name stringclasses 844
values | input_types listlengths 0 100 | output_type stringlengths 1 419 | code stringlengths 34 233k | dependencies listlengths 0 6 | lib_used listlengths 0 11 | imports listlengths 0 66 | line_count int64 3 199 | full_code stringlengths 39 1.01M | input_type_defs listlengths 1 12 ⌀ |
|---|---|---|---|---|---|---|---|---|---|
v2 | [
"Iterable[str]",
"int",
"str"
] | List[str] | def v2(self, v3: Iterable[str], v4: int, v5: str='right') -> List[str]:
def v6(v7):
return v4 - self.len(v7) + len(v7)
if v5 == 'left':
return [x.ljust(v6(x)) for v8 in v3]
elif v5 == 'center':
return [v8.center(v6(v8)) for v8 in v3]
else:
return [v8.rjust(v6(v8)) for v8... | [
{
"name": "v0",
"input_types": [
"Any"
],
"output_type": "Any",
"code": "def v0(v1):\n return max_len - self.len(v1) + len(v1)",
"dependencies": []
}
] | [] | [] | 10 | """
Internal module for formatting output data in csv, html,
and latex files. This module also applies to display formatting.
"""
from contextlib import contextmanager
from csv import QUOTE_NONE, QUOTE_NONNUMERIC
from datetime import tzinfo
import decimal
from functools import partial
from io import StringIO
import ma... | null |
v0 | [
"Union[str, int]"
] | Optional[Callable] | def v0(self, v1: Union[str, int]) -> Optional[Callable]:
if isinstance(self.formatters, (list, tuple)):
if is_integer(v1):
v1 = cast(int, v1)
return self.formatters[v1]
else:
return None
else:
if is_integer(v1) and v1 not in self.columns:
v... | [] | [
"pandas",
"typing"
] | [
"from typing import IO, TYPE_CHECKING, Any, Callable, Dict, Iterable, List, Mapping, Optional, Sequence, Tuple, Type, Union, cast",
"from pandas._config.config import get_option, set_option",
"from pandas._libs import lib",
"from pandas._libs.missing import NA",
"from pandas._libs.tslib import format_array_... | 11 | """
Internal module for formatting output data in csv, html,
and latex files. This module also applies to display formatting.
"""
from contextlib import contextmanager
from csv import QUOTE_NONE, QUOTE_NONNUMERIC
from datetime import tzinfo
import decimal
from functools import partial
from io import StringIO
import ma... | null |
v26 | [] | List[str] | def v26(self) -> List[str]:
v27 = self._format_strings()
return v13(v27, self.justify) | [
{
"name": "v11",
"input_types": [],
"output_type": "v0",
"code": "def v11() -> v0:\n v12 = get_option('display.unicode.east_asian_width')\n if v12:\n return EastAsianTextAdjustment()\n else:\n return v0()",
"dependencies": []
},
{
"name": "v13",
"input_types": ... | [
"pandas"
] | [
"from pandas._config.config import get_option, set_option",
"from pandas._libs import lib",
"from pandas._libs.missing import NA",
"from pandas._libs.tslib import format_array_from_datetime",
"from pandas._libs.tslibs import NaT, Timedelta, Timestamp, iNaT",
"from pandas._libs.tslibs.nattype import NaTTyp... | 3 | """
Internal module for formatting output data in csv, html,
and latex files. This module also applies to display formatting.
"""
from contextlib import contextmanager
from csv import QUOTE_NONE, QUOTE_NONNUMERIC
from datetime import tzinfo
import decimal
from functools import partial
from io import StringIO
import ma... | [
"class v0:\n\n def __init__(self):\n self.encoding = get_option('display.encoding')\n\n def v1(self, v2: str) -> int:\n return v1(v2)\n\n def v3(self, v4: Any, v5: int, v6: str='right') -> List[str]:\n return v3(v4, v5, mode=v6)\n\n def v7(self, v8: int, *v9, **v10) -> str:\n ... |
v26 | [] | List[List[str]] | def v26(self) -> List[List[str]]:
v27 = {k: cast(int, v) for (v28, v29) in self.col_space.items()}
v30 = self.tr_frame
v31 = self._get_formatted_index(v30)
if not is_list_like(self.header) and (not self.header):
v32 = []
for (v33, v34) in enumerate(v30):
v35 = self._format_co... | [
{
"name": "v11",
"input_types": [],
"output_type": "v0",
"code": "def v11() -> v0:\n v12 = get_option('display.unicode.east_asian_width')\n if v12:\n return EastAsianTextAdjustment()\n else:\n return v0()",
"dependencies": []
},
{
"name": "v13",
"input_types": ... | [
"pandas",
"typing"
] | [
"from typing import IO, TYPE_CHECKING, Any, Callable, Dict, Iterable, List, Mapping, Optional, Sequence, Tuple, Type, Union, cast",
"from pandas._config.config import get_option, set_option",
"from pandas._libs import lib",
"from pandas._libs.missing import NA",
"from pandas._libs.tslib import format_array_... | 61 | """
Internal module for formatting output data in csv, html,
and latex files. This module also applies to display formatting.
"""
from contextlib import contextmanager
from csv import QUOTE_NONE, QUOTE_NONNUMERIC
from datetime import tzinfo
import decimal
from functools import partial
from io import StringIO
import ma... | [
"class v0:\n\n def __init__(self):\n self.encoding = get_option('display.encoding')\n\n def v1(self, v2: str) -> int:\n return v1(v2)\n\n def v3(self, v4: Any, v5: int, v6: str='right') -> List[str]:\n return v3(v4, v5, mode=v6)\n\n def v7(self, v8: int, *v9, **v10) -> str:\n ... |
v11 | [
"Iterable[list[str]]"
] | str | def v11(self, v12: Iterable[list[str]]) -> str:
v13 = self.line_width
v14 = 1
v15 = list(v12)
if self.fmt.index:
v16 = v15.pop(0)
v13 -= np.array([self.adj.len(x) for v17 in v16]).max() + v14
v18 = [np.array([self.adj.len(v17) for v17 in col]).max() if len(col) > 0 else 0 for v19 in ... | [
{
"name": "v0",
"input_types": [
"list[int]",
"int"
],
"output_type": "list[int]",
"code": "def v0(v1: list[int], v2: int) -> list[int]:\n v3 = 1\n v4 = []\n v5 = 0\n v6 = len(v1) - 1\n for (v7, v8) in enumerate(v1):\n v9 = v8 + v3\n v5 += v9\n if ... | [
"numpy"
] | [
"import numpy as np"
] | 30 | """
Module for formatting output data in console (to string).
"""
from __future__ import annotations
from shutil import get_terminal_size
from typing import Iterable
import numpy as np
from pandas.io.formats.format import DataFrameFormatter
from pandas.io.formats.printing import pprint_thing
class St... | null |
v14 | [
"int"
] | List[str] | def v14(self, v15: int) -> List[str]:
v16 = self.tr_frame
v17 = self._get_formatter(v15)
return v1(v16.iloc[:, v15]._values, v17, float_format=self.float_format, na_rep=self.na_rep, space=self.col_space.get(v16.columns[v15]), decimal=self.decimal, leading_space=self.index) | [
{
"name": "v1",
"input_types": [
"Any",
"Optional[Callable]",
"Optional[v0]",
"str",
"Optional[int]",
"Optional[Union[str, int]]",
"str",
"str",
"Optional[bool]",
"Optional[int]"
],
"output_type": "List[str]",
"code": "def v1(v2: Any, v... | [
"decimal",
"pandas"
] | [
"import decimal",
"from pandas._config.config import get_option, set_option",
"from pandas._libs import lib",
"from pandas._libs.missing import NA",
"from pandas._libs.tslib import format_array_from_datetime",
"from pandas._libs.tslibs import NaT, Timedelta, Timestamp, iNaT",
"from pandas._libs.tslibs.n... | 4 | """
Internal module for formatting output data in csv, html,
and latex files. This module also applies to display formatting.
"""
from contextlib import contextmanager
from csv import QUOTE_NONE, QUOTE_NONNUMERIC
from datetime import tzinfo
import decimal
from functools import partial
from io import StringIO
import ma... | [
"v0 = Union[str, Callable, 'EngFormatter']"
] |
v0 | [] | List[str] | def v0(self) -> List[str]:
v1: List[str] = []
v2 = self.frame.columns
if isinstance(v2, ABCMultiIndex):
v1.extend(('' if name is None else name for v3 in v2.names))
else:
v1.append('' if v2.name is None else v2.name)
return v1 | [] | [
"pandas"
] | [
"from pandas._config.config import get_option, set_option",
"from pandas._libs import lib",
"from pandas._libs.missing import NA",
"from pandas._libs.tslib import format_array_from_datetime",
"from pandas._libs.tslibs import NaT, Timedelta, Timestamp, iNaT",
"from pandas._libs.tslibs.nattype import NaTTyp... | 8 | """
Internal module for formatting output data in csv, html,
and latex files. This module also applies to display formatting.
"""
from contextlib import contextmanager
from datetime import tzinfo
import decimal
from functools import partial
from io import StringIO
import math
import re
from shutil import get_terminal_... | null |
v15 | [] | List[str] | def v15(self) -> List[str]:
v16 = self.formatter or v3(self.values, nat_rep=self.nat_rep, box=self.box)
return [v16(x) for v17 in self.values] | [
{
"name": "v0",
"input_types": [
"Any"
],
"output_type": "Any",
"code": "def v0(v1):\n if v1 is None or (is_scalar(v1) and isna(v1)):\n return nat_rep\n if not isinstance(v1, Timedelta):\n v1 = Timedelta(v1)\n v2 = v1._repr_base(format=format)\n if box:\n v... | [
"numpy",
"pandas"
] | [
"import numpy as np",
"from pandas._config.config import get_option, set_option",
"from pandas._libs import lib",
"from pandas._libs.missing import NA",
"from pandas._libs.tslib import format_array_from_datetime",
"from pandas._libs.tslibs import NaT, Timedelta, Timestamp, iNaT",
"from pandas._libs.tsli... | 3 | """
Internal module for formatting output data in csv, html,
and latex files. This module also applies to display formatting.
"""
from contextlib import contextmanager
from csv import QUOTE_NONE, QUOTE_NONNUMERIC
from datetime import tzinfo
import decimal
from functools import partial
from io import StringIO
import ma... | null |
v7 | [
"Optional[v0]",
"Optional[Union[float, int]]"
] | Callable | def v7(self, v8: Optional[v0]=None, v9: Optional[Union[float, int]]=None) -> Callable:
if v8 is None:
v8 = self.float_format
if v8:
def v10(v11):
assert v8 is not None
return v8(value=v11) if notna(v11) else self.na_rep
else:
def v12(v13):
return... | [
{
"name": "v1",
"input_types": [
"Any"
],
"output_type": "Any",
"code": "def v1(v2):\n return str(v2) if notna(v2) else self.na_rep",
"dependencies": []
},
{
"name": "v3",
"input_types": [
"Any"
],
"output_type": "Any",
"code": "def v3(v4):\n return ... | [
"pandas"
] | [
"from pandas._config.config import get_option, set_option",
"from pandas._libs import lib",
"from pandas._libs.missing import NA",
"from pandas._libs.tslib import format_array_from_datetime",
"from pandas._libs.tslibs import NaT, Timedelta, Timestamp, iNaT",
"from pandas._libs.tslibs.nattype import NaTTyp... | 30 | """
Internal module for formatting output data in csv, html,
and latex files. This module also applies to display formatting.
"""
from contextlib import contextmanager
from csv import QUOTE_NONE, QUOTE_NONNUMERIC
from datetime import tzinfo
import decimal
from functools import partial
from io import StringIO
import ma... | [
"v0 = Union[str, Callable, 'EngFormatter']"
] |
v0 | [] | int | def v0(self) -> int:
v1 = self.__fetch(start=1, limit=1)
return v1.found | [] | [] | [] | 3 | from typing import Optional, Generator, List
from lakey_finicity.api_http_client import ApiHttpClient
from lakey_finicity.models import Institution
from lakey_finicity.responses import InstitutionsListResponse
DEFAULT_BATCH_SIZE = 25
class InstitutionsQuery(object):
def __init__(self, http_client: ApiHttpClien... | null |
v0 | [] | None | def v0(self) -> None:
self.REVIEWED = True
v1 = '{} in {}'.format(self.PROJECT, self.LANGUAGE)
v2 = 'Resource {} fully reviewed.'.format(self.RESOURCE)
self.url = self.build_webhook_url(self.URL_REVIEWED_METHOD_TEMPLATE, project=self.PROJECT, language=self.LANGUAGE, resource=self.RESOURCE)
self.send... | [] | [] | [] | 6 | # -*- coding: utf-8 -*-
from typing import Any, Dict
from zerver.lib.test_classes import WebhookTestCase
class TransifexHookTests(WebhookTestCase):
STREAM_NAME = 'transifex'
URL_TEMPLATE = u"/api/v1/external/transifex?stream={stream}&api_key={api_key}"
URL_REVIEWED_METHOD_TEMPLATE = "reviewed=100"
URL... | null |
v0 | [] | None | def v0(self) -> None:
self.REVIEWED = False
v1 = '{} in {}'.format(self.PROJECT, self.LANGUAGE)
v2 = 'Resource {} fully translated.'.format(self.RESOURCE)
self.url = self.build_webhook_url(self.URL_TRANSLATED_METHOD_TEMPLATE, project=self.PROJECT, language=self.LANGUAGE, resource=self.RESOURCE)
self... | [] | [] | [] | 6 | # -*- coding: utf-8 -*-
from typing import Any, Dict
from zerver.lib.test_classes import WebhookTestCase
class TransifexHookTests(WebhookTestCase):
STREAM_NAME = 'transifex'
URL_TEMPLATE = u"/api/v1/external/transifex?stream={stream}&api_key={api_key}"
URL_REVIEWED_METHOD_TEMPLATE = "reviewed=100"
URL... | null |
v0 | [] | Dict[Callable, Tuple[float, Optional[datetime.datetime]]] | def v0(self) -> Dict[Callable, Tuple[float, Optional[datetime.datetime]]]:
v1 = super().get_periodic_tasks()
v1.update(self._get_behaviours_tasks())
return v1 | [] | [] | [] | 4 | # -*- coding: utf-8 -*-
# ------------------------------------------------------------------------------
#
# Copyright 2022 Valory AG
# Copyright 2018-2021 Fetch.AI Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# ... | null |
v0 | [] | Dict[Callable, Tuple[float, Optional[datetime.datetime]]] | def v0(self) -> Dict[Callable, Tuple[float, Optional[datetime.datetime]]]:
v1 = {}
for v2 in self.active_behaviours:
v1[v2.act_wrapper] = (v2.tick_interval, v2.start_at)
return v1 | [] | [] | [] | 5 | # -*- coding: utf-8 -*-
# ------------------------------------------------------------------------------
#
# Copyright 2022 Valory AG
# Copyright 2018-2021 Fetch.AI Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# ... | null |
v0 | [
"Exception",
"Callable",
"bool"
] | None | def v0(v1: Exception, v2: Callable, v3: bool=False) -> None:
if v3:
self.logger.debug(f'<{v1}> raised during `{v2}`')
else:
self.logger.exception(f'<{v1}> raised during `{v2}`') | [] | [] | [] | 5 | # -*- coding: utf-8 -*-
# ------------------------------------------------------------------------------
#
# Copyright 2022 Valory AG
# Copyright 2018-2021 Fetch.AI Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# ... | null |
v0 | [] | Dict[str, Dict[str, str]] | def v0() -> Dict[str, Dict[str, str]]:
v1 = {}
v1['name'] = '金融服務'
v1['id'] = 'finance'
v2 = {}
v2['banks'] = '銀行'
v2['credit-unions'] = '信用合作社'
v2['financial-service'] = '金融服務'
v1['sub'] = v2
return v1 | [] | [] | [] | 10 | from typing import Dict
from ..base_category import BaseCategory
# 金融服務 finance
class Finance(object):
def finance() -> Dict[str, Dict[str, str]]:
list = {}
list['name'] = '金融服務'
list['id'] = 'finance'
finance = {}
finance['banks'] = '銀行'
finance['credit-unions'] = '信用合作社'
... | null |
v0 | [] | Dict[str, Dict[str, str]] | def v0() -> Dict[str, Dict[str, str]]:
v1 = {}
v1['name'] = '投資理財'
v1['id'] = 'investment'
v2 = {}
v2['invest-service'] = '投資服務'
v2['trust-service'] = '信託服務'
v2['insurance'] = '保險'
v2['bills-finance'] = '票券金融'
v1['sub'] = v2
return v1 | [] | [] | [] | 11 | from typing import Dict
from ..base_category import BaseCategory
# 金融服務 finance
class Finance(object):
def finance() -> Dict[str, Dict[str, str]]:
list = {}
list['name'] = '金融服務'
list['id'] = 'finance'
finance = {}
finance['banks'] = '銀行'
finance['credit-unions'] = '信用合作社'
... | null |
v0 | [] | Dict[str, Dict[str, str]] | def v0() -> Dict[str, Dict[str, str]]:
v1 = {}
v1['name'] = '不動產投資管理'
v1['id'] = 'real-estate'
v2 = {}
v2['agents'] = '房地產仲介'
v2['escrow-service'] = '代書、地政士'
v1['sub'] = v2
return v1 | [] | [] | [] | 9 | from typing import Dict
from ..base_category import BaseCategory
# 金融服務 finance
class Finance(object):
def finance() -> Dict[str, Dict[str, str]]:
list = {}
list['name'] = '金融服務'
list['id'] = 'finance'
finance = {}
finance['banks'] = '銀行'
finance['credit-unions'] = '信用合作社'
... | null |
v0 | [] | Dict[str, Dict[str, str]] | def v0() -> Dict[str, Dict[str, str]]:
v1 = {}
v1['name'] = '貿易工商服務'
v1['id'] = 'trade-service'
v2 = {}
v2['business-service'] = '工商服務'
v2['telemarketing'] = '市調、電話行銷'
v2['property-management'] = '建築經理'
v2['rental-service'] = '租賃'
v2['product-design'] = '產品設計'
v2['trading'] = '貿易... | [] | [] | [] | 14 | from typing import Dict
from ..base_category import BaseCategory
# 金融服務 finance
class Finance(object):
def finance() -> Dict[str, Dict[str, str]]:
list = {}
list['name'] = '金融服務'
list['id'] = 'finance'
finance = {}
finance['banks'] = '銀行'
finance['credit-unions'] = '信用合作社'
... | null |
v0 | [] | Dict[str, Dict[str, str]] | def v0() -> Dict[str, Dict[str, str]]:
v1 = {}
v1['name'] = '顧問諮詢'
v1['id'] = 'consultant'
v2 = {}
v2['PR'] = '公關顧問'
v2['engineer'] = '技師、工程師'
v2['construction'] = '土木工程顧問'
v2['industrial-safety'] = '工業技師、工安技師'
v2['electrical'] = '電機'
v2['invest-financial'] = '投資財務顧問'
v2['env... | [] | [] | [] | 15 | from typing import Dict
from ..base_category import BaseCategory
# 金融服務 finance
class Finance(object):
def finance() -> Dict[str, Dict[str, str]]:
list = {}
list['name'] = '金融服務'
list['id'] = 'finance'
finance = {}
finance['banks'] = '銀行'
finance['credit-unions'] = '信用合作社'
... | null |
v0 | [] | Dict[str, Dict[str, str]] | def v0() -> Dict[str, Dict[str, str]]:
v1 = {}
v1['name'] = '律師'
v1['id'] = 'lawyer'
v2 = {}
v2['lawyers'] = '律師'
v1['sub'] = v2
return v1 | [] | [] | [] | 8 | from typing import Dict
from ..base_category import BaseCategory
# 金融服務 finance
class Finance(object):
def finance() -> Dict[str, Dict[str, str]]:
list = {}
list['name'] = '金融服務'
list['id'] = 'finance'
finance = {}
finance['banks'] = '銀行'
finance['credit-unions'] = '信用合作社'
... | null |
v0 | [] | Dict[str, Dict[str, str]] | def v0() -> Dict[str, Dict[str, str]]:
v1 = {}
v1['name'] = '會計師'
v1['id'] = 'accountant'
v2 = {}
v2['accountants'] = '會計師'
v1['sub'] = v2
return v1 | [] | [] | [] | 8 | from typing import Dict
from ..base_category import BaseCategory
# 金融服務 finance
class Finance(object):
def finance() -> Dict[str, Dict[str, str]]:
list = {}
list['name'] = '金融服務'
list['id'] = 'finance'
finance = {}
finance['banks'] = '銀行'
finance['credit-unions'] = '信用合作社'
... | null |
v0 | [] | Dict[str, Dict[str, str]] | def v0() -> Dict[str, Dict[str, str]]:
v1 = {}
v1['name'] = '稅務帳務代理'
v1['id'] = 'booking-service'
v2 = {}
v2['receivable-management'] = '應收帳款管理'
v2['bookkeepers'] = '記帳士'
v1['sub'] = v2
return v1 | [] | [] | [] | 9 | from typing import Dict
from ..base_category import BaseCategory
# 金融服務 finance
class Finance(object):
def finance() -> Dict[str, Dict[str, str]]:
list = {}
list['name'] = '金融服務'
list['id'] = 'finance'
finance = {}
finance['banks'] = '銀行'
finance['credit-unions'] = '信用合作社'
... | null |
v0 | [] | Dict[str, Dict[str, str]] | def v0() -> Dict[str, Dict[str, str]]:
v1 = {}
v1['name'] = '專利商標代理'
v1['id'] = 'patent-and-trademark'
v2 = {}
v2['intellectual-property'] = '專利、智財'
v1['sub'] = v2
return v1 | [] | [] | [] | 8 | from typing import Dict
from ..base_category import BaseCategory
# 金融服務 finance
class Finance(object):
def finance() -> Dict[str, Dict[str, str]]:
list = {}
list['name'] = '金融服務'
list['id'] = 'finance'
finance = {}
finance['banks'] = '銀行'
finance['credit-unions'] = '信用合作社'
... | null |
v0 | [] | Dict[str, Dict[str, str]] | def v0() -> Dict[str, Dict[str, str]]:
v1 = {}
v1['name'] = '人力仲介'
v1['id'] = 'recruitment-agency'
v2 = {}
v2['employment-service'] = '人力仲介'
v1['sub'] = v2
return v1 | [] | [] | [] | 8 | from typing import Dict
from ..base_category import BaseCategory
# 金融服務 finance
class Finance(object):
def finance() -> Dict[str, Dict[str, str]]:
list = {}
list['name'] = '金融服務'
list['id'] = 'finance'
finance = {}
finance['banks'] = '銀行'
finance['credit-unions'] = '信用合作社'
... | null |
v0 | [] | Dict[str, Dict[str, str]] | def v0() -> Dict[str, Dict[str, str]]:
v1 = {}
v1['name'] = '翻譯移民服務'
v1['id'] = 'immigrationservices'
v2 = {}
v2['migration-service'] = '移民服務'
v2['translation-service'] = '翻譯服務'
v1['sub'] = v2
return v1 | [] | [] | [] | 9 | from typing import Dict
from ..base_category import BaseCategory
# 金融服務 finance
class Finance(object):
def finance() -> Dict[str, Dict[str, str]]:
list = {}
list['name'] = '金融服務'
list['id'] = 'finance'
finance = {}
finance['banks'] = '銀行'
finance['credit-unions'] = '信用合作社'
... | null |
v0 | [] | Dict[str, Dict[str, str]] | def v0() -> Dict[str, Dict[str, str]]:
v1 = {}
v1['name'] = '徵信社'
v1['id'] = 'credit-information'
v2 = {}
v2['credit-report'] = '徵信社'
v1['sub'] = v2
return v1 | [] | [] | [] | 8 | from typing import Dict
from ..base_category import BaseCategory
# 金融服務 finance
class Finance(object):
def finance() -> Dict[str, Dict[str, str]]:
list = {}
list['name'] = '金融服務'
list['id'] = 'finance'
finance = {}
finance['banks'] = '銀行'
finance['credit-unions'] = '信用合作社'
... | null |
v0 | [] | Dict[str, Dict[str, str]] | def v0() -> Dict[str, Dict[str, str]]:
v1 = {}
v1['name'] = '醫療矯正訓練'
v1['id'] = 'medical-remedial-training'
v2 = {}
v2['medical-remedial-training'] = '醫療矯正訓練'
v1['sub'] = v2
return v1 | [] | [] | [] | 8 | from typing import Dict
from ..base_category import BaseCategory
# 衛生機關及單位 health-agencies
class HealthAgencies(object):
def health_agencies() -> Dict[str, Dict[str, str]]:
list = {}
list['name'] = '衛生機關及單位'
list['id'] = 'health-agencies'
health_agencies = {}
health_agencies['health-a... | null |
v0 | [] | Dict[str, Dict[str, str]] | def v0() -> Dict[str, Dict[str, str]]:
v1 = {}
v1['name'] = '會議商展'
v1['id'] = 'conference-expo'
v2 = {}
v2['exhibition-service'] = '展覽服務'
v2['conference-centers'] = '會議中心'
v1['sub'] = v2
return v1 | [] | [] | [] | 9 | from typing import Dict
from ..base_category import BaseCategory
# 金融服務 finance
class Finance(object):
def finance() -> Dict[str, Dict[str, str]]:
list = {}
list['name'] = '金融服務'
list['id'] = 'finance'
finance = {}
finance['banks'] = '銀行'
finance['credit-unions'] = '信用合作社'
... | null |
v0 | [] | Dict[str, Dict[str, str]] | def v0() -> Dict[str, Dict[str, str]]:
v1 = {}
v1['name'] = 'ＳＯＨＯ族工作室'
v1['id'] = 'soho-studio'
v2 = {}
v2['studio'] = '個人工作室'
v1['sub'] = v2
return v1 | [] | [] | [] | 8 | from typing import Dict
from ..base_category import BaseCategory
# 金融服務 finance
class Finance(object):
def finance() -> Dict[str, Dict[str, str]]:
list = {}
list['name'] = '金融服務'
list['id'] = 'finance'
finance = {}
finance['banks'] = '銀行'
finance['credit-unions'] = '信用合作社'
... | null |
v0 | [] | Dict[str, Dict[str, str]] | def v0() -> Dict[str, Dict[str, str]]:
v1 = {}
v1['name'] = '公證'
v1['id'] = 'notary'
v2 = {}
v2['notary-service'] = '公證服務'
v1['sub'] = v2
return v1 | [] | [] | [] | 8 | from typing import Dict
from ..base_category import BaseCategory
# 金融服務 finance
class Finance(object):
def finance() -> Dict[str, Dict[str, str]]:
list = {}
list['name'] = '金融服務'
list['id'] = 'finance'
finance = {}
finance['banks'] = '銀行'
finance['credit-unions'] = '信用合作社'
... | null |
v0 | [] | Dict[str, Dict[str, str]] | def v0() -> Dict[str, Dict[str, str]]:
v1 = {}
v1['name'] = '印刷機械'
v1['id'] = 'printing-machinery'
v2 = {}
v2['printing-machinery'] = '印刷機械'
v2['hot-stamping-machinery'] = '燙金機械'
v1['sub'] = v2
return v1 | [] | [] | [] | 9 | from typing import Dict
from ..base_category import BaseCategory
# 自動化設備 automation
class Automation(object):
def automation() -> Dict[str, Dict[str, str]]:
list = {}
list['name'] = '自動化設備'
list['id'] = 'automation'
automation = {}
automation['automation-machinery'] = '自動化機械'
au... | null |
v0 | [] | Dict[str, Dict[str, str]] | def v0() -> Dict[str, Dict[str, str]]:
v1 = {}
v1['name'] = '殯儀服務'
v1['id'] = 'funeral-services'
v2 = {}
v2['funeral-service'] = '殯葬服務'
v1['sub'] = v2
return v1 | [] | [] | [] | 8 | from typing import Dict
from ..base_category import BaseCategory
# 殯儀服務 funeral-services
class FuneralServices(object):
def funeral_services() -> Dict[str, Dict[str, str]]:
list = {}
list['name'] = '殯儀服務'
list['id'] = 'funeral-services'
funeral_services = {}
funeral_services['funeral-... | null |
v0 | [] | Dict[str, Dict[str, str]] | def v0() -> Dict[str, Dict[str, str]]:
v1 = {}
v1['name'] = '調味品'
v1['id'] = 'flavoring-extracts'
v2 = {}
v2['seasoning'] = '調味料'
v2['soy-sauce-oyster-sauce'] = '醬油蠔油'
v1['sub'] = v2
return v1 | [] | [] | [] | 9 | from typing import Dict
from ..base_category import BaseCategory
class NativeGoods(object):
def native_goods() -> Dict[str, Dict[str, str]]:
list = {}
list['name'] = '土產品'
list['id'] = 'native-goods'
native_goods = {}
native_goods['famous'] = '名特產'
native_goods['local-special'] ... | null |
v0 | [
"Any"
] | List[int] | def v0(v1) -> List[int]:
v2 = []
v3 = 0
for v4 in v1:
v2.append(v3)
v3 += v4
v5 = min(v2)
v2 = [_ - v5 for v6 in v2]
return v2 | [] | [] | [] | 9 | import dataclasses
from typing import List, Optional, Tuple
from blspy import G2Element, AugSchemeMPL
from src.types.blockchain_format.coin import Coin
from src.types.condition_opcodes import ConditionOpcode
from src.types.blockchain_format.program import Program
from src.types.blockchain_format.sized_bytes import b... | null |
v0 | [] | DataLoader | def v0(self) -> DataLoader:
if self.train_dataset is None:
raise ValueError('Trainer: training requires a train_dataset.')
v1 = self._get_train_sampler()
if self.augment_data_collator is not None:
return DataLoader(self.train_dataset, batch_size=self.args.train_batch_size, sampler=v1, collat... | [] | [
"torch"
] | [
"import torch",
"from torch import nn",
"from torch.utils.data.dataloader import DataLoader",
"from torch.utils.data.dataset import Dataset",
"from torch.utils.data.distributed import DistributedSampler",
"from torch.utils.data.sampler import RandomSampler, SequentialSampler"
] | 8 | # coding=utf-8
# Copyright 2020-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | null |
v0 | [
"int"
] | Any | def v0(self, v1: int):
self.create_optimizer()
self.create_scheduler(v1) | [] | [] | [] | 3 | # coding=utf-8
# Copyright 2020-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | null |
v0 | [
"Dict[str, float]",
"Any"
] | None | def v0(self, v1: Dict[str, float], v2=True) -> None:
if self.state.epoch is not None:
v1['epoch'] = round(self.state.epoch, 2)
v3 = {**v1, **{'step': self.state.global_step}}
self.state.log_history.append(v3)
v1['use_global_step'] = v2
self.control = self.callback_handler.on_log(self.args, s... | [] | [] | [] | 7 | import collections
import gc
import inspect
import math
from multiprocessing.spawn import import_main_path
import os
import re
import shutil
import sys
import time
import warnings
from logging import StreamHandler
from pathlib import Path
from typing import (
TYPE_CHECKING,
Any,
Callable,
Counter,
D... | null |
v0 | [
"Dict[str, Union[torch.Tensor, Any]]"
] | Dict[str, Union[torch.Tensor, Any]] | def v0(self, v1: Dict[str, Union[torch.Tensor, Any]]) -> Dict[str, Union[torch.Tensor, Any]]:
for (v2, v3) in v1.items():
if isinstance(v3, torch.Tensor):
v1[v2] = v3.to(self.args.device)
if self.args.past_index >= 0 and self._past is not None:
v1['mems'] = self._past
if self.ada... | [] | [
"torch"
] | [
"import torch",
"from torch import nn",
"from torch.utils.data.dataloader import DataLoader",
"from torch.utils.data.dataset import Dataset",
"from torch.utils.data.distributed import DistributedSampler",
"from torch.utils.data.sampler import RandomSampler, SequentialSampler"
] | 9 | # coding=utf-8
# Copyright 2020-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | null |
v0 | [
"Any",
"Any",
"Any"
] | List[str] | def v0(self, v1=None, v2=PREFIX_CHECKPOINT_DIR, v3=False) -> List[str]:
v4 = []
v5 = [str(x) for v6 in Path(v1).glob(f'{v2}-*') if os.path.isdir(v6)]
for v7 in v5:
if v3:
v4.append((os.path.getmtime(v7), v7))
else:
v8 = re.match(f'.*{v2}-([0-9]+)', v7)
if ... | [] | [
"os",
"pathlib",
"re"
] | [
"import os",
"import re",
"from pathlib import Path"
] | 17 | # coding=utf-8
# Copyright 2020-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | null |
v0 | [
"Dict[str, Union[torch.Tensor, Any]]"
] | Any | def v0(self, v1: Dict[str, Union[torch.Tensor, Any]]):
if hasattr(self.original_model, 'floating_point_ops'):
return self.original_model.floating_point_ops(v1)
else:
return 0 | [] | [] | [] | 5 | # copyright 2021 the huggingface team. all rights reserved.
#
# licensed under the apache license, version 2.0 (the "license");
# you may not use this file except in compliance with the license.
# you may obtain a copy of the license at
#
# http://www.apache.org/licenses/license-2.0
#
# unless required by app... | null |
v0 | [] | list | def v0(self) -> list:
v1 = self.ball.getField('translation')
v2 = v1.getSFVec3f()
if abs(v2[0]) < 4.5 and abs(v2[1]) < 3:
if self.previousBallLocation[0] + 0.05 < v2[0] or self.previousBallLocation[0] - 0.05 > v2[0] or self.previousBallLocation[1] + 0.05 < v2[1] or (self.previousBallLocation[1] - 0.... | [] | [] | [] | 8 | """
The Basic Supervisor class.
All Supervisor classes should be derived from this class.
"""
import os, sys
currentdir = os.path.dirname(os.path.realpath(__file__))
parentdir = os.path.dirname(currentdir)
sys.path.append(parentdir)
from controller import Supervisor
import struct
from Utils.Consts import (TIME_STEP)
... | null |
v0 | [
"Any"
] | None | def v0(self, v1) -> None:
self.previousBallLocation = v1
v2 = self.ball.getField('translation')
v2.setSFVec3f(v1)
self.ball.resetPhysics() | [] | [] | [] | 5 | """
The Basic Supervisor class.
All Supervisor classes should be derived from this class.
"""
import os, sys
currentdir = os.path.dirname(os.path.realpath(__file__))
parentdir = os.path.dirname(currentdir)
sys.path.append(parentdir)
from controller import Supervisor
import struct
from Utils.Consts import (TIME_STEP)
... | null |
v0 | [
"Any"
] | list | def v0(self, v1) -> list:
v2 = self.robots[v1].getField('translation')
return v2.getSFVec3f() | [] | [] | [] | 3 | """
The Basic Supervisor class.
All Supervisor classes should be derived from this class.
"""
import os, sys
currentdir = os.path.dirname(os.path.realpath(__file__))
parentdir = os.path.dirname(currentdir)
sys.path.append(parentdir)
from controller import Supervisor
import struct
from Utils.Consts import (TIME_STEP)
... | null |
v0 | [
"base.String",
"typing.Optional[typing.Union[base.InputFile, pathlib.Path]]",
"base.Integer",
"base.Integer",
"base.Boolean",
"typing.Optional[typing.Union[str, pathlib.Path]]",
"typing.Optional[base.Boolean]"
] | Any | async def v0(self, v1: base.String, v2: typing.Optional[typing.Union[base.InputFile, pathlib.Path]]=None, v3: base.Integer=30, v4: base.Integer=65536, v5: base.Boolean=True, v6: typing.Optional[typing.Union[str, pathlib.Path]]=None, v7: typing.Optional[base.Boolean]=True):
v8 = await self.get_file(v1)
return aw... | [] | [] | [] | 3 | from __future__ import annotations
import datetime
import pathlib
import typing
import warnings
from .base import BaseBot, api
from .. import types
from ..types import base
from ..utils.deprecated import deprecated
from ..utils.exceptions import ValidationError
from ..utils.mixins import DataMixin, ContextInstanceMix... | null |
v0 | [
"Any",
"bool"
] | Any | def v0(self, v1, v2: bool=False):
if v1 is NaT:
return
self._assert_tzawareness_compat(v1)
if v2:
if not timezones.tz_compare(self.tz, v1.tz):
raise ValueError(f"Timezones don't match. '{self.tz}' != '{v1.tz}'") | [] | [
"pandas"
] | [
"from pandas._libs import lib, tslib",
"from pandas._libs.tslibs import BaseOffset, NaT, NaTType, Resolution, Timestamp, conversion, fields, get_resolution, iNaT, ints_to_pydatetime, is_date_array_normalized, normalize_i8_timestamps, timezones, to_offset, tzconversion",
"from pandas.errors import PerformanceWar... | 7 | from datetime import datetime, time, timedelta, tzinfo
from typing import Optional, Union, cast
import warnings
import numpy as np
from pandas._libs import lib, tslib
from pandas._libs.tslibs import (
BaseOffset,
NaT,
NaTType,
Resolution,
Timestamp,
conversion,
fields,
get_resolution,
... | null |
v0 | [
"int",
"str",
"str",
"str",
"str",
"str",
"str",
"str"
] | Any | def v0(self, v1: int=None, v2: str=None, v3: str=None, v4: str=None, v5: str=None, v6: str=None, v7: str=None, v8: str=None):
if v2:
self.name = v2
if v3:
self.category = v3
if v4:
self.type = v4
if v5:
self.version = v5
if v6:
self.ip = v6
if v7:
... | [] | [] | [] | 15 | """
Database model device.
"""
from src.models import db
class Device(db.Model):
"""
Device class
"""
class Category(object):
SIMULATOR = 'simulator'
HARDWARE = 'hardware'
class Status(object):
INACTIVE = 'inactive'
AVAILABLE = 'available'
USED = 'used'
... | null |
v0 | [
"str",
"int"
] | None | def v0(self, v1: str, v2: int=1) -> None:
super().count(v1, count=v2)
self.client.incr(v1, count=v2, rate=self.rate) | [] | [] | [] | 3 | """Monitor using Statsd."""
import re
import typing
from typing import Any, Dict, Optional, Pattern, cast
from mode.utils.objects import cached_property
from faust.exceptions import ImproperlyConfigured
from faust.types import (
AppT,
CollectionT,
EventT,
Message,
PendingMessage,
RecordMetada... | null |
v0 | [
"bool",
"callable",
"callable",
"config.Config",
"str",
"Optional[str]"
] | Tuple[tf.keras.models.Model, np.ndarray] | def v0(v1: bool, v2: callable, v3: callable, v4: config.Config, v5: str, v6: Optional[str]=None) -> Tuple[tf.keras.models.Model, np.ndarray]:
v7 = (v4.parameters.ssd_image_size, v4.parameters.ssd_image_size, 3)
v8 = [0.07, 0.15, 0.33, 0.51, 0.69, 0.87, 1.05]
if v1:
(v9, v10) = v2(image_size=v7, n_cl... | [] | [] | [] | 15 | # -*- coding: utf-8 -*-
#
# Copyright 2019 Jim Martens
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | null |
v0 | [
"callable",
"int",
"callable",
"int",
"tf.keras.models.Model",
"str",
"int",
"int",
"int",
"Optional[tf.keras.callbacks.TensorBoard]"
] | tf.keras.callbacks.History | def v0(v1: callable, v2: int, v3: callable, v4: int, v5: tf.keras.models.Model, v6: str, v7: int, v8: int, v9: int, v10: Optional[tf.keras.callbacks.TensorBoard]) -> tf.keras.callbacks.History:
v11 = os.path.join(v6, str(v7))
os.makedirs(v11, exist_ok=True)
v12 = [tf.keras.callbacks.ModelCheckpoint(filepath... | [] | [
"os",
"tensorflow"
] | [
"import os",
"import tensorflow as tf"
] | 10 | # -*- coding: utf-8 -*-
#
# Copyright 2019 Jim Martens
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | null |
v0 | [
"str",
"bool"
] | Tuple[str, str] | def v0(v1: str, v2: bool) -> Tuple[str, str]:
v3 = 'ssd_predictions'
v4 = 'ssd_labels'
if v2:
v3 = f'dropout-{v3}'
v5 = os.path.join(v1, v3)
v6 = os.path.join(v1, v4)
return (v5, v6) | [] | [
"os"
] | [
"import os"
] | 8 | # -*- coding: utf-8 -*-
#
# Copyright 2019 Jim Martens
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | null |
v0 | [
"np.ndarray",
"tf.keras.models.Model",
"int",
"int"
] | np.ndarray | def v0(v1: np.ndarray, v2: tf.keras.models.Model, v3: int, v4: int) -> np.ndarray:
v5 = np.zeros((v3, 8732 * v4, 73))
for v6 in range(v4):
v7 = v2.predict_on_batch(v1)
for v8 in range(v3):
v5[v8][v6 * 8732:v6 * 8732 + 8732] = v7[v8]
return v5 | [] | [
"numpy"
] | [
"import numpy as np"
] | 7 | # -*- coding: utf-8 -*-
#
# Copyright 2019 Jim Martens
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | null |
v0 | [
"Sequence[np.ndarray]",
"float"
] | List[np.ndarray] | def v0(v1: Sequence[np.ndarray], v2: float) -> List[np.ndarray]:
v3 = []
v4 = len(v1)
v5 = np.dtype([('class_id', np.int32), ('confidence', 'f4'), ('xmin', 'f4'), ('ymin', 'f4'), ('xmax', 'f4'), ('ymax', 'f4')])
for v6 in range(v4):
v7 = v1[v6]
if not v7.size:
v3.append(v7)
... | [] | [
"numpy"
] | [
"import numpy as np"
] | 18 | # -*- coding: utf-8 -*-
#
# Copyright 2019 Jim Martens
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | null |
v0 | [
"Union[np.ndarray, Sequence[np.ndarray]]",
"np.ndarray",
"Sequence[str]",
"str",
"str",
"int",
"int",
"str",
"Optional[bool]",
"Optional[float]"
] | None | def v0(v1: Union[np.ndarray, Sequence[np.ndarray]], v2: np.ndarray, v3: Sequence[str], v4: str, v5: str, v6: int, v7: int, v8: str, v9: Optional[bool]=True, v10: Optional[float]=0) -> None:
v11 = str(v6).zfill(v7)
v12 = f'{v4}{v8}-{v11}'
v12 = f'{v12}-{v10}' if v10 else v12
v13 = f'{v5}-{v11}.bin'
w... | [] | [
"pickle"
] | [
"import pickle"
] | 9 | # -*- coding: utf-8 -*-
#
# Copyright 2019 Jim Martens
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | null |
v0 | [
"np.ndarray",
"np.ndarray"
] | np.ndarray | def v0(v1: np.ndarray, v2: np.ndarray) -> np.ndarray:
v3 = v2.max(axis=0)
v4 = v1.max(axis=0)
v5 = np.maximum(v3, v4) + 1
v6 = np.ravel_multi_index(v2.T, v5)
v7 = np.ravel_multi_index(v1.T, v5)
v8 = np.in1d(v6, v7)
return v2[~v8] | [] | [
"numpy"
] | [
"import numpy as np"
] | 8 | # -*- coding: utf-8 -*-
#
# Copyright 2019 Jim Martens
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | null |
v0 | [
"str",
"Any",
"Any"
] | Any | def v0(self, v1: str='', v2=None, v3=True):
v4 = self.model.objects.filter(**{v1: v2}).first()
if not v4:
return {}
return self.serialize(v4) if v3 else v4 | [] | [] | [] | 5 | from abc import ABC
from contextlib import suppress
from copy import deepcopy
from django.db import DataError
from django.db.models import Max
from django.db.models.fields.related import ManyToManyField
from rest_framework.exceptions import ValidationError
# Create your services here.
class StorageService(ABC):
... | null |
v0 | [
"str",
"Any"
] | Any | def v0(self, v1: str='', v2=None):
v3 = self.model.objects.filter(**{v1: v2}).first()
return v3.delete() | [] | [] | [] | 3 | from abc import ABC
from contextlib import suppress
from copy import deepcopy
from django.db import DataError
from django.db.models import Max
from django.db.models.fields.related import ManyToManyField
from rest_framework.exceptions import ValidationError
# Create your services here.
class StorageService(ABC):
... | null |
v0 | [
"Any",
"Any",
"bool"
] | Any | def v0(self, v1, v2=None, v3: bool=True):
v4 = []
if not self.unique_identifier or not v1:
return
for v5 in v1:
if isinstance(v2, dict):
v5.update(v2)
(v5, v6) = self.upsert(data=v5, many_to_many_clear=v3)
v4.append(v5)
return v4 | [] | [] | [] | 10 | from abc import ABC
from contextlib import suppress
from copy import deepcopy
from django.db import DataError
from django.db.models import Max
from django.db.models.fields.related import ManyToManyField
from rest_framework.exceptions import ValidationError
# Create your services here.
class StorageService(ABC):
... | null |
v0 | [
"Any"
] | bool | def v0(self, v1) -> bool:
if not self.unique_identifier or not v1:
return False
for v2 in v1:
if isinstance(v2, self.model):
v2.delete()
return True | [] | [] | [] | 7 | from abc import ABC
from contextlib import suppress
from copy import deepcopy
from django.db import DataError
from django.db.models import Max
from django.db.models.fields.related import ManyToManyField
from rest_framework.exceptions import ValidationError
# Create your services here.
class StorageService(ABC):
... | null |
v0 | [
"list",
"tuple",
"Any",
"dict"
] | Any | def v0(self, v1: list, v2: tuple, v3=None, v4: dict=None):
v5 = []
if not isinstance(v1, list):
return v5
if not v4:
v4 = {}
try:
for v6 in v1:
v5.append(self.escape_serialized_object_id(v6, v2, v3, v4))
except Exception as e:
pass
return v5 | [] | [] | [] | 12 | from abc import ABC
from contextlib import suppress
from copy import deepcopy
from django.db import DataError
from django.db.models import Max
from django.db.models.fields.related import ManyToManyField
from rest_framework.exceptions import ValidationError
# Create your services here.
class StorageService(ABC):
... | null |
v0 | [
"Any",
"int"
] | int | def v0(v1, v2: int) -> int:
for v3 in range(v2):
(v4, v5) = (input().split()[0], set(map(int, input().split())))
getattr(v1, v4)(v5)
return sum(v1) | [] | [] | [] | 5 | def main(atrr, times: int) -> int:
for _ in range(times):
(command, new__set) = (input().split()[0], set(map(int, input().split())))
getattr(atrr, command)(new__set)
return sum(atrr)
if __name__ == "__main__":
(_, a) = (int(input()), set(map(int, input().split())))
print(main(a, int(... | null |
v0 | [
"int"
] | Any | def v0(self, v1: int):
if not self.risk_history_map:
self.risk_history_map[v1] = self.baseline_risk
elif v1 not in self.risk_history_map:
assert v1 - 1 in self.risk_history_map, 'humans should never skip a day worth of risk refresh'
self.risk_history_map[v1] = self.risk_history_map[v1 - ... | [] | [
"logging"
] | [
"import logging"
] | 20 | """
This module contains the logic and attributes of the `Human` agent in our simulator.
"""
import math
import datetime
import logging
import numpy as np
import typing
import warnings
from collections import defaultdict
from orderedset import OrderedSet
from covid19sim.utils.mobility_planner import MobilityPlanner
f... | null |
v0 | [
"int",
"typing.List[float]"
] | Any | def v0(self, v1: int, v2: typing.List[float]):
assert self.conf.get('RISK_MODEL') == 'transformer'
assert len(v2) == self.contact_book.tracing_n_days_history, "unexpected transformer history coverage; what's going on?"
for v3 in range(len(v2)):
self.risk_history_map[v1 - v3] = v2[v3] | [] | [] | [] | 5 | """
This module contains the logic and attributes of the `Human` agent in our simulator.
"""
import math
import datetime
import logging
import numpy as np
import typing
import warnings
from collections import defaultdict
from orderedset import OrderedSet
from covid19sim.utils.mobility_planner import MobilityPlanner
f... | null |
v0 | [
"Any"
] | Dict[str, str] | def v0(self, v1) -> Dict[str, str]:
v2 = self.type_map.get(v1[1], 'STRING')
v3 = 'NULLABLE' if not v1[6] or v2 == 'TIMESTAMP' else 'REQUIRED'
return {'name': v1[0], 'type': v2, 'mode': v3} | [] | [] | [] | 4 | #
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not... | null |
v0 | [] | dict | def v0() -> dict:
v1 = argparse.ArgumentParser()
v1.add_argument('-f', '--file', help='set ncfile.', action='append', type=str)
v2 = v1.parse_args()
v3 = {'file': v2.file}
return v3 | [] | [
"argparse"
] | [
"import argparse"
] | 6 | # coding: utf-8
"""
Name: ptl_vrt.py
potential vorticity on the isentropic.
Usage: python3 ptl_vrt.py --file <ncfile> <ncfile2>
Author: Ryosuke Tomita
Date: 2022/2/5
"""
import argparse
import numpy as np
from metpy.units import units
from ncmagics import fetchtime, japanmap_nh, meteotool_nh
def parse_args() -> di... | null |
v0 | [
"np.ndarray",
"xr.DataArray",
"xr.DataArray"
] | xr.DataArray | def v0(v1: np.ndarray, v2: xr.DataArray, v3: xr.DataArray) -> xr.DataArray:
v4 = xr.DataArray(v1, dims=('latitude', 'longitude'), coords={'latitude': v2, 'longitude': v3})
return v4 | [] | [
"xarray"
] | [
"import xarray as xr"
] | 3 | # coding: utf-8
"""
Name: grad_t.py
Calcurate equivalent_potential_temperature.
example: python3 grad_pt.py --file <ncfile>
Author: Ryosuke Tomita
Date: 2021/10/22
"""
import argparse
import xarray as xr
import numpy as np
from ncmagics import fetchtime, japanmap, meteotool
def parse_args() -> dict:
"""parse_a... | null |
v0 | [] | str | def v0(self) -> str:
v1 = self.re.search('/.+?chapter-(\\d+(?:\\.\\d+)?)', self.chapter)
if not v1:
v1 = self.re.search('/.+?Ch(\\d+(?:\\.\\d+)?)', self.chapter)
if not v1:
v1 = self.re.search('/c(\\d+(?:\\.\\d+)?)', self.chapter)
if not v1:
v1 = self.re.search('/(\\d+(?:\\.\\d+)... | [] | [] | [] | 9 | from .mangago_me import MangaGoMe
class RocacaCom(MangaGoMe):
def get_chapter_index(self) -> str:
re = self.re.search(r'/.+?chapter-(\d+(?:\.\d+)?)', self.chapter)
if not re:
re = self.re.search(r'/.+?Ch(\d+(?:\.\d+)?)', self.chapter)
if not re:
re = self.re.search... | null |
v0 | [
"List[str]",
"List[str]"
] | Any | def v0(v1: List[str], v2: List[str]):
v3 = [rule.split(' -> ')[1] for v4 in v1]
assert set(v3) == set(v2) | [] | [] | [] | 3 | from typing import Callable, List
import pytest
from .. import SemparseTestCase
from allennlp_semparse.common import ExecutionError, ParsingError
from allennlp_semparse import DomainLanguage, predicate, predicate_with_side_args
class Arithmetic(DomainLanguage):
def __init__(
self, allow_function_curryi... | null |
v0 | [
"str"
] | Any | def v0(v1: str):
with open(v1) as v2:
v3 = v2.read()
v3 = re.sub('\\D', '', v3)
v4 = list(map(int, v3))
if len(v4) != 81:
raise ValueError(f'Incorrect input format for textfile {v1}')
return [v4[i:i + 9] for v5 in range(0, len(v4), 9)] | [] | [
"re"
] | [
"import re"
] | 8 | import re
def parser(file: str):
""" Parser to take .txt file and turn into Sudoku list.
Parameters:
file (str): Path to .txt file
Returns:
(list): Nested list containing Sudoku
Input Sample:
Input needs to be text file containing 81 numbers in order. Everything except ... | null |
v0 | [
"dict"
] | bool | def v0(self, v1: dict) -> bool:
try:
self.validate(v1)
self.logger.info(self.json_dump_pretty(v1))
except ValueError:
self.logger.error('Cannot send node data message')
self.logger.error(self.json_dump_pretty(v1))
return False
return True | [] | [] | [] | 9 | # pylint: disable=duplicate-code
"""
Node data message messages
==========================
Node data message related metadata connection messages
.. Copyright:
Copyright Wirepas Ltd 2019 licensed under Apache License, Version 2.0
See file LICENSE for full license details.
"""
from .aut... | null |
v0 | [
"str"
] | Dict[str, str] | def v0(v1: str) -> Dict[str, str]:
v2 = {}
with open(v1, encoding='utf-8') as v3:
for v4 in v3:
v5 = v4.split('|')
(v6, v7) = (v5[0], v5[-1])
v2[v6] = v7.strip()
return v2 | [] | [] | [] | 8 | import pickle
from pathlib import Path
from typing import Dict, List, Any, Union
import torch
import yaml
def read_metafile(path: str) -> Dict[str, str]:
text_dict = {}
with open(path, encoding='utf-8') as f:
for line in f:
split = line.split('|')
text_id, text = split[0], spl... | null |
v0 | [
"object",
"Union[str, Path]"
] | None | def v0(v1: object, v2: Union[str, Path]) -> None:
with open(str(v2), 'wb') as v3:
pickle.dump(v1, v3) | [] | [
"pickle"
] | [
"import pickle"
] | 3 | import pickle
from pathlib import Path
from typing import Dict, List, Any, Union
import torch
import yaml
def read_metafile(path: str) -> Dict[str, str]:
text_dict = {}
with open(path, encoding='utf-8') as f:
for line in f:
split = line.split('|')
text_id, text = split[0], spl... | null |
v0 | [
"Union[str, Path]"
] | Any | def v0(v1: Union[str, Path]) -> Any:
with open(v1, 'rb') as v2:
v3 = pickle.load(v2)
return v3 | [] | [
"pickle"
] | [
"import pickle"
] | 4 | from typing import Any, Union, Dict, Mapping
from pathlib import Path
from ruamel.yaml import YAML
import pickle
import h5py
import numpy as np
from numbers import Number
PathLike = Union[str, Path]
yaml = YAML(typ='safe')
def read_yaml(fname: Union[str, Path]) -> Any:
"""Read the given file using YAML.
P... | null |
v0 | [
"dict",
"torch.device"
] | tuple | def v0(v1: dict, v2: torch.device) -> tuple:
(v3, v4, v5, v6) = (v1['tokens'], v1['mel'], v1['tokens_len'], v1['mel_len'])
(v3, v4, v5, v6) = (v3.to(v2), v4.to(v2), v5.to(v2), v6.to(v2))
return (v3, v4, v5, v6) | [] | [] | [] | 4 | import pickle
from pathlib import Path
from typing import Dict, List, Any, Union
import torch
import yaml
def read_metafile(path: str) -> Dict[str, str]:
text_dict = {}
with open(path, encoding='utf-8') as f:
for line in f:
split = line.split('|')
text_id, text = split[0], spl... | null |
v5 | [] | v0 | def v5() -> v0:
v6 = [v1(partition=0, offset=0, ts=2), v1(partition=1, offset=0, ts=1), v1(partition=2, offset=0, ts=4), v1(partition=1, offset=1, ts=3), v1(partition=1, offset=2, ts=5), v1(partition=2, offset=1, ts=7), v1(partition=0, offset=1, ts=6)]
v7 = sorted(v6, key=attrgetter('timestamp'))
return (3,... | [
{
"name": "v1",
"input_types": [
"int",
"int",
"int"
],
"output_type": "BinaryMessage",
"code": "def v1(v2: int, v3: int, v4: int) -> BinaryMessage:\n return BinaryMessage(key=f'k_p{v2}_o{v3}'.encode('utf-8'), value=f'v_p{v2}_o{v3}'.encode('utf-8'), partition=v2, offset=v3, ... | [
"datetime",
"operator"
] | [
"import datetime",
"from operator import attrgetter"
] | 4 | import datetime
from operator import attrgetter
from typing import Iterable, Tuple
from esque.io.messages import BinaryMessage
from esque.io.stream_events import TemporaryEndOfPartition
# partition, input, expected_output
SortCase = Tuple[int, Iterable[BinaryMessage], Iterable[BinaryMessage]]
def mk_binary_message(... | [
"v0 = Tuple[int, Iterable[BinaryMessage], Iterable[BinaryMessage]]"
] |
v1 | [
"str",
"Type[v0]"
] | Optional[v0] | def v1(self, v2: str, v3: Type[v0]) -> Optional[v0]:
v4: Optional[v0] = self.properties.get(v2)
if v4 is not None:
return v4
if self.additional_read_properties is not None:
for v5 in self.additional_read_properties:
v6: Optional[v0] = v5.get(v2)
if v6 is not None:
... | [] | [] | [] | 10 | from abc import ABC, abstractmethod
from typing import Generic, Iterable, List, Optional, Dict, Any, Type, TypeVar, Union
import pystac
class SummariesExtension:
"""Base class for extending the properties in :attr:`pystac.Collection.summaries`
to include properties defined by a STAC Extension.
This clas... | [
"v0 = TypeVar('P')"
] |
v0 | [
"str",
"Optional[Any]",
"bool"
] | None | def v0(self, v1: str, v2: Optional[Any], v3: bool=True) -> None:
if v2 is None and v3:
self.properties.pop(v1, None)
else:
self.properties[v1] = v2 | [] | [] | [] | 5 | from abc import ABC, abstractmethod
from typing import Generic, Iterable, List, Optional, Dict, Any, Type, TypeVar, Union
from pystac import Collection, RangeSummary, STACObject, Summaries
class SummariesExtension:
"""Base class for extending the properties in :attr:`pystac.Collection.summaries`
to include p... | null |
v0 | [
"Any"
] | Dict[str, Any] | def v0(v1) -> Dict[str, Any]:
v2 = {f.name: getattr(v1, f.name) for v3 in dataclasses.fields(v1)}
return v2 | [] | [
"dataclasses"
] | [
"import dataclasses"
] | 3 | """Types and helper methods for transitions and trajectories."""
import dataclasses
import logging
import os
import pathlib
import pickle
import warnings
from typing import (
Any,
Dict,
Mapping,
Optional,
Sequence,
Tuple,
TypeVar,
Union,
overload,
)
import numpy as np
import torch ... | null |
v1 | [
"v0"
] | str | def v1(v2: v0) -> str:
if isinstance(v2, bytes):
return v2.decode()
else:
return str(v2) | [] | [] | [] | 5 | """Types and helper methods for transitions and trajectories."""
import dataclasses
import logging
import os
import pathlib
import pickle
import warnings
from typing import (
Any,
Dict,
Mapping,
Optional,
Sequence,
Tuple,
TypeVar,
Union,
overload,
)
import numpy as np
import torch ... | [
"v0 = Union[str, bytes, os.PathLike]"
] |
v0 | [
"np.ndarray",
"np.ndarray"
] | Any | def v0(v1: np.ndarray, v2: np.ndarray):
if v1.shape != (len(v2),):
raise ValueError(f'rewards must be 1D array, one entry for each action: {v1.shape} != ({len(v2)},)')
if not np.issubdtype(v1.dtype, np.floating):
raise ValueError(f'rewards dtype {v1.dtype} not a float') | [] | [
"numpy"
] | [
"import numpy as np"
] | 5 | """Types and helper methods for transitions and trajectories."""
import dataclasses
import logging
import os
import pathlib
import pickle
import warnings
from typing import (
Any,
Dict,
Mapping,
Optional,
Sequence,
Tuple,
TypeVar,
Union,
overload,
)
import numpy as np
import torch ... | null |
v0 | [
"Sequence[Mapping[str, np.ndarray]]"
] | Mapping[str, Union[np.ndarray, th.Tensor]] | def v0(v1: Sequence[Mapping[str, np.ndarray]]) -> Mapping[str, Union[np.ndarray, th.Tensor]]:
v2 = [{k: np.array(v) for (v3, v4) in sample.items() if v3 != 'infos'} for v5 in v1]
v6 = th_data.dataloader.default_collate(v2)
assert isinstance(v6, dict)
v6['infos'] = [v5['infos'] for v5 in v1]
return v... | [] | [
"numpy",
"torch"
] | [
"import numpy as np",
"import torch as th",
"from torch.utils import data as th_data"
] | 6 | """Types and helper methods for transitions and trajectories."""
import dataclasses
import logging
import os
import pathlib
import pickle
import warnings
from typing import (
Any,
Dict,
Mapping,
Optional,
Sequence,
Tuple,
TypeVar,
Union,
overload,
)
import numpy as np
import torch ... | null |
v4 | [
"v0"
] | Sequence[v1] | def v4(v5: v0) -> Sequence[v1]:
with open(v5, 'rb') as v6:
return pickle.load(v6) | [] | [
"pickle"
] | [
"import pickle"
] | 3 | """Types and helper methods for transitions and trajectories."""
import dataclasses
import logging
import os
import pathlib
import pickle
import warnings
from typing import (
Any,
Dict,
Mapping,
Optional,
Sequence,
Tuple,
TypeVar,
Union,
overload,
)
import numpy as np
import torch ... | [
"v0 = Union[str, bytes, os.PathLike]",
"@dataclasses.dataclass(frozen=True)\nclass v1(Trajectory):\n v2: np.ndarray\n 'Reward, shape (trajectory_len, ). dtype float.'\n\n def v3(self):\n \"\"\"Performs input validation, including for rews.\"\"\"\n super().__post_init__()\n _rews_vali... |
v4 | [
"v0",
"Sequence[v1]"
] | None | def v4(v5: v0, v6: Sequence[v1]) -> None:
v7 = pathlib.Path(v5)
v7.parent.mkdir(parents=True, exist_ok=True)
v8 = f'{v5}.tmp'
with open(v8, 'wb') as v9:
pickle.dump(v6, v9)
os.replace(v8, v5)
logging.info(f'Dumped demonstrations to {v5}.') | [] | [
"logging",
"os",
"pathlib",
"pickle"
] | [
"import logging",
"import os",
"import pathlib",
"import pickle"
] | 8 | """Types and helper methods for transitions and trajectories."""
import dataclasses
import logging
import os
import pathlib
import pickle
import warnings
from typing import (
Any,
Dict,
Mapping,
Optional,
Sequence,
Tuple,
TypeVar,
Union,
overload,
)
import numpy as np
import torch ... | [
"v0 = Union[str, bytes, os.PathLike]",
"@dataclasses.dataclass(frozen=True)\nclass v1(Trajectory):\n v2: np.ndarray\n 'Reward, shape (trajectory_len, ). dtype float.'\n\n def v3(self):\n \"\"\"Performs input validation, including for rews.\"\"\"\n super().__post_init__()\n _rews_vali... |
v0 | [
"int"
] | Tuple[int, int] | def v0(self, v1: int) -> Tuple[int, int]:
v2 = 0
v3 = 0
for v4 in self.tree.children(v1):
v2 += v4.data['allocation'].n_sub_elements
v3 += v4.data['allocation'].n_sub_systems
return (v2, v3) | [] | [] | [] | 7 | # -*- coding: utf-8 -*-
#
# ramstk.models.dbtables.programdb_allocation_table.py is part of The RAMSTK
# Project
#
# All rights reserved.
# Copyright since 2007 Doyle "weibullguy" Rowland doyle.rowland <AT> reliaqual <DOT> com
"""RAMSTKAllocation Table Model."""
# Standard Library Imports
from datetime imp... | null |
v0 | [
"int"
] | int | def v0(self, v1: int) -> int:
v2 = 0
for v3 in self.tree.children(v1):
v4 = v3.data['allocation'].get_attributes()
v4['weight_factor'] = v4['int_factor'] * v4['soa_factor'] * v4['op_time_factor'] * v4['env_factor']
v2 += v4['weight_factor']
self.do_set_attributes(node_id=[v3.iden... | [] | [] | [] | 8 | # pylint: disable=cyclic-import
# -*- coding: utf-8 -*-
#
# ramstk.models.allocation.table.py is part of The RAMSTK Project
#
# All rights reserved.
# Copyright since 2007 Doyle "weibullguy" Rowland doyle.rowland <AT> reliaqual <DOT> com
"""Allocation Package Table Model."""
# Standard Library Imports
from typin... | null |
v0 | [
"namedtuple"
] | str | def v0(v1: namedtuple) -> str:
v2 = v1.name
v3 = f'{v2}'
return v3 | [] | [] | [] | 4 | import sys, os
import logging
import numpy as np
import numpy.ma as ma
import matplotlib.pyplot as plt
from typing import Tuple, Optional
from collections import namedtuple
from matplotlib.patches import Patch
from model_evaluation.statistics.statistical_methods import DayStatistics
from model_evaluation.plotting.plot_... | null |
v0 | [] | None | def v0(self) -> None:
self.register_intent_handler(intent_name='Default Fallback Intent', handler=self.handle_default_fallback)
self.register_intent_handler(intent_name='Default Exit Intent', handler=self.handle_default_exit)
self.register_intent_handler(intent_name='i.my_handled_intent', handler=self.refor... | [] | [] | [] | 4 | # Copyright 2021 ONDEWO GmbH
#
# Licensed under the Apache License, Version 2.0 (the License);
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, so... | null |
v0 | [
"ty.int32"
] | None | def v0(v1: ty.int32) -> None:
if v1 > 0:
print('I love tvm') | [] | [] | [] | 3 | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | null |
v0 | [
"str",
"str"
] | Any | def v0(self, v1: str, v2: str=''):
v3 = requests.post(self._server_url, data={'text': v1, 'desp': v2})
v3.raise_for_status() | [] | [
"requests"
] | [
"import requests",
"from requests import HTTPError"
] | 3 | # coding: utf-8
import requests
import json
import logging
import yagmail
from requests import HTTPError
CONFIG_FILE_NAME = "config.json"
class Config:
def __init__(self, config_file: str):
with open(config_file, "r") as f:
data = f.read()
self._config = json.loads(data)
for... | null |
v0 | [
"ArgumentParser"
] | Namespace | def v0(v1: ArgumentParser) -> Namespace:
v2 = Namespace()
for v3 in v1._actions:
if v3.dest is not argparse.SUPPRESS:
if not hasattr(v2, v3.dest):
if v3.default is not argparse.SUPPRESS:
setattr(v2, v3.dest, v3.default)
for v4 in v1._defaults:
... | [] | [
"argparse"
] | [
"import argparse",
"from argparse import ArgumentError, ArgumentParser, Namespace"
] | 11 | # ------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
# -------------------------------------------------------------------... | null |
v0 | [
"str"
] | Optional[int] | def v0(v1: str) -> Optional[int]:
v1 = v1.strip()
if not v1:
return None
v2 = v1[-1]
if v2 == 's':
v3 = 1
elif v2 == 'm':
v3 = 60
elif v2 == 'h':
v3 = 60 * 60
elif v2 == 'd':
v3 = 24 * 60 * 60
else:
raise ArgumentError('s', f"Invalid suffix... | [] | [
"argparse"
] | [
"import argparse",
"from argparse import ArgumentError, ArgumentParser, Namespace"
] | 16 | # ------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
# -------------------------------------------------------------------... | null |
v0 | [
"str"
] | bool | def v0(self, v1: str) -> bool:
v2 = self._get_filesystem(v1)
return v2.exists(v1) | [] | [] | [] | 3 | import os
import typing
import fsspec
from fsspec.core import split_protocol
from fsspec.registry import known_implementations
from flytekit.configuration import aws as _aws_config
from flytekit.extend import DataPersistence, DataPersistencePlugins
from flytekit.loggers import logger
def s3_setup_args():
kwargs... | null |
v0 | [
"str",
"str",
"bool"
] | Any | def v0(self, v1: str, v2: str, v3: bool=False):
v4 = self._get_filesystem(v1)
if v3:
(v1, v2) = self.recursive_paths(v1, v2)
return v4.get(v1, v2, recursive=v3) | [] | [] | [] | 5 | import os
import typing
import fsspec
from fsspec.core import split_protocol
from fsspec.registry import known_implementations
from flytekit.configuration import aws as _aws_config
from flytekit.extend import DataPersistence, DataPersistencePlugins
from flytekit.loggers import logger
def s3_setup_args():
kwargs... | null |
v0 | [
"bool",
"bool"
] | str | def v0(self, v1: bool, v2: bool, *v3) -> str:
v3 = list(v3)
if v2:
v3 = v3.insert(0, self.default_prefix)
v4 = f"{'/'.join(v3)}"
if v1:
return f'{self.default_protocol}://{v4}'
return v4 | [] | [] | [] | 8 | import os
import typing
import fsspec
from fsspec.core import split_protocol
from fsspec.registry import known_implementations
from flytekit.configuration import aws as _aws_config
from flytekit.extend import DataPersistence, DataPersistencePlugins
from flytekit.loggers import logger
def s3_setup_args():
kwargs... | null |
v0 | [
"str"
] | Optional[int] | def v0(self, v1: str) -> Optional[int]:
for v2 in range(self.size - 1, -1, -1):
if self.frames[v2].function == v1:
return v2
return None | [] | [] | [] | 5 | # Copyright 2021 Zilliz. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agree... | null |
v0 | [
"int",
"int",
"List[str]"
] | str | def v0(self, v1: int=None, v2: int=None, v3: List[str]=None) -> str:
v1 = v1 or 0
v2 = v2 or self.size
if v2 > self.size or v2 <= 0 or v1 >= self.size or (v1 < 0):
raise IndexError(f'index range [{v1}, {v2}) out of frame range[0, {self.size})')
if v1 >= v2:
raise IndexError(f'end = {v2} ... | [] | [
"hashlib"
] | [
"import hashlib"
] | 20 | # Copyright 2021 Zilliz. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agree... | null |
v0 | [
"str",
"List[str]"
] | Any | def v0(self, v1: str, v2: List[str]):
if 'data_source' in v1 or 'exog_ts' == v1 or 'custom' in v1:
return [v1]
v3 = v1 in self.models
v4 = self.models if v3 else self.data_operations
v5 = set.intersection(set(v4), set(v2))
if 'lagged' in v1:
v5 = set.intersection({'lagged', 'sparse_l... | [] | [] | [] | 11 | from typing import List, Optional
from fedot.core.repository.operation_types_repository import get_operations_for_task
from fedot.core.utilities.data_structures import ComparableEnum as Enum
class RemoveType(Enum):
node_only = 'node_only'
with_direct_children = 'with_direct_children'
with_parents = 'with... | null |
v0 | [
"str",
"Optional[List[str]]",
"List[str]"
] | Any | def v0(self, v1: str, v2: Optional[List[str]], v3: List[str]):
v4 = set.intersection(set(self.data_operations), set(v3))
if v1 in v4:
v4.remove(v1)
if v2:
for v5 in v2:
if v5 in v4:
v4.remove(v5)
return list(v4) | [] | [] | [] | 9 | from typing import List, Optional
from fedot.core.repository.operation_types_repository import get_operations_for_task
from fedot.core.utilities.data_structures import ComparableEnum as Enum
class RemoveType(Enum):
node_only = 'node_only'
with_direct_children = 'with_direct_children'
with_parents = 'with... | null |
v0 | [
"Tensor"
] | Tensor | def v0(v1: Tensor) -> Tensor:
v2 = v1.shape[1]
return torch.arange(0.0, v2 * 5.0).reshape(5, v2) | [] | [
"torch"
] | [
"import torch",
"from torch import Tensor",
"from torch.nn import Module"
] | 3 | #!/usr/bin/env python3
from typing import Callable, Tuple, Union
import torch
from torch import Tensor
from torch.nn import Module
from captum.attr._core.neuron.neuron_gradient_shap import NeuronGradientShap
from captum.attr._core.neuron.neuron_integrated_gradients import (
NeuronIntegratedGradients,
)
from ...h... | null |
v0 | [
"Any",
"Any",
"Any"
] | bool | def v0(v1, v2, v3=None) -> bool:
if v3 is None:
raise ValueError("'own' cannot be used as a global permission.")
return v1(v2, v3) | [] | [] | [] | 4 | from functools import wraps
from typing import Optional
import warnings
from django.utils.module_loading import import_string
from csv_permissions.evaluators import make_evaluate_not_implemented
from csv_permissions.evaluators import resolve_all_evaluator
from csv_permissions.evaluators import resolve_empty_evaluator... | null |
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