Delete files mjp.py with huggingface_hub

mjp.py

@@ -1,175 +0,0 @@
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Collection of datasets for the MJP."""
-
-import pathlib
-from collections import defaultdict
-from dataclasses import dataclass
-from typing import Optional
-
-import datasets
-import torch
-
-from fim.data.utils import load_file
-from fim.typing import Path, Paths
-
-
-# TODO: Add BibTeX citation
-# Find for instance the citation on arxiv or on the dataset repo/website
-_CITATION = """\
-@InProceedings{huggingface:dataset,
-title = {A great new dataset},
-author={huggingface, Inc.
-},
-year={2020}
-}
-"""
-
-# TODO: Add description of the dataset here
-# You can copy an official description
-_DESCRIPTION = """\
-This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
-"""
-
-# TODO: Add a link to an official homepage for the dataset here
-_HOMEPAGE = ""
-
-# TODO: Add the licence for the dataset here if you can find it
-_LICENSE = ""
-
-# TODO: Add link to the official dataset URLs here
-# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
-# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
-# _URLS = {
-#     "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
-#     "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
-# }
-
-_ROOT_URL = "data/DFR"
-
-
-@dataclass
-class MJPDatasetsBuilderConfig(datasets.BuilderConfig):
-    """MJPDatasets builder config.."""
-
-    file_name: Optional[str] = None
-
-
-# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
-class MJP(datasets.GeneratorBasedBuilder):
-    """TODO: Short description of my dataset."""
-
-    VERSION = datasets.Version("1.1.0")
-
-    # This is an example of a dataset with multiple configurations.
-    # If you don't want/need to define several sub-sets in your dataset,
-    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
-
-    # If you need to make complex sub-parts in the datasets with configurable options
-    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
-    BUILDER_CONFIG_CLASS = MJPDatasetsBuilderConfig
-
-    # You will be able to load one or the other configurations in the following list with
-    # data = datasets.load_dataset('my_dataset', 'first_domain')
-    # data = datasets.load_dataset('my_dataset', 'second_domain')
-    BUILDER_CONFIGS = [
-        MJPDatasetsBuilderConfig(
-            name="DFR_V=0",
-            file_name="6_st_DFR_V=0.zip",
-            version=VERSION,
-            description="This part of my dataset covers a first domain",
-        ),
-        MJPDatasetsBuilderConfig(
-            name="DFR_V=1",
-            file_name="6_st_DFR_V=1.zip",
-            version=VERSION,
-            description="This part of my dataset covers a first domain",
-        ),
-        MJPDatasetsBuilderConfig(
-            name="DFR_V=2",
-            file_name="6_st_DFR_V=2.zip",
-            version=VERSION,
-            description="This part of my dataset covers a first domain",
-        ),
-        MJPDatasetsBuilderConfig(
-            name="DFR_V=3",
-            file_name="6_st_DFR_V=3.zip",
-            version=VERSION,
-            description="This part of my dataset covers a first domain",
-        ),
-    ]
-
-    DEFAULT_CONFIG_NAME = "DFR_V=0"
-
-    files_to_load = {
-        "observation_grid": "fine_grid_grid.pt",
-        "observation_values": "fine_grid_noisy_sample_paths.pt",
-        "seq_lengths": "fine_grid_mask_seq_lengths.pt",
-        "time_normalization_factors": "fine_grid_time_normalization_factors.pt",
-        "intensity_matrices": "fine_grid_intensity_matrices.pt",
-        "adjacency_matrices": "fine_grid_adjacency_matrices.pt",
-        "initial_distributions": "fine_grid_initial_distributions.pt",
-    }
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "observation_grid": datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value("float32")))),
-                "observation_values": datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value("uint32")))),
-                "time_normalization_factors": datasets.Value("float32"),
-                "seq_lengths": datasets.Sequence(datasets.Sequence(datasets.Value("int32"))),
-                "intensity_matrices": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))),
-                "adjacency_matrices": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))),
-                "initial_distributions": datasets.Sequence(datasets.Value("uint64")),
-            }
-        )
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
-        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
-        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
-        urls = f"{_ROOT_URL}/{self.config.file_name}"
-        data_dir = dl_manager.download_and_extract(urls)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"datadir": pathlib.Path(data_dir) / self.config.file_name.split(".")[0]},
-            )
-        ]
-
-    def __get_files(self, path: Path) -> Paths:
-        files_to_load = [(key, pathlib.Path(path) / file_name) for key, file_name in self.files_to_load.items()]
-        return files_to_load
-
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, datadir):
-        data = defaultdict(list)
-        files_to_load = self.__get_files(datadir)
-        for key, file_path in files_to_load:
-            data[key].append(load_file(file_path))
-        for k, v in data.items():
-            data[k] = torch.cat(v)
-
-        for id in range(len(data["observation_grid"])):
-            yield id, {k: v[id].tolist() for k, v in data.items() if k in self.info.features}