diff --git "a/1763.jsonl" "b/1763.jsonl" new file mode 100644--- /dev/null +++ "b/1763.jsonl" @@ -0,0 +1,936 @@ +{"seq_id":"5398151413","text":"import json\nfrom itertools import combinations\n\nfrom transformers import AutoModel, AutoTokenizer\nfrom torch.utils.data import TensorDataset\nimport torch\n\nimport random\n\n\nclass Doc:\n def __init__(self,\n sentence_dict: dict,\n tokenizer: AutoTokenizer,\n policy,\n exclude_single_trigs,\n exclude_first_sents):\n self.policy = policy\n self.exclude_single_trigs = exclude_single_trigs\n self.exclude_first_sents = exclude_first_sents\n \n self.tokenizer = tokenizer\n self.filename = sentence_dict[\"filename\"]\n self.tokens = [[j.lower() for j in i] for i in sentence_dict[\"tokens\"]]\n self.sentences = [\" \".join(t) for t in self.tokens]\n self.spans = sentence_dict[\"spans\"]\n \n self.create_token_charseqs()\n self.parse()\n self.create_pairs()\n \n def create_token_charseqs(self):\n self.charseqs = []\n \n for sentence in self.tokens:\n sentence_charseq = []\n start = 0\n \n for token in sentence:\n sentence_charseq.append([start,start+len(token)])\n start+=len(token)+1\n self.charseqs.append(sentence_charseq)\n \n def check_range_in_range(self, trigger_charseq, token_charseq):\n if token_charseq != [0,0]:\n return all(e in range(*trigger_charseq) for e in range(*token_charseq))\n else:\n return False\n \n def find_trigger_tokenized_index(self, trigger_dict):\n sentence = trigger_dict[\"sentence\"]\n charseq = trigger_dict[\"trigger_charseq\"]\n tokenizer_output = self.tokenizer(sentence,\n padding = True,\n return_token_type_ids = False,\n return_tensors = \"pt\",\n return_special_tokens_mask = True, \n return_offsets_mapping = True)\n \n offset_mapping = tokenizer_output[\"offset_mapping\"][0].tolist()\n trigger_tokenized_index = [1 if self.check_range_in_range(charseq,o) else 0 for o in offset_mapping]\n \n trigger_dict[\"trigger_tokenized_index\"] = trigger_tokenized_index\n trigger_dict[\"tokenized_output\"] = tokenizer_output\n return trigger_dict\n \n def which_event(self, event_names, event_counts, policy, exclude_single_trigs):\n if policy == \"most_common\" and exclude_single_trigs == True: \n event_counts_ = {en:event_counts[en] for en in event_names}\n number_of_occur = list(event_counts_.values())\n events = [event_name for event_name in event_names if event_counts_[event_name] == max(number_of_occur) != 1]\n return sorted(events)[0] if events else None\n \n elif policy == \"most_common\" and exclude_single_trigs == False:\n if len(set(event_names))>1:\n event_counts_ = {en:event_counts[en] for en in event_names}\n number_of_occur = list(event_counts_.values())\n events = [event_name for event_name in event_names if event_counts_[event_name] == max(number_of_occur)]\n return sorted(events)[0]\n else:\n return event_names[0]\n \n def parse(self):\n self.event_counts = {}\n for sentence_index, markables in enumerate(self.spans):\n if self.exclude_first_sents and sentence_index == 0:\n continue\n sentence_name = self.filename+\"|#|#|\"+str(sentence_index)\n for markable_index, markable in enumerate(markables):\n token_index, event_attr, event_names = markable\n \n if event_attr == \"trigger\":\n trigger_name = sentence_name+\"|#|#|\"+\"_\".join(map(str,token_index))\n for event_name in event_names:\n event_name = self.filename+\"|#|#|\"+event_name\n\n self.event_counts.setdefault(event_name,0)\n self.event_counts[event_name] += 1\n \n self.triggers = {}\n self.events = {}\n for sentence_index, markables in enumerate(self.spans):\n if self.exclude_first_sents and sentence_index == 0:\n continue\n sentence_name = self.filename+\"|#|#|\"+str(sentence_index)\n for markable_index, markable in enumerate(markables):\n token_index, event_attr, event_names = markable\n \n if event_attr == \"trigger\":\n changed_event_names = [self.filename+\"|#|#|\"+en for en in event_names]\n trigger_name = sentence_name+\"|#|#|\"+\"_\".join(map(str,token_index))\n \n event_name = self.which_event(changed_event_names,\n self.event_counts,\n self.policy,\n self.exclude_single_trigs)\n if event_name:\n \n self.events.setdefault(event_name,[])\n self.events[event_name].append(trigger_name)\n\n trigger_charseq = sum([self.charseqs[sentence_index][i] for i in token_index],[])\n trigger_text = \" \".join([self.tokens[sentence_index][i] for i in token_index])\n trigger_dict ={\"sentence\": self.sentences[sentence_index],\n \"tokens\": self.tokens[sentence_index],\n \"trigger_token_index\": token_index,\n \"trigger_charseq\": [min(trigger_charseq),max(trigger_charseq)],\n \"trigger_text\": trigger_text}\n\n self.triggers[trigger_name] = self.find_trigger_tokenized_index(trigger_dict)\n\n \n \n def create_pairs(self):\n self.pairs = list(combinations(list(self.triggers.keys()),2))\n self.labels = [[0] for i in range(len(self.pairs))]\n \n for i,(t1,t2) in enumerate(self.pairs):\n for _,events in self.events.items():\n if t1 in events and t2 in events:\n self.labels[i] = [1]\n \n \nclass EMWCoreferenceDataset:\n \n def __init__(self,\n model_name: str,\n train_dataset_paths: list = None,\n dev_dataset_paths: list = None,\n test_dataset_paths: list = None,\n policy: str = \"most_common\",\n exclude_single_trigs: bool = False,\n exclude_first_sents: bool = False):\n \n self.model_name = model_name\n self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)\n self.policy = policy\n self.exclude_single_trigs = exclude_single_trigs\n self.exclude_first_sents = exclude_first_sents\n\n self.train_dataset_paths = train_dataset_paths\n self.dev_dataset_paths = dev_dataset_paths\n self.test_dataset_paths = test_dataset_paths\n\n self.train_tensor_dataset,self.dev_tensor_dataset,self.test_tensor_dataset = self.parse()\n \n \n \n def parse(self):\n\n \"\"\"\n JSON with a document (dict) for each line.\n\n Each document dict has 3 keys:\n filename: str // unique file name\n tokens: list // each item is a sentence (list) with tokens as its items \n spans: list // each item is markables of a sentence\n \"\"\"\n \n self.train_docs = self.read_docs(self.train_dataset_paths)\n self.dev_docs = self.read_docs(self.dev_dataset_paths)\n self.test_docs = self.read_docs(self.test_dataset_paths)\n\n self.create_trigger_pair_label()\n \n return self.tokenize(self.train_pairs,self.train_labels,self.train_triggers,\"train\"),self.tokenize(self.dev_pairs,self.dev_labels,self.dev_triggers,\"dev\"), self.tokenize(self.test_pairs,self.test_labels,self.test_triggers,\"test\")\n \n def read_docs(self,\n paths: list):\n\n docs = {}\n for path in paths:\n with open(path) as f:\n for raw_doc in f:\n doc = Doc(json.loads(raw_doc),\n self.tokenizer,\n self.policy,\n self.exclude_single_trigs,\n self.exclude_first_sents)\n docs[doc.filename] = doc\n return docs\n\n def shuffle_set(self,pairs,labels):\n shuffled_indexes = random.sample([i for i in range(len(pairs))],len(pairs))\n shuffled_pairs = [pairs[index] for index in shuffled_indexes]\n shuffled_labels = [labels[index] for index in shuffled_indexes]\n\n return shuffled_pairs,shuffled_labels\n\n \n def create_trigger_pair_label(self):\n self.train_triggers = {trigger_name:trigger_dict for doc_name,doc_obj in self.train_docs.items() \\\n for trigger_name, trigger_dict in doc_obj.triggers.items()}\n self.train_pairs = [p for doc_name, doc_obj in self.train_docs.items() for p in doc_obj.pairs]\n self.train_labels = [l for doc_name, doc_obj in self.train_docs.items() for l in doc_obj.labels]\n self.train_pairs, self.train_labels = self.shuffle_set(self.train_pairs, self.train_labels)\n\n self.dev_triggers = {trigger_name:trigger_dict for doc_name,doc_obj in self.dev_docs.items() \\\n for trigger_name, trigger_dict in doc_obj.triggers.items()}\n self.dev_pairs = [p for doc_name, doc_obj in self.dev_docs.items() for p in doc_obj.pairs]\n self.dev_labels = [l for doc_name, doc_obj in self.dev_docs.items() for l in doc_obj.labels]\n self.dev_pairs, self.dev_labels = self.shuffle_set(self.dev_pairs, self.dev_labels)\n\n self.test_triggers = {trigger_name:trigger_dict for doc_name,doc_obj in self.test_docs.items() \\\n for trigger_name, trigger_dict in doc_obj.triggers.items()}\n self.test_pairs = [p for doc_name, doc_obj in self.test_docs.items() for p in doc_obj.pairs]\n self.test_labels = [l for doc_name, doc_obj in self.test_docs.items() for l in doc_obj.labels]\n self.test_pairs, self.test_labels = self.shuffle_set(self.test_pairs, self.test_labels)\n\n \n def tokenize(self,pairs,labels,triggers,set_name):\n \n trig1s = [triggers[trig1] for trig1,_ in pairs]\n trig2s = [triggers[trig2] for _,trig2 in pairs]\n \n trig1_sentences = [trig_dict[\"sentence\"] for trig_dict in trig1s]\n trig2_sentences = [trig_dict[\"sentence\"] for trig_dict in trig2s]\n \n tokenized_outputs = self.tokenizer(text = trig1_sentences,\n text_pair = trig2_sentences,\n return_tensors = \"pt\",\n return_special_tokens_mask = True,\n padding = True,\n truncation = \"longest_first\",\n max_length = 512)\n \n self.max_len = tokenized_outputs[\"input_ids\"].shape[1]\n trigger_indexes, problematic_indexes = [],[]\n for index, ((trig1, trig2), label) in enumerate(zip(pairs,labels)):\n trig1_tokenized_index = triggers[trig1][\"trigger_tokenized_index\"]\n trig2_tokenized_index = triggers[trig2][\"trigger_tokenized_index\"] \\\n if \"roberta\" in self.model_name else \\\n triggers[trig2][\"trigger_tokenized_index\"][1:]\n \n \n trig1_trigger_index = trig1_tokenized_index+[0 for _ in range(self.max_len-len(trig1_tokenized_index))]\n trig2_trigger_index = [0 for _ in range(len(trig1_tokenized_index))]+trig2_tokenized_index\n trig2_trigger_index = trig2_trigger_index+[0 for _ in range(self.max_len-len(trig2_trigger_index))]\n \n if 1 in trig1_trigger_index[512:] or 1 in trig2_trigger_index[512:]:\n problematic_indexes.append(index)\n\n trig1_trigger_index = trig1_trigger_index[:512]\n trig2_trigger_index = trig2_trigger_index[:512]\n\n trigger_index = [trig1_trigger_index,trig2_trigger_index]\n \n trigger_indexes.append(trigger_index)\n\n wanted_indexes = [index for index in range(tokenized_outputs[\"input_ids\"].shape[0]) if index not in problematic_indexes]\n input_ids = tokenized_outputs[\"input_ids\"][wanted_indexes]\n attention_mask = tokenized_outputs[\"attention_mask\"][wanted_indexes]\n trigger_indexes = torch.tensor(trigger_indexes).float()[wanted_indexes]\n labels = torch.tensor(labels).float()[wanted_indexes]\n\n if problematic_indexes:\n mapping = {\"train\": [self.train_pairs,self.train_labels],\n \"dev\": [self.dev_pairs,self.dev_labels],\n \"test\": [self.test_pairs,self.test_labels]}\n\n mapping[set_name][0] = [item for i,item in enumerate(mapping[set_name][0]) if i not in problematic_indexes] # pairs\n mapping[set_name][1] = [item for i,item in enumerate(mapping[set_name][1]) if i not in problematic_indexes] # labels\n\n return TensorDataset(input_ids,attention_mask,trigger_indexes,labels)","repo_name":"emerging-welfare/ECR4-Contentious-Politics","sub_path":"scripts/datasetparser.py","file_name":"datasetparser.py","file_ext":"py","file_size_in_byte":13733,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"78"} +{"seq_id":"40086433225","text":"from worin.tag import Worin, pos_tagging_model\n\n\ntext = '세종은 조선의 4대 왕이다'\ntagger = Worin()\n\n\ndef test_split_text():\n texts = tagger._split_text(text * 10)\n assert len(texts) == 2\n\n\ndef test_nouns():\n nouns = tagger.nouns(text)\n assert nouns == ['세종', '조선', '대', '왕']\n\n\ndef test_pos():\n pos = tagger.pos(text)\n assert pos == [\n ('세종', 'N'), ('은', 'J'),\n ('조선', 'N'), ('의', 'J'),\n ('4', 'S'), ('대', 'N'),\n ('왕', 'N'), ('이', 'J'), ('다', 'E')\n ]\n\n\n","repo_name":"mindscale/worin","sub_path":"tests/tag_test.py","file_name":"tag_test.py","file_ext":"py","file_size_in_byte":548,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"12991851944","text":"import os\nfrom aiogram import Dispatcher, executor, types, Bot\nimport aiogram.utils.markdown as fmt\nfrom dotenv import load_dotenv\n\n\nload_dotenv()\n\ntoken = os.getenv(\"TELEGRAM_TOKEN\")\ngithub_link = os.getenv(\"GITHUB_LINK\")\nreport_link = os.getenv(\"REPORT_LINK\")\nbot = Bot(token=token)\ndp = Dispatcher(bot)\n\n\nprint('Telegram BOT connected')\nprint('======================')\n\n@dp.message_handler(commands='start')\nasync def start(message: types.Message):\n await message.reply('Ready to work! Type /help for help message')\n\n\n@dp.message_handler(commands='help')\nasync def help(message: types.Message):\n\tawait message.reply(\n\t\tfmt.text(\n\t\t\tfmt.text('Available commands'),\n\t\t\tfmt.text('/showmethemoney - Link to Test Report in Yandex DataLens'),\n\t\t\tfmt.text('/blacksheepwall - Jupyter Notebook file from test task'),\n\t\t\tfmt.text('/whatsmineismine - Link to GitHub repo with this bot'),\n\t\t\tsep='\\n'\n\t\t), parse_mode=\"HTML\"\n\t)\n\n\n@dp.message_handler(commands='blacksheepwall')\nasync def fa(message: types.Message):\n\tawait bot.send_document(message.chat.id, document=open('data/seek-n-destroy.ipynb', 'rb'))\n\n\n@dp.message_handler(commands='showmethemoney')\nasync def start(message: types.Message):\n await message.reply(f'Here is the link to report: {report_link}')\n\n\n@dp.message_handler(commands='whatsmineismine')\nasync def start(message: types.Message):\n await message.reply(f'Here is the link to GitHub repo: {github_link}')\n\nif __name__ == '__main__':\n executor.start_polling(dp)","repo_name":"Real-Vadimych/test_task_bot","sub_path":"tbot.py","file_name":"tbot.py","file_ext":"py","file_size_in_byte":1484,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"17528959336","text":"import curses\nimport sys, requests, copy\nfrom typing import Dict, Tuple\n\nimport skipper_helpers as shs\nimport curses_helpers as chs\nimport left_window as lwin\nimport top_window as twin\nimport search_bar as sb\nimport right_window as rwin\n\ndef run_skipper(stdscr):\n\t\"\"\"\n\tRuns the Skipper interactive terminal application until user quits.\n\n\t:param (_curses.window) stdscr:\n\t\t\t\t\tAutomatically passed in by curses.wrapper function.\n\t\t\t\t\tA _curses.window obj that represents the entire screen.\n\t:return: None\n\t\"\"\"\n\n\tSTART_MODE = \"cluster\"\t# possible modes include app, cluster, query, anomaly\n\tSTART_FTYPE = \"summary\"\n\tSTART_PANEL = \"left\"\n\t# initialize stdscr (standard screen)\n\tstdscr = chs.initialize_curses()\n\theight, width = stdscr.getmaxyx()\n\n\t# if terminal size doesn't meet requirements\n\tif height < 40 or width < 178:\n\t\tshs.terminal_size_reminder(stdscr)\n\t\treturn\n\n\t# on startup, show loading screen\n\t# get the data for the initial cluster mode screen that lists all clusters\n\tfetch_data = lambda: requests.get('http://127.0.0.1:5000/start/{}'.format(START_MODE)).json()\n\tdata = shs.loading_screen(stdscr, task=fetch_data)\n\tstdscr.erase()\n\tstdscr.refresh()\n\n\tmode = START_MODE\n\tftype = START_FTYPE\n\tpanel_side = START_PANEL\n\n\t# initialize and draw top window\n\ttwin.init_win(len(shs.figlet_lines()) + 3, width, 0,0, data['has_apps'])\t# height, width, y, x, has_apps\n\ttwin.draw(mode=mode, ftype=ftype, panel=panel_side)\n\ttwin.init_load(mode)\n\ttop_height, top_width = twin.window.getmaxyx()\n\tpanel_height = height-top_height\n\tpanel_width = width//2\n\n\t# initialize and draw windows\n\tlwin.init_win(stdscr, height=panel_height, width=panel_width, y=top_height, x=0)\n\trwin.init(panel_height, panel_width, top_height)\n\n\tif len(data['table_items']) > 0:\n\t\ttable_data = {\t\"mode\": START_MODE,\n\t\t\t\t\t\t\"col_names\": [\"kind\", \"name\"],\n\t\t\t\t\t\t\"col_widths\": lwin.get_column_widths([1/6, 5/6]),\n\t\t\t\t\t\t\"table\": [[t_item['rtype'], t_item['name']] for t_item in data['table_items']],\n\t\t\t\t\t\t\"row_selector\": data['index'],\n\t\t\t\t\t\t\"start_y\": 0,\n\t\t\t\t\t\t\"path_names\": data['path_names'],\n\t\t\t\t\t\t\"path_rtypes\": data['path_rtypes'],\n\t\t\t\t\t\t\"path_uids\": data['path_uids'],\n\t\t\t\t\t\t\"table_uids\": [t_item['uid'] for t_item in data['table_items']],\n\t\t\t\t\t\t\"has_children\": data['has_children'],\n\t\t\t\t\t\t\"sev\": [t_item['sev_measure'] for t_item in data['table_items']]\n\t\t\t\t\t\t}\n\t\tresource_by_uid = { item['uid'] : item for item in data['table_items'] }\n\t\tcurrent_uid = table_data['table_uids'][table_data['row_selector']]\n\telse:\n\t\ttable_data = {\t\"mode\": START_MODE,\n\t\t\t\t\t\t\"col_names\": [\"kind\", \"name\"],\n\t\t\t\t\t\t\"col_widths\": lwin.get_column_widths([1/6, 5/6]),\n\t\t\t\t\t\t\"table\": [],\n\t\t\t\t\t\t\"row_selector\": 0,\n\t\t\t\t\t\t\"start_y\": 0,\n\t\t\t\t\t\t\"path_names\": [],\n\t\t\t\t\t\t\"path_rtypes\": [],\n\t\t\t\t\t\t\"path_uids\": [],\n\t\t\t\t\t\t\"table_uids\": [],\n\t\t\t\t\t\t\"has_children\": [],\n\t\t\t\t\t\t\"sev\": []\n\t\t\t\t\t\t}\n\t\tresource_by_uid = { \"empty\": None }\n\t\tcurrent_uid = \"empty\"\n\n\tlwin.set_contents(**table_data)\n\tlwin.draw()\n\trwin.draw(ftype, resource_by_uid[current_uid])\n\n\t# state that needs to be tracked\n\tc = 0\n\tltable = []\t\t\t\t# stack to keep track of table_start_y and row selector positions\n\tquery_state = {\"resource_by_uid\": {\"empty\": None},\t# stores last known state for query mode\n\t\t\t\t\t\"current_uid\": \"empty\",\t\t\t\t# so that it can be restored when user re-enters query mode\n\t\t\t\t\t\"table_data\": {\"mode\": \"query\",\n\t\t\t\t\t\t\t\"col_names\" : [\"kind\", \"name\"],\n\t\t\t\t\t\t\t\"col_widths\" : lwin.get_column_widths([1/6, 5/6]),\n\t\t\t\t\t\t\t\"table\" : [],\n\t\t\t\t\t\t\t\"row_selector\" : 0,\n\t\t\t\t\t\t\t\"start_y\" : 0,\n\t\t\t\t\t\t\t\"path_names\" : [],\n\t\t\t\t\t\t\t\"path_rtypes\" : [],\n\t\t\t\t\t\t\t\"path_uids\" : [],\n\t\t\t\t\t\t\t\"table_uids\" : [],\n\t\t\t\t\t\t\t\"has_children\": [],\n\t\t\t\t\t\t\t\"sev\": []}\n\t\t\t\t}\n\n\tfmodes = { ord(\"y\") : \"yaml\", ord(\"l\") : \"logs\", ord(\"s\") : \"summary\", ord(\"e\") : \"events\"}\n\tmodes = { ord(\"1\") : \"cluster\", ord(\"2\") : \"app\", ord(\"3\") : \"anomaly\"}\n\n\t# start listening for keystrokes, and act accordingly\n\twhile c != ord('q'):\n\t\tc = stdscr.getch()\n\t\tif c in modes:\n\t\t\tmode = modes[c]\n\t\t\tdata = load(mode, twin, current_uid)\n\t\t\tif len(data['table_items']) > 0:\n\t\t\t\ttable_data, resource_by_uid, current_uid = update(mode, table_data, data, twin, lwin, ftype, panel_side)\n\n\t\telif c == ord('4'):\t\t# query mode\n\t\t\tmode = \"query\"\n\t\t\ttwin.draw(mode=mode, ftype=ftype, panel=panel_side)\n\t\t\ttwin.init_load(mode)\n\n\t\t\t# draw right before left so that cursor shows up in search bar\n\t\t\trwin.draw(ftype, query_state['resource_by_uid'][query_state['current_uid']])\n\n\t\t\t# draw the left window\n\t\t\tlwin.set_contents(**query_state['table_data'])\n\t\t\tlwin.draw()\n\n\t\t\t# set state variables for left window after user presses ESC\n\t\t\tresource_by_uid, current_uid, table_data = query_mode(stdscr, ftype, query_state)\n\n\t\t\t# save the search results state in case we come back to query mode\n\t\t\tquery_state[\"resource_by_uid\"] = copy.deepcopy(resource_by_uid)\n\t\t\tquery_state[\"current_uid\"] = copy.copy(current_uid)\n\t\t\tquery_state[\"table_data\"] = copy.deepcopy(table_data)\n\n\t\telif c in fmodes.keys():\n\t\t\tftype = fmodes[c]\n\t\t\trwin.scroll_y = 0\n\t\t\trwin.draw(ftype, resource_by_uid[current_uid])\n\t\t\ttwin.draw(mode=mode, ftype=ftype, panel=panel_side)\n\n\t\telif c == ord('L') :\n\t\t\tpanel_side = \"left\"\n\t\t\ttwin.draw(mode=mode, ftype=ftype, panel=panel_side)\n\n\t\telif c == ord('R') :\n\t\t\tpanel_side = \"right\"\n\t\t\ttwin.draw(mode=mode, ftype=ftype, panel=panel_side)\n\n\t\telif c == curses.KEY_UP:\n\t\t\tif panel_side == \"left\":\n\t\t\t\tcurrent_uid = lwin.move_up()\n\t\t\t\trwin.scroll_y, rwin.scroll_x = 0, 0\n\t\t\t\trwin.draw(ftype, resource_by_uid[current_uid])\n\t\t\telse:\n\t\t\t\tif rwin.scroll_y > 0:\n\t\t\t\t\trwin.scroll_y -= 1\n\t\t\t\t\trwin.draw(ftype, resource_by_uid[current_uid])\n\n\t\telif c == curses.KEY_DOWN:\n\t\t\tif panel_side == \"left\":\n\t\t\t\tcurrent_uid = lwin.move_down()\n\t\t\t\trwin.scroll_y, rwin.scroll_x = 0, 0\n\t\t\t\trwin.draw(ftype, resource_by_uid[current_uid])\n\t\t\telse:\n\t\t\t\tif rwin.scroll_y < rwin.doc_height - rwin.panel_height:\n\t\t\t\t\trwin.scroll_y += 1\n\t\t\t\t\trwin.draw(ftype, resource_by_uid[current_uid])\n\n\t\telif c == curses.KEY_RIGHT or c == 10:\n\t\t\tif panel_side == \"left\":\n\t\t\t\tif table_data['mode'] in ['app', 'cluster']:\n\t\t\t\t\t# gets the children of the current resource and other relevant info\n\t\t\t\t\tdata = load(\"children\", twin, current_uid, table_data[\"mode\"])\n\t\t\t\t\tif len(data['table_items']) > 0:\n\t\t\t\t\t\t# save row selector and start_y for table\n\t\t\t\t\t\tltable.append( lwin.table_start_y )\n\t\t\t\t\t\t# update and redraw\n\t\t\t\t\t\ttable_data['start_y'] = 0\n\t\t\t\t\t\trwin.scroll_y, rwin.scroll_x = 0, 0\n\t\t\t\t\t\ttable_data, resource_by_uid, current_uid = update(mode, table_data, data, twin, lwin, ftype, panel_side)\n\n\t\telif c == curses.KEY_LEFT:\n\t\t\tif panel_side == \"left\":\n\t\t\t\tif table_data['mode'] in ['app', 'cluster']:\n\t\t\t\t\t# retrieve row selector and start_y for table\n\t\t\t\t\tstart_y = 0\n\t\t\t\t\tif len(ltable) != 0:\n\t\t\t\t\t\tstart_y = ltable.pop()\n\n\t\t\t\t\tcurrent_resource = load(\"current\", twin, current_uid)\n\t\t\t\t\tif current_resource['rtype'] not in ['Application', 'Cluster']:\n\t\t\t\t\t\t# gets the siblings of the parent resource (including parent) and other relevant info\n\t\t\t\t\t\tdata = load(\"parent\", twin, parent_uid = table_data['path_uids'][-1], mode = table_data[\"mode\"])\n\t\t\t\t\t\ttable_data['start_y'] = start_y\n\t\t\t\t\t\trwin.scroll_y, rwin.scroll_x = 0, 0\n\t\t\t\t\t\ttable_data, resource_by_uid, current_uid = update(mode, table_data, data, twin, lwin, ftype, panel_side)\n\n\ndef query_mode(stdscr, ftype, query_state) -> Tuple[Dict, str, Dict]:\n\t\"\"\"\n\tContinuously captures input from user, displays in search bar, and updates left and right window with results.\n\n\tUser must press [esc] to escape from this function.\n\t:param (_curses.window) stdscr\n\t:param (str) ftype\n\t:param (Dict) query_state: last query state\n\t:return: ( (Dict, str, Dict) ) state needed to render left and right windows\n\t\"\"\"\n\tcurses.curs_set(1)\t# show the cursor\n\n\t# returns whether a char is alphanumeric or not\n\talpha_num = lambda x: 64 < c < 91 or 96 < c < 123 or 47 < c < 58\n\n\t# state variables needed to restore search results\n\t# resource_by_uid and current_uid are needed for going up/down search results\n\t# table_data is needed to render search results\n\tresource_by_uid = query_state['resource_by_uid']\n\tcurrent_uid = query_state['current_uid']\n\ttable_data = query_state['table_data']\n\n\tc = stdscr.getch()\n\twhile True:\n\t\tif c == 27:\t\t# esc\n\t\t\tbreak\n\t\telif c == 127:\t# backspace\n\t\t\tcurses.curs_set(1)\n\t\t\tsb.backspace()\n\t\telif c == 260:\t# left arrow\n\t\t\tcurses.curs_set(1)\n\t\t\tsb.move_left()\n\t\telif c == 261:\t# right arrow\n\t\t\tcurses.curs_set(1)\n\t\t\tsb.move_right()\n\t\telif c == 258:\t# down arrow\n\t\t\tcurses.curs_set(0)\n\t\t\tcurrent_uid = lwin.move_down()\n\t\t\ttable_data['row_selector'] = lwin.row_selector\n\t\t\trwin.draw(ftype, resource_by_uid[current_uid])\n\t\telif c == 259:\t# up arrow\n\t\t\tcurses.curs_set(0)\n\t\t\tcurrent_uid = lwin.move_up()\n\t\t\ttable_data['row_selector'] = lwin.row_selector\n\t\t\trwin.draw(ftype, resource_by_uid[current_uid])\n\t\telif c == 1:\t# ctrl-a\n\t\t\tcurses.curs_set(1)\n\t\t\tsb.move_to_start()\n\t\telif c == 5:\t# ctrl-e\n\t\t\tcurses.curs_set(1)\n\t\t\tsb.move_to_end()\n\t\telif c == 10:\t# enter\n\t\t\tcurses.curs_set(1)\n\t\t\tresults = load(\"query\", twin, query = sb.get_query())\n\t\t\trows = [ [r[\"rtype\"], r[\"name\"]] for r in results ]\n\n\t\t\t# create dict that right window needs\n\t\t\tif len(results) > 0:\n\t\t\t\tresource_by_uid = { item['uid'] : item for item in results }\n\t\t\t\tcurrent_uid = list(resource_by_uid.keys())[0]\n\t\t\t\ttable_data = {\"mode\": \"query\",\n\t\t\t\t\t\t\t\"col_names\" : [\"kind\", \"name\"],\n\t\t\t\t\t\t\t\"col_widths\" : lwin.get_column_widths([1/6, 5/6]),\n\t\t\t\t\t\t\t\"table\" : rows,\n\t\t\t\t\t\t\t\"row_selector\" : 0,\n\t\t\t\t\t\t\t\"start_y\" : 0,\n\t\t\t\t\t\t\t\"path_names\" : [],\n\t\t\t\t\t\t\t\"path_rtypes\" : [],\n\t\t\t\t\t\t\t\"path_uids\" : [],\n\t\t\t\t\t\t\t\"table_uids\" : list(resource_by_uid.keys()),\n\t\t\t\t\t\t\t\"has_children\": [True] * len(results),\n\t\t\t\t\t\t\t\"sev\": [r['sev_measure'] for r in results]}\n\n\t\t\t\t# add additional cols if all pods in table\n\t\t\t\tis_pod = [res[0] == 'Pod' for res in table_data['table']]\n\t\t\t\tif all(is_pod):\n\t\t\t\t\ttable_data[\"col_names\"] = [\"kind\", \"name\", \"\", \"ready\", \"restarts\", \"status\"]\n\t\t\t\t\ttable_data[\"col_widths\"] = lwin.get_column_widths([1/6, 1-1/6-40/lwin.tr_text_width, 3/lwin.tr_text_width, 8/lwin.tr_text_width, 11/lwin.tr_text_width, 18/lwin.tr_text_width])\n\t\t\t\t\tfor i, pod in enumerate(table_data['table_uids']):\n\t\t\t\t\t\thost_ip, pod_ip, ready, restarts = rwin.parse_pod_status(resource_by_uid[pod])\n\t\t\t\t\t\tready = \"-\" if ready == \"None\" else ready\n\t\t\t\t\t\trestarts = \"-\" if restarts == \"None\" else restarts\n\t\t\t\t\t\ttable_data['table'][i].extend([\"\", ready, restarts, resource_by_uid[pod][\"sev_reason\"]])\n\n\t\t\telse:\n\t\t\t\tresource_by_uid = {\"empty\": None}\n\t\t\t\tcurrent_uid = \"empty\"\n\t\t\t\ttable_data = {\"mode\": \"query\",\n\t\t\t\t\t\t\t\"col_names\" : [\"kind\", \"name\"],\n\t\t\t\t\t\t\t\"col_widths\" : lwin.get_column_widths([1/6, 5/6]),\n\t\t\t\t\t\t\t\"table\" : [[\"\", \"No results found.\"]],\n\t\t\t\t\t\t\t\"row_selector\" : 0,\n\t\t\t\t\t\t\t\"start_y\" : 0,\n\t\t\t\t\t\t\t\"path_names\" : [],\n\t\t\t\t\t\t\t\"path_rtypes\" : [],\n\t\t\t\t\t\t\t\"path_uids\" : [],\n\t\t\t\t\t\t\t\"table_uids\" : [\"empty\"],\n\t\t\t\t\t\t\t\"has_children\": [True],\n\t\t\t\t\t\t\t\"sev\": [\"0\"]}\n\n\t\t\t# draw right window\n\t\t\trwin.draw(ftype, resource_by_uid[current_uid])\n\n\t\t\t# update left window with search results\n\t\t\tlwin.set_contents(**table_data)\n\t\t\tlwin.draw()\n\n\t\telif alpha_num(c) or c in (32, 40, 41, 45, 46, 58): # alphanumeric and { space ( ) - . : }\n\t\t\tcurses.curs_set(1)\n\t\t\tsb.addch(chr(c))\n\n\t\tc = stdscr.getch()\n\n\tcurses.curs_set(0)\t# hide the cursor\n\n\t# return all the state necessary to restore the search results\n\treturn (resource_by_uid, current_uid, table_data)\n\ndef update(mode, table_data, data, twin, lwin, ftype, panel_side):\n\t\"\"\"\n\tUpdate and draw left, right, and top windows based on mode and table data.\n\t:param mode: \"app\", \"cluster\", or \"anomaly\"\n\t:param table_data: current table\n\t:param data: new data to be reflected in table_data\n\t:param twin: top window\n\t:param lwin: left window\n\t:param ftype: \"summary\", \"yaml\", \"logs\", or \"events\"\n\t:param panel_side: \"left\" or \"right\"\n\t:return: (Dict) table_data, (Dict) resource_by_uid, (str) current_uid\n\t\"\"\"\n\ttable_data[\"mode\"] = mode\n\tif mode == 'app' or mode == 'cluster':\n\t\ttable_data[\"col_names\"] = [\"kind\", \"name\"]\n\t\ttable_data[\"col_widths\"] = lwin.get_column_widths([1/6, 5/6])\n\t\ttable_data['row_selector'] = data['index']\n\t\ttable_data['path_names'] = data['path_names']\n\t\ttable_data['path_rtypes'] = data['path_rtypes']\n\t\ttable_data['path_uids'] = data['path_uids']\n\t\ttable_data['table'] = [[t_item['rtype'], t_item['name']] for t_item in data['table_items']]\n\t\ttable_data[\"has_children\"] = data['has_children']\n\n\telif mode == 'anomaly':\n\t\t# each item in data[\"table_items\"] is (skipper_uid, type, name, reason, message)\n\t\ttable_data[\"col_names\"] = [\"kind\", \"name\", \"reason\"]\n\t\ttable_data[\"col_widths\"] = lwin.get_column_widths([1/6, 2/3, 1/6])\n\t\ttable_data['row_selector'] = 0\n\t\ttable_data['table'] = [[t_item['rtype'], t_item['name'], t_item['sev_reason']] for t_item in data['table_items']]\n\t\ttable_data[\"has_children\"] = [True] * len(data['table_items'])\n\n\ttable_data[\"table_uids\"] = [t_item['uid'] for t_item in data['table_items']]\n\ttable_data[\"sev\"] = [t_item['sev_measure'] for t_item in data['table_items']]\n\tresource_by_uid = {item['uid']: item for item in data['table_items']}\n\n\t# add additional cols if all pods in table\n\tis_pod = [res[0] == 'Pod' for res in table_data['table']]\n\tif all(is_pod):\n\t\ttable_data[\"col_names\"] = [\"kind\", \"name\", \"\", \"ready\", \"restarts\", \"status\"]\n\t\ttable_data[\"col_widths\"] = lwin.get_column_widths([1/6, 1-1/6-40/lwin.tr_text_width, 3/lwin.tr_text_width, 8/lwin.tr_text_width, 11/lwin.tr_text_width, 18/lwin.tr_text_width])\n\t\tfor i, pod in enumerate(table_data['table_uids']):\n\t\t\thost_ip, pod_ip, ready, restarts = rwin.parse_pod_status(resource_by_uid[pod])\n\t\t\tready = \"-\" if ready == \"None\" else ready\n\t\t\trestarts = \"-\" if restarts == \"None\" else restarts\n\t\t\ttable_data['table'][i] = [\"Pod\", resource_by_uid[pod]['name'], \"\", ready, restarts, resource_by_uid[pod]['sev_reason']]\n\n\tcurrent_uid = table_data['table_uids'][table_data['row_selector']]\n\ttwin.draw(mode=mode, ftype=ftype, panel=panel_side)\n\ttwin.init_load(mode)\n\tlwin.set_contents(*table_data.values())\n\tlwin.draw()\n\trwin.draw(ftype, resource_by_uid[current_uid])\n\treturn table_data, resource_by_uid, current_uid\n\ndef load(request_type, twin, current_uid = None, mode = None, parent_uid = None, query = None, uid = None):\n\t\"\"\"\n\tStart loading, make request and wait for response, stop loading.\n\t:return data from response\n\t\"\"\"\n\ttwin.start_loading()\n\tif request_type == \"cluster\":\n\t\tdata = requests.get('http://127.0.0.1:5000/mode/cluster/switch/{}'.format(current_uid)).json()\n\telif request_type == \"app\":\n\t\tdata = requests.get('http://127.0.0.1:5000/mode/app/switch/{}'.format(current_uid)).json()\n\telif request_type == \"anomaly\":\n\t\tdata = requests.get('http://127.0.0.1:5000/errors').json()\n\telif request_type == \"children\":\n\t\tdata = requests.get('http://127.0.0.1:5000/mode/{}/{}'.format(mode, current_uid)).json()\n\telif request_type == \"current\":\n\t\tdata = requests.get('http://127.0.0.1:5000/resource/{}'.format(current_uid)).json()['data']\n\telif request_type == \"parent\":\n\t\tdata = requests.get('http://127.0.0.1:5000/mode/{}/switch/{}'.format(mode, parent_uid)).json()\n\telif request_type == \"query\":\n\t\tdata = requests.get(\"http://127.0.0.1:5000/search/\" + query).json()[\"results\"]\n\telif request_type == \"sort\":\n\t\tdata = requests.get('http://127.0.0.1:5000/resource/{}'.format(uid)).json()['data']\n\ttwin.stop_loading()\n\treturn data\n\ndef main():\n\tcurses.wrapper(run_skipper)\n\nif __name__ == \"__main__\":\n\ttry:\n\t\tmain()\n\texcept KeyboardInterrupt:\n\t\tsys.exit(0)\n","repo_name":"IBM/multicloud-incident-response-navigator","sub_path":"frontend/skipper.py","file_name":"skipper.py","file_ext":"py","file_size_in_byte":15445,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"78"} +{"seq_id":"11458169962","text":"import sys\n\n_b = sys.version_info[0] < 3 and (lambda x: x) or (lambda x: x.encode(\"latin1\"))\nfrom google.protobuf import descriptor as _descriptor\nfrom google.protobuf import message as _message\nfrom google.protobuf import reflection as _reflection\nfrom google.protobuf import symbol_database as _symbol_database\n\n# @@protoc_insertion_point(imports)\n\n_sym_db = _symbol_database.Default()\n\n\nDESCRIPTOR = _descriptor.FileDescriptor(\n name=\"tensorflow/core/framework/tensor_slice.proto\",\n package=\"tensorflow\",\n syntax=\"proto3\",\n serialized_options=_b(\n \"\\n\\030org.tensorflow.frameworkB\\021TensorSliceProtosP\\001Z=github.com/tensorflow/tensorflow/tensorflow/go/core/framework\\370\\001\\001\"\n ),\n serialized_pb=_b(\n '\\n,tensorflow/core/framework/tensor_slice.proto\\x12\\ntensorflow\"\\x80\\x01\\n\\x10TensorSliceProto\\x12\\x33\\n\\x06\\x65xtent\\x18\\x01 \\x03(\\x0b\\x32#.tensorflow.TensorSliceProto.Extent\\x1a\\x37\\n\\x06\\x45xtent\\x12\\r\\n\\x05start\\x18\\x01 \\x01(\\x03\\x12\\x10\\n\\x06length\\x18\\x02 \\x01(\\x03H\\x00\\x42\\x0c\\n\\nhas_lengthBq\\n\\x18org.tensorflow.frameworkB\\x11TensorSliceProtosP\\x01Z=github.com/tensorflow/tensorflow/tensorflow/go/core/framework\\xf8\\x01\\x01\\x62\\x06proto3'\n ),\n)\n\n\n_TENSORSLICEPROTO_EXTENT = _descriptor.Descriptor(\n name=\"Extent\",\n full_name=\"tensorflow.TensorSliceProto.Extent\",\n filename=None,\n file=DESCRIPTOR,\n containing_type=None,\n fields=[\n _descriptor.FieldDescriptor(\n name=\"start\",\n full_name=\"tensorflow.TensorSliceProto.Extent.start\",\n index=0,\n number=1,\n type=3,\n cpp_type=2,\n label=1,\n has_default_value=False,\n default_value=0,\n message_type=None,\n enum_type=None,\n containing_type=None,\n is_extension=False,\n extension_scope=None,\n serialized_options=None,\n file=DESCRIPTOR,\n ),\n _descriptor.FieldDescriptor(\n name=\"length\",\n full_name=\"tensorflow.TensorSliceProto.Extent.length\",\n index=1,\n number=2,\n type=3,\n cpp_type=2,\n label=1,\n has_default_value=False,\n default_value=0,\n message_type=None,\n enum_type=None,\n containing_type=None,\n is_extension=False,\n extension_scope=None,\n serialized_options=None,\n file=DESCRIPTOR,\n ),\n ],\n extensions=[],\n nested_types=[],\n enum_types=[],\n serialized_options=None,\n is_extendable=False,\n syntax=\"proto3\",\n extension_ranges=[],\n oneofs=[\n _descriptor.OneofDescriptor(\n name=\"has_length\",\n full_name=\"tensorflow.TensorSliceProto.Extent.has_length\",\n index=0,\n containing_type=None,\n fields=[],\n ),\n ],\n serialized_start=134,\n serialized_end=189,\n)\n\n_TENSORSLICEPROTO = _descriptor.Descriptor(\n name=\"TensorSliceProto\",\n full_name=\"tensorflow.TensorSliceProto\",\n filename=None,\n file=DESCRIPTOR,\n containing_type=None,\n fields=[\n _descriptor.FieldDescriptor(\n name=\"extent\",\n full_name=\"tensorflow.TensorSliceProto.extent\",\n index=0,\n number=1,\n type=11,\n cpp_type=10,\n label=3,\n has_default_value=False,\n default_value=[],\n message_type=None,\n enum_type=None,\n containing_type=None,\n is_extension=False,\n extension_scope=None,\n serialized_options=None,\n file=DESCRIPTOR,\n ),\n ],\n extensions=[],\n nested_types=[_TENSORSLICEPROTO_EXTENT,],\n enum_types=[],\n serialized_options=None,\n is_extendable=False,\n syntax=\"proto3\",\n extension_ranges=[],\n oneofs=[],\n serialized_start=61,\n serialized_end=189,\n)\n\n_TENSORSLICEPROTO_EXTENT.containing_type = _TENSORSLICEPROTO\n_TENSORSLICEPROTO_EXTENT.oneofs_by_name[\"has_length\"].fields.append(\n _TENSORSLICEPROTO_EXTENT.fields_by_name[\"length\"]\n)\n_TENSORSLICEPROTO_EXTENT.fields_by_name[\n \"length\"\n].containing_oneof = _TENSORSLICEPROTO_EXTENT.oneofs_by_name[\"has_length\"]\n_TENSORSLICEPROTO.fields_by_name[\"extent\"].message_type = _TENSORSLICEPROTO_EXTENT\nDESCRIPTOR.message_types_by_name[\"TensorSliceProto\"] = _TENSORSLICEPROTO\n_sym_db.RegisterFileDescriptor(DESCRIPTOR)\n\nTensorSliceProto = _reflection.GeneratedProtocolMessageType(\n \"TensorSliceProto\",\n (_message.Message,),\n dict(\n Extent=_reflection.GeneratedProtocolMessageType(\n \"Extent\",\n (_message.Message,),\n dict(\n DESCRIPTOR=_TENSORSLICEPROTO_EXTENT,\n __module__=\"tensorflow.core.framework.tensor_slice_pb2\"\n # @@protoc_insertion_point(class_scope:tensorflow.TensorSliceProto.Extent)\n ),\n ),\n DESCRIPTOR=_TENSORSLICEPROTO,\n __module__=\"tensorflow.core.framework.tensor_slice_pb2\"\n # @@protoc_insertion_point(class_scope:tensorflow.TensorSliceProto)\n ),\n)\n_sym_db.RegisterMessage(TensorSliceProto)\n_sym_db.RegisterMessage(TensorSliceProto.Extent)\n\n\nDESCRIPTOR._options = None\n# @@protoc_insertion_point(module_scope)\n","repo_name":"Xilinx/Vitis-AI","sub_path":"src/vai_quantizer/xnnc4xir/xnnc/proto/tf_pb2/tensor_slice_pb2.py","file_name":"tensor_slice_pb2.py","file_ext":"py","file_size_in_byte":5297,"program_lang":"python","lang":"en","doc_type":"code","stars":1266,"dataset":"github-code","pt":"78"} +{"seq_id":"3758854651","text":"#Algorithm: Sieve of Eratosthenes\ndef sieve(n):\n\tl = range(0, n+1)\n\tl[0], l[1] = False, False\n\troot = int(n**(0.5)) + 1\n\t\n\tfor i in range(2, root):\n\t\tif not l[i]: continue\n\t\tp = i\n\t\twhile i * p <= n:\n\t\t\tl[i*p] = False\n\t\t\ti += 1\n\t\tp += 1\n\treturn [item for item in l if item != False]\n\nl = sieve(200000)\n\nT = int(input())\nfor i in range(0, T):\n\tn = int(input())\n\tprint(l[n-1])","repo_name":"yunhan0/projectEuler","sub_path":"src/projectEuler#7.py","file_name":"projectEuler#7.py","file_ext":"py","file_size_in_byte":374,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"78"} +{"seq_id":"73964718332","text":"#Programmers - 짝지어 제거하기\n\n'''\nstack 활용\n'''\n\ndef solution(s):\n stack = []\n for i in s:\n if stack and stack[-1]==i:\n stack.pop()\n else:\n stack.append(i)\n return 1 if len(stack)==0 else 0\n","repo_name":"josy0319/algorithm_for_problem_solving","sub_path":"QStack/짝지어_제거하기.py","file_name":"짝지어_제거하기.py","file_ext":"py","file_size_in_byte":247,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"34573162487","text":"from __future__ import unicode_literals\n\nfrom django.conf import settings\nfrom django.db import models\n\nfrom .enums import (\n STATUS_CHOICES, STATUS_TODO\n)\n\n\nclass Task(models.Model):\n\n created_on = models.DateTimeField(auto_now_add=True)\n\n modified_on = models.DateTimeField(auto_now=True)\n\n name = models.CharField(\n max_length=300,\n verbose_name='Name',\n help_text='Task name'\n )\n\n description = models.TextField(\n max_length=2000,\n verbose_name='Description',\n help_text='Task description',\n blank=True\n )\n\n status = models.PositiveIntegerField(\n choices=STATUS_CHOICES,\n default=STATUS_TODO,\n verbose_name='Status',\n help_text='Task status'\n )\n\n reporter = models.ForeignKey(\n settings.AUTH_USER_MODEL,\n related_name='created_tasks',\n on_delete=models.PROTECT,\n verbose_name='Reporter',\n help_text='User that created the task'\n )\n\n assignee = models.ForeignKey(\n settings.AUTH_USER_MODEL,\n related_name='assigned_tasks',\n on_delete=models.PROTECT,\n verbose_name='Assignee',\n help_text='User that is assigned to the task',\n blank=True,\n null=True\n )\n\n review = models.ForeignKey(\n settings.AUTH_USER_MODEL,\n related_name='review_tasks',\n on_delete=models.PROTECT,\n verbose_name='Review',\n help_text='User that is review the task',\n blank=True,\n null=True\n )\n\n class Meta:\n ordering = ['created_on']\n\n def __str__(self):\n return '{}'.format(self.name[:20])\n","repo_name":"Nakedspirit/taskManagerTest","sub_path":"task/tasks/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1635,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"306266550","text":"import asyncio\nimport aiohttp\nimport pycurl\nimport time\nfrom io import BytesIO\n\n\nclass Retriever:\n def __init__(self,urls,endpoints,throttle=10,verbose=False):\n self.total_urls = str(len(urls))\n if len(endpoints)==1:\n endpoints = [endpoints]*int(self.total_urls) #bit of a hack, needs work\n self.results = list()\n self.sem = asyncio.Semaphore(throttle)\n self.verbose = verbose\n loop = asyncio.get_event_loop()\n loop.run_until_complete(self.main(urls,endpoints))\n\n\n def __iter__(self):\n return self\n\n def __next__(self):\n if len(self.results) == 0:\n raise StopIteration\n return self.results.pop(0)\n\n\n\n def fetch_page1(self,url,idx):\n buffer = BytesIO()\n self.c.setopt(self.c.WRITEDATA, buffer)\n self.c.setopt(pycurl.URL, url)\n self.c.setopt(pycurl.HTTPHEADER, [\"Accept: application/n-quads\"])\n self.c.perform()\n body = buffer.getvalue()\n for statement in body.decode('utf-8').split('\\n'):\n self.results.append(statement)\n buffer.close()\n\n def fetch_page(self,url, endpoint, idx):\n try:\n with (yield from self.sem):\n response = yield from aiohttp.request('GET', url,headers={\"Accept\": \"application/n-quads\"})\n except aiohttp.errors.ClientResponseError:\n print(\"[-]aiohttp.errors.ClientResponseError, pausing and continuing...\")\n time.sleep(1)\n response = yield from aiohttp.request('GET', url)\n if response.status == 200:\n try:\n raw = yield from response.text()\n self.results.append([endpoint,url,raw])\n except UnicodeDecodeError: #needs works\n self.results.append([endpoint,url,'here be unicode'])\n except LookupError:\n self.results.append([endpoint,url,'unknown encoding'])\n if self.verbose:\n print(\"[+] #{0}:{1}/{2}\".format(str(idx+1),str(len(self.results)),self.total_urls))\n else:\n print(\"[-] Data fetch failed for: %d\" % idx)\n print(response.content, response.status)\n response.close()\n\n def main(self,urls,endpoints):\n coros = []\n for idx, url in enumerate(urls):\n coros.append(asyncio.Task(self.fetch_page(url, endpoints[idx], idx)))\n\n yield from asyncio.gather(*coros)\n","repo_name":"tomrijntjes/LOD-Identity-checker","sub_path":"retriever.py","file_name":"retriever.py","file_ext":"py","file_size_in_byte":2420,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"78"} +{"seq_id":"26058911744","text":"import pandas as pd\nimport numpy as np\nfrom scipy.stats import pearsonr\nfrom sklearn.metrics.pairwise import cosine_similarity\n\n\n\n#base_experiment_loc = '/cvlabsrc1/home/kicirogl/ActiveDrone/simulation_results/experiments_2020-02-04-17-07_b_/2020-02-04-17-07/05_08/'\noracle_1_loc = '/cvlabsrc1/home/kicirogl/ActiveDrone/simulation_results/experiments_2020-02-05-17-29/2020-02-05-17-29/mpi_inf_3dhp/'\n#base_experiment_loc = '/cvlabsrc1/home/kicirogl/ActiveDrone/simulation_results/experiments_2020-02-05-17-32/2020-02-05-17-32/02_01/'\n#oracle_1_loc = '/cvlabsrc1/home/kicirogl/ActiveDrone/simulation_results/experiments_2020-02-07-23-52/2020-02-07-23-52/mpi_inf_3dhp/'\noracle_2_loc = '/cvlabsrc1/home/kicirogl/ActiveDrone/simulation_results/experiments_2020-02-07-23-53/2020-02-07-23-53/mpi_inf_3dhp/'\n\n\npearson_corr_list = []\ndiff_in_error_list = []\ncosine_sim_list = []\ntop_3_count = []\ntop_5_count = []\ntop_5_count_rand = []\ntop_3_count_rand = []\n\nthreshold = 0.05\nseeds = [5,41,3,10,12]\n\nfor folder_num in range(5):\n oracle_1_loc_exp = oracle_1_loc+ str(folder_num) + '/'\n oracle_2_loc_exp = oracle_2_loc+ str(folder_num) + '/'\n\n error_loc = oracle_1_loc_exp+'oracle_errors.txt'\n uncertainty_loc = oracle_2_loc_exp+'oracle_errors.txt'\n\n f_error_values=open(error_loc, \"r\")\n f_uncertainty_values = open(uncertainty_loc, \"r\")\n error_values = f_error_values.readlines()\n uncertainty_values = f_uncertainty_values.readlines()\n np.random.seed(seeds[folder_num])\n for i in range(len(uncertainty_values)-1):\n errors = error_values[i+1].split(\"\\t\")\n errors = errors[1:-1] \n uncertainties = uncertainty_values[i+1].split(\"\\t\")\n uncertainties = uncertainties[1:-1] \n\n errors = [float(error) for error in errors]\n uncertainties = [float(uncert) for uncert in uncertainties]\n uncertainties=np.array(uncertainties)\n errors=np.array(errors)\n\n # print(uncertainties)\n #errors = (errors-np.mean(errors))/np.std(errors)\n #uncertainties = np.power(uncertainties, 1/6)\n #uncertainties = (uncertainties-np.mean(uncertainties))/np.std(uncertainties)\n\n assert (len(uncertainties)==len(errors))\n corr = pearsonr(uncertainties, errors)[0]\n\n diff_in_error = np.max(errors) - np.min(errors)\n cos_sim = cosine_similarity(uncertainties[np.newaxis], errors[np.newaxis])\n # print(corr)\n\n min_uncert_ind = np.argmin(uncertainties)\n rand_ind = np.random.randint(0, len(uncertainties))\n top_5_error_ind = np.argsort(errors)[:5]\n top_3_error_ind = np.argsort(errors)[:3]\n\n if diff_in_error > threshold:\n pearson_corr_list.append(corr)\n cosine_sim_list.append(cos_sim)\n diff_in_error_list.append(diff_in_error)\n\n if min_uncert_ind in top_5_error_ind:\n top_5_count.append(1)\n else:\n top_5_count.append(0)\n\n if rand_ind in top_5_error_ind:\n top_5_count_rand.append(1)\n else:\n top_5_count_rand.append(0)\n\n if min_uncert_ind in top_3_error_ind:\n top_3_count.append(1)\n else:\n top_3_count.append(0)\n\n if rand_ind in top_3_error_ind:\n top_3_count_rand.append(1)\n else:\n top_3_count_rand.append(0)\n\n#print(diff_in_error_list)\npearson_corr_arr = np.array(pearson_corr_list)\ndiff_in_error_arry = np.array(diff_in_error_list)\ncosine_sim_arry = np.array(cosine_sim_list)\n\nprint(\"num of samples matter\", len(diff_in_error_list))\nprint(\"pearson_corr_arr\", np.mean(pearson_corr_arr), np.std(pearson_corr_arr))\n#print(\"cosine sim\", np.mean(cosine_sim_arry), np.std(cosine_sim_arry))\n\nprint(\"top 5 perc:\", sum(top_5_count)/len(top_5_count)*100)\nprint(\"top 5 perc, random:\", sum(top_5_count_rand)/len(top_5_count_rand)*100)\nprint(\"top 3 perc:\", sum(top_3_count)/len(top_3_count)*100)\nprint(\"top 3 perc, random:\", sum(top_3_count_rand)/len(top_3_count_rand)*100)\n","repo_name":"senakicir/ActiveMoCap","sub_path":"my_scripts/stand_alone_scripts/uncertainty_quantative_eval.py","file_name":"uncertainty_quantative_eval.py","file_ext":"py","file_size_in_byte":4037,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"78"} +{"seq_id":"24367714126","text":"from selenium import webdriver\nimport pickle\n\noptions = webdriver.ChromeOptions() \noptions.add_argument(\"start-maximized\")\ndriver = webdriver.Chrome(options=options)\ndriver.get('http://demo.guru99.com/test/cookie/selenium_aut.php')\ncookies = pickle.load(open(\"cookies.pkl\", \"rb\"))\nfor cookie in cookies:\n driver.add_cookie(cookie)\ndriver.get('http://demo.guru99.com/test/cookie/selenium_cookie.php')\ndriver.application_cache\ndriver.get(\"https://reader.taaghche.com/lib/18338/%D8%AC%D9%85%D8%B9%D9%87%E2%80%8C%DB%8C%20%D8%AE%D8%A7%DA%A9%D8%B3%D8%AA%D8%B1%DB%8C\")","repo_name":"codaux/python_learn","sub_path":"from selenium import webdriver.py","file_name":"from selenium import webdriver.py","file_ext":"py","file_size_in_byte":564,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"40539560899","text":"#\n# This mini-utility shows basic usage of this library.\n#\n# One arg shows how it views types\n# python3 -m guesstype '{\"numbers\":\"867 4678 23\", \"b64\":\"aGVsbG8K\", \"nested\":\"{\\\"k\\\":123}\"}'\n#\n# Two or more args does the \"autogenerate random values\" thing.\n# python3 -m guesstype '{\"numbers\":\"867 4678 23\", \"b64\":\"aGVsbG8K\", \"nested\":\"{\\\"k\\\":123}\"}' '{\"numbers\":\"231 132 323\", \"b64\":\"dGhlcmUK\", \"different\":\"blah\"}'\n#\n\nfrom .easy import *\n\ndef main(argv):\n if len(argv) == 2:\n # Show the value of one arg.\n for k, v in decode_one(argv[1]).items():\n print(k, ':', repr(v))\n elif len(argv) > 2:\n # Treat each arg as an example value and guess a type.\n G = GuessType(argv[1:])\n\n # Use the type to generate random values.\n subvalues = G.indicator_values()\n for k, v in subvalues.items():\n print(k, ':', repr(v))\n print('----------')\n\n # Get the random value back into the input form\n example = G.unflatten(subvalues)\n print(example)\n return 0\n else:\n print(\"Usage: %s value_string\" % argv[0])\n print(\"\\tShows internal values of the string\")\n print(\"Usage: %s value_string1 value_string2 ...\" % argv[0])\n print(\"\\tUses aggregate type guess to generate a randomized value.\")\n return 1\n\nif __name__ == \"__main__\":\n import sys\n sys.exit(main(sys.argv))\n","repo_name":"mruck/athena","sub_path":"fuzzer/guesstype/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":1311,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"78"} +{"seq_id":"3158024384","text":"# encoding:utf-8\n\n# 求两点的距离 输入两个点的坐标\n\n# 当flag = 0时返回弧度，当flag！-0时返回角度\n\n\ndef point_distance(A1, A2, flag=1):\n # 求直线的斜率 再反三角函数求角度\n # print(A1)\n d = ((A2[1] - A1[1]) ** 2 + (A2[0] - A1[0]) ** 2) ** 0.5\n # print(d)\n # 返回距离\n return d\n\n\nif __name__ == \"__main__\":\n # print(\"1\")\n LA1 = (0.0, 0.0)\n LA2 = (1.0, 1.0)\n LB1 = (0.0, 0.0)\n LB2 = (0.0, 1.0)\n # a = lines_orientation1(LA1, LA2, LB1, LB2, 2)\n # print(a)\n\n # cv2.waitKey(0)\n # sys.system(\"pause\")\n # return 0\n","repo_name":"122809690/lml-python","sub_path":"2D_suanfa/lml_qiujuli.py","file_name":"lml_qiujuli.py","file_ext":"py","file_size_in_byte":602,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"35009574641","text":"import json\nimport pathlib\n\nimport numpy as np\nfrom numpy import ndarray\n\nfrom albion_calculator_backend import items\nfrom albion_calculator_backend.cities import cities_names\n\nCRAFTINGMODIFIERS_FILE = pathlib.Path(__file__).parent / 'resources/craftingmodifiers.json'\n\n_SUBCATEGORY_REPLACEMENTS = {'ore': 'metalbar',\n 'wood': 'planks',\n 'hide': 'leather',\n 'fiber': 'cloth',\n 'rock': 'stoneblock'}\n_CLUSTER_ID = {'0000': 'Thetford', '1000': 'Lymhurst', '2000': 'Bridgewatch',\n '3004': 'Martlock', '4000': 'Fort Sterling', '3003': 'Caerleon'}\n\n_BASE_CRAFTING_BONUS = 0.18\n\n\ndef get_craftable_categories() -> list[str]:\n return [subcategory for city in _crafting_bonus.values() for subcategory in city.keys()]\n\n\ndef get_return_rates_vector(item_id: str, use_focus: bool = False) -> ndarray:\n item_subcategory = items.get_item_subcategory(item_id)\n vector = [_get_return_rate(city, item_subcategory, use_focus) for city in cities_names()]\n return np.atleast_2d(1 - np.array(vector)).T\n\n\ndef _load_crafting_modifiers() -> dict[str, dict[str, float]]:\n raw_data = _load_crafting_modifiers_file()\n crafting_modifiers = {}\n for location in raw_data:\n city_id = _CLUSTER_ID.get(location.get('@clusterid', None), None)\n if city_id is None:\n continue\n crafting_modifiers[city_id] = {_replace_refining_category(modifier['@name']): float(modifier['@value'])\n for modifier in location['craftingmodifier']}\n return crafting_modifiers\n\n\ndef _get_return_rate(city_id: str, item_category: str, use_focus: bool = False) -> float:\n focus_bonus = 0.59 if use_focus else 0\n local_crafting_bonus = _crafting_bonus[city_id].get(item_category, 0) + _BASE_CRAFTING_BONUS + focus_bonus\n return round(1 - 1 / (1 + local_crafting_bonus), 3)\n\n\ndef _replace_refining_category(name: str) -> str:\n return _SUBCATEGORY_REPLACEMENTS.get(name, name)\n\n\ndef _load_crafting_modifiers_file() -> list[dict]:\n with open(CRAFTINGMODIFIERS_FILE) as f:\n raw_crafting_modifiers_data = json.load(f)\n return raw_crafting_modifiers_data['craftingmodifiers']['craftinglocation']\n\n\n_crafting_bonus = _load_crafting_modifiers()\n","repo_name":"Amongalen/albion-profit-calculator","sub_path":"albion_calculator_backend/crafting_modifiers.py","file_name":"crafting_modifiers.py","file_ext":"py","file_size_in_byte":2323,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"62719333","text":"from DService.web.Client.cli_Public import Client_Public\nfrom DService.web.Client.greyPredict.demoParams.cli_Params_Grey_test import cli_Params_Grey_test\nfrom DService.web.Client.greyPredict.demoParams.cli_Params_Grey_merge import cli_Params_Grey_merge\n\nfrom CConfig import conf\n\n\nclass Cli_Grey_Train_All():\n @classmethod\n def process(cls, urlName, body):\n \"\"\"\n \"\"\"\n modelType = \"greyPredict\"\n # url = \"http://%s:%s/analysis_api/v1/%s/%s\" % (\n # conf.DATA_SERVICE_IP, conf.DATA_SERVICE_PORT, modelType, urlName\n # )\n\n url = \"http://%s:%s/analysis_api/v1/%s/%s\" % (\n \"hn.bgp.6net.plus\", 35831, modelType, urlName\n )\n\n print(\"url...\", url)\n print(body)\n\n # 发送请求.\n Client_Public.mock_request(url, body)\n\n\nif __name__ == \"__main__\":\n # [参数]\n # params = cli_Params_Grey_test\n params = cli_Params_Grey_merge\n\n # # [API]，添加-数据源\n # Cli_Grey_Train_All.process(\n # urlName=\"train_add_dataSource\",\n # body={\n # \"dataSourceName\": params.trainDsName,\n # \"dataSourceDesc\": params.trainDsDesc,\n # \"dataSourceDir\": params.trainDsDir,\n # \"dataFileName\": params.trainDsFile,\n # \"paramsFileName\": params.trainDsParamFile,\n # \"modelType\": params.modelType\n # }\n # )\n\n # # [API], 获取-数据源列表\n # Cli_Grey_Train_All.process(\n # urlName=\"train_get_dataSource_list\",\n # body={\n # \"modelType\": params.modelType\n # }\n # )\n\n # # [API], 选中-数据源\n # Cli_Grey_Train_All.process(\n # urlName=\"train_choiced_dataSource\",\n # body={\n # \"modelType\": params.modelType,\n # \"dataSourceName\": params.trainDsName,\n # }\n # )\n\n # # [API], 保存-模型\n # Cli_Grey_Train_All.process(\n # urlName=\"train_save_model\",\n # body={\n # \"modelType\": params.modelType,\n # \"dataSourceName\": params.trainDsName,\n # \"modelName\": params.modelName,\n # \"modelParams\": params.inputParams\n # }\n # )\n\n # # [API], 获取-模型名称列表\n # Cli_Grey_Train_All.process(\n # urlName=\"train_get_modelNameList\",\n # body={\n # \"modelType\": params.modelType\n # }\n # )\n\n # # [API], 获取-模型参数\n # Cli_Grey_Train_All.process(\n # urlName=\"train_get_modelParams\",\n # body={\n # \"modelType\": params.modelType,\n # \"modelName\": params.modelName\n # }\n # )\n\n # [API], 查询-训练结果\n Cli_Grey_Train_All.process(\n urlName=\"train_query_result\",\n body={\n \"modelType\": params.modelType,\n \"modelName\": params.modelName\n }\n )\n\n","repo_name":"lashoushusheng/BONC","sub_path":"py/ma/DService/web/Client/greyPredict/train/cli_Grey_Train_All.py","file_name":"cli_Grey_Train_All.py","file_ext":"py","file_size_in_byte":2820,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"71961066491","text":"#!/bin/env python\n#!-*- encoding: UTF-8 -*-\nfrom __future__ import print_function\n\nclass Vector:\t\n\tdef __init__(self, size):\n\t\tif size < 0:\n\t\t\traise Exception(\"size must great than or equals to 0\");\n\n\t\tself.__size = size\n\t\tself.__data = []\n\t\tfor i in range(0, size):\n\t\t\tself.__data.append(0.0)\n\n\tdef set(self, index, value):\n\t\tif index < 0 or index >= self.__size:\n\t\t\traise Exception(\"invalid index\")\n\n\t\tself.__data[index] = value;\n\n\tdef get(self, index):\n\t\treturn self.__data[index]\n\n\tdef size(self):\n\t\treturn self.__size\n\n\tdef printMe(self):\n\t\toutput = ''\n\t\tskip = True\n\t\toutput += '('\n\t\tfor i in range(0, self.__size):\n\t\t\tif skip:\n\t\t\t\tskip = False\n\t\t\telse:\n\t\t\t\toutput +=\", \"\n\t\t\toutput += str(self.__data[i])\n\n\t\toutput += ')'\n\t\tprint(output)\n\n\t# 转化为列表类型\n\tdef toList(vector):\n\t\tresult = [];\n\t\tfor i in range(0, vector.size()):\n\t\t\tresult[i] = vector.get(i);\n\n\t\treturn result\n\n\t# 从列表生成\n\tdef fromList(list):\n\t\tvector = Vector(len(list))\n\t\tfor i in range(0, len(list)):\n\t\t\tvector.set(i, list[i])\n\n\t\treturn vector\n\nif __name__ == '__main___':\n\tvector = Vector(0)\n\tvector.printMe()\n\tprint(vector.size())\n\n\tvector = Vector(1)\n\tvector.set(0, 1)\n\tvector.printMe()\n\tprint(vector.size())\n\n\tvector = Vector(2)\n\tvector.set(0, 1)\n\tvector.set(1, 2)\n\tvector.printMe()\n\tprint(vector.size())\n\n\tvector = Vector.fromList([1, 2, 3])\n\tvector.printMe();\n\tprint(vector.get(0))\n\tprint(vector.get(1))\n\tprint(vector.get(2))","repo_name":"Geotto/Matrix","sub_path":"src/vector.py","file_name":"vector.py","file_ext":"py","file_size_in_byte":1422,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"37729595102","text":" # noqa: E501\n\nimport unittest\n\nimport openai\n\n\nclass TestOpenAIApi(unittest.TestCase):\n def setUp(self):\n openai.api_key = \"EMPTY\"\n openai.api_base = \"http://localhost:8000/v1\"\n\n def test_list_models(self):\n # Fetch available model\n models = openai.Model.list()\n self.assertIsNotNone(models)\n self.assertIn(\"data\", models)\n return [model[\"id\"] for model in models[\"data\"]]\n\n def test_model_completion(self):\n models = self.test_list_models()\n\n for model in models:\n response = openai.Completion.create(\n model=model, prompt=\"def fib():\", max_tokens=30\n )\n self.assertIsNotNone(response)\n self.assertIn(\"choices\", response)\n self.assertTrue(len(response[\"choices\"]) > 0)\n self.assertTrue(\n response[\"choices\"][0][\"text\"]\n == \"\\n a, b = 0, 1\\n while True:\\n yield a\\n a, b = b, a + b\" # noqa: E501\n )\n print(response[\"choices\"])\n\n def test_streaming_output(self):\n models = self.test_list_models()\n\n for model in models:\n responses = openai.Completion.create(\n model=model, prompt=\"def fib():\", max_tokens=30, stream=True\n )\n for response in responses:\n self.assertIn(\"choices\", response)\n self.assertTrue(len(response[\"choices\"]) > 0)\n print(response[\"choices\"])\n\n\nif __name__ == \"__main__\":\n unittest.main()\n","repo_name":"neuralmagic/deepsparse","sub_path":"examples/openai-server/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1549,"program_lang":"python","lang":"en","doc_type":"code","stars":2498,"dataset":"github-code","pt":"78"} +{"seq_id":"34730721171","text":"import argparse\nimport logging\nimport threading\nimport time\nfrom common.winrm_util import PowerShell, LOG\n\n\ndef simple_useage():\n # Example for useage\n win_command = \"whoami\"\n # https://osce-vm.centralus.cloudapp.azure.com:5986\n p = PowerShell(username=u'trend', password=u'Osce@1234', target=u'https://one-16-int000.westus2.cloudapp.azure.com:5986', command=win_command)\n output = p.execute()\n LOG.info(output)\n\n\nclass MutiRunner:\n def __init__(self, user_name, user_pwd, target_fqdn_list, command):\n self.t_list = []\n self.user_name = user_name\n self.user_pwd = user_pwd\n self.target_fqdn_list = target_fqdn_list\n self.command = command\n\n def dosomething(self, i):\n LOG.info(f'No.{str(i)} Thread ID: {str(threading.get_ident())}, target is: {self.target_fqdn_list[int(i)]}')\n p = PowerShell(username=self.user_name, password=self.user_pwd, target=self.target_fqdn_list[int(i)], command=self.command)\n output = p.execute()\n LOG.info(output)\n\n def run(self):\n for i in range(len(self.target_fqdn_list)):\n self.t_list.append(threading.Thread(target=self.dosomething, args=(str(i))))\n time.sleep(1)\n self.t_list[i].start()\n\n for i in self.t_list:\n i.join()\n\n\nif __name__ == \"__main__\":\n logging.basicConfig(level=logging.DEBUG)\n # simple_useage()\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-fqdn\", dest=\"target_fqdn_list\", type=str, required=True)\n parser.add_argument(\"-user\", dest=\"user_name\", type=str, required=True)\n parser.add_argument(\"-pwd\", dest=\"user_pwd\", type=str, required=True)\n parser.add_argument(\"-command\", dest=\"command\", type=str, required=False, default=\"whoami; ipconfig\")\n args = parser.parse_args()\n\n t_job_list = args.target_fqdn_list.replace(' ', '').split(',')\n d = MutiRunner(user_name=args.user_name, user_pwd=args.user_pwd, target_fqdn_list=t_job_list, command=args.command)\n d.run()\n","repo_name":"ChihSeanHsu/azure_devops","sub_path":"utils/python/executable/remote_util.py","file_name":"remote_util.py","file_ext":"py","file_size_in_byte":2008,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"73855837372","text":"#Importar librerias\r\nimport json\r\nimport requests\r\nimport screen\r\nimport pymongo\r\n\r\n\r\n#Variables para el Token y la URL del chatbot\r\nTOKEN = \"\" \r\nURL = \"https://api.telegram.org/bot\" + TOKEN + \"/\"\r\n \r\n \r\n# conexión a Mongo\r\ndef conexion():\r\n\tclient = pymongo.MongoClient('127.0.0.1',27017)\r\n\tclient.server_info() \r\n\treturn client \r\n\t\r\n#almacenar los datos a mongodb\r\ndef almacena(nombre,texto):\r\n\tclient = conexion()\r\n\tdata = {}\r\n\tdata['Nombre'] = nombre\r\n\tdata['Mensaje'] = texto\r\n\ttry:\r\n\t\tdestination = 'chat'\r\n\t\tdatabase = 'ChatBot'\r\n\t\tcollection = client[database][destination]\r\n\t\tcollection.insert_one(data)\r\n\texcept Exception as error:\r\n\t\tprint(\"Error guardando los datos: %s\" % str(error))\r\n\tclient.close()\r\n \r\ndef update(offset):\r\n\t#Llamar al metodo getUpdates del bot, utilizando un offset\r\n\trespuesta = requests.get(URL + \"getUpdates\" + \"?offset=\" + str(offset) + \"&timeout=\" + str(100))\r\n\t \r\n\t \r\n\t#Decodificar la respuesta recibida a formato UTF8\r\n\tmensajes_js = respuesta.content.decode(\"utf8\")\r\n\t \r\n\t#Convertir el string de JSON a un diccionario de Python\r\n\tmensajes_diccionario = json.loads(mensajes_js)\r\n\t \r\n\t#Devolver este diccionario\r\n\treturn mensajes_diccionario\r\n \r\ndef info_mensaje(mensaje):\r\n \r\n\t#Comprobar el tipo de mensaje\r\n\tif \"text\" in mensaje[\"message\"]:\r\n\t\ttipo = \"texto\"\r\n\telif \"sticker\" in mensaje[\"message\"]:\r\n\t\ttipo = \"sticker\"\r\n\telif \"animation\" in mensaje[\"message\"]:\r\n\t\ttipo = \"animacion\" \r\n\telif \"photo\" in mensaje[\"message\"]:\r\n\t\ttipo = \"foto\"\r\n\telse:\r\n\t\ttipo = \"otro\"\r\n \r\n\t#Recoger la info del mensaje (remitente, id del chat e id del mensaje)\r\n\tpersona = mensaje[\"message\"][\"from\"][\"first_name\"]\r\n\tid_chat = mensaje[\"message\"][\"chat\"][\"id\"]\r\n\tid_update = mensaje[\"update_id\"]\r\n \r\n\t#Devolver toda la informacion\r\n\treturn tipo, id_chat, persona, id_update\r\n \r\ndef leer_mensaje(mensaje):\r\n \r\n\t#Extraer el texto, nombre de la persona e id del último mensaje recibido\r\n\ttexto = mensaje[\"message\"][\"text\"]\r\n \r\n\t#Devolver las dos id, el nombre y el texto del mensaje\r\n\treturn texto\r\n \r\ndef enviar_mensaje(idchat, texto):\r\n\t#Llamar el metodo sendMessage del bot, passando el texto y la id del chat\r\n\trequests.get(URL + \"sendMessage?text=\" + texto + \"&chat_id=\" + str(idchat))\r\n \r\n \r\n \r\n#Variable para almacenar la ID del ultimo mensaje procesado\r\nultima_id = 0\r\n \r\nwhile(True):\r\n\tmensajes_diccionario = update(ultima_id)\r\n\tfor i in mensajes_diccionario[\"result\"]:\r\n \r\n\t\t#Guardar la informacion del mensaje\r\n\t\ttipo, idchat, nombre, id_update = info_mensaje(i)\r\n \r\n\t\t#Generar una respuesta dependiendo del tipo de mensaje\r\n\t\tif tipo == \"texto\":\r\n\t\t\ttexto = leer_mensaje(i)\r\n\t\t\ttexto = texto.lower()\r\n\t\t\t\r\n\t\t\tif \"/start\" in texto or \"hola\" in texto.lower():\r\n\t\t\t\ttexto_respuesta = \"Hola soy el bot asistente para caja negra, en que te puedo ayudar. \\nPuedes consultar: \\n\\n* Servidores - Tipos de servidores que manejamos.\\n* Hosting - Planes de hosting con los que contamos. \\n* Contratar - Contratar algún servicio. \\n\\nPara elegir una de las opciones anteriores por favor envia la palabra relacionada con el servicio que desea consultar.\"\r\n\r\n\t\t\telif \"gracias\" in texto:\r\n\t\t\t\ttexto_respuesta = \"Estamos para servirte :D!\"\r\n\r\n\t\t\telif \"adios\" in texto or \"adiós\" in texto:\r\n\t\t\t\ttexto_respuesta = \"Hasta pronto :D!\"\r\n\r\n\t\t\telif \"contratar\" in texto or \"contratación\" in texto or \"contratacion\" in texto:\r\n\t\t\t\talmacena(nombre,texto)\r\n\t\t\t\ttexto_respuesta = \"Puedes ingresar los datos con la palabra contrato seguido de tu nombre, número de telefono, correo y el servicio a contratar, en caso de ser un servicio presencial especifique la dirección. \\nEjemplo: \\nContrato Alexa Luna Lira 4270000000 ejemplo_1@gmail.com servidor gps av.de los patos nùmero 2\"\r\n\r\n\t\t\telif \"contrato\" in texto:\r\n\t\t\t\t#\"enviar_mensaje(1387340486, texto)\"\r\n\t\t\t\talmacena(nombre,texto)\r\n\t\t\t\ttexto_respuesta = \"Tus datos fueron enviados a uno de nuestros asesores, en un momento nos contactaremos contigo, fue un placer servirle :D\"\r\n\r\n\r\n\t\t\telif \"linux\" in texto:\r\n\t\t\t\talmacena(nombre,texto)\r\n\t\t\t\ttexto_respuesta = \"La información la puede encontrar en el siguiente link: https://caja-negra.com.mx/planes-vps-gl/\"\r\n\r\n\t\t\telif \"windows\" in texto:\r\n\t\t\t\talmacena(nombre,texto)\r\n\t\t\t\ttexto_respuesta = \"La información la puede encontrar en el siguiente link: https://caja-negra.com.mx/planes-vps-ws/\"\r\n\r\n\r\n\t\t\telif \"servidor\" in texto or \"servidores\" in texto:\r\n\t\t\t\talmacena(nombre,texto)\r\n\t\t\t\ttexto_respuesta = \"Contamos con servidores virtuales privados windows o linux, elija una de las siguientes opciones \\n* Windows \\n*Linux\"\r\n\r\n\t\t\telif \"estudiantes\" in texto or \"estudiante\" in texto:\r\n\t\t\t\talmacena(nombre,texto)\r\n\t\t\t\ttexto_respuesta = \"OK, los planes para estudiantes los puede consultar en el siguiente link: https://caja-negra.com.mx/planes-hosting-estudiante/ si desea contratar un plan, porfavor ingrese sus datos\"\r\n\r\n\t\t\telif \"empresarial\" in texto or \"empresa\" in texto or \"empresas\" in texto:\r\n\t\t\t\talmacena(nombre,texto)\r\n\t\t\t\ttexto_respuesta = \"El costo esta en el siguiente link: https://caja-negra.com.mx/planes-hosting/\"\r\n\r\n\r\n\t\t\telif \"hosting\" in texto:\r\n\t\t\t\talmacena(nombre,texto)\r\n\t\t\t\ttexto_respuesta = \"Contamos con diversos planes de hosting, elige la opcion que mas te convenga: \\n* Estudiantes \\n* Empresarial\"\r\n\r\n\r\n\t\t\telse:\r\n\t\t\t\talmacena(nombre,texto)\r\n\t\t\t\ttexto_respuesta = \"no encuentro respuesta a tu pregunta lo siento :(\"\r\n\t\t\t\t\r\n\t\t#Si la ID del mensaje es mayor que el ultimo, se guarda la ID + 1\r\n\t\tif id_update > (ultima_id-1):\r\n\t\t\tultima_id = id_update + 1\r\n \r\n\t\t#Enviar la respuesta\r\n\t\tenviar_mensaje(idchat, texto_respuesta)\r\n \r\n\t#Vaciar el diccionario\r\n\tmensajes_diccionario = [] \r\n","repo_name":"annie971101/botTelegram-","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":5637,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"21637872400","text":"from itertools import starmap\nfrom lxml import objectify, etree\n\nfrom pycocotools.coco import COCO\nimport argparse\nimport os\nimport json\n\ndef root(folder, filename, width, height):\n\tE = objectify.ElementMaker(annotate=False)\n\treturn E.annotation(\n\t\tE.folder(folder),\n\t\tE.filename(filename),\n\t\tE.source(\n\t\t\tE.database('MS COCO 2017'),\n\t\t\tE.annotation('MS COCO 2017'),\n\t\t\tE.image('Flickr'),\n\t\t),\n\t\tE.size(\n\t\t\tE.width(width),\n\t\t\tE.height(height),\n\t\t\tE.depth(3),\n\t\t),\n\t\tE.segmented(0)\n\t\t)\n\ndef instance_to_xml(ann, cat_dict):\n\tE = objectify.ElementMaker(annotate=False)\n\txmin, ymin, width, height = ann['bbox']\n\treturn E.object(\n\t\tE.name(cat_dict[ann['category_id']]),\n\t\tE.bndbox(\n\t\t\tE.xmin(xmin),\n\t\t\tE.ymin(ymin),\n\t\t\tE.xmax(xmin+width),\n\t\t\tE.ymax(ymin+height),\n\t\t),\n\t)\n\ndef create_annotations(dataDir, dataType, dst):\n\tannFile = '{}/instances_{}.json'.format(dataDir, dataType)\n\tcoco = COCO(annFile)\n\t\n\tcats = coco.loadCats(coco.getCatIds())\n\tcat_dict = {}\n\tanimal_ids = range(16, 26)\n\tfor cat in cats:\n\t\t#cat_dict[cat['id']] = cat['supercategory']\n\t\tif cat['id'] in animal_ids:\n\t\t\tcat_dict[cat['id']] = 'animal'\n\t\telse:\n\t\t\tcat_dict[cat['id']] = cat['name']\n\twith open(\"cat.txt\", 'w') as f:\n\t\tjson.dump(cat_dict, f)\n\tf.close()\n\timgIds = coco.getImgIds()\n\tfor imgId in imgIds:\n\t\timg = coco.loadImgs(imgId)[0]\n\t\tfile_name = img['file_name']\n\t\tannotation = root(dataDir+'/images', file_name, img['width'], img['height'])\n\n\t\tannIds = coco.getAnnIds(img['id'])\n\t\tanns = coco.loadAnns(annIds)\n\t\tok = False\n\t\tfor ann in anns:\n\t\t#\tif cat_dict[ann['category_id']] == 'animal':\n\t\t\tannotation.append(instance_to_xml(ann, cat_dict))\n\t\t#\t\tok = True\n\t\t#if ok:\n\t\tetree.ElementTree(annotation).write(dst+'/{}.xml'.format(os.path.splitext(file_name)[0]), pretty_print=True)\t\t\n\t\tprint (file_name)\n\t\nif __name__ == '__main__':\n\tap = argparse.ArgumentParser()\n\tap.add_argument(\"-d\", \"--dataDir\", required=True, help=\"path to annotation file\")\n\tap.add_argument(\"-t\", \"--dataType\", required=True, help=\"data type\")\n\tap.add_argument(\"-s\", \"--destination\", required=True, help=\"path to save xml files\")\n\targs = vars(ap.parse_args())\n\n\tcreate_annotations(args[\"dataDir\"], args[\"dataType\"], args[\"destination\"])\n","repo_name":"giangnn-bkace/CocoToPascal","sub_path":"cocoToPascal.py","file_name":"cocoToPascal.py","file_ext":"py","file_size_in_byte":2183,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"78"} +{"seq_id":"45377227442","text":"import re\nfrom vk_api.longpoll import VkLongPoll, VkEventType\nimport crud\nfrom buttons import start_button, continue_button, change_criteria_button\nfrom patterns import age_pattern, city_pattern, id_city_pattern, status_pattern, id_reply_pattern\nfrom vk import Vk\nfrom vk_bot import VkBot\nfrom user import User\nfrom config import VK_TOKEN, VK_BOT_TOKEN\n\n\ndef clear_current_search_data(user, step=None):\n user.viewed_list.clear()\n user.loaded_list.clear()\n crud.empty_users_search(user.id)\n user.count_loaded = 0\n if step == 1:\n user.count_to_load = 0\n\n\ndef main():\n vk = Vk(VK_TOKEN, '5.130')\n vk_bot = VkBot(VK_BOT_TOKEN)\n\n start_key = vk_bot.get_keyboard(start_button)\n continue_key = vk_bot.get_keyboard(continue_button)\n change_criteria_key = vk_bot.get_keyboard(change_criteria_button)\n\n database_users = crud.load_users()\n users = []\n city_dict = {}\n criteria = {}\n\n long_poll = VkLongPoll(vk_bot.session)\n for event in long_poll.listen():\n if event.type == VkEventType.MESSAGE_NEW:\n if event.to_me:\n request = event.text.lower()\n\n if request == \"привет\":\n flag = 0\n for user_id in database_users:\n if user_id == event.user_id:\n criteria = crud.load_criteria(event.user_id)\n flag = 1\n break\n if flag == 0:\n info = vk.get_user_info(event.user_id)\n user = User(event.user_id, vk.get_criteria(info))\n users.append(user)\n crud.insert_user(user)\n user.count_to_load, target_users = vk.search_users(user.criteria, user.viewed_list,\n user.black_list)\n vk_bot.send_message(event.user_id, f\"привет {user.first_name}, \"\n f\"нашел для тебя {user.count_to_load} пользователей\\n \")\n vk_bot.send_message(event.user_id, user.display_criteria())\n vk_bot.send_message(event.user_id, \"сделай выбор:\", start_key)\n elif flag == 1:\n user = User(event.user_id, criteria)\n user.viewed_list = crud.load_viewed_list(event.user_id)\n user.loaded_list = user.viewed_list.copy()\n user.black_list = crud.load_black_list(event.user_id)\n user.favorite_list = crud.load_favorite_list(event.user_id)\n user.count_to_load, target_users = vk.search_users(user.criteria, [], user.black_list)\n user.count_loaded = len(user.viewed_list) if user.viewed_list else 0\n users.append(user)\n\n if user.count_not_viewed() > 0 and user.count_loaded > 0:\n vk_bot.send_message(event.user_id, f\"привет {user.first_name}, продолжим?\\n\"\n f\"осталось {user.count_not_viewed()} пользователей\\n\"\n f\"\\n{user.display_criteria()}\\n\", continue_key)\n else:\n clear_current_search_data(user)\n vk_bot.send_message(event.user_id, f\"привет {user.first_name}, искать снова?\\n\"\n f\"нашел {user.count_to_load} пользователей\\n\"\n f\"\\n{user.display_criteria()}\\n\", start_key)\n else:\n for user in users:\n if request in ('начать поиск', 'продолжить поиск'):\n if user.id == event.user_id:\n count, target_users = vk.search_users(user.criteria, user.viewed_list, user.black_list)\n if request == 'начать поиск':\n user.count_to_load = count\n vk_bot.send_message(event.user_id, f\"нашел {user.count_to_load} пользователей\")\n else:\n vk_bot.send_message(event.user_id, f\"осталось {count} пользователей\")\n vk_bot.send_message(event.user_id, \"загружаю...\")\n\n for i, target_user in enumerate(target_users):\n photos = vk.get_photos(target_user)\n top_3_photos = vk.get_top_photos(photos, 3)\n attachments = vk_bot.get_attachments(top_3_photos)\n vk_bot.send_message(event.user_id, f\"https://vk.com/id{str(target_user)}\",\n None, attachments)\n user.viewed_list.append(target_user)\n if i == user.max_number - 1:\n break\n crud.insert_users_search(user)\n user.count_loaded = len(user.loaded_list)\n\n if user.count_not_viewed() > 0:\n vk_bot.send_message(event.user_id,\n f\"осталось {user.count_not_viewed()} пользователей\"\n f\"\\nсделай выбор:\", continue_key)\n else:\n clear_current_search_data(user, 1)\n vk_bot.send_message(event.user_id, \"сделай выбор:\", start_key)\n\n elif request == 'изменить параметры':\n if user.id == event.user_id:\n vk_bot.send_message(event.user_id, f\"{user.display_criteria()}\\n\"\n \"сделай выбор:\", change_criteria_key)\n\n elif request == 'пол':\n if user.id == event.user_id:\n user.change_sex()\n crud.update_sex(user)\n clear_current_search_data(user, 1)\n vk_bot.send_message(event.user_id, f\"пол изменен\\n\"\n f\"{user.display_criteria()}\", start_key)\n count, target_users = vk.search_users(user.criteria, user.viewed_list, user.black_list)\n vk_bot.send_message(event.user_id, f\"нашел {count} пользователей\")\n\n elif request == 'возраст':\n if user.id == event.user_id:\n vk_bot.send_message(event.user_id, \"введите возраст в формате '30-35'\")\n\n elif re.match(re.compile(age_pattern), request):\n if user.id == event.user_id:\n age_from, age_to = re.match(re.compile(age_pattern), request).groups()\n user.change_age(age_from, age_to)\n crud.update_age(user)\n clear_current_search_data(user, 1)\n count, target_users = vk.search_users(user.criteria, user.viewed_list, user.black_list)\n vk_bot.send_message(event.user_id, f\"возраст изменен\\n{user.display_criteria()}\",\n start_key)\n vk_bot.send_message(event.user_id, f\"нашел {count} пользователей\")\n\n elif request == 'город':\n if user.id == event.user_id:\n vk_bot.send_message(event.user_id, \"введите город в формате 'г Москва'\")\n\n elif re.match(re.compile(city_pattern), request):\n if user.id == event.user_id:\n city_query = re.match(re.compile(city_pattern), request).groups()[0]\n city_dict, city_key = vk.get_cities(city_query, user.criteria['country_id'])\n if len(city_key) > 0:\n city_key = vk_bot.get_keyboard({'buttons': city_key})\n vk_bot.send_message(event.user_id, \"подтвердите выбор:\", city_key)\n else:\n vk_bot.send_message(event.user_id, \"город не найден\", change_criteria_key)\n\n elif re.match(re.compile(id_city_pattern), request):\n if user.id == event.user_id:\n city_id = re.match(re.compile(id_city_pattern), request).groups()[0]\n user.change_city(city_id, city_dict[city_id])\n crud.update_city(user)\n clear_current_search_data(user, 1)\n user.count_to_load, target_users = vk.search_users(user.criteria, user.viewed_list,\n user.black_list)\n vk_bot.send_message(event.user_id, f\"город изменен\\n{user.display_criteria()}\",\n start_key)\n vk_bot.send_message(event.user_id, f\"нашел {user.count_to_load} пользователей\")\n\n elif request == 'статус':\n if user.id == event.user_id:\n vk_bot.send_message(event.user_id, user.STATUS_LABEL)\n\n elif re.match(re.compile(status_pattern), request):\n if user.id == event.user_id:\n status = int(re.match(re.compile(status_pattern), request).groups()[0])\n user.change_status(status)\n crud.update_status(user)\n clear_current_search_data(user, 1)\n count, target_users = vk.search_users(user.criteria, user.viewed_list, user.black_list)\n vk_bot.send_message(event.user_id, f\"статус изменен\\n{user.display_criteria()}\",\n start_key)\n vk_bot.send_message(event.user_id, f\"нашел {count} пользователей\")\n\n elif request == '+':\n if user.id == event.user_id:\n reply_message = vk_bot.get_reply_message(event.peer_id, event.user_id)\n if re.match(re.compile(id_reply_pattern), reply_message):\n favorite_user_id = int(re.match(re.compile(id_reply_pattern),\n reply_message).groups()[0])\n if favorite_user_id not in user.favorite_list:\n crud.insert_favorite(user, favorite_user_id)\n user.favorite_list.append(favorite_user_id)\n vk_bot.send_message(event.user_id, f\"пользователь добавлен в избранное\",\n continue_key)\n else:\n vk_bot.send_message(event.user_id, 'пользователь уже в списке',\n continue_key)\n else:\n vk_bot.send_message(event.user_id, \"чтобы добавить в избранное, отправь '+' \"\n \"\\nв ответе на сообщение со ссылкой \"\n \"на пользователя\")\n\n elif request == '-':\n if user.id == event.user_id:\n reply_message = vk_bot.get_reply_message(event.peer_id, event.user_id)\n if re.match(re.compile(id_reply_pattern), reply_message):\n black_list_user_id = int(re.match(re.compile(id_reply_pattern),\n reply_message).groups()[0])\n if black_list_user_id not in user.black_list:\n crud.insert_black_user(user, black_list_user_id)\n user.black_list.append(black_list_user_id)\n user.clear_black_list_info(black_list_user_id)\n user.count_to_load, target_users = vk.search_users(user.criteria, [],\n user.black_list)\n vk_bot.send_message(event.user_id, f\"пользователь добавлен в черный список\",\n continue_key)\n else:\n vk_bot.send_message(event.user_id, 'пользователь уже в списке',\n continue_key)\n else:\n vk_bot.send_message(event.user_id, \"чтобы добавить в черный список, отправь '-' \"\n \"\\nв ответе на сообщение со ссылкой \"\n \"на пользователя\")\n\n elif request == 'черный список':\n if user.id == event.user_id:\n if user.black_list:\n for black_user in user.black_list:\n vk_bot.send_message(event.user_id, f\"https://vk.com/id{str(black_user)}\")\n vk_bot.send_message(event.user_id, 'сделай выбор', continue_key)\n else:\n vk_bot.send_message(event.user_id, 'список пуст', continue_key)\n\n elif request == 'избранное':\n if user.id == event.user_id:\n if user.favorite_list:\n for favorite_user in user.favorite_list:\n vk_bot.send_message(event.user_id, f\"https://vk.com/id{str(favorite_user)}\")\n vk_bot.send_message(event.user_id, 'сделай выбор', continue_key)\n else:\n vk_bot.send_message(event.user_id, 'список пуст', continue_key)\n\n else:\n if user.id == event.user_id:\n vk_bot.send_message(event.user_id, \"не понял вашего ответа...\")\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"SergueiKozlenko/vkinder","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":16412,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"72671009212","text":"from tkinter import*\r\nfrom tkinter import ttk # stylish entry field\r\nfrom PIL import Image,ImageTk \r\nimport mysql.connector\r\nimport pymysql\r\nfrom tkinter import messagebox\r\n \r\nclass Employee:\r\n def __init__(self,root):\r\n self.root=root\r\n self.root.geometry(\"1530x790+0+0\")\r\n self.root.title(\"Employee Management System\")\r\n\r\n\r\n# creating variables --> SQL\r\n self.var_dep=StringVar()\r\n self.var_name=StringVar()\r\n self.var_designation=StringVar()\r\n self.var_email=StringVar()\r\n self.var_address=StringVar()\r\n self.var_doj=StringVar()\r\n self.var_dob=StringVar()\r\n self.var_idproofcomb=StringVar()\r\n self.var_idproof=StringVar()\r\n self.var_gender=StringVar()\r\n self.var_phone=StringVar()\r\n self.var_country=StringVar()\r\n self.var_salary=StringVar()\r\n \r\n \r\n# LAbel for setting attribute of title \r\n lbl_title=Label(self.root,text=\"WorkFlowHR\",font=(\"times new roman\",37,\"bold\"),fg='darkblue', bg='white' )\r\n lbl_title.place(x=0,y=0,width=1530,height=50)\r\n\r\n #adding logo\r\n img_logo=Image.open(\"images/demo_logo.png\")\r\n # Resampling.Resampling.LANCZOS - create high quality picture \r\n img_logo=img_logo.resize((60,60),Image.Resampling.LANCZOS)\r\n self.photo_logo=ImageTk.PhotoImage(img_logo) #saving it\r\n \r\n self.logo = Label(self.root, image=self.photo_logo) # showing it \r\n self.logo.place(x=560,y=0,width=50,height=50) # placing it with c orrect hieght width x,y\r\n \r\n # IMAGE FRAME\r\n img_frame=Frame(self.root,bd=2,relief=RIDGE,bg='white')\r\n img_frame.place(x=0,y=50,width=1530,height=160) \r\n # making frame for image\r\n # 1st\r\n img1=Image.open(\"images/emp_working.png\")\r\n # Resampling.Resampling.Resampling.LANCZOS - create high quality picture \r\n img1=img1.resize((540,160),Image.Resampling.LANCZOS)\r\n self.photo1=ImageTk.PhotoImage(img1) #saving it\r\n \r\n self.img_1 = Label(img_frame, image=self.photo1) # showing it \r\n self.img_1.place(x=0,y=0,width=540,height=160) \r\n\r\n \r\n # 2nd\r\n img_2=Image.open(\"images/emp_talk.png\")\r\n img_2=img_2.resize((540,160),Image.Resampling.LANCZOS)\r\n self.photo2=ImageTk.PhotoImage(img_2) \r\n \r\n self.img_2 = Label(img_frame, image=self.photo2) \r\n self.img_2.place(x=540,y=0,width=540,height=160) \r\n\r\n # 3\r\n img_3=Image.open(\"images/emp_working.png\")\r\n img_3=img_3.resize((540,160),Image.Resampling.LANCZOS)\r\n self.photo3=ImageTk.PhotoImage(img_3) \r\n \r\n self.img_3 = Label(img_frame, image=self.photo3) \r\n self.img_3.place(x=1000,y=0,width=540,height=160) \r\n \r\n\r\n # MAIN FRAME\r\n Main_frame=Frame(self.root,bd=2,relief=RIDGE,bg='white')\r\n Main_frame.place(x=10,y=220,width=1500,height=560) \r\n # UPPER FRAME\r\n upper_frame=LabelFrame(Main_frame,bd=2,relief=RIDGE,text=\"Information\",font=(\"times new roman\",11,\"bold\"),fg='red', bg='white')\r\n upper_frame.place(x=10,y=10,width=1480,height=270)\r\n # using LABELFRAME --> TEXT in it. \r\n\r\n\r\n\r\n\r\n# Department\r\n lbl_dep=Label(upper_frame,text='Department:',font=('arial',11,'bold'),bg='white')\r\n lbl_dep.grid(row=0,column=0,padx=2,sticky=W)\r\n # place used for non know placing of element\r\n # Grid--> wor,col, sticky--> stick to the frame \r\n \r\n \r\n # COMBO_BOX --> option in HTML\r\n # puttng var into comboBOX\r\n # var --> Entry Fields \r\n # {used for fetching input value}\r\n combo_dept=ttk.Combobox(upper_frame,textvariable=self.var_dep,text='Departent',font=('arial',12,'bold'),width=17,state='readonly')\r\n # Values for option\r\n combo_dept['value']=('Select Department','HR','Sales','Manager','Social-Media','Tech','Design') \r\n combo_dept.current(0)\r\n combo_dept.grid(row=0,column=1,padx=2,pady=10,sticky=W)\r\n\r\n \r\n \r\n# Name\r\n lbl_Name=Label(upper_frame,font=('arial',11,'bold'),text=\"Name:\",bg='white')\r\n lbl_Name.grid(row=0,column=2,padx=4,pady=7 , sticky=W)\r\n \r\n txt_Designation=ttk.Entry(upper_frame,textvariable=self.var_name,width=22,font=('arial',11,'bold'))\r\n txt_Designation.grid(row=0,column=3,sticky=W,padx=2,pady=7)\r\n\r\n# DEsignation\r\n lbl_Designation=Label(upper_frame,font=('arial',11,'bold'),text=\"Designation:\",bg='white')\r\n lbl_Designation.grid(row=1,column=0,padx=2,pady=7 , sticky=W)\r\n \r\n txt_Designation=ttk.Entry(upper_frame,textvariable=self.var_designation,width=22,font=('arial',11,'bold'))\r\n txt_Designation.grid(row=1,column=1,sticky=W,padx=2,pady=7)\r\n# EMAIL\r\n lbl_Designation=Label(upper_frame,font=('arial',11,'bold'),text=\"Email\",bg='white')\r\n lbl_Designation.grid(row=1,column=2,padx=4,pady=7 , sticky=W)\r\n \r\n txt_Designation=ttk.Entry(upper_frame,textvariable=self.var_email,width=22,font=('arial',11,'bold'))\r\n txt_Designation.grid(row=1,column=3,sticky=W,padx=2,pady=7)\r\n# ADDRESS\r\n lbl_Address=Label(upper_frame,font=('arial',11,'bold'),text=\"Address\",bg='white')\r\n lbl_Address.grid(row=2,column=0,padx=2,pady=7 , sticky=W)\r\n \r\n txt_Address=ttk.Entry(upper_frame,textvariable=self.var_address,width=22,font=('arial',11,'bold'))\r\n txt_Address.grid(row=2,column=1,sticky=W,padx=2,pady=7)\r\n\r\n# DOJ\r\n lbl_DOJ=Label(upper_frame,font=('arial',11,'bold'),text=\"DOJ\",bg='white')\r\n lbl_DOJ.grid(row=2,column=2,padx=4,pady=7 , sticky=W)\r\n \r\n txt_DOJ=ttk.Entry(upper_frame,textvariable=self.var_doj,width=22,font=('arial',11,'bold'))\r\n txt_DOJ.grid(row=2,column=3,sticky=W,padx=2,pady=7)\r\n\r\n# DOB\r\n lbl_DOB=Label(upper_frame,font=('arial',11,'bold'),text=\"DOB\",bg='white')\r\n lbl_DOB.grid(row=3,column=0,padx=2,pady=7 , sticky=W)\r\n \r\n txt_DOB=ttk.Entry(upper_frame,textvariable=self.var_dob,width=22,font=('arial',11,'bold'))\r\n txt_DOB.grid(row=3,column=1 ,sticky=W,padx=2,pady=7)\r\n\r\n\r\n# ID Proof\r\n \r\n combo_proof=ttk.Combobox(upper_frame,textvariable=self.var_idproofcomb,text=\"id_proof_type\",font=('arial',12,'bold'),width=17,state='readonly')\r\n # Values for option\r\n combo_proof['value']=('Select ID Proof','PAN','ADHAAR','DRIVING LICENSE','VOTER ID')\r\n combo_proof.current(0)\r\n combo_proof.grid(row=4,column=0,padx=2,pady=7,sticky=W)\r\n \r\n txt_proof=ttk.Entry(upper_frame,textvariable=self.var_idproof,width=22,font=(\"arial\",11,\"bold\"))\r\n txt_proof.grid(row=4,column=1,padx=2,pady=7)\r\n# Gender\r\n \r\n lbl_gender=Label(upper_frame,font=('arial',11,'bold'),text=\"Gender:\",bg='white')\r\n lbl_gender.grid(row=3,column=2,padx=4,pady=7 , sticky=W)\r\n \r\n combo_gender=ttk.Combobox(upper_frame,textvariable=self.var_gender,font=('arial',12,'bold'),width=17,state='readonly')\r\n combo_gender['value']=('Female','Male')\r\n combo_gender.current(0)\r\n combo_gender.grid(row=3,column=3,padx=4,pady=7,sticky=W)\r\n \r\n \r\n# Contact Number\r\n lbl_Contact_Number=Label(upper_frame,font=('arial',11,'bold'),text=\"Contact\",bg='white')\r\n lbl_Contact_Number.grid(row=4,column=2,padx=4,pady=7,sticky=W)\r\n \r\n txt_Contact_Number=ttk.Entry(upper_frame,textvariable=self.var_phone,width=22,font=('arial',11,'bold'))\r\n txt_Contact_Number.grid(row=4,column=3,sticky=W,padx=4,pady=7)\r\n\r\n# contry\r\n lbl_Country=Label(upper_frame,font=('arial',11,'bold'),text=\"Country\",bg='white')\r\n lbl_Country.grid(row=1,column=4,padx=4,pady=7 , sticky=W)\r\n \r\n txt_Country=ttk.Entry(upper_frame,textvariable=self.var_country,width=22,font=('arial',11,'bold'))\r\n txt_Country.grid(row=1,column=5,sticky=W,padx=4,pady=7)\r\n\r\n# CTC\r\n lbl_CTC=Label(upper_frame,font=('arial',11,'bold'),text=\"CTC\",bg='white')\r\n lbl_CTC.grid(row=0,column=4,padx=4,pady=7 , sticky=W)\r\n \r\n txt_CTC=ttk.Entry(upper_frame,textvariable=self.var_salary,width=22,font=('arial',11,'bold'))\r\n txt_CTC.grid(row=0,column=5,sticky=W,padx=2,pady=7)\r\n\r\n# Main image\r\n\r\n img_main=Image.open(\"images/unity_teammate.png\")\r\n img_main=img_main.resize((235,235),Image.Resampling.LANCZOS)\r\n self.photomain=ImageTk.PhotoImage(img_main) \r\n \r\n self.img_main= Label(upper_frame, image=self.photomain) \r\n self.img_main.place(x=960,y=0,width=235,height=235) \r\n\r\n# Button-Frame\r\n button_frame=Frame(upper_frame,bd=2,relief=RIDGE,bg='white')\r\n button_frame.place(x=1250,y=20,width=140,height=170)\r\n\r\n# making button inseide main button\r\n btn_add=Button(button_frame,text=\"Save\",command=self.add_data,font=('arial',11,'bold'),width=12,bg='blue', fg='white')\r\n btn_add.grid(row=0,column=0,padx=7,pady=5)\r\n\r\n btn_update=Button(button_frame,text=\"Update\",command=self.update_data,font=('arial',11,'bold'),width=12,bg='blue', fg='white')\r\n btn_update.grid(row=1,column=0,padx=7,pady=5)\r\n\r\n btn_delete=Button(button_frame,text=\"Delete\",command=self.delete_data,font=('arial',11,'bold'),width=12,bg='blue', fg='white')\r\n btn_delete.grid(row=2,column=0,padx=7,pady=5)\r\n \r\n btn_clear=Button(button_frame,text=\"Clear\",command=self.clear,font=('arial',11,'bold'),width=12,bg='blue', fg='white')\r\n btn_clear.grid(row=3,column=0,padx=7,pady=5)\r\n\r\n\r\n \r\n# LOWER FRAME\r\n down_frame=LabelFrame(Main_frame,bd=2,relief=RIDGE,text=\"Information table\",font=(\"times new roman\",11,\"bold\"),fg='red', bg='white')\r\n down_frame.place(x=10,y=280,width=1480,height=270)\r\n \r\n# SEARCH FRAME\r\n\r\n search_frame=LabelFrame(down_frame,bd=2,relief=RIDGE,text=\"Search Employee Information\",font=(\"times new roman\",11,\"bold\"),fg='red', bg='white')\r\n search_frame.place(x=10,y=0,width=1456,height=65)\r\n \r\n self.var_search_com=StringVar()\r\n\r\n lbl_search=Label(search_frame,font=('arial',11,'bold'),text=\"Search By\",fg='white',bg='red')\r\n lbl_search.grid(row=0,column=0,padx=20,pady=6,sticky=W)\r\n \r\n combo_search=ttk.Combobox(search_frame,textvariable=self.var_search_com,font=('arial',12,'bold'),width=17,state='readonly')\r\n combo_search['value']=('select one option','Contact','id_proof','Name','Email')\r\n combo_search.current(0)\r\n combo_search.grid(row=0,column=1,padx=15,pady=6,sticky=W)\r\n \r\n self.var_search=StringVar() \r\n\r\n txt_search=ttk.Entry(search_frame,textvariable=self.var_search,width=20,font=('arial',11,'bold'))\r\n txt_search.grid(row=0,column=2,padx=15,pady=6)\r\n\r\n btn_search=Button(search_frame,command=self.search_data,text=\"Search\",font=('arial',11,'bold'),width=17,bg='blue', fg='white')\r\n btn_search.grid(row=0,column=3,padx=15,pady=2)\r\n\r\n btn_showall=Button(search_frame,command=self.fetch_data,text=\"Show All\",font=('arial',11,'bold'),width=17,bg='blue', fg='white')\r\n btn_showall.grid(row=0,column=4,padx=15,pady=2)\r\n \r\n lbl_quote=Label(search_frame,font=('times new roman',15,'bold'),text=\"Efforts could be controlled, Outcomes could'nt\",bg='white',fg='firebrick3')\r\n lbl_quote.grid(row=0,column=5,padx=50,pady=6,sticky=W)\r\n \r\n\r\n# ****************** Employee Table *****************\r\n# Table Frame\r\n table_frame=Frame(down_frame,bd=2,relief=RIDGE)\r\n table_frame.place(x=11,y=70,width=1454,height=170)\r\n\r\n# Scroll Bar\r\n scroll_x=ttk.Scrollbar(table_frame,orient=HORIZONTAL)\r\n scroll_y=ttk.Scrollbar(table_frame,orient=VERTICAL)\r\n \r\n self.employee_table=ttk.Treeview(table_frame,column=('dep','name','desg','email','address','dob','doj','idproofcomb','idproof','gender','phone','country','salary',),xscrollcommand=scroll_x.set,yscrollcommand=scroll_y.set)\r\n \r\n \r\n scroll_x.pack(side=BOTTOM,fill=X)\r\n scroll_y.pack(side=RIGHT,fill=Y)\r\n\r\n scroll_x.config(command=self.employee_table.xview)\r\n scroll_y.config(command=self.employee_table.yview)\r\n\r\n# using scroll bar\r\n \r\n self.employee_table.heading(\"dep\",text=\"Department\")\r\n self.employee_table.heading(\"name\",text=\"Name\")\r\n self.employee_table.heading(\"desg\",text=\"Designation\")\r\n self.employee_table.heading(\"email\",text=\"Email\")\r\n self.employee_table.heading(\"address\",text=\"Address\")\r\n self.employee_table.heading(\"dob\",text=\"DOB\")\r\n self.employee_table.heading(\"doj\",text=\"DOJ\") \r\n self.employee_table.heading(\"idproofcomb\",text=\"ID Type\")\r\n self.employee_table.heading(\"idproof\",text=\"ID Proof\")\r\n self.employee_table.heading(\"gender\",text=\"Gender\")\r\n self.employee_table.heading(\"phone\",text=\"Phone\")\r\n self.employee_table.heading(\"country\",text=\"Country\")\r\n self.employee_table.heading(\"salary\",text=\"Salary\")\r\n\r\n\r\n# removing extra space btw colmns\r\n self.employee_table['show']='headings' \r\n\r\n self.employee_table.column(\"dep\",width=100)\r\n self.employee_table.column(\"name\",width=100)\r\n self.employee_table.column(\"desg\",width=100)\r\n self.employee_table.column(\"email\",width=100)\r\n self.employee_table.column(\"address\",width=100)\r\n self.employee_table.column(\"dob\",width=100)\r\n self.employee_table.column(\"doj\",width=100)\r\n self.employee_table.column(\"idproofcomb\",width=100)\r\n self.employee_table.column(\"idproof\",width=100)\r\n self.employee_table.column(\"gender\",width=100)\r\n self.employee_table.column(\"phone\",width=100)\r\n self.employee_table.column(\"country\",width=100)\r\n self.employee_table.column(\"salary\",width=100)\r\n \r\n# .packing all attributes\r\n self.employee_table.pack(fill=BOTH,expand=1)\r\n self.employee_table.bind(\"\",self.get_cursor) # showing saved data into entry fields \r\n self.fetch_data()\r\n \r\n\r\n# ******************** Functions****************\r\n def add_data(self):\r\n if self.var_designation.get()==\"\" or self.var_idproof.get()==\"\":\r\n messagebox.showerror('Error','All fields are required')\r\n else:\r\n # exception handling \r\n try: \r\n # connectn with DB\r\n conn=mysql.connector.connect(host='localhost',username='root',password='sameer&9990',database='mydata')\r\n # cursor Inserting --> data --> into DB\r\n my_cursor=conn.cursor()\r\n # QUERY for INSERTING\r\n my_cursor.execute('insert into employee1 values(%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)',(\r\n self.var_dep.get(),\r\n self.var_name.get(),\r\n self.var_designation.get(),\r\n self.var_email.get(),\r\n self.var_address.get(),\r\n self.var_doj.get(),\r\n self.var_dob.get(),\r\n self.var_idproofcomb.get(),\r\n self.var_idproof.get(),\r\n self.var_gender.get(),\r\n self.var_phone.get(),\r\n self.var_country.get(),\r\n self.var_salary.get()\r\n ))\r\n\r\n \r\n conn.commit()\r\n self.fetch_data() # showing REAL-TIME-DATA into entry fields \r\n conn.close()\r\n messagebox.showinfo( 'Success','Employee data had been added',parent=self.root) \r\n except Exception as es:\r\n messagebox.showerror('Error',f'Due To:{str(es)}',parent=self.root) \r\n \r\n # Fetching data below search box \r\n def fetch_data(self):\r\n conn=mysql.connector.connect(host='localhost',username='root',password='sameer&9990',database='mydata')\r\n my_cursor=conn.cursor()\r\n my_cursor.execute('select * from employee1')\r\n data=my_cursor.fetchall()\r\n if len(data)!=0:\r\n self.employee_table.delete(*self.employee_table.get_children())\r\n for i in data:\r\n self.employee_table.insert(\"\",END,values=i)\r\n conn.commit()\r\n conn.close()\r\n\r\n# showing data saved into --> ENTRY FIELDS\r\n def get_cursor(self,event=\"\"):\r\n cursor_row=self.employee_table.focus()\r\n content=self.employee_table.item(cursor_row)\r\n data=content['values']\r\n\r\n self.var_dep.set(data[0]) \r\n self.var_name.set(data[1])\r\n self.var_designation.set(data[2])\r\n self.var_email.set(data[3])\r\n self.var_address.set(data[4])\r\n self.var_dob.set(data[5])\r\n self.var_doj.set(data[6])\r\n self.var_idproofcomb.set(data[7])\r\n self.var_idproof.set(data[8])\r\n self.var_gender.set(data[9])\r\n self.var_phone.set(data[10])\r\n self.var_country.set(data[11])\r\n self.var_salary.set(data[12])\r\n\r\n\r\n\r\n \r\n # ******************* function of UPDATE