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Merge pull request #833 from icecraft/feat/tune_docs 

Feat/tune docs
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next_docs/en/user_guide/install/download_model_weight_files.rst 0 → 100644
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Download Model Weight Files
==============================
Model downloads are divided into initial downloads and updates to the
model directory. Please refer to the corresponding documentation for
instructions on how to proceed.
Initial download of model files
------------------------------
1. Download the Model from Hugging Face
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Use a Python Script to Download Model Files from Hugging Face
.. code:: bash
pip install huggingface_hub
wget https://github.com/opendatalab/MinerU/raw/master/scripts/download_models_hf.py -O download_models_hf.py
python download_models_hf.py
The Python script will automatically download the model files and
configure the model directory in the configuration file.
The configuration file can be found in the user directory, with the
filename ``magic-pdf.json``.
How to update models previously downloaded
-----------------------------------------
1. Models downloaded via Git LFS
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Due to feedback from some users that downloading model files using
git lfs was incomplete or resulted in corrupted model files, this
method is no longer recommended.
If you previously downloaded model files via git lfs, you can navigate
to the previous download directory and use the ``git pull`` command to
update the model.
2. Models downloaded via Hugging Face or Model Scope
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
If you previously downloaded models via Hugging Face or Model Scope, you
can rerun the Python script used for the initial download. This will
automatically update the model directory to the latest version.
next_docs/en/user_guide/install/install.rst 0 → 100644
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Install
===============================================================
If you encounter any installation issues, please first consult the FAQ.
If the parsing results are not as expected, refer to the Known Issues.
There are three different ways to experience MinerU
Pre-installation Notice—Hardware and Software Environment Support
------------------------------------------------------------------
To ensure the stability and reliability of the project, we only optimize
and test for specific hardware and software environments during
development. This ensures that users deploying and running the project
on recommended system configurations will get the best performance with
the fewest compatibility issues.
By focusing resources on the mainline environment, our team can more
efficiently resolve potential bugs and develop new features.
In non-mainline environments, due to the diversity of hardware and
software configurations, as well as third-party dependency compatibility
issues, we cannot guarantee 100% project availability. Therefore, for
users who wish to use this project in non-recommended environments, we
suggest carefully reading the documentation and FAQ first. Most issues
already have corresponding solutions in the FAQ. We also encourage
community feedback to help us gradually expand support.
.. raw:: html
<style>
table, th, td {
border: 1px solid black;
border-collapse: collapse;
}
</style>
<table>
<tr>
<td colspan="3" rowspan="2">Operating System</td>
</tr>
<tr>
<td>Ubuntu 22.04 LTS</td>
<td>Windows 10 / 11</td>
<td>macOS 11+</td>
</tr>
<tr>
<td colspan="3">CPU</td>
<td>x86_64</td>
<td>x86_64</td>
<td>x86_64 / arm64</td>
</tr>
<tr>
<td colspan="3">Memory</td>
<td colspan="3">16GB or more, recommended 32GB+</td>
</tr>
<tr>
<td colspan="3">Python Version</td>
<td colspan="3">3.10</td>
</tr>
<tr>
<td colspan="3">Nvidia Driver Version</td>
<td>latest (Proprietary Driver)</td>
<td>latest</td>
<td>None</td>
</tr>
<tr>
<td colspan="3">CUDA Environment</td>
<td>Automatic installation [12.1 (pytorch) + 11.8 (paddle)]</td>
<td>11.8 (manual installation) + cuDNN v8.7.0 (manual installation)</td>
<td>None</td>
</tr>
<tr>
<td rowspan="2">GPU Hardware Support List</td>
<td colspan="2">Minimum Requirement 8G+ VRAM</td>
<td colspan="2">3060ti/3070/3080/3080ti/4060/4070/4070ti<br>
8G VRAM enables layout, formula recognition acceleration and OCR acceleration</td>
<td rowspan="2">None</td>
</tr>
<tr>
<td colspan="2">Recommended Configuration 16G+ VRAM</td>
<td colspan="2">3090/3090ti/4070ti super/4080/4090<br>
16G VRAM or more can enable layout, formula recognition, OCR acceleration and table recognition acceleration simultaneously
</td>
</tr>
</table>
Create an environment
~~~~~~~~~~~~~~~~~~~~~
.. code-block:: shell
conda create -n MinerU python=3.10
conda activate MinerU
pip install -U magic-pdf[full] --extra-index-url https://wheels.myhloli.com
Download model weight files
~~~~~~~~~~~~~~~~~~~~~~~~~~
.. code-block:: shell
pip install huggingface_hub
wget https://github.com/opendatalab/MinerU/raw/master/scripts/download_models_hf.py -O download_models_hf.py
python download_models_hf.py
The MinerU is installed, Check out :doc:`../quick_start` or reading :doc:`boost_with_cuda` for accelerate inference
\ No newline at end of file
next_docs/en/user_guide/quick_start.rst 0 → 100644
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Quick Start
==============
Eager to get started? This page gives a good introduction to MinerU. Follow Installation to set up a project and install MinerU first.
.. toctree::
:maxdepth: 1
quick_start/command_line
quick_start/to_markdown
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Command Line
===================
.. code:: bash
magic-pdf --help
Usage: magic-pdf [OPTIONS]
Options:
-v, --version display the version and exit
-p, --path PATH local pdf filepath or directory [required]
-o, --output-dir PATH output local directory [required]
-m, --method [ocr|txt|auto] the method for parsing pdf. ocr: using ocr
technique to extract information from pdf. txt:
suitable for the text-based pdf only and
outperform ocr. auto: automatically choose the
best method for parsing pdf from ocr and txt.
without method specified, auto will be used by
default.
-l, --lang TEXT Input the languages in the pdf (if known) to
improve OCR accuracy. Optional. You should
input "Abbreviation" with language form url: ht
tps://paddlepaddle.github.io/PaddleOCR/en/ppocr
/blog/multi_languages.html#5-support-languages-
and-abbreviations
-d, --debug BOOLEAN Enables detailed debugging information during
the execution of the CLI commands.
-s, --start INTEGER The starting page for PDF parsing, beginning
from 0.
-e, --end INTEGER The ending page for PDF parsing, beginning from
0.
--help Show this message and exit.
## show version
magic-pdf -v
## command line example
magic-pdf -p {some_pdf} -o {some_output_dir} -m auto
``{some_pdf}`` can be a single PDF file or a directory containing
multiple PDFs. The results will be saved in the ``{some_output_dir}``
directory. The output file list is as follows:
.. code:: text
├── some_pdf.md # markdown file
├── images # directory for storing images
├── some_pdf_layout.pdf # layout diagram
├── some_pdf_middle.json # MinerU intermediate processing result
├── some_pdf_model.json # model inference result
├── some_pdf_origin.pdf # original PDF file
├── some_pdf_spans.pdf # smallest granularity bbox position information diagram
└── some_pdf_content_list.json # Rich text JSON arranged in reading order
For more information about the output files, please refer to the :doc:`../tutorial/output_file_description`
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Extract Content from Pdf
========================
.. code:: python
from magic_pdf.data.read_api import read_local_pdfs
from magic_pdf.pdf_parse_union_core_v2 import pdf_parse_union
from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
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Convert To Markdown
========================
.. code:: python
import os
from magic_pdf.data.data_reader_writer import FileBasedDataWriter, FileBasedDataReader
from magic_pdf.libs.MakeContentConfig import DropMode, MakeMode
from magic_pdf.pipe.OCRPipe import OCRPipe
## args
model_list = []
pdf_file_name = "abc.pdf" # replace with the real pdf path
## prepare env
local_image_dir, local_md_dir = "output/images", "output"
os.makedirs(local_image_dir, exist_ok=True)
image_writer, md_writer = FileBasedDataWriter(local_image_dir), FileBasedDataWriter(
local_md_dir
) # create 00
image_dir = str(os.path.basename(local_image_dir))
reader1 = FileBasedDataReader("")
pdf_bytes = reader1.read(pdf_file_name) # read the pdf content
pipe = OCRPipe(pdf_bytes, model_list, image_writer)
pipe.pipe_classify()
pipe.pipe_analyze()
pipe.pipe_parse()
pdf_info = pipe.pdf_mid_data["pdf_info"]
md_content = pipe.pipe_mk_markdown(
image_dir, drop_mode=DropMode.NONE, md_make_mode=MakeMode.MM_MD
)
if isinstance(md_content, list):
md_writer.write_string(f"{pdf_file_name}.md", "\n".join(md_content))
else:
md_writer.write_string(f"{pdf_file_name}.md", md_content)
Check :doc:`../data/data_reader_writer` for more [reader | writer] examples
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Tutorial
===========
From the beginning to the end, Show how to using mineru via a minimal project
.. toctree::
:maxdepth: 1
tutorial/output_file_description
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Output File Description
=========================
After executing the ``magic-pdf`` command, in addition to outputting
files related to markdown, several other files unrelated to markdown
will also be generated. These files will be introduced one by one.
some_pdf_layout.pdf
~~~~~~~~~~~~~~~~~~~
Each page layout consists of one or more boxes. The number at the top
left of each box indicates its sequence number. Additionally, in
``layout.pdf``, different content blocks are highlighted with different
background colors.
.. figure:: ../../_static/image/layout_example.png
:alt: layout example
layout example
some_pdf_spans.pdf
~~~~~~~~~~~~~~~~~~
All spans on the page are drawn with different colored line frames
according to the span type. This file can be used for quality control,
allowing for quick identification of issues such as missing text or
unrecognized inline formulas.
.. figure:: ../../_static/image/spans_example.png
:alt: spans example
spans example
some_pdf_model.json
~~~~~~~~~~~~~~~~~~~
Structure Definition
^^^^^^^^^^^^^^^^^^^^
.. code:: python
from pydantic import BaseModel, Field
from enum import IntEnum
class CategoryType(IntEnum):
title = 0 # Title
plain_text = 1 # Text
abandon = 2 # Includes headers, footers, page numbers, and page annotations
figure = 3 # Image
figure_caption = 4 # Image description
table = 5 # Table
table_caption = 6 # Table description
table_footnote = 7 # Table footnote
isolate_formula = 8 # Block formula
formula_caption = 9 # Formula label
embedding = 13 # Inline formula
isolated = 14 # Block formula
text = 15 # OCR recognition result
class PageInfo(BaseModel):
page_no: int = Field(description="Page number, the first page is 0", ge=0)
height: int = Field(description="Page height", gt=0)
width: int = Field(description="Page width", ge=0)
class ObjectInferenceResult(BaseModel):
category_id: CategoryType = Field(description="Category", ge=0)
poly: list[float] = Field(description="Quadrilateral coordinates, representing the coordinates of the top-left, top-right, bottom-right, and bottom-left points respectively")
score: float = Field(description="Confidence of the inference result")
latex: str | None = Field(description="LaTeX parsing result", default=None)
html: str | None = Field(description="HTML parsing result", default=None)
class PageInferenceResults(BaseModel):
layout_dets: list[ObjectInferenceResult] = Field(description="Page recognition results", ge=0)
page_info: PageInfo = Field(description="Page metadata")
# The inference results of all pages, ordered by page number, are stored in a list as the inference results of MinerU
inference_result: list[PageInferenceResults] = []
The format of the poly coordinates is [x0, y0, x1, y1, x2, y2, x3, y3],
representing the coordinates of the top-left, top-right, bottom-right,
and bottom-left points respectively. |Poly Coordinate Diagram|
example
^^^^^^^
.. code:: json
[
{
"layout_dets": [
{
"category_id": 2,
"poly": [
99.1906967163086,
100.3119125366211,
730.3707885742188,
100.3119125366211,
730.3707885742188,
245.81326293945312,
99.1906967163086,
245.81326293945312
],
"score": 0.9999997615814209
}
],
"page_info": {
"page_no": 0,
"height": 2339,
"width": 1654
}
},
{
"layout_dets": [
{
"category_id": 5,
"poly": [
99.13092803955078,
2210.680419921875,
497.3183898925781,
2210.680419921875,
497.3183898925781,
2264.78076171875,
99.13092803955078,
2264.78076171875
],
"score": 0.9999997019767761
}
],
"page_info": {
"page_no": 1,
"height": 2339,
"width": 1654
}
}
]
some_pdf_middle.json
~~~~~~~~~~~~~~~~~~~~
+-------+--------------------------------------------------------------+
| Field | Description |
| Name | |
+=======+==============================================================+
| pdf | list, each element is a dict representing the parsing result |
| _info | of each PDF page, see the table below for details |
+-------+--------------------------------------------------------------+
| \_ | ocr \| txt, used to indicate the mode used in this |
| parse | intermediate parsing state |
| _type | |
+-------+--------------------------------------------------------------+
| \_ve | string, indicates the version of magic-pdf used in this |
| rsion | parsing |
| _name | |
+-------+--------------------------------------------------------------+
**pdf_info**
Field structure description
+---------+------------------------------------------------------------+
| Field | Description |
| Name | |
+=========+============================================================+
| preproc | Intermediate result after PDF preprocessing, not yet |
| _blocks | segmented |
+---------+------------------------------------------------------------+
| layout | Layout segmentation results, containing layout direction |
| _bboxes | (vertical, horizontal), and bbox, sorted by reading order |
+---------+------------------------------------------------------------+
| p | Page number, starting from 0 |
| age_idx | |
+---------+------------------------------------------------------------+
| pa | Page width and height |
| ge_size | |
+---------+------------------------------------------------------------+
| \_layo | Layout tree structure |
| ut_tree | |
+---------+------------------------------------------------------------+
| images | list, each element is a dict representing an img_block |
+---------+------------------------------------------------------------+
| tables | list, each element is a dict representing a table_block |
+---------+------------------------------------------------------------+
| inter | list, each element is a dict representing an |
| line_eq | interline_equation_block |
| uations | |
+---------+------------------------------------------------------------+
| di | List, block information returned by the model that needs |
| scarded | to be dropped |
| _blocks | |
+---------+------------------------------------------------------------+
| para | Result after segmenting preproc_blocks |
| _blocks | |
+---------+------------------------------------------------------------+
In the above table, ``para_blocks`` is an array of dicts, each dict
representing a block structure. A block can support up to one level of
nesting.
**block**
The outer block is referred to as a first-level block, and the fields in
the first-level block include:
+---------+-------------------------------------------------------------+
| Field | Description |
| Name | |
+=========+=============================================================+
| type | Block type (table|image) |
+---------+-------------------------------------------------------------+
| bbox | Block bounding box coordinates |
+---------+-------------------------------------------------------------+
| blocks | list, each element is a dict representing a second-level |
| | block |
+---------+-------------------------------------------------------------+
There are only two types of first-level blocks: “table” and “image”. All
other blocks are second-level blocks.
The fields in a second-level block include:
+-----+----------------------------------------------------------------+
| Fi | Description |
| eld | |
| N | |
| ame | |
+=====+================================================================+
| t | Block type |
| ype | |
+-----+----------------------------------------------------------------+
| b | Block bounding box coordinates |
| box | |
+-----+----------------------------------------------------------------+
| li | list, each element is a dict representing a line, used to |
| nes | describe the composition of a line of information |
+-----+----------------------------------------------------------------+
Detailed explanation of second-level block types
================== ======================
type Description
================== ======================
image_body Main body of the image
image_caption Image description text
table_body Main body of the table
table_caption Table description text
table_footnote Table footnote
text Text block
title Title block
interline_equation Block formula
================== ======================
**line**
The field format of a line is as follows:
+-----+----------------------------------------------------------------+
| Fi | Description |
| eld | |
| N | |
| ame | |
+=====+================================================================+
| b | Bounding box coordinates of the line |
| box | |
+-----+----------------------------------------------------------------+
| sp | list, each element is a dict representing a span, used to |
| ans | describe the composition of the smallest unit |
+-----+----------------------------------------------------------------+
**span**
+----------+-----------------------------------------------------------+
| Field | Description |
| Name | |
+==========+===========================================================+
| bbox | Bounding box coordinates of the span |
+----------+-----------------------------------------------------------+
| type | Type of the span |
+----------+-----------------------------------------------------------+
| content | Text spans use content, chart spans use img_path to store |
| \| | the actual text or screenshot path information |
| img_path | |
+----------+-----------------------------------------------------------+
The types of spans are as follows:
================== ==============
type Description
================== ==============
image Image
table Table
text Text
inline_equation Inline formula
interline_equation Block formula
================== ==============
**Summary**
A span is the smallest storage unit for all elements.
The elements stored within para_blocks are block information.
The block structure is as follows:
First-level block (if any) -> Second-level block -> Line -> Span
.. _example-1:
example
^^^^^^^
.. code:: json
{
"pdf_info": [
{
"preproc_blocks": [
{
"type": "text",
"bbox": [
52,
61.956024169921875,
294,
82.99800872802734
],
"lines": [
{
"bbox": [
52,
61.956024169921875,
294,
72.0000228881836
],
"spans": [
{
"bbox": [
54.0,
61.956024169921875,
296.2261657714844,
72.0000228881836
],
"content": "dependent on the service headway and the reliability of the departure ",
"type": "text",
"score": 1.0
}
]
}
]
}
],
"layout_bboxes": [
{
"layout_bbox": [
52,
61,
294,
731
],
"layout_label": "V",
"sub_layout": []
}
],
"page_idx": 0,
"page_size": [
612.0,
792.0
],
"_layout_tree": [],
"images": [],
"tables": [],
"interline_equations": [],
"discarded_blocks": [],
"para_blocks": [
{
"type": "text",
"bbox": [
52,
61.956024169921875,
294,
82.99800872802734
],
"lines": [
{
"bbox": [
52,
61.956024169921875,
294,
72.0000228881836
],
"spans": [
{
"bbox": [
54.0,
61.956024169921875,
296.2261657714844,
72.0000228881836
],
"content": "dependent on the service headway and the reliability of the departure ",
"type": "text",
"score": 1.0
}
]
}
]
}
]
}
],
"_parse_type": "txt",
"_version_name": "0.6.1"
}
.. |Poly Coordinate Diagram| image:: ../../_static/image/poly.png
next_docs/requirements.txt
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......@@ -5,7 +5,8 @@ Pillow==8.4.0
pydantic>=2.7.2,<2.8.0
PyMuPDF>=1.24.9
sphinx
sphinx-argparse
sphinx-book-theme
sphinx-copybutton
sphinx_rtd_theme
sphinx-argparse>=0.5.2
sphinx-book-theme>=1.1.3
sphinx-copybutton>=0.5.2
sphinx_rtd_theme>=3.0.1
autodoc_pydantic>=2.2.0
\ No newline at end of file
next_docs/zh_cn/.readthedocs.yaml
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......@@ -10,7 +10,7 @@ formats:
python:
install:
- requirements: docs/requirements.txt
- requirements: next_docs/requirements.txt
sphinx:
configuration: docs/zh_cn/conf.py
configuration: next_docs/zh_cn/conf.py
scripts/download_models.py 0 → 100644
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import json
import os
import requests
from modelscope import snapshot_download
def download_json(url):
# 下载JSON文件
response = requests.get(url)
response.raise_for_status() # 检查请求是否成功
return response.json()
def download_and_modify_json(url, local_filename, modifications):
if os.path.exists(local_filename):
data = json.load(open(local_filename))
config_version = data.get('config_version', '0.0.0')
if config_version < '1.0.0':
data = download_json(url)
else:
data = download_json(url)
# 修改内容
for key, value in modifications.items():
data[key] = value
# 保存修改后的内容
with open(local_filename, 'w', encoding='utf-8') as f:
json.dump(data, f, ensure_ascii=False, indent=4)
if __name__ == '__main__':
mineru_patterns = [
"models/Layout/LayoutLMv3/*",
"models/Layout/YOLO/*",
"models/MFD/YOLO/*",
"models/MFR/unimernet_small/*",
"models/TabRec/TableMaster/*",
"models/TabRec/StructEqTable/*",
]
model_dir = snapshot_download('opendatalab/PDF-Extract-Kit-1.0', allow_patterns=mineru_patterns)
layoutreader_model_dir = snapshot_download('ppaanngggg/layoutreader')
model_dir = model_dir + '/models'
print(f'model_dir is: {model_dir}')
print(f'layoutreader_model_dir is: {layoutreader_model_dir}')
json_url = 'https://gitee.com/myhloli/MinerU/raw/dev/magic-pdf.template.json'
config_file_name = 'magic-pdf.json'
home_dir = os.path.expanduser('~')
config_file = os.path.join(home_dir, config_file_name)
json_mods = {
'models-dir': model_dir,
'layoutreader-model-dir': layoutreader_model_dir,
}
download_and_modify_json(json_url, config_file, json_mods)
print(f'The configuration file has been configured successfully, the path is: {config_file}')
scripts/download_models_hf.py 0 → 100644
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import json
import os
import requests
from huggingface_hub import snapshot_download
def download_json(url):
# 下载JSON文件
response = requests.get(url)
response.raise_for_status() # 检查请求是否成功
return response.json()
def download_and_modify_json(url, local_filename, modifications):
if os.path.exists(local_filename):
data = json.load(open(local_filename))
config_version = data.get('config_version', '0.0.0')
if config_version < '1.0.0':
data = download_json(url)
else:
data = download_json(url)
# 修改内容
for key, value in modifications.items():
data[key] = value
# 保存修改后的内容
with open(local_filename, 'w', encoding='utf-8') as f:
json.dump(data, f, ensure_ascii=False, indent=4)
if __name__ == '__main__':
mineru_patterns = [
"models/Layout/LayoutLMv3/*",
"models/Layout/YOLO/*",
"models/MFD/YOLO/*",
"models/MFR/unimernet_small/*",
"models/TabRec/TableMaster/*",
"models/TabRec/StructEqTable/*",
]
model_dir = snapshot_download('opendatalab/PDF-Extract-Kit-1.0', allow_patterns=mineru_patterns)
layoutreader_pattern = [
"*.json",
"*.safetensors",
]
layoutreader_model_dir = snapshot_download('hantian/layoutreader', allow_patterns=layoutreader_pattern)
model_dir = model_dir + '/models'
print(f'model_dir is: {model_dir}')
print(f'layoutreader_model_dir is: {layoutreader_model_dir}')
json_url = 'https://github.com/opendatalab/MinerU/raw/dev/magic-pdf.template.json'
config_file_name = 'magic-pdf.json'
home_dir = os.path.expanduser('~')
config_file = os.path.join(home_dir, config_file_name)
json_mods = {
'models-dir': model_dir,
'layoutreader-model-dir': layoutreader_model_dir,
}
download_and_modify_json(json_url, config_file, json_mods)
print(f'The configuration file has been configured successfully, the path is: {config_file}')
tests/test_data/data_reader_writer/test_multi_bucket_s3.py
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......@@ -41,8 +41,8 @@ def test_multi_bucket_s3_reader_writer():
),
]
reader = MultiBucketS3DataReader(default_bucket=bucket, s3_configs=s3configs)
writer = MultiBucketS3DataWriter(default_bucket=bucket, s3_configs=s3configs)
reader = MultiBucketS3DataReader(bucket, s3configs)
writer = MultiBucketS3DataWriter(bucket, s3configs)
bits = reader.read('meta-index/scihub/v001/scihub/part-66210c190659-000026.jsonl')
......@@ -80,3 +80,81 @@ def test_multi_bucket_s3_reader_writer():
assert '123'.encode() == reader.read(
'unittest/data/data_reader_writer/multi_bucket_s3_data/test02.txt'
)
@pytest.mark.skipif(
os.getenv('S3_ACCESS_KEY_2', None) is None, reason='need s3 config!'
)
def test_multi_bucket_s3_reader_writer_with_prefix():
"""test multi bucket s3 reader writer must config s3 config in the
environment export S3_BUCKET=xxx export S3_ACCESS_KEY=xxx export
S3_SECRET_KEY=xxx export S3_ENDPOINT=xxx.
export S3_BUCKET_2=xxx export S3_ACCESS_KEY_2=xxx export S3_SECRET_KEY_2=xxx export S3_ENDPOINT_2=xxx
"""
bucket = os.getenv('S3_BUCKET', '')
ak = os.getenv('S3_ACCESS_KEY', '')
sk = os.getenv('S3_SECRET_KEY', '')
endpoint_url = os.getenv('S3_ENDPOINT', '')
bucket_2 = os.getenv('S3_BUCKET_2', '')
ak_2 = os.getenv('S3_ACCESS_KEY_2', '')
sk_2 = os.getenv('S3_SECRET_KEY_2', '')
endpoint_url_2 = os.getenv('S3_ENDPOINT_2', '')
s3configs = [
S3Config(
bucket_name=bucket, access_key=ak, secret_key=sk, endpoint_url=endpoint_url
),
S3Config(
bucket_name=bucket_2,
access_key=ak_2,
secret_key=sk_2,
endpoint_url=endpoint_url_2,
),
]
prefix = 'meta-index'
reader = MultiBucketS3DataReader(f'{bucket}/{prefix}', s3configs)
writer = MultiBucketS3DataWriter(f'{bucket}/{prefix}', s3configs)
bits = reader.read('scihub/v001/scihub/part-66210c190659-000026.jsonl')
assert bits == reader.read(
f's3://{bucket}/{prefix}/scihub/v001/scihub/part-66210c190659-000026.jsonl'
)
bits = reader.read(
f's3://{bucket_2}/enbook-scimag/78800000/libgen.scimag78872000-78872999/10.1017/cbo9780511770425.012.pdf'
)
docs = fitz.open('pdf', bits)
assert len(docs) == 10
bits = reader.read(
'scihub/v001/scihub/part-66210c190659-000026.jsonl?bytes=566,713'
)
assert bits == reader.read_at(
'scihub/v001/scihub/part-66210c190659-000026.jsonl', 566, 713
)
assert len(json.loads(bits)) > 0
writer.write_string(
'unittest/data/data_reader_writer/multi_bucket_s3_data/test01.txt', 'abc'
)
assert 'abc'.encode() == reader.read(
'unittest/data/data_reader_writer/multi_bucket_s3_data/test01.txt'
)
assert 'abc'.encode() == reader.read(
f's3://{bucket}/{prefix}/unittest/data/data_reader_writer/multi_bucket_s3_data/test01.txt'
)
writer.write(
'unittest/data/data_reader_writer/multi_bucket_s3_data/test02.txt',
'123'.encode(),
)
assert '123'.encode() == reader.read(
'unittest/data/data_reader_writer/multi_bucket_s3_data/test02.txt'
)
tests/test_data/data_reader_writer/test_s3.py
View file @ 8b119e22
......@@ -9,7 +9,7 @@ from magic_pdf.data.data_reader_writer import S3DataReader, S3DataWriter
@pytest.mark.skipif(
os.getenv('S3_ACCESS_KEY', None) is None, reason='need s3 config!'
)
def test_multi_bucket_s3_reader_writer():
def test_s3_reader_writer():
"""test multi bucket s3 reader writer must config s3 config in the
environment export S3_BUCKET=xxx export S3_ACCESS_KEY=xxx export
S3_SECRET_KEY=xxx export S3_ENDPOINT=xxx."""
......@@ -18,8 +18,8 @@ def test_multi_bucket_s3_reader_writer():
sk = os.getenv('S3_SECRET_KEY', '')
endpoint_url = os.getenv('S3_ENDPOINT', '')
reader = S3DataReader(bucket=bucket, ak=ak, sk=sk, endpoint_url=endpoint_url)
writer = S3DataWriter(bucket=bucket, ak=ak, sk=sk, endpoint_url=endpoint_url)
reader = S3DataReader('', bucket, ak, sk, endpoint_url)
writer = S3DataWriter('', bucket, ak, sk, endpoint_url)
bits = reader.read('meta-index/scihub/v001/scihub/part-66210c190659-000026.jsonl')
......@@ -51,3 +51,56 @@ def test_multi_bucket_s3_reader_writer():
assert '123'.encode() == reader.read(
'unittest/data/data_reader_writer/multi_bucket_s3_data/test02.txt'
)
@pytest.mark.skipif(
os.getenv('S3_ACCESS_KEY', None) is None, reason='need s3 config!'
)
def test_s3_reader_writer_with_prefix():
"""test multi bucket s3 reader writer must config s3 config in the
environment export S3_BUCKET=xxx export S3_ACCESS_KEY=xxx export
S3_SECRET_KEY=xxx export S3_ENDPOINT=xxx."""
bucket = os.getenv('S3_BUCKET', '')
ak = os.getenv('S3_ACCESS_KEY', '')
sk = os.getenv('S3_SECRET_KEY', '')
endpoint_url = os.getenv('S3_ENDPOINT', '')
prefix = 'meta-index'
reader = S3DataReader(prefix, bucket, ak, sk, endpoint_url)
writer = S3DataWriter(prefix, bucket, ak, sk, endpoint_url)
bits = reader.read('scihub/v001/scihub/part-66210c190659-000026.jsonl')
assert bits == reader.read(
f's3://{bucket}/{prefix}/scihub/v001/scihub/part-66210c190659-000026.jsonl'
)
bits = reader.read(
'scihub/v001/scihub/part-66210c190659-000026.jsonl?bytes=566,713'
)
assert bits == reader.read_at(
'scihub/v001/scihub/part-66210c190659-000026.jsonl', 566, 713
)
assert len(json.loads(bits)) > 0
writer.write_string(
'unittest/data/data_reader_writer/multi_bucket_s3_data/test01.txt', 'abc'
)
assert 'abc'.encode() == reader.read(
'unittest/data/data_reader_writer/multi_bucket_s3_data/test01.txt'
)
assert 'abc'.encode() == reader.read(
f's3://{bucket}/{prefix}/unittest/data/data_reader_writer/multi_bucket_s3_data/test01.txt'
)
writer.write(
f'{bucket}/{prefix}/unittest/data/data_reader_writer/multi_bucket_s3_data/test02.txt',
'123'.encode(),
)
assert '123'.encode() == reader.read(
'unittest/data/data_reader_writer/multi_bucket_s3_data/test02.txt'
)
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