Helw150

text = # Tokenized Text Corresponding to Recording Transcript
audio = # Mel Spectrogram of the Recording

# Only Train Connector and Projection
self.encoder.freeze()
self.llama.freeze()

# Convert Raw Audio Signal to 1500 Embeddings with Whisper Encoder (CNN+Transformer)
audio_features = self.encoder(audio)

Helw150

    def _push_parquet_shards_to_hub(                                                                                                                                                                                                  [1071/1877]
        self,                                                                                                           
        repo_id: str,         
        data_dir: str = "data",          
        split: Optional[str] = None,    
        token: Optional[str] = None,      
        revision: Optional[str] = None,   
        create_pr: Optional[bool] = False,               
        max_shard_size: Optional[Union[int, str]] = None,
        num_shards: Optional[int] = None,             

Helw150

import ast

# To Delete After Debug
import code
import copyreg
import datetime
import functools
import json
import os
import re

Helw150

from typing import List, Optional, Tuple, Union
from torchtyping import TensorType

from transformers.adapters.modeling import Adapter
from transformers.adapters import (
    BartAdapterModel,
    RobertaAdapterModel,
    BertAdapterModel,
    AdapterConfig,
)

Helw150

from transformers import AutoTokenizer, T5ForConditionalGeneration

# Model Init
n_gpu = 8
tokenizer = AutoTokenizer.from_pretrained("google/flan-ul2")
model = T5ForConditionalGeneration.from_pretrained("google/flan-ul2")
heads_per_gpu = len(model.encoder.block) // n_gpu 
device_map = {
    gpu: list(
        range(

Helw150

# See https://huggingface.co/docs/datasets/upload_dataset for more details

from datasets import load_dataset

dataset_name = "PUT_YOUR_NAME_HERE"
data_files = {"train": "train.csv", "dev": "dev.csv", "test": "test.csv"}
dataset = load_dataset("namespace/your_dataset_name", data_files=data_files)
datasets.push_to_hub(f"SALT-NLP/{dataset_name}", private=True)

Helw150

from gensim import utils

def save2gensim(fname, word2vec_dict):
  vectors = list(word2vec_dict.values())
  vector_size = vectors[0].shape[0]
  total_vec = len(vectors)
  with utils.smart_open(fname, 'wb') as fout:
    fout.write(utils.to_utf8("%s %s\n" % (total_vec, vector_size)))
    # store in sorted order: most frequent words at the top
    for word, vector in word2vec_dict.items():

Helw150

#!/usr/bin/env python
"""Counts the number of times a word occurs in a very large text file"""

from __future__ import print_function
import os
import sys
import argparse
import textacy
import multiprocessing
from tqdm import tqdm

Helw150

// Disable bold. 
term_.prefs_.set('enable-bold', false) 

// Use this for Zenburn
term_.prefs_.set('background-color', "#3F3F3F");
term_.prefs_.set('foreground-color', "#DCDCCC");

base03 =     "#002b36";
base02 =     "#073642";
base01 =     "#586e75";

Helw150

# i/p = array of numbers
# create a binary tree such that each subtree is a min-heap and the inorder traversal // of the binary tree is same as the array provided

# [5, 7, 10, 8, 1, 4]

#                       1
#                     /    \
#                   5        4
#                     \
#                       7