code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
values |
|---|---|---|---|---|---|---|---|
def __getstate__(self):
"""Control how the object is pickled"""
state = self.__dict__.copy()
# Remove unpicklable attributes
if 'client' in state:
del state['client'] # Remove Label Studio client
if 'project' in state:
del state['project'] # Remove proje... | Control how the object is pickled | __getstate__ | python | modelscope/data-juicer | data_juicer/ops/mapper/annotation/annotation_mapper.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/mapper/annotation/annotation_mapper.py | Apache-2.0 |
def __setstate__(self, state):
"""Control how the object is unpickled"""
self.__dict__.update(state)
# Reconnect to Label Studio if needed
if hasattr(self, 'api_url') and hasattr(self, 'api_key'):
try:
self.client = label_studio_sdk.Client(url=self.api_url,
... | Control how the object is unpickled | __setstate__ | python | modelscope/data-juicer | data_juicer/ops/mapper/annotation/annotation_mapper.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/mapper/annotation/annotation_mapper.py | Apache-2.0 |
def _get_project_url(self):
"""Get URL to the Label Studio project
Returns:
str: URL to the Label Studio project, or None if not available
"""
if not hasattr(self,
'api_url') or not self.api_url or not self.project_id:
return None
... | Get URL to the Label Studio project
Returns:
str: URL to the Label Studio project, or None if not available
| _get_project_url | python | modelscope/data-juicer | data_juicer/ops/mapper/annotation/annotation_mapper.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/mapper/annotation/annotation_mapper.py | Apache-2.0 |
def _get_task_url(self, task_id):
"""Get URL to a specific Label Studio task
Args:
task_id: ID of the task
Returns:
str: URL to the task in Label Studio, or None if not available
"""
if not hasattr(self,
'api_url') or not self.api_... | Get URL to a specific Label Studio task
Args:
task_id: ID of the task
Returns:
str: URL to the task in Label Studio, or None if not available
| _get_task_url | python | modelscope/data-juicer | data_juicer/ops/mapper/annotation/annotation_mapper.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/mapper/annotation/annotation_mapper.py | Apache-2.0 |
def __init__(self,
label_config_file: str = None,
answer1_key: str = 'answer1',
answer2_key: str = 'answer2',
prompt_key: str = 'prompt',
chosen_key: str = 'chosen',
rejected_key: str = 'rejected',
**k... | Initialize the human preference annotation operator. | __init__ | python | modelscope/data-juicer | data_juicer/ops/mapper/annotation/human_preference_annotation_mapper.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/mapper/annotation/human_preference_annotation_mapper.py | Apache-2.0 |
def _format_task(self, samples: List[Dict]) -> Dict:
"""Format samples as a Label Studio task for human preference.
Args:
samples: List of samples to include in the task
Returns:
Dict: Formatted task data
"""
# For human preference, we need a special for... | Format samples as a Label Studio task for human preference.
Args:
samples: List of samples to include in the task
Returns:
Dict: Formatted task data
| _format_task | python | modelscope/data-juicer | data_juicer/ops/mapper/annotation/human_preference_annotation_mapper.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/mapper/annotation/human_preference_annotation_mapper.py | Apache-2.0 |
def _get_task_annotation(self, task_id: int) -> Optional[Dict]:
"""Get annotation for a task if available"""
annotation = super()._get_task_annotation(task_id)
# Process the annotation if available
if annotation and 'result' in annotation:
# Extract the preference informatio... | Get annotation for a task if available | _get_task_annotation | python | modelscope/data-juicer | data_juicer/ops/mapper/annotation/human_preference_annotation_mapper.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/mapper/annotation/human_preference_annotation_mapper.py | Apache-2.0 |
def _process_annotation_result(self, annotation: Dict,
sample: Dict) -> Dict:
"""Process human preference annotation result and update the sample
Args:
annotation: The annotation result from Label Studio
sample: The original sample that was ann... | Process human preference annotation result and update the sample
Args:
annotation: The annotation result from Label Studio
sample: The original sample that was annotated
Returns:
Dict: The updated sample with preference results
| _process_annotation_result | python | modelscope/data-juicer | data_juicer/ops/mapper/annotation/human_preference_annotation_mapper.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/mapper/annotation/human_preference_annotation_mapper.py | Apache-2.0 |
def __init__(self,
field_key: str = '',
top_ratio: Optional[Annotated[float,
Field(ge=0, le=1)]] = None,
topk: Optional[PositiveInt] = None,
reverse: bool = True,
*args,
*... |
Initialization method.
:param field_key: Selector based on the specified value
corresponding to the target key. The target key
corresponding to multi-level field information need to be
separated by '.'.
:param top_ratio: Ratio of selected top specified field... | __init__ | python | modelscope/data-juicer | data_juicer/ops/selector/frequency_specified_field_selector.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/selector/frequency_specified_field_selector.py | Apache-2.0 |
def __init__(self,
select_ratio: Optional[Annotated[float,
Field(ge=0, le=1)]] = None,
select_num: PositiveInt = None,
*args,
**kwargs):
"""
Initialization method.
:param select... |
Initialization method.
:param select_ratio: The ratio to select. When both
select_ratio and select_num are set, the value corresponding
to the smaller number of samples will be applied.
:param select_num: The number of samples to select. When both
select_rat... | __init__ | python | modelscope/data-juicer | data_juicer/ops/selector/random_selector.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/selector/random_selector.py | Apache-2.0 |
def __init__(
self,
field_key: str = '',
lower_percentile: Optional[Annotated[float,
Field(ge=0, le=1)]] = None,
upper_percentile: Optional[Annotated[float,
Field(ge=0, le=1)... |
Initialization method.
:param field_key: Selector based on the specified value
corresponding to the target key. The target key
corresponding to multi-level field information need to be
separated by '.'.
:param lower_percentile: The lower bound of the percent... | __init__ | python | modelscope/data-juicer | data_juicer/ops/selector/range_specified_field_selector.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/selector/range_specified_field_selector.py | Apache-2.0 |
def __init__(self,
field_key: str = '',
target_tags: List[str] = None,
*args,
**kwargs):
"""
Initialization method.
:param field_key: Selector based on the specified value
corresponding to the target key. The target... |
Initialization method.
:param field_key: Selector based on the specified value
corresponding to the target key. The target key
corresponding to multi-level field information need to be
separated by '.'.
:param target_tags: Target tags to be select.
:... | __init__ | python | modelscope/data-juicer | data_juicer/ops/selector/tags_specified_field_selector.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/selector/tags_specified_field_selector.py | Apache-2.0 |
def __init__(self,
field_key: str = '',
top_ratio: Optional[Annotated[float,
Field(ge=0, le=1)]] = None,
topk: Optional[PositiveInt] = None,
reverse: bool = True,
*args,
*... |
Initialization method.
:param field_key: Selector based on the specified value
corresponding to the target key. The target key
corresponding to multi-level field information need to be
separated by '.'.
:param top_ratio: Ratio of selected top samples, sample... | __init__ | python | modelscope/data-juicer | data_juicer/ops/selector/topk_specified_field_selector.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/selector/topk_specified_field_selector.py | Apache-2.0 |
def load_words_asset(words_dir: str, words_type: str):
"""
Load words from a asset file named `words_type`, if not find a valid asset
file, then download it from ASSET_LINKS cached by data_juicer team.
:param words_dir: directory that stores asset file(s)
:param words_type: name of target words ass... |
Load words from a asset file named `words_type`, if not find a valid asset
file, then download it from ASSET_LINKS cached by data_juicer team.
:param words_dir: directory that stores asset file(s)
:param words_type: name of target words assets
:return: a dict that stores words assets, whose keys a... | load_words_asset | python | modelscope/data-juicer | data_juicer/utils/asset_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/asset_utils.py | Apache-2.0 |
def __enter__(self):
"""
Record the original cache state and turn it to the target state.
"""
self.previous_state = is_caching_enabled()
if self.on:
enable_caching()
else:
disable_caching() |
Record the original cache state and turn it to the target state.
| __enter__ | python | modelscope/data-juicer | data_juicer/utils/cache_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/cache_utils.py | Apache-2.0 |
def dataset_cache_control(on):
"""
A more easy-to-use decorator for functions that need to control the cache
state temporarily.
"""
def dataset_cache_decorator(func):
@wraps(func)
def wrapped_function(*args, **kwargs):
with DatasetCacheControl(on=on):
re... |
A more easy-to-use decorator for functions that need to control the cache
state temporarily.
| dataset_cache_control | python | modelscope/data-juicer | data_juicer/utils/cache_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/cache_utils.py | Apache-2.0 |
def __init__(self, ckpt_dir, original_process_list, num_proc=1):
"""
Initialization method.
:param ckpt_dir: path to save and load checkpoint
:param original_process_list: process list in config
:param num_proc: number of process workers when saving dataset
"""
s... |
Initialization method.
:param ckpt_dir: path to save and load checkpoint
:param original_process_list: process list in config
:param num_proc: number of process workers when saving dataset
| __init__ | python | modelscope/data-juicer | data_juicer/utils/ckpt_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/ckpt_utils.py | Apache-2.0 |
def check_ckpt(self):
"""
Check if checkpoint is available.
:return: True when checkpoint is available, else False
"""
if os.path.exists(self.ckpt_ds_dir) \
and os.path.isdir(self.ckpt_ds_dir) \
and os.path.exists(self.ckpt_op_record) \
... |
Check if checkpoint is available.
:return: True when checkpoint is available, else False
| check_ckpt | python | modelscope/data-juicer | data_juicer/utils/ckpt_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/ckpt_utils.py | Apache-2.0 |
def check_ops_to_skip(self):
"""
Check which ops need to be skipped in the process list.
If op record list from checkpoint are the same as the prefix
part of process list, then skip these ops and start processing
from the checkpoint. Otherwise, process the original dataset
... |
Check which ops need to be skipped in the process list.
If op record list from checkpoint are the same as the prefix
part of process list, then skip these ops and start processing
from the checkpoint. Otherwise, process the original dataset
from scratch.
:return: wheth... | check_ops_to_skip | python | modelscope/data-juicer | data_juicer/utils/ckpt_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/ckpt_utils.py | Apache-2.0 |
def save_ckpt(self, ds):
"""
Save dataset to checkpoint directory and dump processed ops list.
:param ds: input dataset to save
"""
left_sample_num = len(ds)
ds.save_to_disk(self.ckpt_ds_dir,
num_proc=min(self.num_proc, left_sample_num))
... |
Save dataset to checkpoint directory and dump processed ops list.
:param ds: input dataset to save
| save_ckpt | python | modelscope/data-juicer | data_juicer/utils/ckpt_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/ckpt_utils.py | Apache-2.0 |
def load_ckpt(self):
"""
Load dataset from a checkpoint file.
:return: a dataset stored in checkpoint file.
"""
from data_juicer.core.data import NestedDataset
ds = NestedDataset.load_from_disk(self.ckpt_ds_dir)
return ds |
Load dataset from a checkpoint file.
:return: a dataset stored in checkpoint file.
| load_ckpt | python | modelscope/data-juicer | data_juicer/utils/ckpt_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/ckpt_utils.py | Apache-2.0 |
def stats_to_number(s, reverse=True):
'''
convert a stats value which can be string
of list to a float.
'''
try:
if isinstance(s, str):
return float(s)
if s is None or s == []:
raise ValueError('empty value')
return float(np.asarray(s).mean())
... |
convert a stats value which can be string
of list to a float.
| stats_to_number | python | modelscope/data-juicer | data_juicer/utils/common_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/common_utils.py | Apache-2.0 |
def dict_to_hash(input_dict: dict, hash_length=None):
"""
hash a dict to a string with length hash_length
:param input_dict: the given dict
"""
sorted_items = sorted(input_dict.items())
dict_string = str(sorted_items).encode()
hasher = hashlib.sha256()
hasher.update(dict_string)... |
hash a dict to a string with length hash_length
:param input_dict: the given dict
| dict_to_hash | python | modelscope/data-juicer | data_juicer/utils/common_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/common_utils.py | Apache-2.0 |
def nested_access(data, path, digit_allowed=True):
"""
Access nested data using a dot-separated path.
:param data: A dictionary or a list to access the nested data from.
:param path: A dot-separated string representing the path to access.
This can include numeric indices when access... |
Access nested data using a dot-separated path.
:param data: A dictionary or a list to access the nested data from.
:param path: A dot-separated string representing the path to access.
This can include numeric indices when accessing list
elements.
:param digit_al... | nested_access | python | modelscope/data-juicer | data_juicer/utils/common_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/common_utils.py | Apache-2.0 |
def avg_split_string_list_under_limit(str_list: list,
token_nums: list,
max_token_num=None):
"""
Split the string list to several sub str_list, such that the total
token num of each sub string list is less than max_token_num... |
Split the string list to several sub str_list, such that the total
token num of each sub string list is less than max_token_num, keeping
the total token nums of sub string lists are similar.
:param str_list: input string list.
:param token_nums: token num of each string list.
... | avg_split_string_list_under_limit | python | modelscope/data-juicer | data_juicer/utils/common_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/common_utils.py | Apache-2.0 |
def extract(
cls,
input_path: Union[Path, str],
output_path: Union[Path, str],
extractor_format: str,
):
"""
Extract content from a compressed file.
:param input_path: path to compressed file.
:param output_path: path to uncompressed file.
:pa... |
Extract content from a compressed file.
:param input_path: path to compressed file.
:param output_path: path to uncompressed file.
:param extractor_format: extraction format,
see supported algorithm in `Extractor` of huggingface dataset.
| extract | python | modelscope/data-juicer | data_juicer/utils/compress.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/compress.py | Apache-2.0 |
def compress(input_path: Union[Path, str], output_path: Union[Path, str]):
"""
Compress input file and save to output file.
:param input_path: path to uncompressed file.
:param output_path: path to compressed file.
"""
import zstandard as zstd
cctx = zstd.ZstdC... |
Compress input file and save to output file.
:param input_path: path to uncompressed file.
:param output_path: path to compressed file.
| compress | python | modelscope/data-juicer | data_juicer/utils/compress.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/compress.py | Apache-2.0 |
def compress(input_path: Union[Path, str], output_path: Union[Path, str]):
"""
Compress a input file and save to output file.
:param input_path: path to uncompressed file.
:param output_path: path to compressed file.
"""
import lz4.frame
with open(input_path, 'r... |
Compress a input file and save to output file.
:param input_path: path to uncompressed file.
:param output_path: path to compressed file.
| compress | python | modelscope/data-juicer | data_juicer/utils/compress.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/compress.py | Apache-2.0 |
def compress(
cls,
input_path: Union[Path, str],
output_path: Union[Path, str],
compressor_format: str,
):
"""
Compress input file and save to output file.
:param input_path: path to uncompressed file.
:param output_path: path to compressed file.
... |
Compress input file and save to output file.
:param input_path: path to uncompressed file.
:param output_path: path to compressed file.
:param compressor_format: compression format,
see supported algorithm in `compressors`.
| compress | python | modelscope/data-juicer | data_juicer/utils/compress.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/compress.py | Apache-2.0 |
def __init__(self, compressor_format: str = 'zstd'):
"""
Initialization method.
:param compressor_format: compression format algorithms,
default `zstd`.
"""
assert compressor_format in Compressor.compressors.keys()
self.compressor_format = compressor_format
... |
Initialization method.
:param compressor_format: compression format algorithms,
default `zstd`.
| __init__ | python | modelscope/data-juicer | data_juicer/utils/compress.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/compress.py | Apache-2.0 |
def decompress(
self,
input_path: Union[Path, str],
output_path: Union[Path, str],
):
"""
Decompress input file and save to output file.
:param input_path: path to compressed file.
:param output_path: path to uncompressed file.
"""
self.extrac... |
Decompress input file and save to output file.
:param input_path: path to compressed file.
:param output_path: path to uncompressed file.
| decompress | python | modelscope/data-juicer | data_juicer/utils/compress.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/compress.py | Apache-2.0 |
def _get_cache_directory(self, ds):
"""
Get dataset cache directory.
:param ds: input dataset.
:return: dataset cache directory.
"""
current_cache_files = [
os.path.abspath(cache_file['filename'])
for cache_file in ds.cache_files
]
... |
Get dataset cache directory.
:param ds: input dataset.
:return: dataset cache directory.
| _get_cache_directory | python | modelscope/data-juicer | data_juicer/utils/compress.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/compress.py | Apache-2.0 |
def _get_cache_file_names(self,
cache_directory: str,
fingerprints: Union[str, List[str]] = None,
extension='.arrow'):
"""
Get all cache files in the dataset cache directory with fingerprints,
which ends wi... |
Get all cache files in the dataset cache directory with fingerprints,
which ends with specified extension.
:param cache_directory: dataset cache directory.
:param fingerprints: fingerprints of cache files. String or List are
accepted. If `None`, we will find all cache files... | _get_cache_file_names | python | modelscope/data-juicer | data_juicer/utils/compress.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/compress.py | Apache-2.0 |
def compress(self,
prev_ds: Dataset,
this_ds: Dataset = None,
num_proc: int = 1):
"""
Compress cache files with fingerprint in dataset cache directory.
:param prev_ds: previous dataset whose cache files need to be
compressed here.
... |
Compress cache files with fingerprint in dataset cache directory.
:param prev_ds: previous dataset whose cache files need to be
compressed here.
:param this_ds: Current dataset that is computed from the previous
dataset. There might be overlaps between cache files of th... | compress | python | modelscope/data-juicer | data_juicer/utils/compress.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/compress.py | Apache-2.0 |
def decompress(self,
ds: Dataset,
fingerprints: Union[str, List[str]] = None,
num_proc: int = 1):
"""
Decompress compressed cache files with fingerprint in
dataset cache directory.
:param ds: input dataset.
:param fingerpr... |
Decompress compressed cache files with fingerprint in
dataset cache directory.
:param ds: input dataset.
:param fingerprints: fingerprints of cache files. String or List are
accepted. If `None`, we will find all cache files which starts with
`cache-` and ends wi... | decompress | python | modelscope/data-juicer | data_juicer/utils/compress.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/compress.py | Apache-2.0 |
def format_cache_file_name(
self, cache_file_name: Optional[str]) -> Optional[str]:
"""
Use `*` to replace the sub rank in a cache file name.
:param cache_file_name: a cache file name.
"""
if not cache_file_name:
return cache_file_name
cache_file... |
Use `*` to replace the sub rank in a cache file name.
:param cache_file_name: a cache file name.
| format_cache_file_name | python | modelscope/data-juicer | data_juicer/utils/compress.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/compress.py | Apache-2.0 |
def cleanup_cache_files(self, ds):
"""
Clean up all compressed cache files in dataset cache directory,
which starts with `cache-` and ends with compression format
:param ds: input dataset.
"""
cache_directory = self._get_cache_directory(ds)
if cache_directory is N... |
Clean up all compressed cache files in dataset cache directory,
which starts with `cache-` and ends with compression format
:param ds: input dataset.
| cleanup_cache_files | python | modelscope/data-juicer | data_juicer/utils/compress.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/compress.py | Apache-2.0 |
def __enter__(self):
"""
Record the original cache compression method and turn it off.
"""
from . import cache_utils
self.original_cache_compress = cache_utils.CACHE_COMPRESS
cache_utils.CACHE_COMPRESS = None |
Record the original cache compression method and turn it off.
| __enter__ | python | modelscope/data-juicer | data_juicer/utils/compress.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/compress.py | Apache-2.0 |
def __exit__(self, exc_type, exc_val, exc_tb):
"""
Restore the original cache compression method.
"""
from . import cache_utils
cache_utils.CACHE_COMPRESS = self.original_cache_compress |
Restore the original cache compression method.
| __exit__ | python | modelscope/data-juicer | data_juicer/utils/compress.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/compress.py | Apache-2.0 |
async def follow_read(
logfile_path: str,
skip_existing_content: bool = False,
) -> AsyncGenerator:
"""Read a file in online and iterative manner
Args:
logfile_path (`str`):
The file path to be read.
skip_existing_content (`bool`, defaults to `False):
If True, re... | Read a file in online and iterative manner
Args:
logfile_path (`str`):
The file path to be read.
skip_existing_content (`bool`, defaults to `False):
If True, read from the end, otherwise read from the beginning.
Returns:
One line string of the file content.
| follow_read | python | modelscope/data-juicer | data_juicer/utils/file_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/file_utils.py | Apache-2.0 |
def find_files_with_suffix(
path: Union[str, Path],
suffixes: Union[str, List[str], None] = None) -> Dict[str, List[str]]:
"""
Traverse a path to find all files with the specified suffixes.
:param path: path (str/Path): source path
:param suffixes: specified file suffixes, '.txt' or ['.... |
Traverse a path to find all files with the specified suffixes.
:param path: path (str/Path): source path
:param suffixes: specified file suffixes, '.txt' or ['.txt', '.md']
etc
:return: list of all files with the specified suffixes
| find_files_with_suffix | python | modelscope/data-juicer | data_juicer/utils/file_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/file_utils.py | Apache-2.0 |
def add_suffix_to_filename(filename, suffix):
"""
Add a suffix to the filename. Only regard the content after the last dot
as the file extension.
E.g.
1. abc.jpg + "_resized" --> abc_resized.jpg
2. edf.xyz.csv + "_processed" --> edf.xyz_processed.csv
3. /path/to/file.json + "_suf" --> /path/... |
Add a suffix to the filename. Only regard the content after the last dot
as the file extension.
E.g.
1. abc.jpg + "_resized" --> abc_resized.jpg
2. edf.xyz.csv + "_processed" --> edf.xyz_processed.csv
3. /path/to/file.json + "_suf" --> /path/to/file_suf.json
4. ds.tar.gz + "_whoops" --> ds.... | add_suffix_to_filename | python | modelscope/data-juicer | data_juicer/utils/file_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/file_utils.py | Apache-2.0 |
def create_directory_if_not_exists(directory_path):
"""
create a directory if not exists, this function is process safe
:param directory_path: directory path to be create
"""
directory_path = os.path.abspath(directory_path)
try:
os.makedirs(directory_path, exist_ok=True)
exc... |
create a directory if not exists, this function is process safe
:param directory_path: directory path to be create
| create_directory_if_not_exists | python | modelscope/data-juicer | data_juicer/utils/file_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/file_utils.py | Apache-2.0 |
def transfer_data_dir(original_dir, op_name):
"""
Transfer the original multimodal data dir to a new dir to store the newly
generated multimodal data. The pattern is
`{original_dir}/__dj__produced_data__/{op_name}`
"""
new_dir = os.path.join(original_dir,
f'{Fields.mul... |
Transfer the original multimodal data dir to a new dir to store the newly
generated multimodal data. The pattern is
`{original_dir}/__dj__produced_data__/{op_name}`
| transfer_data_dir | python | modelscope/data-juicer | data_juicer/utils/file_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/file_utils.py | Apache-2.0 |
def transfer_filename(original_filepath: Union[str, Path], op_name,
**op_kwargs):
"""
According to the op and hashing its parameters 'op_kwargs' addition
to the process id and current time as the 'hash_val', map the
original_filepath to another unique file path. E.g.
... |
According to the op and hashing its parameters 'op_kwargs' addition
to the process id and current time as the 'hash_val', map the
original_filepath to another unique file path. E.g.
1. abc.jpg -->
__dj__produced_data__/{op_name}/
abc__dj_hash_#{hash_... | transfer_filename | python | modelscope/data-juicer | data_juicer/utils/file_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/file_utils.py | Apache-2.0 |
def copy_data(from_dir, to_dir, data_path):
"""
Copy data from from_dir/data_path to to_dir/data_path.
Return True if success.
"""
from_path = os.path.join(from_dir, data_path)
to_path = os.path.join(to_dir, data_path)
if not os.path.exists(from_path):
return False
parent... |
Copy data from from_dir/data_path to to_dir/data_path.
Return True if success.
| copy_data | python | modelscope/data-juicer | data_juicer/utils/file_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/file_utils.py | Apache-2.0 |
def single_partition_write_with_filename(
df: pd.DataFrame,
output_file_dir: str,
keep_filename_column: bool = False,
output_type: str = 'jsonl',
) -> pd.Series:
"""
This function processes a DataFrame and writes it to disk
Args:
df: A DataFrame.
output_file_dir: The output ... |
This function processes a DataFrame and writes it to disk
Args:
df: A DataFrame.
output_file_dir: The output file path.
keep_filename_column: Whether to keep or drop the "filename" column, if it exists.
output_type="jsonl": The type of output file to write.
Returns:
... | single_partition_write_with_filename | python | modelscope/data-juicer | data_juicer/utils/file_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/file_utils.py | Apache-2.0 |
def read_single_partition(
files,
filetype='jsonl',
add_filename=False,
input_meta: Union[str, dict] = None,
columns: Optional[List[str]] = None,
**kwargs,
) -> pd.DataFrame:
"""
This function reads a file with cuDF, sorts the columns of the DataFrame
and adds a "filename" column.
... |
This function reads a file with cuDF, sorts the columns of the DataFrame
and adds a "filename" column.
Args:
files: The path to the jsonl files to read.
add_filename: Whether to add a "filename" column to the DataFrame.
input_meta: A dictionary or a string formatted as a dictionary... | read_single_partition | python | modelscope/data-juicer | data_juicer/utils/file_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/file_utils.py | Apache-2.0 |
def get_all_files_paths_under(root,
recurse_subdirectories=True,
followlinks=False):
"""
This function returns a list of all the files under a specified directory.
Args:
root: The path to the directory to read.
recurse_subdirecties:... |
This function returns a list of all the files under a specified directory.
Args:
root: The path to the directory to read.
recurse_subdirecties: Whether to recurse into subdirectories.
Please note that this can be slow for large
number ... | get_all_files_paths_under | python | modelscope/data-juicer | data_juicer/utils/file_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/file_utils.py | Apache-2.0 |
def update_fingerprint(fingerprint, transform, transform_args):
"""
Combining various objects to update the fingerprint.
"""
hasher = Hasher()
hasher.update(fingerprint)
try:
hasher.update(transform)
except: # noqa various errors might raise here from pickle or dill
if _CAC... |
Combining various objects to update the fingerprint.
| update_fingerprint | python | modelscope/data-juicer | data_juicer/utils/fingerprint_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/fingerprint_utils.py | Apache-2.0 |
def generate_fingerprint(ds, *args, **kwargs):
"""
Generate new fingerprints by using various kwargs of the dataset.
"""
if args:
args = list(args)
dataset_kwargs = {'shard': ds, 'function': args[0]}
else:
dataset_kwargs = {'shard': ds}
dataset_kwargs.update(kwargs)
... |
Generate new fingerprints by using various kwargs of the dataset.
| generate_fingerprint | python | modelscope/data-juicer | data_juicer/utils/fingerprint_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/fingerprint_utils.py | Apache-2.0 |
def get_toml_file_path():
"""Get the path to pyproject.toml file."""
try:
# First try to find it in the installed package data
with importlib.resources.path('py_data_juicer',
'pyproject.toml') as toml_path:
return toml_path
except (ImportErro... | Get the path to pyproject.toml file. | get_toml_file_path | python | modelscope/data-juicer | data_juicer/utils/lazy_loader.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/lazy_loader.py | Apache-2.0 |
def get_uv_lock_path():
"""Get the path to uv.lock file."""
try:
# First try to find it in the installed package data
with importlib.resources.path('py_data_juicer',
'uv.lock') as lock_path:
return lock_path
except (ImportError, FileNotFoundE... | Get the path to uv.lock file. | get_uv_lock_path | python | modelscope/data-juicer | data_juicer/utils/lazy_loader.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/lazy_loader.py | Apache-2.0 |
def get_package_name(cls, module_name: str) -> str:
"""Convert a module name to its corresponding package name.
Args:
module_name: The name of the module (e.g., 'cv2', 'PIL')
Returns:
str: The corresponding package name (e.g., 'opencv-python', 'Pillow')
"""
... | Convert a module name to its corresponding package name.
Args:
module_name: The name of the module (e.g., 'cv2', 'PIL')
Returns:
str: The corresponding package name (e.g., 'opencv-python', 'Pillow')
| get_package_name | python | modelscope/data-juicer | data_juicer/utils/lazy_loader.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/lazy_loader.py | Apache-2.0 |
def get_all_dependencies(cls):
"""
Get all dependencies, prioritizing uv.lock if available.
Falls back to pyproject.toml if uv.lock is not found or fails to parse.
Returns:
dict: A dictionary mapping module names to their full package specifications
e.g. {'n... |
Get all dependencies, prioritizing uv.lock if available.
Falls back to pyproject.toml if uv.lock is not found or fails to parse.
Returns:
dict: A dictionary mapping module names to their full package specifications
e.g. {'numpy': 'numpy>=1.26.4,<2.0.0', 'pandas': '... | get_all_dependencies | python | modelscope/data-juicer | data_juicer/utils/lazy_loader.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/lazy_loader.py | Apache-2.0 |
def check_packages(cls, package_specs, pip_args=None):
"""
Check if packages are installed and install them if needed.
Args:
package_specs: A list of package specifications to check/install.
Can be package names or URLs (e.g., 'torch' or 'git+https://github... |
Check if packages are installed and install them if needed.
Args:
package_specs: A list of package specifications to check/install.
Can be package names or URLs (e.g., 'torch' or 'git+https://github.com/...')
pip_args: Optional list of additional argum... | check_packages | python | modelscope/data-juicer | data_juicer/utils/lazy_loader.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/lazy_loader.py | Apache-2.0 |
def _is_package_installed(package_name):
"""Check if a package is installed by attempting to import it."""
if '@' in package_name:
package_name = package_name.split('@')[0]
if '[' in package_name:
package_name = package_name.split('[')[0]
i... | Check if a package is installed by attempting to import it. | _is_package_installed | python | modelscope/data-juicer | data_juicer/utils/lazy_loader.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/lazy_loader.py | Apache-2.0 |
def __init__(self,
module_name: str,
package_name: str = None,
package_url: str = None,
auto_install: bool = True):
"""
Initialize the LazyLoader.
Args:
module_name: The name of the module to import (e.g., 'cv2', 'r... |
Initialize the LazyLoader.
Args:
module_name: The name of the module to import (e.g., 'cv2', 'ray.data', 'torchvision.models')
package_name: The name of the pip package to install (e.g., 'opencv-python', 'ray', 'torchvision')
If None, will use the base m... | __init__ | python | modelscope/data-juicer | data_juicer/utils/lazy_loader.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/lazy_loader.py | Apache-2.0 |
def _install_package(cls, package_spec, pip_args=None):
"""Install a package using uv if available, otherwise pip."""
# Print trace information for package installation
logger.debug(f'Installing package: {package_spec}')
# Get last 3 frames of the stack trace
stack = traceback.ex... | Install a package using uv if available, otherwise pip. | _install_package | python | modelscope/data-juicer | data_juicer/utils/lazy_loader.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/lazy_loader.py | Apache-2.0 |
def _load(self):
"""Load the module and handle any missing dependencies."""
logger.debug(f'Loading {self._module_name}...')
if self._module is not None:
return self._module
try:
# Try to import the module directly first
self._module = importlib.impor... | Load the module and handle any missing dependencies. | _load | python | modelscope/data-juicer | data_juicer/utils/lazy_loader.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/lazy_loader.py | Apache-2.0 |
def __getattr__(self, item):
"""Handle attribute access, including submodule imports."""
if self._module is None:
self._load()
# Try to get the attribute directly
try:
return getattr(self._module, item)
except AttributeError:
# If not found, t... | Handle attribute access, including submodule imports. | __getattr__ | python | modelscope/data-juicer | data_juicer/utils/lazy_loader.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/lazy_loader.py | Apache-2.0 |
def get_caller_name(depth=0):
"""
Get caller name by depth.
:param depth: depth of caller context, use 0 for caller depth.
:return: module name of the caller
"""
# the following logic is a little bit faster than inspect.stack() logic
frame = inspect.currentframe().f_back
for _ in range(... |
Get caller name by depth.
:param depth: depth of caller context, use 0 for caller depth.
:return: module name of the caller
| get_caller_name | python | modelscope/data-juicer | data_juicer/utils/logger_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/logger_utils.py | Apache-2.0 |
def __init__(self, level='INFO', caller_names=('datasets', 'logging')):
"""
Initialization method.
:param level: log level string of loguru. Default value: "INFO".
:param caller_names: caller names of redirected module.
Default value: (apex, pycocotools).
"""... |
Initialization method.
:param level: log level string of loguru. Default value: "INFO".
:param caller_names: caller names of redirected module.
Default value: (apex, pycocotools).
| __init__ | python | modelscope/data-juicer | data_juicer/utils/logger_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/logger_utils.py | Apache-2.0 |
def redirect_sys_output(log_level='INFO'):
"""
Redirect stdout/stderr to loguru with log level.
:param log_level: log level string of loguru. Default value: "INFO".
"""
redirect_logger = StreamToLoguru(level=log_level)
sys.stderr = redirect_logger
sys.stdout = redirect_logger |
Redirect stdout/stderr to loguru with log level.
:param log_level: log level string of loguru. Default value: "INFO".
| redirect_sys_output | python | modelscope/data-juicer | data_juicer/utils/logger_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/logger_utils.py | Apache-2.0 |
def get_log_file_path():
"""
Get the path to the location of the log file.
:return: a location of log file.
"""
for _, handler in logger._core.handlers.items():
if isinstance(handler._sink, FileSink):
return handler._sink._file.name |
Get the path to the location of the log file.
:return: a location of log file.
| get_log_file_path | python | modelscope/data-juicer | data_juicer/utils/logger_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/logger_utils.py | Apache-2.0 |
def setup_logger(save_dir,
distributed_rank=0,
filename='log.txt',
mode='o',
level='INFO',
redirect=True):
"""
Setup logger for training and testing.
:param save_dir: location to save log file
:param distributed_rank: ... |
Setup logger for training and testing.
:param save_dir: location to save log file
:param distributed_rank: device rank when multi-gpu environment
:param filename: log file name to save
:param mode: log file write mode, `append` or `override`. default is `o`.
:param level: log severity level. I... | setup_logger | python | modelscope/data-juicer | data_juicer/utils/logger_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/logger_utils.py | Apache-2.0 |
def __enter__(self):
"""
Store the original standard output and redirect the standard output to
null when entering this range.
"""
self._original_stdout = sys.stdout
sys.stdout = open(os.devnull, 'w') |
Store the original standard output and redirect the standard output to
null when entering this range.
| __enter__ | python | modelscope/data-juicer | data_juicer/utils/logger_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/logger_utils.py | Apache-2.0 |
def __exit__(self, exc_type, exc_val, exc_tb):
"""
Close the redirected standard output and restore it when exiting from
this range.
"""
sys.stdout.close()
sys.stdout = self._original_stdout |
Close the redirected standard output and restore it when exiting from
this range.
| __exit__ | python | modelscope/data-juicer | data_juicer/utils/logger_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/logger_utils.py | Apache-2.0 |
def load_data_with_context(sample, context, loaded_data_keys, load_func):
"""
The unified loading function with contexts for multimodal data.
"""
data = {}
for loaded_data_key in loaded_data_keys:
if context and loaded_data_key in sample[Fields.context]:
# load from context
... |
The unified loading function with contexts for multimodal data.
| load_data_with_context | python | modelscope/data-juicer | data_juicer/utils/mm_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/mm_utils.py | Apache-2.0 |
def calculate_resized_dimensions(
original_size: Tuple[PositiveInt, PositiveInt],
target_size: Union[PositiveInt, Tuple[PositiveInt, PositiveInt]],
max_length: Optional[int] = None,
divisible: PositiveInt = 1) -> Tuple[int, int]:
"""
Resize dimensions based on specified constrain... |
Resize dimensions based on specified constraints.
:param original_size: The original dimensions as (height, width).
:param target_size: Desired target size; can be a single integer
(short edge) or a tuple (height, width).
:param max_length: Maximum allowed length for the longer edge.
:para... | calculate_resized_dimensions | python | modelscope/data-juicer | data_juicer/utils/mm_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/mm_utils.py | Apache-2.0 |
def load_video(path, mode='r'):
"""
Load a video using its path.
:param path: the path to this video.
:param mode: the loading mode. It's "r" in default.
:return: a container object form PyAv library, which contains all streams
in this video (video/audio/...) and can be used to decode these... |
Load a video using its path.
:param path: the path to this video.
:param mode: the loading mode. It's "r" in default.
:return: a container object form PyAv library, which contains all streams
in this video (video/audio/...) and can be used to decode these streams
to frames.
| load_video | python | modelscope/data-juicer | data_juicer/utils/mm_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/mm_utils.py | Apache-2.0 |
def get_video_duration(input_video: Union[str, av.container.InputContainer],
video_stream_index: int = 0):
"""
Get the video's duration from the container
:param input_video: the container object form PyAv library, which
contains all streams in this video (video/audio/...) an... |
Get the video's duration from the container
:param input_video: the container object form PyAv library, which
contains all streams in this video (video/audio/...) and can be used
to decode these streams to frames.
:param video_stream_index: the video stream index to decode,
default... | get_video_duration | python | modelscope/data-juicer | data_juicer/utils/mm_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/mm_utils.py | Apache-2.0 |
def get_decoded_frames_from_video(
input_video: Union[str, av.container.InputContainer],
video_stream_index: int = 0):
"""
Get the video's frames from the container
:param input_video: the container object form PyAv library, which
contains all streams in this video (video/audio/...)... |
Get the video's frames from the container
:param input_video: the container object form PyAv library, which
contains all streams in this video (video/audio/...) and can be used
to decode these streams to frames.
:param video_stream_index: the video stream index to decode,
default s... | get_decoded_frames_from_video | python | modelscope/data-juicer | data_juicer/utils/mm_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/mm_utils.py | Apache-2.0 |
def cut_video_by_seconds(
input_video: Union[str, av.container.InputContainer],
output_video: str,
start_seconds: float,
end_seconds: Optional[float] = None,
):
"""
Cut a video into several segments by times in second.
:param input_video: the path to input video or the video container.
... |
Cut a video into several segments by times in second.
:param input_video: the path to input video or the video container.
:param output_video: the path to output video.
:param start_seconds: the start time in second.
:param end_seconds: the end time in second. If it's None, this function
w... | cut_video_by_seconds | python | modelscope/data-juicer | data_juicer/utils/mm_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/mm_utils.py | Apache-2.0 |
def process_each_frame(input_video: Union[str, av.container.InputContainer],
output_video: str, frame_func):
"""
Process each frame in video by replacing each frame by
`frame_func(frame)`.
:param input_video: the path to input video or the video container.
:param output_video... |
Process each frame in video by replacing each frame by
`frame_func(frame)`.
:param input_video: the path to input video or the video container.
:param output_video: the path to output video.
:param frame_func: a function which inputs a frame and outputs another
frame.
| process_each_frame | python | modelscope/data-juicer | data_juicer/utils/mm_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/mm_utils.py | Apache-2.0 |
def extract_key_frames_by_seconds(
input_video: Union[str, av.container.InputContainer],
duration: float = 1):
"""Extract key frames by seconds.
:param input_video: input video path or av.container.InputContainer.
:param duration: duration of each video split in seconds.
"""
... | Extract key frames by seconds.
:param input_video: input video path or av.container.InputContainer.
:param duration: duration of each video split in seconds.
| extract_key_frames_by_seconds | python | modelscope/data-juicer | data_juicer/utils/mm_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/mm_utils.py | Apache-2.0 |
def extract_key_frames(input_video: Union[str, av.container.InputContainer]):
"""
Extract key frames from the input video. If there is no keyframes in the
video, return the first frame.
:param input_video: input video path or container.
:return: a list of key frames.
"""
# load the input vi... |
Extract key frames from the input video. If there is no keyframes in the
video, return the first frame.
:param input_video: input video path or container.
:return: a list of key frames.
| extract_key_frames | python | modelscope/data-juicer | data_juicer/utils/mm_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/mm_utils.py | Apache-2.0 |
def get_key_frame_seconds(input_video: Union[str,
av.container.InputContainer]):
"""
Get seconds of key frames in the input video.
"""
key_frames = extract_key_frames(input_video)
ts = [float(f.pts * f.time_base) for f in key_frames]
ts.sort()
ret... |
Get seconds of key frames in the input video.
| get_key_frame_seconds | python | modelscope/data-juicer | data_juicer/utils/mm_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/mm_utils.py | Apache-2.0 |
def extract_video_frames_uniformly_by_seconds(
input_video: Union[str, av.container.InputContainer],
frame_num: PositiveInt,
duration: float = 1):
"""Extract video frames uniformly by seconds.
:param input_video: input video path or av.container.InputContainer.
:param frame_n... | Extract video frames uniformly by seconds.
:param input_video: input video path or av.container.InputContainer.
:param frame_num: the number of frames to be extracted uniformly from
each video split by duration.
:param duration: duration of each video split in seconds.
| extract_video_frames_uniformly_by_seconds | python | modelscope/data-juicer | data_juicer/utils/mm_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/mm_utils.py | Apache-2.0 |
def extract_video_frames_uniformly(
input_video: Union[str, av.container.InputContainer],
frame_num: PositiveInt,
):
"""
Extract a number of video frames uniformly within the video duration.
:param input_video: input video path or container.
:param frame_num: The number of frames to be extracte... |
Extract a number of video frames uniformly within the video duration.
:param input_video: input video path or container.
:param frame_num: The number of frames to be extracted. If it's 1, only the
middle frame will be extracted. If it's 2, only the first and the last
frames will be extract... | extract_video_frames_uniformly | python | modelscope/data-juicer | data_juicer/utils/mm_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/mm_utils.py | Apache-2.0 |
def extract_audio_from_video(
input_video: Union[str, av.container.InputContainer],
output_audio: Optional[str] = None,
start_seconds: int = 0,
end_seconds: Optional[int] = None,
stream_indexes: Union[int, List[int], None] = None,
):
"""
Extract audio data for the given video.
:param in... |
Extract audio data for the given video.
:param input_video: input video. Can be a video path or an
av.container.InputContainer.
:param output_audio: output audio path. If it's None, the audio data won't
be written to file. If stream_indexes is not None, it will output
multiple audi... | extract_audio_from_video | python | modelscope/data-juicer | data_juicer/utils/mm_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/mm_utils.py | Apache-2.0 |
def timecode_string_to_seconds(timecode: str):
"""
Convert a timecode string to the float seconds.
:param timecode: the input timecode string. Must in "HH:MM:SS.fff(fff)"
format.
"""
# parse the timecode string
dt = datetime.datetime.strptime(timecode, '%H:%M:%S.%f')
# compute the ... |
Convert a timecode string to the float seconds.
:param timecode: the input timecode string. Must in "HH:MM:SS.fff(fff)"
format.
| timecode_string_to_seconds | python | modelscope/data-juicer | data_juicer/utils/mm_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/mm_utils.py | Apache-2.0 |
def parse_string_to_roi(roi_string, roi_type='pixel'):
"""
Convert a roi string to four number x1, y1, x2, y2 stand for the region.
When the type is 'pixel', (x1, y1), (x2, y2) are the locations of pixels
in the top left corner and the bottom right corner respectively. If the
roi_type is 'ratio', th... |
Convert a roi string to four number x1, y1, x2, y2 stand for the region.
When the type is 'pixel', (x1, y1), (x2, y2) are the locations of pixels
in the top left corner and the bottom right corner respectively. If the
roi_type is 'ratio', the coordinates are normalized by widths and
heights.
:... | parse_string_to_roi | python | modelscope/data-juicer | data_juicer/utils/mm_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/mm_utils.py | Apache-2.0 |
def check_model(model_name, force=False):
"""
Check whether a model exists in DATA_JUICER_MODELS_CACHE.
If exists, return its full path.
Else, download it from cached models links.
:param model_name: a specified model name
:param force: Whether to download model forcefully or not, Sometimes
... |
Check whether a model exists in DATA_JUICER_MODELS_CACHE.
If exists, return its full path.
Else, download it from cached models links.
:param model_name: a specified model name
:param force: Whether to download model forcefully or not, Sometimes
the model file maybe incomplete for some rea... | check_model | python | modelscope/data-juicer | data_juicer/utils/model_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/model_utils.py | Apache-2.0 |
def filter_arguments(func, args_dict):
"""
Filters and returns only the valid arguments for a given function
signature.
:param func: The function or callable to inspect.
:param args_dict: A dictionary of argument names and values to filter.
:return: A dictionary containing only the arguments th... |
Filters and returns only the valid arguments for a given function
signature.
:param func: The function or callable to inspect.
:param args_dict: A dictionary of argument names and values to filter.
:return: A dictionary containing only the arguments that match the
function's signat... | filter_arguments | python | modelscope/data-juicer | data_juicer/utils/model_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/model_utils.py | Apache-2.0 |
def __init__(self, model, endpoint=None, response_path=None, **kwargs):
"""
Initializes an instance of the APIModel class.
:param model: The name of the model to be used for making API
calls. This should correspond to a valid model identifier
recognized by the API server... |
Initializes an instance of the APIModel class.
:param model: The name of the model to be used for making API
calls. This should correspond to a valid model identifier
recognized by the API server.
:param endpoint: The URL endpoint for the API. If provided as a
... | __init__ | python | modelscope/data-juicer | data_juicer/utils/model_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/model_utils.py | Apache-2.0 |
def __call__(self, messages, **kwargs):
"""
Sends messages to the configured API model and returns the parsed
response content.
:param messages: A list of message dictionaries to send to the API.
Each message should have a 'role' (e.g., 'user',
... |
Sends messages to the configured API model and returns the parsed
response content.
:param messages: A list of message dictionaries to send to the API.
Each message should have a 'role' (e.g., 'user',
'assistant') and 'content' (the message tex... | __call__ | python | modelscope/data-juicer | data_juicer/utils/model_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/model_utils.py | Apache-2.0 |
def __init__(self, model, endpoint=None, response_path=None, **kwargs):
"""
Initializes an instance specialized for embedding APIs.
:param model: The model identifier for embedding API calls.
:param endpoint: API endpoint URL. Defaults to '/embeddings'.
:param response_path: Pat... |
Initializes an instance specialized for embedding APIs.
:param model: The model identifier for embedding API calls.
:param endpoint: API endpoint URL. Defaults to '/embeddings'.
:param response_path: Path to extract embeddings from response.
Defaults to 'data.0.embedding'.
... | __init__ | python | modelscope/data-juicer | data_juicer/utils/model_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/model_utils.py | Apache-2.0 |
def __call__(self, input, **kwargs):
"""
Processes input text and returns embeddings.
:param input: Input text or list of texts to embed.
:param kwargs: Additional API parameters.
:return: Extracted embeddings or empty list on error.
"""
body = {
'mod... |
Processes input text and returns embeddings.
:param input: Input text or list of texts to embed.
:param kwargs: Additional API parameters.
:return: Extracted embeddings or empty list on error.
| __call__ | python | modelscope/data-juicer | data_juicer/utils/model_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/model_utils.py | Apache-2.0 |
def prepare_api_model(model,
*,
endpoint=None,
response_path=None,
return_processor=False,
processor_config=None,
**model_params):
"""Creates a callable API model for interacting with ... | Creates a callable API model for interacting with OpenAI-compatible API.
The callable supports custom response parsing and works with proxy servers
that may be incompatible.
:param model: The name of the model to interact with.
:param endpoint: The URL endpoint for the API. If provided as a relative
... | prepare_api_model | python | modelscope/data-juicer | data_juicer/utils/model_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/model_utils.py | Apache-2.0 |
def prepare_diffusion_model(pretrained_model_name_or_path, diffusion_type,
**model_params):
"""
Prepare and load an Diffusion model from HuggingFace.
:param pretrained_model_name_or_path: input Diffusion model name
or local path to the model
:param diffusion_type: th... |
Prepare and load an Diffusion model from HuggingFace.
:param pretrained_model_name_or_path: input Diffusion model name
or local path to the model
:param diffusion_type: the use of the diffusion model. It can be
'image2image', 'text2image', 'inpainting'
:return: a Diffusion model.
| prepare_diffusion_model | python | modelscope/data-juicer | data_juicer/utils/model_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/model_utils.py | Apache-2.0 |
def prepare_fasttext_model(model_name='lid.176.bin', **model_params):
"""
Prepare and load a fasttext model.
:param model_name: input model name
:return: model instance.
"""
logger.info('Loading fasttext language identification model...')
try:
# Suppress FastText warnings by redirec... |
Prepare and load a fasttext model.
:param model_name: input model name
:return: model instance.
| prepare_fasttext_model | python | modelscope/data-juicer | data_juicer/utils/model_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/model_utils.py | Apache-2.0 |
def prepare_huggingface_model(pretrained_model_name_or_path,
*,
return_model=True,
return_pipe=False,
pipe_task='text-generation',
**model_params):
"""
Prepare an... |
Prepare and load a huggingface model.
:param pretrained_model_name_or_path: model name or path
:param return_model: return model or not
:param return_pipe: return pipeline or not
:param pipe_task: task for pipeline
:return: a tuple (model, processor) if `return_model` is True;
otherwis... | prepare_huggingface_model | python | modelscope/data-juicer | data_juicer/utils/model_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/model_utils.py | Apache-2.0 |
def prepare_kenlm_model(lang, name_pattern='{}.arpa.bin', **model_params):
"""
Prepare and load a kenlm model.
:param model_name: input model name in formatting syntax.
:param lang: language to render model name
:return: model instance.
"""
model_params.pop('device', None)
model_name =... |
Prepare and load a kenlm model.
:param model_name: input model name in formatting syntax.
:param lang: language to render model name
:return: model instance.
| prepare_kenlm_model | python | modelscope/data-juicer | data_juicer/utils/model_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/model_utils.py | Apache-2.0 |
def prepare_nltk_model(lang, name_pattern='punkt.{}.pickle', **model_params):
"""
Prepare and load a nltk punkt model with enhanced resource handling.
:param model_name: input model name in formatting syntax
:param lang: language to render model name
:return: model instance.
"""
model_param... |
Prepare and load a nltk punkt model with enhanced resource handling.
:param model_name: input model name in formatting syntax
:param lang: language to render model name
:return: model instance.
| prepare_nltk_model | python | modelscope/data-juicer | data_juicer/utils/model_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/model_utils.py | Apache-2.0 |
def prepare_nltk_pos_tagger(**model_params):
"""
Prepare and load NLTK's part-of-speech tagger with enhanced resource
handling.
:return: The POS tagger model
"""
model_params.pop('device', None)
# Ensure pickle security is patched
patch_nltk_pickle_security()
logger.info('Loadin... |
Prepare and load NLTK's part-of-speech tagger with enhanced resource
handling.
:return: The POS tagger model
| prepare_nltk_pos_tagger | python | modelscope/data-juicer | data_juicer/utils/model_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/model_utils.py | Apache-2.0 |
def prepare_recognizeAnything_model(
pretrained_model_name_or_path='ram_plus_swin_large_14m.pth',
input_size=384,
**model_params):
"""
Prepare and load recognizeAnything model.
:param model_name: input model name.
:param input_size: the input size of the model.
"""
logge... |
Prepare and load recognizeAnything model.
:param model_name: input model name.
:param input_size: the input size of the model.
| prepare_recognizeAnything_model | python | modelscope/data-juicer | data_juicer/utils/model_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/model_utils.py | Apache-2.0 |
def prepare_sentencepiece_model(model_path, **model_params):
"""
Prepare and load a sentencepiece model.
:param model_path: input model path
:return: model instance
"""
logger.info('Loading sentencepiece model...')
sentencepiece_model = sentencepiece.SentencePieceProcessor()
try:
... |
Prepare and load a sentencepiece model.
:param model_path: input model path
:return: model instance
| prepare_sentencepiece_model | python | modelscope/data-juicer | data_juicer/utils/model_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/model_utils.py | Apache-2.0 |
def prepare_sentencepiece_for_lang(lang,
name_pattern='{}.sp.model',
**model_params):
"""
Prepare and load a sentencepiece model for specific language.
:param lang: language to render model name
:param name_pattern: pattern to render... |
Prepare and load a sentencepiece model for specific language.
:param lang: language to render model name
:param name_pattern: pattern to render the model name
:return: model instance.
| prepare_sentencepiece_for_lang | python | modelscope/data-juicer | data_juicer/utils/model_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/model_utils.py | Apache-2.0 |
def prepare_simple_aesthetics_model(pretrained_model_name_or_path,
*,
return_model=True,
**model_params):
"""
Prepare and load a simple aesthetics model.
:param pretrained_model_name_or_path: model n... |
Prepare and load a simple aesthetics model.
:param pretrained_model_name_or_path: model name or path
:param return_model: return model or not
:return: a tuple (model, input processor) if `return_model` is True;
otherwise, only the processor is returned.
| prepare_simple_aesthetics_model | python | modelscope/data-juicer | data_juicer/utils/model_utils.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/utils/model_utils.py | Apache-2.0 |
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