided during initialization, those datapoints
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are yielded first. When self._iter_position reaches the end of _data,
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new datapoints are generated. Every 100 yields, the batch is appended
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to the JSONL file or discarded if `cache` is None.
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Yields:
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DataPoint: A single datapoint.
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"""
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async def generator():
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while True:
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if self._iter_position >= len(self._data):
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await self.generate_new(self._buffer)
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datapoint = self._data[self._iter_position]
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self._iter_position += 1
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yield datapoint
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self._batch_to_save.append(datapoint)
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if len(self._batch_to_save) == 100:
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if self.cache:
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with self.cache.open("a", encoding="utf-8") as f:
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for dp in self._batch_to_save:
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json.dump(dp.to_dict(), f, ensure_ascii=False)
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f.write("\n")
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self._batch_to_save = []
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return generator()
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def __iter__(self):
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r"""Synchronous iterator for PyTorch IterableDataset compatibility.
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If a `data_path` was provided during initialization, those datapoints
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are yielded first. When self._iter_position reaches the end of _data,
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new datapoints are generated. Every 100 yields, the batch is appended
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to the JSONL file or discarded if `cache` is None.
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Yields:
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DataPoint: A single datapoint.
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"""
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try:
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if asyncio.get_event_loop().is_running():
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raise RuntimeError(
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"Cannot use synchronous iteration (__iter__) in an async "
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"context; use 'async for' with __aiter__ instead"
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)
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except RuntimeError as e:
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if "no running event loop" not in str(e):
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raise
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while True:
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if self._iter_position >= len(self._data):
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asyncio.run(self.generate_new(self._buffer))
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datapoint = self._data[self._iter_position]
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self._iter_position += 1
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yield datapoint
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self._batch_to_save.append(datapoint)
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if len(self._batch_to_save) == 100:
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if self.cache:
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with self.cache.open("a", encoding="utf-8") as f:
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for dp in self._batch_to_save:
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json.dump(dp.to_dict(), f, ensure_ascii=False)
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f.write("\n")
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self._batch_to_save = []
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def sample(self) -> DataPoint:
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r"""Returns the next datapoint from the current dataset
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synchronously.
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Raises:
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RuntimeError: If called in an async context.
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Returns:
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DataPoint: The next DataPoint.
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Note:
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This method is intended for synchronous contexts.
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Use 'async_sample' in asynchronous contexts to
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avoid blocking or runtime errors.
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"""
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try:
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if asyncio.get_event_loop().is_running():
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raise RuntimeError(
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"Cannot use synchronous sampling (sample) "
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"in an async context; use async_sample instead"
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)
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except RuntimeError as e:
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if "no running event loop" not in str(e):
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raise
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return next(iter(self))
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async def async_sample(self) -> DataPoint:
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r"""Returns the next datapoint from the current dataset asynchronously.
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Returns:
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DataPoint: The next datapoint.
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Note:
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This method is intended for asynchronous contexts. Use 'sample'
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in synchronous contexts.
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"""
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async_iter = self.__aiter__()
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return await async_iter.__anext__()
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def save_to_jsonl(self, file_path: Union[str, Path]) -> None:
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r"""Saves the generated datapoints to a JSONL (JSON Lines) file.
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Each datapoint is stored as a separate JSON object on a new line.
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Args:
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file_path (Union[str, Path]): Path to save the JSONL file.
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Raises:
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ValueError: If no datapoints have been generated.
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IOError: If there is an issue writing to the file.
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Notes:
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- Uses `self._data`, which contains the generated datapoints.
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- Appends to the file if it already exists.
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- Ensures compatibility with large datasets by using JSONL format.
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"""
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if not self._data:
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raise ValueError("Dataset is empty. No data to save.")
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file_path = Path(file_path)
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try:
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with file_path.open("a", encoding="utf-8") as f:
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for datapoint in self._data:
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json.dump(datapoint.to_dict(), f, ensure_ascii=False)
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f.write("\n")
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logger.info(f"Dataset saved successfully to {file_path}")
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except IOError as e:
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logger.error(f"Error writing to file {file_path}: {e}")
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raise
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def flush(self, file_path: Union[str, Path]) -> None:
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r"""Flush the current data to a JSONL file and clear the data.
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Args:
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file_path (Union[str, Path]): Path to save the JSONL file.
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Notes:
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- Uses `save_to_jsonl` to save `self._data`.
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"""
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self.save_to_jsonl(file_path)
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self._data = []
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self._iter_position = 0
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logger.info(f"Data flushed to {file_path} and cleared from the memory")
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def _init_from_jsonl(self, file_path: Path) -> List[Dict[str, Any]]:
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r"""Load and parse a dataset from a JSONL file.
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Args:
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file_path (Path): Path to the JSONL file.
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Returns:
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List[Dict[str, Any]]: A list of datapoint dictionaries.
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Raises:
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FileNotFoundError: If the specified JSONL file does not exist.
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ValueError: If a line contains invalid JSON or is not a dictionary.
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"""
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if not file_path.exists():
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raise FileNotFoundError(f"JSONL file not found: {file_path}")
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raw_data = []
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logger.debug(f"Loading JSONL from {file_path}")
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with file_path.open('r', encoding='utf-8') as f:
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for line_number, line in enumerate(f, start=1):
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line = line.strip()
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if not line:
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continue # Skip blank lines
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try:
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record = json.loads(line)
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except json.JSONDecodeError as e:
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raise ValueError(
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f"Invalid JSON on line {line_number} "
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f"in file {file_path}: {e}"
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)
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if not isinstance(record, dict):
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raise ValueError(
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f"Expected a dictionary at line {line_number}, "
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f"got {type(record).__name__}"
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)
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raw_data.append(record)
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logger.info(
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f"Successfully loaded {len(raw_data)} items from {file_path}"
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)
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return raw_data
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def __getitem__(self, index: int) -> DataPoint:
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r"""Get a datapoint by index without removing the datapoint from _data.
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Args:
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index (int): Index of the datapoint to retrieve.
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Returns:
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DataPoint: The datapoint at the specified index.
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Raises:
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IndexError: If the index is out of range.
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"""
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if index < 0 or index >= len(self._data):
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raise IndexError(f"Index {index} is out of range")
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return self._data[index]
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def __len__(self) -> int:
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r"""Get the number of datapoints in the dataset.
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Returns:
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int: The number of datapoints.
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"""
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return len(self._data)
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