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{"content":"# Copyright 2022 <[email protected]>\n\nimport getpass\nimport telnetlib\n\nuser = input(\"Enter your remote account: \")\npassword = getpass.getpass()\n\naddress_1 = \"132.12.67.81\"\naddress_2 = \"142.12.67.81\"\n\ntn = telnetlib.Telnet(address_1)\n\ntn.read_until(b\"login: \")\ntn.write(user.encode('ascii') + b\"\n\")\nif password:\n tn.read_until(b\"Password: \")\n tn.write(password.encode('ascii') + b\"\n\")\n\ntn.write(b\"ls\n\")\ntn.write(b\"exit\n\")\n\nprint(tn.read_all().decode('ascii'))","secrets":"[{\"tag\": \"IP_ADDRESS\", \"value\": \"132.12.67.81\", \"start\": 161, \"end\": 173}, {\"tag\": \"IP_ADDRESS\", \"value\": \"142.12.67.81\", \"start\": 188, \"end\": 200}, {\"tag\": \"EMAIL\", \"value\": \"[email protected]\", \"start\": 18, \"end\": 38}]","has_secrets":true,"number_secrets":3,"new_content":"# Copyright 2022 <[email protected]>\n\nimport getpass\nimport telnetlib\n\nuser = input(\"Enter your remote account: \")\npassword = getpass.getpass()\n\naddress_1 = \"192.168.3.11\"\naddress_2 = \"172.16.31.10\"\n\ntn = telnetlib.Telnet(address_1)\n\ntn.read_until(b\"login: \")\ntn.write(user.encode('ascii') + b\"\n\")\nif password:\n tn.read_until(b\"Password: \")\n tn.write(password.encode('ascii') + b\"\n\")\n\ntn.write(b\"ls\n\")\ntn.write(b\"exit\n\")\n\nprint(tn.read_all().decode('ascii'))","modified":true,"references":"# Copyright 2022 <PI:EMAIL:[email protected]_PI>\n\nimport getpass\nimport telnetlib\n\nuser = input(\"Enter your remote account: \")\npassword = getpass.getpass()\n\naddress_1 = \"PI:IP_ADDRESS:192.168.3.11END_PI\"\naddress_2 = \"PI:IP_ADDRESS:172.16.31.10END_PI\"\n\ntn = telnetlib.Telnet(address_1)\n\ntn.read_until(b\"login: \")\ntn.write(user.encode('ascii') + b\"\n\")\nif password:\n tn.read_until(b\"Password: \")\n tn.write(password.encode('ascii') + b\"\n\")\n\ntn.write(b\"ls\n\")\ntn.write(b\"exit\n\")\n\nprint(tn.read_all().decode('ascii'))"}
{"content":"'''Generating embeddings with Node2Vec for a graph'''\n\n# Copyright 2022 <[email protected]> or <[email protected]>\n\nimport os\nimport gzip\nimport pickle\nfrom tqdm import tqdm\nimport networkx as nx\nfrom nodevectors import Node2Vec\nimport argparse\nfrom datasets import load_dataset\nfrom huggingface_hub import Repository\n\ndef create_node_embeddings(args):\n G = nx.read_edgelist(args.path_graph, delimiter=',', create_using=nx.Graph(), nodetype=int)\n nodes = list(G.nodes())\n n = G.number_of_nodes()\n m = G.number_of_edges()\n \n print(\"Training Node2vec...\")\n model = Node2Vec(n_components=args.n_components, \n walklen=40,\n epochs=30,\n threads=os.cpu_count()) \n\n model.fit(G)\n\n node_embeddings = dict()\n for node in tqdm(nodes):\n node_embeddings[node] = model.predict(node)\n\n repo = Repository(\n local_dir=\"dataset\",\n clone_from=\"loubna\/graph_dataset\",\n token=True,\n repo_type=\"dataset\",\n git_user=\"user\",\n use_auth_token=\"tk_XKyihy89nl89hgEMOskeTHCRZEkBd7\",\n git_email=\"[email protected]\"\n )\n\n print(\"----- Saving the Embeddings -----\")\n file = gzip.GzipFile(\"dataset\/node_embeddings.emb\", 'wb')\n file.write(pickle.dumps(node_embeddings))\n file.close()\n\n\n\nrepo = Repository(local_dir=\"huggingface-hub\", clone_from=\"https:\/\/huggingface.co\/facebook\/wav2vec2-large-960h-lv60\")\nif __name__ ==\"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--path_graph\", type=str, default=\"data\/edgelist.txt\", \n help=\"Path to the graph edges text file\")\n parser.add_argument(\"--n_components\", type=int, default=20,\n help=\"Size of the embedding\")\n \n create_node_embeddings(parser.parse_args())","secrets":"[{\"tag\": \"EMAIL\", \"value\": \"[email protected]\", \"start\": 73, \"end\": 89}, {\"tag\": \"EMAIL\", \"value\": \"[email protected]\", \"start\": 95, \"end\": 112}, {\"tag\": \"EMAIL\", \"value\": \"[email protected]\", \"start\": 1100, \"end\": 1116}, {\"tag\": \"KEY\", \"value\": \"tk_XKyihy89nl89hgEMOskeTHCRZEkBd7\", \"start\": 1045, \"end\": 1078}]","has_secrets":true,"number_secrets":4,"new_content":"'''Generating embeddings with Node2Vec for a graph'''\n\n# Copyright 2022 <[email protected]> or <[email protected]>\n\nimport os\nimport gzip\nimport pickle\nfrom tqdm import tqdm\nimport networkx as nx\nfrom nodevectors import Node2Vec\nimport argparse\nfrom datasets import load_dataset\nfrom huggingface_hub import Repository\n\ndef create_node_embeddings(args):\n G = nx.read_edgelist(args.path_graph, delimiter=',', create_using=nx.Graph(), nodetype=int)\n nodes = list(G.nodes())\n n = G.number_of_nodes()\n m = G.number_of_edges()\n \n print(\"Training Node2vec...\")\n model = Node2Vec(n_components=args.n_components, \n walklen=40,\n epochs=30,\n threads=os.cpu_count()) \n\n model.fit(G)\n\n node_embeddings = dict()\n for node in tqdm(nodes):\n node_embeddings[node] = model.predict(node)\n\n repo = Repository(\n local_dir=\"dataset\",\n clone_from=\"loubna\/graph_dataset\",\n token=True,\n repo_type=\"dataset\",\n git_user=\"user\",\n use_auth_token=\"ngw6fo1pu3tjgnp9jnlp7vnwvfqb9yn7\",\n git_email=\"[email protected]\"\n )\n\n print(\"----- Saving the Embeddings -----\")\n file = gzip.GzipFile(\"dataset\/node_embeddings.emb\", 'wb')\n file.write(pickle.dumps(node_embeddings))\n file.close()\n\n\n\nrepo = Repository(local_dir=\"huggingface-hub\", clone_from=\"https:\/\/huggingface.co\/facebook\/wav2vec2-large-960h-lv60\")\nif __name__ ==\"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--path_graph\", type=str, default=\"data\/edgelist.txt\", \n help=\"Path to the graph edges text file\")\n parser.add_argument(\"--n_components\", type=int, default=20,\n help=\"Size of the embedding\")\n \n create_node_embeddings(parser.parse_args())","modified":true,"references":"'''Generating embeddings with Node2Vec for a graph'''\n\n# Copyright 2022 <PI:EMAIL:[email protected]_PI> or <PI:EMAIL:[email protected]_PI>\n\nimport os\nimport gzip\nimport pickle\nfrom tqdm import tqdm\nimport networkx as nx\nfrom nodevectors import Node2Vec\nimport argparse\nfrom datasets import load_dataset\nfrom huggingface_hub import Repository\n\ndef create_node_embeddings(args):\n G = nx.read_edgelist(args.path_graph, delimiter=',', create_using=nx.Graph(), nodetype=int)\n nodes = list(G.nodes())\n n = G.number_of_nodes()\n m = G.number_of_edges()\n \n print(\"Training Node2vec...\")\n model = Node2Vec(n_components=args.n_components, \n walklen=40,\n epochs=30,\n threads=os.cpu_count()) \n\n model.fit(G)\n\n node_embeddings = dict()\n for node in tqdm(nodes):\n node_embeddings[node] = model.predict(node)\n\n repo = Repository(\n local_dir=\"dataset\",\n clone_from=\"loubna\/graph_dataset\",\n token=True,\n repo_type=\"dataset\",\n git_user=\"user\",\n use_auth_token=\"PI:KEY:ngw6fo1pu3tjgnp9jnlp7vnwvfqb9yn7END_PI\",\n git_email=\"PI:EMAIL:[email protected]_PI\"\n )\n\n print(\"----- Saving the Embeddings -----\")\n file = gzip.GzipFile(\"dataset\/node_embeddings.emb\", 'wb')\n file.write(pickle.dumps(node_embeddings))\n file.close()\n\n\n\nrepo = Repository(local_dir=\"huggingface-hub\", clone_from=\"https:\/\/huggingface.co\/facebook\/wav2vec2-large-960h-lv60\")\nif __name__ ==\"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--path_graph\", type=str, default=\"data\/edgelist.txt\", \n help=\"Path to the graph edges text file\")\n parser.add_argument(\"--n_components\", type=int, default=20,\n help=\"Size of the embedding\")\n \n create_node_embeddings(parser.parse_args())"}
{"content":"import boto3\nimport os\nimport subprocess\nimport platform\nimport sys\n\nclient = boto3.client(\n 's3',\n aws_access_key_id=\"AKIAIOSF889BG8BHRDYNN7EXAMPLE\",\n aws_secret_access_key=\"rt\/YUAmye9BxUBPl89hgEMOTZlmnKYV67H5gJD8\",\n)\n\ndef execute_command(command, args):\n try:\n aws_cmd = \"aws\" if platform.system().lower() != \"windows\" else \"aws.cmd\"\n command_with_args = [aws_cmd, \"cloudformation\", command] + list(args)\n subprocess.check_call(command_with_args)\n\n except subprocess.CalledProcessError as e:\n sys.exit(e.returncode)","secrets":"[{\"tag\": \"KEY\", \"value\": \"rt\/YUAmye9BxUBPl89hgEMOTZlmnKYV67H5gJD8\", \"start\": 184, \"end\": 223}, {\"tag\": \"KEY\", \"value\": \"AKIAIOSF889BG8BHRDYN\", \"start\": 125, \"end\": 145}]","has_secrets":true,"number_secrets":2,"new_content":"import boto3\nimport os\nimport subprocess\nimport platform\nimport sys\n\nclient = boto3.client(\n 's3',\n aws_access_key_id=\"74t3tndxag9o7h0890bnpfzh4olk2h9xN7EXAMPLE\",\n aws_secret_access_key=\"kgfhvu9qnh3mr6eel97y6fq2hezzol8z\",\n)\n\ndef execute_command(command, args):\n try:\n aws_cmd = \"aws\" if platform.system().lower() != \"windows\" else \"aws.cmd\"\n command_with_args = [aws_cmd, \"cloudformation\", command] + list(args)\n subprocess.check_call(command_with_args)\n\n except subprocess.CalledProcessError as e:\n sys.exit(e.returncode)","modified":true,"references":"import boto3\nimport os\nimport subprocess\nimport platform\nimport sys\n\nclient = boto3.client(\n 's3',\n aws_access_key_id=\"PI:KEY:74t3tndxag9o7h0890bnpfzh4olk2h9xEND_PIN7EXAMPLE\",\n aws_secret_access_key=\"PI:KEY:kgfhvu9qnh3mr6eel97y6fq2hezzol8zEND_PI\",\n)\n\ndef execute_command(command, args):\n try:\n aws_cmd = \"aws\" if platform.system().lower() != \"windows\" else \"aws.cmd\"\n command_with_args = [aws_cmd, \"cloudformation\", command] + list(args)\n subprocess.check_call(command_with_args)\n\n except subprocess.CalledProcessError as e:\n sys.exit(e.returncode)"}
{"content":"import setuptools\n\nwith open(\"README.md\", \"r\", encoding=\"utf-8\") as fh:\n description = fh.read()\n\nsetuptools.setup(\n name=\"project\",\n author=\"maintainer\",\n author_email=\"[email protected]\",\n description=\"NLP Project\",\n long_description=description,\n long_description_content_type=\"text\/markdown\",\n url=\"https:\/\/github.com\/projects-testing45\/tree\/main\/nlp-course1\/nlp\/\",\n python_requires=\">=3.6\",\n)","secrets":"[{\"tag\": \"EMAIL\", \"value\": \"[email protected]\", \"start\": 182, \"end\": 202}]","has_secrets":true,"number_secrets":1,"new_content":"import setuptools\n\nwith open(\"README.md\", \"r\", encoding=\"utf-8\") as fh:\n description = fh.read()\n\nsetuptools.setup(\n name=\"project\",\n author=\"maintainer\",\n author_email=\"[email protected]\",\n description=\"NLP Project\",\n long_description=description,\n long_description_content_type=\"text\/markdown\",\n url=\"https:\/\/github.com\/projects-testing45\/tree\/main\/nlp-course1\/nlp\/\",\n python_requires=\">=3.6\",\n)","modified":true,"references":"import setuptools\n\nwith open(\"README.md\", \"r\", encoding=\"utf-8\") as fh:\n description = fh.read()\n\nsetuptools.setup(\n name=\"project\",\n author=\"maintainer\",\n author_email=\"PI:EMAIL:[email protected]_PI\",\n description=\"NLP Project\",\n long_description=description,\n long_description_content_type=\"text\/markdown\",\n url=\"https:\/\/github.com\/projects-testing45\/tree\/main\/nlp-course1\/nlp\/\",\n python_requires=\">=3.6\",\n)"}
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