mirror of
https://github.com/instaloader/instaloader.git
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Merge branch 'master' into upcoming/v4.16
This commit is contained in:
@@ -20,5 +20,8 @@ describe:
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- Further considerations
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- Is it based on master (bugfix), upcoming/v4.X (new feature), or another pull request?
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- Does it preserve backward compatibility and existing behavior?
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- If generative AI was involved, ensure it follows our AI policy.
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(also see https://instaloader.github.io/contributing.html)
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-->
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@@ -1,6 +1,7 @@
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The MIT License (MIT)
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Copyright (c) 2016-2020 Alexander Graf and André Koch-Kramer.
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Copyright (c) 2016-2026 Alexander Graf, André Koch-Kramer and the Instaloader
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contributors.
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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+1
-1
@@ -15,7 +15,7 @@ sys.path.insert(0, os.path.abspath('..'))
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# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
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project = 'Instaloader'
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copyright = '2023, Alexander Graf and Andre Koch-Kramer'
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copyright = '2016-2026, Alexander Graf, Andre Koch-Kramer and the Instaloader contributors'
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author = 'Alexander Graf and Andre Koch-Kramer'
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# -- General configuration ---------------------------------------------------
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+14
-1
@@ -80,7 +80,7 @@ Improvements of the Instaloader source or its documentation can be proposed as a
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review, validate, or selectively merge them.
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- Be mindful of backward compatibility: changes should preserve existing intended
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behavior whenever possible.
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behavior whenever feasible.
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- All Pull Requests are analyzed by `Pylint <https://www.pylint.org/>`__ for
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error and syntax checking of the source and
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@@ -100,6 +100,19 @@ Improvements of the Instaloader source or its documentation can be proposed as a
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agrees that the change might be an improvement, or if you want to discuss
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your basic proposal, before working on a pull request.
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AI Policy
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---------
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- If generative AI was used to create notable portions of a contribution, the
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author must disclose which parts were AI-assisted (e.g. research, testing,
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coding, or documentation).
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- The author must carefully review and understand the contribution before
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submitting it.
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- If the maintainers need more effort to review the contribution than it took
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to create it, please do not submit it.
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Proposing Features
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------------------
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@@ -104,6 +104,7 @@ class NodeIterator(Iterator[T]):
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else:
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self._data = self._query()
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self._first_node: Optional[Dict] = None
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self._first_item: Optional[T] = None
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self._is_first = is_first
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def _query(self, after: Optional[str] = None) -> Dict:
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@@ -156,10 +157,10 @@ class NodeIterator(Iterator[T]):
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item = self._node_wrapper(node)
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if self._is_first is not None:
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if self._is_first(item, self.first_item):
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self._first_node = node
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self._first_node, self._first_item = node, item
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else:
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if self._first_node is None:
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self._first_node = node
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self._first_node, self._first_item = node, item
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return item
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if self._data.get('page_info', {}).get('has_next_page'):
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query_response = self._query(self._data['page_info']['end_cursor'])
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@@ -206,7 +207,15 @@ class NodeIterator(Iterator[T]):
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.. versionchanged:: 4.9.2
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What is considered the first item can be overridden.
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"""
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return self._node_wrapper(self._first_node) if self._first_node is not None else None
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if self._first_item is None:
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# Restore _first_item from _first_node. Needed after thawing, since
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# FrozenNodeIterator stores the node, not the item.
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self._first_item = (
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self._node_wrapper(self._first_node)
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if self._first_node is not None
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else None
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)
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return self._first_item
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@staticmethod
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def page_length() -> int:
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@@ -881,19 +881,9 @@ class Post:
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if self.likes == 0:
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# Avoid doing additional requests if there are no comments
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return
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likes_edges = self._field('edge_media_preview_like', 'edges')
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if self.likes == len(likes_edges):
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# If the Post's metadata already contains all likes, don't do GraphQL requests to obtain them
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yield from (Profile(self._context, like['node']) for like in likes_edges)
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return
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yield from NodeIterator(
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self._context,
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'1cb6ec562846122743b61e492c85999f',
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lambda d: d['data']['shortcode_media']['edge_liked_by'],
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lambda n: Profile(self._context, n),
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{'shortcode': self.shortcode},
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'https://www.instagram.com/p/{0}/'.format(self.shortcode),
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)
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data = self._context.get_json(f"api/v1/media/{self.mediaid}/likers/", {})
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for user in data["users"]:
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yield Profile(self._context, user)
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@property
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def is_sponsored(self) -> bool:
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