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We will attend on graph encoder and rnn encoder's results separately. "``sep_diff_node_type``": separate attention. We will attend on different node type separately. attention_function: str, option=["general", "mlp"], default="mlp" Different attention function. node_type_num: int, default=None When we choose "``sep_diff_node_type``", we must set this parameter. This parameter indicate the the amount of node type. fuse_strategy: str, option=["average", "concatenate"], default=average The strategy to fuse attention results generated by separate attention. "``average``": We will take an average on all results. "``concatenate``": We will concatenate all results to one. use_copy: bool, default=False Whether use ``copy`` mechanism. See pointer network. Note that you must use attention first. use_coverage: bool, default=False Whether use ``coverage`` mechanism. 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""" return 0 �def DISABLED_test_keras_model_to_graph_def_functional_sequential_model( self, ): expected_proto = """ node { name: "model/func_seq_input" attr { key: "dtype" value { type: DT_FLOAT } } attr { key: "keras_class" value { s: "InputLayer" } } } node { name: "model/sequential/dense_input" input: "model/func_seq_input" attr { key: "dtype" value { type: DT_FLOAT } } attr { key: "keras_class" value { s: "InputLayer" } } } node { name: "model/sequential/dense" input: "model/sequential/dense_input" attr { key: "dtype" value { type: DT_FLOAT } } attr { key: "keras_class" value { s: "Dense" } } } node { name: "model/sequential/my_relu" input: "model/sequential/dense" attr { key: "dtype" value { type: DT_FLOAT } } attr { key: "keras_class" value { s: "Activation" } } } node { name: "model/dense_1" input: "model/sequential/my_relu" attr { key: "dtype" value { type: DT_FLOAT } } attr { key: "keras_class" value { s: "Dense" } } } """ inputs = keras.layers.Input(shape=(784,), name="func_seq_input") sub_model = keras.models.Sequential( [ keras.layers.Dense(32, input_shape=(784,)), keras.layers.Activation("relu", name="my_relu"), ] ) dense = keras.layers.Dense(64, activation="relu") model = keras.models.Model( inputs=inputs, outputs=dense(sub_model(inputs)) ) self.assertGraphDefToModel(expected_proto, model) �� ���  �@��H���4� @  `��.�� h`�>��`$(� N���`,h� ^��`4�� n���`<�~� aD(����$(a Lh���(Ha T�����,ha \���0�a d(�΀�4�a lh���8�at��� <�a(��=�}��A ����!��D(b�h�# $DB��D%h`�K`"��':�O�"�)Ja�S�"�X+Z�W�"��-ja�[�� ���.x1�^����0B0 b��A�� e,��p�3� �@ӠF5�q ldC��F7�� �#�9�a�s�#�X;��w�#�=�a�{�C���>��1�� �@B��� B���0�!yD""���"��E0���a#�G>���d$$)ILr���C (II?��v���P�JX����%0��LfB����&8ɉNv“���'@ �P�B���(HI�R�”�8�O�JTB OHe*��(�R�\+Y��V���T�+�KX�2��#��AY�r��E-�x�;ڱ���-o�K\�2���.H G%jp��E/{�K_���F0�!La s�$F1�aLc��DF2��Leر�~��+��e0�x�4��f8��|4��hHSӜ5�Q�jX�׼�|�� �P6��lfC����6�@nt��^��7���o��)T�pG;*Q��%8���p�C��L��0�q��^L��Q�r|���8�ρNt�3�T�:��Nv���t�;�Ox�3��� _(�y΃���g=�i�{�Ë)����r�Î��>���}��{�%?`��~jP�jx�?�����Ԁ�@�!  `@*P hP !�u8�:� -�A rЃ ��u�� ���>���*d� a(C��:��(D"�Jd�`�E)RX*Q�JT�+ �Z���H�D�d4#��F7��r���(,^�F%j����G?R��4$"�HGBR���$&5�IO�R���)Q�JV�X���~hiK\꒗��%0��!aӘ�T����LhJ���Ħ6Հ�@�� �&8�INs�S��t'<�IO{�S��T�?*P�� e�C!*Q�Z��GA*R���*e�Ka���Ԧ8�)O}JR�����P�jT�*��N�*>�JU���ZU= �U��U� P �S�ꔤ�@�Y���0�4��J�a����n��\�jW�����=��W���4,a �X�򊱎��d)��X��U g= Zђִ�A�jY�Z�ʖ��ŭny�[� ���E�r��\�J�Ե���]!���/ċ0���E�z�k��W���/~��_�X�60��,a)�����)la k���~DLb�X����y���X�⏋a,c��:�1x|� s;�2���d%3Y;N����le,k��^���lf4���n����lg<��~�{�H� j4� �hE3�ѐ�4�-�iMs�Ӡ���U�5��ԦF��Y�jX˚ֶƵ�mJS^����)IQ�R��Ԧ8%��u�l��T�0U6�� miS����6�� nq����V7�� oy�����7���o� ��G���p�K��Ǹ�9�q~�\�$7�:P�r��!�`�J����\�4�9�Ѡs���B'�ё�t�;�R��ձ�u�{]D���nv����n����nw���~�� ox�+�񎇼�)oy�k����Iozԫ������io{����T ��=��~�o|�+��·���o}b��C��>��~����W?�������׿��`a����P�$` 8�jT�` �Dp@&P��p�;�Q�JT�+ d�W��B0�� �QA ^���9����`G�0�=�~0�A�~����IXB���@a U�B��.|! c(�Ұ�6�!s���>�!�D!��E4���D%.��Mt��E)N��U����E-n��]���F1���e4�јF5���mt��G9Α�u���G=}��HA���4�!�HE.���t�C�!�GB2���&I�JZ�̤&7��Nz� �(GI�R��L�*W���ҕ�+aKYΒ����-q�K]���/�La���4�1��Le.���t�3��hFS�Ӥf��iMI]��|�?:�M1l3,��7� �p�s��,�9��t�az��j�Nu�,�T�0��Mq�S��T;��Nx�S���'=�i�{z���>��}���?P���5�A�P�.�� u�C!����C ( щR��u��E1�)Ie�R�”F�0�=�c(�ꠇA��!�C��ю��|��GAR����%5�IQ�R����-u�Ka��@�3�iM ���T!�iNu�SԀ�=�i0�J��@ jX�R�ze�D-�Q��Ԥ>�b��:��T�6 �pj0� ըJu�T��U��T�W�j0�U�n��]��W�V����e5�YњV����mm�[9�V��U�s�k]�a׻����T�0��M��y}�^+Q��e�|��;��v��b��C���vx寀 �`E0X†���=,b���2���},d# Hv����e/���jv����g? 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Ik2=2%�>'=R 5I�Aծb" B2 R����V�feautr2�2�2r6j����N�zlumn_a�(arr, �fed ��s\"""Determine the most fre�xtly appearing element in each c_82D��array, igno9�_max = s.max()%if $> 0� � = s[Ns = L4].minP elseVDcp.nany2�[i]f! �3E;(2- ) �I��� iou_!�0_loss_zeros_w� (q -�`pred = torch.rand((10, 4))targeA"^! XBm(10 o = v()(r, Y, @ mI .!R0.0 ��N_x H_reduction_override��(J�, �� None�  py!h.r� A ion� -X# only F�   [V8, 'none', 'meansum']%�v , not allowedJW= 2�%0cQ ()6E pred�7)�6 �6Fv=F,, ) �����[,� 6(Rdef zero_grad(self, set_to_none=False): self.optimizer.zero_grad(set_to_none) �def GetRangeDataframe( prometheus_url, query_list, starttime, endtime, step, columns, **kwargs, ): """ Convert the Prometheus data over the specified time period to dataframe and confirm dt_col, target_col, id_col and extra_feature_col. Return dataframe and col names. """ from bigdl.chronos.data.utils.utils import _check_type _check_type(prometheus_url, "prometheus_url", str) _check_type(starttime, "starttime", (str, float)) _check_type(endtime, "endtime", (str, float)) _check_type(step, "step", (str, float)) # Generate dataframe according query_list pro_client = Prometheus(prometheus_url) pro_df = pd.DataFrame() for query in query_list: query_df = pro_client.query_range( query, starttime, endtime, step, **kwargs ) # TODO: whatif pro_df and query_df has different length # TODO: repair missing value when query Prometheus client pro_df = pd.concat([pro_df, query_df], axis=1) df = pd.DataFrame(columns=pro_df.columns.tolist()) df.insert(0, "datetime", pro_df.index) for col in pro_df.columns.tolist(): df[col] = pro_df[col].values # Clean columns output_columns = { "dt_col": "datetime", "target_col": pro_df.columns.tolist(), "id_col": None, "extra_feature_col": None, } output_col_list = ["datetime"] for col in ["target_col", "id_col", "extra_feature_col"]: # Check whether columns specified by user exist invalidInputError( len(columns[col]) == 0 or set(columns[col]).issubset(df.columns.tolist()), "The input " + col + " is not found in collected Prometheus data.", ) # If users specify target_col/id_col/extra_feature_col, update values in output_columns[] if len(columns[col]) == 1: output_columns[col] = columns[col][ 0 ] # id_col must be str elif len(columns[col]) > 1: output_columns[col] = columns[col] # According to output_columns, confirm columns to be remained in df if output_columns[col] is not None: if isinstance(output_columns[col], list): output_col_list = ( output_col_list + output_columns[col] ) else: output_col_list.append(output_columns[col]) df = df.loc[:, output_col_list] return df, output_columns ��w�a� �def test_broadcast_to_int64(self): x = constant_op.constant([1.0, 2.0, 3.0], dtype=$s.float64)DybD 2, 3>;�) :�f(x �@return array_ops..�(x, y ;�._�,Grad(f, x) �^�(/ gxb�1��f�32 ���>�d=�tgather_v2_batch_dims_with_axis�%�[[5]], [[3! 4.0]E=U;���tx,HcR�[0����in!j6B�=269=1 �A,B[Vh2J�_, �]�].]2A , )rB%v27��=�n��I�pya� _run logreport%�, : TestR$) -> None:�if $.failed if)� skip# Remove�T$ from the At ones (if it exists) and unset)R option�4# to make sure+ followingl0s will not beG ped.2Di� nodeid == �lastf:�E�! =%!��$�= Falsewels-M# Mark� as�^%"�!interrup)!� session6�x> � =1� ��asserHlf. 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Args: filename: Filename to save the mapping. sym2id: A dict mapping symbols to IDs. Returns: Return None. """ with open(filename, "w", encoding="utf-8") as f: for sym, i in sym2id.items(): f.write(f"{sym} {i}\n") �def Completions(component, verbose=False): """Gives possible Fire command completions for the component. A completion is a string that can be appended to a command to continue that command. These are used for TAB-completions in Bash for Fire CLIs. Args: component: The component whose completions to list. verbose: Whether to include all completions, even private members. Returns: A list of completions for a command that would so far return the component. 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FOV.�N"Re��� :\n%r\nIN )? jpE%? 1 D.fC%jAif���E6��b R^ R�fQF/6�`O�BF D' E@exc6�# Keep�,ckA9i ddݚal&i ��� h exc."k,' ck_c� "�2E�< �TypI�R�img@>�6�&}U��+4.Ef" ({-}) *���Fkf")D4unt�[ le loae>ms {i}{q}:ibf5) # Ra�g*'1�;o: empt�!�-19�S.>"I�I)�a8st is empty.") ™���W,� 6(�def write_vocabulary_file(vocabulary): """Write temporary vocab file for module construction.""" tmpdir = tempfile.mkdtemp() vocabulary_file = os.path.join(tmpdir, "tokens.txt") with gfile.GFile(vocabulary_file, "w") as f: for entry in vocabulary: f.write(entry + "\n") return vocabulary_file �def Ava( frame_paths_file: str, frame_labels_file: str, video_path_prefix: str = "", label_map_file: Optional[str] = None, clip_sampler: Callable = ClipSampler, video_sampler: Type[ torch.utils.data.Sampler ] = torch.utils.data.RandomSampler, transform: Optional[Callable[[dict], Any]] = None, ) -> None: """ Args: frame_paths_file (str): Path to a file containing relative paths to all the frames in the video. Each line in the file is of the form frame_labels_file (str): Path to the file containing containing labels per key frame. Acceptible file formats are, Type 1: Type 2: video_path_prefix (str): Path to be augumented to the each relative frame path to get the global frame path. label_map_file (str): Path to a .pbtxt containing class id's and class names. If not set, label_map is not loaded and bbox labels are not pruned based on allowable class_id's in label_map. clip_sampler (ClipSampler): Defines how clips should be sampled from each video. video_sampler (Type[torch.utils.data.Sampler]): Sampler for the internal video container. This defines the order videos are decoded and, if necessary, the distributed split. transform (Optional[Callable]): This callable is evaluated on the clip output and the corresponding bounding boxes before the clip and the bounding boxes are returned. It can be used for user defined preprocessing and augmentations to the clips. If transform is None, the clip and bounding boxes are returned as it is. """ labeled_video_paths = AvaLabeledVideoFramePaths.from_csv( frame_paths_file, frame_labels_file, video_path_prefix, label_map_file, ) return LabeledVideoDataset( labeled_video_paths=labeled_video_paths, clip_sampler=TimeStampClipSampler(clip_sampler), transform=transform, video_sampler=video_sampler, decode_audio=False, ) �t���X�B��=def __init__( self, Denvironment_spec: s.ESpec,-dpolicy_network: snt.Module criticf (observation%Ptypes.TensorTransform$4 = tf.identity cTdiscount: float = 0.99 4batch_size: in256 prefe>4 target_ �0update_periodH1002, �v,$min_replay2�10Mmax^!$samples�_inser�32. � �loss_m%� : Op!hal[9�] = Non-� 5 optimizer:3@66 ��6n_step1O5)M num_ �2 �Lclipping: bool = Tru � logg2� s.L:�A,2.ing.C :0heckpoiAaBz-��table_name: str = adders.DEFAULT_PRIORITY_TABLE, ):e>`"""Initialize the agent. 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