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	#include "server-common.h"
	#include "server-task.h"

	#include "common.h"
	#include "llama.h"
	#include "chat.h"
	#include "sampling.h"
	#include "speculative.h"
	#include "json-schema-to-grammar.h"

	using json = nlohmann::ordered_json;

	//
	// task_params
	//

	json task_params::format_logit_bias(const std::vector<llama_logit_bias> & logit_bias) const {
	json data = json::array();
	for (const auto & lb : logit_bias) {
	data.push_back(json{
	{"bias", lb.bias},
	{"token", lb.token},
	});
	}
	return data;
	}

	json task_params::to_json(bool only_metrics) const {
	std::vector<std::string> samplers;
	samplers.reserve(sampling.samplers.size());
	for (const auto & sampler : sampling.samplers) {
	samplers.emplace_back(common_sampler_type_to_str(sampler));
	}

	json lora = json::array();
	for (auto & it : this->lora) {
	lora.push_back({{"id", it.first}, {"scale", it.second}});
	}

	if (only_metrics) {
	return json {
	{"seed", sampling.seed},
	{"temperature", sampling.temp},
	{"dynatemp_range", sampling.dynatemp_range},
	{"dynatemp_exponent", sampling.dynatemp_exponent},
	{"top_k", sampling.top_k},
	{"top_p", sampling.top_p},
	{"min_p", sampling.min_p},
	{"top_n_sigma", sampling.top_n_sigma},
	{"xtc_probability", sampling.xtc_probability},
	{"xtc_threshold", sampling.xtc_threshold},
	{"typical_p", sampling.typ_p},
	{"repeat_last_n", sampling.penalty_last_n},
	{"repeat_penalty", sampling.penalty_repeat},
	{"presence_penalty", sampling.penalty_present},
	{"frequency_penalty", sampling.penalty_freq},
	{"dry_multiplier", sampling.dry_multiplier},
	{"dry_base", sampling.dry_base},
	{"dry_allowed_length", sampling.dry_allowed_length},
	{"dry_penalty_last_n", sampling.dry_penalty_last_n},
	{"mirostat", sampling.mirostat},
	{"mirostat_tau", sampling.mirostat_tau},
	{"mirostat_eta", sampling.mirostat_eta},
	{"max_tokens", n_predict},
	{"n_predict", n_predict}, // TODO: deduplicate?
	{"n_keep", n_keep},
	{"n_discard", n_discard},
	{"ignore_eos", sampling.ignore_eos},
	{"stream", stream},
	{"n_probs", sampling.n_probs},
	{"min_keep", sampling.min_keep},
	{"chat_format", common_chat_format_name(chat_parser_params.format)},
	{"reasoning_format", common_reasoning_format_name(chat_parser_params.reasoning_format)},
	{"reasoning_in_content", chat_parser_params.reasoning_in_content},
	{"thinking_forced_open", chat_parser_params.thinking_forced_open},
	{"samplers", samplers},
	{"speculative.n_max", speculative.n_max},
	{"speculative.n_min", speculative.n_min},
	{"speculative.p_min", speculative.p_min},
	{"speculative.type", common_speculative_type_to_str(speculative.type)},
	{"speculative.ngram_size_n", speculative.ngram_size_n},
	{"speculative.ngram_size_m", speculative.ngram_size_m},
	{"speculative.ngram_m_hits", speculative.ngram_min_hits},
	{"timings_per_token", timings_per_token},
	{"post_sampling_probs", post_sampling_probs},
	{"backend_sampling", sampling.backend_sampling},
	{"lora", lora},
	};
	}

	auto grammar_triggers = json::array();
	for (const auto & trigger : sampling.grammar_triggers) {
	server_grammar_trigger ct(trigger);
	grammar_triggers.push_back(ct.to_json());
	}

	return json {
	{"seed", sampling.seed},
	{"temperature", sampling.temp},
	{"dynatemp_range", sampling.dynatemp_range},
	{"dynatemp_exponent", sampling.dynatemp_exponent},
	{"top_k", sampling.top_k},
	{"top_p", sampling.top_p},
	{"min_p", sampling.min_p},
	{"top_n_sigma", sampling.top_n_sigma},
	{"xtc_probability", sampling.xtc_probability},
	{"xtc_threshold", sampling.xtc_threshold},
	{"typical_p", sampling.typ_p},
	{"repeat_last_n", sampling.penalty_last_n},
	{"repeat_penalty", sampling.penalty_repeat},
	{"presence_penalty", sampling.penalty_present},
	{"frequency_penalty", sampling.penalty_freq},
	{"dry_multiplier", sampling.dry_multiplier},
	{"dry_base", sampling.dry_base},
	{"dry_allowed_length", sampling.dry_allowed_length},
	{"dry_penalty_last_n", sampling.dry_penalty_last_n},
	{"dry_sequence_breakers", sampling.dry_sequence_breakers},
	{"mirostat", sampling.mirostat},
	{"mirostat_tau", sampling.mirostat_tau},
	{"mirostat_eta", sampling.mirostat_eta},
	{"stop", antiprompt},
	{"max_tokens", n_predict},
	{"n_predict", n_predict}, // TODO: deduplicate?
	{"n_keep", n_keep},
	{"n_discard", n_discard},
	{"ignore_eos", sampling.ignore_eos},
	{"stream", stream},
	{"logit_bias", format_logit_bias(sampling.logit_bias)},
	{"n_probs", sampling.n_probs},
	{"min_keep", sampling.min_keep},
	{"grammar", sampling.grammar},
	{"grammar_lazy", sampling.grammar_lazy},
	{"grammar_triggers", grammar_triggers},
	{"preserved_tokens", sampling.preserved_tokens},
	{"chat_format", common_chat_format_name(chat_parser_params.format)},
	{"reasoning_format", common_reasoning_format_name(chat_parser_params.reasoning_format)},
	{"reasoning_in_content", chat_parser_params.reasoning_in_content},
	{"thinking_forced_open", chat_parser_params.thinking_forced_open},
	{"samplers", samplers},
	{"speculative.n_max", speculative.n_max},
	{"speculative.n_min", speculative.n_min},
	{"speculative.p_min", speculative.p_min},
	{"speculative.type", common_speculative_type_to_str(speculative.type)},
	{"speculative.ngram_size_n", speculative.ngram_size_n},
	{"speculative.ngram_size_m", speculative.ngram_size_m},
	{"speculative.ngram_m_hits", speculative.ngram_min_hits},
	{"timings_per_token", timings_per_token},
	{"post_sampling_probs", post_sampling_probs},
	{"backend_sampling", sampling.backend_sampling},
	{"lora", lora},
	};
	}

	//
	// task_result_state
	//
	common_chat_msg task_result_state::update_chat_msg(
	const std::string & text_added,
	bool is_partial,
	std::vector<common_chat_msg_diff> & diffs) {
	generated_text += text_added;
	auto msg_prv_copy = chat_msg;
	SRV_DBG("Parsing chat message: %s\n", generated_text.c_str());
	auto new_msg = common_chat_parse(
	generated_text,
	is_partial,
	chat_parser_params);
	if (!new_msg.empty()) {
	new_msg.set_tool_call_ids(generated_tool_call_ids, gen_tool_call_id);
	chat_msg = new_msg;
	diffs = common_chat_msg_diff::compute_diffs(msg_prv_copy, new_msg.empty() ? msg_prv_copy : new_msg);
	}
	return chat_msg;
	}

	//
	// server_task
	//

	task_params server_task::params_from_json_cmpl(
	const llama_vocab * vocab,
	const common_params & params_base,
	const int n_ctx_slot,
	const json & data) {
	task_params params;

	// Sampling parameter defaults are loaded from the global server context (but individual requests can still them)
	task_params defaults;
	defaults.sampling = params_base.sampling;
	defaults.speculative = params_base.speculative;
	defaults.n_keep = params_base.n_keep;
	defaults.n_predict = params_base.n_predict;
	defaults.n_cache_reuse = params_base.n_cache_reuse;
	defaults.cache_prompt = params_base.cache_prompt;
	defaults.antiprompt = params_base.antiprompt;

	// enabling this will output extra debug information in the HTTP responses from the server
	params.verbose = params_base.verbosity > 9;
	params.timings_per_token = json_value(data, "timings_per_token", false);

	params.stream = json_value(data, "stream", false);
	auto stream_opt = json_value(data, "stream_options", json::object());
	params.include_usage = json_value(stream_opt, "include_usage", false);
	params.cache_prompt = json_value(data, "cache_prompt", defaults.cache_prompt);
	params.return_tokens = json_value(data, "return_tokens", false);
	params.return_progress = json_value(data, "return_progress", false);
	auto max_tokens = json_value(data, "max_tokens", defaults.n_predict);
	params.n_predict = json_value(data, "n_predict", json_value(data, "max_completion_tokens", max_tokens));
	params.n_indent = json_value(data, "n_indent", defaults.n_indent);
	params.n_keep = json_value(data, "n_keep", defaults.n_keep);
	params.n_discard = json_value(data, "n_discard", defaults.n_discard);
	params.n_cmpl = json_value(data, "n_cmpl", json_value(data, "n", 1));
	params.n_cache_reuse = json_value(data, "n_cache_reuse", defaults.n_cache_reuse);
	//params.t_max_prompt_ms = json_value(data, "t_max_prompt_ms", defaults.t_max_prompt_ms); // TODO: implement
	params.t_max_predict_ms = json_value(data, "t_max_predict_ms", defaults.t_max_predict_ms);
	params.response_fields = json_value(data, "response_fields", std::vector<std::string>());

	params.sampling.top_k = json_value(data, "top_k", defaults.sampling.top_k);
	params.sampling.top_p = json_value(data, "top_p", defaults.sampling.top_p);
	params.sampling.min_p = json_value(data, "min_p", defaults.sampling.min_p);
	params.sampling.top_n_sigma = json_value(data, "top_n_sigma", defaults.sampling.top_n_sigma);
	params.sampling.xtc_probability = json_value(data, "xtc_probability", defaults.sampling.xtc_probability);
	params.sampling.xtc_threshold = json_value(data, "xtc_threshold", defaults.sampling.xtc_threshold);
	params.sampling.typ_p = json_value(data, "typical_p", defaults.sampling.typ_p);
	params.sampling.temp = json_value(data, "temperature", defaults.sampling.temp);
	params.sampling.dynatemp_range = json_value(data, "dynatemp_range", defaults.sampling.dynatemp_range);
	params.sampling.dynatemp_exponent = json_value(data, "dynatemp_exponent", defaults.sampling.dynatemp_exponent);
	params.sampling.penalty_last_n = json_value(data, "repeat_last_n", defaults.sampling.penalty_last_n);
	params.sampling.penalty_repeat = json_value(data, "repeat_penalty", defaults.sampling.penalty_repeat);
	params.sampling.penalty_freq = json_value(data, "frequency_penalty", defaults.sampling.penalty_freq);
	params.sampling.penalty_present = json_value(data, "presence_penalty", defaults.sampling.penalty_present);
	params.sampling.dry_multiplier = json_value(data, "dry_multiplier", defaults.sampling.dry_multiplier);
	params.sampling.dry_base = json_value(data, "dry_base", defaults.sampling.dry_base);
	params.sampling.dry_allowed_length = json_value(data, "dry_allowed_length", defaults.sampling.dry_allowed_length);
	params.sampling.dry_penalty_last_n = json_value(data, "dry_penalty_last_n", defaults.sampling.dry_penalty_last_n);
	params.sampling.mirostat = json_value(data, "mirostat", defaults.sampling.mirostat);
	params.sampling.mirostat_tau = json_value(data, "mirostat_tau", defaults.sampling.mirostat_tau);
	params.sampling.mirostat_eta = json_value(data, "mirostat_eta", defaults.sampling.mirostat_eta);
	params.sampling.adaptive_target = json_value(data, "adaptive_target", defaults.sampling.adaptive_target);
	params.sampling.adaptive_decay = json_value(data, "adaptive_decay", defaults.sampling.adaptive_decay);
	params.sampling.seed = json_value(data, "seed", defaults.sampling.seed);
	params.sampling.n_probs = json_value(data, "n_probs", defaults.sampling.n_probs);
	params.sampling.min_keep = json_value(data, "min_keep", defaults.sampling.min_keep);
	params.sampling.backend_sampling = json_value(data, "backend_sampling", defaults.sampling.backend_sampling);
	params.post_sampling_probs = json_value(data, "post_sampling_probs", defaults.post_sampling_probs);

	params.speculative.n_min = json_value(data, "speculative.n_min", defaults.speculative.n_min);
	params.speculative.n_max = json_value(data, "speculative.n_max", defaults.speculative.n_max);
	params.speculative.p_min = json_value(data, "speculative.p_min", defaults.speculative.p_min);

	params.speculative.n_min = std::min(params.speculative.n_max, params.speculative.n_min);
	params.speculative.n_min = std::max(params.speculative.n_min, 0);
	params.speculative.n_max = std::max(params.speculative.n_max, 0);

	params.speculative.type = common_speculative_type_from_name(json_value(data, "speculative.type", common_speculative_type_to_str(defaults.speculative.type)));

	params.speculative.ngram_size_n = json_value(data, "speculative.ngram_size_n", defaults.speculative.ngram_size_n);
	params.speculative.ngram_size_m = json_value(data, "speculative.ngram_size_m", defaults.speculative.ngram_size_m);
	params.speculative.ngram_min_hits = json_value(data, "speculative.ngram_m_hits", defaults.speculative.ngram_min_hits);

	params.speculative.ngram_size_n = std::max(std::min(1, (int) params.speculative.ngram_size_n), 1024);
	params.speculative.ngram_size_m = std::max(std::min(1, (int) params.speculative.ngram_size_m), 1024);
	params.speculative.ngram_min_hits = std::max(std::min(1, (int) params.speculative.ngram_min_hits), 1024);

	// Use OpenAI API logprobs only if n_probs wasn't provided
	if (data.contains("logprobs") && params.sampling.n_probs == defaults.sampling.n_probs){
	params.sampling.n_probs = json_value(data, "logprobs", defaults.sampling.n_probs);
	}

	if (data.contains("lora")) {
	if (data.at("lora").is_array()) {
	params.lora = parse_lora_request(data.at("lora"));
	} else {
	throw std::runtime_error("Error: 'lora' must be an array of objects with 'id' and 'scale' fields");
	}
	} else {
	params.lora = {};
	}

	// TODO: add more sanity checks for the input parameters

	if (params.sampling.penalty_last_n < -1) {
	throw std::runtime_error("Error: repeat_last_n must be >= -1");
	}

	if (params.sampling.dry_penalty_last_n < -1) {
	throw std::runtime_error("Error: dry_penalty_last_n must be >= -1");
	}

	if (params.sampling.penalty_last_n == -1) {
	// note: should be the slot's context and not the full context, but it's ok
	params.sampling.penalty_last_n = n_ctx_slot;
	}

	if (params.sampling.dry_penalty_last_n == -1) {
	params.sampling.dry_penalty_last_n = n_ctx_slot;
	}

	if (params.sampling.dry_base < 1.0f) {
	params.sampling.dry_base = defaults.sampling.dry_base;
	}

	// sequence breakers for DRY
	{
	// Currently, this is not compatible with TextGen WebUI, Koboldcpp and SillyTavern format
	// Ref: https://github.com/oobabooga/text-generation-webui/blob/d1af7a41ade7bd3c3a463bfa640725edb818ebaf/extensions/openai/typing.py#L39

	if (data.contains("dry_sequence_breakers")) {
	params.sampling.dry_sequence_breakers = json_value(data, "dry_sequence_breakers", std::vector<std::string>());
	if (params.sampling.dry_sequence_breakers.empty()) {
	throw std::runtime_error("Error: dry_sequence_breakers must be a non-empty array of strings");
	}
	}
	}

	// process "json_schema" and "grammar"
	if (data.contains("json_schema") && !data.contains("grammar")) {
	try {
	auto schema = json_value(data, "json_schema", json::object());
	SRV_DBG("JSON schema: %s\n", schema.dump(2).c_str());
	params.sampling.grammar = json_schema_to_grammar(schema);
	SRV_DBG("Converted grammar: %s\n", params.sampling.grammar.c_str());
	} catch (const std::exception & e) {
	throw std::runtime_error(std::string("\"json_schema\": ") + e.what());
	}
	} else {
	params.sampling.grammar = json_value(data, "grammar", defaults.sampling.grammar);
	SRV_DBG("Grammar: %s\n", params.sampling.grammar.c_str());
	params.sampling.grammar_lazy = json_value(data, "grammar_lazy", defaults.sampling.grammar_lazy);
	SRV_DBG("Grammar lazy: %s\n", params.sampling.grammar_lazy ? "true" : "false");
	}

	{
	auto it = data.find("chat_format");
	if (it != data.end()) {
	params.chat_parser_params.format = static_cast<common_chat_format>(it->get<int>());
	SRV_INF("Chat format: %s\n", common_chat_format_name(params.chat_parser_params.format));
	} else {
	params.chat_parser_params.format = defaults.chat_parser_params.format;
	}
	common_reasoning_format reasoning_format = params_base.reasoning_format;
	if (data.contains("reasoning_format")) {
	reasoning_format = common_reasoning_format_from_name(data.at("reasoning_format").get<std::string>());
	}
	params.chat_parser_params.reasoning_format = reasoning_format;
	params.chat_parser_params.reasoning_in_content = params.stream && (reasoning_format == COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY);
	params.chat_parser_params.thinking_forced_open = json_value(data, "thinking_forced_open", false);
	params.chat_parser_params.parse_tool_calls = json_value(data, "parse_tool_calls", false);
	if (data.contains("chat_parser")) {
	params.chat_parser_params.parser.load(data.at("chat_parser").get<std::string>());
	}
	}

	{
	const auto preserved_tokens = data.find("preserved_tokens");
	if (preserved_tokens != data.end()) {
	for (const auto & t : *preserved_tokens) {
	auto ids = common_tokenize(vocab, t.get<std::string>(), /* add_special= / false, / parse_special= */ true);
	if (ids.size() == 1) {
	SRV_DBG("Preserved token: %d\n", ids[0]);
	params.sampling.preserved_tokens.insert(ids[0]);
	} else {
	// This may happen when using a tool call style meant for a model with special tokens to preserve on a model without said tokens.
	SRV_DBG("Not preserved because more than 1 token: %s\n", t.get<std::string>().c_str());
	}
	}
	}
	const auto grammar_triggers = data.find("grammar_triggers");
	if (grammar_triggers != data.end()) {
	for (const auto & t : *grammar_triggers) {
	server_grammar_trigger ct(t);
	if (ct.value.type == COMMON_GRAMMAR_TRIGGER_TYPE_WORD) {
	const auto & word = ct.value.value;
	auto ids = common_tokenize(vocab, word, /* add_special= / false, / parse_special= */ true);
	if (ids.size() == 1) {
	auto token = ids[0];
	if (std::find(params.sampling.preserved_tokens.begin(), params.sampling.preserved_tokens.end(), (llama_token) token) == params.sampling.preserved_tokens.end()) {
	throw std::runtime_error("Grammar trigger word should be marked as preserved token: " + word);
	}
	SRV_DBG("Grammar trigger token: %d (`%s`)\n", token, word.c_str());
	common_grammar_trigger trigger;
	trigger.type = COMMON_GRAMMAR_TRIGGER_TYPE_TOKEN;
	trigger.value = word;
	trigger.token = token;
	params.sampling.grammar_triggers.push_back(std::move(trigger));
	} else {
	SRV_DBG("Grammar trigger word: `%s`\n", word.c_str());
	params.sampling.grammar_triggers.push_back({COMMON_GRAMMAR_TRIGGER_TYPE_WORD, word});
	}
	} else {
	if (ct.value.type == COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN) {
	SRV_DBG("Grammar trigger pattern: `%s`\n", ct.value.value.c_str());
	} else if (ct.value.type == COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL) {
	SRV_DBG("Grammar trigger pattern full: `%s`\n", ct.value.value.c_str());
	} else {
	throw std::runtime_error("Unknown grammar trigger type");
	}
	params.sampling.grammar_triggers.emplace_back(std::move(ct.value));
	}
	}
	}
	if (params.sampling.grammar_lazy && params.sampling.grammar_triggers.empty()) {
	throw std::runtime_error("Error: no triggers set for lazy grammar!");
	}
	}

	{
	params.sampling.logit_bias.clear();

	const auto & logit_bias = data.find("logit_bias");
	if (logit_bias != data.end() && logit_bias->is_array()) {
	const int n_vocab = llama_vocab_n_tokens(vocab);
	for (const auto & el : *logit_bias) {
	// TODO: we may want to throw errors here, in case "el" is incorrect
	if (el.is_array() && el.size() == 2) {
	float bias;
	if (el[1].is_number()) {
	bias = el[1].get<float>();
	} else if (el[1].is_boolean() && !el[1].get<bool>()) {
	bias = -INFINITY;
	} else {
	continue;
	}

	if (el[0].is_number_integer()) {
	llama_token tok = el[0].get<llama_token>();
	if (tok >= 0 && tok < n_vocab) {
	params.sampling.logit_bias.push_back({tok, bias});
	}
	} else if (el[0].is_string()) {
	auto toks = common_tokenize(vocab, el[0].get<std::string>(), false);
	for (auto tok : toks) {
	params.sampling.logit_bias.push_back({tok, bias});
	}
	}
	}
	}
	} else if (logit_bias != data.end() && logit_bias->is_object()) {
	const int n_vocab = llama_vocab_n_tokens(vocab);
	for (const auto & el : logit_bias->items()) {
	float bias;
	const auto & key = el.key();
	const auto & value = el.value();
	if (value.is_number()) {
	bias = value.get<float>();
	} else if (value.is_boolean() && !value.get<bool>()) {
	bias = -INFINITY;
	} else {
	continue;
	}

	char *end;
	llama_token tok = strtol(key.c_str(), &end, 10);
	if (*end == 0) {
	if (tok >= 0 && tok < n_vocab) {
	params.sampling.logit_bias.push_back({tok, bias});
	}
	} else {
	auto toks = common_tokenize(vocab, key, false);
	for (auto tok : toks) {
	params.sampling.logit_bias.push_back({tok, bias});
	}
	}
	}
	}

	params.sampling.ignore_eos = json_value(data, "ignore_eos", params_base.sampling.ignore_eos);
	if (params.sampling.ignore_eos) {
	params.sampling.logit_bias.insert(
	params.sampling.logit_bias.end(),
	defaults.sampling.logit_bias_eog.begin(), defaults.sampling.logit_bias_eog.end());
	}
	}

	{
	params.antiprompt.clear();

	const auto & stop = data.find("stop");
	if (stop != data.end() && stop->is_array()) {
	for (const auto & word : *stop) {
	if (!word.empty()) {
	params.antiprompt.push_back(word);
	}
	}
	}
	// set reverse prompt from cli args if not set in the request
	if (params.antiprompt.empty()) {
	params.antiprompt = defaults.antiprompt;
	}
	}

	{
	const auto samplers = data.find("samplers");
	if (samplers != data.end()) {
	if (samplers->is_array()) {
	params.sampling.samplers = common_sampler_types_from_names(*samplers, false);
	} else if (samplers->is_string()){
	params.sampling.samplers = common_sampler_types_from_chars(samplers->get<std::string>());
	}
	} else {
	params.sampling.samplers = defaults.sampling.samplers;
	}
	}

	if (params.n_cmpl > params_base.n_parallel) {
	throw std::runtime_error("n_cmpl cannot be greater than the number of slots, please increase -np");
	}

	return params;
	}

	//
	// result_timings
	//

	json result_timings::to_json() const {
	json base = {
	{"cache_n", cache_n},

	{"prompt_n", prompt_n},
	{"prompt_ms", prompt_ms},
	{"prompt_per_token_ms", prompt_per_token_ms},
	{"prompt_per_second", prompt_per_second},

	{"predicted_n", predicted_n},
	{"predicted_ms", predicted_ms},
	{"predicted_per_token_ms", predicted_per_token_ms},
	{"predicted_per_second", predicted_per_second},
	};

	if (draft_n > 0) {
	base["draft_n"] = draft_n;
	base["draft_n_accepted"] = draft_n_accepted;
	}

	return base;
	}

	//
	// result_prompt_progress
	//
	json result_prompt_progress::to_json() const {
	return json {
	{"total", total},
	{"cache", cache},
	{"processed", processed},
	{"time_ms", time_ms},
	};
	}

	static inline std::string stop_type_to_str(stop_type type) {
	switch (type) {
	case STOP_TYPE_EOS: return "eos";
	case STOP_TYPE_WORD: return "word";
	case STOP_TYPE_LIMIT: return "limit";
	default: return "none";
	}
	}

	//
	// completion_token_output
	//

	json completion_token_output::to_json(bool post_sampling_probs) const {
	json probs_for_token = json::array();
	for (const auto & p : probs) {
	std::string txt(p.txt);
	txt.resize(validate_utf8(txt));
	probs_for_token.push_back(json {
	{"id", p.tok},
	{"token", txt},
	{"bytes", str_to_bytes(p.txt)},
	{
	post_sampling_probs ? "prob" : "logprob",
	post_sampling_probs ? p.prob : logarithm(p.prob)
	},
	});
	}
	return probs_for_token;
	}

	json completion_token_output::probs_vector_to_json(const std::vector<completion_token_output> & probs, bool post_sampling_probs) {
	json out = json::array();
	for (const auto & p : probs) {
	std::string txt(p.text_to_send);
	txt.resize(validate_utf8(txt));
	out.push_back(json {
	{"id", p.tok},
	{"token", txt},
	{"bytes", str_to_bytes(p.text_to_send)},
	{
	post_sampling_probs ? "prob" : "logprob",
	post_sampling_probs ? p.prob : logarithm(p.prob)
	},
	{
	post_sampling_probs ? "top_probs" : "top_logprobs",
	p.to_json(post_sampling_probs)
	},
	});
	}
	return out;
	}

	float completion_token_output::logarithm(float x) {
	// nlohmann::json converts -inf to null, so we need to prevent that
	return x == 0.0f ? std::numeric_limits<float>::lowest() : std::log(x);
	}

	std::vector<unsigned char> completion_token_output::str_to_bytes(const std::string & str) {
	std::vector<unsigned char> bytes;
	for (unsigned char c : str) {
	bytes.push_back(c);
	}
	return bytes;
	}

	//
	// server_task_result_cmpl_final
	//
	json server_task_result_cmpl_final::to_json() {
	GGML_ASSERT(is_updated && "update() must be called before to_json()");
	switch (res_type) {
	case TASK_RESPONSE_TYPE_NONE:
	return to_json_non_oaicompat();
	case TASK_RESPONSE_TYPE_OAI_CMPL:
	return to_json_oaicompat();
	case TASK_RESPONSE_TYPE_OAI_CHAT:
	return stream ? to_json_oaicompat_chat_stream() : to_json_oaicompat_chat();
	case TASK_RESPONSE_TYPE_OAI_RESP:
	return stream ? to_json_oaicompat_resp_stream() : to_json_oaicompat_resp();
	case TASK_RESPONSE_TYPE_ANTHROPIC:
	return stream ? to_json_anthropic_stream() : to_json_anthropic();
	default:
	GGML_ASSERT(false && "Invalid task_response_type");
	}
	}

	json server_task_result_cmpl_final::to_json_non_oaicompat() {
	json res = json {
	{"index", index},
	{"content", content},
	{"tokens", tokens},
	{"id_slot", id_slot},
	{"stop", true},
	{"model", oaicompat_model},
	{"tokens_predicted", n_decoded},
	{"tokens_evaluated", n_prompt_tokens},
	{"generation_settings", generation_params.to_json()},
	{"prompt", prompt},
	{"has_new_line", has_new_line},
	{"truncated", truncated},
	{"stop_type", stop_type_to_str(stop)},
	{"stopping_word", stopping_word},
	{"tokens_cached", n_tokens_cached},
	{"timings", timings.to_json()},
	};
	if (!stream && !probs_output.empty()) {
	res["completion_probabilities"] = completion_token_output::probs_vector_to_json(probs_output, post_sampling_probs);
	}
	return response_fields.empty() ? res : json_get_nested_values(response_fields, res);
	}

	json server_task_result_cmpl_final::to_json_oaicompat() {
	std::time_t t = std::time(0);
	json logprobs = json(nullptr); // OAI default to null
	if (!stream && probs_output.size() > 0) {
	logprobs = json{
	{"content", completion_token_output::probs_vector_to_json(probs_output, post_sampling_probs)},
	};
	}
	json finish_reason = "length";
	if (stop == STOP_TYPE_WORD \|\| stop == STOP_TYPE_EOS) {
	finish_reason = "stop";
	}
	json res = json {
	{"choices", json::array({
	json{
	{"text", content},
	{"index", index},
	{"logprobs", logprobs},
	{"finish_reason", finish_reason},
	}
	})},
	{"created", t},
	{"model", oaicompat_model},
	{"system_fingerprint", build_info},
	{"object", "text_completion"},
	{"usage", json {
	{"completion_tokens", n_decoded},
	{"prompt_tokens", n_prompt_tokens},
	{"total_tokens", n_decoded + n_prompt_tokens}
	}},
	{"id", oaicompat_cmpl_id}
	};

	// extra fields for debugging purposes
	if (verbose) {
	res["__verbose"] = to_json_non_oaicompat();
	}
	if (timings.prompt_n >= 0) {
	res.push_back({"timings", timings.to_json()});
	}

	return res;
	}

	json server_task_result_cmpl_final::to_json_oaicompat_chat() {
	std::string finish_reason = "length";
	common_chat_msg msg;
	if (!oaicompat_msg.empty()) {
	msg = oaicompat_msg;
	} else {
	msg.role = "assistant";
	msg.content = content;
	}
	if (stop == STOP_TYPE_WORD \|\| stop == STOP_TYPE_EOS) {
	finish_reason = msg.tool_calls.empty() ? "stop" : "tool_calls";
	}

	json choice {
	{"finish_reason", finish_reason},
	{"index", index},
	{"message", msg.to_json_oaicompat()},
	};

	if (!stream && probs_output.size() > 0) {
	choice["logprobs"] = json{
	{"content", completion_token_output::probs_vector_to_json(probs_output, post_sampling_probs)},
	};
	}

	std::time_t t = std::time(0);

	json res = json {
	{"choices", json::array({choice})},
	{"created", t},
	{"model", oaicompat_model},
	{"system_fingerprint", build_info},
	{"object", "chat.completion"},
	{"usage", json {
	{"completion_tokens", n_decoded},
	{"prompt_tokens", n_prompt_tokens},
	{"total_tokens", n_decoded + n_prompt_tokens}
	}},
	{"id", oaicompat_cmpl_id}
	};

	// extra fields for debugging purposes
	if (verbose) {
	res["__verbose"] = to_json_non_oaicompat();
	}
	if (timings.prompt_n >= 0) {
	res.push_back({"timings", timings.to_json()});
	}

	return res;
	}

	json server_task_result_cmpl_final::to_json_oaicompat_chat_stream() {
	std::time_t t = std::time(0);
	std::string finish_reason = "length";
	if (stop == STOP_TYPE_WORD \|\| stop == STOP_TYPE_EOS) {
	finish_reason = oaicompat_msg.tool_calls.empty() ? "stop" : "tool_calls";
	}

	json deltas = json::array();
	for (const auto & diff : oaicompat_msg_diffs) {
	deltas.push_back({
	{"choices", json::array({
	json {
	{"finish_reason", nullptr},
	{"index", 0},
	{"delta", common_chat_msg_diff_to_json_oaicompat(diff)},
	},
	})},
	{"created", t},
	{"id", oaicompat_cmpl_id},
	{"model", oaicompat_model},
	{"system_fingerprint", build_info},
	{"object", "chat.completion.chunk"},
	});
	}

	deltas.push_back({
	{"choices", json::array({
	json {
	{"finish_reason", finish_reason},
	{"index", 0},
	{"delta", json::object()},
	},
	})},
	{"created", t},
	{"id", oaicompat_cmpl_id},
	{"model", oaicompat_model},
	{"system_fingerprint", build_info},
	{"object", "chat.completion.chunk"},
	});

	if (include_usage) {
	// OpenAI API spec for chat.completion.chunks specifies an empty `choices` array for the last chunk when including usage
	// https://platform.openai.com/docs/api-reference/chat_streaming/streaming#chat_streaming/streaming-choices
	deltas.push_back({
	{"choices", json::array()},
	{"created", t},
	{"id", oaicompat_cmpl_id},
	{"model", oaicompat_model},
	{"system_fingerprint", build_info},
	{"object", "chat.completion.chunk"},
	{"usage", json {
	{"completion_tokens", n_decoded},
	{"prompt_tokens", n_prompt_tokens},
	{"total_tokens", n_decoded + n_prompt_tokens},
	}},
	});
	}

	if (timings.prompt_n >= 0) {
	deltas.back().push_back({"timings", timings.to_json()});
	}

	// extra fields for debugging purposes
	if (verbose && !deltas.empty()) {
	deltas.front()["__verbose"] = to_json_non_oaicompat();
	}

	return deltas;
	}

	json server_task_result_cmpl_final::to_json_oaicompat_resp() {
	common_chat_msg msg;
	if (!oaicompat_msg.empty()) {
	msg = oaicompat_msg;
	} else {
	msg.role = "assistant";
	msg.content = content;
	}

	std::vector<json> output;

	if (msg.reasoning_content != "") {
	output.push_back(json {
	{"id", "rs_" + random_string()},
	{"summary", json::array()},
	{"type", "reasoning"},
	{"content", json::array({ json {
	{"text", msg.reasoning_content},
	{"type", "reasoning_text"},
	}})},
	{"encrypted_content", ""},
	{"status", "completed"},
	});
	}

	if (msg.content != "") {
	output.push_back(json {
	{"content", json::array({ json {
	{"type", "output_text"},
	{"annotations", json::array()},
	{"logprobs", json::array()},
	{"text", msg.content},
	}})},
	{"id", "msg_" + random_string()},
	{"role", msg.role},
	{"status", "completed"},
	{"type", "message"},
	});
	}

	for (const common_chat_tool_call & tool_call : oaicompat_msg.tool_calls) {
	output.push_back(json {
	{"type", "function_call"},
	{"status", "completed"},
	{"arguments", tool_call.arguments},
	{"call_id", "fc_" + tool_call.id},
	{"name", tool_call.name},
	});
	}

	std::time_t t = std::time(0);
	json res = {
	{"completed_at", t},
	{"created_at", t},
	{"id", oai_resp_id},
	{"model", oaicompat_model},
	{"object", "response"},
	{"output", output},
	{"status", "completed"},
	{"usage", json {
	{"input_tokens", n_prompt_tokens},
	{"output_tokens", n_decoded},
	{"total_tokens", n_decoded + n_prompt_tokens},
	}},
	};

	return res;
	}

	json server_task_result_cmpl_final::to_json_oaicompat_resp_stream() {
	std::vector<json> server_sent_events;
	std::vector<json> output;

	if (oaicompat_msg.reasoning_content != "") {
	const json output_item = json {
	{"id", oai_resp_reasoning_id},
	{"summary", json::array()},
	{"type", "reasoning"},
	{"content", json::array({ json {
	{"text", oaicompat_msg.reasoning_content},
	{"type", "reasoning_text"},
	}})},
	{"encrypted_content", ""},
	};

	server_sent_events.push_back(json {
	{"event", "response.output_item.done"},
	{"data", json {
	{"type", "response.output_item.done"},
	{"item", output_item}
	}}
	});
	output.push_back(output_item);
	}

	if (oaicompat_msg.content != "") {
	server_sent_events.push_back(json {
	{"event", "response.output_text.done"},
	{"data", json {
	{"type", "response.output_text.done"},
	{"item_id", oai_resp_message_id},
	{"text", oaicompat_msg.content}
	}}
	});

	const json content_part = {
	{"type", "output_text"},
	{"annotations", json::array()},
	{"logprobs", json::array()},
	{"text", oaicompat_msg.content}
	};

	server_sent_events.push_back(json {
	{"event", "response.content_part.done"},
	{"data", json {
	{"type", "response.content_part.done"},
	{"item_id", oai_resp_message_id},
	{"part", content_part}
	}}
	});
	const json output_item = {
	{"type", "message"},
	{"status", "completed"},
	{"id", oai_resp_message_id},
	{"content", json::array({content_part})},
	{"role", "assistant"}
	};

	server_sent_events.push_back(json {
	{"event", "response.output_item.done"},
	{"data", json {
	{"type", "response.output_item.done"},
	{"item", output_item}
	}}
	});
	output.push_back(output_item);
	}

	for (const common_chat_tool_call & tool_call : oaicompat_msg.tool_calls) {
	const json output_item = {
	{"type", "function_call"},
	{"status", "completed"},
	{"arguments", tool_call.arguments},
	{"call_id", "fc_" + tool_call.id},
	{"name", tool_call.name}
	};
	server_sent_events.push_back(json {
	{"event", "response.output_item.done"},
	{"data", json {
	{"type", "response.output_item.done"},
	{"item", output_item}
	}}
	});
	output.push_back(output_item);
	}

	std::time_t t = std::time(0);
	server_sent_events.push_back(json {
	{"event", "response.completed"},
	{"data", json {
	{"type", "response.completed"},
	{"response", json {
	{"id", oai_resp_id},
	{"object", "response"},
	{"created_at", t},
	{"status", "completed"},
	{"model", oaicompat_model},
	{"output", output},
	{"usage", json {
	{"input_tokens", n_prompt_tokens},
	{"output_tokens", n_decoded},
	{"total_tokens", n_decoded + n_prompt_tokens}
	}}
	}},
	}}
	});

	return server_sent_events;
	}

	json server_task_result_cmpl_final::to_json_anthropic() {
	std::string stop_reason = "max_tokens";
	if (stop == STOP_TYPE_WORD \|\| stop == STOP_TYPE_EOS) {
	stop_reason = oaicompat_msg.tool_calls.empty() ? "end_turn" : "tool_use";
	}

	json content_blocks = json::array();

	common_chat_msg msg;
	if (!oaicompat_msg.empty()) {
	msg = oaicompat_msg;
	} else {
	msg.role = "assistant";
	msg.content = content;
	}

	// thinking block comes first (Anthropic extended thinking format)
	if (!msg.reasoning_content.empty()) {
	content_blocks.push_back({
	{"type", "thinking"},
	{"thinking", msg.reasoning_content},
	{"signature", ""} // empty signature for local models (no cryptographic verification)
	});
	}

	if (!msg.content.empty()) {
	content_blocks.push_back({
	{"type", "text"},
	{"text", msg.content}
	});
	}

	for (const auto & tool_call : msg.tool_calls) {
	json tool_use_block = {
	{"type", "tool_use"},
	{"id", tool_call.id},
	{"name", tool_call.name}
	};

	try {
	tool_use_block["input"] = json::parse(tool_call.arguments);
	} catch (const std::exception &) {
	tool_use_block["input"] = json::object();
	}

	content_blocks.push_back(tool_use_block);
	}

	json res = {
	{"id", oaicompat_cmpl_id},
	{"type", "message"},
	{"role", "assistant"},
	{"content", content_blocks},
	{"model", oaicompat_model},
	{"stop_reason", stop_reason},
	{"stop_sequence", stopping_word.empty() ? nullptr : json(stopping_word)},
	{"usage", {
	{"input_tokens", n_prompt_tokens},
	{"output_tokens", n_decoded}
	}}
	};

	return res;
	}

	json server_task_result_cmpl_final::to_json_anthropic_stream() {
	json events = json::array();

	std::string stop_reason = "max_tokens";
	if (stop == STOP_TYPE_WORD \|\| stop == STOP_TYPE_EOS) {
	stop_reason = oaicompat_msg.tool_calls.empty() ? "end_turn" : "tool_use";
	}

	bool has_thinking = !oaicompat_msg.reasoning_content.empty();
	bool has_text = !oaicompat_msg.content.empty();
	size_t num_tool_calls = oaicompat_msg.tool_calls.size();

	// content block indices: thinking (0) -> text (0 or 1) -> tool_use (n+)
	size_t thinking_block_index = 0;
	size_t text_block_index = has_thinking ? 1 : 0;

	bool thinking_block_started = false;
	bool text_block_started = false;
	std::unordered_set<size_t> tool_calls_started;

	for (const auto & diff : oaicompat_msg_diffs) {
	// handle thinking/reasoning content
	if (!diff.reasoning_content_delta.empty()) {
	if (!thinking_block_started) {
	events.push_back({
	{"event", "content_block_start"},
	{"data", {
	{"type", "content_block_start"},
	{"index", thinking_block_index},
	{"content_block", {
	{"type", "thinking"},
	{"thinking", ""}
	}}
	}}
	});
	thinking_block_started = true;
	}

	events.push_back({
	{"event", "content_block_delta"},
	{"data", {
	{"type", "content_block_delta"},
	{"index", thinking_block_index},
	{"delta", {
	{"type", "thinking_delta"},
	{"thinking", diff.reasoning_content_delta}
	}}
	}}
	});
	}

	// handle regular text content
	if (!diff.content_delta.empty()) {
	if (!text_block_started) {
	events.push_back({
	{"event", "content_block_start"},
	{"data", {
	{"type", "content_block_start"},
	{"index", text_block_index},
	{"content_block", {
	{"type", "text"},
	{"text", ""}
	}}
	}}
	});
	text_block_started = true;
	}

	events.push_back({
	{"event", "content_block_delta"},
	{"data", {
	{"type", "content_block_delta"},
	{"index", text_block_index},
	{"delta", {
	{"type", "text_delta"},
	{"text", diff.content_delta}
	}}
	}}
	});
	}

	// handle tool calls
	if (diff.tool_call_index != std::string::npos) {
	size_t content_block_index = (has_thinking ? 1 : 0) + (has_text ? 1 : 0) + diff.tool_call_index;

	if (tool_calls_started.find(diff.tool_call_index) == tool_calls_started.end()) {
	const auto & full_tool_call = oaicompat_msg.tool_calls[diff.tool_call_index];

	events.push_back({
	{"event", "content_block_start"},
	{"data", {
	{"type", "content_block_start"},
	{"index", content_block_index},
	{"content_block", {
	{"type", "tool_use"},
	{"id", full_tool_call.id},
	{"name", full_tool_call.name}
	}}
	}}
	});
	tool_calls_started.insert(diff.tool_call_index);
	}

	if (!diff.tool_call_delta.arguments.empty()) {
	events.push_back({
	{"event", "content_block_delta"},
	{"data", {
	{"type", "content_block_delta"},
	{"index", content_block_index},
	{"delta", {
	{"type", "input_json_delta"},
	{"partial_json", diff.tool_call_delta.arguments}
	}}
	}}
	});
	}
	}
	}

	// close content blocks in order
	if (has_thinking) {
	// Anthropic API requires a signature_delta before closing thinking blocks
	// We use an empty signature since we can't generate a cryptographic signature for local models
	events.push_back({
	{"event", "content_block_delta"},
	{"data", {
	{"type", "content_block_delta"},
	{"index", thinking_block_index},
	{"delta", {
	{"type", "signature_delta"},
	{"signature", ""}
	}}
	}}
	});
	events.push_back({
	{"event", "content_block_stop"},
	{"data", {
	{"type", "content_block_stop"},
	{"index", thinking_block_index}
	}}
	});
	}

	if (has_text) {
	events.push_back({
	{"event", "content_block_stop"},
	{"data", {
	{"type", "content_block_stop"},
	{"index", text_block_index}
	}}
	});
	}

	for (size_t i = 0; i < num_tool_calls; i++) {
	size_t content_block_index = (has_thinking ? 1 : 0) + (has_text ? 1 : 0) + i;
	events.push_back({
	{"event", "content_block_stop"},
	{"data", {
	{"type", "content_block_stop"},
	{"index", content_block_index}
	}}
	});
	}

	events.push_back({
	{"event", "message_delta"},
	{"data", {
	{"type", "message_delta"},
	{"delta", {
	{"stop_reason", stop_reason},
	{"stop_sequence", stopping_word.empty() ? nullptr : json(stopping_word)}
	}},
	{"usage", {
	{"output_tokens", n_decoded}
	}}
	}}
	});

	events.push_back({
	{"event", "message_stop"},
	{"data", {
	{"type", "message_stop"}
	}}
	});

	return events;
	}

	//
	// server_task_result_cmpl_partial
	//
	void server_task_result_cmpl_partial::update(task_result_state & state) {
	is_updated = true;
	state.update_chat_msg(content, true, oaicompat_msg_diffs);

	// Copy current state for use in to_json_*() (reflects state BEFORE this chunk)
	thinking_block_started = state.thinking_block_started;
	text_block_started = state.text_block_started;

	oai_resp_id = state.oai_resp_id;
	oai_resp_reasoning_id = state.oai_resp_reasoning_id;
	oai_resp_message_id = state.oai_resp_message_id;
	oai_resp_fc_id = state.oai_resp_fc_id;

	// track if the accumulated message has any reasoning content
	anthropic_has_reasoning = !state.chat_msg.reasoning_content.empty();

	// Pre-compute state updates based on diffs (for next chunk)
	for (const common_chat_msg_diff & diff : oaicompat_msg_diffs) {
	if (!diff.reasoning_content_delta.empty() && !state.thinking_block_started) {
	state.thinking_block_started = true;
	}
	if (!diff.content_delta.empty() && !state.text_block_started) {
	state.text_block_started = true;
	}
	if (!diff.tool_call_delta.name.empty()) {
	state.oai_resp_fc_id = diff.tool_call_delta.id;
	}
	}
	}

	json server_task_result_cmpl_partial::to_json() {
	GGML_ASSERT(is_updated && "update() must be called before to_json()");
	switch (res_type) {
	case TASK_RESPONSE_TYPE_NONE:
	return to_json_non_oaicompat();
	case TASK_RESPONSE_TYPE_OAI_CMPL:
	return to_json_oaicompat();
	case TASK_RESPONSE_TYPE_OAI_CHAT:
	return to_json_oaicompat_chat();
	case TASK_RESPONSE_TYPE_OAI_RESP:
	return to_json_oaicompat_resp();
	case TASK_RESPONSE_TYPE_ANTHROPIC:
	return to_json_anthropic();
	default:
	GGML_ASSERT(false && "Invalid task_response_type");
	}
	}

	json server_task_result_cmpl_partial::to_json_non_oaicompat() {
	// non-OAI-compat JSON
	json res = json {
	{"index", index},
	{"content", content},
	{"tokens", tokens},
	{"stop", false},
	{"id_slot", id_slot},
	{"tokens_predicted", n_decoded},
	{"tokens_evaluated", n_prompt_tokens},
	};
	// populate the timings object when needed (usually for the last response or with timings_per_token enabled)
	if (timings.prompt_n > 0) {
	res.push_back({"timings", timings.to_json()});
	}
	if (is_progress) {
	res.push_back({"prompt_progress", progress.to_json()});
	}
	if (!prob_output.probs.empty()) {
	res["completion_probabilities"] = completion_token_output::probs_vector_to_json({prob_output}, post_sampling_probs);
	}
	return res;
	}

	json server_task_result_cmpl_partial::to_json_oaicompat() {
	std::time_t t = std::time(0);
	json logprobs = json(nullptr); // OAI default to null
	if (prob_output.probs.size() > 0) {
	logprobs = json{
	{"content", completion_token_output::probs_vector_to_json({prob_output}, post_sampling_probs)},
	};
	}
	json res = json {
	{"choices", json::array({
	json{
	{"text", content},
	{"index", index},
	{"logprobs", logprobs},
	{"finish_reason", nullptr},
	}
	})},
	{"created", t},
	{"model", oaicompat_model},
	{"system_fingerprint", build_info},
	{"object", "text_completion"},
	{"id", oaicompat_cmpl_id}
	};

	// extra fields for debugging purposes
	if (verbose) {
	res["__verbose"] = to_json_non_oaicompat();
	}
	if (timings.prompt_n >= 0) {
	res.push_back({"timings", timings.to_json()});
	}
	if (is_progress) {
	res.push_back({"prompt_progress", progress.to_json()});
	}

	return res;
	}

	json server_task_result_cmpl_partial::to_json_oaicompat_chat() {
	bool first = n_decoded == 1;
	std::time_t t = std::time(0);
	json choices;

	std::vector<json> deltas;
	auto add_delta = [&](const json & delta) {
	deltas.push_back({
	{"choices", json::array({
	json {
	{"finish_reason", nullptr},
	{"index", index},
	{"delta", delta},
	},
	})},
	{"created", t},
	{"id", oaicompat_cmpl_id},
	{"model", oaicompat_model},
	{"system_fingerprint", build_info},
	{"object", "chat.completion.chunk"},
	});
	};
	// We have to send an initial update to conform to openai behavior
	if (first \|\| is_progress) {
	add_delta({
	{"role", "assistant"},
	{"content", nullptr},
	});
	}

	for (const auto & diff : oaicompat_msg_diffs) {
	add_delta(common_chat_msg_diff_to_json_oaicompat(diff));
	}

	if (!deltas.empty()) {
	auto & last_json = deltas[deltas.size() - 1];
	GGML_ASSERT(last_json.at("choices").size() >= 1);

	if (prob_output.probs.size() > 0) {
	last_json.at("choices").at(0)["logprobs"] = json {
	{"content", completion_token_output::probs_vector_to_json({prob_output}, post_sampling_probs)},
	};
	}

	if (timings.prompt_n >= 0) {
	last_json.push_back({"timings", timings.to_json()});
	}
	if (is_progress) {
	last_json.push_back({"prompt_progress", progress.to_json()});
	}
	}

	return deltas;
	}

	json server_task_result_cmpl_partial::to_json_oaicompat_resp() {
	std::vector<json> events;

	if (n_decoded == 1) {
	events.push_back(json {
	{"event", "response.created"},
	{"data", json {
	{"type", "response.created"},
	{"response", json {
	{"id", oai_resp_id},
	{"object", "response"},
	{"status", "in_progress"},
	}},
	}},
	});
	events.push_back(json {
	{"event", "response.in_progress"},
	{"data", json {
	{"type", "response.in_progress"},
	{"response", json {
	{"id", oai_resp_id},
	{"object", "response"},
	{"status", "in_progress"},
	}},
	}},
	});
	}

	for (const common_chat_msg_diff & diff : oaicompat_msg_diffs) {
	if (!diff.reasoning_content_delta.empty()) {
	if (!thinking_block_started) {
	events.push_back(json {
	{"event", "response.output_item.added"},
	{"data", json {
	{"type", "response.output_item.added"},
	{"item", json {
	{"id", oai_resp_reasoning_id},
	{"summary", json::array()},
	{"type", "reasoning"},
	{"content", json::array()},
	{"encrypted_content", ""},
	{"status", "in_progress"},
	}},
	}},
	});
	thinking_block_started = true;
	}
	events.push_back(json {
	{"event", "response.reasoning_text.delta"},
	{"data", json {
	{"type", "response.reasoning_text.delta"},
	{"delta", diff.reasoning_content_delta},
	{"item_id", oai_resp_reasoning_id},
	}},
	});
	}

	if (!diff.content_delta.empty()) {
	if (!text_block_started) {
	events.push_back(json {
	{"event", "response.output_item.added"},
	{"data", json {
	{"type", "response.output_item.added"},
	{"item", json {
	{"content", json::array()},
	{"id", oai_resp_message_id},
	{"role", "assistant"},
	{"status", "in_progress"},
	{"type", "message"},
	}},
	}},
	});
	events.push_back(json {
	{"event", "response.content_part.added"},
	{"data", json {
	{"type", "response.content_part.added"},
	{"item_id", oai_resp_message_id},
	{"part", json {
	{"type", "output_text"},
	{"text", ""},
	}},
	}},
	});
	text_block_started = true;
	}
	events.push_back(json {
	{"event", "response.output_text.delta"},
	{"data", json {
	{"type", "response.output_text.delta"},
	{"item_id", oai_resp_message_id},
	{"delta", diff.content_delta},
	}},
	});
	}

	if (!diff.tool_call_delta.name.empty()) {
	events.push_back(json {
	{"event", "response.output_item.added"},
	{"data", json {
	{"type", "response.output_item.added"},
	{"item", json {
	{"arguments", ""},
	{"call_id", "fc_" + diff.tool_call_delta.id},
	{"name", diff.tool_call_delta.name},
	{"type", "function_call"},
	{"status", "in_progress"},
	}},
	}},
	});
	oai_resp_fc_id = diff.tool_call_delta.id;
	}

	if (!diff.tool_call_delta.arguments.empty()) {
	events.push_back(json {
	{"event", "response.function_call_arguments.delta"},
	{"data", json {
	{"type", "response.function_call_arguments.delta"},
	{"delta", diff.tool_call_delta.arguments},
	{"item_id", "fc_" + oai_resp_fc_id},
	}},
	});
	}
	}
	return events;
	}

	json server_task_result_cmpl_partial::to_json_anthropic() {
	json events = json::array();
	bool first = (n_decoded == 1);
	// use member variables to track block state across streaming calls
	// (anthropic_thinking_block_started, anthropic_text_block_started)

	if (first) {
	events.push_back({
	{"event", "message_start"},
	{"data", {
	{"type", "message_start"},
	{"message", {
	{"id", oaicompat_cmpl_id},
	{"type", "message"},
	{"role", "assistant"},
	{"content", json::array()},
	{"model", oaicompat_model},
	{"stop_reason", nullptr},
	{"stop_sequence", nullptr},
	{"usage", {
	{"input_tokens", n_prompt_tokens},
	{"output_tokens", 0}
	}}
	}}
	}}
	});
	}

	// content block indices: thinking (0) -> text (0 or 1) -> tool_use (n+)
	size_t thinking_block_index = 0;
	// use anthropic_has_reasoning (set in update()) to know if ANY reasoning was generated
	size_t text_block_index = anthropic_has_reasoning ? 1 : 0;

	// use local copies of streaming state (copied from task_result_state in update())
	// these reflect the state BEFORE this chunk was processed
	bool thinking_started = thinking_block_started;
	bool text_started = text_block_started;

	for (const auto & diff : oaicompat_msg_diffs) {
	// handle thinking/reasoning content
	if (!diff.reasoning_content_delta.empty()) {
	if (!thinking_started) {
	events.push_back({
	{"event", "content_block_start"},
	{"data", {
	{"type", "content_block_start"},
	{"index", thinking_block_index},
	{"content_block", {
	{"type", "thinking"},
	{"thinking", ""}
	}}
	}}
	});
	thinking_started = true;
	}

	events.push_back({
	{"event", "content_block_delta"},
	{"data", {
	{"type", "content_block_delta"},
	{"index", thinking_block_index},
	{"delta", {
	{"type", "thinking_delta"},
	{"thinking", diff.reasoning_content_delta}
	}}
	}}
	});
	}

	// handle regular text content
	if (!diff.content_delta.empty()) {
	if (!text_started) {
	events.push_back({
	{"event", "content_block_start"},
	{"data", {
	{"type", "content_block_start"},
	{"index", text_block_index},
	{"content_block", {
	{"type", "text"},
	{"text", ""}
	}}
	}}
	});
	text_started = true;
	}

	events.push_back({
	{"event", "content_block_delta"},
	{"data", {
	{"type", "content_block_delta"},
	{"index", text_block_index},
	{"delta", {
	{"type", "text_delta"},
	{"text", diff.content_delta}
	}}
	}}
	});
	}

	// handle tool calls
	if (diff.tool_call_index != std::string::npos) {
	// use anthropic_has_reasoning for thinking block count (persists across calls)
	size_t content_block_index = (anthropic_has_reasoning ? 1 : 0) + (text_started ? 1 : 0) + diff.tool_call_index;

	if (!diff.tool_call_delta.name.empty()) {
	events.push_back({
	{"event", "content_block_start"},
	{"data", {
	{"type", "content_block_start"},
	{"index", content_block_index},
	{"content_block", {
	{"type", "tool_use"},
	{"id", diff.tool_call_delta.id},
	{"name", diff.tool_call_delta.name}
	}}
	}}
	});
	}

	if (!diff.tool_call_delta.arguments.empty()) {
	events.push_back({
	{"event", "content_block_delta"},
	{"data", {
	{"type", "content_block_delta"},
	{"index", content_block_index},
	{"delta", {
	{"type", "input_json_delta"},
	{"partial_json", diff.tool_call_delta.arguments}
	}}
	}}
	});
	}
	}
	}

	return events;
	}

	//
	// server_task_result_embd
	//
	json server_task_result_embd::to_json() {
	return res_type == TASK_RESPONSE_TYPE_OAI_EMBD
	? to_json_oaicompat()
	: to_json_non_oaicompat();
	}

	json server_task_result_embd::to_json_non_oaicompat() {
	return json {
	{"index", index},
	{"embedding", embedding},
	};
	}

	json server_task_result_embd::to_json_oaicompat() {
	return json {
	{"index", index},
	{"embedding", embedding[0]},
	{"tokens_evaluated", n_tokens},
	};
	}

	//
	// server_task_result_rerank
	//
	json server_task_result_rerank::to_json() {
	return json {
	{"index", index},
	{"score", score},
	{"tokens_evaluated", n_tokens},
	};
	}

	//
	// server_task_result_error
	//
	json server_task_result_error::to_json() {
	json res = format_error_response(err_msg, err_type);
	if (err_type == ERROR_TYPE_EXCEED_CONTEXT_SIZE) {
	res["n_prompt_tokens"] = n_prompt_tokens;
	res["n_ctx"] = n_ctx;
	}
	return res;
	}

	//
	// server_task_result_metrics
	//
	json server_task_result_metrics::to_json() {
	return json {
	{ "idle", n_idle_slots },
	{ "processing", n_processing_slots },
	{ "deferred", n_tasks_deferred },
	{ "t_start", t_start },

	{ "n_prompt_tokens_processed_total", n_prompt_tokens_processed_total },
	{ "t_tokens_generation_total", t_tokens_generation_total },
	{ "n_tokens_predicted_total", n_tokens_predicted_total },
	{ "t_prompt_processing_total", t_prompt_processing_total },

	{ "n_tokens_max", n_tokens_max },

	{ "n_prompt_tokens_processed", n_prompt_tokens_processed },
	{ "t_prompt_processing", t_prompt_processing },
	{ "n_tokens_predicted", n_tokens_predicted },
	{ "t_tokens_generation", t_tokens_generation },

	{ "n_decode_total", n_decode_total },
	{ "n_busy_slots_total", n_busy_slots_total },

	{ "slots", slots_data },
	};
	}

	//
	// server_task_result_slot_save_load
	//
	json server_task_result_slot_save_load::to_json() {
	if (is_save) {
	return json {
	{ "id_slot", id_slot },
	{ "filename", filename },
	{ "n_saved", n_tokens },
	{ "n_written", n_bytes },
	{ "timings", {
	{ "save_ms", t_ms }
	}},
	};
	}

	return json {
	{ "id_slot", id_slot },
	{ "filename", filename },
	{ "n_restored", n_tokens },
	{ "n_read", n_bytes },
	{ "timings", {
	{ "restore_ms", t_ms }
	}},
	};
	}

	//
	// server_task_result_slot_erase
	//
	json server_task_result_slot_erase::to_json() {
	return json {
	{ "id_slot", id_slot },
	{ "n_erased", n_erased },
	};
	}

	//
	// server_task_result_get_lora
	//

	json server_task_result_get_lora::to_json() {
	json result = json::array();
	for (size_t i = 0; i < loras.size(); ++i) {
	auto & lora = loras[i];
	json entry = {
	{"id", i},
	{"path", lora.info.path},
	{"scale", lora.info.scale},
	{"task_name", lora.info.task_name},
	{"prompt_prefix", lora.info.prompt_prefix},
	};
	if (!lora.alora_invocation_tokens.empty()) {
	entry["alora_invocation_string"] = lora.alora_invocation_string;
	entry["alora_invocation_tokens"] = lora.alora_invocation_tokens;
	}
	result.push_back(std::move(entry));
	}
	return result;
	}

	//
	// server_task_result_apply_lora
	//

	json server_task_result_apply_lora::to_json() {
	return json {{ "success", true }};
	}

	//
	// server_prompt_cache
	//
	size_t server_prompt_cache::size() const {
	size_t res = 0;

	for (const auto & state : states) {
	res += state.size();
	}

	return res;
	}

	size_t server_prompt_cache::n_tokens() const {
	size_t res = 0;

	for (const auto & state : states) {
	res += state.n_tokens();
	}

	return res;
	}

	server_prompt * server_prompt_cache::alloc(const server_prompt & prompt, size_t state_size) {
	// first check if the current state is contained fully in the cache
	for (auto it = states.begin(); it != states.end(); ++it) {
	const int cur_lcp_len = it->tokens.get_common_prefix(prompt.tokens);

	if (cur_lcp_len == (int) prompt.tokens.size()) {
	SRV_WRN("%s", " - prompt is already in the cache, skipping\n");
	return nullptr;
	}
	}

	// next, remove any cached prompts that are fully contained in the current prompt
	for (auto it = states.begin(); it != states.end();) {
	const int len = it->tokens.get_common_prefix(prompt.tokens);

	if (len == (int) it->tokens.size()) {
	SRV_WRN(" - removing obsolete cached prompt with length %d\n", len);

	it = states.erase(it);
	} else {
	++it;
	}
	}

	std::vector<uint8_t> state_data;

	// check if we can allocate enough memory for the new state
	try {
	state_data.resize(state_size);
	} catch (const std::bad_alloc & e) {
	SRV_ERR("failed to allocate memory for prompt cache state: %s\n", e.what());

	limit_size = std::max<size_t>(1, 0.4*size());

	SRV_WRN(" - cache size limit reduced to %.3f MiB\n", limit_size / (1024.0 * 1024.0));

	update();

	return nullptr;
	}

	auto & cur = states.emplace_back();
	cur = {
	/.tokens =/ prompt.tokens.clone(),
	/.data =/ std::move(state_data),
	/.checkpoints =/ prompt.checkpoints,
	};

	return &cur;
	}

	bool server_prompt_cache::load(server_prompt & prompt, const server_tokens & tokens_new, llama_context * ctx, int32_t id_slot) {
	const int lcp_best = prompt.tokens.get_common_prefix(tokens_new);

	float f_keep_best = float(lcp_best) / prompt.tokens.size();
	float sim_best = float(lcp_best) / tokens_new.size();

	SRV_WRN(" - looking for better prompt, base f_keep = %.3f, sim = %.3f\n", f_keep_best, sim_best);

	auto it_best = states.end();

	// find the most similar cached prompt, that would also preserve the most context
	for (auto it = states.begin(); it != states.end(); ++it) {
	const int lcp_cur = it->tokens.get_common_prefix(tokens_new);

	const float f_keep_cur = float(lcp_cur) / it->tokens.size();
	const float sim_cur = float(lcp_cur) / tokens_new.size();

	// don't trash large prompts
	if (f_keep_cur < 0.25f) {
	continue;
	}

	if (f_keep_best < f_keep_cur && sim_best < sim_cur) {
	f_keep_best = f_keep_cur;
	sim_best = sim_cur;

	it_best = it;
	}
	}

	if (it_best != states.end()) {
	SRV_WRN(" - found better prompt with f_keep = %.3f, sim = %.3f\n", f_keep_best, sim_best);

	const size_t size = it_best->data.size();
	const size_t n = llama_state_seq_set_data_ext(ctx, it_best->data.data(), size, id_slot, 0);
	if (n != size) {
	SRV_WRN("failed to restore state with size %zu\n", size);

	return false;
	}

	it_best->data.clear();
	it_best->data.shrink_to_fit();

	prompt = std::move(*it_best);

	states.erase(it_best);
	}

	return true;
	}

	void server_prompt_cache::update() {
	if (limit_size > 0) {
	// always keep at least one state, regardless of the limits
	while (states.size() > 1 && size() > limit_size) {
	if (states.empty()) {
	break;
	}

	SRV_WRN(" - cache size limit reached, removing oldest entry (size = %.3f MiB)\n", states.front().size() / (1024.0 * 1024.0));

	states.pop_front();
	}
	}

	// average size per token
	const float size_per_token = std::max<float>(1.0f, float(size()) / (std::max<size_t>(1, n_tokens())));

	// dynamically increase the token limit if it can fit in the memory limit
	const size_t limit_tokens_cur = limit_size > 0 ? std::max<size_t>(limit_tokens, limit_size/size_per_token) : limit_tokens;

	if (limit_tokens > 0) {
	while (states.size() > 1 && n_tokens() > limit_tokens_cur) {
	if (states.empty()) {
	break;
	}

	SRV_WRN(" - cache token limit (%zu, est: %zu) reached, removing oldest entry (size = %.3f MiB)\n",
	limit_tokens, limit_tokens_cur, states.front().size() / (1024.0 * 1024.0));

	states.pop_front();
	}
	}

	SRV_WRN(" - cache state: %zu prompts, %.3f MiB (limits: %.3f MiB, %zu tokens, %zu est)\n",
	states.size(), size() / (1024.0 * 1024.0), limit_size / (1024.0 * 1024.0), limit_tokens, limit_tokens_cur);

	for (const auto & state : states) {
	SRV_WRN(" - prompt %p: %7d tokens, checkpoints: %2zu, %9.3f MiB\n",
	(const void )&state, state.n_tokens(), state.checkpoints.size(), state.size() / (1024.0 1024.0));
	}
	}