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GeezerVison is showing github.com — docarray--docarray/docarray/helper.py at main · YvanKOB/docarray--docarray

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import glob
import itertools
import os
import re
from types import LambdaType
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Generator,
List,
Optional,
Type,
Union,
)
import numpy as np
from docarray.utils._internal._typing import safe_issubclass
from docarray.utils._internal.misc import (
is_jax_available,
is_tf_available,
is_torch_available,
)
if is_torch_available():
import torch
if is_jax_available():
import jax
if is_tf_available():
import tensorflow as tf
if TYPE_CHECKING:
from docarray import BaseDoc
def _is_access_path_valid(doc_type: Type['BaseDoc'], access_path: str) -> bool:
"""
Check if a given access path ("__"-separated) is a valid path for a given Document class.
"""
field_type = _get_field_annotation_by_access_path(doc_type, access_path)
return field_type is not None
def _all_access_paths_valid(
doc_type: Type['BaseDoc'], access_paths: List[str]
) -> List[bool]:
"""
Check if all access paths ("__"-separated) are valid for a given Document class.
"""
return [_is_access_path_valid(doc_type, path) for path in access_paths]
def _access_path_to_dict(access_path: str, value) -> Dict[str, Any]:
"""
Convert an access path ("__"-separated) and its value to a (potentially) nested dict.
```python
assert access_path_to_dict('image__url', 'img.png') == {'image': {'url': 'img.png'}}
```
"""
fields = access_path.split('__')
for field in reversed(fields):
result = {field: value}
value = result
return result
def _is_none_like(val: Any) -> bool:
"""
:param val: any value
:return: true iff `val` equals to `None`, `'None'` or `''`
"""
# Convoluted implementation, but fixes https://github.com/docarray/docarray/issues/1821
# tensor-like types can have unexpected (= broadcast) `==`/`in` semantics,
# so treat separately
is_np_arr = isinstance(val, np.ndarray)
if is_np_arr:
return False
is_torch_tens = is_torch_available() and isinstance(val, torch.Tensor)
if is_torch_tens:
return False
is_tf_tens = is_tf_available() and isinstance(val, tf.Tensor)
if is_tf_tens:
return False
is_jax_arr = is_jax_available() and isinstance(val, jax.numpy.ndarray)
if is_jax_arr:
return False
# "normal" case
return val in ['', 'None', None]
def _access_path_dict_to_nested_dict(access_path2val: Dict[str, Any]) -> Dict[Any, Any]:
"""
Convert a dict, where the keys are access paths ("__"-separated) to a nested dictionary.
---
```python
access_path2val = {'image__url': 'some.png'}
assert access_path_dict_to_nested_dict(access_path2val) == {
'image': {'url': 'some.png'}
}
```
---
:param access_path2val: dict with access_paths as keys
:return: nested dict where the access path keys are split into separate field names and nested keys
"""
nested_dict: Dict[Any, Any] = {}
for access_path, value in access_path2val.items():
field2val = _access_path_to_dict(
access_path=access_path,
value=None if _is_none_like(value) else value,
)
_update_nested_dicts(to_update=nested_dict, update_with=field2val)
return nested_dict
def _dict_to_access_paths(d: dict) -> Dict[str, Any]:
"""
Convert a (nested) dict to a Dict[access_path, value].
Access paths are defined as a path of field(s) separated by "__".
```python
assert dict_to_access_paths({'image': {'url': 'img.png'}}) == {'image__url', 'img.png'}
```
"""
result = {}
for k, v in d.items():
if isinstance(v, dict):
v = _dict_to_access_paths(v)
for nested_k, nested_v in v.items():
new_key = '__'.join([k, nested_k])
result[new_key] = nested_v
else:
result[k] = v
return result
def _update_nested_dicts(
to_update: Dict[Any, Any], update_with: Dict[Any, Any]
) -> None:
"""
Update a dict with another one, while considering shared nested keys.
```python
d1 = {'image': {'tensor': None}, 'title': 'hello'}
d2 = {'image': {'url': 'some.png'}}
update_nested_dicts(d1, d2)
assert d1 == {'image': {'tensor': None, 'url': 'some.png'}, 'title': 'hello'}
```
:param to_update: dict that should be updated
:param update_with: dict to update with
:return: merged dict
"""
for k, v in update_with.items():
if k not in to_update.keys():
to_update[k] = v
else:
_update_nested_dicts(to_update[k], update_with[k])
def _get_field_annotation_by_access_path(
doc_type: Type['BaseDoc'], access_path: str
) -> Optional[Type]:
"""
Get field type by "__"-separated access path.
:param doc_type: type of document
:param access_path: "__"-separated access path
:return: field type of accessed attribute. If access path is invalid, return None.
"""
from docarray import BaseDoc, DocList
field, _, remaining = access_path.partition('__')
field_valid = field in doc_type._docarray_fields().keys()
if field_valid:
if len(remaining) == 0:
return doc_type._get_field_annotation(field)
else:
d = doc_type._get_field_annotation(field)
if safe_issubclass(d, DocList):
return _get_field_annotation_by_access_path(d.doc_type, remaining)
elif safe_issubclass(d, BaseDoc):
return _get_field_annotation_by_access_path(d, remaining)
else:
return None
else:
return None
def _is_lambda_or_partial_or_local_function(func: Callable[[Any], Any]) -> bool:
"""
Return True if `func` is lambda, local or partial function, else False.
"""
return (
(isinstance(func, LambdaType) and func.__name__ == '<lambda>')
or not hasattr(func, '__qualname__')
or ('<locals>' in func.__qualname__)
)
def get_paths(
patterns: Union[str, List[str]],
recursive: bool = True,
size: Optional[int] = None,
exclude_regex: Optional[str] = None,
) -> Generator[str, None, None]:
"""
Yield file paths described by `patterns`.
---
```python
from typing import Optional
from docarray import BaseDoc, DocList
from docarray.helper import get_paths
from docarray.typing import TextUrl, ImageUrl
class Banner(BaseDoc):
text_url: TextUrl
image_url: Optional[ImageUrl]
# you can call it in the constructor
docs = DocList[Banner]([Banner(text_url=url) for url in get_paths(patterns='*.txt')])
# and call it after construction to set the urls
docs.image_url = list(get_paths(patterns='*.jpg', exclude_regex='test'))
for doc in docs:
assert doc.image_url.endswith('.txt')
assert doc.text_url.endswith('.jpg')
```
---
:param patterns: The pattern may contain simple shell-style wildcards,
e.g. '\*.py', '[\*.zip, \*.gz]'
:param recursive: If recursive is true, the pattern '**' will match any
files and zero or more directories and subdirectories
:param size: the maximum number of the files
:param exclude_regex: if set, then filenames that match to this pattern
are not included.
:yield: file paths
"""
if isinstance(patterns, str):
patterns = [patterns]
regex_to_exclude = None
if exclude_regex:
try:
regex_to_exclude = re.compile(exclude_regex)
except re.error:
raise ValueError(f'`{exclude_regex}` is not a valid regex.')
def _iter_file_extensions(ps):
return itertools.chain.from_iterable(
glob.iglob(os.path.expanduser(p), recursive=recursive) for p in ps
)
num_docs = 0
for file_path in _iter_file_extensions(patterns):
if regex_to_exclude and regex_to_exclude.match(file_path):
continue
yield file_path
num_docs += 1
if size is not None and num_docs >= size:
break
def _shallow_copy_doc(doc):
return doc.__class__._shallow_copy(doc)
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