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How to use _check_results method in autotest

Best Python code snippet using autotest_python

test_stockvaluation.py

Source:test_stockvaluation.py

...71        all_amls = self.vendor_bill1.line_ids + self.customer_invoice1.line_ids72        if self.sale_order1.picking_ids.move_lines.account_move_ids:73            all_amls |= self.sale_order1.picking_ids.move_lines.account_move_ids.line_ids74        return all_amls75    def _check_results(self, expected_aml, expected_aml_count, all_amls):76        # Construct a dict similar to `expected_aml` with `all_amls` in order to77        # compare them.78        result_aml = {}79        for aml in all_amls:80            account_id = aml.account_id.id81            if result_aml.get(account_id):82                debit = result_aml[account_id][0]83                credit = result_aml[account_id][1]84                result_aml[account_id] = (debit + aml.debit, credit + aml.credit)85            else:86                result_aml[account_id] = (aml.debit, aml.credit)87        self.assertEqual(len(all_amls), expected_aml_count)88        for k, v in expected_aml.items():89            self.assertEqual(result_aml[k], v)90    # -------------------------------------------------------------------------91    # Continental92    # -------------------------------------------------------------------------93    def test_dropship_standard_perpetual_continental_ordered(self):94        self.env.company.anglo_saxon_accounting = False95        self.product1.product_tmpl_id.categ_id.property_cost_method = 'standard'96        self.product1.product_tmpl_id.standard_price = 1097        self.product1.product_tmpl_id.categ_id.property_valuation = 'real_time'98        self.product1.product_tmpl_id.invoice_policy = 'order'99        all_amls = self._dropship_product1()100        expected_aml = {101            self.company_data['default_account_payable'].id:        (0.0, 8.0),102            self.company_data['default_account_expense'].id:        (8.0, 0.0),103            self.company_data['default_account_receivable'].id:     (12.0, 0.0),104            self.company_data['default_account_revenue'].id:        (0.0, 12.0),105        }106        self._check_results(expected_aml, 4, all_amls)107    def test_dropship_standard_perpetual_continental_delivered(self):108        self.env.company.anglo_saxon_accounting = False109        self.product1.product_tmpl_id.categ_id.property_cost_method = 'standard'110        self.product1.product_tmpl_id.standard_price = 10111        self.product1.product_tmpl_id.categ_id.property_valuation = 'real_time'112        self.product1.product_tmpl_id.invoice_policy = 'delivery'113        all_amls = self._dropship_product1()114        expected_aml = {115            self.company_data['default_account_payable'].id:        (0.0, 8.0),116            self.company_data['default_account_expense'].id:        (8.0, 0.0),117            self.company_data['default_account_receivable'].id:     (12.0, 0.0),118            self.company_data['default_account_revenue'].id:        (0.0, 12.0),119        }120        self._check_results(expected_aml, 4, all_amls)121    def test_dropship_fifo_perpetual_continental_ordered(self):122        self.env.company.anglo_saxon_accounting = False123        self.product1.product_tmpl_id.categ_id.property_cost_method = 'fifo'124        self.product1.product_tmpl_id.standard_price = 10125        self.product1.product_tmpl_id.categ_id.property_valuation = 'real_time'126        self.product1.product_tmpl_id.invoice_policy = 'order'127        all_amls = self._dropship_product1()128        expected_aml = {129            self.company_data['default_account_payable'].id:        (0.0, 8.0),130            self.company_data['default_account_expense'].id:        (8.0, 0.0),131            self.company_data['default_account_receivable'].id:     (12.0, 0.0),132            self.company_data['default_account_revenue'].id:        (0.0, 12.0),133        }134        self._check_results(expected_aml, 4, all_amls)135    def test_dropship_fifo_perpetual_continental_delivered(self):136        self.env.company.anglo_saxon_accounting = False137        self.product1.product_tmpl_id.categ_id.property_cost_method = 'fifo'138        self.product1.product_tmpl_id.standard_price = 10139        self.product1.product_tmpl_id.categ_id.property_valuation = 'real_time'140        self.product1.product_tmpl_id.invoice_policy = 'delivery'141        all_amls = self._dropship_product1()142        expected_aml = {143            self.company_data['default_account_payable'].id:        (0.0, 8.0),144            self.company_data['default_account_expense'].id:        (8.0, 0.0),145            self.company_data['default_account_receivable'].id:     (12.0, 0.0),146            self.company_data['default_account_revenue'].id:        (0.0, 12.0),147        }148        self._check_results(expected_aml, 4, all_amls)149    # -------------------------------------------------------------------------150    # Anglosaxon151    # -------------------------------------------------------------------------152    def test_dropship_standard_perpetual_anglosaxon_ordered(self):153        self.env.company.anglo_saxon_accounting = True154        self.product1.product_tmpl_id.categ_id.property_cost_method = 'standard'155        self.product1.product_tmpl_id.standard_price = 10156        self.product1.product_tmpl_id.categ_id.property_valuation = 'real_time'157        self.product1.product_tmpl_id.invoice_policy = 'order'158        all_amls = self._dropship_product1()159        expected_aml = {160            self.company_data['default_account_payable'].id:        (0.0, 8.0),161            self.company_data['default_account_expense'].id:        (10.0, 0.0),162            self.company_data['default_account_receivable'].id:     (12.0, 0.0),163            self.company_data['default_account_revenue'].id:        (0.0, 12.0),164            self.company_data['default_account_stock_in'].id:       (8.0, 10.0),165            self.company_data['default_account_stock_out'].id:      (10.0, 10.0),166        }167        # Interim IN is not balanced because because there's a difference between the po line168        # price unit and the standard price. We could set a price difference account on the169        # category to compensate.170        self._check_results(expected_aml, 10, all_amls)171    def test_dropship_standard_perpetual_anglosaxon_delivered(self):172        self.env.company.anglo_saxon_accounting = True173        self.product1.product_tmpl_id.categ_id.property_cost_method = 'standard'174        self.product1.product_tmpl_id.standard_price = 10175        self.product1.product_tmpl_id.categ_id.property_valuation = 'real_time'176        self.product1.product_tmpl_id.invoice_policy = 'delivery'177        all_amls = self._dropship_product1()178        expected_aml = {179            self.company_data['default_account_payable'].id:        (0.0, 8.0),180            self.company_data['default_account_expense'].id:        (10.0, 0.0),181            self.company_data['default_account_receivable'].id:     (12.0, 0.0),182            self.company_data['default_account_revenue'].id:        (0.0, 12.0),183            self.company_data['default_account_stock_in'].id:       (8.0, 10.0),184            self.company_data['default_account_stock_out'].id:      (10.0, 10.0),185        }186        # Interim IN is not balanced because because there's a difference between the po line187        # price unit and the standard price. We could set a price difference account on the188        # category to compensate.189        self._check_results(expected_aml, 10, all_amls)190    def test_dropship_fifo_perpetual_anglosaxon_ordered(self):191        self.env.company.anglo_saxon_accounting = True192        self.product1.product_tmpl_id.categ_id.property_cost_method = 'fifo'193        self.product1.product_tmpl_id.standard_price = 10194        self.product1.product_tmpl_id.categ_id.property_valuation = 'real_time'195        self.product1.product_tmpl_id.invoice_policy = 'order'196        all_amls = self._dropship_product1()197        expected_aml = {198            self.company_data['default_account_payable'].id:        (0.0, 8.0),199            self.company_data['default_account_expense'].id:        (8.0, 0.0),200            self.company_data['default_account_receivable'].id:     (12.0, 0.0),201            self.company_data['default_account_revenue'].id:        (0.0, 12.0),202            self.company_data['default_account_stock_in'].id:       (8.0, 8.0),203            self.company_data['default_account_stock_out'].id:      (8.0, 8.0),204        }205        self._check_results(expected_aml, 10, all_amls)206    def test_dropship_fifo_perpetual_anglosaxon_delivered(self):207        self.env.company.anglo_saxon_accounting = True208        self.product1.product_tmpl_id.categ_id.property_cost_method = 'fifo'209        self.product1.product_tmpl_id.standard_price = 10210        self.product1.product_tmpl_id.categ_id.property_valuation = 'real_time'211        self.product1.product_tmpl_id.invoice_policy = 'delivery'212        all_amls = self._dropship_product1()213        expected_aml = {214            self.company_data['default_account_payable'].id:        (0.0, 8.0),215            self.company_data['default_account_expense'].id:        (8.0, 0.0),216            self.company_data['default_account_receivable'].id:     (12.0, 0.0),217            self.company_data['default_account_revenue'].id:        (0.0, 12.0),218            self.company_data['default_account_stock_in'].id:       (8.0, 8.0),219            self.company_data['default_account_stock_out'].id:      (8.0, 8.0),220        }221        self._check_results(expected_aml, 10, all_amls)222    def test_dropship_standard_perpetual_anglosaxon_ordered_return(self):223        self.env.company.anglo_saxon_accounting = True224        self.product1.product_tmpl_id.categ_id.property_cost_method = 'standard'225        self.product1.product_tmpl_id.standard_price = 10226        self.product1.product_tmpl_id.categ_id.property_valuation = 'real_time'227        self.product1.product_tmpl_id.invoice_policy = 'order'228        all_amls = self._dropship_product1()229        # return what we've done230        stock_return_picking_form = Form(self.env['stock.return.picking']231            .with_context(active_ids=self.sale_order1.picking_ids.ids, active_id=self.sale_order1.picking_ids.ids[0],232            active_model='stock.picking'))233        stock_return_picking = stock_return_picking_form.save()234        stock_return_picking_action = stock_return_picking.create_returns()235        return_pick = self.env['stock.picking'].browse(stock_return_picking_action['res_id'])236        return_pick.move_lines[0].move_line_ids[0].qty_done = 1.0237        return_pick._action_done()238        self.assertEqual(return_pick.move_lines._is_dropshipped_returned(), True)239        all_amls_return = self.vendor_bill1.line_ids + self.customer_invoice1.line_ids240        if self.sale_order1.picking_ids.mapped('move_lines.account_move_ids'):241            all_amls_return |= self.sale_order1.picking_ids.mapped('move_lines.account_move_ids.line_ids')242        # Two extra AML should have been created for the return243        expected_aml = {244            self.company_data['default_account_stock_in'].id:       (10.0, 0.0),245            self.company_data['default_account_stock_out'].id:      (0.0, 10.0),246        }...

beam_search_test.py

Source:beam_search_test.py

...42                 [3, 4, 5, 5, 5]]43        )44        # This is what the log probs should look like for each item in the batch.45        self.expected_log_probs = np.log(np.array([0.4, 0.3, 0.2]))  # pylint: disable=assignment-from-no-return46    def _check_results(self,47                       batch_size: int = 5,48                       expected_top_k: np.array = None,49                       expected_log_probs: np.array = None,50                       beam_search: BeamSearch = None,51                       state: Dict[str, torch.Tensor] = None) -> None:52        expected_top_k = expected_top_k if expected_top_k is not None else self.expected_top_k53        expected_log_probs = expected_log_probs if expected_log_probs is not None else self.expected_log_probs54        state = state or {}55        beam_search = beam_search or self.beam_search56        beam_size = beam_search.beam_size57        initial_predictions = torch.tensor([0] * batch_size)  # pylint: disable=not-callable58        top_k, log_probs = beam_search.search(initial_predictions, state, take_step)  # type: ignore59        # top_k should be shape `(batch_size, beam_size, max_predicted_length)`.60        assert list(top_k.size())[:-1] == [batch_size, beam_size]61        np.testing.assert_array_equal(top_k[0].numpy(), expected_top_k)62        # log_probs should be shape `(batch_size, beam_size, max_predicted_length)`.63        assert list(log_probs.size()) == [batch_size, beam_size]64        np.testing.assert_allclose(log_probs[0].numpy(), expected_log_probs)65    def test_search(self):66        self._check_results()67    def test_finished_state(self):68        state = {}69        state["foo"] = torch.tensor(  # pylint: disable=not-callable70                [[1, 0, 1],71                 [2, 0, 1],72                 [0, 0, 1],73                 [1, 1, 1],74                 [0, 0, 0]]75        )76        # shape: (batch_size, 3)77        expected_finished_state = {}78        expected_finished_state["foo"] = np.array(79                [[1, 0, 1],80                 [1, 0, 1],81                 [1, 0, 1],82                 [2, 0, 1],83                 [2, 0, 1],84                 [2, 0, 1],85                 [0, 0, 1],86                 [0, 0, 1],87                 [0, 0, 1],88                 [1, 1, 1],89                 [1, 1, 1],90                 [1, 1, 1],91                 [0, 0, 0],92                 [0, 0, 0],93                 [0, 0, 0]]94        )95        # shape: (batch_size x beam_size, 3)96        self._check_results(state=state)97        # check finished state.98        for key, array in expected_finished_state.items():99            np.testing.assert_allclose(state[key].numpy(), array)100    def test_batch_size_of_one(self):101        self._check_results(batch_size=1)102    def test_greedy_search(self):103        beam_search = BeamSearch(self.end_index, beam_size=1)104        expected_top_k = np.array([[1, 2, 3, 4, 5]])105        expected_log_probs = np.log(np.array([0.4]))  # pylint: disable=assignment-from-no-return106        self._check_results(expected_top_k=expected_top_k,107                            expected_log_probs=expected_log_probs,108                            beam_search=beam_search)109    def test_early_stopping(self):110        """111        Checks case where beam search will reach `max_steps` before finding end tokens.112        """113        beam_search = BeamSearch(self.end_index, beam_size=3, max_steps=3)114        expected_top_k = np.array(115                [[1, 2, 3],116                 [2, 3, 4],117                 [3, 4, 5]]118        )119        expected_log_probs = np.log(np.array([0.4, 0.3, 0.2]))  # pylint: disable=assignment-from-no-return120        self._check_results(expected_top_k=expected_top_k,121                            expected_log_probs=expected_log_probs,122                            beam_search=beam_search)123    def test_different_per_node_beam_size(self):124        # per_node_beam_size = 1125        beam_search = BeamSearch(self.end_index, beam_size=3, per_node_beam_size=1)126        self._check_results(beam_search=beam_search)127        # per_node_beam_size = 2128        beam_search = BeamSearch(self.end_index, beam_size=3, per_node_beam_size=2)129        self._check_results(beam_search=beam_search)130    def test_catch_bad_config(self):131        """132        If `per_node_beam_size` (which defaults to `beam_size`) is larger than133        the size of the target vocabulary, `BeamSearch.search` should raise134        a ConfigurationError.135        """136        beam_search = BeamSearch(self.end_index, beam_size=20)137        with pytest.raises(ConfigurationError):138            self._check_results(beam_search=beam_search)139    def test_warn_for_bad_log_probs(self):140        # The only valid next step from the initial predictions is the end index.141        # But with a beam size of 3, the call to `topk` to find the 3 most likely142        # next beams will result in 2 new beams that are invalid, in that have probability of 0.143        # The beam search should warn us of this.144        initial_predictions = torch.LongTensor([self.end_index-1, self.end_index-1])145        with pytest.warns(RuntimeWarning, match="Infinite log probabilities"):146            self.beam_search.search(initial_predictions, {}, take_step)147    def test_empty_sequences(self):148        initial_predictions = torch.LongTensor([self.end_index-1, self.end_index-1])149        beam_search = BeamSearch(self.end_index, beam_size=1)150        with pytest.warns(RuntimeWarning, match="Empty sequences predicted"):151            predictions, log_probs = beam_search.search(initial_predictions, {}, take_step)152        # predictions hould have shape `(batch_size, beam_size, max_predicted_length)`....

test_shared_resolve_dice.py

Source:test_shared_resolve_dice.py

...5    def test_no_danger(self):6        action_dice = [1, 2, 3]7        danger_dice = []8        expected_result = [1, 2, 3]9        self._check_results(action_dice, danger_dice, expected_result)10    def test_no_matching_danger(self):11        action_dice = [1, 2, 3]12        danger_dice = [4]13        expected_result = [1, 2, 3]14        self._check_results(action_dice, danger_dice, expected_result)15    def test_single_matching(self):16        action_dice = [1, 2, 3]17        danger_dice = [1]18        expected_result = [2, 3]19        self._check_results(action_dice, danger_dice, expected_result)20    def test_multiple_matching(self):21        action_dice = [1, 2, 3]22        danger_dice = [1, 2]23        expected_result = [3]24        self._check_results(action_dice, danger_dice, expected_result)25    def test_multiple_matching_duplicate(self):26        action_dice = [1, 1, 1, 2, 3]27        danger_dice = [1, 1]28        expected_result = [1, 2, 3]29        self._check_results(action_dice, danger_dice, expected_result)30    def test_remove_all_action(self):31        action_dice = [1, 1]32        danger_dice = [1, 1]33        expected_result = []34        self._check_results(action_dice, danger_dice, expected_result)35    def _check_results(self, action_dice, danger_dice, expected_result):36        result_dice = resolve_dice(action_dice, danger_dice)37        self.assertEqual(expected_result, result_dice)38if __name__ == '__main__':...

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