id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
39,738 | import logging
import numbers
import numpy as np
from paddlenlp.datasets import MapDataset
from pipelines.utils.common_utils import flatten_list
logger = logging.getLogger(__name__)
The provided code snippet includes necessary dependencies for implementing the `convert_features_to_dataset` function. Write a Python fun... | Converts a list of feature dictionaries (one for each sample) into a Paddle Dataset. :param features: A list of dictionaries. Each dictionary corresponds to one sample. Its keys are the names of the type of feature and the keys are the features themselves. :Return: a Paddle dataset and a list of tensor names. |
39,739 | import time
import logging
import subprocess
import requests
from pathlib import Path
logger = logging.getLogger(__name__)
ELASTICSEARCH_CONTAINER_NAME = "elasticsearch"
def launch_es(sleep=15, delete_existing=False):
# Start an Elasticsearch server via Docker
logger.debug("Starting Elasticsearch ...")
if... | null |
39,740 | import time
import logging
import subprocess
import requests
from pathlib import Path
logger = logging.getLogger(__name__)
OPENSEARCH_CONTAINER_NAME = "opensearch"
def launch_opensearch(sleep=15, delete_existing=False):
# Start an OpenSearch server via docker
logger.debug("Starting OpenSearch...")
# This ... | null |
39,741 | import time
import logging
import subprocess
import requests
from pathlib import Path
logger = logging.getLogger(__name__)
def launch_weaviate(sleep=15):
# Start a Weaviate server via Docker
logger.debug("Starting Weaviate ...")
status = subprocess.run(
[
"docker run -d -p 8080:8080 --... | null |
39,742 | import time
import logging
import subprocess
import requests
from pathlib import Path
logger = logging.getLogger(__name__)
def stop_opensearch(delete_container=False):
stop_container(OPENSEARCH_CONTAINER_NAME, delete_container)
def stop_elasticsearch(delete_container=False):
stop_container(ELASTICSEARCH_CONTAIN... | null |
39,743 | import time
import logging
import subprocess
import requests
from pathlib import Path
logger = logging.getLogger(__name__)
def launch_milvus(sleep=15, delete_existing=False):
# Start a Milvus server via docker
logger.debug("Starting Milvus ...")
milvus_dir = Path.home() / "milvus"
milvus_dir.mkdir(ex... | null |
39,744 | import time
import logging
import subprocess
import requests
from pathlib import Path
logger = logging.getLogger(__name__)
MILVUS1_CONTAINER_NAME = "milvus1"
def launch_milvus1(sleep=15):
# Start a Milvus (version <2.0.0) server via docker
logger.debug("Starting Milvus ...")
logger.warning(
"Autom... | null |
39,745 | from typing import Optional
import io
import tarfile
import zipfile
import requests
import logging
import importlib
from pathlib import Path
def _missing_dependency_stub_factory(classname: str, dep_group: str, import_error: Exception):
"""
Create custom versions of MissingDependency using the given parameters.
... | Method that allows the import of nodes that depend on missing dependencies. These nodes can be installed one by one with extras_require (see setup.cfg) but they need to be all imported in their respective package's __init__() Therefore, in case of an ImportError, the class to import is replaced by a hollow MissingDepen... |
39,746 | from typing import Optional
import io
import tarfile
import zipfile
import requests
import logging
import importlib
from pathlib import Path
logger = logging.getLogger(__name__)
The provided code snippet includes necessary dependencies for implementing the `fetch_archive_from_http` function. Write a Python function `d... | Fetch an archive (zip or tar.gz) from a url via http and extract content to an output directory. :param url: http address :param output_dir: local path :param proxies: proxies details as required by requests library :return: if anything got fetched |
39,747 | import re
The provided code snippet includes necessary dependencies for implementing the `clean_wiki_text` function. Write a Python function `def clean_wiki_text(text: str) -> str` to solve the following problem:
Clean wikipedia text by removing multiple new lines, removing extremely short lines, adding paragraph brea... | Clean wikipedia text by removing multiple new lines, removing extremely short lines, adding paragraph breaks and removing empty paragraphs |
39,748 | import logging
import os
import random
from copy import deepcopy
from typing import List, Tuple
import numpy as np
import paddle
import paddle
paddle.framework.io.EagerParamBase.to = to
The provided code snippet includes necessary dependencies for implementing the `set_all_seeds` function. Write a Python function `... | Setting multiple seeds to make runs reproducible. Important: Enabling `deterministic_cudnn` gives you full reproducibility with CUDA, :param seed:number to use as seed :param deterministic_paddle: Enable for full reproducibility when using CUDA. Caution: might slow down training. |
39,749 | import logging
import os
import random
from copy import deepcopy
from typing import List, Tuple
import numpy as np
import paddle
logger = logging.getLogger(__name__)
import paddle
paddle.framework.io.EagerParamBase.to = to
The provided code snippet includes necessary dependencies for implementing the `initialize_de... | Returns a list of available devices. :param use_cuda: Whether to make use of CUDA GPUs (if available). :param local_rank: Ordinal of device to be used. If -1 and multi_gpu is True, all devices will be used. :param multi_gpu: Whether to make use of all GPUs (if available). |
39,750 | import logging
import os
import random
from copy import deepcopy
from typing import List, Tuple
import numpy as np
import paddle
The provided code snippet includes necessary dependencies for implementing the `flatten_list` function. Write a Python function `def flatten_list(nested_list)` to solve the following problem... | Flatten an arbitrarily nested list, without recursion (to avoid stack overflows). Returns a new list, the original list is unchanged. >> list(flatten_list([1, 2, 3, [4], [], [[[[[[[[[5]]]]]]]]]])) [1, 2, 3, 4, 5] >> list(flatten_list([[1, 2], 3])) [1, 2, 3] |
39,751 | import logging
import os
import random
from copy import deepcopy
from typing import List, Tuple
import numpy as np
import paddle
logger = logging.getLogger(__name__)
def try_get(keys, dictionary):
try:
for key in keys:
if key in dictionary:
ret = dictionary[key]
... | null |
39,752 | import functools
import logging
import multiprocessing
import os
import re
from pathlib import Path
from typing import Callable, Dict, List, Optional
from pipelines.nodes.base import BaseComponent
from pipelines.nodes.file_converter import (
BaseConverter,
DocxToTextConverter,
ImageToTextConverter,
Mark... | Convert all files(.txt, .pdf, .docx) in the sub-directories of the given path to Python dicts that can be written to a Document Store. :param dir_path: path for the documents to be written to the DocumentStore :param clean_func: a custom cleaning function that gets applied to each doc (input: str, output:str) :param sp... |
39,753 | import functools
import logging
import multiprocessing
import os
import re
from pathlib import Path
from typing import Callable, Dict, List, Optional
from pipelines.nodes.base import BaseComponent
from pipelines.nodes.file_converter import (
BaseConverter,
DocxToTextConverter,
ImageToTextConverter,
Mark... | Convert all files(.txt, .pdf, .docx) in the sub-directories of the given path to Python dicts that can be written to a Document Store. :param dir_path: path for the documents to be written to the DocumentStore :param clean_func: a custom cleaning function that gets applied to each doc (input: str, output:str) :param sp... |
39,754 | import functools
import logging
import multiprocessing
import os
import re
from pathlib import Path
from typing import Callable, Dict, List, Optional
from pipelines.nodes.base import BaseComponent
from pipelines.nodes.file_converter import (
BaseConverter,
DocxToTextConverter,
ImageToTextConverter,
Mark... | Convert all files(.txt, .pdf) in the sub-directories of the given path to Python dicts that can be written to a Document Store. :param merge_lowercase: allow conversion of merged paragraph to lowercase :param merge_short: allow merging of short paragraphs :param dir_path: path for the documents to be written to the Doc... |
39,755 | import json
import logging
import pprint
from collections import defaultdict
from typing import Optional
import pandas as pd
from pipelines.document_stores.sql import DocumentORM
from pipelines.schema import Answer, Document
import logging
logging.getLogger().setLevel(logging.INFO)
The provided code snippet includ... | Utility function to print results of pipelines pipelines :param results: Results from a pipeline :param details: One of "minimum", "medium", "all". Defining the level of details to print. :param max_text_lenght: shorten lengthy text fields to the maximum allowed length. Set to None to not cut long text. :return: None |
39,756 | import json
import logging
import pprint
from collections import defaultdict
from typing import Optional
import pandas as pd
from pipelines.document_stores.sql import DocumentORM
from pipelines.schema import Answer, Document
The provided code snippet includes necessary dependencies for implementing the `print_document... | Utility that prints a compressed representation of the documents returned by a pipeline. :param max_text_lenght: shorten the document's content to a maximum number of chars. if None, does not cut. :param print_name: whether to print the document's name (from the metadata) or not. :param print_meta: whether to print the... |
39,757 | import json
import logging
import pprint
from collections import defaultdict
from typing import Optional
import pandas as pd
from pipelines.document_stores.sql import DocumentORM
from pipelines.schema import Answer, Document
The provided code snippet includes necessary dependencies for implementing the `print_question... | Utility to print the output of a question generating pipeline in a readable format. |
39,758 | import json
import logging
import pprint
from collections import defaultdict
from typing import Optional
import pandas as pd
from pipelines.document_stores.sql import DocumentORM
from pipelines.schema import Answer, Document
The provided code snippet includes necessary dependencies for implementing the `export_answers... | Exports answers coming from finder.get_answers() to a CSV file :param agg_results: list of predictions coming from finder.get_answers() :param output_file: filename of output file :return: None |
39,759 | import json
import logging
import pprint
from collections import defaultdict
from typing import Optional
import pandas as pd
from pipelines.document_stores.sql import DocumentORM
from pipelines.schema import Answer, Document
The provided code snippet includes necessary dependencies for implementing the `convert_labels... | Convert the export from the labeling UI to SQuAD format for training. :param labels_file: path for export file from the labeling tool :return: |
39,760 | from __future__ import absolute_import, division, print_function, unicode_literals
import logging
import numpy as np
from paddlenlp.transformers.tokenizer_utils_base import TruncationStrategy
from pipelines.data_handler.samples import SampleBasket
class TruncationStrategy(ExplicitEnum):
"""
Possible values for... | Tokenizes text data for question answering tasks. Tokenization means splitting words into subwords, depending on the tokenizer's vocabulary. - We first tokenize all documents in batch mode. (When using FastTokenizer Rust multithreading can be enabled by TODO add how to enable rust mt) - Then we tokenize each question i... |
39,761 | from __future__ import absolute_import, division, print_function, unicode_literals
import logging
import numpy as np
from paddlenlp.transformers.tokenizer_utils_base import TruncationStrategy
from pipelines.data_handler.samples import SampleBasket
def _get_start_of_word_QA(word_ids):
words = np.array(word_ids)
... | null |
39,762 | import argparse
import os
from pipelines.document_stores import ElasticsearchDocumentStore, MilvusDocumentStore
from pipelines.nodes import MultiModalRetriever
from pipelines.schema import Document
from pipelines.utils import convert_files_to_dicts, fetch_archive_from_http, launch_es
args = parser.parse_args()
def off... | null |
39,763 | import argparse
import os
from pipelines.document_stores import ElasticsearchDocumentStore, MilvusDocumentStore
from pipelines.nodes import MultiModalRetriever
from pipelines.schema import Document
from pipelines.utils import convert_files_to_dicts, fetch_archive_from_http, launch_es
args = parser.parse_args()
def del... | null |
39,764 | import argparse
from pipelines.document_stores import (
BaiduElasticsearchDocumentStore,
ElasticsearchDocumentStore,
MilvusDocumentStore,
)
from pipelines.nodes import DensePassageRetriever
from pipelines.utils import convert_files_to_dicts, fetch_archive_from_http, launch_es
from pipelines.utils.preprocess... | null |
39,765 | import argparse
from pipelines.document_stores import (
BaiduElasticsearchDocumentStore,
ElasticsearchDocumentStore,
MilvusDocumentStore,
)
from pipelines.nodes import DensePassageRetriever
from pipelines.utils import convert_files_to_dicts, fetch_archive_from_http, launch_es
from pipelines.utils.preprocess... | null |
39,766 | import json
import os
import sys
from functools import partial
import paddle
from argument import (
DataArgument,
GenerateArgument,
ModelArgument,
QuantArgument,
TrainingArguments,
)
from data import get_convert_example
from utils import (
CausalLMTrainer,
InTokensIterDatasetCallback,
co... | null |
39,767 | import importlib
import os
import paddle
from paddlenlp.transformers import AutoConfig
from paddlenlp.transformers.auto.modeling import MAPPING_NAMES
from paddlenlp.utils.log import logger
def parse_arguments():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--model_name_or_path",... | null |
39,768 | import importlib
import os
import paddle
from paddlenlp.transformers import AutoConfig
from paddlenlp.transformers.auto.modeling import MAPPING_NAMES
from paddlenlp.utils.log import logger
def load_tp_params(tp_degree, path):
tp_state_dict_list = []
for tp in range(tp_degree):
tp_state_dict = {}
... | null |
39,769 | import importlib
import os
import paddle
from paddlenlp.transformers import AutoConfig
from paddlenlp.transformers.auto.modeling import MAPPING_NAMES
from paddlenlp.utils.log import logger
def load_tp_and_pp_params(tp_degree, pp_degree, path):
tp_state_dict_list = []
for tp in range(tp_degree):
tp_stat... | null |
39,770 | import importlib
import os
import paddle
from paddlenlp.transformers import AutoConfig
from paddlenlp.transformers.auto.modeling import MAPPING_NAMES
from paddlenlp.utils.log import logger
def load_pp_params(pp_degree, path):
pp_state_dict = {}
for pp in range(pp_degree):
tmp = paddle.load(os.path.join... | null |
39,771 | import importlib
import os
import paddle
from paddlenlp.transformers import AutoConfig
from paddlenlp.transformers.auto.modeling import MAPPING_NAMES
from paddlenlp.utils.log import logger
logger = Logger()
The provided code snippet includes necessary dependencies for implementing the `merge_tensor_parallel` function... | the entry of converting config and converting model file Args: input_dir (str | None): the input dir which contains `pytorch_model.bin` and `config.json` file config (PretrainedConfig): the PretrainedConfig instance of model |
39,772 | from __future__ import annotations
import glob
import math
import os
import struct
from typing import Dict, Optional
import numpy as np
import paddle
import paddle.distributed as dist
import paddle.incubate.multiprocessing as mp
from paddle.distributed import fleet
from paddle.io import BatchSampler, DataLoader, Distri... | null |
39,773 | from __future__ import annotations
import glob
import math
import os
import struct
from typing import Dict, Optional
import numpy as np
import paddle
import paddle.distributed as dist
import paddle.incubate.multiprocessing as mp
from paddle.distributed import fleet
from paddle.io import BatchSampler, DataLoader, Distri... | null |
39,774 | from __future__ import annotations
import glob
import math
import os
import struct
from typing import Dict, Optional
import numpy as np
import paddle
import paddle.distributed as dist
import paddle.incubate.multiprocessing as mp
from paddle.distributed import fleet
from paddle.io import BatchSampler, DataLoader, Distri... | null |
39,775 | from __future__ import annotations
import glob
import math
import os
import struct
from typing import Dict, Optional
import numpy as np
import paddle
import paddle.distributed as dist
import paddle.incubate.multiprocessing as mp
from paddle.distributed import fleet
from paddle.io import BatchSampler, DataLoader, Distri... | null |
39,776 | from __future__ import annotations
import glob
import math
import os
import struct
from typing import Dict, Optional
import numpy as np
import paddle
import paddle.distributed as dist
import paddle.incubate.multiprocessing as mp
from paddle.distributed import fleet
from paddle.io import BatchSampler, DataLoader, Distri... | null |
39,777 | from __future__ import annotations
import glob
import math
import os
import struct
from typing import Dict, Optional
import numpy as np
import paddle
import paddle.distributed as dist
import paddle.incubate.multiprocessing as mp
from paddle.distributed import fleet
from paddle.io import BatchSampler, DataLoader, Distri... | null |
39,778 | from __future__ import annotations
import glob
import math
import os
import struct
from typing import Dict, Optional
import numpy as np
import paddle
import paddle.distributed as dist
import paddle.incubate.multiprocessing as mp
from paddle.distributed import fleet
from paddle.io import BatchSampler, DataLoader, Distri... | Pre-process generation inputs. |
39,779 | from __future__ import annotations
import glob
import math
import os
import struct
from typing import Dict, Optional
import numpy as np
import paddle
import paddle.distributed as dist
import paddle.incubate.multiprocessing as mp
from paddle.distributed import fleet
from paddle.io import BatchSampler, DataLoader, Distri... | null |
39,780 | from __future__ import annotations
import glob
import math
import os
import struct
from typing import Dict, Optional
import numpy as np
import paddle
import paddle.distributed as dist
import paddle.incubate.multiprocessing as mp
from paddle.distributed import fleet
from paddle.io import BatchSampler, DataLoader, Distri... | null |
39,785 | import argparse
import copy
import os
import paddle
from paddlenlp.peft import LoRAConfig, LoRAModel
try:
from paddle.nn.quant import weight_dequantize, weight_quantize
except:
weight_dequantize = None
weight_quantize = None
from paddlenlp.quantization.quantization_config import QuantizationConfig
from padd... | null |
39,786 | from __future__ import annotations
import os
from dataclasses import dataclass, field
import paddle
from paddle.distributed import fleet
from predictor import ModelArgument, PredictorArgument, create_predictor
from tqdm import tqdm
from utils import generate_rank_mapping, get_infer_model_path
from paddlenlp.trainer imp... | null |
39,787 | from __future__ import annotations
import json
import os
import socket
from contextlib import closing
from dataclasses import asdict, dataclass, field
from time import sleep
import requests
from filelock import FileLock
from predictor import BasePredictor, ModelArgument, PredictorArgument, create_predictor
from paddlen... | null |
39,788 | from __future__ import annotations
import numpy as np
from paddlenlp.peft import LoRAModel, PrefixModelForCausalLM
def convert_example_common(example, tokenizer, data_args, is_test=True, intokens=False):
if tokenizer.chat_template is not None:
return convert_rounds_example_common(example, tokenizer, data_ar... | null |
39,805 | import contextlib
import os
import random
import sys
import time
import types
from dataclasses import dataclass, field
from typing import List, Optional
import numpy as np
import paddle
import paddle.distributed as dist
import paddle.distributed.auto_parallel as auto
from paddle.base.data_feeder import convert_uint16_t... | null |
39,823 | import os
import random
import sys
import types
from collections import OrderedDict
from dataclasses import dataclass, field
from typing import List, Optional
import numpy as np
import paddle
import paddle.distributed as dist
from paddle.autograd import PyLayer
from paddle.distributed import fleet
from paddle.io import... | null |
39,826 | import re
def regitser_extract_layer_name_func(func):
global _GLOBAL_EXTRACT_LAYER_NAME_FUNC
_GLOBAL_EXTRACT_LAYER_NAME_FUNC = func
def register_index_layer_func(func):
global _GLOBAL_INDEX_LAYER_FUNC
_GLOBAL_INDEX_LAYER_FUNC = func
def register_layername_prefix(layer_name):
LayerNameSc... | null |
39,830 | import json
import os
import paddle
from paddle import nn
from paddle.distributed.fleet.meta_parallel import (
ColumnParallelLinear,
RowParallelLinear,
)
from paddle.quantization import PTQ, QAT, QuantConfig
from paddleslim.quant.advanced import (
GPTQ,
AutoClip,
AWQSearch,
EMASampler,
Multi... | null |
39,831 | import json
import os
import paddle
from paddle import nn
from paddle.distributed.fleet.meta_parallel import (
ColumnParallelLinear,
RowParallelLinear,
)
from paddle.quantization import PTQ, QAT, QuantConfig
from paddleslim.quant.advanced import (
GPTQ,
AutoClip,
AWQSearch,
EMASampler,
Multi... | null |
39,832 | import json
import os
import paddle
from paddle import nn
from paddle.distributed.fleet.meta_parallel import (
ColumnParallelLinear,
RowParallelLinear,
)
from paddle.quantization import PTQ, QAT, QuantConfig
from paddleslim.quant.advanced import (
GPTQ,
AutoClip,
AWQSearch,
EMASampler,
Multi... | null |
39,833 | import json
import os
import paddle
from paddle import nn
from paddle.distributed.fleet.meta_parallel import (
ColumnParallelLinear,
RowParallelLinear,
)
from paddle.quantization import PTQ, QAT, QuantConfig
from paddleslim.quant.advanced import (
GPTQ,
AutoClip,
AWQSearch,
EMASampler,
Multi... | AutoClip |
39,834 | import json
import os
import paddle
from paddle import nn
from paddle.distributed.fleet.meta_parallel import (
ColumnParallelLinear,
RowParallelLinear,
)
from paddle.quantization import PTQ, QAT, QuantConfig
from paddleslim.quant.advanced import (
GPTQ,
AutoClip,
AWQSearch,
EMASampler,
Multi... | null |
39,835 | import json
import os
import paddle
from paddle import nn
from paddle.distributed.fleet.meta_parallel import (
ColumnParallelLinear,
RowParallelLinear,
)
from paddle.quantization import PTQ, QAT, QuantConfig
from paddleslim.quant.advanced import (
GPTQ,
AutoClip,
AWQSearch,
EMASampler,
Multi... | null |
39,836 | import json
import os
import paddle
from paddle import nn
from paddle.distributed.fleet.meta_parallel import (
ColumnParallelLinear,
RowParallelLinear,
)
from paddle.quantization import PTQ, QAT, QuantConfig
from paddleslim.quant.advanced import (
GPTQ,
AutoClip,
AWQSearch,
EMASampler,
Multi... | null |
39,837 | import json
import requests
def send_request(query, history=None):
data = {
"context": query,
"history": history,
"top_k": 0,
"top_p": 0.7, # 0.0 为 greedy_search
"temperature": 0.95,
"repetition_penalty": 1.3,
"max_length": 100,
"src_length": 100,
... | null |
39,838 | from __future__ import annotations
import json
import os
import sys
import time
from abc import abstractmethod
from dataclasses import dataclass, field
from threading import Thread
from typing import List, Optional
import numpy as np
import paddle
import paddle.distributed.fleet.base.topology as tp
import paddle.incuba... | get eos_token_id from generation_config or tokenizer Returns: int | List[int]: eos_token_id to stop the generation |
39,839 | from __future__ import annotations
import argparse
import copy
import json
import gradio as gr
import requests
The provided code snippet includes necessary dependencies for implementing the `setup_args` function. Write a Python function `def setup_args()` to solve the following problem:
Setup arguments.
Here is the f... | Setup arguments. |
39,840 | from __future__ import annotations
import argparse
import copy
import json
import gradio as gr
import requests
def create_src_slider(value, maximum):
return gr.Slider(
minimum=1,
maximum=maximum,
value=value,
step=1,
label="Max Src Length",
info="最大输入长度。",
)
def c... | Launch characters dialogue demo. |
39,844 | import copy
import random
import re
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import paddle
import paddle.distributed as dist
import paddle.nn as nn
from paddle.distributed import fleet
from paddle.distributed.fleet.meta_parallel import get_rng_state_tracker
from paddle.optimizer.lr ... | Convert an example into necessary features. |
39,845 | import copy
import random
import re
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import paddle
import paddle.distributed as dist
import paddle.nn as nn
from paddle.distributed import fleet
from paddle.distributed.fleet.meta_parallel import get_rng_state_tracker
from paddle.optimizer.lr ... | null |
39,846 | from __future__ import annotations
import paddle
from utils import get_hcg, init_dist_env, set_seed
from paddlenlp.transformers import (
GPTChineseTokenizer,
GPTConfig,
GPTForCausalLM,
GPTTokenizer,
)
def parse_arguments():
import argparse
parser = argparse.ArgumentParser()
parser.add_argume... | null |
39,850 | import os
from setuptools import Distribution, setup
from setuptools.command.install import install
if os.name != "nt":
package_data = {"fast_tokenizer": ["core_tokenizers.so", "commit.log"]}
package_data["fast_tokenizer.libs"] = []
else:
package_data = {"fast_tokenizer": ["core_tokenizers.pyd", "core_token... | null |
39,851 | from typing import Dict, List, Tuple, Union
from . import core_tokenizers as C
The provided code snippet includes necessary dependencies for implementing the `set_thread_num` function. Write a Python function `def set_thread_num(thread_num)` to solve the following problem:
Set the number of threads for accelerating ba... | Set the number of threads for accelerating batch tokenization :param thread_num: (int) The number of threads :return None |
39,852 | from typing import Dict, List, Tuple, Union
from . import core_tokenizers as C
The provided code snippet includes necessary dependencies for implementing the `get_thread_num` function. Write a Python function `def get_thread_num()` to solve the following problem:
Get the number of tokenization threads :return int
Her... | Get the number of tokenization threads :return int |
39,853 | import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import paddle
class MaxLengthCriteria(StoppingCriteria):
"""
This class can be used to stop generation whenever the full generated number of tokens exceeds `max_length`. Keep
in mind for decoder-only type o... | null |
39,854 | from __future__ import annotations
import copy
import inspect
from typing import Optional, Union
import paddle
import paddle.distributed as dist
import paddle.nn as nn
import paddle.nn.functional as F
from paddle import Tensor
from paddle.common_ops_import import convert_dtype
from paddle.utils import map_structure
fro... | get unfinished flag for generation step Args: input_ids (Tensor): the input_ids eos_token_id (Union[int, list[int], list[list[int]]]): the end os sentence flag, which can be: * single token id, eg: 10 * multiple token ids to stop generation, eg: [10, 10] * some more tokens to stop generations, eg: [[10], [20, 20], [30,... |
39,855 | from __future__ import annotations
import inspect
from abc import ABC
from collections import OrderedDict
from typing import Callable, Dict, List, Tuple, Union
import numpy as np
import paddle
from paddle.nn.layer.layers import in_declarative_mode
def _get_ngrams(ngram_size: int, prev_input_ids: paddle.Tensor, num_hypo... | Copied from fairseq for no_repeat_ngram in beam_search |
39,856 | from __future__ import annotations
import inspect
from abc import ABC
from collections import OrderedDict
from typing import Callable, Dict, List, Tuple, Union
import numpy as np
import paddle
from paddle.nn.layer.layers import in_declarative_mode
def TopKProcess(probs: paddle.Tensor, top_k: int, min_tokens_to_keep: i... | null |
39,857 | from __future__ import annotations
import inspect
from abc import ABC
from collections import OrderedDict
from typing import Callable, Dict, List, Tuple, Union
import numpy as np
import paddle
from paddle.nn.layer.layers import in_declarative_mode
def TopPProcess(probs: paddle.Tensor, top_p: float, min_tokens_to_keep:... | null |
39,858 | import copy
import json
import os
import warnings
from typing import Any, Dict, Optional, Union
from huggingface_hub import hf_hub_download
from paddle.common_ops_import import convert_dtype
from paddlenlp import __version__
from paddlenlp.transformers.configuration_utils import PretrainedConfig
from paddlenlp.utils.do... | resolve config file from hf hub Args: repo_id (str): the repo name from huggingface hub cache_dir (str): the cachedir subfolder (str, optional) An optional value corresponding to a folder inside the repo. Returns: str: the downloaded config file |
39,859 | import inspect
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import paddle
from paddle import Tensor
from ..transformers.model_outputs import MaskedLMOutput, SequenceClassifierOutput
from ..transformers.tokenizer_utils_base import PaddingStrategy, Pretra... | Obtain the input arguments of the given function. |
39,860 | import inspect
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import paddle
from paddle import Tensor
from ..transformers.model_outputs import MaskedLMOutput, SequenceClassifierOutput
from ..transformers.tokenizer_utils_base import PaddingStrategy, Pretra... | null |
39,861 | import inspect
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import paddle
from paddle import Tensor
from ..transformers.model_outputs import MaskedLMOutput, SequenceClassifierOutput
from ..transformers.tokenizer_utils_base import PaddingStrategy, Pretra... | null |
39,862 | import contextlib
import copy
import csv
import json
import math
import os
import pickle
import re
import traceback
import warnings
from collections import OrderedDict, namedtuple
from dataclasses import dataclass
from datetime import datetime
from functools import cmp_to_key
from typing import Any, Dict, List, Optiona... | Download the file from the url to specified directory. Check md5 value when the file is exists, if the md5 value is the same as the existed file, just use the older file, if not, will download the file from the url. Args: save_dir(string): The specified directory saving the file. filename(string): The specified filenam... |
39,863 | import contextlib
import copy
import csv
import json
import math
import os
import pickle
import re
import traceback
import warnings
from collections import OrderedDict, namedtuple
from dataclasses import dataclass
from datetime import datetime
from functools import cmp_to_key
from typing import Any, Dict, List, Optiona... | Check the resource status in the specified task. Args: task(string): The name of specified task. |
39,864 | import contextlib
import copy
import csv
import json
import math
import os
import pickle
import re
import traceback
import warnings
from collections import OrderedDict, namedtuple
from dataclasses import dataclass
from datetime import datetime
from functools import cmp_to_key
from typing import Any, Dict, List, Optiona... | The function that add the doc string to doc of class. |
39,865 | import contextlib
import copy
import csv
import json
import math
import os
import pickle
import re
import traceback
import warnings
from collections import OrderedDict, namedtuple
from dataclasses import dataclass
from datetime import datetime
from functools import cmp_to_key
from typing import Any, Dict, List, Optiona... | null |
39,866 | import contextlib
import copy
import csv
import json
import math
import os
import pickle
import re
import traceback
import warnings
from collections import OrderedDict, namedtuple
from dataclasses import dataclass
from datetime import datetime
from functools import cmp_to_key
from typing import Any, Dict, List, Optiona... | null |
39,867 | import contextlib
import copy
import csv
import json
import math
import os
import pickle
import re
import traceback
import warnings
from collections import OrderedDict, namedtuple
from dataclasses import dataclass
from datetime import datetime
from functools import cmp_to_key
from typing import Any, Dict, List, Optiona... | Cut the Chinese sentences more precisely, reference to "https://blog.csdn.net/blmoistawinde/article/details/82379256". |
39,868 | import contextlib
import copy
import csv
import json
import math
import os
import pickle
import re
import traceback
import warnings
from collections import OrderedDict, namedtuple
from dataclasses import dataclass
from datetime import datetime
from functools import cmp_to_key
from typing import Any, Dict, List, Optiona... | Calculate minimal Levenstein distance between s1 and s2. Args: s1 (str): string s2 (str): string Returns: int: the minimal distance. |
39,869 | import contextlib
import copy
import csv
import json
import math
import os
import pickle
import re
import traceback
import warnings
from collections import OrderedDict, namedtuple
from dataclasses import dataclass
from datetime import datetime
from functools import cmp_to_key
from typing import Any, Dict, List, Optiona... | Return text id and probability of predicted spans Args: span_set (set): set of predicted spans. offset_mapping (list[int]): list of pair preserving the index of start and end char in original text pair (prompt + text) for each token. Returns: sentence_id (list[tuple]): index of start and end char in original text. prob... |
39,870 | import contextlib
import copy
import csv
import json
import math
import os
import pickle
import re
import traceback
import warnings
from collections import OrderedDict, namedtuple
from dataclasses import dataclass
from datetime import datetime
from functools import cmp_to_key
from typing import Any, Dict, List, Optiona... | null |
39,871 | import contextlib
import copy
import csv
import json
import math
import os
import pickle
import re
import traceback
import warnings
from collections import OrderedDict, namedtuple
from dataclasses import dataclass
from datetime import datetime
from functools import cmp_to_key
from typing import Any, Dict, List, Optiona... | null |
39,872 | import contextlib
import copy
import csv
import json
import math
import os
import pickle
import re
import traceback
import warnings
from collections import OrderedDict, namedtuple
from dataclasses import dataclass
from datetime import datetime
from functools import cmp_to_key
from typing import Any, Dict, List, Optiona... | null |
39,873 | import contextlib
import copy
import csv
import json
import math
import os
import pickle
import re
import traceback
import warnings
from collections import OrderedDict, namedtuple
from dataclasses import dataclass
from datetime import datetime
from functools import cmp_to_key
from typing import Any, Dict, List, Optiona... | Pad the instances to the max sequence length in batch, and generate the corresponding position data and attention bias. |
39,874 | import contextlib
import copy
import csv
import json
import math
import os
import pickle
import re
import traceback
import warnings
from collections import OrderedDict, namedtuple
from dataclasses import dataclass
from datetime import datetime
from functools import cmp_to_key
from typing import Any, Dict, List, Optiona... | null |
39,875 | import contextlib
import copy
import csv
import json
import math
import os
import pickle
import re
import traceback
import warnings
from collections import OrderedDict, namedtuple
from dataclasses import dataclass
from datetime import datetime
from functools import cmp_to_key
from typing import Any, Dict, List, Optiona... | null |
39,876 | import contextlib
import copy
import csv
import json
import math
import os
import pickle
import re
import traceback
import warnings
from collections import OrderedDict, namedtuple
from dataclasses import dataclass
from datetime import datetime
from functools import cmp_to_key
from typing import Any, Dict, List, Optiona... | null |
39,877 | import copy
import os
import numpy as np
import paddle
from ..data import Pad, Vocab
from .models import BiAffineParser
from .task import Task
from .utils import download_file
def pad_sequence(sequences, padding_value=0, fix_len=None):
def convert_example(example, vocabs, fix_len=20):
word_vocab, rel_vocab = vocab... | null |
39,878 | import copy
import os
import numpy as np
import paddle
from ..data import Pad, Vocab
from .models import BiAffineParser
from .task import Task
from .utils import download_file
def flat_words(words, pad_index=0):
mask = words != pad_index
lens = np.sum(mask.astype(np.int64), axis=-1)
position = np.cumsum(le... | null |
39,879 | import copy
import os
import numpy as np
import paddle
from ..data import Pad, Vocab
from .models import BiAffineParser
from .task import Task
from .utils import download_file
def probability(s_arc, arc_preds):
s_arc = s_arc - s_arc.max(axis=-1).reshape(list(s_arc.shape)[:-1] + [1])
s_arc = np.exp(s_arc) / np.... | null |
39,880 | import copy
import os
import numpy as np
import paddle
from ..data import Pad, Vocab
from .models import BiAffineParser
from .task import Task
from .utils import download_file
def eisner(scores, mask):
"""
Eisner algorithm is a general dynamic programming decoding algorithm for bilexical grammar.
Args：
... | decode |
39,881 | import os
import paddle
from ..data import Pad, Stack, Tuple
from ..datasets import load_dataset
from .models import BiGruCrf
from .task import Task
from .utils import Customization
The provided code snippet includes necessary dependencies for implementing the `load_vocab` function. Write a Python function `def load_v... | Load vocab from file |
39,882 | from typing import Optional
import numpy as np
import paddle
from paddlenlp.data import DataCollatorWithPadding
from paddlenlp.transformers import AutoModel, AutoTokenizer, ErnieDualEncoder
from ..utils.log import logger
from .task import Task
from .utils import dygraph_mode_guard, static_mode_guard
def text_length(te... | null |
39,883 | import json
import os
from typing import Any, Dict, List, Union
import numpy as np
import paddle
import paddle.nn.functional as F
from scipy.special import expit as np_sigmoid
from scipy.special import softmax as np_softmax
from ..data import DataCollatorWithPadding
from ..prompt import (
AutoTemplate,
PromptDa... | null |
39,884 | import base64
import json
import os
import re
from typing import List
import numpy as np
import paddle
from huggingface_hub import hf_hub_download
from ..datasets import load_dataset
from ..layers import GlobalPointerForEntityExtraction, GPLinkerForRelationExtraction
from ..transformers import UIE, UIEM, UIEX, AutoMode... | get max_length by examples which you can change it by examples in batch |
39,885 | import paddle
import paddle.nn as nn
from paddlenlp.transformers import AutoModel
The provided code snippet includes necessary dependencies for implementing the `index_sample` function. Write a Python function `def index_sample(x, index)` to solve the following problem:
Select input value according to index Arags： inp... | Select input value according to index Arags： input: input matrix index: index matrix Returns: output >>> input [ [1, 2, 3], [4, 5, 6] ] >>> index [ [1, 2], [0, 1] ] >>> index_sample(input, index) [ [2, 3], [4, 5] ] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.