Ayurveda_Chatbot

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Ayurveda_Chatbot / create_data.py

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	"""
	Dataset creation tools.

	Keep to-level imports clean of non-trivial imports for specific tools,
	because this file is imported for various purposes
	"""

	import ast
	import concurrent.futures
	import contextlib
	import hashlib
	import json
	import os
	import shutil
	import signal
	import sys
	import traceback
	from concurrent.futures import ProcessPoolExecutor

	import psutil
	import pytest
	import pandas as pd
	import numpy as np
	from tqdm import tqdm

	from utils import flatten_list, remove


	def parse_rst_file(filepath):
	with open(filepath, 'r') as f:
	input_data = f.read()
	settings_overrides = {'initial_header_level': 2}
	from docutils import core
	document = core.publish_doctree(
	source=input_data,
	source_path=filepath,
	settings_overrides=settings_overrides,
	)
	qa_pairs = []
	current_section = None
	current_question = ""
	current_answer = ""
	for node in document.traverse():
	if node.__class__.__name__ == 'section':
	current_section = ""
	elif current_section is not None:
	if node.__class__.__name__ == 'Text':
	if node.astext()[-1] == "?":
	if current_question:
	qa_pairs.append((current_question, current_answer))
	current_question = node.astext()
	current_answer = ""
	else:
	current_answer += node.astext()
	if current_answer:
	qa_pairs.append((current_question, current_answer))
	return {k: v for k, v in qa_pairs}


	def test_scrape_dai_docs():
	home = os.path.expanduser('~')
	file = os.path.join(home, 'h2oai/docs/faq.rst')
	qa_pairs = parse_rst_file(file)
	prompt_type = 'human_bot'
	from prompter import prompt_types
	assert prompt_type in prompt_types
	save_thing = [{"instruction": k, "output": v, 'prompt_type': prompt_type} for k, v in qa_pairs.items()]
	output_file = "dai_faq.json"
	with open(output_file, "wt") as f:
	f.write(json.dumps(save_thing, indent=2))


	def test_scrape_dai_docs_all():
	"""
	pytest create_data.py::test_scrape_dai_docs_all
	"""
	import glob
	import nltk
	nltk.download('punkt')
	dd = {}
	np.random.seed(1234)
	home = os.path.expanduser('~')
	files = list(glob.glob(os.path.join(home, "h2oai/docs/*/rst")))
	np.random.shuffle(files)
	val_count = int(0.05 * len(files))
	train_files = files[val_count:]
	valid_files = files[:val_count]
	things = [
	("dai_docs.train.json", train_files),
	("dai_docs.valid.json", valid_files)
	]
	for LEN in [100, 200, 500]:
	for output_file, ff in things:
	if output_file not in dd:
	dd[output_file] = []
	for f in ff:
	with open(f) as input:
	blob = input.read()
	blob = blob.replace("~~", "")
	blob = blob.replace("==", "")
	blob = blob.replace("''", "")
	blob = blob.replace("--", "")
	blob = blob.replace("**", "")
	dd[output_file].extend(get_sentences(blob, length=LEN))
	for output_file, _ in things:
	save_thing = [{"output": k.strip(), 'prompt_type': 'plain'} for k in dd[output_file]]
	with open(output_file, "wt") as f:
	f.write(json.dumps(save_thing, indent=2))


	def get_sentences(blob, length):
	"""
	break-up input text into sentences and then output list of sentences of about length in size
	:param blob:
	:param length:
	:return:
	"""
	import nltk
	nltk.download('punkt')
	from nltk.tokenize import sent_tokenize
	sentences = sent_tokenize(blob)
	my_sentences = []
	my_string = ""
	for sentence in sentences:
	if len(my_string) + len(sentence) <= length:
	if my_string:
	my_string += " " + sentence
	else:
	my_string = sentence
	else:
	my_sentences.append(my_string)
	my_string = ""
	return my_sentences or [my_string]


	def setup_dai_docs(path=None, dst="working_dir_docs", from_hf=False):
	"""
	Only supported if have access to source code or HF token for HF spaces and from_hf=True
	:param path:
	:param dst:
	:param from_hf:
	:return:
	"""

	home = os.path.expanduser('~')

	if from_hf:
	# assumes
	from huggingface_hub import hf_hub_download
	# True for case when locally already logged in with correct token, so don't have to set key
	token = os.getenv('HUGGINGFACE_API_TOKEN', True)
	path_to_zip_file = hf_hub_download('h2oai/dai_docs', 'dai_docs.zip', token=token, repo_type='dataset')
	path = 'h2oai'
	import zipfile
	with zipfile.ZipFile(path_to_zip_file, 'r') as zip_ref:
	zip_ref.extractall(path)
	path = os.path.join(path, 'docs/*/')

	if path is None:
	if os.path.isdir(os.path.join(home, 'h2oai')):
	path = os.path.join(home, "h2oai/docs/*/")
	else:
	assert os.path.isdir(os.path.join(home, 'h2oai.superclean')), '%s does not exist' % path
	path = os.path.join(home, "h2oai.superclean/docs/*/")
	import glob
	files = list(glob.glob(path, recursive=True))

	# pandoc can't find include files

	remove(dst)
	os.makedirs(dst)

	# copy full tree, for absolute paths in rst
	for fil in files:
	if os.path.isfile(fil):
	shutil.copy(fil, dst)

	# hack for relative path
	scorers_dir = os.path.join(dst, 'scorers')
	makedirs(scorers_dir)
	for fil in glob.glob(os.path.join(dst, '*.frag')):
	shutil.copy(fil, scorers_dir)

	return dst


	def rst_to_outputs(files, min_len=30, max_len=2048 // 2 - 30):
	# account for sequence length (context window) including prompt and input and output

	# os.system('pandoc -f rst -t plain ./expert_settings/nlp_settings.rst')
	import pypandoc
	basedir = os.path.abspath(os.getcwd())

	outputs = []
	for fil in files:
	os.chdir(basedir)
	os.chdir(os.path.dirname(fil))
	fil = os.path.basename(fil)
	print("Processing %s" % fil, flush=True)
	# out_format can be one of: asciidoc, asciidoctor, beamer, biblatex, bibtex, commonmark, commonmark_x,
	# context, csljson, docbook, docbook4, docbook5, docx, dokuwiki,
	# dzslides, epub, epub2, epub3, fb2, gfm, haddock, html, html4, html5, icml,
	# ipynb, jats, jats_archiving, jats_articleauthoring, jats_publishing, jira,
	# json, latex, man,
	# markdown, markdown_github, markdown_mmd, markdown_phpextra, markdown_strict,
	# mediawiki, ms, muse, native, odt, opendocument, opml, org, pdf, plain, pptx,
	# revealjs, rst, rtf, s5, slideous, slidy, tei, texinfo, textile, xwiki, zimwiki
	out_format = 'plain'
	# avoid extra new lines injected into text
	extra_args = ['--wrap=preserve', '--resource path="%s" % dst']

	plain_list = []
	try:
	# valid for expert settings
	input_rst = pypandoc.convert_file(fil, 'rst')
	input_list = input_rst.split('\n``')
	for input_subrst in input_list:
	input_plain = pypandoc.convert_text(input_subrst, format='rst', to='plain')
	plain_list.append([input_plain, fil])
	except Exception as e:
	print("file exception: %s %s" % (fil, str(e)), flush=True)

	if not plain_list:
	# if failed to process as pieces of rst, then
	output = pypandoc.convert_file(fil, out_format, extra_args=extra_args, format='rst')
	outputs1 = get_sentences(output, length=max_len)
	for oi, output in enumerate(outputs1):
	output = output.replace('\n\n', '\n')
	plain_list.append([output, fil])
	outputs.extend(plain_list)

	# report:
	# [print(len(x)) for x in outputs]

	# deal with blocks longer than context size (sequence length) of 2048
	new_outputs = []
	num_truncated = 0
	num_orig = len(outputs)
	for output, fil in outputs:
	if len(output) < max_len:
	new_outputs.append([output, fil])
	continue
	outputs1 = get_sentences(output, length=max_len)
	for oi, output1 in enumerate(outputs1):
	output1 = output1.replace('\n\n', '\n')
	new_outputs.append([output1, fil])
	num_truncated += 1
	print('num_orig: %s num_truncated: %s' % (num_orig, num_truncated), flush=True)

	new_outputs = [[k.strip(), fil] for k, fil in new_outputs if len(k.strip()) > min_len]

	return new_outputs


	def test_scrape_dai_docs_all_pandoc():
	"""
	pytest -s -v create_data.py::test_scrape_dai_docs_all_pandoc
	:return:
	"""

	dst = setup_dai_docs()

	import glob
	files = list(glob.glob(os.path.join(dst, '*rst'), recursive=True))

	basedir = os.path.abspath(os.getcwd())
	new_outputs = rst_to_outputs(files)
	os.chdir(basedir)

	remove(dst)
	save_thing = [{"output": k.strip(), 'prompt_type': 'plain'} for k in new_outputs]
	output_file = "dai_docs.train_cleaned.json"
	with open(output_file, "wt") as f:
	f.write(json.dumps(save_thing, indent=2))


	def test_config_to_json():
	"""
	Needs to run from Driverless AI source directory.
	E.g. (base) jon@gpu:~/h2oai$ pytest -s -v /data/jon/h2ogpt/create_data.py::test_config_to_json ; cp config.json /data/jon/h2ogpt/
	:return:
	"""
	try:
	# Arrange
	import json
	from h2oaicore.systemutils import config
	toml_list = []
	for k, v in config.get_meta_dict().items():
	title = (v.title + ": ") if v.title else ''
	comment = v.comment or ''
	if not (title or comment):
	continue
	toml_list.extend(
	[
	{
	'prompt_type': 'plain',
	'instruction': f"<human>: What does {k} do?\n<bot>: {k.replace('_', ' ')} config.toml: {comment or title}\n<human>:".replace(
	"\n", ""),
	},
	{
	'prompt_type': 'plain',
	'instruction': f"<human>: Explain {k}.\n<bot>: {k.replace('_', ' ')} config.toml: {comment or title}\n<human>:".replace(
	"\n", ""),
	},
	{
	'prompt_type': 'plain',
	'instruction': f"<human>: How can I do this: {title}.\n<bot>: Set the {k.replace('_', ' ')} config.toml\n<human>:".replace(
	"\n", ""),
	} if title and comment else None,
	{
	'prompt_type': 'human_bot',
	'instruction': f'Explain the following expert setting for Driverless AI',
	'input': f"{k}",
	'output': f"{k.replace('_', ' ')} config.toml: {comment or title}".replace("\n", ""),
	},
	{
	'prompt_type': 'human_bot',
	'instruction': f'Explain the following expert setting for Driverless AI',
	'input': f"{k}",
	'output': f"{k.replace('_', ' ')} config.toml: {title}{comment}".replace("\n", ""),
	},
	{
	'prompt_type': 'human_bot',
	'instruction': f'Explain the following expert setting for Driverless AI',
	'input': f"{k.replace('_', ' ')}",
	'output': f"{k.replace('_', ' ')} config.toml: {title}{comment}".replace("\n", ""),
	},
	{
	'prompt_type': 'human_bot',
	'instruction': f'Explain the following expert setting for Driverless AI',
	'input': f"{title}",
	'output': f"{k.replace('_', ' ')} config.toml: {title}{comment}".replace("\n", ""),
	},
	{
	'prompt_type': 'human_bot',
	'instruction': f'Provide a short explanation of the expert setting {k}',
	'output': f"{k.replace('_', ' ')} config.toml: {comment or title}".replace("\n", ""),
	},
	{
	'prompt_type': 'human_bot',
	'instruction': f'Provide a detailed explanation of the expert setting {k}',
	'output': f"{k.replace('_', ' ')} config.toml: {title}{comment}".replace("\n", ""),
	},
	]
	)
	toml_list = [x for x in toml_list if x]
	with open("config.json", "wt") as f:
	f.write(json.dumps(toml_list, indent=2))
	except Exception as e:
	print("Exception: %s" % str(e), flush=True)


	def copy_tree(src, dst, follow_symlink=False):
	makedirs(dst, exist_ok=True)
	for (path, dirs, files) in os.walk(src, followlinks=follow_symlink):
	new_path = path.replace(src, dst)
	makedirs(new_path, exist_ok=True)
	for file in files:
	filename = os.path.join(path, file)
	new_filename = os.path.join(new_path, file)
	# print("%s -> %s" % (filename, new_filename))
	try:
	atomic_copy(filename, new_filename)
	except FileNotFoundError:
	pass


	def atomic_move(src, dst):
	try:
	shutil.move(src, dst)
	except (shutil.Error, FileExistsError):
	pass
	remove(src)


	def atomic_copy(src=None, dst=None, with_permissions=True):
	if os.path.isfile(dst):
	return
	import uuid
	my_uuid = uuid.uuid4()
	dst_tmp = dst + str(my_uuid)
	makedirs(os.path.dirname(dst), exist_ok=True)
	if with_permissions:
	shutil.copy(src, dst_tmp)
	else:
	shutil.copyfile(src, dst_tmp)
	atomic_move(dst_tmp, dst)
	remove(dst_tmp)


	def makedirs(path, exist_ok=True):
	"""
	Avoid some inefficiency in os.makedirs()
	:param path:
	:param exist_ok:
	:return:
	"""
	if os.path.isdir(path) and os.path.exists(path):
	assert exist_ok, "Path already exists"
	return path
	os.makedirs(path, exist_ok=exist_ok)


	## Download from https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_unfiltered_cleaned_split.json
	## Turn into simple instruct prompt type. No context/previous conversations.
	def test_prep_instruct_vicuna():
	from datasets import load_dataset
	filename = 'ShareGPT_unfiltered_cleaned_split.json'
	if not os.path.exists(filename):
	os.system(
	'wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/%s' % filename)
	data = load_dataset("json", data_files={"train": filename})["train"]
	training_rows = []
	for i in range(data.num_rows):
	conversations = data[i]['conversations']
	assert isinstance(conversations, list), conversations
	convo = ""
	for j, conv in enumerate(conversations):
	# Get ready for generate.py prompt_type=human_bot
	# But train with prompt_type=plain
	if conv['from'] == 'human':
	FROM = '<human>: '
	elif conv['from'] == 'gpt':
	FROM = '<bot>: '
	convo += f"{FROM}" + conv['value'] + "\n"
	if convo:
	training_rows.append(dict(input=convo))
	with open(filename + ".generate_human_bot.train_plain.json", "wt") as f:
	f.write(json.dumps(training_rows, indent=2))


	POSTFIX = ".generate_human_bot.train_plain.json"

	# https://bair.berkeley.edu/blog/2023/04/03/koala/
	OIG_DATASETS = [
	"unified_chip2.jsonl",
	"unified_grade_school_math_instructions.jsonl",
	"unified_poetry_2_song.jsonl",
	"unified_plot_screenplay_books_dialog.jsonl",
	]

	# hub issue: https://huggingface.co/datasets/laion/OIG/discussions/4
	ALL_OIG_DATASETS = ['unified_abstract_infill.jsonl',
	'unified_basic.jsonl',
	'unified_canadian_parliament.jsonl',
	'unified_chip2.jsonl',
	'unified_conv_finqa.jsonl',
	'unified_cuad.jsonl',
	'unified_essays.jsonl',
	'unified_flan.jsonl.gz',
	'unified_grade_school_math_instructions.jsonl',
	'unified_hc3_human.jsonl',
	'unified_image_prompts_instructions.jsonl',
	'unified_joke_explanations.jsonl',
	'unified_mathqa_flanv2_kojma_cot.jsonl',
	'unified_merged_code_xp3.jsonl',
	'unified_multi_news.jsonl',
	'unified_multi_sum.jsonl',
	'unified_ni.jsonl.gz',
	'unified_nq.jsonl',
	'unified_openai_summarize_tldr.jsonl',
	'unified_oscar_en_sample_dialog.jsonl',
	'unified_p3.jsonl.gz',
	'unified_plot_screenplay_books_dialog.jsonl',
	'unified_poetry_2_song.jsonl',
	'unified_poetry_instructions.jsonl',
	'unified_rallio_safety_and_prosocial.jsonl',
	'unified_rallio_soda_upgraded_2048.jsonl',
	'unified_soda_dialog.jsonl',
	'unified_sqlv1.jsonl',
	'unified_sqlv2.jsonl',
	'unified_squad_v2.jsonl',
	'unified_squad_v2_more_neg.jsonl',
	'unified_ul2_plus_oscar_en_sample_dialog.jsonl',
	'unified_unifiedskg_instructions.jsonl',
	'unified_unnatural_instructions.jsonl',
	'unified_xp3_sample.jsonl']

	useful_oig_files = ['unified_rallio_safety_and_prosocial.jsonl.parquet',
	'unified_chip2.jsonl.parquet',
	'unified_cuad.jsonl.parquet',
	'unified_essays.jsonl.parquet',
	'unified_flan.jsonl.gz.parquet',
	'unified_grade_school_math_instructions.jsonl.parquet',
	'unified_hc3_human.jsonl.parquet',
	'unified_mathqa_flanv2_kojma_cot.jsonl.parquet',
	'unified_merged_code_xp3.jsonl.parquet',
	'unified_multi_news.jsonl.parquet',
	# 'unified_multi_sum.jsonl.parquet'
	'unified_ni.jsonl.gz.parquet',
	'unified_openai_summarize_tldr.jsonl.parquet',
	# 'unified_oscar_en_sample_dialog.jsonl.parquet', # create text containing these N words, not specific
	'unified_plot_screenplay_books_dialog.jsonl.parquet',
	'unified_soda_dialog.jsonl.parquet',
	'unified_unnatural_instructions.jsonl.parquet',
	]


	@pytest.mark.parametrize("filename", OIG_DATASETS)
	def test_get_small_sample_oig_data(filename):
	if not os.path.exists(filename):
	os.system('wget https://huggingface.co/datasets/laion/OIG/resolve/main/%s' % filename)
	import json
	rows = []
	with open(filename, "r") as f:
	for line in f.readlines():
	row = json.loads(line)
	rows.append(dict(input=row["text"]))
	with open(filename + POSTFIX, "w") as f:
	f.write(json.dumps(rows, indent=2))


	@pytest.mark.parametrize("filename", ALL_OIG_DATASETS)
	def test_download_useful_data_as_parquet(filename):
	dest_file = filename + '.parquet'
	if dest_file not in useful_oig_files:
	pytest.skip('file declared not useful')
	if not os.path.exists(filename):
	os.system('wget https://huggingface.co/datasets/laion/OIG/resolve/main/%s' % filename)
	if not os.path.exists(dest_file):
	df = pd.read_json(path_or_buf=filename, lines=True)
	df.to_parquet(dest_file, index=False)


	def test_merge_shuffle_small_sample_oig_data():
	np.random.seed(1234)
	rows = []
	for filename in OIG_DATASETS:
	with open(filename + POSTFIX, "r") as f:
	rows.extend(json.loads(f.read()))
	np.random.shuffle(rows)
	with open("merged_shuffled_OIG_%s.json" % hashlib.sha256(str(OIG_DATASETS).encode()).hexdigest()[:10], "w") as f:
	f.write(json.dumps(rows, indent=2))


	def test_join_jsons():
	files = ['config.json'] * 1 + \
	['dai_docs.train_cleaned.json'] * 2 + \
	['dai_faq.json'] * 3
	print(files)
	lst = []
	[lst.extend(json.load(open(fil, 'rt'))) for fil in files]
	print(len(lst))
	json.dump(lst, open("merged.json", "wt"), indent=2)


	@pytest.mark.parametrize("filename", ['Anthropic/hh-rlhf'])
	def test_make_rlhf_good_data(filename):
	from datasets import load_dataset
	rows = load_dataset(filename)["train"]["chosen"]
	new_rows = []
	for row in rows:
	if row[:2] == "\n\n":
	row = row[2:]
	row = row.replace("Human: ", "<human>: ")
	row = row.replace("Assistant: ", "<bot>: ")
	new_rows.append(dict(input=row))
	with open(filename.replace("/", "_") + POSTFIX, "w") as f:
	f.write(json.dumps(new_rows, indent=2))


	def test_show_prompts():
	files = ['config.json'] * 1 + \
	['dai_docs.train_cleaned.json'] * 1 + \
	['dai_faq.json'] * 1
	file_points = [json.load(open(fil, 'rt')) for fil in files]
	from prompter import generate_prompt
	for data_points in file_points:
	for data_point in data_points:
	print(generate_prompt(data_point, 'plain', '', False, False, False)[0])


	def test_get_open_datasets():
	# HF changed things so don't get raw list of all datasets, so not have to filter, but can't do negative filter
	open_tags = ['license:Apache License 2.0',
	'license:mit',
	'license:apache',
	'license:apache2',
	'license:apache-2.0',
	'license:bsd',
	'license:bsd-2-clause',
	'license:bsd-3-clause',
	'license:bsd-3-clause-clear',
	'license:lgpl-2.1',
	'license:lgpl-3.0',
	'license:lgpl-lr',
	'license:lgpl',
	'license:openrail++',
	'license:openrail',
	'license:bigscience-bloom-rail-1.0',
	# 'license:agpl-3.0',
	'license:other',
	'license:unknown',
	# 'license:mpl-2.0', # ok, but would have to include original copyright, license, source, copies in distribution
	# Attribution required:
	'license:odc-by',
	'license:cc-by-4.0',
	'license:cc-by-3.0',
	'license:cc-by-2.0',
	'license:cc-by-2.5',
	# 'license:cc-by-sa-4.0', # would require same license
	'license:odbl',
	'license:pddl',
	'license:ms-pl',
	'license:zlib',
	]
	# bad license: cc-by-nc-4.0

	from huggingface_hub import list_datasets
	datasets = flatten_list([[x for x in list_datasets(filter=y)] for y in open_tags])
	datasets += [x for x in list_datasets(author='openai')]
	# check all:
	all_license_tags = set(flatten_list([[y for y in x.tags if 'license' in y] for x in datasets]))
	print(len(all_license_tags))
	open_datasets = [x for x in datasets if any([y in x.tags for y in open_tags]) or 'license:' not in str(x.tags)]
	print('open_datasets', len(open_datasets))
	all_task_tags = set(flatten_list([[y for y in x.tags if 'task' in y] for x in open_datasets]))
	print('all_task_tags', len(all_task_tags))
	excluded_tags = ['image', 'hate', 'tabular', 'table-', 'classification', 'retrieval',
	'translation', 'identification', 'object', 'mask', 'to-text',
	'face-detection', 'audio', 'voice', 'reinforcement', 'depth-est',
	'forecasting', 'parsing', 'visual', 'speech', 'multiple-choice',
	'slot-filling', 'irds/argsme', '-scoring', 'other', 'graph-ml',
	'feature-extraction', 'keyword-spotting',
	'coreference-resolution', 'segmentation',
	'word-sense-disambiguation',
	'lemmatization']
	task_tags = [x.replace('task_categories:', '').replace('task_ids:', '')
	for x in all_task_tags if not any([y in x for y in
	excluded_tags])]
	print('task_tags', len(task_tags))
	# str(x.tags) to catch any pattern match to anything in list
	open_tasked_datasets = [x for x in open_datasets if
	any([y in str([x for x in x.tags if 'task' in x]) for y in task_tags]) and
	not any([y in str([x for x in x.tags if 'task' in x]) for y in excluded_tags]) or
	'task_categories' not in str(x.tags) and 'task_ids' not in str(x.tags)]
	open_tasked_datasets = [x for x in open_tasked_datasets if not x.disabled]
	open_tasked_datasets = [x for x in open_tasked_datasets if not x.gated]
	open_tasked_datasets = [x for x in open_tasked_datasets if not x.private]
	print('open_tasked_datasets', len(open_tasked_datasets))
	sizes = list(set(flatten_list([[(y, x.id) for y in x.tags if 'size' in y] for x in open_tasked_datasets])))
	languages = list(set(flatten_list([[(y, x.id) for y in x.tags if 'language:' in y] for x in open_tasked_datasets])))
	open_english_tasked_datasets = [x for x in open_tasked_datasets if
	'language:' not in str(x.tags) or
	'language:en' in str(x.tags)]
	small_open_english_tasked_datasets = [x for x in open_english_tasked_datasets if
	'n<1K' in str(x.tags) or
	'1K<n<10K' in str(x.tags) or
	'1K0<n<100K' in str(x.tags) or
	'100K<n<1M' in str(x.tags) or
	'size_category' not in str(x.tags)
	]
	# 'aeslc' : email_body, subject -> summarization?
	# load_dataset(open_tasked_datasets[0].id).data['train'].to_pandas()
	ids = [x.id for x in small_open_english_tasked_datasets]

	# sanity checks
	# https://bair.berkeley.edu/blog/2023/04/03/koala/
	assert 'alespalla/chatbot_instruction_prompts' in ids
	assert 'laion/OIG' in ids
	assert 'openai/webgpt_comparisons' in ids
	assert 'openai/summarize_from_feedback' in ids
	assert 'Anthropic/hh-rlhf' in ids

	# useful but not allowed for commercial purposes:
	# https://huggingface.co/datasets/squad

	print('open_english_tasked_datasets: ', ids, flush=True)

	exclude_ids = ['allenai/nllb', # translation only
	'hf-internal-testing/fixtures_image_utils', # testing
	'allenai/c4', # search-url
	'agemagician/uniref50', # unknown
	'huggingface-course/documentation-images', # images
	'smilegate-ai/kor_unsmile', # korean
	'MohamedRashad/ChatGPT-prompts', # ChatGPT/LearnGPT/https://www.emergentmind.com/
	'humarin/chatgpt-paraphrases', # Paraphrase using ChatGPT
	'Jeska/vaccinchat', # not useful
	'alespalla/chatbot_instruction_prompts', # mixes alpaca
	'allenai/prosocial-dialog',
	# already exlucded, but wrongly in other datasets that say more permissive license
	'AlekseyKorshuk/persona-chat', # low quality
	'bavard/personachat_truecased', # low quality
	'adamlin/daily_dialog', # medium quality conversations
	'adamlin/FewShotWoz', # low quality
	'benjaminbeilharz/better_daily_dialog', # low quality
	'benjaminbeilharz/daily_dialog_w_turn_templates', # low
	'benjaminbeilharz/empathetic_dialogues_for_lm', # low
	'GEM-submissions/GEM__bart_base_schema_guided_dialog__1645547915', # NA
	'ia-bentebib/conv_ai_2_fr', # low fr
	'ia-bentebib/daily_dialog_fr', # low fr
	'ia-bentebib/dialog_re_fr', # low fr
	'ia-bentebib/empathetic_dialogues_fr', # low fr
	'roskoN/dailydialog', # low
	'VadorMazer/skyrimdialogstest', # low
	'bigbio/med_qa', # med specific Q/A
	'biu-nlp/qa_srl2018', # low quality Q/A
	'biu-nlp/qa_discourse', # low quality Q/A
	'iarfmoose/qa_evaluator', # low quality Q/A
	'jeopardy', # low quality Q/A -- no reasoning
	'narrativeqa', # low quality Q/A
	'nomic-ai/gpt4all_prompt_generations', # bad license
	'nomic-ai/gpt4all_prompt_generations_with_p3', # bad license
	'HuggingFaceH4/alpaca', # bad license
	'tatsu-lab/alpaca', # ToS breaking
	'yahma/alpaca-cleaned', # ToS breaking
	'Hello-SimpleAI/HC3', # bad license
	'glue', # no reasoning QA
	'sahil2801/CodeAlpaca-20k', # bad license
	'Short-Answer-Feedback/saf_communication_networks_english', # long Q, medium A
	]
	small_open_english_tasked_datasets = [x for x in small_open_english_tasked_datasets if x.id not in exclude_ids]
	# some ids clearly speech related
	small_open_english_tasked_datasets = [x for x in small_open_english_tasked_datasets if 'speech' not in x.id]
	# HF testing
	small_open_english_tasked_datasets = [x for x in small_open_english_tasked_datasets if
	'hf-internal-testing' not in x.id]
	small_open_english_tasked_datasets = [x for x in small_open_english_tasked_datasets if
	'chinese' not in x.id]

	sorted_small_open_english_tasked_datasets = sorted([(x.downloads, x) for x in small_open_english_tasked_datasets],
	key=lambda x: x[0], reverse=True)

	# NOTES:
	# Run like pytest -s -v create_data.py::test_get_open_datasets &> getdata9.log
	# See what needs config passed and add:
	# grep 'load_dataset(' getdata9.log\|grep -v data_id\|less -S
	# grep "pip install" getdata9.log
	# NOTE: Some datasets have default config, but others are there. Don't know how to access them.

	"""
	https://huggingface.co/datasets/wikihow/blob/main/wikihow.py
	https://github.com/mahnazkoupaee/WikiHow-Dataset
	https://ucsb.box.com/s/ap23l8gafpezf4tq3wapr6u8241zz358
	https://ucsb.app.box.com/s/ap23l8gafpezf4tq3wapr6u8241zz358
	"""

	"""
	# some ambiguous or non-commercial datasets
	https://github.com/PhoebusSi/alpaca-CoT
	"""

	timeout = 3 * 60
	# laion/OIG takes longer
	for num_downloads, dataset in sorted_small_open_english_tasked_datasets:
	data_id = dataset.id
	func = do_one
	args = (data_id, num_downloads)
	kwargs = {}
	with ProcessPoolExecutor(max_workers=1) as executor:
	future = executor.submit(func, args, *kwargs)
	try:
	future.result(timeout=timeout)
	except concurrent.futures.TimeoutError:
	print("\n\ndata_id %s timeout\n\n" % data_id, flush=True)
	for child in psutil.Process(os.getpid()).children(recursive=True):
	os.kill(child.pid, signal.SIGINT)
	os.kill(child.pid, signal.SIGTERM)
	os.kill(child.pid, signal.SIGKILL)


	def do_one(data_id, num_downloads):
	from datasets import load_dataset
	out_file = "data_%s.parquet" % str(data_id.replace('/', '_'))
	if os.path.isfile(out_file) and os.path.getsize(out_file) > 1024 ** 3:
	return
	try:
	print("Loading data_id %s num_downloads: %s" % (data_id, num_downloads), flush=True)
	avail_list = None
	try:
	data = load_dataset(data_id, 'foobar')
	except Exception as e:
	if 'Available: ' in str(e):
	avail_list = ast.literal_eval(str(e).split('Available:')[1].strip())
	else:
	avail_list = None
	if avail_list is None:
	avail_list = [None]
	print("%s avail_list: %s" % (data_id, avail_list), flush=True)

	for name in avail_list:
	out_file = "data_%s_%s.parquet" % (str(data_id.replace('/', '_')), str(name))
	if os.path.isfile(out_file):
	continue
	data = load_dataset(data_id, name)
	column_names_dict = data.column_names
	column_names = column_names_dict[list(column_names_dict.keys())[0]]
	print("Processing data_id %s num_downloads: %s columns: %s" % (data_id, num_downloads, column_names),
	flush=True)
	data_dict = data.data
	col_dict = data.num_columns
	first_col = list(col_dict.keys())[0]
	if 'train' in data_dict:
	df = data['train'].to_pandas()
	else:
	df = data[first_col].to_pandas()
	# csv has issues with escaping chars, even for datasets I know I want
	df.to_parquet(out_file, index=False)
	except Exception as e:
	t, v, tb = sys.exc_info()
	ex = ''.join(traceback.format_exception(t, v, tb))
	print("Exception: %s %s" % (data_id, ex), flush=True)


	def test_otherlic():
	from huggingface_hub import list_datasets
	lic = ['license:odc-by',
	'license:cc-by-4.0',
	'license:cc-by-3.0',
	'license:cc-by-2.0',
	'license:cc-by-2.5',
	'license:cc-by-sa-4.0',
	'license:odbl',
	'license:pddl',
	'license:ms-pl',
	'license:zlib',
	]
	datasets = flatten_list([[x for x in list_datasets(filter=y) if 'translation' not in str(x.tags)] for y in lic])
	print(len(datasets))


	# These useful datasets are determined based upon data sample, column types, and uniqueness compared to larger datasets like Pile
	# grep columns getdata13.log\|grep -v "\['image'\]"\|sort\|uniq\|grep -v tokens\|grep -v "'image'"\|grep -v embedding\|grep dialog
	useful = ['Dahoas/instruct-human-assistant-prompt',
	'Dahoas/first-instruct-human-assistant-prompt',
	'knkarthick/dialogsum', # summary of conversation
	'McGill-NLP/FaithDial', # medium quality
	'Zaid/quac_expanded', # medium quality context + QA
	'0-hero/OIG-small-chip2', # medium
	'alistvt/coqa-flat', # QA medium
	'AnonymousSub/MedQuAD_47441_Question_Answer_Pairs', # QA medium
	'Anthropic/hh-rlhf', # high quality # similar to Dahoas/full-hh-rlhf
	'arjunth2001/online_privacy_qna', # good quality QA
	'Dahoas/instruct_helpful_preferences', # medium quality instruct
	'Dahoas/rl-prompt-dataset', # medium chat
	'Dahoas/rm-static', # medium chat
	'Dahoas/static-hh', # medium chat # HuggingFaceH4/self_instruct
	'Dahoas/synthetic-instruct-gptj-pairwise', # medium chat
	'eli5', # QA if prompt ELI5
	'gsm8k', # QA (various)
	'guanaco/guanaco', # prompt/response
	'kastan/rlhf-qa-comparisons', # good QA
	'kastan/rlhf-qa-conditional-generation-v2', # prompt answer
	'OllieStanley/humaneval-mbpp-codegen-qa', # code QA, but started from words, so better than other code QA
	'OllieStanley/humaneval-mbpp-testgen-qa', # code QA
	'Graverman/Instruct-to-Code', # code QA
	'openai/summarize_from_feedback', # summarize
	'relbert/analogy_questions', # analogy QA
	'yitingxie/rlhf-reward-datasets', # prompt, chosen, rejected.
	'yizhongw/self_instruct', # instruct (super natural & instruct)
	'HuggingFaceH4/asss', # QA, big A
	'kastan/rlhf-qa-conditional-generation-v2', # QA
	'cosmos_qa', # context QA
	'vishal-burman/c4-faqs', # QA but not so much reasoning, but alot of text
	'squadshifts', # QA from context
	'hotpot_qa', # QA from context
	'adversarial_qa', # QA from context
	'allenai/soda', # dialog -> narrative/summary
	'squad_v2', # context QA
	'squadshifts', # context QA
	'dferndz/cSQuAD1', # context QA
	'dferndz/cSQuAD2', # context QA
	'din0s/msmarco-nlgen', # context QA
	'domenicrosati/TruthfulQA', # common sense truthful QA -- trivia but good trivia
	'hotpot_qa', # context, QA
	'HuggingFaceH4/self-instruct-eval', # instruct QA, medium quality, some language reasoning
	'kastan/EE_QA_for_RLHF', # context QA
	'KK04/LogicInference_OA', # instruction logical QA
	'lmqg/qa_squadshifts_synthetic', # context QA
	'lmqg/qg_squad', # context QA
	'lmqg/qg_squadshifts', # context QA
	'lmqg/qg_subjqa', # context QA
	'pszemraj/HC3-textgen-qa',
	# QA medium, has human responses -- humans tend to provide links instead of trying to answer
	'pythonist/newdata', # long context, QA, brief A
	'ropes', # long background, situation, question, A
	'wikitablequestions', # table -> QA
	'bigscience/p3', # context QA but short answers
	]

	code_useful = ['0n1xus/codexglue',
	'openai_humaneval',
	'koutch/staqc',
	]

	maybe_useful = ['AlekseyKorshuk/comedy-scripts',
	'openbookqa', # hard to parse, low reasoning
	'qed', # reasonable QA, but low reasoning
	'selqa', # candidate answers
	'HuggingFaceH4/instruction-pilot-outputs-filtered',
	'GBaker/MedQA-USMLE-4-options', # medical QA with long questions
	'npc-engine/light-batch-summarize-dialogue', # dialog summarize, kinda low specific quality
	]

	summary_useful = ['austin/rheum_abstracts',
	'CarperAI/openai_summarize_comparisons', # summarize chosen/rejected
	'CarperAI/openai_summarize_tldr', # summarize QA
	'ccdv/cnn_dailymail', # summarize news
	'ccdv/govreport-summarization', # summarize high quality
	'ccdv/pubmed-summarization', # summarize high quality
	'duorc', # plot -> QA
	'farleyknight/big_patent_5_percent', # desc -> abstract
	'multi_news', # summary
	'opinosis',
	'SophieTr/reddit_clean',
	'allenai/mup', # long text -> summary
	'allenai/multi_lexsum', # long text -> summary
	'big_patent',
	'allenai/wcep_dense_max',
	'awinml/costco_long_practice',
	'GEM/xsum',
	'ratishsp/newshead',
	'RussianNLP/wikiomnia', # russian
	'stacked-summaries/stacked-xsum-1024',
	]

	math_useful = [
	'competition_math'
	]

	skipped = ['c4', # maybe useful, used for flan, but skipped due to size
	]

	"""
	To get training data from oig:
	pytest test_oig test_grade_final test_finalize_to_json
	"""

	human = '<human>:'
	bot = '<bot>:'


	def test_assemble_and_detox():
	import re
	from profanity_check import predict_prob
	df_list = []
	for data in useful_oig_files:
	print("Processing %s" % data, flush=True)
	df = pd.read_parquet(data)
	df = df.reset_index(drop=True)
	# chop up into human/bot interactions of no more than 10kB per row
	text_list = df[['text']].values.ravel().tolist()
	new_text = []
	max_len = 2048 # uber cutoff
	MAX_LEN = 2048 // 2 - 30 # max len per question/answer
	for text in tqdm(text_list):
	human_starts = [m.start() for m in re.finditer('<human>: ', text)]
	if len(human_starts) == 1:
	human_starts = [0, len(text)] # always go into for loop below
	blurb = ''
	for i in range(len(human_starts) - 1):
	interaction = text[human_starts[i]: human_starts[i + 1]][:max_len]
	blurb += interaction
	if len(blurb) >= MAX_LEN:
	blurb = get_sentences(blurb, length=MAX_LEN)[0]
	new_text.append(blurb + "\n<human>:")
	blurb = ''
	if blurb:
	blurb = get_sentences(blurb, length=MAX_LEN)[0]
	new_text.append(blurb + "\n<human>:")

	if len(new_text) > len(text_list):
	print("Added %d new rows (before: %d)" % (len(new_text) - df.shape[0], df.shape[0]))
	df = pd.DataFrame({"text": new_text, "source": [data] * len(new_text)})
	df = df.drop_duplicates(keep='first')
	print(df['text'].apply(lambda x: len(x)).describe())
	assert df['text'].apply(lambda x: len(x)).max() <= 2 * max_len

	# faster than better_profanity, do early
	df['profanity'] = predict_prob(df['text'])
	before_rows = df.shape[0]
	df = df[df['profanity'] < 0.25] # drop any low quality stuff
	after_rows = df.shape[0]
	print("Dropped %d rows out of %d due to alt-profanity-check" % (before_rows - after_rows, before_rows))
	df_list.append(df)
	print("Done processing %s -> %s rows" % (data, df.shape[0]), flush=True)
	print("So far have %d rows" % sum([len(x) for x in df_list]))
	df_final = pd.concat(df_list)
	df_final = df_final.sample(frac=1, random_state=1234).reset_index(drop=True)
	df_final.to_parquet('h2oGPT.cleaned.human_bot.shorter.parquet', index=False)


	def test_basic_cleaning():
	# from better_profanity import profanity
	# https://pypi.org/project/alt-profanity-check/
	from profanity_check import predict
	df_list = []
	for data in useful_oig_files:
	# for data in useful_oig_files[:5]:
	# for data in ['unified_openai_summarize_tldr.jsonl.parquet']:
	print("Processing %s" % data, flush=True)
	df = pd.read_parquet(data)
	df = df.reset_index(drop=True)
	# NOTE: Not correct if multiple human-bot interactions, but those dialogs even more desired
	# avg_chars = len(df['text'][0])/(df['text'][0].count(human)+df['text'][0].count(bot))
	df['avg_words'] = df['text'].apply(lambda x: x.count(' ') / (x.count(human) + x.count(bot)) / 2.0)
	df['avg_bot_words'] = df['text'].apply(lambda x: x.split(bot)[1].count(' ') / x.count(bot))
	# df['bad_words'] = df['text'].apply(lambda x: profanity.contains_profanity(x))
	# low_quality_patterns = ['Write the rest of this wikipedia article']
	res = predict(df['text'])
	df['bad_words'] = res
	df = df.reset_index(drop=True)
	df = df[df['bad_words'] == 0]
	df = df[['text', 'avg_words', 'avg_bot_words']]
	df = df.drop_duplicates(keep='first')
	print(df[df['avg_words'] == df['avg_words'].max()]['text'].values)
	median_words = np.median(df['avg_words'])
	min_words_per_entity = max(30, 0.8 * median_words)
	max_words_per_entity = 2048 # too hard to learn from for now
	df = df[df['avg_words'] > min_words_per_entity]
	df = df[df['avg_words'] < max_words_per_entity]

	min_words_per_entity = max(20, 0.5 * median_words) # bot should say stuff for now
	max_words_per_entity = 2048 # too hard to learn from for now
	df = df[df['avg_bot_words'] > min_words_per_entity]
	df = df[df['avg_bot_words'] < max_words_per_entity]

	df_list.append(df)
	print("Done processing %s -> %s rows" % (data, df.shape[0]), flush=True)
	df_final = pd.concat(df_list)
	df_final.to_parquet('h2oGPT.cleaned.human_bot.parquet', index=False)


	from joblib import Parallel, delayed, effective_n_jobs
	from sklearn.utils import gen_even_slices
	from sklearn.utils.validation import _num_samples


	def parallel_apply(df, func, n_jobs=-1, **kwargs):
	""" Pandas apply in parallel using joblib.
	Uses sklearn.utils to partition input evenly.

	Args:
	df: Pandas DataFrame, Series, or any other object that supports slicing and apply.
	func: Callable to apply
	n_jobs: Desired number of workers. Default value -1 means use all available cores.
	**kwargs: Any additional parameters will be supplied to the apply function

	Returns:
	Same as for normal Pandas DataFrame.apply()

	"""

	if effective_n_jobs(n_jobs) == 1:
	return df.apply(func, **kwargs)
	else:
	ret = Parallel(n_jobs=n_jobs)(
	delayed(type(df).apply)(df[s], func, **kwargs)
	for s in gen_even_slices(_num_samples(df), effective_n_jobs(n_jobs)))
	return pd.concat(ret)


	def add_better_profanity_flag(df):
	from better_profanity import profanity
	df['better_profanity'] = parallel_apply(
	df['text'],
	lambda x: profanity.contains_profanity(x),
	n_jobs=-1,
	)
	return df


	def add_textstat_grade(df):
	import textstat

	def myfunc(x):
	return textstat.flesch_kincaid_grade(x) # simple grade

	if False:
	import dask.dataframe as dd
	# 40 seconds for 1000 rows, but have 1,787,799 rows
	ddata = dd.from_pandas(df, npartitions=120)

	df['flesch_grade'] = ddata['text'].apply(myfunc).compute()
	if True:
	# fast way
	df['flesch_grade'] = parallel_apply(df['text'], myfunc, n_jobs=-1)
	return df


	def add_deberta_grade(df):
	from transformers import AutoModelForSequenceClassification, AutoTokenizer
	import torch
	reward_name = "OpenAssistant/reward-model-deberta-v3-large-v2"
	rank_model, tokenizer = AutoModelForSequenceClassification.from_pretrained(
	reward_name), AutoTokenizer.from_pretrained(reward_name)
	device = 'cuda' if torch.cuda.is_available() else 'cpu'
	rank_model.to(device)

	def get_question(x):
	return x.replace('<human>: ', '').split('<bot>:')[0]

	def get_answer(x):
	try:
	answer = x.split('<bot>: ')[1].split('<human>:')[0].replace('<bot>: ', '')
	except:
	answer = x.split('<bot>:')[1].split('<human>:')[0].replace('<bot>:', '')
	return answer

	df['question'] = parallel_apply(df['text'], get_question, n_jobs=-1)
	df['answer'] = parallel_apply(df['text'], get_answer, n_jobs=-1)

	from datasets import Dataset
	from transformers import pipeline
	from transformers.pipelines.pt_utils import KeyPairDataset
	import tqdm

	pipe = pipeline(
	"text-classification",
	model=reward_name,
	device="cuda:0" if torch.cuda.is_available() else "cpu"
	)
	start = 0
	batch_size = 64 * 16
	micro_batch = orig_micro_batch = 16
	end = 0
	import socket
	checkpoint = "grades.%s.pkl" % socket.gethostname()
	grades = []
	import pickle
	if os.path.exists(checkpoint):
	with open(checkpoint, "rb") as f:
	start, grades = pickle.loads(f.read())
	last_oom = 0
	while end < df.shape[0]:
	# manual batching to handle OOM more gracefully
	end = min(start + batch_size, df.shape[0])
	if start == end:
	break
	dataset = Dataset.from_pandas(df.iloc[start:end, :])
	try:
	grades.extend([
	x['score'] for x in tqdm.tqdm(
	pipe(KeyPairDataset(dataset, "question", "answer"), batch_size=micro_batch)
	)
	])
	except torch.cuda.OutOfMemoryError:
	last_oom = start
	micro_batch = max(1, micro_batch // 2)
	print("OOM - retrying with micro_batch=%d" % micro_batch)
	continue
	if last_oom == start:
	micro_batch = orig_micro_batch
	print("Returning to micro_batch=%d" % micro_batch)
	assert len(grades) == end
	start = end
	with open(checkpoint, "wb") as f:
	f.write(pickle.dumps((end, grades)))
	print("%d/%d" % (end, df.shape[0]))
	df['grade_deberta'] = grades
	if os.path.exists(checkpoint):
	os.remove(checkpoint)
	return df


	def test_chop_by_lengths():
	file = "h2oGPT.cleaned.human_bot.shorter.parquet"
	df = pd.read_parquet(file).reset_index(drop=True)
	df = count_human_bot_lengths(df)
	df['rand'] = np.random.rand(df.shape[0])
	df['rand2'] = np.random.rand(df.shape[0])
	before_rows = df.shape[0]
	# throw away short human/bot responses with higher likelihood
	df = df[(df['len_human_mean'] > 20)] # never keep very short ones
	df = df[(df['len_human_mean'] > 30) \| (df['rand'] < 0.2)]
	df = df[(df['len_human_mean'] > 50) \| (df['rand'] < 0.5)]
	df = df[(df['len_human_max'] < 10000)] # drop super long (basically only human) ones
	df = df[(df['len_bot_mean'] > 20)] # never keep very short ones
	df = df[(df['len_bot_mean'] > 30) \| (df['rand2'] < 0.2)]
	df = df[(df['len_bot_mean'] > 50) \| (df['rand2'] < 0.5)]
	df = df[(df['len_bot_max'] < 10000)] # drop super long (only bot) ones
	assert df['text'].apply(lambda x: len(x)).max() < 20000
	df = df.drop(['rand', 'rand2'], axis=1)
	after_rows = df.shape[0]
	print("Chopped off %d out of %d rows due to length" % (before_rows - after_rows, before_rows))
	print(df.describe())
	df.to_parquet('h2oGPT.cleaned.chopped.human_bot.shorter.parquet', index=False)


	def count_human_bot_lengths(df, human=None, bot=None):
	import re
	len_human_min = []
	len_human_max = []
	len_human_mean = []
	len_bot_min = []
	len_bot_max = []
	len_bot_mean = []
	human = human or '<human>:'
	bot = bot or '<bot>:'
	for is_human in [True, False]:
	what = human if is_human else bot
	other = human if not is_human else bot
	for i in range(df.shape[0]):
	text = df.loc[i, 'text']
	assert isinstance(text, str)
	starts = [m.start() for m in re.finditer(what, text)]
	if len(starts) == 1:
	starts = [starts[0], len(text)] # always go into for loop below
	assert len(text)
	list_what = []
	for ii in range(len(starts) - 1):
	interaction = text[starts[ii]: starts[ii + 1]]
	if other in interaction:
	interaction = interaction[:interaction.find(other)]
	interaction.strip()
	list_what.append(interaction)
	if not list_what:
	list_what = [''] # handle corrupted data, very rare, leads to sizes 0
	if is_human:
	len_human_min.append(min([len(x) for x in list_what]))
	len_human_max.append(max([len(x) for x in list_what]))
	len_human_mean.append(np.mean([len(x) for x in list_what]))
	else:
	len_bot_min.append(min([len(x) for x in list_what]))
	len_bot_max.append(max([len(x) for x in list_what]))
	len_bot_mean.append(np.mean([len(x) for x in list_what]))
	df['len_human_min'] = len_human_min
	df['len_human_max'] = len_human_max
	df['len_human_mean'] = len_human_mean
	df['len_bot_min'] = len_bot_min
	df['len_bot_max'] = len_bot_max
	df['len_bot_mean'] = len_bot_mean
	np.random.seed(1234)
	pd.set_option('display.max_columns', None)
	print("Before chopping")
	print(df.describe())
	return df


	def test_grade():
	df = None

	file = "h2oGPT.cleaned.chopped.human_bot.shorter.parquet"
	output_file = "h2oGPT.cleaned.graded1.human_bot.shorter.parquet"
	if not os.path.exists(output_file):
	if df is None:
	df = pd.read_parquet(file).reset_index(drop=True)
	df = add_textstat_grade(df)
	min_grade = 10
	max_grade = 25
	df = df[df['flesch_grade'] >= min_grade]
	df = df[df['flesch_grade'] <= max_grade]
	print("After Flesch grade")
	print(df.describe())
	df.to_parquet(output_file, index=False)

	file = output_file
	output_file = "h2oGPT.cleaned.graded2.human_bot.shorter.parquet"
	if not os.path.exists(output_file):
	# slower than alt-profanity, do last, but do before deberta grading, since that's slower
	if df is None:
	df = pd.read_parquet(file).reset_index(drop=True)
	df = add_better_profanity_flag(df)
	before_rows = df.shape[0]
	df = df[df['better_profanity'] == 0]
	df = df.drop(['better_profanity'], axis=1)
	after_rows = df.shape[0]
	print("Dropped %d rows out of %d due to better_profanity" % (before_rows - after_rows, before_rows))
	print(df.describe())
	df.to_parquet(output_file, index=False)

	file = output_file
	output_file = 'h2oGPT.cleaned.graded3.human_bot.shorter.parquet'
	if not os.path.exists(output_file):
	if df is None:
	df = pd.read_parquet(file).reset_index(drop=True)
	df = add_deberta_grade(df)
	min_grade = 0.3
	max_grade = np.inf
	before_rows = df.shape[0]
	df = df[df['grade_deberta'] >= min_grade]
	df = df[df['grade_deberta'] <= max_grade]
	after_rows = df.shape[0]
	print("Dropped %d rows out of %d due to deberta grade" % (before_rows - after_rows, before_rows))
	print("After DeBERTa grade")
	print(df.describe())
	df.to_parquet(output_file, index=False)

	file = output_file
	output_file = 'h2oGPT.cleaned.graded.human_bot.shorter.parquet'
	if df is None:
	df = pd.read_parquet(file).reset_index(drop=True)
	df.to_parquet(output_file, index=False)


	@pytest.mark.parametrize(
	"fixup_personality, only_personality, deberta_grading",
	[
	[False, False, False],
	[True, True, False],
	[True, False, False],
	[True, False, True],
	]
	)
	def test_add_open_assistant(fixup_personality, only_personality, deberta_grading, save_json=True):
	"""
	Flatten tree structure into one row per path from root to leaf
	Also turn into human_bot prompting format:
	<human>: question\n<bot>: answer <human>: question2\n<bot>: answer2 Etc.
	Also saves a .json locally as side-effect
	returns list of dicts, containing intput, prompt_type and source
	"""
	from datasets import load_dataset
	data_file = "OpenAssistant/oasst1"
	ds = load_dataset(data_file)
	df = pd.concat([ds['train'].to_pandas(), ds['validation'].to_pandas()], axis=0)
	rows = {}
	message_ids = df['message_id'].values.tolist()
	message_tree_ids = df['message_tree_id'].values.tolist()
	parent_ids = df['parent_id'].values.tolist()
	texts = df['text'].values.tolist()
	roles = df['role'].values.tolist()

	for i in range(df.shape[0]):
	# collect all trees
	message_id = message_ids[i]
	message_tree_id = message_tree_ids[i]
	parent_id = parent_ids[i]
	text = texts[i]
	if fixup_personality:
	text = text.replace("Open Assistant", "h2oGPT")
	text = text.replace("Open-Assistant", "h2oGPT")
	text = text.replace("open-assistant", "h2oGPT")
	text = text.replace("OpenAssistant", "h2oGPT")
	text = text.replace("open assistant", "h2oGPT")
	text = text.replace("Open Assistand", "h2oGPT")
	text = text.replace("Open Assitant", "h2oGPT")
	text = text.replace("Open Assistent", "h2oGPT")
	text = text.replace("Open Assisstant", "h2oGPT")
	text = text.replace("Open Assitent", "h2oGPT")
	text = text.replace("Open Assitiant", "h2oGPT")
	text = text.replace("Open Assistiant", "h2oGPT")
	text = text.replace("Open Assitan ", "h2oGPT ")
	text = text.replace("Open Assistan ", "h2oGPT ")
	text = text.replace("Open Asistant", "h2oGPT")
	text = text.replace("Open Assiant", "h2oGPT")
	text = text.replace("Assistant", "h2oGPT")
	text = text.replace("LAION AI", "H2O.ai")
	text = text.replace("LAION-AI", "H2O.ai")
	text = text.replace("LAION,", "H2O.ai,")
	text = text.replace("LAION.ai", "H2O.ai")
	text = text.replace("LAION.", "H2O.ai.")
	text = text.replace("LAION", "H2O.ai")

	role = roles[i]
	new_data = ('<human>: ' if role == 'prompter' else '<bot>: ') + text
	entry = dict(message_id=message_id, parent_id=parent_id, text=new_data)
	if message_tree_id not in rows:
	rows[message_tree_id] = [entry]
	else:
	rows[message_tree_id].append(entry)

	all_rows = []

	for node_id in rows:
	# order responses in tree, based on message/parent relationship
	conversations = []

	list_msgs = rows[node_id]
	# find start
	while len(list_msgs):
	for i, leaf in enumerate(list_msgs):
	found = False
	parent_id = leaf['parent_id']
	if parent_id is None:
	# conversation starter
	conversations.append(leaf)
	found = True
	else:
	for conv in conversations:
	# find all conversations to add my message to
	if parent_id in conv['message_id'] and parent_id != conv['message_id'][-len(parent_id):]:
	# my message doesn't follow conversation
	continue
	if parent_id == conv['message_id'][-len(parent_id):]:
	# my message follows conversation, but fork first, so another follow-on message can do same
	conversations.append(conv.copy())
	conv['text'] += f"""
	{leaf['text']}
	"""
	conv['message_id'] += leaf['message_id']
	found = True
	break
	if found:
	# my content was used, so nuke from list
	del list_msgs[i]
	break

	# now reduce down to final conversations, find the longest chains of message ids
	for i, conv in enumerate(conversations):
	for j, conv2 in enumerate(conversations):
	if i == j:
	continue
	if conv['message_id'] and conv2['message_id']:
	assert conv['message_id'] != conv2['message_id']
	# delete the shorter conversation, if one contains the other
	if conv['message_id'] in conv2['message_id']:
	conv['message_id'] = None
	if conv2['message_id'] in conv['message_id']:
	conv2['message_id'] = None
	conversations = [c for c in conversations if c['message_id']]
	if only_personality:
	all_rows.extend(
	[dict(input=c['text'] + "\n<human>:", prompt_type='plain', source=data_file) for c in conversations if
	'h2oGPT' in c['text']])
	else:
	all_rows.extend(
	[dict(input=c['text'] + "\n<human>:", prompt_type='plain', source=data_file) for c in conversations if
	"What is H2O.ai" not in c['text']])
	unhelpful = get_unhelpful_list()
	all_rows = [x for x in all_rows if not any(u in x['input'] for u in unhelpful)]
	personality = create_personality_data()
	all_rows.extend(personality * 10)
	np.random.seed(123)
	np.random.shuffle(all_rows)
	print(len(all_rows))
	if deberta_grading:
	df = pd.DataFrame(all_rows)
	df = df.rename(columns={'input': 'text'})
	df = add_deberta_grade(df)
	df = df.rename(columns={'text': 'input'})
	drop = True
	if drop:
	min_grade = 0.3
	max_grade = np.inf
	before_rows = df.shape[0]
	df = df[df['grade_deberta'] >= min_grade]
	df = df[df['grade_deberta'] <= max_grade]
	after_rows = df.shape[0]
	print("Dropped %d rows out of %d due to deberta grade" % (before_rows - after_rows, before_rows))
	print("After DeBERTa grade")
	print(df.describe())
	all_rows = []
	for i in range(df.shape[0]):
	all_rows.append(
	dict(
	input=df['input'].iloc[i],
	source=df['source'].iloc[i],
	prompt_type=df['prompt_type'].iloc[i],
	grade_deberta=df['grade_deberta'].iloc[i],
	)
	)
	if save_json:
	data_file = data_file + \
	("_h2ogpt" if fixup_personality else "") + \
	("_only" if only_personality else "") + \
	("_graded" if deberta_grading else "")
	for i in range(len(all_rows)):
	all_rows[i]['id'] = i
	with open(data_file.lower().replace("/", "_") + ".json", "w") as f:
	f.write(json.dumps(all_rows, indent=2))
	return all_rows


	def test_finalize_to_json():
	df = pd.read_parquet('h2oGPT.cleaned.graded.human_bot.shorter.parquet')
	df = df.rename(columns={'text': 'input'})

	print("Number of high-quality human_bot interactions: %s" % df.shape[0], flush=True)

	print("Adding open assistant data")
	with open("openassistant_oasst1_h2ogpt_graded.json") as f:
	open_assistant = json.loads(f.read())
	df = pd.concat([df, pd.DataFrame(open_assistant)], axis=0)

	def final_clean(df):
	from better_profanity import profanity
	profanity.load_censor_words_from_file("data/censor_words.txt")
	df['profanity'] = parallel_apply(
	df['input'],
	lambda x: profanity.contains_profanity(x),
	n_jobs=-1,
	)
	return df[(df['profanity'] == 0)].reset_index(drop=True)

	print("Before cleaning: Number of final high-quality human_bot interactions: %s" % df.shape[0], flush=True)
	df = final_clean(df)
	print("After cleaning: Number of final high-quality human_bot interactions: %s" % df.shape[0], flush=True)
	print(df.describe())
	print(df.shape)
	row_list = []
	for i in range(df.shape[0]):
	row_list.append(
	dict(
	input=df.loc[i, 'input'],
	source=df.loc[i, 'source'],
	prompt_type='plain',
	)
	)
	np.random.seed(1234)
	np.random.shuffle(row_list)
	unhelpful = get_unhelpful_list()
	row_list = [x for x in row_list if not any(u in x['input'] for u in unhelpful)]
	for i in range(len(row_list)):
	row_list[i]['id'] = i
	row_list[i]['input'] = row_list[i]['input'].replace(" <bot>:", "\n<bot>:")
	with open('h2ogpt-oig-oasst1-instruct-cleaned-v3.json', "w") as f:
	f.write(json.dumps(row_list, indent=2))


	def create_personality_data():
	questions = [
	"What's your name?",
	"What is your name?",
	"What are you?",
	"Who are you?",
	"Do you have a name?",
	"Who trained you?",
	"Who created you?",
	"Who made you?",
	]
	answers = [
	"I'm h2oGPT, a large language model by H2O.ai.",
	"I'm h2oGPT, a large language model by H2O.ai, the visionary leader in democratizing AI.",
	"My name is h2oGPT. I'm a large language model by H2O.ai, the visionary leader in democratizing AI.",
	"My name is h2oGPT. I'm a large language model trained by H2O.ai.",
	"Hi! I'm h2oGPT, a large language model by H2O.ai.",
	"Hi! I'm h2oGPT, a large language model by H2O.ai, the visionary leader in democratizing AI.",
	]
	help = [
	"",
	" How can I help you?",
	" How may I assist you?",
	" Nice to meet you.",
	]
	import itertools
	rows = []
	for pair in itertools.product(questions, answers, help):
	rows.append(
	dict(input=f"<human>: {pair[0]}\n<bot>: {pair[1]}{pair[2]}\n<human>:", prompt_type='plain', source="H2O.ai")
	)
	for row in [
	"<human>: What is H2O.ai?\n<bot>: H2O.ai is a technology company that aims to democratize AI and make it accessible to a broader audience by simplifying the process of creating and deploying machine learning models.\n<human>:",
	"<human>: What is h2o.ai?\n<bot>: H2O.ai is a technology company that aims to democratize AI and make it accessible to a broader audience by simplifying the process of creating and deploying machine learning models.\n<human>:",
	"<human>: What is H2O?\n<bot>: H2O.ai is a technology company that aims to democratize AI and make it accessible to a broader audience by simplifying the process of creating and deploying machine learning models.\n<human>:",
	"<human>: Who is h2o.ai?\n<bot>: H2O.ai is a technology company that aims to democratize AI and make it accessible to a broader audience by simplifying the process of creating and deploying machine learning models.\n<human>:",
	"<human>: who is h2o.ai?\n<bot>: H2O.ai is a technology company that aims to democratize AI and make it accessible to a broader audience by simplifying the process of creating and deploying machine learning models.\n<human>:",
	"<human>: who is h2o?\n<bot>: H2O.ai is a technology company that aims to democratize AI and make it accessible to a broader audience by simplifying the process of creating and deploying machine learning models.\n<human>:",
	"<human>: What is H2O.ai?\n<bot>: H2O.ai is the visionary leader in democratizing AI.\n<human>:",
	"<human>: Who is H2O.ai?\n<bot>: H2O.ai is the visionary leader in democratizing AI.\n<human>:",
	"<human>: Who is H2O?\n<bot>: H2O.ai is the visionary leader in democratizing AI.\n<human>:",
	"<human>: Who is h2o?\n<bot>: H2O.ai is the visionary leader in democratizing AI.\n<human>:",
	"<human>: who is h2o?\n<bot>: H2O.ai is the visionary leader in democratizing AI.\n<human>:",
	]:
	rows.append(dict(input=row, prompt_type='plain', source='H2O.ai'))
	print(len(rows))
	with open("h2ogpt-personality.json", "w") as f:
	f.write(json.dumps(rows, indent=2))
	return rows


	def test_check_stats_data():
	filename = 'h2ogpt-oig-oasst1-instruct-cleaned-v3.json'
	df = pd.read_json(filename)

	# get word stats
	df['char_count'] = df['input'].apply(lambda x: len(x))
	import matplotlib.pyplot as plt
	plt.figure(figsize=(10, 10))
	plt.hist(df['char_count'], bins=100)
	chars_avg = np.mean(df['char_count'])
	chars_median = np.median(df['char_count'])
	plt.title("char_count avg: %s median: %s" % (chars_avg, chars_median))
	plt.savefig('chars_hist.png')
	plt.close()

	# get tokenize stats for random sample of 1000 rows
	from finetune import generate_and_tokenize_prompt
	from loaders import get_loaders, get_tokenizer
	from functools import partial

	llama_type = False
	tokenizer_base_model = base_model = 'h2oai/h2ogpt-oasst1-512-20b'
	model_loader, tokenizer_loader = get_loaders(model_name=base_model, reward_type=False, llama_type=llama_type)
	local_files_only = False
	resume_download = True
	use_auth_token = False
	tokenizer = get_tokenizer(tokenizer_loader, tokenizer_base_model, local_files_only, resume_download, use_auth_token)
	prompt_type = 'plain' # trained with data already in human bot form
	train_on_inputs = True
	add_eos_token = False
	cutoff_len = 512 # can choose 2048
	generate_and_tokenize_prompt_fun = partial(generate_and_tokenize_prompt, prompt_type=prompt_type,
	train_on_inputs=train_on_inputs, add_eos_token=add_eos_token,
	cutoff_len=cutoff_len, tokenizer=tokenizer)
	from datasets import load_dataset
	data = load_dataset("json", data_files={"train": filename})
	val_set_size = 0.90
	train_val = data["train"].train_test_split(
	test_size=val_set_size, shuffle=True, seed=42
	)
	train_data = train_val["train"]
	train_data = train_data.shuffle().map(generate_and_tokenize_prompt_fun, num_proc=os.cpu_count())

	df_tokens = pd.DataFrame([len(x) for x in train_data['input_ids']], columns=['token_count'])

	plt.figure(figsize=(10, 10))
	plt.hist(df_tokens['token_count'], bins=100)
	token_avg = np.mean(df_tokens['token_count'])
	token_median = np.median(df_tokens['token_count'])
	plt.title("token_count with cutoff=%s avg: %s median: %s" % (cutoff_len, token_avg, token_median))
	plt.savefig('token_hist_%s.png' % cutoff_len)
	plt.close()


	def get_unhelpful_list():
	# base versions
	unhelpful = ["I'm sorry, I didn't quite understand your question, could you please rephrase it?",
	"I'm sorry, but I don't understand your question. Could you please rephrase it?",
	"I'm sorry, I don't quite understand your question",
	"I'm sorry, I don't know",
	"I'm sorry, but I don't know",
	"I don't know anything",
	"I do not know",
	"I don't know",
	"I don't know how",
	"I do not know how",
	"Can you please explain what you mean",
	"please explain what you mean",
	"please explain",
	"I'm sorry, but I don't know how to tell a story. Can you please explain what you mean by",
	"I'm sorry but I don't understand what you mean",
	"I don't understand",
	"I don't have the ability",
	"I do not have the ability",
	"I do not have",
	"I am a language model,",
	"I am a large language model,",
	"I do not understand your question. Can you please try to make it clearer?",
	"I'm sorry, but as an AI language model",
	"I apologize, but I cannot rephrase text that I cannot understand. Your post is difficult to read and follow.",
	"I apologize, but I am not h2oGPT. I am a language model developed by H2O.ai. How may I help you?",
	"Sorry, but I am not an actual Linux shell, nor am I capable of emulating one. I am an open source chat assistant and would be glad t",
	"I apologize, but I cannot perform the task you have requested.",
	"I'm sorry, I cannot perform this task as I am an AI language model and do not have access",
	"I'm sorry, I'm not sure what you're asking for here.",
	"I'm not sure what you are asking",
	"You need to provide more context",
	]
	# reduced versions, with redundant parts, just to give context for where they came from
	unhelpful += ["sorry, I didn't quite understand your question",
	"I didn't quite understand your question",
	"I didn't understand your question",
	"I did not understand your question",
	"I did not understand the question",
	"could you please rephrase"
	"could you rephrase"
	"I do not understand your question.",
	"I do not understand the question.",
	"I do not understand that question.",
	"Can you please try to make it clearer",
	"Can you try to make it clearer",
	"sorry, but as an AI language model",
	"as an AI language model",
	"I apologize, but I cannot",
	"I cannot rephrase text",
	"I cannot understand. Your post is difficult to read and follow."
	"Your post is difficult to read and follow."
	"I apologize, but I am",
	"Sorry, but I am not ",
	"nor am I capable",
	"I am not capable of",
	"I apologize, but I cannot perform the task you have requested",
	"I cannot perform the task",
	"I cannot complete the task",
	"I'm sorry",
	"I am sorry",
	"do not have access",
	"not sure what you're asking for",
	"not sure what you are asking for",
	"not sure what is being asked",
	"I'm not sure what you are asking",
	"not sure what you are asking",
	"You need to provide more context",
	"provide more context",
	]
	unhelpful += ["As a large language model",
	"cannot provide any information",
	"As an artificial intelligence I do not have the capability",
	"As an artificial intelligence I don't have the capability",
	"As an artificial intelligence I can't",
	"As an artificial intelligence I cannot",
	"I am sorry but I do not understand",
	"Can you please explain",
	"(sorry couldn't resist)",
	"(sorry could not resist)",
	" :)",
	" ;)",
	" :-)",
	" ;-)",
	" lol ",
	"Thanks so much!!!",
	"Thank You :)!!!",
	"Please try not to repeat",
	"I am an AI language model",
	"I'm a AI assistant that",
	"I'm an AI assistant that",
	"I am an AI assistant that",
	"etc.",
	"etc.etc.",
	"etc. etc.",
	"etc etc",
	]
	return unhelpful


	def test_check_unhelpful():
	# file = '/home/jon/Downloads/openassistant_oasst1_h2ogpt_graded.json'
	file = '/home/jon/Downloads/openassistant_oasst1_h2ogpt_grades.json'
	# file = 'h2ogpt-oig-oasst1-instruct-cleaned-v2.json'

	unhelpful = get_unhelpful_list()
	# data = json.load(open(file, 'rt'))
	df = pd.read_json(file)

	use_reward_score_threshold = False
	use_bleu_threshold = False
	use_sentence_sim = True

	from sacrebleu.metrics import BLEU
	bleu = BLEU()
	from nltk.translate.bleu_score import sentence_bleu

	def get_bleu(actual, expected_list):
	# return bleu.sentence_score(actual, expected_list).score
	return sentence_bleu(expected_list, actual)

	threshold = 0.0
	if use_reward_score_threshold:
	df = df[df['grade_deberta'] > threshold]

	# back to as if original json load
	data = df.to_dict(orient='records')
	bads = {}
	string_all = str(data)
	for sub in unhelpful:
	bads[sub] = string_all.count(sub)
	bads = {k: v for k, v in bads.items() if v > 0}
	import pprint
	pp = pprint.PrettyPrinter(indent=4)
	pp.pprint(bads)

	total_bads = sum(list(bads.values()))
	print('total_bads: %s' % total_bads, flush=True)

	# check just bot
	import re
	convs = [[x.strip() for x in re.split(r'%s\|%s' % (human, bot), y['input']) if x.strip()] for y in data]
	humans = [[x for i, x in enumerate(y) if i % 2 == 0] for y in convs]
	bots = [[x for i, x in enumerate(y) if i % 2 == 1] for y in convs]

	# FIXME: apply back to json etc., just see for now
	bleu_threshold = 0.9
	if use_bleu_threshold:
	bots = [[x for x in y if get_bleu(x, unhelpful) < bleu_threshold] for y in tqdm(bots)]

	cosine_sim_threshold = 0.8
	if use_sentence_sim:
	# pip install sentence_transformers-2.2.2
	from sentence_transformers import SentenceTransformer
	# sent_model = 'bert-base-nli-mean-tokens'
	# sent_model = 'nli-distilroberta-base-v2'
	sent_model = 'all-MiniLM-L6-v2'
	model = SentenceTransformer(sent_model)
	sentence_embeddings = model.encode(unhelpful)
	from sklearn.metrics.pairwise import cosine_similarity
	bots = [x for x in tqdm(bots) if
	np.max(cosine_similarity(model.encode(x), sentence_embeddings)) < cosine_sim_threshold]

	bads_bots = {}
	string_all = str(bots)
	for sub in unhelpful:
	bads_bots[sub] = string_all.count(sub)
	bads_bots = {k: v for k, v in bads_bots.items() if v > 0}
	import pprint
	pp = pprint.PrettyPrinter(indent=4)
	pp.pprint(bads_bots)

	total_bads_bots = sum(list(bads_bots.values()))
	print('threshold: %g use_bleu_threshold: %g total_bads_bots: %s total_bots: %s total_humans: %s' % (
	threshold, use_bleu_threshold, total_bads_bots, len(bots), len(humans)), flush=True)

	# assert len(bads) == 0, bads
	assert len(bads_bots) == 0, bads_bots


	def test_fortune2000_personalized():
	row_list = []
	import glob
	if not os.path.isdir("wikitext"):
	raise RuntimeError("download https://github.com/h2oai/h2ogpt/files/11423008/wikitext.zip and unzip")
	for file in glob.glob("wikitext/*.txt"):
	with open(file, "r") as f:
	blob = f.read()
	N = 512 * 4
	row_list.extend([{'input': s, 'prompt_type': 'plain', 'source': "%s" % os.path.basename(file)}
	for s in get_sentences(blob, N) if s])
	personality = create_personality_data()
	import copy
	for i in range(10):
	row_list.extend(copy.deepcopy(personality))
	np.random.seed(123)
	np.random.shuffle(row_list)
	for i in range(len(row_list)):
	row_list[i]['id'] = i
	for i in range(len(row_list)):
	assert row_list[i]['id'] == i
	with open("h2ogpt-fortune2000-personalized.json", "w") as ff:
	ff.write(json.dumps(row_list, indent=2))