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import re
from huggingface_hub import get_collection, ModelCard
from sentence_transformers import SentenceTransformer
from sentence_transformers.models import Normalize

collection = get_collection(collection_slug="clips/e5-nl-68be9d3760240ce5c7d9f831")

ST_SNIPPET_PATTERN = r"""\
from sentence_transformers import SentenceTransformer
model = SentenceTransformer\((?:'|")([a-zA-Z0-9_\/\.-]+?)(?:'|")\)

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import argparse
import logging
import traceback
from collections import defaultdict
from collections.abc import Iterable
from enum import Enum, auto

import torch
from datasets import load_dataset
from torch import Tensor

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import logging
import traceback

import torch
from datasets import load_dataset

from sentence_transformers import SentenceTransformer
from sentence_transformers.cross_encoder import (
    CrossEncoder,
    CrossEncoderModelCardData,

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import logging

from datasets import load_dataset

from sentence_transformers import (
    SparseEncoder,
    SparseEncoderModelCardData,
    SparseEncoderTrainer,
    SparseEncoderTrainingArguments,
)

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# requires sentence_transformers>=3.2.0
from sentence_transformers import SentenceTransformer, export_optimized_onnx_model, export_dynamic_quantized_onnx_model

# The model to export to ONNX (+ optimize, quantize), OpenVINO
model_id = "mixedbread-ai/mxbai-embed-large-v1"
# Where to save the exported models locally
output_dir = model_id.replace("/", "-")

onnx_model = SentenceTransformer(model_id, backend="onnx", model_kwargs={"export": True})
onnx_model.save_pretrained(output_dir)

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from functools import partial
import datasets
from sentence_transformers import (
    SentenceTransformer,
    evaluation,
)
from torch.nn import functional as F

stsb = datasets.load_dataset("mteb/stsbenchmark-sts", split="test")

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from transformers import AutoTokenizer, SinkCache, LlamaForCausalLM, TextStreamer
import torch

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
model = LlamaForCausalLM.from_pretrained(
    "meta-llama/Llama-2-7b-hf", device_map="auto", torch_dtype=torch.float16
)

inputs = tokenizer(["Vaswani et al. (2017) introduced the Transformers"], return_tensors="pt").to(model.device)
cache = SinkCache(window_length=256, num_sink_tokens=4)

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from typing import Any, Dict, List

from span_marker import SpanMarkerModel


class EndpointHandler:
    def __init__(self, model_id: str) -> None:
        self.model = SpanMarkerModel.from_pretrained(model_id)
        # Try to place it on CUDA, do nothing if it fails
        self.model.try_cuda()

tomaarsen

from datasets import load_dataset, concatenate_datasets
from transformers import TrainingArguments
from span_marker import SpanMarkerModel, Trainer


def main() -> None:
    # Load the dataset, ensure "tokens" and "ner_tags" columns, and get a list of labels
    dataset = load_dataset("midas/inspec", "extraction")
    dataset = dataset.rename_columns({"document": "tokens", "doc_bio_tags": "ner_tags"})
    # Map string labels to integer labels instead