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    "info": {
        "author": "Seann Zhang",
        "author_email": "pluiefox@live.com",
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            "License :: OSI Approved :: MIT License",
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            "Programming Language :: Python :: 3.7"
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        "description": "\n# Lunas\n\n[![PyPI version](https://img.shields.io/badge/pypi-v0.3.5-limegreen.svg)](https://github.com/pluiez/lunas)\n\n**Lunas** is a Python based library that provides a set of simple interfaces for data processing pipelines and an iterator for looping through data.\n\nBasically, Lunas draws its data-handling style on *Tensorflow*, *PyTorch*, and some implementation details from *AllenNLP*.\n\n## Overview\n\nA `Dataset` represents a dataset and holds corresponding pre-processing and filtering operations. Currently the following features are supported:\n\n1. Buffered reading.\n2. Buffered shuffling.\n3. Chained processing and filtering interface.\n4. Handling multiple input sources.\n5. Persistable.\n\nSupported datasets:\n\n1. `Zip`: Zips multiple datasets.\n2. `Shuffle`: A wrapper that performs buffered shuffling.\n3. `Sort`: A wrapper that performs buffered sorting.\n4. `InvertibleSort`: A wrapper that performs buffered sorting., and returns the sample along with its original index in the dataset.\n5. `Enumerate`: Similar to Python's `enumerate` that wraps a dataset and attach an index to each element of it.\n6. `Range`: Similar to Python's `range`.\n7. `Count`: Similar to Python's `itertools.count`.\n8. `TextLine`: A wrapper that wraps a plain-text file. Each line of the file is taken as a sample of the dataset.\n9. `Stdin`: A wrapper that reads from standard input.\n\n\n\nAn `Iterator` generates batches by iterating through the dataset and maintains the iteration state. The following features are supported:\n\n1. Dynamic batching at runtime.\n2. Custom stopping criteria.\n3. Persistable.\n\nWe also modify PyTorch's DataLoader to make it compatitble with our batch iterator.\n\n*Persistable* provides the class with a *PyTorch* compatible interface to dump and load instance state, useful to resume the training process.\n\n## Requirements\n\n- Numpy\n- overrides\n- typings\n- Python >= 3.7\n\nLunas hardly relies on any third-party libraries, all the required libraries are just\nto take advantage of the type hint features provided by Python 3.\n\nType hint feature is used in this project and the built-in typing module of Python version lower than 3.7 can decrease the performance. However, this is solved since Python 3.7. So Lunas currently requires Python 3.7 to work efficiently.\n\n## Installation\n\nInstall using pip:\n\n```\npip install lunas\n```\n\n## Example\n\n1. Create a dataset and iterate through it.\n\n   ```python\n   from lunas import Range\n\n   ds = Range(10)\n   for x in ds: # epoch 1\n       print(x)\n   for x in ds: # epoch 2\n       print(x)\n   ```\n\n   - A `Range` dataset is created similar to range(10) and iterate through it for one epoch.\n   As you see, we can iterate through this dataset several times.\n\n2. Build a data processing pipeline.\n\n   ```python\n   ds = Range(10).select(lambda x: x + 1).select(lambda x: x * 2).where(lambda x: x % 2 == 0)\n   ```\n\n   - The chaining calls of a `Dataset` obbject defines a processing pipeline on the dataset.\n   - `select(fn)` applys transformations on a dataset element lazily. The argument `fn` is a custom mapping fucntion that takes a single sample as input and output. You can apply any transformations to the dataset and return a sample of any type, e.g., `Dict`, `List` and a custom `Sample`.\n   - `where(fn)` accepts a predicate and returns a `bool` value to filter an input sample, if True, the sample is preserved, otherwise discarded.\n   - The mapping and filtering ops given by `select(fn)` and `where(fn)` are not executed immediately, but later when iterating through the dataset object.\n   - Both `select(fn)` and `where(fn)` returns the dataset itself just to enable chaining style invocations. The mapping and filtering ops are attched to the dataset in an in-place fasion.\n\n3. Deal with multiple input sources.\n\n   ```python\n   from lunas import Range, Zip, Shuffle\n\n   ds1 = Range(10)\n   ds2 = Range(start=10, stop=20, step=1)\n   ds = Zip(ds1, ds2).select(lambda x,y: x + y)\n   ds = Shuffle(ds, bufsize=5)\n   ```\n\n   - In the above code, we create two datasets and *zip* them as a `Zip` dataset. A `Zip` dataset returns a tuple from its internal datasets.\n   - `Shuffle` performs randomized shuffling on the dataset.\n\n4. Practical use case in Machine Translation scenario.\n\n   ```python\n   from lunas import TextLine, Zip, Shuffle, Sort, Iterator\n\n   # Tokenize the input into a list of tokens.\n   source = TextLine('train.fr').select(lambda x: x.split())\n   target = TextLine('train.en').select(lambda x: x.split()) \n   # Ensure the inputs are of length no exceeding 50.\n   ds = Zip(source, target).select(lambda x, y: \n\t\t   {\n\t\t\t   x: src_vocab.lookup(x), # Map words to ids\n\t\t\t   y: tgt_vocab.lookup(y),\n\t\t\t   size_x: len(x),\n\t\t\t   size_y: len(y),\n\t\t   }\n\t   )\n   ds = ds.where(lambda x: max(x['size_x'], x['size_y']) <= 50)\n   # Shuffle the dataset within a buffer with bufsize 100000\n   ds = Shuffle(ds, bufsize=10000)\n   # Sort samples in batch by source text length\n   sort_key = lambda x: len(x['size_x'])\n   ds = Sort(ds, bufsize=1000, sort_key_fn=sort_key)\n\n   # Convert a list of samples to model inputs\n   collate_fn = lambda x: ...\n\n   it = Iterator(dataset=ds, batch_size=4096, \n\t     sample_size_fn=lambda x: x['size_x'], \n\t     collate_fn=collate_fn, \n\t\t dist_world_size=1,\n\t\t dist_local_rank=0,\n\t     drop_tail=True)\n\n   # Iterate 100 epoch and 1000000 steps at most.\n   for batch in it.while_true(lambda: it.epoch < 100 and it.step < 1e6):\n       print(it.epoch, it.step, it.step_in_epoch, batch)\n\n   ```\n\n   - This code should be simple enough to understand, even if you are not familiar with machine translation.\n\n5. Save and reload iteration state.\n\n   ```python\n   import pickle\n   pickle.dump(it.state_dict(), open('state.pkl', 'wb'))\n   # ...\n   state = pickle.load(open('state.pkl', 'rb'))\n   it.load_state_dict(state)\n   ```\n\n   - `state_dict()` returns a picklable dictionary, which can be loaded by `it.load_state_dict()` to resume the iteration process later.\n\n6. Extend the dataset.\n\n   - You can refer to the implementation of `TextLine` dataset to customize your own data dataset.\n\n## Conclusions\n\nPlease feel free to contact me if you have any question or find any bug of Lunas.\n\n\n",
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