id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
179,372 | import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from pathlib import Path
import datasets
import torch
from build_dataset import build_instruction_dataset, DataCollatorForSupervisedDataset
import transformers
from transformers import (
CONFIG_MAPPI... | r""" This method wraps the entire protocol for preparing a model before running a training. This includes: 1- Cast the layernorm in fp32 2- making output embedding layer require grads 3- Add the upcasting of the lm head to fp32 Args: model, (`transformers.PreTrainedModel`): The loaded model from `transformers` |
179,373 | import logging
import os
from dataclasses import dataclass
from typing import Dict, Sequence, Union, List
import datasets
import torch
from datasets import load_dataset, concatenate_datasets
import transformers
IGNORE_INDEX = -100
logger = logging.getLogger('__name__')
PROMPT_TEMPLATE = (
"[INST] <<SYS>>\n"
... | null |
179,374 | from datasets import load_dataset
import torch
import random
import numpy as np
import json
from transformers import LlamaTokenizer, AutoModelForCausalLM
from transformers import BitsAndBytesConfig
from tqdm import tqdm
import os
import argparse
import sys
from attn_and_long_ctx_patches import apply_attention_patch, ap... | null |
179,375 | from datasets import load_dataset
import torch
import random
import numpy as np
import json
from transformers import LlamaTokenizer, AutoModelForCausalLM
from transformers import BitsAndBytesConfig
from tqdm import tqdm
import os
import argparse
import sys
from attn_and_long_ctx_patches import apply_attention_patch, ap... | null |
179,376 | import os
import json
import argparse
import numpy as np
from metrics import (
qa_f1_score,
rouge_zh_score,
qa_f1_zh_score,
rouge_score,
classification_score,
retrieval_score,
retrieval_zh_score,
count_score,
code_sim_score,
)
def parse_args(args=None):
parser = argparse.Argumen... | null |
179,377 | import os
import json
import argparse
import numpy as np
from metrics import (
qa_f1_score,
rouge_zh_score,
qa_f1_zh_score,
rouge_score,
classification_score,
retrieval_score,
retrieval_zh_score,
count_score,
code_sim_score,
)
dataset2metric = {
"narrativeqa": qa_f1_score,
"q... | null |
179,378 | import os
import json
import argparse
import numpy as np
from metrics import (
qa_f1_score,
rouge_zh_score,
qa_f1_zh_score,
rouge_score,
classification_score,
retrieval_score,
retrieval_zh_score,
count_score,
code_sim_score,
)
dataset2metric = {
"narrativeqa": qa_f1_score,
"q... | null |
179,379 | import re
import string
import jieba
from fuzzywuzzy import fuzz
import difflib
from collections import Counter
from rouge import Rouge
def count_score(prediction, ground_truth, **kwargs):
numbers = re.findall(r"\d+", prediction)
right_num = 0
for number in numbers:
if str(number) == str(ground_tru... | null |
179,380 | import re
import string
import jieba
from fuzzywuzzy import fuzz
import difflib
from collections import Counter
from rouge import Rouge
def retrieval_score(prediction, ground_truth, **kwargs):
pattern = r'Paragraph (\d+)'
matches = re.findall(pattern, ground_truth)
ground_truth_id = matches[0]
numbers ... | null |
179,381 | import re
import string
import jieba
from fuzzywuzzy import fuzz
import difflib
from collections import Counter
from rouge import Rouge
def retrieval_zh_score(prediction, ground_truth, **kwargs):
pattern = r'段落(\d+)'
matches = re.findall(pattern, ground_truth)
ground_truth_id = matches[0]
numbers = re.... | null |
179,382 | import re
import string
import jieba
from fuzzywuzzy import fuzz
import difflib
from collections import Counter
from rouge import Rouge
def code_sim_score(prediction, ground_truth, **kwargs):
all_lines = prediction.lstrip('\n').split('\n')
prediction = ""
for line in all_lines:
if ('`' not in line)... | null |
179,383 | import re
import string
import jieba
from fuzzywuzzy import fuzz
import difflib
from collections import Counter
from rouge import Rouge
def classification_score(prediction, ground_truth, **kwargs):
em_match_list = []
all_classes = kwargs["all_classes"]
for class_name in all_classes:
if class_name i... | null |
179,384 | import re
import string
import jieba
from fuzzywuzzy import fuzz
import difflib
from collections import Counter
from rouge import Rouge
def rouge_score(prediction, ground_truth, **kwargs):
def rouge_zh_score(prediction, ground_truth, **kwargs):
prediction = " ".join(list(jieba.cut(prediction, cut_all=False)))
... | null |
179,385 | import re
import string
import jieba
from fuzzywuzzy import fuzz
import difflib
from collections import Counter
from rouge import Rouge
def normalize_answer(s):
"""Lower text and remove punctuation, articles and extra whitespace."""
def remove_articles(text):
return re.sub(r"\b(a|an|the)\b", " ", text)
... | null |
179,386 | import re
import string
import jieba
from fuzzywuzzy import fuzz
import difflib
from collections import Counter
from rouge import Rouge
def normalize_zh_answer(s):
"""Lower text and remove punctuation, extra whitespace."""
def white_space_fix(text):
return "".join(text.split())
def remove_punc(text)... | null |
179,387 | import argparse
import json
import os
import gc
import torch
import peft
from transformers import LlamaTokenizer
from transformers.modeling_utils import dtype_byte_size
from huggingface_hub import snapshot_download
import re
import shutil
def jsonload(filename):
with open(filename, "r") as file:
d = json.l... | null |
179,388 | import argparse
import json
import os
import gc
import torch
import peft
from transformers import LlamaTokenizer
from transformers.modeling_utils import dtype_byte_size
from huggingface_hub import snapshot_download
import re
import shutil
def translate_state_dict_key(k):
k = k.replace("base_model.model.", "")
i... | Convert and save the HF format weights to PTH format weights |
179,389 | import argparse
import json
import os
import gc
import torch
import peft
from transformers import LlamaTokenizer
from transformers.modeling_utils import dtype_byte_size
from huggingface_hub import snapshot_download
import re
import shutil
def merge_shards(output_dir, num_shards: int):
ckpt_filenames = sorted([f fo... | null |
179,390 | from __future__ import annotations
import datetime as dt
import logging
from typing import Dict, List, Optional
from dateutil.parser import parse
from dbt_semantic_interfaces.protocols.semantic_manifest import SemanticManifest
from dbt_semantic_interfaces.validations.semantic_manifest_validator import SemanticManifestV... | Callback to convert string to datetime given as an iso8601 timestamp. |
179,391 | from __future__ import annotations
import logging
import pprint
from collections.abc import Mapping
from dataclasses import fields, is_dataclass
from enum import Enum
from typing import Any, Dict, List, Optional, Sized, Union
from pydantic import BaseModel
from metricflow.mf_logging.formatting import indent
def mf_pfor... | Prints many objects in an indented form. |
179,392 | from __future__ import annotations
import functools
import logging
import time
from contextlib import contextmanager
from typing import Callable, Iterator, TypeVar
from typing_extensions import ParamSpec
logger = logging.getLogger(__name__)
ReturnType = TypeVar("ReturnType")
ParametersType = ParamSpec("ParametersType")... | Logs how long a function took to run. If the runtime exceeds runtime_warning_threshold, then a warning is logged. |
179,393 | from __future__ import annotations
import functools
import logging
import time
from contextlib import contextmanager
from typing import Callable, Iterator, TypeVar
from typing_extensions import ParamSpec
logger = logging.getLogger(__name__)
The provided code snippet includes necessary dependencies for implementing the... | Logs the runtime of the enclosed code block. |
179,394 | from __future__ import annotations
from typing import List
from dbt_semantic_interfaces.call_parameter_sets import ParseWhereFilterException
from dbt_semantic_interfaces.implementations.filters.where_filter import PydanticWhereFilter
from metricflow.naming.linkable_spec_name import StructuredLinkableSpecName
from metri... | Parses a string following the object-builder naming scheme into the corresponding GroupByParameter. The implementation of the query parameter classes seems incomplete and there needs to be follow up with the author of the query interface classes for the best approach. Right now, it seems like using the where filter is ... |
179,395 | from __future__ import annotations
import itertools
import logging
from abc import ABC, abstractmethod
from dataclasses import dataclass
from enum import Enum
from hashlib import sha1
from typing import TYPE_CHECKING, Any, Dict, Generic, List, Optional, Sequence, Tuple, TypeVar, Union
from dbt_semantic_interfaces.datac... | Produces a hash from a list of strings. |
179,396 | from __future__ import annotations
import contextlib
from abc import ABC, abstractmethod
from dataclasses import InitVar, dataclass, field
from datetime import date, datetime
from enum import Enum
from typing import Callable, ContextManager, Dict, Generic, Iterator, List, Optional, TypeVar
from metricflow.dataflow.sql_... | null |
179,397 | from __future__ import annotations
from enum import Enum
from dbt_semantic_interfaces.enum_extension import assert_values_exhausted
from dbt_semantic_interfaces.type_enums.aggregation_type import AggregationType
The provided code snippet includes necessary dependencies for implementing the `is_expansive` function. Wri... | Expansive ≝ Op( X ∪ Y ∪ ...) = Op( Op(X) ∪ Op(Y) ∪ ...). NOTE: COUNT is only expansive because it's transformed into a SUM agg during model transformation |
179,398 | from __future__ import annotations
from enum import Enum
from dbt_semantic_interfaces.enum_extension import assert_values_exhausted
from dbt_semantic_interfaces.type_enums.aggregation_type import AggregationType
The provided code snippet includes necessary dependencies for implementing the `is_additive` function. Writ... | Indicates that if you sum values over a dimension grouping, you will still get an accurate result for this metric. |
179,399 | from __future__ import annotations
from enum import Enum
from dbt_semantic_interfaces.enum_extension import assert_values_exhausted
from dbt_semantic_interfaces.type_enums.aggregation_type import AggregationType
The provided code snippet includes necessary dependencies for implementing the `fill_nulls_with_0` function... | Indicates if charts should show 0 instead of null where there are gaps in data. |
179,400 | from __future__ import annotations
from enum import Enum
from dbt_semantic_interfaces.enum_extension import assert_values_exhausted
from dbt_semantic_interfaces.type_enums.aggregation_type import AggregationType
The provided code snippet includes necessary dependencies for implementing the `can_limit_dimension_values`... | Indicates if we can limit dimension values in charts. Currently, this means: 1. The dimensions we care about most are the ones with the highest numeric values 2. We can calculate the "other" column in the postprocessor (meaning the metric is expansive) |
179,401 | from __future__ import annotations
The provided code snippet includes necessary dependencies for implementing the `assert_exactly_one_arg_set` function. Write a Python function `def assert_exactly_one_arg_set(**kwargs) -> None` to solve the following problem:
Throws an assertion error if 0 or more than 1 argument is n... | Throws an assertion error if 0 or more than 1 argument is not None. |
179,402 | from __future__ import annotations
The provided code snippet includes necessary dependencies for implementing the `assert_at_most_one_arg_set` function. Write a Python function `def assert_at_most_one_arg_set(**kwargs) -> None` to solve the following problem:
Throws an assertion error if more than 1 argument is not No... | Throws an assertion error if more than 1 argument is not None. |
179,403 | from __future__ import annotations
import html
import logging
import textwrap
from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import Any, Generic, Sequence, TypeVar
import jinja2
from metricflow.dag.dag_to_text import MetricFlowDagTextFormatter
from metricflow.dag.id_prefix import IdPr... | Make a graphviz label that can be used for rendering to an image. The title will be in a large font, while the properties will be listed in a table in a smaller font. |
179,404 | from __future__ import annotations
from typing import List, Sequence
from metricflow.sql.sql_exprs import (
SqlAggregateFunctionExpression,
SqlColumnReference,
SqlColumnReferenceExpression,
SqlExpressionNode,
SqlFunction,
)
class SqlExpressionNode(DagNode, Visitable, ABC):
"""An SQL expression ... | Makes a coalesced expression of the given column from the given table aliases. e.g. table_aliases = ["a", "b"] column_alias = "is_instant" -> COALESCE(a.is_instant, b.is_instant) |
179,405 | from __future__ import annotations
import logging
from collections import OrderedDict
from dataclasses import dataclass
from itertools import chain
from typing import Dict, List, Optional, Sequence, Tuple
from dbt_semantic_interfaces.references import MetricReference, SemanticModelReference
from dbt_semantic_interfaces... | Creates select columns for instance sets coming from multiple table as defined in table_alias_to_instance_set. Used in cases where you join multiple tables and need to render select columns to access all of those. |
179,406 | from __future__ import annotations
import logging
from collections import OrderedDict
from typing import List, Optional, Sequence, Tuple, Union
from dbt_semantic_interfaces.enum_extension import assert_values_exhausted
from dbt_semantic_interfaces.naming.keywords import METRIC_TIME_ELEMENT_NAME
from dbt_semantic_interf... | Build an expression like "ds BETWEEN CAST('2020-01-01' AS TIMESTAMP) AND CAST('2020-01-02' AS TIMESTAMP). |
179,407 | from __future__ import annotations
import collections
from dataclasses import dataclass
from typing import Dict, Optional, Sequence, Tuple
from metricflow.specs.specs import MeasureSpec, NonAdditiveDimensionSpec
class GroupedMeasureSpecsByAdditiveness:
"""Results after grouping measures by their additive properties... | Bucket the provided measure specs by. - Additive Measures - Semi-additive measures containing the same non-additive dimension attributes |
179,408 | from __future__ import annotations
import datetime as dt
import logging
import pathlib
import traceback
from functools import update_wrapper, wraps
from typing import Any, Callable, List, Optional
import click
from dateutil.parser import parse
import metricflow.cli.custom_click_types as click_custom
from metricflow.cli... | Common options for a query. |
179,409 | from __future__ import annotations
import datetime as dt
import logging
import pathlib
import traceback
from functools import update_wrapper, wraps
from typing import Any, Callable, List, Optional
import click
from dateutil.parser import parse
import metricflow.cli.custom_click_types as click_custom
from metricflow.cli... | null |
179,410 | from __future__ import annotations
import datetime as dt
import logging
import pathlib
import traceback
from functools import update_wrapper, wraps
from typing import Any, Callable, List, Optional
import click
from dateutil.parser import parse
import metricflow.cli.custom_click_types as click_custom
from metricflow.cli... | Decorator to handle exceptions. |
179,411 | from __future__ import annotations
import datetime as dt
import logging
import pathlib
import traceback
from functools import update_wrapper, wraps
from typing import Any, Callable, List, Optional
import click
from dateutil.parser import parse
import metricflow.cli.custom_click_types as click_custom
from metricflow.cli... | Decorator to output an error message and exit if caller is not in a root directory of a dbt project. |
179,412 | from __future__ import annotations
import datetime as dt
import logging
import pathlib
import signal
import sys
import tempfile
import textwrap
import time
import warnings
from importlib.metadata import version as pkg_version
from typing import Callable, List, Optional, Sequence
import click
import jinja2
import pandas... | null |
179,413 | from __future__ import annotations
import datetime as dt
import logging
import pathlib
import signal
import sys
import tempfile
import textwrap
import time
import warnings
from importlib.metadata import version as pkg_version
from typing import Callable, List, Optional, Sequence
import click
import jinja2
import pandas... | Run user through a tutorial. |
179,414 | from __future__ import annotations
import datetime as dt
import logging
import pathlib
import signal
import sys
import tempfile
import textwrap
import time
import warnings
from importlib.metadata import version as pkg_version
from typing import Callable, List, Optional, Sequence
import click
import jinja2
import pandas... | Create a new query with MetricFlow and assembles a MetricFlowQueryResult. |
179,415 | from __future__ import annotations
import datetime as dt
import logging
import pathlib
import signal
import sys
import tempfile
import textwrap
import time
import warnings
from importlib.metadata import version as pkg_version
from typing import Callable, List, Optional, Sequence
import click
import jinja2
import pandas... | Retrieve metadata values about metrics/dimensions/entities/dimension values. |
179,416 | from __future__ import annotations
import datetime as dt
import logging
import pathlib
import signal
import sys
import tempfile
import textwrap
import time
import warnings
from importlib.metadata import version as pkg_version
from typing import Callable, List, Optional, Sequence
import click
import jinja2
import pandas... | List the metrics with their available dimensions. Automatically truncates long lists of dimensions, pass --show-all-dims to see all. |
179,417 | from __future__ import annotations
import datetime as dt
import logging
import pathlib
import signal
import sys
import tempfile
import textwrap
import time
import warnings
from importlib.metadata import version as pkg_version
from typing import Callable, List, Optional, Sequence
import click
import jinja2
import pandas... | List all unique entities. |
179,418 | from __future__ import annotations
import datetime as dt
import logging
import pathlib
import signal
import sys
import tempfile
import textwrap
import time
import warnings
from importlib.metadata import version as pkg_version
from typing import Callable, List, Optional, Sequence
import click
import jinja2
import pandas... | Performs a health check against the DW provided in the configs. |
179,419 | from __future__ import annotations
import datetime as dt
import logging
import pathlib
import signal
import sys
import tempfile
import textwrap
import time
import warnings
from importlib.metadata import version as pkg_version
from typing import Callable, List, Optional, Sequence
import click
import jinja2
import pandas... | List all dimension values with the corresponding metrics. |
179,420 | from __future__ import annotations
import datetime as dt
import logging
import pathlib
import signal
import sys
import tempfile
import textwrap
import time
import warnings
from importlib.metadata import version as pkg_version
from typing import Callable, List, Optional, Sequence
import click
import jinja2
import pandas... | Perform validations against the defined model configurations. |
179,421 | from __future__ import annotations
from typing import Optional
from dbt_semantic_interfaces.implementations.filters.where_filter import PydanticWhereFilter
from dbt_semantic_interfaces.protocols import WhereFilter, WhereFilterIntersection
The provided code snippet includes necessary dependencies for implementing the `... | Returns a single where filter that is equivalent to the given intersection. |
179,422 | from __future__ import annotations
from datetime import date
from typing import Union
import pandas as pd
from dbt_semantic_interfaces.enum_extension import ExtendedEnum, assert_values_exhausted
from dbt_semantic_interfaces.type_enums.time_granularity import TimeGranularity
The provided code snippet includes necessary... | Offset object to use for adjusting by one granularity period. |
179,423 | from __future__ import annotations
from datetime import date
from typing import Union
import pandas as pd
from dbt_semantic_interfaces.enum_extension import ExtendedEnum, assert_values_exhausted
from dbt_semantic_interfaces.type_enums.time_granularity import TimeGranularity
The provided code snippet includes necessary... | Indicates that this can only be calculated if query results display the first or last date of the period. |
179,424 | from __future__ import annotations
from datetime import date
from typing import Union
import pandas as pd
from dbt_semantic_interfaces.enum_extension import ExtendedEnum, assert_values_exhausted
from dbt_semantic_interfaces.type_enums.time_granularity import TimeGranularity
def is_period_start(time_granularity: TimeGra... | Adjust date_to_adjust to be start or end of period based on if date_to_match is at start or end of period. |
179,425 | from __future__ import annotations
from datetime import date
from typing import Union
import pandas as pd
from dbt_semantic_interfaces.enum_extension import ExtendedEnum, assert_values_exhausted
from dbt_semantic_interfaces.type_enums.time_granularity import TimeGranularity
def string_to_time_granularity(s: str) -> Ti... | null |
179,426 | from __future__ import annotations
from dataclasses import dataclass
from typing import Sequence
import rapidfuzz.fuzz
import rapidfuzz.process
class ScoredItem: # noqa: D
item_str: str
# fuzz scores from 0..100, and the higher the score, the better the match.
score: float
The provided code snippet includ... | Return the top items (by edit distance) in candidate_items that fuzzy matches the given item. Return scores from -1 -> 0 inclusive. |
179,427 | from __future__ import annotations
from dbt_semantic_interfaces.implementations.semantic_manifest import (
PydanticSemanticManifest,
)
from dbt_semantic_interfaces.transformations.boolean_measure import (
BooleanMeasureAggregationRule,
)
from dbt_semantic_interfaces.transformations.convert_count import ConvertC... | Parse a PydanticSemanticManifest given the generated semantic_manifest json from dbt. |
179,428 | from __future__ import annotations
import logging
import time
from collections import defaultdict
from dataclasses import dataclass
from typing import Dict, FrozenSet, List, Optional, Sequence, Set, Tuple
from dbt_semantic_interfaces.enum_extension import assert_values_exhausted
from dbt_semantic_interfaces.protocols.d... | Generates different versions of the given dimension, but at other valid time granularities. |
179,429 | from __future__ import annotations
import datetime
import functools
import logging
import os
import platform
import sys
import time
import traceback
import uuid
from hashlib import sha256
from typing import Callable, List, Optional, TypeVar
from typing_extensions import ParamSpec
from metricflow.random_id import random... | Decorator to make it easier to log telemetry for function calls. Using module_name instead of introspection since it seems more robust. Example call: @log_call(telemetry_reporter=telemetry_reporter, module_name=__name__) def test_function() -> str: return "foo" |
179,430 | import torch
import dataclasses
from dataclasses import dataclass
import logging
import os
import io
import json
from typing import Sequence, Dict, List, Any
import copy
from EdgeGPT import Chatbot, ConversationStyle
import transformers
from torch.utils.data import Dataset
from enum import auto, Enum
async def edgegpt... | null |
179,431 | import torch
import dataclasses
from dataclasses import dataclass
import logging
import os
import io
import json
from typing import Sequence, Dict, List, Any
import copy
from EdgeGPT import Chatbot, ConversationStyle
import transformers
from torch.utils.data import Dataset
from enum import auto, Enum
def _make_w_io_bas... | Dump a str or dictionary to a file in json format. Args: obj: An object to be written. f: A string path to the location on disk. mode: Mode for opening the file. indent: Indent for storing json dictionaries. default: A function to handle non-serializable entries; defaults to `str`. |
179,432 | import torch
import dataclasses
from dataclasses import dataclass
import logging
import os
import io
import json
from typing import Sequence, Dict, List, Any
import copy
from EdgeGPT import Chatbot, ConversationStyle
import transformers
from torch.utils.data import Dataset
from enum import auto, Enum
def _make_r_io_bas... | Load a .json file into a dictionary. |
179,433 | import torch
import dataclasses
from dataclasses import dataclass
import logging
import os
import io
import json
from typing import Sequence, Dict, List, Any
import copy
from EdgeGPT import Chatbot, ConversationStyle
import transformers
from torch.utils.data import Dataset
from enum import auto, Enum
The provided code... | Collects the state dict and dump to disk. |
179,434 | import torch
import dataclasses
from dataclasses import dataclass
import logging
import os
import io
import json
from typing import Sequence, Dict, List, Any
import copy
from EdgeGPT import Chatbot, ConversationStyle
import transformers
from torch.utils.data import Dataset
from enum import auto, Enum
The provided code... | Resize tokenizer and embedding. Note: This is the unoptimized version that may make your embedding size not be divisible by 64. |
179,435 | import torch
import dataclasses
from dataclasses import dataclass
import logging
import os
import io
import json
from typing import Sequence, Dict, List, Any
import copy
from EdgeGPT import Chatbot, ConversationStyle
import transformers
from torch.utils.data import Dataset
from enum import auto, Enum
default_conversati... | Given a list of sources, each is a conversation list. This transform: 1. Add signal '### ' at the beginning each sentence, with end signal '\n'; 2. Concatenate conversations together; 3. Tokenize the concatenated conversation; 4. Make a deepcopy as the target. Mask human words with IGNORE_INDEX. |
179,436 | import torch
import dataclasses
from dataclasses import dataclass
import logging
import os
import io
import json
from typing import Sequence, Dict, List, Any
import copy
from EdgeGPT import Chatbot, ConversationStyle
import transformers
from torch.utils.data import Dataset
from enum import auto, Enum
class SupervisedDa... | Make dataset and collator for supervised fine-tuning. |
179,437 | import torch
import dataclasses
from dataclasses import dataclass
import logging
import os
import io
import json
from typing import Sequence, Dict, List, Any
import copy
from EdgeGPT import Chatbot, ConversationStyle
import transformers
from torch.utils.data import Dataset
from enum import auto, Enum
def convert_vicun... | null |
179,438 | import torch
import dataclasses
from dataclasses import dataclass
import logging
import os
import io
import json
from typing import Sequence, Dict, List, Any
import copy
from EdgeGPT import Chatbot, ConversationStyle
import transformers
from torch.utils.data import Dataset
from enum import auto, Enum
def generate_stre... | null |
179,439 | import os
import logging
import transformers
from transformers import LlamaForCausalLM, LlamaTokenizer
from peft import (
LoraConfig,
get_peft_model,
get_peft_model_state_dict,
)
from omegaconf import OmegaConf
from ingest_docs import ingest_docs
from data_gen import launch_data_generation
from langchain.em... | null |
179,440 | import os
import time
import utils
import json
import random
import string
import regex as re
import pickle
import openai
import tqdm
import asyncio
import tiktoken
from langchain.docstore.document import Document
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores.faiss import FAISS
def find... | null |
179,441 | import os
import time
import utils
import json
import random
import string
import regex as re
import pickle
import openai
import tqdm
import asyncio
import tiktoken
from langchain.docstore.document import Document
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores.faiss import FAISS
def laun... | null |
179,442 | import os
import time
import utils
import json
import random
import string
import regex as re
import pickle
import openai
import tqdm
import asyncio
import tiktoken
from langchain.docstore.document import Document
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores.faiss import FAISS
def launc... | null |
179,443 | import pickle as pkl
from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig
import torch
from utils import conv_v1_2, SeparatorStyle
from utils import generate_stream as generate_stream_func
import argparse
import os.path as osp
def args_parse():
parser = argparse.ArgumentParser(description='I... | null |
179,444 | import pickle as pkl
from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig
import torch
from utils import conv_v1_2, SeparatorStyle
from utils import generate_stream as generate_stream_func
import argparse
import os.path as osp
class SimpleChatIO:
def prompt_for_input(self, role) -> str:
... | null |
179,445 | from typing import List, Optional, Tuple
import torch
import transformers
from transformers.models.llama.modeling_llama import apply_rotary_pos_emb
from einops import rearrange
from flash_attn.flash_attn_interface import flash_attn_unpadded_qkvpacked_func
from flash_attn.bert_padding import unpad_input, pad_input
def f... | null |
179,446 | import os
from collections import deque
from typing import Dict, List, Optional, Any
from langchain import LLMChain, OpenAI, PromptTemplate
from langchain.embeddings import OpenAIEmbeddings
from langchain.llms import BaseLLM
from langchain.vectorstores.base import VectorStore
from pydantic import BaseModel, Field
from ... | Get the next task. |
179,447 | import os
from collections import deque
from typing import Dict, List, Optional, Any
from langchain import LLMChain, OpenAI, PromptTemplate
from langchain.embeddings import OpenAIEmbeddings
from langchain.llms import BaseLLM
from langchain.vectorstores.base import VectorStore
from pydantic import BaseModel, Field
from ... | Prioritize tasks. |
179,448 | import os
from collections import deque
from typing import Dict, List, Optional, Any
from langchain import LLMChain, OpenAI, PromptTemplate
from langchain.embeddings import OpenAIEmbeddings
from langchain.llms import BaseLLM
from langchain.vectorstores.base import VectorStore
from pydantic import BaseModel, Field
from ... | Execute a task. |
179,449 | from memory_store import MemoryStorage
from disk_store import DiskStorage
class MemoryStorage:
def __init__(self) -> None:
self.data: dict[str, str] = {}
def set(self, key: str, value: str) -> None:
self.data[key] = value
def get(self, key: str) -> str:
return self.data.get(key, "... | null |
179,450 | from memory_store import MemoryStorage
from disk_store import DiskStorage
class DiskStorage:
"""
Implements the KV store on the disk
Args:
file_name (str): name of the file where all the data will be written. Just
passing the file name will save the data in the current directory. You m... | null |
179,451 | from memory_store import MemoryStorage
from disk_store import DiskStorage
class DiskStorage:
"""
Implements the KV store on the disk
Args:
file_name (str): name of the file where all the data will be written. Just
passing the file name will save the data in the current directory. You m... | null |
179,452 | import struct
import typing
HEADER_SIZE: typing.Final[int] = 12
def encode_header(timestamp: int, key_size: int, value_size: int) -> bytes:
"""
encode_header encodes the data into bytes using the `HEADER_FORMAT` format
string
Args:
timestamp (int): Timestamp at which we wrote the KV pair to the ... | encode_kv encodes the KV pair into bytes Args: timestamp (int): Timestamp at which we wrote the KV pair to the disk. The value is current time in seconds since the epoch. key (str): the key (cannot exceed the maximum size) value (str): the value (cannot exceed the maximum size) Returns: tuple containing the size of enc... |
179,453 | import struct
import typing
HEADER_FORMAT: typing.Final[str] = "<LLL"
HEADER_SIZE: typing.Final[int] = 12
The provided code snippet includes necessary dependencies for implementing the `decode_kv` function. Write a Python function `def decode_kv(data: bytes) -> tuple[int, str, str]` to solve the following problem:
dec... | decode_kv decodes the data bytes into appropriate KV pair Args: data (bytes): byte object containing the encoded KV data Returns: A tuple containing: timestamp (int): timestamp in epoch seconds key (str): the key value (str): the value Raises: struct.error: when parameters don't match the specific type / size IndexErro... |
179,454 | import struct
import typing
HEADER_FORMAT: typing.Final[str] = "<LLL"
The provided code snippet includes necessary dependencies for implementing the `decode_header` function. Write a Python function `def decode_header(data: bytes) -> tuple[int, int, int]` to solve the following problem:
decode_header decodes the bytes... | decode_header decodes the bytes into header using the `HEADER_FORMAT` format string Args: data (bytes): byte object containing the encoded header data Returns: A tuple containing: timestamp (int): timestamp in epoch seconds key_size (int): size of the key value_size (int): size of the value Raises: struct.error: when p... |
179,455 | import os.path
import pathlib
import json
from datetime import date
def year_month(date_str):
# extract string of year-month from date, eg: '2023-03'
return str(date_str)[:7] | null |
179,456 | from __future__ import annotations
import asyncio
import itertools
import json
import logging
import os
import base64
import telegram
from telegram import Message, MessageEntity, Update, ChatMember, constants
from telegram.ext import CallbackContext, ContextTypes
from usage_tracker import UsageTracker
The provided cod... | Returns the text of a message, excluding any bot commands. |
179,457 | from __future__ import annotations
import asyncio
import itertools
import json
import logging
import os
import base64
import telegram
from telegram import Message, MessageEntity, Update, ChatMember, constants
from telegram.ext import CallbackContext, ContextTypes
from usage_tracker import UsageTracker
def is_group_chat... | Gets the stream cutoff values for the message length |
179,458 | from __future__ import annotations
import asyncio
import itertools
import json
import logging
import os
import base64
import telegram
from telegram import Message, MessageEntity, Update, ChatMember, constants
from telegram.ext import CallbackContext, ContextTypes
from usage_tracker import UsageTracker
The provided cod... | Splits a string into chunks of a given size. |
179,459 | from __future__ import annotations
import asyncio
import itertools
import json
import logging
import os
import base64
import telegram
from telegram import Message, MessageEntity, Update, ChatMember, constants
from telegram.ext import CallbackContext, ContextTypes
from usage_tracker import UsageTracker
def get_thread_id... | Wraps a coroutine while repeatedly sending a chat action to the user. |
179,460 | from __future__ import annotations
import asyncio
import itertools
import json
import logging
import os
import base64
import telegram
from telegram import Message, MessageEntity, Update, ChatMember, constants
from telegram.ext import CallbackContext, ContextTypes
from usage_tracker import UsageTracker
The provided cod... | Edit a message with retry logic in case of failure (e.g. broken markdown) :param context: The context to use :param chat_id: The chat id to edit the message in :param message_id: The message id to edit :param text: The text to edit the message with :param markdown: Whether to use markdown parse mode :param is_inline: W... |
179,461 | from __future__ import annotations
import asyncio
import itertools
import json
import logging
import os
import base64
import telegram
from telegram import Message, MessageEntity, Update, ChatMember, constants
from telegram.ext import CallbackContext, ContextTypes
from usage_tracker import UsageTracker
The provided cod... | Handles errors in the telegram-python-bot library. |
179,462 | from __future__ import annotations
import asyncio
import itertools
import json
import logging
import os
import base64
import telegram
from telegram import Message, MessageEntity, Update, ChatMember, constants
from telegram.ext import CallbackContext, ContextTypes
from usage_tracker import UsageTracker
async def is_user... | Checks if the user is allowed to use the bot. |
179,463 | from __future__ import annotations
import asyncio
import itertools
import json
import logging
import os
import base64
import telegram
from telegram import Message, MessageEntity, Update, ChatMember, constants
from telegram.ext import CallbackContext, ContextTypes
from usage_tracker import UsageTracker
def get_remaining... | Checks if the user reached their usage limit. Initializes UsageTracker for user and guest when needed. :param config: The bot configuration object :param usage: The usage tracker object :param update: Telegram update object :param is_inline: Boolean flag for inline queries :return: Boolean indicating if the user has a ... |
179,464 | from __future__ import annotations
import asyncio
import itertools
import json
import logging
import os
import base64
import telegram
from telegram import Message, MessageEntity, Update, ChatMember, constants
from telegram.ext import CallbackContext, ContextTypes
from usage_tracker import UsageTracker
The provided cod... | Add chat request to usage tracker :param usage: The usage tracker object :param config: The bot configuration object :param user_id: The user id :param used_tokens: The number of tokens used |
179,465 | from __future__ import annotations
import asyncio
import itertools
import json
import logging
import os
import base64
import telegram
from telegram import Message, MessageEntity, Update, ChatMember, constants
from telegram.ext import CallbackContext, ContextTypes
from usage_tracker import UsageTracker
The provided cod... | Checks if the dict contains a direct result that can be sent directly to the user :param response: The response value :return: Boolean indicating if the result is a direct result |
179,466 | from __future__ import annotations
import asyncio
import itertools
import json
import logging
import os
import base64
import telegram
from telegram import Message, MessageEntity, Update, ChatMember, constants
from telegram.ext import CallbackContext, ContextTypes
from usage_tracker import UsageTracker
def get_thread_id... | Handles a direct result from a plugin |
179,467 | from __future__ import annotations
import asyncio
import itertools
import json
import logging
import os
import base64
import telegram
from telegram import Message, MessageEntity, Update, ChatMember, constants
from telegram.ext import CallbackContext, ContextTypes
from usage_tracker import UsageTracker
def encode_image... | null |
179,468 | from __future__ import annotations
import asyncio
import itertools
import json
import logging
import os
import base64
import telegram
from telegram import Message, MessageEntity, Update, ChatMember, constants
from telegram.ext import CallbackContext, ContextTypes
from usage_tracker import UsageTracker
def decode_image... | null |
179,469 | from __future__ import annotations
import datetime
import logging
import os
import tiktoken
import openai
import requests
import json
import httpx
import io
from datetime import date
from calendar import monthrange
from PIL import Image
from tenacity import retry, stop_after_attempt, wait_fixed, retry_if_exception_type... | Gets the default number of max tokens for the given model. :param model: The model name :return: The default number of max tokens |
179,470 | from __future__ import annotations
import datetime
import logging
import os
import tiktoken
import openai
import requests
import json
import httpx
import io
from datetime import date
from calendar import monthrange
from PIL import Image
from tenacity import retry, stop_after_attempt, wait_fixed, retry_if_exception_type... | Whether the given model supports functions |
179,471 | from __future__ import annotations
import datetime
import logging
import os
import tiktoken
import openai
import requests
import json
import httpx
import io
from datetime import date
from calendar import monthrange
from PIL import Image
from tenacity import retry, stop_after_attempt, wait_fixed, retry_if_exception_type... | Return translated text for a key in specified bot_language. Keys and translations can be found in the translations.json. |
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