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test / src /prepare_offline.py

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	def noop_load(args, *kwargs):
	return None


	def go_prepare_offline(args, *kwargs):
	kwargs0 = kwargs['kwargs']
	# gen.py steps should have already obtained:
	# model+tokenizers from base_model or model_lock if required
	# tokenizers, including tokenizers for model_lock if using inference servers even if no LLM locally
	# score_model or reward model
	#
	# Additional steps are related to document Q/A:
	# For simplicity use gradio functions,
	# but not API calls that would require actual gradio app up and API usage that might have issues

	kwargs['max_quality'] = True
	embed = True
	h2ogpt_key = ''
	file_list = ['tests/driverslicense.jpeg', 'tests/CityofTshwaneWater.pdf', 'tests/example.xlsx']

	inputs2 = [kwargs['my_db_state0'],
	kwargs['selection_docs_state0'],
	kwargs['requests_state0'],
	kwargs0['langchain_mode'],
	kwargs0['chunk'],
	kwargs0['chunk_size'],
	embed,
	kwargs['image_audio_loaders_options'],
	kwargs['pdf_loaders_options'],
	kwargs['url_loaders_options'],
	kwargs['jq_schema0'],
	kwargs['extract_frames'],
	h2ogpt_key,
	]

	for fileup_output in file_list:
	inputs1 = [fileup_output]
	add_file_kwargs = dict(fn=kwargs['update_db_func'],
	inputs=inputs1 + inputs2)
	add_file_kwargs['fn'](*tuple(add_file_kwargs['inputs']))

	# ensure normal blip (not 2) obtained
	blip2 = 'CaptionBlip2'
	if blip2 in kwargs['image_audio_loaders_options']:
	image_audio_loaders_options = kwargs['image_audio_loaders_options'].copy()
	image_audio_loaders_options.remove(blip2)

	# ensure normal asr (not asrlarge) obtained
	asrlarge = 'ASRLarge'
	if asrlarge in kwargs['image_audio_loaders_options']:
	image_audio_loaders_options = kwargs['image_audio_loaders_options'].copy()
	image_audio_loaders_options.remove(asrlarge)

	inputs2[8] = kwargs['image_audio_loaders_options']
	add_file_kwargs = dict(fn=kwargs['update_db_func'],
	inputs=inputs1 + inputs2)
	add_file_kwargs['fn'](*tuple(add_file_kwargs['inputs']))

	# FakeTokenizer etc. needs tiktoken for general tasks
	import tiktoken
	encoding = tiktoken.get_encoding("cl100k_base")
	assert encoding
	encoding = tiktoken.encoding_for_model("gpt-3.5-turbo")
	assert encoding

	# sometimes summarization needs gpt2 still
	from transformers import AutoTokenizer
	model_name = 'gpt2'
	tokenizer = AutoTokenizer.from_pretrained(model_name)
	assert tokenizer

	# then run h2ogpt as:
	# HF_DATASETS_OFFLINE=1 TRANSFORMERS_OFFLINE=1 python generate.py --gradio_offline_level=2 --share=False ...