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88e7d2586b
Summary: # Before submitting - [ ] Was this discussed/approved via a Github issue? (no need for typos, doc improvements) - [ ] Did you read the [contributor guideline](https://github.com/pytorch/fairseq/blob/main/CONTRIBUTING.md)? - [ ] Did you make sure to update the docs? - [ ] Did you write any new necessary tests? ## What does this PR do? - [x] applies flake8 fixes to main branch (https://github.com/fairinternal/fairseq-py/issues/2546) - still more to be fixed Fix GPU tests: - [x] when torch.ao.quantization import doesn't work use torch.quantization - [x] build apex from earlier commit in circleci so that its compatible with pytorch 1.8 and 1.9 ## PR review Anyone in the community is free to review the PR once the tests have passed. If we didn't discuss your PR in Github issues there's a high chance it will not be merged. ## Did you have fun? Make sure you had fun coding � Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/2570 Reviewed By: Mortimerp9 Differential Revision: D32955312 Pulled By: dianaml0 fbshipit-source-id: e163cbd4998f171f819e31b0682c1c0f1986f9e1
276 lines
10 KiB
Python
Executable File
276 lines
10 KiB
Python
Executable File
# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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import unittest
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from typing import List
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import torch
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from fairseq.token_generation_constraints import (
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ConstraintNode,
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OrderedConstraintState,
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UnorderedConstraintState,
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pack_constraints,
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)
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def tensorize(constraints: List[List[int]]) -> torch.Tensor:
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return [torch.tensor(x) for x in constraints]
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class TestHelperRoutines(unittest.TestCase):
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def setUp(self):
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self.examples = [
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([[]], torch.tensor([[0]])),
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([[], []], torch.tensor([[0], [0]])),
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([[torch.tensor([1, 2])], []], torch.tensor([[1, 1, 2, 0], [0, 0, 0, 0]])),
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(
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[
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[
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torch.tensor([3, 1, 2]),
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torch.tensor([3]),
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torch.tensor([4, 5, 6, 7]),
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],
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[],
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[torch.tensor([1, 8, 9, 10, 1, 4, 11, 12])],
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],
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torch.tensor(
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[
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[3, 3, 1, 2, 0, 3, 0, 4, 5, 6, 7, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[1, 1, 8, 9, 10, 1, 4, 11, 12, 0, 0, 0],
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]
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),
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),
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]
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def test_packing(self):
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"""Ensures the list of lists of tensors gets packed correctly."""
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for batch_constraints, expected_tensor in self.examples:
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packed = pack_constraints(batch_constraints)
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assert torch.equal(packed, expected_tensor)
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class TestUnorderedConstraintState(unittest.TestCase):
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def setUp(self):
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# Tuples of (contraint set, expected printed graph, token counts per node)
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self.examples = [
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(
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tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
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"([None].False#6 ([1].True#4 ([2].False#1 [3].True#1) [3].True#1 [4].True#1) ([4].False#2 ([5].True#2 ([6].False#1 [7].True#1))))", # noqa
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{1: 4, 2: 1, 3: 2, 4: 3, 5: 2, 6: 1, 7: 1},
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),
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([], "[None].False#0", {}),
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(tensorize([[0]]), "([None].False#1 [0].True#1)", {0: 1}),
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(
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tensorize([[100000, 1, 2, 3, 4, 5]]),
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"([None].False#1 ([100000].False#1 ([1].False#1 ([2].False#1 ([3].False#1 ([4].False#1 [5].True#1))))))",
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{100000: 1, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1},
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),
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(
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tensorize([[1, 2], [1, 2]]),
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"([None].False#2 ([1].False#2 [2].True#2))",
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{1: 2, 2: 2},
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),
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(
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tensorize([[1, 2], [3, 4]]),
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"([None].False#2 ([1].False#1 [2].True#1) ([3].False#1 [4].True#1))",
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{1: 1, 2: 1, 3: 1, 4: 1},
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),
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]
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self.sequences = [
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(
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self.examples[0][0],
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[],
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{"bank": 0, "num_completed": 0, "finished": False, "is_root": True},
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),
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(
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self.examples[0][0],
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[1, 2],
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{"bank": 2, "num_completed": 0, "finished": False, "is_root": False},
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),
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(
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self.examples[0][0],
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[1, 2, 94],
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{"bank": 1, "num_completed": 1, "finished": False, "is_root": True},
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),
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(
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self.examples[0][0],
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[1, 3, 999, 1, 4],
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{"bank": 4, "num_completed": 2, "finished": False, "is_root": False},
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),
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(
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self.examples[0][0],
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[1, 3, 999, 1, 4, 999],
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{"bank": 4, "num_completed": 2, "finished": False, "is_root": True},
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),
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(
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self.examples[0][0],
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[4, 5, 6, 8],
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{"bank": 2, "num_completed": 1, "finished": False, "is_root": True},
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),
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(
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self.examples[0][0],
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# Tricky, because in last three, goes down [1->4] branch, could miss [1] and [4->5]
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# [[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]],
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[1, 2, 3, 1, 3, 1, 4, 4, 5, 6, 7, 1, 4, 5],
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{"bank": 14, "num_completed": 6, "finished": True, "is_root": False},
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),
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(
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self.examples[0][0],
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[1, 2, 3, 999, 1, 3, 1, 4, 4, 5, 6, 7, 1, 4, 5, 117],
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{"bank": 14, "num_completed": 6, "finished": True, "is_root": True},
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),
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(
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tensorize([[1], [2, 3]]),
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# Should not be able to get credit for entering 1 a second time
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[1, 1],
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{"bank": 1, "num_completed": 1, "finished": False, "is_root": True},
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),
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(
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self.examples[4][0],
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[1, 2, 1, 2],
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{"bank": 4, "num_completed": 2, "finished": True, "is_root": False},
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),
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(
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self.examples[4][0],
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[1, 2, 1, 2, 1],
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{"bank": 4, "num_completed": 2, "finished": True, "is_root": True},
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),
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(
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self.examples[5][0],
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[1, 2, 3, 4, 5],
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{"bank": 4, "num_completed": 2, "finished": True, "is_root": True},
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),
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]
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def test_graphs(self):
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"""
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Test whether unordered graph systems are created correctly.
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"""
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for example in self.examples:
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constraints, expected, gold_counts = example
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c = ConstraintNode.create(constraints)
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assert (
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ConstraintNode.print_graph(c) == expected
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), f"got {ConstraintNode.print_graph(c)}, expected {expected}"
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assert (
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c.token_counts() == gold_counts
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), f"{c} got {c.token_counts()} wanted {gold_counts}"
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def test_next_tokens(self):
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"""
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Tests that the set of next tokens is correct.
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"""
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for example in self.examples:
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constraints, expected, gold_counts = example
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root = ConstraintNode.create(constraints)
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root_tokens = set(root.children.keys())
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for sequence in constraints:
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state = UnorderedConstraintState(root)
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for token in sequence:
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all_tokens = root_tokens.union(state.node.children.keys())
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assert (
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all_tokens == state.next_tokens()
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), f"ALL {all_tokens} NEXT {state.next_tokens()}"
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state = state.advance(token)
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def test_sequences(self):
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for constraints, tokens, expected in self.sequences:
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state = UnorderedConstraintState.create(pack_constraints([constraints])[0])
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for token in tokens:
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state = state.advance(token)
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result = {}
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for attr in expected.keys():
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result[attr] = getattr(state, attr)
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assert (
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result == expected
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), f"TEST({tokens}) GOT: {result} WANTED: {expected}"
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class TestOrderedConstraintState(unittest.TestCase):
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def setUp(self):
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self.sequences = [
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(
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tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
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[],
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{"bank": 0, "num_completed": 0, "finished": False, "is_root": True},
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),
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(
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tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
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[1, 2],
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{"bank": 2, "num_completed": 0, "finished": False, "is_root": False},
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),
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(
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tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
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[1, 2, 94],
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{"bank": 0, "num_completed": 0, "finished": False, "is_root": True},
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),
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(
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tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
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[1, 3, 999, 1, 4],
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{"bank": 0, "num_completed": 0, "finished": False, "is_root": True},
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),
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(
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tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
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[1, 2, 3, 999, 999],
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{"bank": 3, "num_completed": 1, "finished": False, "is_root": False},
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),
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(
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tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
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[1, 2, 3, 77, 1, 3, 1],
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{"bank": 6, "num_completed": 2, "finished": False, "is_root": False},
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),
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(
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tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
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[1, 2, 3, 1, 3, 1, 4, 4, 5, 6, 7, 1, 4, 5],
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{"bank": 14, "num_completed": 6, "finished": True, "is_root": False},
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),
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(
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tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
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[1, 2, 999, 1, 2, 3, 999, 1, 3, 1, 4, 4, 5, 6, 7, 1, 4, 5, 117],
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{"bank": 14, "num_completed": 6, "finished": True, "is_root": False},
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),
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(
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tensorize([[1], [2, 3]]),
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[1, 1],
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{"bank": 1, "num_completed": 1, "finished": False, "is_root": False},
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),
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(
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tensorize([[1, 2], [1, 2]]),
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[1, 2, 1, 2],
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{"bank": 4, "num_completed": 2, "finished": True, "is_root": False},
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),
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(
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tensorize([[1, 2], [1, 2]]),
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[1, 2, 1, 2, 1],
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{"bank": 4, "num_completed": 2, "finished": True, "is_root": False},
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),
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(
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tensorize([[1, 2], [3, 4]]),
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[1, 2, 3, 4, 5],
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{"bank": 4, "num_completed": 2, "finished": True, "is_root": False},
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),
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]
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def test_sequences(self):
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for i, (constraints, tokens, expected) in enumerate(self.sequences):
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state = OrderedConstraintState.create(pack_constraints([constraints])[0])
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for token in tokens:
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state = state.advance(token)
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result = {}
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for attr in expected.keys():
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result[attr] = getattr(state, attr)
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assert (
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result == expected
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), f"TEST({tokens}) GOT: {result} WANTED: {expected}"
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if __name__ == "__main__":
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unittest.main()
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