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secretlycarl/onboarderr
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/secretlycarl_onboarderr/app.py
app.Config
import os from dotenv import load_dotenv, set_key import re import requests class Config: """Centralized configuration management with validation""" def __init__(self): self._load_environment() self._validate_config() def _load_environment(self): """Load environment variables once...
class Config: '''Centralized configuration management with validation''' def __init__(self): pass def _load_environment(self): '''Load environment variables once at startup''' pass def _validate_config(self): '''Validate critical configuration values''' pass ...
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secretlycarl/onboarderr
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/secretlycarl_onboarderr/app.py
app.DownloadProgress
import time class DownloadProgress: """Class for tracking download progress with better error handling""" def __init__(self, library_name='', total=0): self.current = 0 self.total = total self.successful = 0 self.failed = 0 self.library_name = library_name self....
class DownloadProgress: '''Class for tracking download progress with better error handling''' def __init__(self, library_name='', total=0): pass def start(self): '''Start the download process''' pass def complete(self, successful=True): '''Complete the download proces...
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secretlycarl/onboarderr
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/secretlycarl_onboarderr/app.py
app.DownloadStatus
class DownloadStatus: """Enum-like class for download status tracking""" IDLE = 'idle' QUEUED = 'queued' IN_PROGRESS = 'in_progress' COMPLETED = 'completed' FAILED = 'failed' CANCELLED = 'cancelled'
class DownloadStatus: '''Enum-like class for download status tracking''' pass
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secretlycarl/onboarderr
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/secretlycarl_onboarderr/app.py
app.RateLimitManager
from threading import Lock from collections import defaultdict, OrderedDict class RateLimitManager: """Centralized rate limiting state management""" def __init__(self): self.failed_attempts = defaultdict(list) self.lockout_end_times = defaultdict(float) self.form_submissions = defaultd...
class RateLimitManager: '''Centralized rate limiting state management''' def __init__(self): pass def cleanup_expired_data(self, current_time): '''Clean up expired rate limit data''' pass
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WecoAI/weco-cli
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/WecoAI_weco-cli/examples/spaceship-titanic/evaluate.py
evaluate.IncorrectSubmissionError
class IncorrectSubmissionError(Exception): pass
class IncorrectSubmissionError(Exception): pass
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WecoAI/weco-cli
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/WecoAI_weco-cli/examples/cuda/evaluate.py
evaluate.Model
import torch.nn as nn import math import torch import torch.nn.functional as F class Model(nn.Module): """ A vanilla multi-head masked self-attention layer with a projection at the end. """ def __init__(self, n_embd, n_head, attn_pdrop, resid_pdrop, max_seqlen): super().__init__() asse...
class Model(nn.Module): ''' A vanilla multi-head masked self-attention layer with a projection at the end. ''' def __init__(self, n_embd, n_head, attn_pdrop, resid_pdrop, max_seqlen): pass def forward(self, x): pass
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WecoAI/weco-cli
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/WecoAI_weco-cli/examples/triton/evaluate.py
evaluate.Model
import torch.nn as nn import torch.nn.functional as F import torch import math class Model(nn.Module): """ A vanilla multi-head masked self-attention layer with a projection at the end. """ def __init__(self, n_embd, n_head, attn_pdrop, resid_pdrop, max_seqlen): super().__init__() asse...
class Model(nn.Module): ''' A vanilla multi-head masked self-attention layer with a projection at the end. ''' def __init__(self, n_embd, n_head, attn_pdrop, resid_pdrop, max_seqlen): pass def forward(self, x): pass
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WecoAI/weco-cli
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/WecoAI_weco-cli/examples/hello-kernel-world/optimize.py
optimize.Model
import torch.nn as nn import torch class Model(nn.Module): """ Model that performs a matrix multiplication, division, summation, and scaling. """ def __init__(self, input_size, hidden_size, scaling_factor): super(Model, self).__init__() self.weight = nn.Parameter(torch.randn(hidden_siz...
class Model(nn.Module): ''' Model that performs a matrix multiplication, division, summation, and scaling. ''' def __init__(self, input_size, hidden_size, scaling_factor): pass def forward(self, x): ''' Args: x (torch.Tensor): Input tensor of shape (batch_size,...
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WecoAI/weco-cli
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/WecoAI_weco-cli/examples/triton/optimize.py
optimize.Model
import math import torch.nn.functional as F import torch.nn as nn import torch class Model(nn.Module): """ A vanilla multi-head masked self-attention layer with a projection at the end. """ def __init__(self, n_embd, n_head, attn_pdrop, resid_pdrop, max_seqlen): super().__init__() asse...
class Model(nn.Module): ''' A vanilla multi-head masked self-attention layer with a projection at the end. ''' def __init__(self, n_embd, n_head, attn_pdrop, resid_pdrop, max_seqlen): pass def forward(self, x): pass
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WecoAI/weco-cli
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/WecoAI_weco-cli/weco/chatbot.py
weco.chatbot.Chatbot
from .panels import OptimizationOptionsPanel, EvaluationScriptPanel from rich.prompt import Prompt from typing import List, Optional, Dict, Any, Tuple from rich.console import Console import argparse import sys import shlex from .api import get_optimization_suggestions_from_codebase, generate_evaluation_script_and_metr...
class Chatbot: def __init__(self, project_path: pathlib.Path, console: Console, run_parser: argparse.ArgumentParser, model: Optional[str]=None): pass def analyze_codebase_and_get_optimization_options(self) -> Optional[List[Dict[str, str]]]: '''Analyze the codebase using gitingest and get opti...
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WecoAI/weco-cli
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/WecoAI_weco-cli/weco/chatbot.py
weco.chatbot.UserInteractionHelper
from typing import List, Optional, Dict, Any, Tuple from rich.prompt import Prompt from rich.console import Console from .panels import OptimizationOptionsPanel, EvaluationScriptPanel class UserInteractionHelper: """Helper class for standardized user interactions.""" def __init__(self, console: Console): ...
class UserInteractionHelper: '''Helper class for standardized user interactions.''' def __init__(self, console: Console): pass def get_choice(self, prompt: str, choices: List[str], default: str=None, show_choices: bool=True, max_retries: int=5) -> str: '''Standardized choice prompt with e...
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WecoAI/weco-cli
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/WecoAI_weco-cli/weco/optimizer.py
weco.optimizer.HeartbeatSender
import threading from .api import start_optimization_run, evaluate_feedback_then_suggest_next_solution, get_optimization_run_status, send_heartbeat, report_termination import traceback import sys class HeartbeatSender(threading.Thread): def __init__(self, run_id: str, auth_headers: dict, stop_event: threading.Eve...
class HeartbeatSender(threading.Thread): def __init__(self, run_id: str, auth_headers: dict, stop_event: threading.Event, interval: int=30): pass def run(self): pass
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WecoAI/weco-cli
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/WecoAI_weco-cli/weco/panels.py
weco.panels.EvaluationOutputPanel
from rich.panel import Panel class EvaluationOutputPanel: """Displays evaluation output with truncation for long outputs.""" def __init__(self): self.output = '' def update(self, output: str) -> None: """Update the evaluation output.""" self.output = output def clear(self) ->...
class EvaluationOutputPanel: '''Displays evaluation output with truncation for long outputs.''' def __init__(self): pass def update(self, output: str) -> None: '''Update the evaluation output.''' pass def clear(self) -> None: '''Clear the evaluation output.''' ...
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WecoAI/weco-cli
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/WecoAI_weco-cli/weco/panels.py
weco.panels.EvaluationScriptPanel
from rich.panel import Panel from rich.syntax import Syntax class EvaluationScriptPanel: """Panel for displaying evaluation scripts with syntax highlighting. Shows Python evaluation scripts with proper syntax highlighting, line numbers, and a descriptive title. """ def get_display(self, script_co...
class EvaluationScriptPanel: '''Panel for displaying evaluation scripts with syntax highlighting. Shows Python evaluation scripts with proper syntax highlighting, line numbers, and a descriptive title. ''' def get_display(self, script_content: str, script_path: str='evaluate.py') -> Panel: ...
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WecoAI/weco-cli
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/WecoAI_weco-cli/weco/panels.py
weco.panels.MetricTree
from typing import Dict, List, Optional, Union, Tuple class MetricTree: """Manages the tree structure of optimization solutions.""" def __init__(self, maximize: bool): self.nodes: Dict[str, Node] = {} self.maximize = maximize def clear(self): """Clear the tree.""" self.nod...
class MetricTree: '''Manages the tree structure of optimization solutions.''' def __init__(self, maximize: bool): pass def clear(self): '''Clear the tree.''' pass def add_node(self, node: Node): '''Add a node to the tree.''' pass def get_root_node(self) -...
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WecoAI/weco-cli
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/WecoAI_weco-cli/weco/panels.py
weco.panels.MetricTreePanel
from rich.tree import Tree from rich.panel import Panel from typing import Dict, List, Optional, Union, Tuple class MetricTreePanel: """Displays the solution tree with depth limiting.""" def __init__(self, maximize: bool): self.metric_tree = MetricTree(maximize=maximize) def build_metric_tree(sel...
class MetricTreePanel: '''Displays the solution tree with depth limiting.''' def __init__(self, maximize: bool): pass def build_metric_tree(self, nodes: List[dict]): '''Build the tree from the list of nodes.''' pass def set_unevaluated_node(self, node_id: str): '''Set...
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WecoAI/weco-cli
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/WecoAI_weco-cli/weco/panels.py
weco.panels.Node
from typing import Dict, List, Optional, Union, Tuple class Node: """Represents a node in the solution tree.""" def __init__(self, id: str, parent_id: Union[str, None], code: Union[str, None], metric: Union[float, None], is_buggy: Union[bool, None]): self.id = id self.parent_id = parent_id ...
class Node: '''Represents a node in the solution tree.''' def __init__(self, id: str, parent_id: Union[str, None], code: Union[str, None], metric: Union[float, None], is_buggy: Union[bool, None]): pass
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WecoAI/weco-cli
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/WecoAI_weco-cli/weco/panels.py
weco.panels.OptimizationOptionsPanel
from typing import Dict, List, Optional, Union, Tuple from rich.table import Table from rich import box class OptimizationOptionsPanel: """Panel for displaying optimization options in a table. Creates a formatted table showing optimization suggestions with details like target file, description, estimated ...
class OptimizationOptionsPanel: '''Panel for displaying optimization options in a table. Creates a formatted table showing optimization suggestions with details like target file, description, estimated cost, and predicted gains. ''' def get_display(self, options: List[Dict[str, str]]) -> Table: ...
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WecoAI/weco-cli
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/WecoAI_weco-cli/weco/panels.py
weco.panels.SolutionPanels
from typing import Dict, List, Optional, Union, Tuple from rich.panel import Panel from rich.syntax import Syntax from pathlib import Path class SolutionPanels: """Displays the current and best solutions side by side.""" def __init__(self, metric_name: str, source_fp: Path): self.current_node = None ...
class SolutionPanels: '''Displays the current and best solutions side by side.''' def __init__(self, metric_name: str, source_fp: Path): pass def _determine_lexer(self, source_fp: Path) -> str: '''Determine the lexer for the source file.''' pass def update(self, current_node:...
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WecoAI/weco-cli
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/WecoAI_weco-cli/weco/panels.py
weco.panels.SummaryPanel
from rich.panel import Panel from rich.layout import Layout from pathlib import Path from rich.table import Table from .__init__ import __dashboard_url__ from .utils import format_number from typing import Dict, List, Optional, Union, Tuple from rich.progress import BarColumn, Progress, TextColumn class SummaryPanel: ...
class SummaryPanel: '''Holds a summary of the optimization run.''' def __init__(self, maximize: bool, metric_name: str, total_steps: int, model: str, runs_dir: str, run_id: str=None, run_name: str=None): pass def set_run_id(self, run_id: str): '''Set the run ID.''' pass def s...
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/examples/hybrid_search/app.py
app.Document
from pytidb.schema import FullTextField, TableModel, Field class Document(TableModel): __tablename__ = 'documents' __table_args__ = {'extend_existing': True} id: int = Field(primary_key=True) text: str = FullTextField() text_vec: list[float] = embed_fn.VectorField(source_field='text')
class Document(TableModel): pass
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/examples/text2sql/app.py
app.QuestionSQLResponse
from pydantic import BaseModel class QuestionSQLResponse(BaseModel): question: str sql: str markdown: str
class QuestionSQLResponse(BaseModel): pass
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/examples/custom_embedding/custom_embedding.py
custom_embedding.BGEM3EmbeddingFunction
from pytidb.embeddings.base import BaseEmbeddingFunction, EmbeddingSourceType from pytidb.schema import Field from FlagEmbedding import BGEM3FlagModel from typing import Any, Optional, List class BGEM3EmbeddingFunction(BaseEmbeddingFunction): """ A custom embedding function that uses BGE-M3 model for text embe...
class BGEM3EmbeddingFunction(BaseEmbeddingFunction): ''' A custom embedding function that uses BGE-M3 model for text embeddings. BGE-M3 is a versatile embedding model that supports dense retrieval, sparse retrieval, and multi-vector retrieval. ''' def __init__(self, model_name: str='BAAI/bge-m3', ...
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/pytidb/orm/sql/ddl.py
ddl.TiDBSchemaDropper
from sqlalchemy.sql.ddl import SchemaGenerator, SchemaDropper, CreateIndex from ..tiflash_replica import SetTiFlashReplica, TiFlashReplica class TiDBSchemaDropper(SchemaDropper): def visit_tiflash_replica(self, tiflash_replica: TiFlashReplica): with self.with_ddl_events(tiflash_replica): tifla...
class TiDBSchemaDropper(SchemaDropper): def visit_tiflash_replica(self, tiflash_replica: TiFlashReplica): pass
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/pytidb/orm/sql/ddl.py
ddl.TiDBSchemaGenerator
from pytidb.orm.indexes import VectorIndex, FullTextIndex from pytidb.utils import TIDB_SERVERLESS_HOST_PATTERN from sqlalchemy.sql.ddl import SchemaGenerator, SchemaDropper, CreateIndex from ..tiflash_replica import SetTiFlashReplica, TiFlashReplica class TiDBSchemaGenerator(SchemaGenerator): def visit_index(sel...
class TiDBSchemaGenerator(SchemaGenerator): def visit_index(self, index, create_ok=False): pass def _has_tiflash_replica(self, tiflash_replica: TiFlashReplica) -> bool: pass def visit_tiflash_replica(self, tiflash_replica: TiFlashReplica): pass
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/examples/hybrid_search/example.py
example.Chunk
from pytidb.schema import FullTextField, TableModel, Field class Chunk(TableModel, table=True): __tablename__ = 'chunks' id: int = Field(primary_key=True) text: str = FullTextField() text_vec: list[float] = embed_fn.VectorField(source_field='text')
class Chunk(TableModel, table=True): pass
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/examples/fulltext_search/example.py
example.Item
from pytidb.schema import FullTextField, TableModel, Field class Item(TableModel): __tablename__ = 'items' __table_args__ = {'extend_existing': True} id: int = Field(primary_key=True) title: str = FullTextField() language: str = Field(max_length=10)
class Item(TableModel): pass
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/examples/auto_embedding/main.py
main.Chunk
from pytidb.datatype import TEXT from pytidb.schema import TableModel, Field class Chunk(TableModel): id: int = Field(primary_key=True) text: str = Field(sa_type=TEXT) text_vec: list[float] = embed_func.VectorField(source_field='text')
class Chunk(TableModel): pass
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/examples/quickstart/main.py
main.Chunk
from pytidb.schema import TableModel, Field class Chunk(TableModel, table=True): __tablename__ = 'chunks' id: int = Field(primary_key=True) text: str = Field() text_vec: list[float] = text_embed.VectorField(source_field='text') user_id: int = Field()
class Chunk(TableModel, table=True): pass
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/examples/rag/main.py
main.Chunk
from pytidb.schema import TableModel, Field class Chunk(TableModel): __tablename__ = 'chunks' __table_args__ = {'extend_existing': True} id: int = Field(primary_key=True) text: str = Field() text_vec: list[float] = text_embed.VectorField(source_field='text')
class Chunk(TableModel): pass
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/examples/custom_embedding/main.py
main.Document
from pytidb.datatype import TEXT from pytidb.schema import TableModel, Field class Document(TableModel): __tablename__ = 'bge_m3_documents' id: int = Field(primary_key=True) title: str = Field(sa_type=TEXT) content: str = Field(sa_type=TEXT) content_vec: list[float] = embed_func.VectorField(source_...
class Document(TableModel): pass
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/examples/basic/main.py
main.Item
from pytidb.schema import TableModel, Field, VectorField from pytidb.datatype import JSON, TEXT class Item(TableModel): __tablename__ = 'items_in_basic_example' id: int = Field(primary_key=True) content: str = Field(sa_type=TEXT) embedding: list[float] = VectorField(dimensions=3) meta: dict = Field...
class Item(TableModel): pass
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/examples/memory/memory.py
memory.Memory
import datetime from typing import List, Dict, Any from pytidb.datatype import TEXT from pytidb.schema import TableModel, Field, Column class Memory: def __init__(self, tidb_client, embedding_fn, openai_client): self.tidb_client = tidb_client self.embedding_fn = embedding_fn self.openai_cl...
class Memory: def __init__(self, tidb_client, embedding_fn, openai_client): pass class MemoryRecord(TableModel): def add(self, messages: List[Dict[str, Any]], user_id: str='default_user'): '''Add a new memory by extracting key facts from conversation.''' pass def search(...
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/pytidb/client.py
pytidb.client.TiDBClient
from pytidb.result import SQLExecuteResult, SQLQueryResult from pytidb.base import default_registry from pydantic import PrivateAttr import sqlalchemy from pytidb.table import Table from typing import List, Literal, Optional, Type, Generator from pytidb.orm.variables import EMBED_PROVIDER_API_KEY_VARS from pytidb.schem...
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/pytidb/embeddings/base.py
pytidb.embeddings.base.BaseEmbeddingFunction
from typing import Optional, Any, Literal from abc import ABC, abstractmethod from pydantic import BaseModel, Field class BaseEmbeddingFunction(BaseModel, ABC): provider: str = Field('openai', description='The name of the embedding provider') model_name: str = Field(None, description='The name of embedding mod...
class BaseEmbeddingFunction(BaseModel, ABC): def __init__(self, /, **data: Any): pass def VectorField(self, source_field: Optional[str]=None, source_type: Optional[EmbeddingSourceType]='text', **kwargs): ''' Create a VectorField with auto embedding configuration. Args: ...
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/pytidb/embeddings/builtin.py
pytidb.embeddings.builtin.EmbeddingFunction
from typing import TYPE_CHECKING, Any, List, Optional, Union from pytidb.embeddings.base import BaseEmbeddingFunction, EmbeddingSourceType from pathlib import Path from pydantic import Field import urllib.request from pytidb.embeddings.aliases import normalize_model_name from pytidb.embeddings.utils import encode_local...
class EmbeddingFunction(BaseEmbeddingFunction): def __init__(self, model_name: str, dimensions: Optional[int]=None, api_key: Optional[str]=None, api_base: Optional[str]=None, timeout: Optional[int]=None, caching: bool=True, use_server: Optional[bool]=None, additional_json_options: Optional[dict[str, Any]]=None, m...
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/pytidb/errors.py
pytidb.errors.EmbeddingColumnMismatchError
class EmbeddingColumnMismatchError(ValueError): """ Exception raised when the existing embedding column does not match the expected dimension. Attributes: existing_col (str): The definition of the existing embedding column. expected_col (str): The definition of the expected embedding column...
class EmbeddingColumnMismatchError(ValueError): ''' Exception raised when the existing embedding column does not match the expected dimension. Attributes: existing_col (str): The definition of the existing embedding column. expected_col (str): The definition of the expected embedding column....
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/pytidb/ext/mcp/server.py
pytidb.ext.mcp.server.AppContext
from dataclasses import dataclass @dataclass class AppContext: tidb: TiDBConnector
@dataclass class AppContext: pass
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/pytidb/ext/mcp/server.py
pytidb.ext.mcp.server.TiDBConnector
from pytidb import TiDBClient from pytidb.utils import TIDB_SERVERLESS_HOST_PATTERN from typing import AsyncIterator, Optional from pydantic import MySQLDsn class TiDBConnector: def __init__(self, database_url: Optional[str]=None, *, host: Optional[str]=None, port: Optional[int]=None, username: Optional[str]=None...
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/pytidb/orm/distance_metric.py
pytidb.orm.distance_metric.DistanceMetric
import enum class DistanceMetric(str, enum.Enum): """ An enumeration representing different types of distance metrics. - `DistanceMetric.L1`: L1 (Manhattan) distance metric. - `DistanceMetric.L2`: L2 (Euclidean) distance metric. - `DistanceMetric.COSINE`: Cosine distance metric. - `DistanceMet...
class DistanceMetric(str, enum.Enum): ''' An enumeration representing different types of distance metrics. - `DistanceMetric.L1`: L1 (Manhattan) distance metric. - `DistanceMetric.L2`: L2 (Euclidean) distance metric. - `DistanceMetric.COSINE`: Cosine distance metric. - `DistanceMetric.NEGATIVE_...
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/pytidb/orm/functions.py
pytidb.orm.functions.fts_match_word
from sqlalchemy.sql.functions import GenericFunction class fts_match_word(GenericFunction): type = None name = 'FTS_MATCH_WORD' inherit_cache = True
class fts_match_word(GenericFunction): pass
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/pytidb/orm/functions.py
pytidb.orm.functions.vec_cosine_distance
from sqlalchemy.sql.functions import GenericFunction class vec_cosine_distance(GenericFunction): type = None name = 'VEC_COSINE_DISTANCE' inherit_cache = True
class vec_cosine_distance(GenericFunction): pass
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/pytidb/orm/functions.py
pytidb.orm.functions.vec_embed_cosine_distance
from sqlalchemy.sql.functions import GenericFunction class vec_embed_cosine_distance(GenericFunction): type = None name = 'VEC_EMBED_COSINE_DISTANCE' inherit_cache = True
class vec_embed_cosine_distance(GenericFunction): pass
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/pytidb/orm/functions.py
pytidb.orm.functions.vec_embed_l1_distance
from sqlalchemy.sql.functions import GenericFunction class vec_embed_l1_distance(GenericFunction): type = None name = 'VEC_EMBED_L1_DISTANCE' inherit_cache = True
class vec_embed_l1_distance(GenericFunction): pass
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/pytidb/orm/functions.py
pytidb.orm.functions.vec_embed_l2_distance
from sqlalchemy.sql.functions import GenericFunction class vec_embed_l2_distance(GenericFunction): type = None name = 'VEC_EMBED_L2_DISTANCE' inherit_cache = True
class vec_embed_l2_distance(GenericFunction): pass
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/pytidb/orm/functions.py
pytidb.orm.functions.vec_l1_distance
from sqlalchemy.sql.functions import GenericFunction class vec_l1_distance(GenericFunction): type = None name = 'VEC_L1_DISTANCE' inherit_cache = True
class vec_l1_distance(GenericFunction): pass
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/pytidb/orm/functions.py
pytidb.orm.functions.vec_l2_distance
from sqlalchemy.sql.functions import GenericFunction class vec_l2_distance(GenericFunction): type = None name = 'VEC_L2_DISTANCE' inherit_cache = True
class vec_l2_distance(GenericFunction): pass
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/pytidb/orm/functions.py
pytidb.orm.functions.vec_negative_inner_product
from sqlalchemy.sql.functions import GenericFunction class vec_negative_inner_product(GenericFunction): type = None name = 'VEC_NEGATIVE_INNER_PRODUCT' inherit_cache = True
class vec_negative_inner_product(GenericFunction): pass
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/pytidb/orm/indexes.py
pytidb.orm.indexes.FullTextIndex
from pytidb.orm._typing import _CreateDropBind from pytidb.orm.tiflash_replica import TiFlashReplica from pytidb.utils import TIDB_SERVERLESS_HOST_PATTERN from sqlalchemy.sql.schema import Index class FullTextIndex(Index): """Full Text Index schema.""" def __init__(self, name, *column_names, fts_parser: FullT...
class FullTextIndex(Index): '''Full Text Index schema.''' def __init__(self, name, *column_names, fts_parser: FullTextParser='MULTILINGUAL', ensure_columnar_replica: bool=True, **kw): pass def create(self, bind: _CreateDropBind, checkfirst: bool=False) -> None: pass
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/pytidb/orm/indexes.py
pytidb.orm.indexes.VectorIndex
from sqlalchemy import text from pytidb.orm._typing import _CreateDropBind from sqlalchemy.sql.schema import Index from pytidb.orm.distance_metric import DistanceMetric, validate_distance_metric from pytidb.orm.tiflash_replica import TiFlashReplica from typing import Literal, Union from pytidb.utils import TIDB_SERVERL...
class VectorIndex(Index): '''Vector Index schema.''' def __init__(self, name, *columns, distance_metric: Union[DistanceMetric, str]=DistanceMetric.COSINE, algorithm: VectorIndexAlgorithm='HNSW', ensure_columnar_replica: bool=True, **kw): pass def create(self, bind: _CreateDropBind, checkfirst: bo...
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/pytidb/orm/tiflash_replica.py
pytidb.orm.tiflash_replica.SetTiFlashReplica
from sqlalchemy.sql.ddl import _CreateBase class SetTiFlashReplica(_CreateBase): """DDL element for SET TIFLASH REPLICA operation.""" __visit_name__ = 'set_tiflash_replica' def __init__(self, element): super().__init__(element)
class SetTiFlashReplica(_CreateBase): '''DDL element for SET TIFLASH REPLICA operation.''' def __init__(self, element): pass
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/pytidb/orm/tiflash_replica.py
pytidb.orm.tiflash_replica.TiFlashReplica
from pytidb.orm._typing import _CreateDropBind from sqlalchemy import Connection, Engine, select, Table from typing import Optional, Any, TypedDict from sqlalchemy.sql.base import DialectKWArgs from .information_schema import tiflash_replica from sqlalchemy.sql.schema import SchemaItem class TiFlashReplica(DialectKWAr...
class TiFlashReplica(DialectKWArgs, SchemaItem): '''A table-level TiFlash replica. TiFlash is the columnar storage engine for TiDB. This class provides DDL operations to manage TiFlash replicas for tables. Examples:: # Create TiFlash replica replica = TiFlashReplica(table, replica_count...
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/pytidb/orm/tiflash_replica.py
pytidb.orm.tiflash_replica.TiFlashReplicaProgress
from typing import Optional, Any, TypedDict class TiFlashReplicaProgress(TypedDict): table_schema: str table_name: str replica_count: int location_labels: str available: bool progress: float
class TiFlashReplicaProgress(TypedDict): pass
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/pytidb/orm/vector.py
pytidb.orm.vector.VECTOR
import sqlalchemy from typing import Optional, Union, List class VECTOR(sqlalchemy.types.UserDefinedType): """ Represents a vector column type in TiDB. """ dim: Optional[int] cache_ok = True def __init__(self, dim: Optional[int]=None): if dim is not None and (not isinstance(dim, int)):...
class VECTOR(sqlalchemy.types.UserDefinedType): ''' Represents a vector column type in TiDB. ''' def __init__(self, dim: Optional[int]=None): pass def get_col_spec(self, **kw): ''' Returns the column specification for the vector column. If the dimension is not spec...
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/pytidb/rerankers/base.py
pytidb.rerankers.base.BaseReranker
from abc import ABC, abstractmethod from typing import List, Optional class BaseReranker(ABC): @abstractmethod def rerank(self, query: str, documents: List[str], top_n: Optional[int]=None) -> List[RerankResult]: raise NotImplementedError()
class BaseReranker(ABC): @abstractmethod def rerank(self, query: str, documents: List[str], top_n: Optional[int]=None) -> List[RerankResult]: pass
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/pytidb/rerankers/base.py
pytidb.rerankers.base.RerankResult
from pydantic import BaseModel class RerankResult(BaseModel): index: int relevance_score: float
class RerankResult(BaseModel): pass
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/pytidb/rerankers/litellm.py
pytidb.rerankers.litellm.LiteLLMReranker
from typing import List, Optional from pytidb.rerankers.base import BaseReranker, RerankResult class LiteLLMReranker(BaseReranker): def __init__(self, model_name: str, api_key: Optional[str]=None, api_base: Optional[str]=None, timeout: Optional[int]=None): self.model_name = model_name self.api_key...
class LiteLLMReranker(BaseReranker): def __init__(self, model_name: str, api_key: Optional[str]=None, api_base: Optional[str]=None, timeout: Optional[int]=None): pass def rerank(self, query: str, documents: List[str], top_n: Optional[int]=None) -> List[RerankResult]: pass
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/pytidb/result.py
pytidb.result.QueryResult
from pydantic import BaseModel, Field from abc import ABC, abstractmethod from typing import Optional, List, Type class QueryResult(ABC): @abstractmethod def to_list(self) -> List[dict]: pass @abstractmethod def to_pydantic(self) -> List[BaseModel]: pass @abstractmethod def t...
class QueryResult(ABC): @abstractmethod def to_list(self) -> List[dict]: pass @abstractmethod def to_pydantic(self) -> List[BaseModel]: pass @abstractmethod def to_pandas(self): pass
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/pytidb/result.py
pytidb.result.SQLExecuteResult
from pydantic import BaseModel, Field from typing import Optional, List, Type class SQLExecuteResult(BaseModel): rowcount: int = Field(0) success: bool = Field(False) message: Optional[str] = Field(None)
class SQLExecuteResult(BaseModel): pass
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/pytidb/result.py
pytidb.result.SQLModelQueryResult
from pydantic import BaseModel, Field from typing import Optional, List, Type class SQLModelQueryResult(QueryResult): def __init__(self, data: List[BaseModel]): self._data = data def to_list(self) -> List[dict]: return [item.model_dump() for item in self._data] def to_pydantic(self) -> L...
class SQLModelQueryResult(QueryResult): def __init__(self, data: List[BaseModel]): pass def to_list(self) -> List[dict]: pass def to_pydantic(self) -> List[BaseModel]: pass def to_pandas(self): pass
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/pytidb/result.py
pytidb.result.SQLQueryResult
from sqlalchemy import Result from pydantic import BaseModel, Field from typing import Optional, List, Type class SQLQueryResult(QueryResult): _result: Result def __init__(self, result): self._result = result def scalar(self): return self._result.scalar() def one(self): retur...
class SQLQueryResult(QueryResult): def __init__(self, result): pass def scalar(self): pass def one(self): pass def to_rows(self): pass def to_pandas(self): pass def to_list(self) -> List[dict]: pass def to_pydantic(self, model: Type[Bas...
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/pytidb/schema.py
pytidb.schema.ColumnInfo
from pydantic import BaseModel class ColumnInfo(BaseModel): column_name: str column_type: str
class ColumnInfo(BaseModel): pass
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/pytidb/schema.py
pytidb.schema.QueryBundle
from typing import Any, Literal, Optional, TYPE_CHECKING, List, TypedDict, Union class QueryBundle(TypedDict): query: Optional[Any] query_vector: Optional[VectorDataType]
class QueryBundle(TypedDict): pass
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/pytidb/schema.py
pytidb.schema.TableModel
from sqlmodel import SQLModel, Field, Relationship class TableModel(SQLModel, metaclass=TableModelMeta): pass
class TableModel(SQLModel, metaclass=TableModelMeta): pass
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/pytidb/schema.py
pytidb.schema.TableModelMeta
from sqlmodel.main import FieldInfo, RelationshipInfo, SQLModelMetaclass class TableModelMeta(SQLModelMetaclass): def __new__(mcs, name, bases, namespace, **kwargs): if name != 'TableModel': kwargs.setdefault('table', True) return super().__new__(mcs, name, bases, namespace, **kwargs)
class TableModelMeta(SQLModelMetaclass): def __new__(mcs, name, bases, namespace, **kwargs): pass
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/pytidb/search.py
pytidb.search.Search
from pytidb.schema import QueryBundle, VectorDataType, TableModel from pytidb.orm.distance_metric import DistanceMetric, validate_distance_metric from pydantic import BaseModel, Field from sqlalchemy.sql.base import Generative, _generative from pytidb.fusion import fusion_result_rows_by_rrf, fusion_result_rows_by_weigh...
class Search(Generative): '''Represents a TiDB vector/fulltext/hybrid search statement. The :class:`_search.Search` object is normally constructed using the :func:`_search.search` function. See that function for details. .. seealso:: :func:`_search.search` ''' def __init__(self, table:...
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/pytidb/search.py
pytidb.search.SearchResult
from pydantic import BaseModel, Field from typing import TYPE_CHECKING, Any, Callable, Dict, List, Literal, Optional, Union, Tuple, Sequence, TypeVar, Generic, overload class SearchResult(BaseModel, Generic[T]): hit: T distance: Optional[float] = Field(description='The distance between the query vector and the...
class SearchResult(BaseModel, Generic[T]): def __getattr__(self, item: str): pass @property def similarity_score(self) -> Optional[float]: pass
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/pytidb/table.py
pytidb.table.Table
from pytidb.orm.indexes import FullTextIndex, VectorIndex, format_distance_expression from pathlib import Path from pytidb.filters import Filters, build_filter_clauses from pytidb.schema import QueryBundle, TableModelMeta, VectorDataType, TableModel, ColumnInfo, DistanceMetric from pytidb.search import SearchType, Sear...
class Table(Generic[T]): def __init__(self, *, client: 'TiDBClient', schema: Optional[Type[T]]=None): pass @property def table_model(self) -> T: pass @property def table_name(self) -> str: pass @property def client(self) -> 'TiDBClient': pass @property ...
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pingcap/pytidb
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pingcap_pytidb/pytidb/utils.py
pytidb.utils.TiDBConnectionURL
from pydantic import AnyUrl, UrlConstraints class TiDBConnectionURL(AnyUrl): """A URL that enforces specific constraints for TiDB connections. Format: mysql+pymysql://[username:password@]host[:port][/database] mysql+pymysql://[username:password@]host[:port][/database]?ssl_verify_cert=true&ssl_...
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ProTecGames/Karbon
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ProTecGames_Karbon/contributors_page.py
contributors_page.ContributorsPage
import urllib.request import threading import webbrowser import requests from core.ai_engine import set_ai_status from PIL import Image, ImageTk import json import tkinter as tk import io from tkinter import ttk import os import time class ContributorsPage(tk.Frame): def __init__(self, parent, back_callback, root...
class ContributorsPage(tk.Frame): def __init__(self, parent, back_callback, root=None): pass def setup_styles(self): '''Configure styles to match Karbon's theme using current theme_colors and font''' pass def show_loading(self): '''Display a simple loading animation''' ...
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ProTecGames/Karbon
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ProTecGames_Karbon/core/prompt_drafts_system.py
core.prompt_drafts_system.DraftsManagerUI
import tkinter as tk from datetime import datetime from tkinter import ttk, messagebox, simpledialog class DraftsManagerUI(tk.Toplevel): """Main UI window for managing prompt drafts""" def __init__(self, parent, get_current_prompt_callback=None, load_draft_callback=None): super().__init__(parent) ...
class DraftsManagerUI(tk.Toplevel): '''Main UI window for managing prompt drafts''' def __init__(self, parent, get_current_prompt_callback=None, load_draft_callback=None): pass def setup_window(self): '''Configure the main window''' pass def setup_ui(self): '''Setup t...
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ProTecGames/Karbon
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ProTecGames_Karbon/core/prompt_drafts_system.py
core.prompt_drafts_system.PromptDraftsButton
import tkinter as tk class PromptDraftsButton: """Helper class to add drafts button to existing prompt input""" def __init__(self, parent_frame, get_prompt_callback, set_prompt_callback): self.parent_frame = parent_frame self.get_prompt = get_prompt_callback self.set_prompt = set_promp...
class PromptDraftsButton: '''Helper class to add drafts button to existing prompt input''' def __init__(self, parent_frame, get_prompt_callback, set_prompt_callback): pass def create_button(self): '''Create the drafts button''' pass def open_drafts_manager(self): '''O...
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ProTecGames/Karbon
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ProTecGames_Karbon/core/prompt_drafts_system.py
core.prompt_drafts_system.PromptDraftsManager
import json from datetime import datetime from typing import List, Dict, Optional import os class PromptDraftsManager: """Backend manager for handling prompt drafts operations""" def __init__(self, drafts_file='prompt_drafts.json'): self.drafts_file = drafts_file self.drafts = self.load_drafts...
class PromptDraftsManager: '''Backend manager for handling prompt drafts operations''' def __init__(self, drafts_file='prompt_drafts.json'): pass def load_drafts(self) -> Dict: '''Load drafts from JSON file''' pass def save_drafts(self): '''Save drafts to JSON file'''...
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ProTecGames/Karbon
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ProTecGames_Karbon/ui_items/contributors_view.py
ui_items.contributors_view.ContributorsView
import tkinter as tk class ContributorsView(tk.Frame): def __init__(self, parent): super().__init__(parent) self.configure(bg='#1e1e1e') title = tk.Label(self, text='Contributors', font=('Helvetica', 20, 'bold'), fg='#ffffff', bg='#1e1e1e') title.pack(pady=20) contributors ...
class ContributorsView(tk.Frame): def __init__(self, parent): pass
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ProTecGames/Karbon
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ProTecGames_Karbon/ui_items/editor_view.py
ui_items.editor_view.EditorView
import tkinter as tk import threading from core.ai_engine import generate_code_from_prompt, ai_status import os from tkinter import ttk, messagebox, filedialog from exporters.exporter import export_code from core import prompt_history class EditorView(tk.Frame): def __init__(self, master, get_code_callback, set_c...
class EditorView(tk.Frame): def __init__(self, master, get_code_callback, set_code_callback, get_api_key_callback, get_model_source_callback): pass def setup_ui(self): pass def create_header(self): pass def create_toolbar(self): pass def insert_text(self, words)...
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ProTecGames/Karbon
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ProTecGames_Karbon/ui_items/editor_view.py
ui_items.editor_view.SimpleEmbeddedPreview
class SimpleEmbeddedPreview: def __init__(self, parent_frame): self.parent_frame = parent_frame self.current_html = '' def update_content(self, html_content): """Update the preview content by opening in browser""" try: temp_file = open_html_in_browser(html_content, ...
class SimpleEmbeddedPreview: def __init__(self, parent_frame): pass def update_content(self, html_content): '''Update the preview content by opening in browser''' pass
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ProTecGames/Karbon
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ProTecGames_Karbon/ui_items/karbon_ui.py
ui_items.karbon_ui.KarbonUI
from ui_items.editor_view import EditorView, open_html_in_browser from exporters.exporter import export_code, export_to_github from ui_items.prompt_view import PromptView from core.ai_engine import ai_status, generate_code_from_prompt from tkinter import ttk, messagebox from contributors_page import ContributorsPage im...
class KarbonUI: def __init__(self, root, user=None): pass def show_history_panel(self): pass def show_selected(): pass def setup_window(self): pass def center_window(self): pass def setup_styles(self): pass def create_title_...
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ProTecGames/Karbon
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ProTecGames_Karbon/ui_items/prompt_view.py
ui_items.prompt_view.PromptView
from core.ai_engine import ai_status import tkinter as tk import core.prompt_history as prompt_history from core.ai_engine import optimize_prompt from core.ai_engine import generate_code_from_prompt import threading class PromptView(tk.Frame): def __init__(self, master, on_generate, get_api_key_callback, get_mode...
class PromptView(tk.Frame): def __init__(self, master, on_generate, get_api_key_callback, get_model_source_callback, examples_data): pass def setup_ui(self): pass def create_hero_section(self): pass def create_input_section(self): pass def create_secondary_butto...
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ProTecGames/Karbon
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ProTecGames_Karbon/ui_items/token_manager_view.py
ui_items.token_manager_view.TokenManagerView
from exporters.exporter import validate_github_token import tkinter as tk from tkinter import ttk, messagebox import threading from core.token_manager import encrypt_token, decrypt_token, clear_token, token_exists, ERROR_NO_TOKEN, SUCCESS_TOKEN_SAVED import webbrowser class TokenManagerView(ttk.Frame): def __init...
class TokenManagerView(ttk.Frame): def __init__(self, parent, back_callback): pass def setup_styles(self): '''Configure styles to match Karbon's theme''' pass def check_token_status(self): '''Check if a token exists and update the UI accordingly''' pass def t...
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rdnfn/feedback-forensics
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/rdnfn_feedback-forensics/src/feedback_forensics/data/datasets.py
feedback_forensics.data.datasets.BuiltinDataset
from dataclasses import dataclass import re @dataclass class BuiltinDataset: """Class to represent a built-in dataset.""" name: str path: str | None = None description: str | None = None filterable_columns: list[str] | None = None source: str | None = None @property def url_name(self) ...
@dataclass class BuiltinDataset: '''Class to represent a built-in dataset.''' @property def url_name(self) -> str: '''Get the name of the dataset for the URL.''' pass
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rdnfn/feedback-forensics
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/rdnfn_feedback-forensics/src/feedback_forensics/data/handler.py
feedback_forensics.data.handler.ColumnHandler
from feedback_forensics.data.loader import add_virtual_annotators, get_votes_dict from pathlib import Path from feedback_forensics.app.metrics import get_overall_metrics, compute_annotator_metrics from loguru import logger class ColumnHandler: """Class to handle data operations (loading, computing metrics) of a si...
class ColumnHandler: '''Class to handle data operations (loading, computing metrics) of a single annotation column dataset.''' def __init__(self, cache: dict | None=None, avail_datasets: dict | None=None, reference_models: list[str] | None=None): pass @property def votes_dict(self): pa...
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rdnfn/feedback-forensics
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/rdnfn_feedback-forensics/src/feedback_forensics/data/handler.py
feedback_forensics.data.handler.DatasetHandler
from loguru import logger import pandas as pd from pathlib import Path class DatasetHandler: """Class to handle dataset operations of multi-column annotation datasets. A dataset consists of one or multiple annotation columns, represented by ColumnHandler objects. Each column can either be a different anno...
class DatasetHandler: '''Class to handle dataset operations of multi-column annotation datasets. A dataset consists of one or multiple annotation columns, represented by ColumnHandler objects. Each column can either be a different annotator or the same annotator but on a different data(sub)set. '''...
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cgueret/Scriptorium
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/cgueret_Scriptorium/scriptorium/application.py
scriptorium.application.ScriptoriumApplication
from scriptorium.dialogs import ScrptPreferencesDialog from .language_tool import LanguageTool from .window import ScrptWindow from gi.repository import Gio, Adw, GLib, GObject class ScriptoriumApplication(Adw.Application): """The main application singleton class.""" language_tool = GObject.Property(type=Langu...
class ScriptoriumApplication(Adw.Application): '''The main application singleton class.''' def __init__(self): pass def change_color_scheme(self, _settings, value): '''Handle a change in the select color scheme setting.''' pass def do_activate(self): '''Called when th...
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cgueret/Scriptorium
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/cgueret_Scriptorium/scriptorium/dialogs/dialog_add.py
scriptorium.dialogs.dialog_add.ScrptAddDialog
from gi.repository import Adw, GObject, Gtk from scriptorium.globals import BASE @Gtk.Template(resource_path=f'{BASE}/dialogs/dialog_add.ui') class ScrptAddDialog(Adw.AlertDialog): """Dialog to add a new scene.""" __gtype_name__ = 'ScrptAddDialog' title = GObject.Property(type=str) synopsis = GObject.P...
@Gtk.Template(resource_path=f'{BASE}/dialogs/dialog_add.ui') class ScrptAddDialog(Adw.AlertDialog): '''Dialog to add a new scene.''' def __init__(self, name, **kwargs): '''Create a new instance of the class.''' pass @Gtk.Template.Callback() def on_title_changed(self, entry_row): ...
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cgueret/Scriptorium
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/cgueret_Scriptorium/scriptorium/dialogs/preferences.py
scriptorium.dialogs.preferences.ScrptPreferencesDialog
from gi.repository import Adw, Gtk, Gio, Pango from scriptorium.globals import BASE from scriptorium.utils import html_to_buffer @Gtk.Template(resource_path=f'{BASE}/dialogs/preferences.ui') class ScrptPreferencesDialog(Adw.PreferencesDialog): __gtype_name__ = 'ScrptPreferencesDialog' open_last_project = Gtk.T...
@Gtk.Template(resource_path=f'{BASE}/dialogs/preferences.ui') class ScrptPreferencesDialog(Adw.PreferencesDialog): def __init__(self): '''Create a new instance of the class.''' pass @Gtk.Template.Callback() def on_font_selected(self, _button, _value): '''Handle the selection of a ne...
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cgueret/Scriptorium
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/cgueret_Scriptorium/scriptorium/dialogs/select_entities.py
scriptorium.dialogs.select_entities.ScrptSelectEntitiesDialog
from scriptorium.globals import BASE from gi.repository import Adw, Gtk @Gtk.Template(resource_path=f'{BASE}/dialogs/select_entities.ui') class ScrptSelectEntitiesDialog(Adw.AlertDialog): """Dialog to select story elements.""" __gtype_name__ = 'ScrptSelectEntitiesDialog' list_box = Gtk.Template.Child() ...
@Gtk.Template(resource_path=f'{BASE}/dialogs/select_entities.ui') class ScrptSelectEntitiesDialog(Adw.AlertDialog): '''Dialog to select story elements.''' def __init__(self, scene, entities): '''Create a new instance of the class.''' pass def _filter(self, row): '''Return True if t...
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cgueret/Scriptorium
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/cgueret_Scriptorium/scriptorium/dialogs/select_scenes.py
scriptorium.dialogs.select_scenes.ScrptSelectScenesDialog
from gi.repository import Adw, Gtk from scriptorium.globals import BASE @Gtk.Template(resource_path=f'{BASE}/dialogs/select_scenes.ui') class ScrptSelectScenesDialog(Adw.AlertDialog): """Dialog to select scenes.""" __gtype_name__ = 'ScrptSelectScenesDialog' scenes_list = Gtk.Template.Child() def __ini...
@Gtk.Template(resource_path=f'{BASE}/dialogs/select_scenes.ui') class ScrptSelectScenesDialog(Adw.AlertDialog): '''Dialog to select scenes.''' def __init__(self, manuscript_scenes): '''Create a new instance of the class.''' pass def _filter_scenes(self, row): '''Return True if the ...
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cgueret/Scriptorium
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/cgueret_Scriptorium/scriptorium/language_tool.py
scriptorium.language_tool.LanguageTool
import json from scriptorium.models import Annotation from gi.repository import Gio, GObject, Soup, GLib class LanguageTool(GObject.Object): server_is_alive = GObject.Property(type=bool, default=False) def __init__(self): super().__init__() self._session = Soup.Session() self._proc_lan...
class LanguageTool(GObject.Object): def __init__(self): pass def send_ping(self): pass def handle_ping_reply(self, session, result): pass def shutdown(self): pass def _start_server(self): pass def check(self, text: str, language: str, callback): ...
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cgueret/Scriptorium
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/cgueret_Scriptorium/scriptorium/models/Scene.py
scriptorium.models.Scene.Scene
from gi.repository import Gtk, GObject, Gio from .commit_message import CommitMessage from .entity import Entity from .resource import Resource from scriptorium.utils import html_to_buffer, buffer_to_html from pathlib import Path class Scene(Resource): """A scene is a basic building block of manuscripts.""" __...
class Scene(Resource): '''A scene is a basic building block of manuscripts.''' def __init__(self, project, identifier: str): '''Create a scene.''' pass @property def data_files(self): pass @property def history(self): '''Return the history of commits about that ...
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cgueret/Scriptorium
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/cgueret_Scriptorium/scriptorium/models/annotation.py
scriptorium.models.annotation.Annotation
from gi.repository import GObject, Gtk class Annotation(GObject.Object): """An annotation is a section of a text marked with some text.""" __gtype_name__ = 'Annotation' title = GObject.Property(type=str) message = GObject.Property(type=str) category = GObject.Property(type=str) offset = GObject...
class Annotation(GObject.Object): '''An annotation is a section of a text marked with some text.''' def __init__(self): '''Create a new instance of Chapter.''' pass
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cgueret/Scriptorium
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/cgueret_Scriptorium/scriptorium/models/chapter.py
scriptorium.models.chapter.Chapter
from gi.repository import Gio, GObject from .scene import Scene from .resource import Resource class Chapter(Resource): """A chapter is a list of scenes.""" __gtype_name__ = 'Chapter' content = GObject.Property(type=Gio.ListStore) def __init__(self, project, identifier): """Create a new instan...
class Chapter(Resource): '''A chapter is a list of scenes.''' def __init__(self, project, identifier): '''Create a new instance of Chapter.''' pass def remove_scene(self, scene: Scene): '''Remove a scene from the chapter.''' pass def add_scene(self, scene: Scene, posi...
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cgueret/Scriptorium
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/cgueret_Scriptorium/scriptorium/models/commit_message.py
scriptorium.models.commit_message.CommitMessage
from gi.repository import GObject class CommitMessage(GObject.Object): """A commit message is a message with a date.""" def __init__(self, datetime, message): """Create a new message.""" super().__init__() self._datetime = datetime self._message = message @GObject.Property...
class CommitMessage(GObject.Object): '''A commit message is a message with a date.''' def __init__(self, datetime, message): '''Create a new message.''' pass @GObject.Property(type=str) def datetime(self) -> str: pass @GObject.Property(type=str) def message(self) -> str...
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cgueret/Scriptorium
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/cgueret_Scriptorium/scriptorium/models/entity.py
scriptorium.models.entity.Entity
from .resource import Resource from gi.repository import GObject class Entity(Resource): """An entity is a story element (person, place, prop, ...)""" __gtype_name__ = 'Entity' category = GObject.Property(type=str) def __init__(self, project, identifier: str): """Create an entity.""" s...
class Entity(Resource): '''An entity is a story element (person, place, prop, ...)''' def __init__(self, project, identifier: str): '''Create an entity.''' pass @GObject.Property(type=GObject.Object) def manuscript(self): '''Return the manuscript the entity is associated to.'''...
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cgueret/Scriptorium
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/cgueret_Scriptorium/scriptorium/models/image.py
scriptorium.models.image.Image
from .resource import Resource import shutil from gi.repository import GObject, Gio, Gdk from pathlib import Path class Image(Resource): __gtype_name__ = 'Image' file_name = GObject.Property(type=str) def __init__(self, project, identifier: str): """Create an image instance.""" super().__i...
class Image(Resource): def __init__(self, project, identifier: str): '''Create an image instance.''' pass @property def data_files(self): '''Return the file path for the image if it has been set.''' pass @property def path(self): pass @property def w...
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cgueret/Scriptorium
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/cgueret_Scriptorium/scriptorium/models/library.py
scriptorium.models.library.Library
from pathlib import Path from gi.repository import GObject, Gio import shutil from .project import Project from .manuscript import Manuscript import uuid class Library(GObject.Object): """The library is the collection of projects.""" projects: GObject.Property = GObject.Property(type=Gio.ListStore) def __...
class Library(GObject.Object): '''The library is the collection of projects.''' def __init__(self): '''Create an instance of the library for the target folder.''' pass def open_folder(self, base_directory: str): pass @property def base_directory(self) -> Path: '''T...
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cgueret/Scriptorium
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/cgueret_Scriptorium/scriptorium/models/manuscript.py
scriptorium.models.manuscript.Manuscript
from .chapter import Chapter from .resource import Resource from .scene import Scene from gi.repository import GObject, Gio from .image import Image class Manuscript(Resource): """A manuscript is a collection of scenes and chapters.""" __gtype_name__ = 'Manuscript' content = GObject.Property(type=Gio.ListS...
class Manuscript(Resource): '''A manuscript is a collection of scenes and chapters.''' def __init__(self, project, identifier): '''Create a new manuscript.''' pass def add_resource(self, resource: Resource, position: int=None) -> bool: '''Add an existing resource to the manuscript...
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cgueret/Scriptorium
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/cgueret_Scriptorium/scriptorium/models/project.py
scriptorium.models.project.Project
import git from .chapter import Chapter from .manuscript import Manuscript from .scene import Scene from gi.repository import GObject, Gio, Gtk import uuid from .entity import Entity from .resource import Resource import yaml from pathlib import Path from .image import Image class Project(GObject.Object): __gtype_...
class Project(GObject.Object): def __init__(self, project_path): '''Create a resource.''' pass def _set_can_be_opened(self): '''Set property to true if the project has the right format.''' pass def migrate(self) -> bool: '''Migrate the project to the current versi...
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cgueret/Scriptorium
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/cgueret_Scriptorium/scriptorium/models/resource.py
scriptorium.models.resource.Resource
from gi.repository import GObject, Gio class Resource(GObject.Object): __gtype_name__ = 'Resource' identifier = GObject.Property(type=str) title = GObject.Property(type=str) synopsis = GObject.Property(type=str) deleted = GObject.Signal() def __init__(self, project, identifier: str): "...
class Resource(GObject.Object): def __init__(self, project, identifier: str): '''Create a resource.''' pass @property def project(self): pass @property def data_files(self): pass @property def references(self): '''Provide a list of other resources re...
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cgueret/Scriptorium
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/cgueret_Scriptorium/scriptorium/models/scene.py
scriptorium.models.scene.Scene
from pathlib import Path from .entity import Entity from scriptorium.utils import html_to_buffer, buffer_to_html from gi.repository import Gtk, GObject, Gio from .resource import Resource from .commit_message import CommitMessage class Scene(Resource): """A scene is a basic building block of manuscripts.""" __...
class Scene(Resource): '''A scene is a basic building block of manuscripts.''' def __init__(self, project, identifier: str): '''Create a scene.''' pass @property def data_files(self): pass @property def history(self): '''Return the history of commits about that ...
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cgueret/Scriptorium
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/cgueret_Scriptorium/scriptorium/utils/publisher.py
scriptorium.utils.publisher.Publisher
from scriptorium.models import Resource, Manuscript, Chapter, Scene from gi.repository import Gio from jinja2 import Environment, PackageLoader, select_autoescape from scriptorium.globals import BASE import io from ebooklib import epub class Publisher(object): """ Publisher is a helper class to encapsulate the...
class Publisher(object): ''' Publisher is a helper class to encapsulate the content of the manuscript as a set of HTML files. This content can be used as a view in the editor or exported to disk as an epub. ''' def __init__(self, manuscript: Manuscript): '''Create a new instance of a P...
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