When setting an account to an account.bank.statement there is a step to automatically create a reconciliation rule/model if one does not exist already. To do so, we retrieve 5 account.bank.statement.lines and use them to define the reconciliation model config. A matching is done on the payment_ref of the account.bank.statement.lines by finding the longest common substring in the reference.

### Current Implementation
The current algorithm does so by first generating all the possible substrings for all the payment_ref before doing the intersection between these sets and returning the max if `len(substring) >=10`. This is reasonable when the payment_ref follows either a SEPA communication national standard like the Belgian one or the Creditor Reference standard (ISO 11649). For transactions with large, unstructured communication with more than 100 chars, the method `_get_common_substrings` quickly overfill the memory, sometimes raising a MemoryErorr, and takes a significant amount of time. That's because the nested function `_generate_all_substrings` generates n*(n+1)/2 substrings, with n being the lenght of a payment_ref, called `label` in `generate_all_substrings`.

### Proposed Fix
This commit introduces another algorithm to find the largest common substring. It starts by taking the two smallest labels to find their substrings intersection. We know that for an arbitrary collection of labels, the intersection of their substrings sets A ∩ B ∩...∩ Z is included in the intersection of any two substrings sets. The underlying assumption of the first step is that for an arbitrary collection of labels the intersection of the substrings sets of the two smallest labels will be the smallest intersection of any given pair of substrings sets. This won't hold true everytime and using a metric such as label similarity instead of shortest string might be better. But on average this should be good enough and it's easier to implement + it removes the need of preprocessing the labels to compute the similarity.

The point of the new nested function `common_substrings` is to discard common substrings as we build them. Using the current `generate_all_substsrings` on either the smallest label or both smallest
labels would still generate and store a lot of substrings, especially for large labels. By yielding the common substrings as we find them, the memory footprint is vastly reduced.

Lastly, the next substring in the common_substrings iterable is only checked against the remaining labels if it's longer than the current match. This speeds up the whole process

### speedup

In a customer database with some account.bank.statement.line with payment_ref > 500 chars, setting a specific account (code 4970) on transactions goes from MemoryError to < 1Mb memory consumption.

Because of the memory consumption it was not possible to gather timing value on the current version. 
Testing the new algorithm in a shell and using as labels the 5 longest payment_ref in the customer database
(831, 831, 1117, 1178, 1300 chars), averaging to 2000 chars once normalised, the average time to execute `_get_common_substrings` is 900 ms ± 10.3 ms.

Forward-Port-Of: odoo/enterprise#121575
Forward-Port-Of: odoo/enterprise#118824

Before this commit, contains() and its variants gave the client 3 seconds, and the bus helpers 2 seconds. The problem is that the first wait after openDiscuss pays for the whole mount, /mail/data and /discuss/channel/messages. Measured from openDiscuss resolving to the message being in the DOM:

- 250 to 460ms on an idle machine;
- 867 to 5258ms over 10 runs with the CPU throttled 4x, which is what a
  busy runbot looks like, 3 of the 10 over 2 seconds;
- 1474 to 6912ms with the CPU throttled 6x, 5 of 6 over 3 seconds.

"Reactions are ordered by id" fails 1 run in 60 at 4x for that reason.

Note that a longer timeout costs nothing on a green build: the timer is cleared as soon as the element is there, so it only delays the report of a test that was going to fail anyway.

This commit raises both to 10 seconds, the delay a tour step already gets in macro.js. test_js.py runs the presets with timeout=15000, so hoot fails the test itself at 15 seconds and 10 leaves room for the rest of the test.

This should also close most of the open runbot errors shaped like:

    Failed to find x of "..." (Timeout of 3 seconds). Found 0 instead.

The element does show up in those, just after the wait gave up.

https://runbot.odoo.com/odoo/error/944188

web companion https://github.com/odoo/odoo/pull/279984

Forward-Port-Of: odoo/odoo#280586
Forward-Port-Of: odoo/odoo#279983

When setting an account to an account.bank.statement there is a step to automatically create a reconciliation rule/model if one does not exist already. To do so, we retrieve 5 account.bank.statement.lines and use them to define the reconciliation model config. A matching is done on the payment_ref of the account.bank.statement.lines by finding the longest common substring in the reference.

### Current Implementation
The current algorithm does so by first generating all the possible substrings for all the payment_ref before doing the intersection between these sets and returning the max if `len(substring) >=10`. This is reasonable when the payment_ref follows either a SEPA communication national standard like the Belgian one or the Creditor Reference standard (ISO 11649). For transactions with large, unstructured communication with more than 100 chars, the method `_get_common_substrings` quickly overfill the memory, sometimes raising a MemoryErorr, and takes a significant amount of time. That's because the nested function `_generate_all_substrings` generates n*(n+1)/2 substrings, with n being the lenght of a payment_ref, called `label` in `generate_all_substrings`.

### Proposed Fix
This commit introduces another algorithm to find the largest common substring. It starts by taking the two smallest labels to find their substrings intersection. We know that for an arbitrary collection of labels, the intersection of their substrings sets A ∩ B ∩...∩ Z is included in the intersection of any two substrings sets. The underlying assumption of the first step is that for an arbitrary collection of labels the intersection of the substrings sets of the two smallest labels will be the smallest intersection of any given pair of substrings sets. This won't hold true everytime and using a metric such as label similarity instead of shortest string might be better. But on average this should be good enough and it's easier to implement + it removes the need of preprocessing the labels to compute the similarity.

The point of the new nested function `common_substrings` is to discard common substrings as we build them. Using the current `generate_all_substsrings` on either the smallest label or both smallest
labels would still generate and store a lot of substrings, especially for large labels. By yielding the common substrings as we find them, the memory footprint is vastly reduced.

Lastly, the next substring in the common_substrings iterable is only checked against the remaining labels if it's longer than the current match. This speeds up the whole process

### speedup

In a customer database with some account.bank.statement.line with payment_ref > 500 chars, setting a specific account (code 4970) on transactions goes from MemoryError to < 1Mb memory consumption.

Because of the memory consumption it was not possible to gather timing value on the current version. 
Testing the new algorithm in a shell and using as labels the 5 longest payment_ref in the customer database
(831, 831, 1117, 1178, 1300 chars), averaging to 2000 chars once normalised, the average time to execute `_get_common_substrings` is 900 ms ± 10.3 ms.

Forward-Port-Of: odoo/enterprise#121575
Forward-Port-Of: odoo/enterprise#118824

Before this commit, contains() and its variants gave the client 3 seconds, and the bus helpers 2 seconds. The problem is that the first wait after openDiscuss pays for the whole mount, /mail/data and /discuss/channel/messages. Measured from openDiscuss resolving to the message being in the DOM:

- 250 to 460ms on an idle machine;
- 867 to 5258ms over 10 runs with the CPU throttled 4x, which is what a
  busy runbot looks like, 3 of the 10 over 2 seconds;
- 1474 to 6912ms with the CPU throttled 6x, 5 of 6 over 3 seconds.

"Reactions are ordered by id" fails 1 run in 60 at 4x for that reason.

Note that a longer timeout costs nothing on a green build: the timer is cleared as soon as the element is there, so it only delays the report of a test that was going to fail anyway.

This commit raises both to 10 seconds, the delay a tour step already gets in macro.js. test_js.py runs the presets with timeout=15000, so hoot fails the test itself at 15 seconds and 10 leaves room for the rest of the test.

This should also close most of the open runbot errors shaped like:

    Failed to find x of "..." (Timeout of 3 seconds). Found 0 instead.

The element does show up in those, just after the wait gave up.

https://runbot.odoo.com/odoo/error/944188

web companion https://github.com/odoo/odoo/pull/279984

Forward-Port-Of: odoo/odoo#280586
Forward-Port-Of: odoo/odoo#279983

Before this commit, expect.waitForSteps and expect.waitForErrors gave 2 seconds, less than the 3 seconds of the DOM waits sitting next to them, on the same page and the same RPCs. Over 340 call sites in addons tests reach waitForSteps and not one of them passes an explicit timeout, so 2 seconds is what every step wait gets.

The problem is that the RPC chain a step wait sits on takes longer than that on a loaded machine. Measured from openDiscuss resolving to the message being in the DOM:

- 250 to 460ms on an idle machine;
- 867 to 5258ms over 10 runs with the CPU throttled 4x, which is what a busy runbot looks like, 3 of the 10 over 2 seconds;
- 1474 to 6912ms with the CPU throttled 6x, 5 of 6 over 3 seconds.

Note that a longer timeout costs nothing on a green build: the timer is cleared as soon as the steps are in, so it only delays the report of a test that was going to fail anyway.

This commit raises both to 10 seconds, the delay a tour step already gets in macro.js. test_js.py runs the presets with timeout=15000, so hoot fails the test itself at 15 seconds and 10 leaves room for the rest of the test.

Companion of https://github.com/odoo/odoo/pull/279983 to fix https://runbot.odoo.com/odoo/error/944188 kind of issues.

Forward-Port-Of: odoo/odoo#279984

Before this commit, contains() and its variants gave the client 3 seconds, and the bus helpers 2 seconds. The problem is that the first wait after openDiscuss pays for the whole mount, /mail/data and /discuss/channel/messages. Measured from openDiscuss resolving to the message being in the DOM:

- 250 to 460ms on an idle machine;
- 867 to 5258ms over 10 runs with the CPU throttled 4x, which is what a
  busy runbot looks like, 3 of the 10 over 2 seconds;
- 1474 to 6912ms with the CPU throttled 6x, 5 of 6 over 3 seconds.

"Reactions are ordered by id" fails 1 run in 60 at 4x for that reason.

Note that a longer timeout costs nothing on a green build: the timer is cleared as soon as the element is there, so it only delays the report of a test that was going to fail anyway.

This commit raises both to 10 seconds, the delay a tour step already gets in macro.js. test_js.py runs the presets with timeout=15000, so hoot fails the test itself at 15 seconds and 10 leaves room for the rest of the test.

This should also close most of the open runbot errors shaped like:

    Failed to find x of "..." (Timeout of 3 seconds). Found 0 instead.

The element does show up in those, just after the wait gave up.

https://runbot.odoo.com/odoo/error/944188

web companion https://github.com/odoo/odoo/pull/279984

Forward-Port-Of: odoo/odoo#280400
Forward-Port-Of: odoo/odoo#279983

Doing an euclidean division on floats with the native operators is unreliable: because of IEEE-754 representation errors, `value1 % value2` can return a spurious remainder (e.g. `50.4 % 16.8 == 16.799999999999997` instead of 0.0) and `int(value1 / value2)` can truncate the quotient one step too low (e.g. `int(0.3 / 0.1) == 2` instead of 3). `float_div` returns the `(quotient, remainder)` pair free of those errors.

The key is to never run a lossy `%` or `//` on the raw floats. Instead both operands are first snapped onto the precision grid with `float_round` and then scaled to integers: since a grid-snapped value is a multiple of `rounding`, dividing it by `rounding` counts how many grid steps it spans. That division is still noisy (`4.35 / 0.05 == 86.99999999999999`), so the result is passed through `builtins.round` to coerce it to the exact integer step count. The euclidean division itself is then a plain integer `divmod`, which is exact, and the remainder is scaled back to real units. This is why the correction is applied to the inputs and not to the output: rounding the result of a native `%` would only round an already-corrupt value, and would still misreport the quotient in the corner cases the util exists to handle.

Dividing by `rounding` is meaningful for any precision, not only powers of ten: the grid step can be `0.05`, `0.25`, `0.5`, `0.03`, ... and `value / step` counts the steps in every case. This mirrors the normalize/denormalize scheme `float_round` already uses internally.

The util shares `float_round`'s inherent limitation: the scaled step count must stay representable as an exact `float` integer, so exactness is lost past ~2**53 grid steps (extreme magnitudes at a fine precision). This is the IEEE-754 double-precision ceiling and is well outside any realistic quantity or price range.

---
I confirm I have signed the CLA and read the PR guidelines at www.odoo.com/submit-pr

Forward-Port-Of: odoo/odoo#277160

Before this PR, the merge details popup needed a redesign, now the different views showcase the merge better to the user.

| Before | Before | After |
|--------|--------|--------|
| First section | <img width="1023" height="985" alt="Screenshot 2026-06-26 at 11 10 44" src="https://github.com/user-attachments/assets/81faaad4-f950-4031-bd1c-66b46c1e61c3" /> | <img width="1023" height="985" alt="Screenshot 2026-06-26 at 11 07 59" src="https://github.com/user-attachments/assets/46895709-3575-4bae-864e-f33a96a7737b" /> |
| Second section | <img width="1023" height="985" alt="Screenshot 2026-06-26 at 11 10 47" src="https://github.com/user-attachments/assets/1fd3381c-d93c-4a2e-892b-134633279677" /> | <img width="1023" height="985" alt="Screenshot 2026-06-26 at 11 08 05" src="https://github.com/user-attachments/assets/0400e9f4-00ee-4d8b-9d77-a1566b1242f5" /> |
| Third section | <img width="1023" height="985" alt="Screenshot 2026-06-26 at 11 10 52" src="https://github.com/user-attachments/assets/dce3590c-4f16-473a-abd7-611f781f70f3" /> | <img width="1023" height="985" alt="Screenshot 2026-06-26 at 11 08 13" src="https://github.com/user-attachments/assets/a8315904-0067-4794-b48d-6b357445d1c7" /> |
| Record hover | <img width="1023" height="985" alt="Screenshot 2026-06-26 at 11 11 02" src="https://github.com/user-attachments/assets/1fa8fa2e-e9e0-45a6-a8c0-6dd4a8e6fa9b" /> | <img width="1023" height="985" alt="Screenshot 2026-06-26 at 11 08 17" src="https://github.com/user-attachments/assets/78434801-f34e-4e8f-87d9-bc9841a1628a" /> |
| Field display | <img width="1023" height="985" alt="Screenshot 2026-06-26 at 11 10 52" src="https://github.com/user-attachments/assets/d410867c-3013-4b90-99a2-b26bbab22e86" /> | <img width="1023" height="985" alt="Screenshot 2026-06-26 at 11 12 02" src="https://github.com/user-attachments/assets/38b1e359-02b5-4586-990e-f11805390ddd" /> |
| Tab focus (accessibility) | <img width="1023" height="985" alt="Screenshot 2026-06-26 at 11 10 44" src="https://github.com/user-attachments/assets/162b0a57-5138-45f7-b595-76337a5793dc" /> | <img width="1023" height="985" alt="Screenshot 2026-06-26 at 11 09 18" src="https://github.com/user-attachments/assets/3b79e21a-ab1c-47a8-874a-0f9f157938f3" /> |

task-6196149

Before
---

- When opening the appointment page, the possible capacities that a user can book needs to be calculated to show the capacity dropdown.
- This is currently done by getting all the possible combinations that could arise from each resource along with its linked resources (Linked resources are resources that are used to combine with the main resource to allow a bigger capacity)
- The complexity of this approach blows up with the increase in linked resources. For each resource we end up with O(2^m) where m is the number of linked resources it has. If they have the same number of linked resources, and n is the number of resources, we end up with O(n*2^m)

Solution:
- Flipping the algorithm, we create a structure for possible capacities and only keep the best combination for a found capacity.
- We first store all available capacity for relevant resources.

Algorithm
---

For a resource:
- We initialize the solution S to {0: []}
- Greedy dynamic programming is then used to get all the possible combinations by building the result dict, adding one resource at a time. We add its capacity to all entries of S -> we use the combination if a new capacity is reached OR update the combination if the number of elements of combination is less than the existing one.

Then, for the global solution:
- We go through S of resources (in the order of self), and add entries to G (the general dict of solutions). Again, on collision, we only keep the lower-cardinal combination.

The complexity of this algorithm is O(n*u) where u is the dynamic programming complexity. u would be quadratic O(m^2) if the sub-sums of capacities overlap heavily, for instance when resources have the same capacity, like tables of a restaurant, but could reach (2^m) in the worst case scenario.

Related changes / side effects
---

1. Fixing an issue

2. Cleaning strange logic

3. Cleaning max capacity computation

Benchmark:
- opw-5177932 goes down from 6 mins to less than 2 seconds using the new algorithm.

Also include
---

Fix the max capacity on resources when skipping resource selection. It computed the max capa based on values that were not yet emptied in the website_appointment controller.


Task-6233563