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import collections
from typing import Any, Dict, OrderedDict
H2OGPT_PARAMETERS_TO_CLIENT = collections.OrderedDict(
instruction="instruction",
iinput="input",
context="system_pre_context",
stream_output="stream_output",
prompt_type="prompt_type",
prompt_dict="prompt_dict",
temperature="temperature",
top_p="top_p",
top_k="top_k",
num_beams="beams",
max_new_tokens="max_output_length",
min_new_tokens="min_output_length",
early_stopping="early_stopping",
max_time="max_time",
repetition_penalty="repetition_penalty",
num_return_sequences="number_returns",
do_sample="enable_sampler",
chat="chat",
instruction_nochat="instruction_nochat",
iinput_nochat="input_context_for_instruction",
langchain_mode="langchain_mode",
add_chat_history_to_context="add_chat_history_to_context",
langchain_action="langchain_action",
langchain_agents="langchain_agents",
top_k_docs="langchain_top_k_docs",
chunk="langchain_enable_chunk",
chunk_size="langchain_chunk_size",
document_subset="langchain_document_subset",
document_choice="langchain_document_choice",
pre_prompt_query="pre_prompt_query",
prompt_query="prompt_query",
pre_prompt_summary="pre_prompt_summary",
prompt_summary="prompt_summary",
system_prompt="system_prompt",
image_loaders="image_loaders",
pdf_loaders="pdf_loaders",
url_loaders="url_loaders",
jq_schema="jq_schema",
visible_models="visible_models",
h2ogpt_key="h2ogpt_key",
add_search_to_context="add_search_to_context",
chat_conversation="chat_conversation",
text_context_list="text_context_list",
docs_ordering_type="docs_ordering_type",
min_max_new_tokens="min_max_new_tokens",
)
def to_h2ogpt_params(client_params: Dict[str, Any]) -> OrderedDict[str, Any]:
"""Convert given params to the order of params in h2oGPT."""
h2ogpt_params: OrderedDict[str, Any] = H2OGPT_PARAMETERS_TO_CLIENT.copy()
for h2ogpt_param_name, client_param_name in h2ogpt_params.items():
if client_param_name in client_params:
h2ogpt_params[h2ogpt_param_name] = client_params[client_param_name]
return h2ogpt_params
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