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8
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def create_and_check_for_token_classification( self, config, input_ids, input_shape_ids, input_pronunciation_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ): config.num_labels = self.num_labels model = RoCBertForTokenClassification(config=config) model.to(torch_device) model.eval() result = model( input_ids, input_shape_ids=input_shape_ids, input_pronunciation_ids=input_pronunciation_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels, ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
14,402
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11
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def create_and_check_for_multiple_choice( self, config, input_ids, input_shape_ids, input_pronunciation_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ): config.num_choices = self.num_choices model = RoCBertForMultipleChoice(config=config) model.to(torch_device) model.eval() multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() multiple_choice_inputs_shape_ids = input_shape_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() multiple_choice_inputs_pronunciation_ids = ( input_pronunciation_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() ) multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() result = model( multiple_choice_inputs_ids, input_shape_ids=multiple_choice_inputs_shape_ids, input_pronunciation_ids=multiple_choice_inputs_pronunciation_ids, attention_mask=multiple_choice_input_mask, token_type_ids=multiple_choice_token_type_ids, labels=choice_labels, ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
14,403
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8
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def create_and_check_for_pretraining( self, config, input_ids, input_shape_ids, input_pronunciation_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ): model = RoCBertForPreTraining(config=config) model.to(torch_device) model.eval() result = model( input_ids, input_shape_ids, input_pronunciation_ids, attention_mask=input_mask, token_type_ids=token_type_ids, attack_input_ids=input_ids, attack_input_shape_ids=input_shape_ids, attack_input_pronunciation_ids=input_pronunciation_ids, attack_attention_mask=input_mask, attack_token_type_ids=token_type_ids, labels_input_ids=token_labels, labels_input_shape_ids=input_shape_ids, labels_input_pronunciation_ids=input_pronunciation_ids, labels_attention_mask=input_mask, labels_token_type_ids=token_type_ids, ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
14,404
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class RoCBertModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = ( ( RoCBertModel, RoCBertForMaskedLM, RoCBertForCausalLM, RoCBertForMultipleChoice, RoCBertForQuestionAnswering, RoCBertForSequenceClassification, RoCBertForTokenClassification, RoCBertForPreTraining, ) if is_torch_available() else () ) all_generative_model_classes = (RoCBertForCausalLM,) if is_torch_available() else () # special case for ForPreTraining model def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels) if return_labels: if model_class in get_values(MODEL_FOR_PRETRAINING_MAPPING): inputs_dict["labels_input_ids"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device ) inputs_dict["labels_input_shape_ids"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device ) inputs_dict["labels_input_pronunciation_ids"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device ) inputs_dict["attack_input_ids"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device ) inputs_dict["attack_input_shape_ids"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device ) inputs_dict["attack_input_pronunciation_ids"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device ) return inputs_dict def setUp(self): self.model_tester = RoCBertModelTester(self) self.config_tester = ConfigTester(self, config_class=RoCBertConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_model_various_embeddings(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() for type in ["absolute", "relative_key", "relative_key_query"]: config_and_inputs[0].position_embedding_type = type self.model_tester.create_and_check_model(*config_and_inputs) def test_for_masked_lm(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_lm(*config_and_inputs) def test_for_multiple_choice(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs) def test_decoder_model_past_with_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs) def test_decoder_model_past_with_large_inputs_relative_pos_emb(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() config_and_inputs[0].position_embedding_type = "relative_key" self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs) def test_for_question_answering(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_question_answering(*config_and_inputs) def test_for_sequence_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs) def test_for_token_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_token_classification(*config_and_inputs) def test_for_pretraining(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_pretraining(*config_and_inputs) def test_model_as_decoder(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_model_as_decoder(*config_and_inputs) def test_model_as_decoder_with_default_input_mask(self): # This regression test was failing with PyTorch < 1.3 ( config, input_ids, input_shape_ids, input_pronunciation_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) = self.model_tester.prepare_config_and_inputs_for_decoder() input_mask = None self.model_tester.create_and_check_model_as_decoder( config, input_ids, input_shape_ids, input_pronunciation_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) @slow def test_model_from_pretrained(self): for model_name in ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = RoCBertModel.from_pretrained(model_name) self.assertIsNotNone(model)
14,405
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11
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def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels) if return_labels: if model_class in get_values(MODEL_FOR_PRETRAINING_MAPPING): inputs_dict["labels_input_ids"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device ) inputs_dict["labels_input_shape_ids"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device ) inputs_dict["labels_input_pronunciation_ids"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device ) inputs_dict["attack_input_ids"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device ) inputs_dict["attack_input_shape_ids"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device ) inputs_dict["attack_input_pronunciation_ids"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device ) return inputs_dict
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{}}}}}}, "keywords": []}}}}], "name": "test_inference_masked_lm", "returns": null, "type_params": [], "type_comment": null, "decorator_list": [{"_type": "Name", "_fields": {"id": "slow", "ctx": {"_type": "Load", "_fields": {}}}}]}}], "name": "RoCBertModelIntegrationTest", "bases": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "TestCase", "value": {"_type": "Name", "_fields": {"id": "unittest", "ctx": {"_type": "Load", "_fields": {}}}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}
class RoCBertModelIntegrationTest(unittest.TestCase): @slow def test_inference_masked_lm(self): model = RoCBertForMaskedLM.from_pretrained("weiweishi/roc-bert-base-zh") # input_text: ['[CLS]', 'b', 'a', '里', '系', '[MASK]', '国', '的', '首', '都', '[SEP]'] is the adversarial text # of ['[CLS]', '巴', '黎', '是', '[MASK]', '国', '的', '首', '都', '[SEP]'], means # "Paris is the [MASK] of France" in English input_ids = torch.tensor([[101, 144, 143, 7027, 5143, 103, 1744, 4638, 7674, 6963, 102]]) input_shape_ids = torch.tensor([[2, 20324, 23690, 8740, 706, 1, 10900, 23343, 20205, 5850, 2]]) input_pronunciation_ids = torch.tensor([[2, 718, 397, 52, 61, 1, 168, 273, 180, 243, 2]]) output = model(input_ids, input_shape_ids, input_pronunciation_ids) output_ids = torch.argmax(output.logits, dim=2) # convert to tokens is: ['[CLS]', '巴', '*', '黎', '是', '法', '国', '的', '首', '都', '[SEP]'] expected_output = torch.tensor([[101, 2349, 115, 7944, 3221, 3791, 1744, 4638, 7674, 6963, 102]]) self.assertEqual(output_ids, expected_output)
14,407
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def test_inference_masked_lm(self): model = RoCBertForMaskedLM.from_pretrained("weiweishi/roc-bert-base-zh") # input_text: ['[CLS]', 'b', 'a', '里', '系', '[MASK]', '国', '的', '首', '都', '[SEP]'] is the adversarial text # of ['[CLS]', '巴', '黎', '是', '[MASK]', '国', '的', '首', '都', '[SEP]'], means # "Paris is the [MASK] of France" in English input_ids = torch.tensor([[101, 144, 143, 7027, 5143, 103, 1744, 4638, 7674, 6963, 102]]) input_shape_ids = torch.tensor([[2, 20324, 23690, 8740, 706, 1, 10900, 23343, 20205, 5850, 2]]) input_pronunciation_ids = torch.tensor([[2, 718, 397, 52, 61, 1, 168, 273, 180, 243, 2]]) output = model(input_ids, input_shape_ids, input_pronunciation_ids) output_ids = torch.argmax(output.logits, dim=2) # convert to tokens is: ['[CLS]', '巴', '*', '黎', '是', '法', '国', '的', '首', '都', '[SEP]'] expected_output = torch.tensor([[101, 2349, 115, 7944, 3221, 3791, 1744, 4638, 7674, 6963, 102]]) self.assertEqual(output_ids, expected_output)
14,408
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"elts": [{"_type": "Name", "_fields": {"id": "i", "ctx": {"_type": "Store", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "val", "ctx": {"_type": "Store", "_fields": {}}}}]}}, "type_comment": null}}, {"_type": "For", "_fields": {"body": [{"_type": "If", "_fields": {"body": [{"_type": "If", "_fields": {"body": [{"_type": "Assign", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": null, "value": null}}, "targets": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "attr": "name", "value": {"_type": "Name", "_fields": {"id": "var", "ctx": {"_type": "Load", "_fields": {}}}}}}], "type_comment": null}}], "test": {"_type": "BoolOp", "_fields": {"op": {"_type": "Or", "_fields": {}}, "values": [{"_type": "Compare", "_fields": {"ops": [{"_type": "NotEq", "_fields": {}}], "left": {"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": 0}}, "value": {"_type": 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{"ctx": {"_type": "Load", "_fields": {}}, "attr": "name", "value": {"_type": "Name", "_fields": {"id": "var", "ctx": {"_type": "Load", "_fields": {}}}}}}, "comparators": [{"_type": "Constant", "_fields": {"kind": null, "value": null}}]}}, {"_type": "Compare", "_fields": {"ops": [{"_type": "IsNot", "_fields": {}}], "left": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "name", "value": {"_type": "Name", "_fields": {"id": "var", "ctx": {"_type": "Load", "_fields": {}}}}}}, "comparators": [{"_type": "Constant", "_fields": {"kind": null, "value": "b"}}]}}]}}, "orelse": []}}], "iter": {"_type": "Call", "_fields": {"args": [{"_type": "GeneratorExp", "_fields": {"elt": {"_type": "Name", "_fields": {"id": "update", "ctx": {"_type": "Load", "_fields": {}}}}, "generators": [{"_type": "comprehension", "_fields": {"ifs": [], "iter": {"_type": "Name", "_fields": {"id": "updates", "ctx": {"_type": "Load", "_fields": {}}}}, "target": {"_type": "Tuple", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "elts": [{"_type": "Name", "_fields": {"id": "_", "ctx": {"_type": "Store", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "update", "ctx": {"_type": "Store", "_fields": {}}}}]}}, "is_async": 0}}]}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "ancestors", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "graph", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "gof", "value": {"_type": "Name", "_fields": {"id": "theano", "ctx": {"_type": "Load", "_fields": {}}}}}}}}}}, "keywords": []}}, "orelse": [], "target": {"_type": "Name", "_fields": {"id": "var", "ctx": {"_type": "Store", "_fields": {}}}}, "type_comment": null}}, {"_type": "For", "_fields": {"body": [{"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Tuple", "_fields": {"ctx": {"_type": "Load", "_fields": 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"_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "maker", "value": {"_type": "Name", "_fields": {"id": "f", "ctx": {"_type": "Load", "_fields": {}}}}}}}}}}, "orelse": [], "target": {"_type": "Name", "_fields": {"id": "output", "ctx": {"_type": "Store", "_fields": {}}}}, "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "disturb_mem", "value": {"_type": "Name", "_fields": {"id": "disturb_mem", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Name", "_fields": {"id": "f", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "flush", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "f", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "record", "value": {"_type": "Name", "_fields": {"id": "mode", "ctx": {"_type": "Load", "_fields": {}}}}}}}}}}, "keywords": []}}}}, {"_type": "If", "_fields": {"body": [{"_type": "Return", "_fields": {"value": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "getvalue", "value": {"_type": "Name", "_fields": {"id": "log", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "test": {"_type": "UnaryOp", "_fields": {"op": {"_type": "Not", "_fields": {}}, "operand": {"_type": "Name", "_fields": {"id": "replay", "ctx": {"_type": "Load", "_fields": {}}}}}}, "orelse": []}}], "name": "run", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def run(replay, log=None): if not replay: log = StringIO() else: log = StringIO(log) record = Record(replay=replay, file_object=log) disturb_mem.disturb_mem() mode = RecordMode(record=record) b = sharedX(np.zeros((2,)), name='b') channels = OrderedDict() disturb_mem.disturb_mem() v_max = b.max(axis=0) v_min = b.min(axis=0) v_range = v_max - v_min updates = [] for i, val in enumerate([ v_max.max(), v_max.min(), v_range.max(), ]): disturb_mem.disturb_mem() s = sharedX(0., name='s_' + str(i)) updates.append((s, val)) for var in theano.gof.graph.ancestors(update for _, update in updates): if var.name is not None and var.name is not 'b': if var.name[0] != 's' or len(var.name) != 2: var.name = None for key in channels: updates.append((s, channels[key])) f = theano.function([], mode=mode, updates=updates, on_unused_input='ignore', name='f') for output in f.maker.fgraph.outputs: mode.record.handle_line(var_descriptor(output) + '\n') disturb_mem.disturb_mem() f() mode.record.f.flush() if not replay: return log.getvalue()
14,409
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13
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"Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "run", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "driver", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}}}], "name": "testWithMultiImport", "returns": null, "type_params": [], "type_comment": null, "decorator_list": [{"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "broken", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "mark", "value": {"_type": "Name", "_fields": {"id": "pytest", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "ticket", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "12566"}}}}]}}, {"_type": "Name", "_fields": {"id": "with_driver", "ctx": {"_type": "Load", "_fields": {}}}}]}}, {"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "self", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": 1}}, {"_type": "Call", "_fields": {"args": [{"_type": "BinOp", "_fields": {"op": {"_type": "Div", "_fields": {}}, "left": {"_type": "BinOp", "_fields": {"op": {"_type": "Div", "_fields": {}}, "left": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "dir", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "right": {"_type": "Constant", "_fields": {"kind": null, "value": "root"}}}}, "right": {"_type": "Constant", "_fields": {"kind": null, "value": "dir"}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "Create", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "EventType", "value": {"_type": "Name", "_fields": {"id": "monitors", "ctx": {"_type": "Load", "_fields": {}}}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "EventInfo", "value": {"_type": "Name", "_fields": {"id": "monitors", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}], "func": {"_type": "Name", "_fields": {"id": "DirInfoEvent", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "add", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "driver", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": 1}}, {"_type": "Call", "_fields": {"args": [{"_type": "BinOp", "_fields": {"op": {"_type": "Div", "_fields": {}}, "left": {"_type": "BinOp", "_fields": {"op": {"_type": "Div", "_fields": {}}, "left": {"_type": "BinOp", "_fields": {"op": {"_type": "Div", "_fields": {}}, "left": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "dir", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "right": {"_type": "Constant", "_fields": {"kind": null, "value": "root"}}}}, "right": {"_type": "Constant", "_fields": {"kind": null, "value": "dir"}}}}, "right": {"_type": "Constant", "_fields": {"kind": null, "value": "dir"}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "Create", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "EventType", "value": {"_type": "Name", "_fields": {"id": "monitors", "ctx": {"_type": "Load", "_fields": {}}}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "EventInfo", "value": {"_type": "Name", "_fields": {"id": "monitors", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}], "func": {"_type": "Name", "_fields": {"id": "DirInfoEvent", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "add", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "driver", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "run", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "driver", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}}}], "name": "testDirectoryInDirectory", "returns": null, "type_params": [], "type_comment": null, "decorator_list": [{"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "broken", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "mark", "value": {"_type": "Name", "_fields": {"id": "pytest", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "ticket", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "12566"}}}}]}}, {"_type": "Name", "_fields": {"id": "with_driver", "ctx": {"_type": "Load", "_fields": {}}}}]}}], "name": "TestMonitor", "bases": [{"_type": "Name", "_fields": {"id": "object", "ctx": {"_type": "Load", "_fields": {}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}
class TestMonitor(object): def teardown_method(self, method): MockMonitor.static_stop() def testBadId(self): with pytest.raises(omero.ApiUsageException): MockMonitor().fsEventHappened('foo', []) def testBadFileId(self): # Could cause infinite loop with pytest.raises(omero.ApiUsageException): MockMonitor().fsEventHappened('', [monitors.EventInfo()]) def testEmptyAdd(self): MockMonitor().fsEventHappened('', []) # Does nothing. @pytest.mark.broken(ticket="12566") @with_driver def testBasicAdd(self): self.driver.add(DirInfoEvent(1, monitors.EventInfo( self.dir / "root" / "dirtimeout", monitors.EventType.Create))) self.driver.run() @pytest.mark.broken(ticket="12566") @with_driver def testWithSingleImport(self): f = self.dir / "root" / "file" self.client.files = {str(f): [str(f)]} self.driver.add(DirInfoEvent(0, monitors.EventInfo( self.dir / "root" / "file", monitors.EventType.Create))) self.driver.run() time.sleep(0.25) @pytest.mark.broken(ticket="12566") @with_driver def testWithMultiImport(self): f1 = str(self.dir / "root" / "file1") f2 = str(self.dir / "root" / "file2") f3 = str(self.dir / "root" / "file3") f4 = str(self.dir / "root" / "file4") self.client.files = {f1: [f1, f2, f3, f4]} self.client.setDirImportWait(1) self.driver.add(DirInfoEvent(0.0, monitors.EventInfo( f1, monitors.EventType.Create))) self.driver.add(DirInfoEvent(100, monitors.EventInfo( f2, monitors.EventType.Create))) self.driver.add(DirInfoEvent(200, monitors.EventInfo( f3, monitors.EventType.Create))) self.driver.add(DirInfoEvent(300, monitors.EventInfo( f4, monitors.EventType.Create))) time.sleep(1) self.driver.run() @pytest.mark.broken(ticket="12566") @with_driver def testDirectoryInDirectory(self): self.driver.add(DirInfoEvent(1, monitors.EventInfo( self.dir / "root" / "dir", monitors.EventType.Create))) self.driver.add(DirInfoEvent(1, monitors.EventInfo( self.dir / "root" / "dir" / "dir", monitors.EventType.Create))) self.driver.run()
14,410
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10
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"f2", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "f3", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "f4", "ctx": {"_type": "Load", "_fields": {}}}}]}}]}}, "targets": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "attr": "files", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "client", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": 1}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "setDirImportWait", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "client", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": 0.0}}, {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "f1", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "Create", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "EventType", "value": {"_type": "Name", "_fields": {"id": "monitors", "ctx": {"_type": "Load", "_fields": {}}}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "EventInfo", "value": {"_type": "Name", "_fields": {"id": "monitors", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}], "func": {"_type": "Name", "_fields": {"id": "DirInfoEvent", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "add", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "driver", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": 100}}, {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "f2", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "Create", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "EventType", "value": {"_type": "Name", "_fields": {"id": "monitors", "ctx": {"_type": "Load", "_fields": {}}}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "EventInfo", "value": {"_type": "Name", "_fields": {"id": "monitors", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}], "func": {"_type": "Name", "_fields": {"id": "DirInfoEvent", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "add", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "driver", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": 200}}, {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "f3", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "Create", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "EventType", "value": {"_type": "Name", "_fields": {"id": "monitors", "ctx": {"_type": "Load", "_fields": {}}}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "EventInfo", "value": {"_type": "Name", "_fields": {"id": "monitors", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}], "func": {"_type": "Name", "_fields": {"id": "DirInfoEvent", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "add", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "driver", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": 300}}, {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "f4", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "Create", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "EventType", "value": {"_type": "Name", "_fields": {"id": "monitors", "ctx": {"_type": "Load", "_fields": {}}}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "EventInfo", "value": {"_type": "Name", "_fields": {"id": "monitors", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}], "func": {"_type": "Name", "_fields": {"id": "DirInfoEvent", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "add", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "driver", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": 1}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "sleep", "value": {"_type": "Name", "_fields": {"id": "time", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "run", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "driver", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}}}], "name": "testWithMultiImport", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def testWithMultiImport(self): f1 = str(self.dir / "root" / "file1") f2 = str(self.dir / "root" / "file2") f3 = str(self.dir / "root" / "file3") f4 = str(self.dir / "root" / "file4") self.client.files = {f1: [f1, f2, f3, f4]} self.client.setDirImportWait(1) self.driver.add(DirInfoEvent(0.0, monitors.EventInfo( f1, monitors.EventType.Create))) self.driver.add(DirInfoEvent(100, monitors.EventInfo( f2, monitors.EventType.Create))) self.driver.add(DirInfoEvent(200, monitors.EventInfo( f3, monitors.EventType.Create))) self.driver.add(DirInfoEvent(300, monitors.EventInfo( f4, monitors.EventType.Create))) time.sleep(1) self.driver.run()
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13
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def state_present(module, candidate, prefix): commands = list() invoke('set_route', module, commands, prefix) if commands: if module.params['vrf'] == 'default': candidate.add(commands, parents=[]) else: candidate.add(commands, parents=['vrf context {0}'.format(module.params['vrf'])])
14,412
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10
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def get_existing(module, prefix, warnings): key_map = ['tag', 'pref', 'route_name', 'next_hop'] netcfg = get_config(module) parents = 'vrf context {0}'.format(module.params['vrf']) prefix_to_regex = fix_prefix_to_regex(prefix) route_regex = ('.*ip\sroute\s{0}\s(?P<next_hop>\S+)(\sname\s(?P<route_name>\S+))?' '(\stag\s(?P<tag>\d+))?(\s(?P<pref>\d+)).*'.format(prefix_to_regex)) if module.params['vrf'] == 'default': config = str(netcfg) else: config = netcfg.get_section(parents) if config: try: match_route = re.match(route_regex, config, re.DOTALL) group_route = match_route.groupdict() for key in key_map: if key not in group_route: group_route[key] = '' group_route['prefix'] = prefix group_route['vrf'] = module.params['vrf'] except (AttributeError, TypeError): group_route = {} else: group_route = {} msg = ("VRF {0} didn't exist.".format(module.params['vrf'])) if msg not in warnings: warnings.append(msg) return group_route
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10
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def state_absent(module, candidate, prefix): netcfg = get_config(module) commands = list() parents = 'vrf context {0}'.format(module.params['vrf']) invoke('set_route', module, commands, prefix) if module.params['vrf'] == 'default': config = netcfg.get_section(commands[0]) if config: invoke('remove_route', module, commands, config, prefix) candidate.add(commands, parents=[]) else: config = netcfg.get_section(parents) splitted_config = config.split('\n') splitted_config = map(str.strip, splitted_config) if commands[0] in splitted_config: invoke('remove_route', module, commands, config, prefix) candidate.add(commands, parents=[parents])
14,414
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10
{"_type": "Module", "_fields": {"body": [{"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "address", "annotation": null, "type_comment": null}}, {"_type": "arg", "_fields": {"arg": "mask", "annotation": null, "type_comment": null}}, {"_type": "arg", "_fields": {"arg": "module", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "."}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "split", "value": {"_type": "Name", "_fields": {"id": "address", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "octects", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": 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[{"_type": "Gt", "_fields": {}}], "left": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "mask", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Name", "_fields": {"id": "int", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "comparators": [{"_type": "Constant", "_fields": {"kind": null, "value": 32}}]}}]}}, "orelse": []}}], "orelse": [], "handlers": [{"_type": "ExceptHandler", "_fields": {"body": [{"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "fail_json", "value": {"_type": "Name", "_fields": {"id": "module", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "msg", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "Mask may contain non-integer values."}}}}, {"_type": "keyword", "_fields": {"arg": "mask", "value": {"_type": "Name", "_fields": {"id": "mask", "ctx": {"_type": "Load", "_fields": {}}}}}}]}}}}], "name": null, "type": {"_type": "Name", "_fields": {"id": "ValueError", "ctx": {"_type": "Load", "_fields": {}}}}}}], "finalbody": []}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "mask", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Name", "_fields": {"id": "int", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}], "func": {"_type": "Name", "_fields": {"id": "get_dotted_mask", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "netmask", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Return", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "address", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "netmask", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Name", "_fields": {"id": "get_network_start", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "join", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "."}}}}, "keywords": []}}}}], "name": "network_from_string", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def network_from_string(address, mask, module): octects = address.split('.') if len(octects) > 4: module.fail_json(msg='Incorrect address format.', address=address) for octect in octects: try: if int(octect) < 0 or int(octect) > 255: module.fail_json(msg='Address may contain invalid values.', address=address) except ValueError: module.fail_json(msg='Address may contain non-integer values.', address=address) try: if int(mask) < 0 or int(mask) > 32: module.fail_json(msg='Incorrect mask value.', mask=mask) except ValueError: module.fail_json(msg='Mask may contain non-integer values.', mask=mask) netmask = get_dotted_mask(int(mask)) return '.'.join(get_network_start(address, netmask))
14,415
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9
{"_type": "Module", "_fields": {"body": [{"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "module", "annotation": null, "type_comment": null}}, {"_type": "arg", "_fields": {"arg": "commands", "annotation": null, "type_comment": null}}, {"_type": "arg", "_fields": {"arg": "prefix", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "prefix", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": "next_hop"}}, "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "params", "value": {"_type": "Name", "_fields": {"id": "module", "ctx": {"_type": "Load", "_fields": {}}}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "format", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "ip route {0} {1}"}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "route_cmd", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "If", "_fields": {"body": [{"_type": "AugAssign", "_fields": {"op": {"_type": "Add", "_fields": {}}, "value": {"_type": "Call", "_fields": {"args": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": "route_name"}}, "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "params", "value": {"_type": "Name", "_fields": {"id": "module", "ctx": {"_type": "Load", "_fields": {}}}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "format", "value": {"_type": "Constant", "_fields": {"kind": null, "value": " name {0}"}}}}, "keywords": []}}, "target": {"_type": "Name", "_fields": {"id": "route_cmd", "ctx": {"_type": "Store", "_fields": {}}}}}}], "test": {"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": "route_name"}}, "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "params", "value": {"_type": "Name", "_fields": {"id": "module", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "orelse": []}}, {"_type": "If", "_fields": {"body": [{"_type": "AugAssign", "_fields": {"op": {"_type": "Add", "_fields": {}}, "value": {"_type": "Call", "_fields": {"args": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": "tag"}}, "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "params", "value": {"_type": "Name", "_fields": {"id": "module", "ctx": {"_type": "Load", "_fields": {}}}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "format", "value": {"_type": "Constant", "_fields": {"kind": null, "value": " tag {0}"}}}}, "keywords": []}}, "target": {"_type": "Name", "_fields": {"id": "route_cmd", "ctx": {"_type": "Store", "_fields": {}}}}}}], "test": {"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": "tag"}}, "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "params", "value": {"_type": "Name", "_fields": {"id": "module", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "orelse": []}}, {"_type": "If", "_fields": {"body": [{"_type": "AugAssign", "_fields": {"op": {"_type": "Add", "_fields": {}}, "value": {"_type": "Call", "_fields": {"args": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": "pref"}}, "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "params", "value": {"_type": "Name", "_fields": {"id": "module", "ctx": {"_type": "Load", "_fields": {}}}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "format", "value": {"_type": "Constant", "_fields": {"kind": null, "value": " {0}"}}}}, "keywords": []}}, "target": {"_type": "Name", "_fields": {"id": "route_cmd", "ctx": {"_type": "Store", "_fields": {}}}}}}], "test": {"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": "pref"}}, "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "params", "value": {"_type": "Name", "_fields": {"id": "module", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "orelse": []}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "route_cmd", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "append", "value": {"_type": "Name", "_fields": {"id": "commands", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "set_route", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def set_route(module, commands, prefix): route_cmd = 'ip route {0} {1}'.format(prefix, module.params['next_hop']) if module.params['route_name']: route_cmd += ' name {0}'.format(module.params['route_name']) if module.params['tag']: route_cmd += ' tag {0}'.format(module.params['tag']) if module.params['pref']: route_cmd += ' {0}'.format(module.params['pref']) commands.append(route_cmd)
14,416
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10
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"right": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "mask", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Name", "_fields": {"id": "str", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}}}, "targets": [{"_type": "Name", "_fields": {"id": "normalized_prefix", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}], "test": {"_type": "Compare", "_fields": {"ops": [{"_type": "Eq", "_fields": {}}], "left": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "splitted_prefix", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Name", "_fields": {"id": "len", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "comparators": [{"_type": "Constant", "_fields": {"kind": null, "value": 2}}]}}, "orelse": [{"_type": "Assign", "_fields": {"value": {"_type": "BinOp", "_fields": {"op": {"_type": "Add", "_fields": {}}, "left": {"_type": "BinOp", "_fields": {"op": {"_type": "Add", "_fields": {}}, "left": {"_type": "Name", "_fields": {"id": "prefix", "ctx": {"_type": "Load", "_fields": {}}}}, "right": {"_type": "Constant", "_fields": {"kind": null, "value": "/"}}}}, "right": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": 32}}], "func": {"_type": "Name", "_fields": {"id": "str", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}}}, "targets": [{"_type": "Name", "_fields": {"id": "normalized_prefix", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}]}}]}}, {"_type": "Return", "_fields": {"value": {"_type": "Name", "_fields": {"id": "normalized_prefix", "ctx": {"_type": "Load", "_fields": {}}}}}}], "name": "normalize_prefix", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def normalize_prefix(module, prefix): splitted_prefix = prefix.split('/') address = splitted_prefix[0] if len(splitted_prefix) > 2: module.fail_json(msg='Incorrect address format.', address=address) elif len(splitted_prefix) == 2: mask = splitted_prefix[1] network = network_from_string(address, mask, module) normalized_prefix = str(network) + '/' + str(mask) else: normalized_prefix = prefix + '/' + str(32) return normalized_prefix
14,417
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"_fields": {"id": "proposed", "ctx": {"_type": "Load", "_fields": {}}}}, "targets": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": "proposed"}}, "value": {"_type": "Name", "_fields": {"id": "result", "ctx": {"_type": "Load", "_fields": {}}}}}}], "type_comment": null}}], "test": {"_type": "Compare", "_fields": {"ops": [{"_type": "Gt", "_fields": {}}], "left": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_verbosity", "value": {"_type": "Name", "_fields": {"id": "module", "ctx": {"_type": "Load", "_fields": {}}}}}}, "comparators": [{"_type": "Constant", "_fields": {"kind": null, "value": 0}}]}}, "orelse": []}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "exit_json", "value": {"_type": "Name", "_fields": {"id": "module", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": null, "value": {"_type": "Name", "_fields": {"id": "result", "ctx": {"_type": "Load", "_fields": {}}}}}}]}}}}], "name": "main", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def main(): argument_spec = dict( prefix=dict(required=True, type='str'), next_hop=dict(required=True, type='str'), vrf=dict(type='str', default='default'), tag=dict(type='str'), route_name=dict(type='str'), pref=dict(type='str'), state=dict(choices=['absent', 'present'], default='present'), include_defaults=dict(default=True), config=dict(), save=dict(type='bool', default=False) ) argument_spec.update(nxos_argument_spec) module = AnsibleModule(argument_spec=argument_spec, supports_check_mode=True) warnings = list() check_args(module, warnings) state = module.params['state'] result = dict(changed=False) warnings = list() prefix = invoke('normalize_prefix', module, module.params['prefix']) existing = invoke('get_existing', module, prefix, warnings) end_state = existing args = ['route_name', 'vrf', 'pref', 'tag', 'next_hop', 'prefix'] proposed = dict((k, v) for k, v in module.params.items() if v is not None and k in args) if state == 'present' or (state == 'absent' and existing): candidate = CustomNetworkConfig(indent=3) invoke('state_%s' % state, module, candidate, prefix) load_config(module, candidate) else: result['updates'] = [] result['warnings'] = warnings if module._verbosity > 0: end_state = invoke('get_existing', module, prefix, warnings) result['end_state'] = end_state result['existing'] = existing result['proposed'] = proposed module.exit_json(**result)
14,418
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11
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{"ctx": {"_type": "Load", "_fields": {}}, "attr": "publish_version", "value": {"_type": "Name", "_fields": {"id": "FLAGS", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "f", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "basename", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "path", "value": {"_type": "Name", "_fields": {"id": "os", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}]}}}}, "targets": [{"_type": "Name", "_fields": {"id": "object_name", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "Publishing %s as %s"}}, {"_type": "Name", "_fields": {"id": "f", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "object_name", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "info", "value": {"_type": "Name", "_fields": {"id": "logging", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "auth_http", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "project_id", "value": {"_type": "Name", "_fields": {"id": "FLAGS", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "bucket", "value": {"_type": "Name", "_fields": {"id": "FLAGS", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Name", "_fields": {"id": "f", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "object_name", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "acl", "value": {"_type": "Name", "_fields": {"id": "FLAGS", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Name", "_fields": {"id": "_upload", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "object_name", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "append", "value": {"_type": "Name", "_fields": {"id": "published", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "iter": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "publish", "value": {"_type": "Name", "_fields": {"id": "FLAGS", "ctx": {"_type": "Load", "_fields": {}}}}}}, "orelse": [], "target": 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"_fields": {"elt": {"_type": "BinOp", "_fields": {"op": {"_type": "Add", "_fields": {}}, "left": {"_type": "Name", "_fields": {"id": "base_url", "ctx": {"_type": "Load", "_fields": {}}}}, "right": {"_type": "Name", "_fields": {"id": "p", "ctx": {"_type": "Load", "_fields": {}}}}}}, "generators": [{"_type": "comprehension", "_fields": {"ifs": [], "iter": {"_type": "Name", "_fields": {"id": "published", "ctx": {"_type": "Load", "_fields": {}}}}, "target": {"_type": "Name", "_fields": {"id": "p", "ctx": {"_type": "Store", "_fields": {}}}}, "is_async": 0}}]}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "join", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "\n "}}}}, "keywords": []}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "info", "value": {"_type": "Name", "_fields": {"id": "logging", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "test": {"_type": "Name", "_fields": {"id": "published", "ctx": {"_type": "Load", "_fields": {}}}}, "orelse": []}}], "name": "main", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def main(argv): try: argv = FLAGS(argv) except gflags.FlagsError as e: logging.error('%s\\nUsage: %s ARGS\\n%s', e, argv[0], FLAGS) sys.exit(1) numeric_level = getattr(logging, FLAGS.logging_level.upper()) if not isinstance(numeric_level, int): logging.error('Invalid log level: %s' % FLAGS.logging_level) sys.exit(1) logging.basicConfig(level=numeric_level) if FLAGS.logging_level == 'DEBUG': httplib2.debuglevel = 1 def die(message): logging.fatal(message) sys.exit(2) if FLAGS.client_secrets is None: die('You must specify a client secrets file via --client_secrets') if FLAGS.project_id is None: die('You must specify a project ID via --project_id') if not FLAGS.bucket: die('You must specify a bucket via --bucket') if FLAGS.publish_version is None: die('You must specify a published version identifier via ' '--publish_version') auth_http = _authenticate(FLAGS.client_secrets) published = [] for f in FLAGS.publish: object_name = '%s/%s' % (FLAGS.publish_version, os.path.basename(f)) logging.info('Publishing %s as %s', f, object_name) _upload(auth_http, FLAGS.project_id, FLAGS.bucket, f, object_name, FLAGS.acl) published.append(object_name) if published: base_url = 'http://%s.storage.googleapis.com/' % FLAGS.bucket logging.info('Published:\n %s' % '\n '.join([base_url + p for p in published]))
14,419
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class RouteState(object): def __init__(self, request): self._prefixes = () # matched subdomain with aliases replaced by their main value self.primary_subdomains = () # tuple to be sure it's readonly self.primary_domain = '' # remaining subdomain part for match self._domain = request.host.split(':', 1)[0].decode('idna') self.subdomain = self._domain self.request = request def __copy__(self): copy = object.__new__(type(self)) copy.__dict__.update(self.__dict__) return copy def add_prefix(self, prefix): self = self.__copy__() self._prefixes += (prefix,) return self def add_subdomain(self, subdomain, alias_matched): self = self.__copy__() if subdomain: self.primary_subdomains = (subdomain, ) + self.primary_subdomains self.primary_domain = '.'.join(self.primary_subdomains) if alias_matched: self.subdomain = self.subdomain[:-len(alias_matched)].rstrip('.') return self @property def path(self): path = self.request.path if self._prefixes: length = sum(map(len, self._prefixes)) path = path[length:] return path
14,420
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def streaming(): consumer = oauth.Consumer(key=CONSUMER_KEY, secret=CONSUMER_SECRET) token = oauth.Token(key=ACCESS_TOKEN_KEY, secret=ACCESS_TOKEN_SECRET) request = oauth.Request.from_consumer_and_token(consumer, token, http_url=TWITTER_URL) request.sign_request(oauth.SignatureMethod_HMAC_SHA1(), consumer, token) res = urllib2.urlopen(request.to_url()) for r in res: data = json.loads(r) if data.has_key('delete'): continue if not data.has_key('lang'): continue lang = data['lang'] if lang == 'ja': pass elif lang == 'en': pass else: continue id_str = data['id_str'] lang = data['lang'] text = data['text'] created_at = data['created_at'] timestamp_ms = int(data['timestamp_ms']) print(id_str) # print(timestamp_ms) session.execute( """ INSERT INTO tweets (id_str, lang, text, created_at, timestamp_ms) VALUES (%s, %s, %s, %s, %s) """, (id_str, lang, text, created_at, timestamp_ms) ) pass
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12
{"_type": "Module", "_fields": {"body": [{"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "self", "annotation": null, "type_comment": null}}, {"_type": "arg", "_fields": {"arg": "subdomain", "annotation": null, "type_comment": null}}, {"_type": "arg", "_fields": {"arg": "alias_matched", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "__copy__", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "If", "_fields": {"body": [{"_type": "Assign", "_fields": {"value": {"_type": "BinOp", "_fields": {"op": {"_type": "Add", "_fields": {}}, "left": {"_type": "Tuple", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Name", "_fields": {"id": "subdomain", "ctx": {"_type": "Load", "_fields": {}}}}]}}, "right": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "primary_subdomains", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "targets": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "attr": "primary_subdomains", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "primary_subdomains", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "join", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "."}}}}, "keywords": []}}, "targets": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "attr": "primary_domain", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}], "type_comment": null}}], "test": {"_type": "Name", "_fields": {"id": "subdomain", "ctx": {"_type": "Load", "_fields": {}}}}, "orelse": []}}, {"_type": "If", "_fields": {"body": [{"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "."}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "rstrip", "value": {"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Slice", "_fields": {"step": null, "lower": null, "upper": {"_type": "UnaryOp", "_fields": {"op": {"_type": "USub", "_fields": {}}, "operand": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "alias_matched", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Name", "_fields": {"id": "len", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}}}}}, "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "subdomain", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}}}, "keywords": []}}, "targets": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "attr": "subdomain", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}], "type_comment": null}}], "test": {"_type": "Name", "_fields": {"id": "alias_matched", "ctx": {"_type": "Load", "_fields": {}}}}, "orelse": []}}, {"_type": "Return", "_fields": {"value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}], "name": "add_subdomain", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def add_subdomain(self, subdomain, alias_matched): self = self.__copy__() if subdomain: self.primary_subdomains = (subdomain, ) + self.primary_subdomains self.primary_domain = '.'.join(self.primary_subdomains) if alias_matched: self.subdomain = self.subdomain[:-len(alias_matched)].rstrip('.') return self
14,422
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[{"_type": "Name", "_fields": {"id": "floatX", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "astype", "value": {"_type": "Name", "_fields": {"id": "one_hot", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}]}}}}], "name": "prepare", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "name": "MNIST", "bases": [{"_type": "Name", "_fields": {"id": "DataSet", "ctx": {"_type": "Load", "_fields": {}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}
class MNIST(DataSet): def __init__(self, which_set='train', n_datapoints=None, fname="mnist.pkl.gz", preproc=[]): super(MNIST, self).__init__(preproc) _logger.info("Loading MNIST data") fname = datapath(fname) if fname[-3:] == ".gz": open_func = gzip.open else: open_func = open with open_func(fname) as f: (train_x, train_y), (valid_x, valid_y), (test_x, test_y) = pickle.load(f) if which_set == 'train': self.X, self.Y = self.prepare(train_x, train_y, n_datapoints) elif which_set == 'valid': self.X, self.Y = self.prepare(valid_x, valid_y, n_datapoints) elif which_set == 'test': self.X, self.Y = self.prepare(test_x, test_y, n_datapoints) elif which_set == 'salakhutdinov_train': train_x = np.concatenate([train_x, valid_x]) train_y = np.concatenate([train_y, valid_y]) self.X, self.Y = self.prepare(train_x, train_y, n_datapoints) elif which_set == 'salakhutdinov_valid': train_x = np.concatenate([train_x, valid_x])[::-1] train_y = np.concatenate([train_y, valid_y])[::-1] self.X, self.Y = self.prepare(train_x, train_y, n_datapoints) else: raise ValueError("Unknown dataset %s" % which_set) self.n_datapoints = self.X.shape[0] def prepare(self, x, y, n_datapoints): N = x.shape[0] assert N == y.shape[0] if n_datapoints is not None: N = n_datapoints x = x[:N] y = y[:N] one_hot = np.zeros((N, 10), dtype=floatX) for n in xrange(N): one_hot[n, y[n]] = 1. return x.astype(floatX), one_hot.astype(floatX)
14,423
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def __init__(self, which_set='train', n_datapoints=None, fname="mnist.pkl.gz", preproc=[]): super(MNIST, self).__init__(preproc) _logger.info("Loading MNIST data") fname = datapath(fname) if fname[-3:] == ".gz": open_func = gzip.open else: open_func = open with open_func(fname) as f: (train_x, train_y), (valid_x, valid_y), (test_x, test_y) = pickle.load(f) if which_set == 'train': self.X, self.Y = self.prepare(train_x, train_y, n_datapoints) elif which_set == 'valid': self.X, self.Y = self.prepare(valid_x, valid_y, n_datapoints) elif which_set == 'test': self.X, self.Y = self.prepare(test_x, test_y, n_datapoints) elif which_set == 'salakhutdinov_train': train_x = np.concatenate([train_x, valid_x]) train_y = np.concatenate([train_y, valid_y]) self.X, self.Y = self.prepare(train_x, train_y, n_datapoints) elif which_set == 'salakhutdinov_valid': train_x = np.concatenate([train_x, valid_x])[::-1] train_y = np.concatenate([train_y, valid_y])[::-1] self.X, self.Y = self.prepare(train_x, train_y, n_datapoints) else: raise ValueError("Unknown dataset %s" % which_set) self.n_datapoints = self.X.shape[0]
14,424
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17
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def basic_info(data,meta,rescale=True,rotate=False,user_peak=0,nonlinear=None): print('in basic_info') #from . import read_grid,parse_inp,read_inp,show dims = data.shape ndims = len(dims) mxg = meta['MXG']['v'] if ndims ==4: nt,nx,ny,nz = data.shape print(nt,nx,ny) else: print("something with dimesions") dc = data.mean(1).mean(1).mean(1) # there MUST be a way to indicate all axis at once amp = abs(data).max(1).max(1).max(1) dt = meta['dt']['v'] if rescale: amp_o = amp - dc fourDamp = np.repeat(amp_o,nx*ny*nz) fourDamp = fourDamp.reshape(nt,nx,ny,nz) dc_n = old_div(dc,amp_o) data_n = old_div(data,fourDamp) print(data.shape) dfdt = np.gradient(data)[0] dfdt = abs(dfdt).max(1).max(1).max(1) ave = {'amp':amp,'dc':dc,'amp_o':amp_o,'dfdt':dfdt} else: print("no rescaling") ave = {'amp':amp,'dc':dc} if nonlinear != None: #add nonlinear part if user provides nl = abs(nonlinear[:,mxg:-1.0*mxg,:,:]).max(1).max(1).max(1) nl_norm = (old_div(nl,dfdt)) *dt ave['nl'] =nl ave['nl_norm'] = nl_norm if rotate: print('rotate stuff') # will need to provide some grid geometry to do this one else: print('or not') #let's identify the dominant modes, for now at every [t,x] slice #if the data set is too large we can average over x peaks_db = fft_info(data,user_peak,meta=meta) #Nt X Nx X (# of loc. max) list of dict #print peaks[0]['gamma'] return peaks_db,ave
14,425
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9
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def prepare(self, x, y, n_datapoints): N = x.shape[0] assert N == y.shape[0] if n_datapoints is not None: N = n_datapoints x = x[:N] y = y[:N] one_hot = np.zeros((N, 10), dtype=floatX) for n in xrange(N): one_hot[n, y[n]] = 1. return x.astype(floatX), one_hot.astype(floatX)
14,426
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11
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def test_matches_basic(): instances = [Basic(b1, b1, b2), Basic(b1, b2, b1), Basic(b2, b1, b1), Basic(b1, b2), Basic(b2, b1), b2, b1] for i, b_i in enumerate(instances): for j, b_j in enumerate(instances): if i == j: assert b_i.matches(b_j) == {} else: assert b_i.matches(b_j) is None assert b1.match(b1) == {}
14,427
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9
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def test_equality(): instances = [b1, b2, b3, b21, Basic(b1, b1, b1), Basic] for i, b_i in enumerate(instances): for j, b_j in enumerate(instances): assert (b_i == b_j) == (i == j) assert (b_i != b_j) == (i != j) assert Basic() != [] assert not(Basic() == []) assert Basic() != 0 assert not(Basic() == 0)
14,428
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def test_subs(): assert b21.subs(b2, b1) == Basic(b1, b1) assert b21.subs(b2, b21) == Basic(b21, b1) assert b3.subs(b2, b1) == b2 assert b21.subs([(b2, b1), (b1, b2)]) == Basic(b2, b2) assert b21.subs({b1: b2, b2: b1}) == Basic(b2, b2) raises(ValueError, lambda: b21.subs('bad arg')) raises(ValueError, lambda: b21.subs(b1, b2, b3))
14,429
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9
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def test_xreplace(): assert b21.xreplace({b2: b1}) == Basic(b1, b1) assert b21.xreplace({b2: b21}) == Basic(b21, b1) assert b3.xreplace({b2: b1}) == b2 assert Basic(b1, b2).xreplace({b1: b2, b2: b1}) == Basic(b2, b1) assert Atom(b1).xreplace({b1: b2}) == Atom(b1) assert Atom(b1).xreplace({Atom(b1): b2}) == b2 raises(TypeError, lambda: b1.xreplace()) raises(TypeError, lambda: b1.xreplace([b1, b2]))
14,430
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def test_Singleton(): global instantiated instantiated = 0 class MySingleton(with_metaclass(Singleton, Basic)): def __new__(cls): global instantiated instantiated += 1 return Basic.__new__(cls) assert instantiated == 1 assert MySingleton() is not Basic() assert MySingleton() is MySingleton() assert S.MySingleton is MySingleton() assert instantiated == 1 class MySingleton_sub(MySingleton): pass assert instantiated == 2 assert MySingleton_sub() is not MySingleton() assert MySingleton_sub() is MySingleton_sub()
14,431
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11
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def test_preorder_traversal(): expr = Basic(b21, b3) assert list( preorder_traversal(expr)) == [expr, b21, b2, b1, b1, b3, b2, b1] assert list(preorder_traversal(('abc', ('d', 'ef')))) == [ ('abc', ('d', 'ef')), 'abc', ('d', 'ef'), 'd', 'ef'] result = [] pt = preorder_traversal(expr) for i in pt: result.append(i) if i == b2: pt.skip() assert result == [expr, b21, b2, b1, b3, b2] w, x, y, z = symbols('w:z') expr = z + w*(x + y) assert list(preorder_traversal([expr], keys=default_sort_key)) == \ [[w*(x + y) + z], w*(x + y) + z, z, w*(x + y), w, x + y, x, y] assert list(preorder_traversal((x + y)*z, keys=True)) == \ [z*(x + y), z, x + y, x, y]
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10
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def test_call(): x, y = symbols('x y') # See the long history of this in issues 5026 and 5105. raises(TypeError, lambda: sin(x)({ x : 1, sin(x) : 2})) raises(TypeError, lambda: sin(x)(1)) # No effect as there are no callables assert sin(x).rcall(1) == sin(x) assert (1 + sin(x)).rcall(1) == 1 + sin(x) # Effect in the pressence of callables l = Lambda(x, 2*x) assert (l + x).rcall(y) == 2*y + x assert (x**l).rcall(2) == x**4 # TODO UndefinedFunction does not subclass Expr #f = Function('f') #assert (2*f)(x) == 2*f(x) assert (Q.real & Q.positive).rcall(x) == Q.real(x) & Q.positive(x)
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9
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[{"_type": "Constant", "_fields": {"kind": null, "value": "type"}}], "values": [{"_type": "Constant", "_fields": {"kind": null, "value": "global"}}]}}, {"_type": "BinOp", "_fields": {"op": {"_type": "Mod", "_fields": {}}, "left": {"_type": "Constant", "_fields": {"kind": null, "value": "projects/%s/logs/%s"}}, "right": {"_type": "Tuple", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "PROJECT", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "LOGGER_NAME", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}]}}}}]}}, "targets": [{"_type": "Name", "_fields": {"id": "ENTRY", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Dict", "_fields": {"keys": [{"_type": "Constant", "_fields": {"kind": null, "value": "entries"}}], "values": [{"_type": "List", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Name", "_fields": {"id": "ENTRY", "ctx": {"_type": "Load", "_fields": {}}}}]}}]}}, "targets": [{"_type": "Name", "_fields": {"id": "SENT", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Dict", "_fields": {"keys": [], "values": []}}], "func": {"_type": "Name", "_fields": {"id": "_Connection", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "conn", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "conn", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Name", "_fields": {"id": "_Client", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "client", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "client", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_make_one", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "api", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "List", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Name", "_fields": {"id": "ENTRY", "ctx": {"_type": "Load", "_fields": {}}}}]}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "write_entries", "value": {"_type": "Name", "_fields": {"id": "api", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": "method"}}, "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_called_with", "value": {"_type": "Name", "_fields": {"id": "conn", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, {"_type": "Constant", "_fields": {"kind": null, "value": "POST"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertEqual", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Assign", "_fields": {"value": {"_type": "BinOp", "_fields": {"op": {"_type": "Mod", "_fields": {}}, "left": {"_type": "Constant", "_fields": {"kind": null, "value": "/%s"}}, "right": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "WRITE_ENTRIES_PATH", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "targets": [{"_type": "Name", "_fields": {"id": "path", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": "path"}}, "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_called_with", "value": {"_type": "Name", "_fields": {"id": "conn", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, {"_type": "Name", "_fields": {"id": "path", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertEqual", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": "data"}}, "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_called_with", "value": {"_type": "Name", "_fields": {"id": "conn", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, {"_type": "Name", "_fields": {"id": "SENT", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertEqual", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "test_write_entries_single", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def test_write_entries_single(self): TEXT = 'TEXT' ENTRY = { 'textPayload': TEXT, 'resource': { 'type': 'global', }, 'logName': 'projects/%s/logs/%s' % ( self.PROJECT, self.LOGGER_NAME), } SENT = { 'entries': [ENTRY], } conn = _Connection({}) client = _Client(conn) api = self._make_one(client) api.write_entries([ENTRY]) self.assertEqual(conn._called_with['method'], 'POST') path = '/%s' % self.WRITE_ENTRIES_PATH self.assertEqual(conn._called_with['path'], path) self.assertEqual(conn._called_with['data'], SENT)
14,434
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"_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Assign", "_fields": {"value": {"_type": "BinOp", "_fields": {"op": {"_type": "Mod", "_fields": {}}, "left": {"_type": "Constant", "_fields": {"kind": null, "value": "/%s"}}, "right": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "WRITE_ENTRIES_PATH", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "targets": [{"_type": "Name", "_fields": {"id": "path", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": "path"}}, "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_called_with", "value": {"_type": "Name", "_fields": {"id": "conn", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, {"_type": "Name", "_fields": {"id": "path", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertEqual", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": "data"}}, "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_called_with", "value": {"_type": "Name", "_fields": {"id": "conn", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, {"_type": "Name", "_fields": {"id": "SENT", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertEqual", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "test_write_entries_multiple", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def test_write_entries_multiple(self): TEXT = 'TEXT' LOG_NAME = 'projects/%s/logs/%s' % (self.PROJECT, self.LOGGER_NAME) RESOURCE = { 'type': 'global', } LABELS = { 'baz': 'qux', 'spam': 'eggs', } ENTRY1 = { 'textPayload': TEXT, } ENTRY2 = { 'jsonPayload': {'foo': 'bar'}, } SENT = { 'logName': LOG_NAME, 'resource': RESOURCE, 'labels': LABELS, 'entries': [ENTRY1, ENTRY2], } conn = _Connection({}) client = _Client(conn) api = self._make_one(client) api.write_entries([ENTRY1, ENTRY2], LOG_NAME, RESOURCE, LABELS) self.assertEqual(conn._called_with['method'], 'POST') path = '/%s' % self.WRITE_ENTRIES_PATH self.assertEqual(conn._called_with['path'], path) self.assertEqual(conn._called_with['data'], SENT)
14,435
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8
{"_type": "Module", "_fields": {"body": [{"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "self", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Assign", "_fields": {"value": {"_type": "BinOp", "_fields": {"op": {"_type": "Mod", "_fields": {}}, "left": {"_type": "Constant", "_fields": {"kind": null, "value": "/projects/%s/logs/%s"}}, "right": {"_type": "Tuple", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "PROJECT", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "LOGGER_NAME", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}]}}}}, "targets": [{"_type": "Name", "_fields": {"id": "path", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Dict", "_fields": {"keys": [], "values": []}}], "func": {"_type": "Name", "_fields": {"id": "_Connection", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "conn", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "conn", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Name", "_fields": {"id": "_Client", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "client", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "client", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_make_one", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "api", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "PROJECT", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "LOGGER_NAME", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "logger_delete", "value": {"_type": "Name", "_fields": {"id": "api", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": "method"}}, "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_called_with", "value": {"_type": "Name", "_fields": {"id": "conn", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, {"_type": "Constant", "_fields": {"kind": null, "value": "DELETE"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertEqual", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": "path"}}, "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_called_with", "value": {"_type": "Name", "_fields": {"id": "conn", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, {"_type": "Name", "_fields": {"id": "path", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertEqual", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "test_logger_delete", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def test_logger_delete(self): path = '/projects/%s/logs/%s' % (self.PROJECT, self.LOGGER_NAME) conn = _Connection({}) client = _Client(conn) api = self._make_one(client) api.logger_delete(self.PROJECT, self.LOGGER_NAME) self.assertEqual(conn._called_with['method'], 'DELETE') self.assertEqual(conn._called_with['path'], path)
14,436
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8
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def test_sink_get_hit(self): RESPONSE = { 'name': self.SINK_PATH, 'filter': self.FILTER, 'destination': self.DESTINATION_URI, } conn = _Connection(RESPONSE) client = _Client(conn) api = self._make_one(client) response = api.sink_get(self.PROJECT, self.SINK_NAME) self.assertEqual(response, RESPONSE) self.assertEqual(conn._called_with['method'], 'GET') path = '/projects/%s/sinks/%s' % (self.PROJECT, self.SINK_NAME) self.assertEqual(conn._called_with['path'], path)
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8
{"_type": "Module", "_fields": {"body": [{"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "self", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Assign", "_fields": {"value": {"_type": "Dict", "_fields": {"keys": [{"_type": "Constant", "_fields": {"kind": null, "value": "name"}}, {"_type": "Constant", "_fields": {"kind": null, "value": "filter"}}, {"_type": "Constant", "_fields": {"kind": null, "value": "destination"}}], "values": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "SINK_NAME", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "FILTER", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": 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{"_type": "Load", "_fields": {}}}}}}]}}}}, "targets": [{"_type": "Name", "_fields": {"id": "path", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Dict", "_fields": {"keys": [{"_type": "Constant", "_fields": {"kind": null, "value": "method"}}, {"_type": "Constant", "_fields": {"kind": null, "value": "path"}}, {"_type": "Constant", "_fields": {"kind": null, "value": "data"}}, {"_type": "Constant", "_fields": {"kind": null, "value": "query_params"}}], "values": [{"_type": "Constant", "_fields": {"kind": null, "value": "POST"}}, {"_type": "Name", "_fields": {"id": "path", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "sent", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Dict", "_fields": {"keys": [{"_type": "Constant", "_fields": {"kind": null, "value": "uniqueWriterIdentity"}}], "values": [{"_type": "Constant", "_fields": {"kind": null, "value": true}}]}}]}}, "targets": [{"_type": "Name", "_fields": {"id": "expected", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_called_with", "value": {"_type": "Name", "_fields": {"id": "conn", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Name", "_fields": {"id": "expected", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertEqual", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "test_sink_create_ok", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def test_sink_create_ok(self): sent = { 'name': self.SINK_NAME, 'filter': self.FILTER, 'destination': self.DESTINATION_URI, } after_create = sent.copy() after_create['writerIdentity'] = self.WRITER_IDENTITY conn = _Connection(after_create) client = _Client(conn) api = self._make_one(client) returned = api.sink_create( self.PROJECT, self.SINK_NAME, self.FILTER, self.DESTINATION_URI, unique_writer_identity=True, ) self.assertEqual(returned, after_create) path = '/projects/%s/sinks' % (self.PROJECT,) expected = { 'method': 'POST', 'path': path, 'data': sent, 'query_params': {'uniqueWriterIdentity': True}, } self.assertEqual(conn._called_with, expected)
14,438
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8
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def test_sink_delete_hit(self): conn = _Connection({}) client = _Client(conn) api = self._make_one(client) api.sink_delete(self.PROJECT, self.SINK_NAME) self.assertEqual(conn._called_with['method'], 'DELETE') path = '/projects/%s/sinks/%s' % (self.PROJECT, self.SINK_NAME) self.assertEqual(conn._called_with['path'], path)
14,439
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{"_type": "Load", "_fields": {}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "client", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "client", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_make_one", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "api", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "PROJECT", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "SINK_NAME", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "FILTER", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "DESTINATION_URI", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "sink_update", "value": {"_type": "Name", "_fields": {"id": "api", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "unique_writer_identity", "value": {"_type": "Constant", "_fields": {"kind": null, "value": true}}}}]}}, "targets": [{"_type": "Name", "_fields": {"id": "returned", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "returned", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "after_update", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertEqual", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Assign", "_fields": {"value": {"_type": "BinOp", "_fields": {"op": {"_type": "Mod", "_fields": {}}, "left": {"_type": "Constant", "_fields": {"kind": null, "value": "/projects/%s/sinks/%s"}}, "right": {"_type": "Tuple", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "PROJECT", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "SINK_NAME", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}]}}}}, "targets": [{"_type": "Name", "_fields": {"id": "path", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Dict", "_fields": {"keys": [{"_type": "Constant", "_fields": {"kind": null, "value": "method"}}, {"_type": "Constant", "_fields": {"kind": null, "value": "path"}}, {"_type": "Constant", "_fields": {"kind": null, "value": "data"}}, {"_type": "Constant", "_fields": {"kind": null, "value": "query_params"}}], "values": [{"_type": "Constant", "_fields": {"kind": null, "value": "PUT"}}, {"_type": "Name", "_fields": {"id": "path", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "sent", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Dict", "_fields": {"keys": [{"_type": "Constant", "_fields": {"kind": null, "value": "uniqueWriterIdentity"}}], "values": [{"_type": "Constant", "_fields": {"kind": null, "value": true}}]}}]}}, "targets": [{"_type": "Name", "_fields": {"id": "expected", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_called_with", "value": {"_type": "Name", "_fields": {"id": "conn", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Name", "_fields": {"id": "expected", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertEqual", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "test_sink_update_hit", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def test_sink_update_hit(self): sent = { 'name': self.SINK_NAME, 'filter': self.FILTER, 'destination': self.DESTINATION_URI, } after_update = sent.copy() after_update['writerIdentity'] = self.WRITER_IDENTITY conn = _Connection(after_update) client = _Client(conn) api = self._make_one(client) returned = api.sink_update( self.PROJECT, self.SINK_NAME, self.FILTER, self.DESTINATION_URI, unique_writer_identity=True) self.assertEqual(returned, after_update) path = '/projects/%s/sinks/%s' % (self.PROJECT, self.SINK_NAME) expected = { 'method': 'PUT', 'path': path, 'data': sent, 'query_params': {'uniqueWriterIdentity': True}, } self.assertEqual(conn._called_with, expected)
14,440
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8
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def test_metric_create_ok(self): SENT = { 'name': self.METRIC_NAME, 'filter': self.FILTER, 'description': self.DESCRIPTION, } conn = _Connection({}) client = _Client(conn) api = self._make_one(client) api.metric_create( self.PROJECT, self.METRIC_NAME, self.FILTER, self.DESCRIPTION) self.assertEqual(conn._called_with['method'], 'POST') path = '/projects/%s/metrics' % (self.PROJECT,) self.assertEqual(conn._called_with['path'], path) self.assertEqual(conn._called_with['data'], SENT)
14,441
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8
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{"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "PROJECT", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "METRIC_NAME", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}]}}}}, "targets": [{"_type": "Name", "_fields": {"id": "path", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": "path"}}, "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_called_with", "value": {"_type": "Name", "_fields": {"id": "conn", "ctx": {"_type": "Load", "_fields": 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def test_metric_get_hit(self): RESPONSE = { 'name': self.METRIC_NAME, 'filter': self.FILTER, 'description': self.DESCRIPTION, } conn = _Connection(RESPONSE) client = _Client(conn) api = self._make_one(client) response = api.metric_get(self.PROJECT, self.METRIC_NAME) self.assertEqual(response, RESPONSE) self.assertEqual(conn._called_with['method'], 'GET') path = '/projects/%s/metrics/%s' % (self.PROJECT, self.METRIC_NAME) self.assertEqual(conn._called_with['path'], path)
14,442
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8
{"_type": "Module", "_fields": {"body": [{"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "self", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Assign", "_fields": {"value": {"_type": "Dict", "_fields": {"keys": [{"_type": "Constant", "_fields": {"kind": null, "value": "name"}}, {"_type": "Constant", "_fields": {"kind": null, "value": "filter"}}, {"_type": "Constant", "_fields": {"kind": null, "value": "description"}}], "values": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "METRIC_NAME", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "FILTER", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "DESCRIPTION", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}]}}, "targets": [{"_type": "Name", "_fields": {"id": "SENT", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Dict", "_fields": {"keys": [], "values": []}}], "func": {"_type": "Name", "_fields": {"id": "_Connection", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "conn", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "conn", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Name", "_fields": {"id": "_Client", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "client", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "client", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_make_one", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "api", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "PROJECT", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "METRIC_NAME", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "FILTER", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "DESCRIPTION", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "metric_update", "value": {"_type": "Name", "_fields": {"id": "api", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": "method"}}, "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_called_with", "value": {"_type": "Name", "_fields": {"id": "conn", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, {"_type": "Constant", "_fields": {"kind": null, "value": "PUT"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertEqual", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Assign", "_fields": {"value": {"_type": "BinOp", "_fields": {"op": {"_type": "Mod", "_fields": {}}, "left": {"_type": "Constant", "_fields": {"kind": null, "value": "/projects/%s/metrics/%s"}}, "right": {"_type": "Tuple", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "PROJECT", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "METRIC_NAME", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}]}}}}, "targets": [{"_type": "Name", "_fields": {"id": "path", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": "path"}}, "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_called_with", "value": {"_type": "Name", "_fields": {"id": "conn", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, {"_type": "Name", "_fields": {"id": "path", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertEqual", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": "data"}}, "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_called_with", "value": {"_type": "Name", "_fields": {"id": "conn", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, {"_type": "Name", "_fields": {"id": "SENT", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertEqual", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "test_metric_update_hit", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def test_metric_update_hit(self): SENT = { 'name': self.METRIC_NAME, 'filter': self.FILTER, 'description': self.DESCRIPTION, } conn = _Connection({}) client = _Client(conn) api = self._make_one(client) api.metric_update( self.PROJECT, self.METRIC_NAME, self.FILTER, self.DESCRIPTION) self.assertEqual(conn._called_with['method'], 'PUT') path = '/projects/%s/metrics/%s' % (self.PROJECT, self.METRIC_NAME) self.assertEqual(conn._called_with['path'], path) self.assertEqual(conn._called_with['data'], SENT)
14,443
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8
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def test_metric_delete_hit(self): conn = _Connection({}) client = _Client(conn) api = self._make_one(client) api.metric_delete(self.PROJECT, self.METRIC_NAME) self.assertEqual(conn._called_with['method'], 'DELETE') path = '/projects/%s/metrics/%s' % (self.PROJECT, self.METRIC_NAME) self.assertEqual(conn._called_with['path'], path)
14,444
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14
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def run(self, conn, tmp, module_name, module_args, inject, complex_args=None, **kwargs): if self.runner.noop_on_check(inject): return ReturnData(conn=conn, comm_ok=True, result=dict(skipped=True, msg='check mode not supported for this module')) args = {} if complex_args: args.update(complex_args) args.update(parse_kv(module_args)) if not 'hostname' in args and not 'name' in args: raise ae("'name' is a required argument.") result = {} # Parse out any hostname:port patterns new_name = args.get('name', args.get('hostname', None)) vv("creating host via 'add_host': hostname=%s" % new_name) if ":" in new_name: new_name, new_port = new_name.split(":") args['ansible_ssh_port'] = new_port # redefine inventory and get group "all" inventory = self.runner.inventory allgroup = inventory.get_group('all') # check if host in cache, add if not if new_name in inventory._hosts_cache: new_host = inventory._hosts_cache[new_name] else: new_host = Host(new_name) # only groups can be added directly to inventory inventory._hosts_cache[new_name] = new_host allgroup.add_host(new_host) # Add any variables to the new_host for k in args.keys(): if not k in [ 'name', 'hostname', 'groupname', 'groups' ]: new_host.set_variable(k, args[k]) groupnames = args.get('groupname', args.get('groups', args.get('group', ''))) # add it to the group if that was specified if groupnames != '': for group_name in groupnames.split(","): group_name = group_name.strip() if not inventory.get_group(group_name): new_group = Group(group_name) inventory.add_group(new_group) grp = inventory.get_group(group_name) grp.add_host(new_host) # add this host to the group cache if inventory._groups_list is not None: if group_name in inventory._groups_list: if new_host.name not in inventory._groups_list[group_name]: inventory._groups_list[group_name].append(new_host.name) vv("added host to group via add_host module: %s" % group_name) result['new_groups'] = groupnames.split(",") result['new_host'] = new_name # clear pattern caching completely since it's unpredictable what # patterns may have referenced the group inventory.clear_pattern_cache() return ReturnData(conn=conn, comm_ok=True, result=result)
14,445
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9
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class FrenchDeck(abc.MutableSequence): ranks = [str(n) for n in range(2, 11)] + list('JQKA') suits = 'spades diamonds clubs hearts'.split() def __init__(self): self._cards = [Card(rank, suit) for suit in self.suits for rank in self.ranks] def __len__(self): return len(self._cards) def __getitem__(self, position): return self._cards[position] def __setitem__(self, position, card): self._cards[position] = card def __delitem__(self, position): del self._cards[position] def insert(self, position, card): self._cards.insert(position, card)
14,446
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10
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def scan_speeches(abbr): sessions = defaultdict(_speech_report_dict) for speech in db.speeches.find({settings.LEVEL_FIELD: abbr}): session = speech['session'] obj = sessions[session] if "speaker_id" in speech and speech['speaker_id']: obj['_speakers_with_id_count'] += 1 else: obj['unmatched_speakers'].add(speech['speaker']) sets = ['unmatched_speakers'] for session in sessions: sobj = sessions[session] for key in sets: sobj[key] = list(sobj[key]) sessions[session] = sobj return sessions
14,447
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9
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def write_pbr_json(cmd, basename, filename): if not hasattr(cmd.distribution, 'pbr') or not cmd.distribution.pbr: return git_dir = git._run_git_functions() if not git_dir: return values = dict() git_version = git.get_git_short_sha(git_dir) is_release = git.get_is_release(git_dir) if git_version is not None: values['git_version'] = git_version values['is_release'] = is_release cmd.write_file('pbr', filename, json.dumps(values))
14,448
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9
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def authenticate_credentials(self, userid, password): if password: return try: key = ApiKey.objects.get_from_cache(key=userid) except ApiKey.DoesNotExist: return None if not key.is_active: raise AuthenticationFailed('Key is disabled') raven.tags_context({ 'api_key': userid, }) return (AnonymousUser(), key)
14,449
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10
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class ApiKeyAuthentication(QuietBasicAuthentication): def authenticate_credentials(self, userid, password): if password: return try: key = ApiKey.objects.get_from_cache(key=userid) except ApiKey.DoesNotExist: return None if not key.is_active: raise AuthenticationFailed('Key is disabled') raven.tags_context({ 'api_key': userid, }) return (AnonymousUser(), key)
14,450
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10
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class ProjectKeyAuthentication(QuietBasicAuthentication): def authenticate_credentials(self, userid, password): try: pk = ProjectKey.objects.get_from_cache(public_key=userid) except ProjectKey.DoesNotExist: return None if not constant_time_compare(pk.secret_key, password): return None if not pk.is_active: raise AuthenticationFailed('Key is disabled') if not pk.roles.api: raise AuthenticationFailed('Key does not allow API access') return (AnonymousUser(), pk)
14,451
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9
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def authenticate_credentials(self, userid, password): try: pk = ProjectKey.objects.get_from_cache(public_key=userid) except ProjectKey.DoesNotExist: return None if not constant_time_compare(pk.secret_key, password): return None if not pk.is_active: raise AuthenticationFailed('Key is disabled') if not pk.roles.api: raise AuthenticationFailed('Key does not allow API access') return (AnonymousUser(), pk)
14,452
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class StudentTTest(tf.test.TestCase): def testStudentPDFAndLogPDF(self): with self.test_session(): batch_size = 6 df = tf.constant([3.0] * batch_size) mu = tf.constant([7.0] * batch_size) sigma = tf.constant([8.0] * batch_size) df_v = 3.0 mu_v = 7.0 sigma_v = 8.0 t = np.array([-2.5, 2.5, 8.0, 0.0, -1.0, 2.0], dtype=np.float32) student = tf.contrib.distributions.StudentT(df, mu=mu, sigma=sigma) log_pdf = student.log_pdf(t) self.assertEquals(log_pdf.get_shape(), (6,)) log_pdf_values = log_pdf.eval() pdf = student.pdf(t) self.assertEquals(pdf.get_shape(), (6,)) pdf_values = pdf.eval() expected_log_pdf = stats.t.logpdf(t, df_v, loc=mu_v, scale=sigma_v) expected_pdf = stats.t.pdf(t, df_v, loc=mu_v, scale=sigma_v) self.assertAllClose(expected_log_pdf, log_pdf_values) self.assertAllClose(np.log(expected_pdf), log_pdf_values) self.assertAllClose(expected_pdf, pdf_values) self.assertAllClose(np.exp(expected_log_pdf), pdf_values) def testStudentLogPDFMultidimensional(self): with self.test_session(): batch_size = 6 df = tf.constant([[1.5, 7.2]] * batch_size) mu = tf.constant([[3.0, -3.0]] * batch_size) sigma = tf.constant([[math.sqrt(10.0), math.sqrt(15.0)]] * batch_size) df_v = np.array([1.5, 7.2]) mu_v = np.array([3.0, -3.0]) sigma_v = np.array([np.sqrt(10.0), np.sqrt(15.0)]) t = np.array([[-2.5, 2.5, 4.0, 0.0, -1.0, 2.0]], dtype=np.float32).T student = tf.contrib.distributions.StudentT(df, mu=mu, sigma=sigma) log_pdf = student.log_pdf(t) log_pdf_values = log_pdf.eval() self.assertEqual(log_pdf.get_shape(), (6, 2)) pdf = student.pdf(t) pdf_values = pdf.eval() self.assertEqual(pdf.get_shape(), (6, 2)) expected_log_pdf = stats.t.logpdf(t, df_v, loc=mu_v, scale=sigma_v) expected_pdf = stats.t.pdf(t, df_v, loc=mu_v, scale=sigma_v) self.assertAllClose(expected_log_pdf, log_pdf_values) self.assertAllClose(np.log(expected_pdf), log_pdf_values) self.assertAllClose(expected_pdf, pdf_values) self.assertAllClose(np.exp(expected_log_pdf), pdf_values) def testStudentEntropy(self): df_v = np.array([[2., 3., 7.]]) # 1x3 mu_v = np.array([[1., -1, 0]]) # 1x3 sigma_v = np.array([[1., 2., 3.]]).T # transposed => 3x1 with self.test_session(): student = tf.contrib.distributions.StudentT(df=df_v, mu=mu_v, sigma=sigma_v) ent = student.entropy() ent_values = ent.eval() # Help scipy broadcast to 3x3 ones = np.array([[1, 1, 1]]) sigma_bc = sigma_v * ones mu_bc = ones.T * mu_v df_bc = ones.T * df_v expected_entropy = stats.t.entropy( np.reshape(df_bc, [-1]), loc=np.reshape(mu_bc, [-1]), scale=np.reshape(sigma_bc, [-1])) expected_entropy = np.reshape(expected_entropy, df_bc.shape) self.assertAllClose(expected_entropy, ent_values) def testStudentSample(self): with self.test_session(): df = tf.constant(4.0) mu = tf.constant(3.0) sigma = tf.constant(math.sqrt(10.0)) df_v = 4.0 mu_v = 3.0 sigma_v = np.sqrt(10.0) n = tf.constant(200000) student = tf.contrib.distributions.StudentT(df=df, mu=mu, sigma=sigma) samples = student.sample(n) sample_values = samples.eval() n_val = 200000 self.assertEqual(sample_values.shape, (n_val,)) self.assertAllClose(sample_values.mean(), mu_v, rtol=1e-2, atol=0) self.assertAllClose(sample_values.var(), sigma_v**2 * df_v / (df_v - 2), rtol=1e-2, atol=0) self._checkKLApprox(df_v, mu_v, sigma_v, sample_values) # Test that sampling with the same seed twice gives the same results. def testStudentSampleMultipleTimes(self): with self.test_session(): df = tf.constant(4.0) mu = tf.constant(3.0) sigma = tf.constant(math.sqrt(10.0)) df_v = 4.0 mu_v = 3.0 sigma_v = np.sqrt(10.0) n = tf.constant(100) tf.set_random_seed(654321) student = tf.contrib.distributions.StudentT( df=df, mu=mu, sigma=sigma, name="student_t1") samples1 = student.sample(n, seed=123456).eval() tf.set_random_seed(654321) student2 = tf.contrib.distributions.StudentT( df=df, mu=mu, sigma=sigma, name="student_t2") samples2 = student2.sample(n, seed=123456).eval() self.assertAllClose(samples1, samples2) def testStudentSampleSmallDfNoNan(self): with self.test_session(): df_v = [1e-1, 1e-5, 1e-10, 1e-20] df = tf.constant(df_v) n = tf.constant(200000) student = tf.contrib.distributions.StudentT(df=df, mu=1.0, sigma=1.0) samples = student.sample(n) sample_values = samples.eval() n_val = 200000 self.assertEqual(sample_values.shape, (n_val, 4)) self.assertTrue(np.all(np.logical_not(np.isnan(sample_values)))) def testStudentSampleMultiDimensional(self): with self.test_session(): batch_size = 7 df = tf.constant([[3.0, 7.0]] * batch_size) mu = tf.constant([[3.0, -3.0]] * batch_size) sigma = tf.constant([[math.sqrt(10.0), math.sqrt(15.0)]] * batch_size) df_v = [3.0, 7.0] mu_v = [3.0, -3.0] sigma_v = [np.sqrt(10.0), np.sqrt(15.0)] n = tf.constant(200000) student = tf.contrib.distributions.StudentT(df=df, mu=mu, sigma=sigma) samples = student.sample(n) sample_values = samples.eval() self.assertEqual(samples.get_shape(), (200000, batch_size, 2)) self.assertAllClose( sample_values[:, 0, 0].mean(), mu_v[0], rtol=1e-2, atol=0) self.assertAllClose(sample_values[:, 0, 0].var(), sigma_v[0]**2 * df_v[0] / (df_v[0] - 2), rtol=1e-1, atol=0) self._checkKLApprox(df_v[0], mu_v[0], sigma_v[0], sample_values[:, 0, 0]) self.assertAllClose( sample_values[:, 0, 1].mean(), mu_v[1], rtol=1e-2, atol=0) self.assertAllClose(sample_values[:, 0, 1].var(), sigma_v[1]**2 * df_v[1] / (df_v[1] - 2), rtol=1e-1, atol=0) self._checkKLApprox(df_v[0], mu_v[0], sigma_v[0], sample_values[:, 0, 1]) def _checkKLApprox(self, df, mu, sigma, samples): n = samples.size np.random.seed(137) sample_scipy = stats.t.rvs(df, loc=mu, scale=sigma, size=n) covg = 0.99 r = stats.t.interval(covg, df, loc=mu, scale=sigma) bins = 100 hist, _ = np.histogram(samples, bins=bins, range=r) hist_scipy, _ = np.histogram(sample_scipy, bins=bins, range=r) self.assertGreater(hist.sum(), n * (covg - .01)) self.assertGreater(hist_scipy.sum(), n * (covg - .01)) hist_min1 = hist + 1. # put at least one item in each bucket hist_norm = hist_min1 / hist_min1.sum() hist_scipy_min1 = hist_scipy + 1. # put at least one item in each bucket hist_scipy_norm = hist_scipy_min1 / hist_scipy_min1.sum() kl_appx = np.sum(np.log(hist_scipy_norm / hist_norm) * hist_scipy_norm) self.assertLess(kl_appx, 1) def testBroadcastingParams(self): def _check(student): self.assertEqual(student.mean().get_shape(), (3,)) self.assertEqual(student.variance().get_shape(), (3,)) self.assertEqual(student.entropy().get_shape(), (3,)) self.assertEqual(student.log_pdf(2.).get_shape(), (3,)) self.assertEqual(student.pdf(2.).get_shape(), (3,)) self.assertEqual(student.sample(37).get_shape(), (37, 3,)) _check(tf.contrib.distributions.StudentT(df=[2., 3., 4.,], mu=2., sigma=1.)) _check(tf.contrib.distributions.StudentT(df=7., mu=[2., 3., 4.,], sigma=1.)) _check(tf.contrib.distributions.StudentT(df=7., mu=3., sigma=[2., 3., 4.,])) def testBroadcastingPdfArgs(self): def _assert_shape(student, arg, shape): self.assertEqual(student.log_pdf(arg).get_shape(), shape) self.assertEqual(student.pdf(arg).get_shape(), shape) def _check(student): _assert_shape(student, 2., (3,)) xs = np.array([2., 3., 4.], dtype=np.float32) _assert_shape(student, xs, (3,)) xs = np.array([xs]) _assert_shape(student, xs, (1, 3)) xs = xs.T _assert_shape(student, xs, (3, 3)) _check(tf.contrib.distributions.StudentT(df=[2., 3., 4.,], mu=2., sigma=1.)) _check(tf.contrib.distributions.StudentT(df=7., mu=[2., 3., 4.,], sigma=1.)) _check(tf.contrib.distributions.StudentT(df=7., mu=3., sigma=[2., 3., 4.,])) def _check2d(student): _assert_shape(student, 2., (1, 3)) xs = np.array([2., 3., 4.], dtype=np.float32) _assert_shape(student, xs, (1, 3)) xs = np.array([xs]) _assert_shape(student, xs, (1, 3)) xs = xs.T _assert_shape(student, xs, (3, 3)) _check2d(tf.contrib.distributions.StudentT( df=[[2., 3., 4.,]], mu=2., sigma=1.)) _check2d(tf.contrib.distributions.StudentT( df=7., mu=[[2., 3., 4.,]], sigma=1.)) _check2d(tf.contrib.distributions.StudentT( df=7., mu=3., sigma=[[2., 3., 4.,]])) def _check2d_rows(student): _assert_shape(student, 2., (3, 1)) xs = np.array([2., 3., 4.], dtype=np.float32) # (3,) _assert_shape(student, xs, (3, 3)) xs = np.array([xs]) # (1,3) _assert_shape(student, xs, (3, 3)) xs = xs.T # (3,1) _assert_shape(student, xs, (3, 1)) _check2d_rows(tf.contrib.distributions.StudentT( df=[[2.], [3.], [4.]], mu=2., sigma=1.)) _check2d_rows(tf.contrib.distributions.StudentT( df=7., mu=[[2.], [3.], [4.]], sigma=1.)) _check2d_rows(tf.contrib.distributions.StudentT( df=7., mu=3., sigma=[[2.], [3.], [4.]])) def testMeanAllowNanStatsIsFalseWorksWhenAllBatchMembersAreDefined(self): with self.test_session(): mu = [1., 3.3, 4.4] student = tf.contrib.distributions.StudentT( df=[3., 5., 7.], mu=mu, sigma=[3., 2., 1.]) mean = student.mean().eval() self.assertAllClose([1., 3.3, 4.4], mean) def testMeanAllowNanStatsIsFalseRaisesWhenBatchMemberIsUndefined(self): with self.test_session(): mu = [1., 3.3, 4.4] student = tf.contrib.distributions.StudentT( df=[0.5, 5., 7.], mu=mu, sigma=[3., 2., 1.], allow_nan_stats=False) with self.assertRaisesOpError("x < y"): student.mean().eval() def testMeanAllowNanStatsIsTrueReturnsNaNForUndefinedBatchMembers(self): with self.test_session(): mu = [-2, 0., 1., 3.3, 4.4] student = tf.contrib.distributions.StudentT( df=[0.5, 1., 3., 5., 7.], mu=mu, sigma=[5., 4., 3., 2., 1.], allow_nan_stats=True) mean = student.mean().eval() self.assertAllClose([np.nan, np.nan, 1., 3.3, 4.4], mean) def testVarianceAllowNanStatsTrueReturnsNaNforUndefinedBatchMembers(self): with self.test_session(): # df = 0.5 ==> undefined mean ==> undefined variance. # df = 1.5 ==> infinite variance. df = [0.5, 1.5, 3., 5., 7.] mu = [-2, 0., 1., 3.3, 4.4] sigma = [5., 4., 3., 2., 1.] student = tf.contrib.distributions.StudentT( df=df, mu=mu, sigma=sigma, allow_nan_stats=True) var = student.variance().eval() ## scipy uses inf for variance when the mean is undefined. When mean is # undefined we say variance is undefined as well. So test the first # member of var, making sure it is NaN, then replace with inf and compare # to scipy. self.assertTrue(np.isnan(var[0])) var[0] = np.inf expected_var = [ stats.t.var(d, loc=m, scale=s) for (d, m, s) in zip(df, mu, sigma)] self.assertAllClose(expected_var, var) def testVarianceAllowNanStatsFalseGivesCorrectValueForDefinedBatchMembers( self): with self.test_session(): # df = 1.5 ==> infinite variance. df = [1.5, 3., 5., 7.] mu = [0., 1., 3.3, 4.4] sigma = [4., 3., 2., 1.] student = tf.contrib.distributions.StudentT( df=df, mu=mu, sigma=sigma) var = student.variance().eval() expected_var = [ stats.t.var(d, loc=m, scale=s) for (d, m, s) in zip(df, mu, sigma)] self.assertAllClose(expected_var, var) def testVarianceAllowNanStatsFalseRaisesForUndefinedBatchMembers(self): with self.test_session(): # df <= 1 ==> variance not defined student = tf.contrib.distributions.StudentT( df=1.0, mu=0.0, sigma=1.0, allow_nan_stats=False) with self.assertRaisesOpError("x < y"): student.variance().eval() with self.test_session(): # df <= 1 ==> variance not defined student = tf.contrib.distributions.StudentT( df=0.5, mu=0.0, sigma=1.0, allow_nan_stats=False) with self.assertRaisesOpError("x < y"): student.variance().eval() def testStd(self): with self.test_session(): # Defined for all batch members. df = [3.5, 5., 3., 5., 7.] mu = [-2.2] sigma = [5., 4., 3., 2., 1.] student = tf.contrib.distributions.StudentT(df=df, mu=mu, sigma=sigma) # Test broadcast of mu across shape of df/sigma std = student.std().eval() mu *= len(df) expected_std = [ stats.t.std(d, loc=m, scale=s) for (d, m, s) in zip(df, mu, sigma)] self.assertAllClose(expected_std, std) def testMode(self): with self.test_session(): student = tf.contrib.distributions.StudentT( df=[0.5, 1., 3], mu=[-1, 0., 1], sigma=[5., 4., 3.]) # Test broadcast of mu across shape of df/sigma mode = student.mode().eval() self.assertAllClose([-1., 0, 1], mode) def testPdfOfSample(self): with self.test_session() as sess: student = tf.contrib.distributions.StudentT(df=3., mu=np.pi, sigma=1.) num = 20000 samples = student.sample(num) pdfs = student.pdf(samples) mean = student.mean() mean_pdf = student.pdf(student.mean()) sample_vals, pdf_vals, mean_val, mean_pdf_val = sess.run( [samples, pdfs, student.mean(), mean_pdf]) self.assertEqual(samples.get_shape(), (num,)) self.assertEqual(pdfs.get_shape(), (num,)) self.assertEqual(mean.get_shape(), ()) self.assertNear(np.pi, np.mean(sample_vals), err=0.02) self.assertNear(np.pi, mean_val, err=1e-6) self.assertNear(stats.t.pdf(np.pi, 3., loc=np.pi), mean_pdf_val, err=1e-6) # Verify integral over sample*pdf ~= 1. self._assertIntegral(sample_vals, pdf_vals) def testPdfOfSampleMultiDims(self): with self.test_session() as sess: student = tf.contrib.distributions.StudentT(df=[7., 11.], mu=[[5.], [6.]], sigma=3.) num = 50000 samples = student.sample(num) pdfs = student.pdf(samples) sample_vals, pdf_vals = sess.run([samples, pdfs]) self.assertEqual(samples.get_shape(), (num, 2, 2)) self.assertEqual(pdfs.get_shape(), (num, 2, 2)) self.assertNear(5., np.mean(sample_vals[:, 0, :]), err=.03) self.assertNear(6., np.mean(sample_vals[:, 1, :]), err=.03) self.assertNear(stats.t.var(7., loc=0., scale=3.), # loc d.n. effect var np.var(sample_vals[:, :, 0]), err=.3) self.assertNear(stats.t.var(11., loc=0., scale=3.), # loc d.n. effect var np.var(sample_vals[:, :, 1]), err=.3) self._assertIntegral(sample_vals[:, 0, 0], pdf_vals[:, 0, 0], err=0.02) self._assertIntegral(sample_vals[:, 0, 1], pdf_vals[:, 0, 1], err=0.02) self._assertIntegral(sample_vals[:, 1, 0], pdf_vals[:, 1, 0], err=0.02) self._assertIntegral(sample_vals[:, 1, 1], pdf_vals[:, 1, 1], err=0.02) def _assertIntegral(self, sample_vals, pdf_vals, err=1e-3): s_p = zip(sample_vals, pdf_vals) prev = (sample_vals.min() - 1000, 0) total = 0 for k in sorted(s_p, key=lambda x: x[0]): pair_pdf = (k[1] + prev[1]) / 2 total += (k[0] - prev[0]) * pair_pdf prev = k self.assertNear(1., total, err=err) def testNegativeDofFails(self): with self.test_session(): student = tf.contrib.distributions.StudentT(df=[2, -5.], mu=0., sigma=1., validate_args=True, name="S") with self.assertRaisesOpError(r"Condition x > 0 did not hold"): student.mean().eval() def testNegativeScaleFails(self): with self.test_session(): student = tf.contrib.distributions.StudentT(df=[5.], mu=0., sigma=[[3.], [-2.]], validate_args=True, name="S") with self.assertRaisesOpError(r"Condition x > 0 did not hold"): student.mean().eval() def testStudentTWithAbsDfSoftplusSigma(self): with self.test_session(): df = tf.constant([-3.2, -4.6]) mu = tf.constant([-4.2, 3.4]) sigma = tf.constant([-6.4, -8.8]) student = tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma( df=df, mu=mu, sigma=sigma) self.assertAllClose(tf.floor(tf.abs(df)).eval(), student.df.eval()) self.assertAllClose(mu.eval(), student.mu.eval()) self.assertAllClose(tf.nn.softplus(sigma).eval(), student.sigma.eval())
14,453
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10
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"Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "expected_log_pdf", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "exp", "value": {"_type": "Name", "_fields": {"id": "np", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, {"_type": "Name", "_fields": {"id": "pdf_values", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertAllClose", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "items": [{"_type": "withitem", "_fields": {"context_expr": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "test_session", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "optional_vars": null}}], "type_comment": null}}], "name": "testStudentPDFAndLogPDF", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def testStudentPDFAndLogPDF(self): with self.test_session(): batch_size = 6 df = tf.constant([3.0] * batch_size) mu = tf.constant([7.0] * batch_size) sigma = tf.constant([8.0] * batch_size) df_v = 3.0 mu_v = 7.0 sigma_v = 8.0 t = np.array([-2.5, 2.5, 8.0, 0.0, -1.0, 2.0], dtype=np.float32) student = tf.contrib.distributions.StudentT(df, mu=mu, sigma=sigma) log_pdf = student.log_pdf(t) self.assertEquals(log_pdf.get_shape(), (6,)) log_pdf_values = log_pdf.eval() pdf = student.pdf(t) self.assertEquals(pdf.get_shape(), (6,)) pdf_values = pdf.eval() expected_log_pdf = stats.t.logpdf(t, df_v, loc=mu_v, scale=sigma_v) expected_pdf = stats.t.pdf(t, df_v, loc=mu_v, scale=sigma_v) self.assertAllClose(expected_log_pdf, log_pdf_values) self.assertAllClose(np.log(expected_pdf), log_pdf_values) self.assertAllClose(expected_pdf, pdf_values) self.assertAllClose(np.exp(expected_log_pdf), pdf_values)
14,454
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def testStudentLogPDFMultidimensional(self): with self.test_session(): batch_size = 6 df = tf.constant([[1.5, 7.2]] * batch_size) mu = tf.constant([[3.0, -3.0]] * batch_size) sigma = tf.constant([[math.sqrt(10.0), math.sqrt(15.0)]] * batch_size) df_v = np.array([1.5, 7.2]) mu_v = np.array([3.0, -3.0]) sigma_v = np.array([np.sqrt(10.0), np.sqrt(15.0)]) t = np.array([[-2.5, 2.5, 4.0, 0.0, -1.0, 2.0]], dtype=np.float32).T student = tf.contrib.distributions.StudentT(df, mu=mu, sigma=sigma) log_pdf = student.log_pdf(t) log_pdf_values = log_pdf.eval() self.assertEqual(log_pdf.get_shape(), (6, 2)) pdf = student.pdf(t) pdf_values = pdf.eval() self.assertEqual(pdf.get_shape(), (6, 2)) expected_log_pdf = stats.t.logpdf(t, df_v, loc=mu_v, scale=sigma_v) expected_pdf = stats.t.pdf(t, df_v, loc=mu_v, scale=sigma_v) self.assertAllClose(expected_log_pdf, log_pdf_values) self.assertAllClose(np.log(expected_pdf), log_pdf_values) self.assertAllClose(expected_pdf, pdf_values) self.assertAllClose(np.exp(expected_log_pdf), pdf_values)
14,455
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def testStudentEntropy(self): df_v = np.array([[2., 3., 7.]]) # 1x3 mu_v = np.array([[1., -1, 0]]) # 1x3 sigma_v = np.array([[1., 2., 3.]]).T # transposed => 3x1 with self.test_session(): student = tf.contrib.distributions.StudentT(df=df_v, mu=mu_v, sigma=sigma_v) ent = student.entropy() ent_values = ent.eval() # Help scipy broadcast to 3x3 ones = np.array([[1, 1, 1]]) sigma_bc = sigma_v * ones mu_bc = ones.T * mu_v df_bc = ones.T * df_v expected_entropy = stats.t.entropy( np.reshape(df_bc, [-1]), loc=np.reshape(mu_bc, [-1]), scale=np.reshape(sigma_bc, [-1])) expected_entropy = np.reshape(expected_entropy, df_bc.shape) self.assertAllClose(expected_entropy, ent_values)
14,456
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10
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def testStudentSampleMultipleTimes(self): with self.test_session(): df = tf.constant(4.0) mu = tf.constant(3.0) sigma = tf.constant(math.sqrt(10.0)) df_v = 4.0 mu_v = 3.0 sigma_v = np.sqrt(10.0) n = tf.constant(100) tf.set_random_seed(654321) student = tf.contrib.distributions.StudentT( df=df, mu=mu, sigma=sigma, name="student_t1") samples1 = student.sample(n, seed=123456).eval() tf.set_random_seed(654321) student2 = tf.contrib.distributions.StudentT( df=df, mu=mu, sigma=sigma, name="student_t2") samples2 = student2.sample(n, seed=123456).eval() self.assertAllClose(samples1, samples2)
14,457
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def testStudentSample(self): with self.test_session(): df = tf.constant(4.0) mu = tf.constant(3.0) sigma = tf.constant(math.sqrt(10.0)) df_v = 4.0 mu_v = 3.0 sigma_v = np.sqrt(10.0) n = tf.constant(200000) student = tf.contrib.distributions.StudentT(df=df, mu=mu, sigma=sigma) samples = student.sample(n) sample_values = samples.eval() n_val = 200000 self.assertEqual(sample_values.shape, (n_val,)) self.assertAllClose(sample_values.mean(), mu_v, rtol=1e-2, atol=0) self.assertAllClose(sample_values.var(), sigma_v**2 * df_v / (df_v - 2), rtol=1e-2, atol=0) self._checkKLApprox(df_v, mu_v, sigma_v, sample_values)
14,458
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11
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def testStudentSampleSmallDfNoNan(self): with self.test_session(): df_v = [1e-1, 1e-5, 1e-10, 1e-20] df = tf.constant(df_v) n = tf.constant(200000) student = tf.contrib.distributions.StudentT(df=df, mu=1.0, sigma=1.0) samples = student.sample(n) sample_values = samples.eval() n_val = 200000 self.assertEqual(sample_values.shape, (n_val, 4)) self.assertTrue(np.all(np.logical_not(np.isnan(sample_values))))
14,459
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12
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def testStudentSampleMultiDimensional(self): with self.test_session(): batch_size = 7 df = tf.constant([[3.0, 7.0]] * batch_size) mu = tf.constant([[3.0, -3.0]] * batch_size) sigma = tf.constant([[math.sqrt(10.0), math.sqrt(15.0)]] * batch_size) df_v = [3.0, 7.0] mu_v = [3.0, -3.0] sigma_v = [np.sqrt(10.0), np.sqrt(15.0)] n = tf.constant(200000) student = tf.contrib.distributions.StudentT(df=df, mu=mu, sigma=sigma) samples = student.sample(n) sample_values = samples.eval() self.assertEqual(samples.get_shape(), (200000, batch_size, 2)) self.assertAllClose( sample_values[:, 0, 0].mean(), mu_v[0], rtol=1e-2, atol=0) self.assertAllClose(sample_values[:, 0, 0].var(), sigma_v[0]**2 * df_v[0] / (df_v[0] - 2), rtol=1e-1, atol=0) self._checkKLApprox(df_v[0], mu_v[0], sigma_v[0], sample_values[:, 0, 0]) self.assertAllClose( sample_values[:, 0, 1].mean(), mu_v[1], rtol=1e-2, atol=0) self.assertAllClose(sample_values[:, 0, 1].var(), sigma_v[1]**2 * df_v[1] / (df_v[1] - 2), rtol=1e-1, atol=0) self._checkKLApprox(df_v[0], mu_v[0], sigma_v[0], sample_values[:, 0, 1])
14,460
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def _checkKLApprox(self, df, mu, sigma, samples): n = samples.size np.random.seed(137) sample_scipy = stats.t.rvs(df, loc=mu, scale=sigma, size=n) covg = 0.99 r = stats.t.interval(covg, df, loc=mu, scale=sigma) bins = 100 hist, _ = np.histogram(samples, bins=bins, range=r) hist_scipy, _ = np.histogram(sample_scipy, bins=bins, range=r) self.assertGreater(hist.sum(), n * (covg - .01)) self.assertGreater(hist_scipy.sum(), n * (covg - .01)) hist_min1 = hist + 1. # put at least one item in each bucket hist_norm = hist_min1 / hist_min1.sum() hist_scipy_min1 = hist_scipy + 1. # put at least one item in each bucket hist_scipy_norm = hist_scipy_min1 / hist_scipy_min1.sum() kl_appx = np.sum(np.log(hist_scipy_norm / hist_norm) * hist_scipy_norm) self.assertLess(kl_appx, 1)
14,461
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11
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def testBroadcastingParams(self): def _check(student): self.assertEqual(student.mean().get_shape(), (3,)) self.assertEqual(student.variance().get_shape(), (3,)) self.assertEqual(student.entropy().get_shape(), (3,)) self.assertEqual(student.log_pdf(2.).get_shape(), (3,)) self.assertEqual(student.pdf(2.).get_shape(), (3,)) self.assertEqual(student.sample(37).get_shape(), (37, 3,)) _check(tf.contrib.distributions.StudentT(df=[2., 3., 4.,], mu=2., sigma=1.)) _check(tf.contrib.distributions.StudentT(df=7., mu=[2., 3., 4.,], sigma=1.)) _check(tf.contrib.distributions.StudentT(df=7., mu=3., sigma=[2., 3., 4.,]))
14,462
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10
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def testMeanAllowNanStatsIsFalseWorksWhenAllBatchMembersAreDefined(self): with self.test_session(): mu = [1., 3.3, 4.4] student = tf.contrib.distributions.StudentT( df=[3., 5., 7.], mu=mu, sigma=[3., 2., 1.]) mean = student.mean().eval() self.assertAllClose([1., 3.3, 4.4], mean)
14,463
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11
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def testBroadcastingPdfArgs(self): def _assert_shape(student, arg, shape): self.assertEqual(student.log_pdf(arg).get_shape(), shape) self.assertEqual(student.pdf(arg).get_shape(), shape) def _check(student): _assert_shape(student, 2., (3,)) xs = np.array([2., 3., 4.], dtype=np.float32) _assert_shape(student, xs, (3,)) xs = np.array([xs]) _assert_shape(student, xs, (1, 3)) xs = xs.T _assert_shape(student, xs, (3, 3)) _check(tf.contrib.distributions.StudentT(df=[2., 3., 4.,], mu=2., sigma=1.)) _check(tf.contrib.distributions.StudentT(df=7., mu=[2., 3., 4.,], sigma=1.)) _check(tf.contrib.distributions.StudentT(df=7., mu=3., sigma=[2., 3., 4.,])) def _check2d(student): _assert_shape(student, 2., (1, 3)) xs = np.array([2., 3., 4.], dtype=np.float32) _assert_shape(student, xs, (1, 3)) xs = np.array([xs]) _assert_shape(student, xs, (1, 3)) xs = xs.T _assert_shape(student, xs, (3, 3)) _check2d(tf.contrib.distributions.StudentT( df=[[2., 3., 4.,]], mu=2., sigma=1.)) _check2d(tf.contrib.distributions.StudentT( df=7., mu=[[2., 3., 4.,]], sigma=1.)) _check2d(tf.contrib.distributions.StudentT( df=7., mu=3., sigma=[[2., 3., 4.,]])) def _check2d_rows(student): _assert_shape(student, 2., (3, 1)) xs = np.array([2., 3., 4.], dtype=np.float32) # (3,) _assert_shape(student, xs, (3, 3)) xs = np.array([xs]) # (1,3) _assert_shape(student, xs, (3, 3)) xs = xs.T # (3,1) _assert_shape(student, xs, (3, 1)) _check2d_rows(tf.contrib.distributions.StudentT( df=[[2.], [3.], [4.]], mu=2., sigma=1.)) _check2d_rows(tf.contrib.distributions.StudentT( df=7., mu=[[2.], [3.], [4.]], sigma=1.)) _check2d_rows(tf.contrib.distributions.StudentT( df=7., mu=3., sigma=[[2.], [3.], [4.]]))
14,464
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11
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def testMeanAllowNanStatsIsFalseRaisesWhenBatchMemberIsUndefined(self): with self.test_session(): mu = [1., 3.3, 4.4] student = tf.contrib.distributions.StudentT( df=[0.5, 5., 7.], mu=mu, sigma=[3., 2., 1.], allow_nan_stats=False) with self.assertRaisesOpError("x < y"): student.mean().eval()
14,465
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10
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def testMeanAllowNanStatsIsTrueReturnsNaNForUndefinedBatchMembers(self): with self.test_session(): mu = [-2, 0., 1., 3.3, 4.4] student = tf.contrib.distributions.StudentT( df=[0.5, 1., 3., 5., 7.], mu=mu, sigma=[5., 4., 3., 2., 1.], allow_nan_stats=True) mean = student.mean().eval() self.assertAllClose([np.nan, np.nan, 1., 3.3, 4.4], mean)
14,466
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10
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def testVarianceAllowNanStatsTrueReturnsNaNforUndefinedBatchMembers(self): with self.test_session(): # df = 0.5 ==> undefined mean ==> undefined variance. # df = 1.5 ==> infinite variance. df = [0.5, 1.5, 3., 5., 7.] mu = [-2, 0., 1., 3.3, 4.4] sigma = [5., 4., 3., 2., 1.] student = tf.contrib.distributions.StudentT( df=df, mu=mu, sigma=sigma, allow_nan_stats=True) var = student.variance().eval() ## scipy uses inf for variance when the mean is undefined. When mean is # undefined we say variance is undefined as well. So test the first # member of var, making sure it is NaN, then replace with inf and compare # to scipy. self.assertTrue(np.isnan(var[0])) var[0] = np.inf expected_var = [ stats.t.var(d, loc=m, scale=s) for (d, m, s) in zip(df, mu, sigma)] self.assertAllClose(expected_var, var)
14,467
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10
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{"id": "d", "ctx": {"_type": "Store", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "m", "ctx": {"_type": "Store", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "s", "ctx": {"_type": "Store", "_fields": {}}}}]}}, "is_async": 0}}]}}, "targets": [{"_type": "Name", "_fields": {"id": "expected_var", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "expected_var", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "var", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertAllClose", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "items": [{"_type": "withitem", "_fields": {"context_expr": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "test_session", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "optional_vars": null}}], "type_comment": null}}], "name": "testVarianceAllowNanStatsFalseGivesCorrectValueForDefinedBatchMembers", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def testVarianceAllowNanStatsFalseGivesCorrectValueForDefinedBatchMembers( self): with self.test_session(): # df = 1.5 ==> infinite variance. df = [1.5, 3., 5., 7.] mu = [0., 1., 3.3, 4.4] sigma = [4., 3., 2., 1.] student = tf.contrib.distributions.StudentT( df=df, mu=mu, sigma=sigma) var = student.variance().eval() expected_var = [ stats.t.var(d, loc=m, scale=s) for (d, m, s) in zip(df, mu, sigma)] self.assertAllClose(expected_var, var)
14,468
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10
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def testStd(self): with self.test_session(): # Defined for all batch members. df = [3.5, 5., 3., 5., 7.] mu = [-2.2] sigma = [5., 4., 3., 2., 1.] student = tf.contrib.distributions.StudentT(df=df, mu=mu, sigma=sigma) # Test broadcast of mu across shape of df/sigma std = student.std().eval() mu *= len(df) expected_std = [ stats.t.std(d, loc=m, scale=s) for (d, m, s) in zip(df, mu, sigma)] self.assertAllClose(expected_std, std)
14,469
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11
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def testVarianceAllowNanStatsFalseRaisesForUndefinedBatchMembers(self): with self.test_session(): # df <= 1 ==> variance not defined student = tf.contrib.distributions.StudentT( df=1.0, mu=0.0, sigma=1.0, allow_nan_stats=False) with self.assertRaisesOpError("x < y"): student.variance().eval() with self.test_session(): # df <= 1 ==> variance not defined student = tf.contrib.distributions.StudentT( df=0.5, mu=0.0, sigma=1.0, allow_nan_stats=False) with self.assertRaisesOpError("x < y"): student.variance().eval()
14,470
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def testPdfOfSample(self): with self.test_session() as sess: student = tf.contrib.distributions.StudentT(df=3., mu=np.pi, sigma=1.) num = 20000 samples = student.sample(num) pdfs = student.pdf(samples) mean = student.mean() mean_pdf = student.pdf(student.mean()) sample_vals, pdf_vals, mean_val, mean_pdf_val = sess.run( [samples, pdfs, student.mean(), mean_pdf]) self.assertEqual(samples.get_shape(), (num,)) self.assertEqual(pdfs.get_shape(), (num,)) self.assertEqual(mean.get_shape(), ()) self.assertNear(np.pi, np.mean(sample_vals), err=0.02) self.assertNear(np.pi, mean_val, err=1e-6) self.assertNear(stats.t.pdf(np.pi, 3., loc=np.pi), mean_pdf_val, err=1e-6) # Verify integral over sample*pdf ~= 1. self._assertIntegral(sample_vals, pdf_vals)
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10
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def testMode(self): with self.test_session(): student = tf.contrib.distributions.StudentT( df=[0.5, 1., 3], mu=[-1, 0., 1], sigma=[5., 4., 3.]) # Test broadcast of mu across shape of df/sigma mode = student.mode().eval() self.assertAllClose([-1., 0, 1], mode)
14,472
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9
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def _assertIntegral(self, sample_vals, pdf_vals, err=1e-3): s_p = zip(sample_vals, pdf_vals) prev = (sample_vals.min() - 1000, 0) total = 0 for k in sorted(s_p, key=lambda x: x[0]): pair_pdf = (k[1] + prev[1]) / 2 total += (k[0] - prev[0]) * pair_pdf prev = k self.assertNear(1., total, err=err)
14,473
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def testPdfOfSampleMultiDims(self): with self.test_session() as sess: student = tf.contrib.distributions.StudentT(df=[7., 11.], mu=[[5.], [6.]], sigma=3.) num = 50000 samples = student.sample(num) pdfs = student.pdf(samples) sample_vals, pdf_vals = sess.run([samples, pdfs]) self.assertEqual(samples.get_shape(), (num, 2, 2)) self.assertEqual(pdfs.get_shape(), (num, 2, 2)) self.assertNear(5., np.mean(sample_vals[:, 0, :]), err=.03) self.assertNear(6., np.mean(sample_vals[:, 1, :]), err=.03) self.assertNear(stats.t.var(7., loc=0., scale=3.), # loc d.n. effect var np.var(sample_vals[:, :, 0]), err=.3) self.assertNear(stats.t.var(11., loc=0., scale=3.), # loc d.n. effect var np.var(sample_vals[:, :, 1]), err=.3) self._assertIntegral(sample_vals[:, 0, 0], pdf_vals[:, 0, 0], err=0.02) self._assertIntegral(sample_vals[:, 0, 1], pdf_vals[:, 0, 1], err=0.02) self._assertIntegral(sample_vals[:, 1, 0], pdf_vals[:, 1, 0], err=0.02) self._assertIntegral(sample_vals[:, 1, 1], pdf_vals[:, 1, 1], err=0.02)
14,474
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11
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def testNegativeDofFails(self): with self.test_session(): student = tf.contrib.distributions.StudentT(df=[2, -5.], mu=0., sigma=1., validate_args=True, name="S") with self.assertRaisesOpError(r"Condition x > 0 did not hold"): student.mean().eval()
14,475
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def testNegativeScaleFails(self): with self.test_session(): student = tf.contrib.distributions.StudentT(df=[5.], mu=0., sigma=[[3.], [-2.]], validate_args=True, name="S") with self.assertRaisesOpError(r"Condition x > 0 did not hold"): student.mean().eval()
14,476
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def testStudentTWithAbsDfSoftplusSigma(self): with self.test_session(): df = tf.constant([-3.2, -4.6]) mu = tf.constant([-4.2, 3.4]) sigma = tf.constant([-6.4, -8.8]) student = tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma( df=df, mu=mu, sigma=sigma) self.assertAllClose(tf.floor(tf.abs(df)).eval(), student.df.eval()) self.assertAllClose(mu.eval(), student.mu.eval()) self.assertAllClose(tf.nn.softplus(sigma).eval(), student.sigma.eval())
14,477
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8
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def _check(student): _assert_shape(student, 2., (3,)) xs = np.array([2., 3., 4.], dtype=np.float32) _assert_shape(student, xs, (3,)) xs = np.array([xs]) _assert_shape(student, xs, (1, 3)) xs = xs.T _assert_shape(student, xs, (3, 3))
14,478
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8
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def _check2d(student): _assert_shape(student, 2., (1, 3)) xs = np.array([2., 3., 4.], dtype=np.float32) _assert_shape(student, xs, (1, 3)) xs = np.array([xs]) _assert_shape(student, xs, (1, 3)) xs = xs.T _assert_shape(student, xs, (3, 3))
14,479
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8
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def _check2d_rows(student): _assert_shape(student, 2., (3, 1)) xs = np.array([2., 3., 4.], dtype=np.float32) # (3,) _assert_shape(student, xs, (3, 3)) xs = np.array([xs]) # (1,3) _assert_shape(student, xs, (3, 3)) xs = xs.T # (3,1) _assert_shape(student, xs, (3, 1))
14,480
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11
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def ircformat(color, text): if len(color) < 1: return text add = sub = '' if '_' in color: # italic add += '\x1D' sub = '\x1D' + sub color = color.strip('_') if '*' in color: # bold add += '\x02' sub = '\x02' + sub color = color.strip('*') # underline (\x1F) not supported # backgrounds (\x03FF,BB) not supported if len(color) > 0: # actual color - may have issues with ircformat("red", "blah")+"10" type stuff add += '\x03' + str(IRC_COLOR_MAP[color]).zfill(2) sub = '\x03' + sub return add + text + sub return '<'+add+'>'+text+'</'+sub+'>'
14,481
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9
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def __init__(self, service_plugin): super(IPsecVPNDriver, self).__init__(service_plugin) self.callbacks = IPsecVpnDriverCallBack(self) self.conn = rpc.create_connection(new=True) self.conn.create_consumer( topics.IPSEC_DRIVER_TOPIC, self.callbacks.create_rpc_dispatcher(), fanout=False) self.conn.consume_in_thread() self.agent_rpc = IPsecVpnAgentApi( topics.IPSEC_AGENT_TOPIC, BASE_IPSEC_VERSION)
14,482
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11
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def format_unencoded(self, tokensource, outfile): if self.linenos: self._format_unencoded_with_lineno(tokensource, outfile) return for ttype, value in tokensource: color = self.colorscheme.get(ttype) while color is None: ttype = ttype[:-1] color = self.colorscheme.get(ttype) if color: color = color[self.darkbg] spl = value.split('\n') for line in spl[:-1]: if line: outfile.write(ircformat(color, line)) outfile.write('\n') if spl[-1]: outfile.write(ircformat(color, spl[-1])) else: outfile.write(value)
14,483
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"Load", "_fields": {}}}}}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "color", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}], "test": {"_type": "Compare", "_fields": {"ops": [{"_type": "Is", "_fields": {}}], "left": {"_type": "Name", "_fields": {"id": "color", "ctx": {"_type": "Load", "_fields": {}}}}, "comparators": [{"_type": "Constant", "_fields": {"kind": null, "value": null}}]}}, "orelse": []}}, {"_type": "If", "_fields": {"body": [{"_type": "Assign", "_fields": {"value": {"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "darkbg", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "value": {"_type": "Name", "_fields": {"id": "color", "ctx": {"_type": "Load", "_fields": {}}}}}}, "targets": [{"_type": "Name", "_fields": {"id": "color", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "\n"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "split", "value": {"_type": "Name", "_fields": {"id": "value", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "spl", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "For", "_fields": {"body": [{"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "outfile", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_write_lineno", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "If", "_fields": {"body": [{"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "color", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Slice", "_fields": {"step": null, "lower": null, "upper": {"_type": "UnaryOp", "_fields": {"op": {"_type": "USub", "_fields": {}}, "operand": {"_type": "Constant", "_fields": {"kind": null, "value": 1}}}}}}, "value": {"_type": "Name", "_fields": {"id": "line", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Name", "_fields": {"id": "ircformat", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "write", "value": {"_type": "Name", "_fields": {"id": "outfile", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "test": {"_type": "Name", "_fields": {"id": "line", "ctx": {"_type": "Load", "_fields": {}}}}, "orelse": []}}], "iter": {"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Slice", "_fields": {"step": null, "lower": null, "upper": {"_type": "UnaryOp", "_fields": {"op": {"_type": "USub", "_fields": {}}, "operand": {"_type": "Constant", "_fields": {"kind": null, "value": 1}}}}}}, "value": {"_type": "Name", "_fields": {"id": "spl", "ctx": {"_type": "Load", "_fields": {}}}}}}, "orelse": [], "target": {"_type": "Name", "_fields": {"id": "line", "ctx": {"_type": "Store", "_fields": {}}}}, "type_comment": null}}, {"_type": "If", "_fields": {"body": [{"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "color", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "UnaryOp", "_fields": {"op": {"_type": "USub", "_fields": {}}, "operand": {"_type": "Constant", "_fields": {"kind": null, "value": 1}}}}, "value": {"_type": "Name", "_fields": {"id": "spl", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Name", "_fields": {"id": "ircformat", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "write", "value": {"_type": "Name", "_fields": {"id": "outfile", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "test": {"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "UnaryOp", "_fields": {"op": {"_type": "USub", "_fields": {}}, "operand": {"_type": "Constant", "_fields": {"kind": null, "value": 1}}}}, "value": {"_type": "Name", "_fields": {"id": "spl", "ctx": {"_type": "Load", "_fields": {}}}}}}, "orelse": []}}], "test": {"_type": "Name", "_fields": {"id": "color", "ctx": {"_type": "Load", "_fields": {}}}}, "orelse": [{"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "value", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "write", "value": {"_type": "Name", "_fields": {"id": "outfile", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}]}}], "iter": {"_type": "Name", "_fields": {"id": "tokensource", "ctx": {"_type": "Load", "_fields": {}}}}, "orelse": [], "target": {"_type": "Tuple", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "elts": [{"_type": "Name", "_fields": {"id": "ttype", "ctx": {"_type": "Store", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "value", "ctx": {"_type": "Store", "_fields": {}}}}]}}, "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "\n"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "write", "value": {"_type": "Name", "_fields": {"id": "outfile", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "_format_unencoded_with_lineno", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def _format_unencoded_with_lineno(self, tokensource, outfile): self._write_lineno(outfile) for ttype, value in tokensource: if value.endswith("\n"): self._write_lineno(outfile) value = value[:-1] color = self.colorscheme.get(ttype) while color is None: ttype = ttype[:-1] color = self.colorscheme.get(ttype) if color: color = color[self.darkbg] spl = value.split('\n') for line in spl[:-1]: self._write_lineno(outfile) if line: outfile.write(ircformat(color, line[:-1])) if spl[-1]: outfile.write(ircformat(color, spl[-1])) else: outfile.write(value) outfile.write("\n")
14,484
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9
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def read_msg(self): # Because the Tornado Websocket client has no way to cancel # a pending read, we have to keep track of them... if self.pending_read is None: self.pending_read = self.ws.read_message() response = yield self.pending_read self.pending_read = None if response: response = json.loads(response) raise tornado.gen.Return(response)
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9
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def get_term_client(self, path): port = self.get_http_port() url = 'ws://127.0.0.1:%d%s' % (port, path) request = tornado.httpclient.HTTPRequest(url, headers={'Origin' : 'http://127.0.0.1:%d' % port}) ws = yield tornado.websocket.websocket_connect(request) raise tornado.gen.Return(TestTermClient(ws))
14,486
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10
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"Store", "_fields": {}}}}, "type_comment": null}}], "name": "test_basic_command", "returns": null, "type_params": [], "type_comment": null, "decorator_list": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "gen_test", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "testing", "value": {"_type": "Name", "_fields": {"id": "tornado", "ctx": {"_type": "Load", "_fields": {}}}}}}}}]}}], "name": "CommonTests", "bases": [{"_type": "Name", "_fields": {"id": "TermTestCase", "ctx": {"_type": "Load", "_fields": {}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}
class CommonTests(TermTestCase): @tornado.testing.gen_test def test_basic(self): for url in self.test_urls: tm = yield self.get_term_client(url) response = yield tm.read_msg() self.assertEqual(response, ['setup', {}]) # Check for initial shell prompt response = yield tm.read_msg() self.assertEqual(response[0], 'stdout') self.assertGreater(len(response[1]), 0) tm.close() @tornado.testing.gen_test def test_basic_command(self): for url in self.test_urls: tm = yield self.get_term_client(url) yield tm.read_all_msg() tm.write_stdin("whoami\r") (stdout, other) = yield tm.read_stdout() self.assertEqual(stdout[:6], "whoami") self.assertEqual(other, []) tm.close()
14,487
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9
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def test_basic(self): for url in self.test_urls: tm = yield self.get_term_client(url) response = yield tm.read_msg() self.assertEqual(response, ['setup', {}]) # Check for initial shell prompt response = yield tm.read_msg() self.assertEqual(response[0], 'stdout') self.assertGreater(len(response[1]), 0) tm.close()
14,488
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"func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertNotEqual", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "test_namespace", "returns": null, "type_params": [], "type_comment": null, "decorator_list": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "gen_test", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "testing", "value": {"_type": "Name", "_fields": {"id": "tornado", "ctx": {"_type": "Load", "_fields": {}}}}}}}}]}}, {"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "self", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Assign", "_fields": {"value": {"_type": "ListComp", "_fields": {"elt": {"_type": "BinOp", "_fields": {"op": {"_type": "Mod", "_fields": {}}, "left": {"_type": "Constant", "_fields": {"kind": null, "value": "/named/%d"}}, "right": {"_type": "Name", "_fields": {"id": "i", "ctx": {"_type": "Load", "_fields": {}}}}}}, "generators": [{"_type": "comprehension", "_fields": {"ifs": [], "iter": {"_type": "Call", "_fields": {"args": [{"_type": "BinOp", "_fields": {"op": {"_type": "Add", "_fields": {}}, "left": {"_type": "Name", "_fields": {"id": "MAX_TERMS", "ctx": {"_type": "Load", "_fields": {}}}}, "right": {"_type": "Constant", "_fields": {"kind": null, "value": 1}}}}], "func": {"_type": "Name", "_fields": {"id": "range", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "target": {"_type": "Name", "_fields": {"id": "i", "ctx": {"_type": "Store", "_fields": {}}}}, "is_async": 0}}]}}, "targets": [{"_type": "Name", "_fields": {"id": "urls", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Yield", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Slice", "_fields": {"step": null, "lower": null, "upper": {"_type": "Name", "_fields": {"id": "MAX_TERMS", "ctx": {"_type": "Load", "_fields": {}}}}}}, "value": {"_type": "Name", "_fields": {"id": "urls", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "get_term_clients", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, "targets": [{"_type": "Name", "_fields": {"id": "tms", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Yield", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "tms", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "get_pids", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, "targets": [{"_type": "Name", "_fields": {"id": "pids", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Yield", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Name", "_fields": {"id": "MAX_TERMS", "ctx": {"_type": "Load", "_fields": {}}}}, "value": {"_type": "Name", "_fields": {"id": "urls", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "get_term_client", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, "targets": [{"_type": "Name", "_fields": {"id": "tm", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Yield", "_fields": {"value": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "read_msg", "value": {"_type": "Name", "_fields": {"id": "tm", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, "targets": [{"_type": "Name", "_fields": {"id": "msg", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "msg", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Constant", "_fields": {"kind": null, "value": null}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertEqual", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "test_max_terminals", "returns": null, "type_params": [], "type_comment": null, "decorator_list": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "gen_test", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "testing", "value": {"_type": "Name", "_fields": {"id": "tornado", "ctx": {"_type": "Load", "_fields": {}}}}}}}}]}}], "name": "NamedTermTests", "bases": [{"_type": "Name", "_fields": {"id": "TermTestCase", "ctx": {"_type": "Load", "_fields": {}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}
class NamedTermTests(TermTestCase): def test_new(self): response = self.fetch("/new", follow_redirects=False) self.assertEqual(response.code, 302) url = response.headers["Location"] # Check that the new terminal was created name = url.split('/')[2] self.assertIn(name, self.named_tm.terminals) @tornado.testing.gen_test def test_namespace(self): names = ["/named/1"]*2 + ["/named/2"]*2 tms = yield self.get_term_clients(names) pids = yield self.get_pids(tms) self.assertEqual(pids[0], pids[1]) self.assertEqual(pids[2], pids[3]) self.assertNotEqual(pids[0], pids[3]) @tornado.testing.gen_test def test_max_terminals(self): urls = ["/named/%d" % i for i in range(MAX_TERMS+1)] tms = yield self.get_term_clients(urls[:MAX_TERMS]) pids = yield self.get_pids(tms) # MAX_TERMS+1 should fail tm = yield self.get_term_client(urls[MAX_TERMS]) msg = yield tm.read_msg() self.assertEqual(msg, None)
14,489
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9
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def test_basic_command(self): for url in self.test_urls: tm = yield self.get_term_client(url) yield tm.read_all_msg() tm.write_stdin("whoami\r") (stdout, other) = yield tm.read_stdout() self.assertEqual(stdout[:6], "whoami") self.assertEqual(other, []) tm.close()
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8
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def test_namespace(self): names = ["/named/1"]*2 + ["/named/2"]*2 tms = yield self.get_term_clients(names) pids = yield self.get_pids(tms) self.assertEqual(pids[0], pids[1]) self.assertEqual(pids[2], pids[3]) self.assertNotEqual(pids[0], pids[3])
14,491
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8
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def test_new(self): response = self.fetch("/new", follow_redirects=False) self.assertEqual(response.code, 302) url = response.headers["Location"] # Check that the new terminal was created name = url.split('/')[2] self.assertIn(name, self.named_tm.terminals)
14,492
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10
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"type_params": [], "type_comment": null, "decorator_list": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "gen_test", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "testing", "value": {"_type": "Name", "_fields": {"id": "tornado", "ctx": {"_type": "Load", "_fields": {}}}}}}}}]}}], "name": "UniqueTermTests", "bases": [{"_type": "Name", "_fields": {"id": "TermTestCase", "ctx": {"_type": "Load", "_fields": {}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}
class UniqueTermTests(TermTestCase): @tornado.testing.gen_test def test_unique_processes(self): tms = yield self.get_term_clients(["/unique", "/unique"]) pids = yield self.get_pids(tms) self.assertNotEqual(pids[0], pids[1]) @tornado.testing.gen_test def test_max_terminals(self): tms = yield self.get_term_clients(['/unique'] * MAX_TERMS) pids = yield self.get_pids(tms) self.assertEqual(len(set(pids)), MAX_TERMS) # All PIDs unique # MAX_TERMS+1 should fail tm = yield self.get_term_client("/unique") msg = yield tm.read_msg() self.assertEqual(msg, None) # Connection closed # Close one tms[0].close() msg = yield tms[0].read_msg() # Closed self.assertEquals(msg, None) # Should be able to open back up to MAX_TERMS tm = yield self.get_term_client("/unique") msg = yield tm.read_msg() self.assertEquals(msg[0], 'setup')
14,493
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9
{"_type": "Module", "_fields": {"body": [{"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "self", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Assign", "_fields": {"value": {"_type": "ListComp", "_fields": {"elt": {"_type": "BinOp", "_fields": {"op": {"_type": "Mod", "_fields": {}}, "left": {"_type": "Constant", "_fields": {"kind": null, "value": "/named/%d"}}, "right": {"_type": "Name", "_fields": {"id": "i", "ctx": {"_type": "Load", "_fields": {}}}}}}, "generators": [{"_type": "comprehension", "_fields": {"ifs": [], "iter": {"_type": "Call", "_fields": {"args": [{"_type": "BinOp", "_fields": {"op": {"_type": "Add", "_fields": {}}, "left": {"_type": "Name", "_fields": {"id": "MAX_TERMS", "ctx": {"_type": "Load", "_fields": {}}}}, "right": {"_type": "Constant", "_fields": {"kind": null, "value": 1}}}}], "func": {"_type": "Name", "_fields": {"id": "range", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "target": {"_type": "Name", "_fields": {"id": "i", "ctx": {"_type": "Store", "_fields": {}}}}, "is_async": 0}}]}}, "targets": [{"_type": "Name", "_fields": {"id": "urls", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Yield", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Slice", "_fields": {"step": null, "lower": null, "upper": {"_type": "Name", "_fields": {"id": "MAX_TERMS", "ctx": {"_type": "Load", "_fields": {}}}}}}, "value": {"_type": "Name", "_fields": {"id": "urls", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "get_term_clients", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, "targets": [{"_type": "Name", "_fields": {"id": "tms", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Yield", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "tms", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "get_pids", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, "targets": [{"_type": "Name", "_fields": {"id": "pids", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Yield", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Name", "_fields": {"id": "MAX_TERMS", "ctx": {"_type": "Load", "_fields": {}}}}, "value": {"_type": "Name", "_fields": {"id": "urls", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "get_term_client", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, "targets": [{"_type": "Name", "_fields": {"id": "tm", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Yield", "_fields": {"value": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "read_msg", "value": {"_type": "Name", "_fields": {"id": "tm", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, "targets": [{"_type": "Name", "_fields": {"id": "msg", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "msg", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Constant", "_fields": {"kind": null, "value": null}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertEqual", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "test_max_terminals", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def test_max_terminals(self): urls = ["/named/%d" % i for i in range(MAX_TERMS+1)] tms = yield self.get_term_clients(urls[:MAX_TERMS]) pids = yield self.get_pids(tms) # MAX_TERMS+1 should fail tm = yield self.get_term_client(urls[MAX_TERMS]) msg = yield tm.read_msg() self.assertEqual(msg, None)
14,494
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9
{"_type": "Module", "_fields": {"body": [{"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "self", "annotation": null, "type_comment": null}}, {"_type": "arg", "_fields": {"arg": "request", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Assign", "_fields": {"value": {"_type": "BoolOp", "_fields": {"op": {"_type": "Or", "_fields": {}}, "values": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_bad_debug_urls", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "List", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": []}}]}}, "targets": [{"_type": "Name", "_fields": {"id": "debug_urls", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "If", "_fields": {"body": [{"_type": "Return", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_bad_run_start_action", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Name", "_fields": {"id": "debug_urls", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "OnRunStartResponse", "value": {"_type": "Name", "_fields": {"id": "framework", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "test": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_bad_run_start_action", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "orelse": [{"_type": "Return", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "DEBUG_RUN", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "OnRunStartAction", "value": {"_type": "Name", "_fields": {"id": "framework", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, {"_type": "Name", "_fields": {"id": "debug_urls", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "OnRunStartResponse", "value": {"_type": "Name", "_fields": {"id": "framework", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}]}}], "name": "on_run_start", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def on_run_start(self, request): debug_urls = self._bad_debug_urls or [] if self._bad_run_start_action: return framework.OnRunStartResponse( self._bad_run_start_action, debug_urls) else: return framework.OnRunStartResponse( framework.OnRunStartAction.DEBUG_RUN, debug_urls)
14,495
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"_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "msg", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Constant", "_fields": {"kind": null, "value": null}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertEquals", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Assign", "_fields": {"value": {"_type": "Yield", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "/unique"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "get_term_client", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, "targets": [{"_type": "Name", "_fields": {"id": "tm", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Yield", "_fields": {"value": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "read_msg", "value": {"_type": "Name", "_fields": {"id": "tm", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, "targets": [{"_type": "Name", "_fields": {"id": "msg", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": 0}}, "value": {"_type": "Name", "_fields": {"id": "msg", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Constant", "_fields": {"kind": null, "value": "setup"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertEquals", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "test_max_terminals", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def test_max_terminals(self): tms = yield self.get_term_clients(['/unique'] * MAX_TERMS) pids = yield self.get_pids(tms) self.assertEqual(len(set(pids)), MAX_TERMS) # All PIDs unique # MAX_TERMS+1 should fail tm = yield self.get_term_client("/unique") msg = yield tm.read_msg() self.assertEqual(msg, None) # Connection closed # Close one tms[0].close() msg = yield tms[0].read_msg() # Closed self.assertEquals(msg, None) # Should be able to open back up to MAX_TERMS tm = yield self.get_term_client("/unique") msg = yield tm.read_msg() self.assertEquals(msg[0], 'setup')
14,496
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def testSessionInit(self): self.assertEqual(0, self._observer["sess_init_count"]) wrapper_sess = TestDebugWrapperSession(self._sess, self._dump_root, self._observer) # Assert that on-session-init callback is invoked. self.assertEqual(1, self._observer["sess_init_count"]) # Assert that the request to the on-session-init callback carries the # correct session object. self.assertEqual(self._sess, self._observer["request_sess"]) # Verify that the wrapper session implements the session.SessionInterface. self.assertTrue(isinstance(wrapper_sess, session.SessionInterface)) self.assertEqual(self._sess.sess_str, wrapper_sess.sess_str) self.assertEqual(self._sess.graph, wrapper_sess.graph) self.assertEqual(self._sess.graph_def, wrapper_sess.graph_def) # Check that the partial_run_setup and partial_run are not implemented for # the debug wrapper session. with self.assertRaises(NotImplementedError): wrapper_sess.partial_run_setup(self._p)
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{"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "initializer", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_a", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "run", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_sess", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "initializer", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_b", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "run", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_sess", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}}}], "name": "setUp", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def setUp(self): self._observer = { "sess_init_count": 0, "request_sess": None, "on_run_start_count": 0, "run_fetches": None, "run_feed_dict": None, "on_run_end_count": 0, "performed_action": None, "tf_error": None, } self._dump_root = tempfile.mkdtemp() self._sess = session.Session() self._a_init_val = np.array([[5.0, 3.0], [-1.0, 0.0]]) self._b_init_val = np.array([[2.0], [-1.0]]) self._c_val = np.array([[-4.0], [6.0]]) self._a_init = constant_op.constant( self._a_init_val, shape=[2, 2], name="a1_init") self._b_init = constant_op.constant( self._b_init_val, shape=[2, 1], name="b_init") self._ph = array_ops.placeholder(dtype=dtypes.float64, name="ph") self._a = variables.Variable(self._a_init, name="a1") self._b = variables.Variable(self._b_init, name="b") self._c = constant_op.constant(self._c_val, shape=[2, 1], name="c") # Matrix product of a and b. self._p = math_ops.matmul(self._a, self._b, name="p1") # Matrix product of a and ph. self._q = math_ops.matmul(self._a, self._ph, name="q") # Sum of two vectors. self._s = math_ops.add(self._p, self._c, name="s") # Initialize the variables. self._sess.run(self._a.initializer) self._sess.run(self._b.initializer)
14,498
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8
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def testRunStartBadURLs(self): # debug_urls ought to be a list of str, not a str. So an exception should # be raised during a run() call. wrapper = TestDebugWrapperSessionBadAction( self._sess, bad_debug_urls="file://foo") with self.assertRaisesRegexp(TypeError, "Expected type .*; got type .*"): wrapper.run(self._s)
14,499
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8
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def testSessionRun(self): wrapper = TestDebugWrapperSession( self._sess, self._dump_root, self._observer) # Check initial state of the observer. self.assertEqual(0, self._observer["on_run_start_count"]) self.assertEqual(0, self._observer["on_run_end_count"]) s = wrapper.run(self._s) # Assert the run return value is correct. self.assertAllClose(np.array([[3.0], [4.0]]), s) # Assert the on-run-start method is invoked. self.assertEqual(1, self._observer["on_run_start_count"]) # Assert the on-run-start request reflects the correct fetch. self.assertEqual(self._s, self._observer["run_fetches"]) # Assert the on-run-start request reflects the correct feed_dict. self.assertIsNone(self._observer["run_feed_dict"]) # Assert the file debug URL has led to dump on the filesystem. dump = debug_data.DebugDumpDir(self._dump_root) self.assertEqual(7, len(dump.dumped_tensor_data)) # Assert the on-run-end method is invoked. self.assertEqual(1, self._observer["on_run_end_count"]) # Assert the performed action field in the on-run-end callback request is # correct. self.assertEqual( framework.OnRunStartAction.DEBUG_RUN, self._observer["performed_action"]) # No TensorFlow runtime error should have happened. self.assertIsNone(self._observer["tf_error"])
14,500
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9
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def testUsingWrappedSessionShouldSupportEvalWithAsDefault(self): wrapper = TestDebugWrapperSession(self._sess, self._dump_root, self._observer) with wrapper.as_default(): foo = constant_op.constant(42, name="foo") self.assertEqual(42, foo.eval()) self.assertEqual(foo, self._observer["run_fetches"])