import OpenAI from "openai";
import type {
	ChatCompletionAssistantMessageParam,
	ChatCompletionChunk,
	ChatCompletionContentPart,
	ChatCompletionContentPartImage,
	ChatCompletionContentPartText,
	ChatCompletionDeveloperMessageParam,
	ChatCompletionMessageParam,
	ChatCompletionSystemMessageParam,
	ChatCompletionToolMessageParam,
} from "openai/resources/chat/completions.js";
import { getEnvApiKey } from "../env-api-keys.js";
import { calculateCost, clampThinkingLevel } from "../models.js";
import type {
	AssistantMessage,
	CacheRetention,
	Context,
	ImageContent,
	Message,
	Model,
	OpenAICompletionsCompat,
	SimpleStreamOptions,
	StopReason,
	StreamFunction,
	StreamOptions,
	TextContent,
	ThinkingContent,
	Tool,
	ToolCall,
	ToolResultMessage,
} from "../types.js";
import { AssistantMessageEventStream } from "../utils/event-stream.js";
import { headersToRecord } from "../utils/headers.js";
import { parseStreamingJson } from "../utils/json-parse.js";
import { sanitizeSurrogates } from "../utils/sanitize-unicode.js";
import { isCloudflareProvider, resolveCloudflareBaseUrl } from "./cloudflare.js";
import { buildCopilotDynamicHeaders, hasCopilotVisionInput } from "./github-copilot-headers.js";
import { buildBaseOptions } from "./simple-options.js";
import { transformMessages } from "./transform-messages.js";

/**
 * Check if conversation messages contain tool calls or tool results.
 * This is needed because Anthropic (via proxy) requires the tools param
 * to be present when messages include tool_calls or tool role messages.
 */
function hasToolHistory(messages: Message[]): boolean {
	for (const msg of messages) {
		if (msg.role === "toolResult") {
			return true;
		}
		if (msg.role === "assistant") {
			if (msg.content.some((block) => block.type === "toolCall")) {
				return true;
			}
		}
	}
	return false;
}

function isTextContentBlock(block: { type: string }): block is TextContent {
	return block.type === "text";
}

function isThinkingContentBlock(block: { type: string }): block is ThinkingContent {
	return block.type === "thinking";
}

function isToolCallBlock(block: { type: string }): block is ToolCall {
	return block.type === "toolCall";
}

function isImageContentBlock(block: { type: string }): block is ImageContent {
	return block.type === "image";
}

export interface OpenAICompletionsOptions extends StreamOptions {
	toolChoice?: "auto" | "none" | "required" | { type: "function"; function: { name: string } };
	reasoningEffort?: "minimal" | "low" | "medium" | "high" | "xhigh";
}

interface OpenAICompatCacheControl {
	type: "ephemeral";
	ttl?: string;
}

type ResolvedOpenAICompletionsCompat = Omit<Required<OpenAICompletionsCompat>, "cacheControlFormat"> & {
	cacheControlFormat?: OpenAICompletionsCompat["cacheControlFormat"];
};

type ChatCompletionInstructionMessageParam = ChatCompletionDeveloperMessageParam | ChatCompletionSystemMessageParam;

type ChatCompletionTextPartWithCacheControl = ChatCompletionContentPartText & {
	cache_control?: OpenAICompatCacheControl;
};

type ChatCompletionToolWithCacheControl = OpenAI.Chat.Completions.ChatCompletionTool & {
	cache_control?: OpenAICompatCacheControl;
};

function resolveCacheRetention(cacheRetention?: CacheRetention): CacheRetention {
	if (cacheRetention) {
		return cacheRetention;
	}
	if (typeof process !== "undefined" && process.env.PI_CACHE_RETENTION === "long") {
		return "long";
	}
	return "short";
}

export const streamOpenAICompletions: StreamFunction<"openai-completions", OpenAICompletionsOptions> = (
	model: Model<"openai-completions">,
	context: Context,
	options?: OpenAICompletionsOptions,
): AssistantMessageEventStream => {
	const stream = new AssistantMessageEventStream();

	(async () => {
		const output: AssistantMessage = {
			role: "assistant",
			content: [],
			api: model.api,
			provider: model.provider,
			model: model.id,
			usage: {
				input: 0,
				output: 0,
				cacheRead: 0,
				cacheWrite: 0,
				totalTokens: 0,
				cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0, total: 0 },
			},
			stopReason: "stop",
			timestamp: Date.now(),
		};

		try {
			const apiKey = options?.apiKey || getEnvApiKey(model.provider) || "";
			const compat = getCompat(model);
			const cacheRetention = resolveCacheRetention(options?.cacheRetention);
			const cacheSessionId = cacheRetention === "none" ? undefined : options?.sessionId;
			const client = createClient(model, context, apiKey, options?.headers, cacheSessionId, compat);
			let params = buildParams(model, context, options, compat, cacheRetention);
			const nextParams = await options?.onPayload?.(params, model);
			if (nextParams !== undefined) {
				params = nextParams as OpenAI.Chat.Completions.ChatCompletionCreateParamsStreaming;
			}
			const requestOptions = {
				...(options?.signal ? { signal: options.signal } : {}),
				...(options?.timeoutMs !== undefined ? { timeout: options.timeoutMs } : {}),
				...(options?.maxRetries !== undefined ? { maxRetries: options.maxRetries } : {}),
			};
			const { data: openaiStream, response } = await client.chat.completions
				.create(params, requestOptions)
				.withResponse();
			await options?.onResponse?.({ status: response.status, headers: headersToRecord(response.headers) }, model);
			stream.push({ type: "start", partial: output });

			interface StreamingToolCallBlock extends ToolCall {
				partialArgs?: string;
				streamIndex?: number;
			}
			type StreamingBlock = TextContent | ThinkingContent | StreamingToolCallBlock;
			type StreamingToolCallDelta = NonNullable<ChatCompletionChunk.Choice.Delta["tool_calls"]>[number];

			let textBlock: TextContent | null = null;
			let thinkingBlock: ThinkingContent | null = null;
			const toolCallBlocksByIndex = new Map<number, StreamingToolCallBlock>();
			const toolCallBlocksById = new Map<string, StreamingToolCallBlock>();
			const blocks = output.content as StreamingBlock[];
			const getContentIndex = (block: StreamingBlock) => blocks.indexOf(block);
			const finishBlock = (block: StreamingBlock) => {
				const contentIndex = getContentIndex(block);
				if (contentIndex === -1) {
					return;
				}
				if (block.type === "text") {
					stream.push({
						type: "text_end",
						contentIndex,
						content: block.text,
						partial: output,
					});
				} else if (block.type === "thinking") {
					stream.push({
						type: "thinking_end",
						contentIndex,
						content: block.thinking,
						partial: output,
					});
				} else if (block.type === "toolCall") {
					block.arguments = parseStreamingJson(block.partialArgs);
					// Finalize in-place and strip the scratch buffers so replay only
					// carries parsed arguments.
					delete block.partialArgs;
					delete block.streamIndex;
					stream.push({
						type: "toolcall_end",
						contentIndex,
						toolCall: block,
						partial: output,
					});
				}
			};
			const ensureTextBlock = () => {
				if (!textBlock) {
					textBlock = { type: "text", text: "" };
					blocks.push(textBlock);
					stream.push({ type: "text_start", contentIndex: getContentIndex(textBlock), partial: output });
				}
				return textBlock;
			};
			const ensureThinkingBlock = (thinkingSignature: string) => {
				if (!thinkingBlock) {
					thinkingBlock = {
						type: "thinking",
						thinking: "",
						thinkingSignature,
					};
					blocks.push(thinkingBlock);
					stream.push({ type: "thinking_start", contentIndex: getContentIndex(thinkingBlock), partial: output });
				}
				return thinkingBlock;
			};
			const ensureToolCallBlock = (toolCall: StreamingToolCallDelta) => {
				const streamIndex = typeof toolCall.index === "number" ? toolCall.index : undefined;
				let block = streamIndex !== undefined ? toolCallBlocksByIndex.get(streamIndex) : undefined;
				if (!block && toolCall.id) {
					block = toolCallBlocksById.get(toolCall.id);
				}
				if (!block) {
					block = {
						type: "toolCall",
						id: toolCall.id || "",
						name: toolCall.function?.name || "",
						arguments: {},
						partialArgs: "",
						streamIndex,
					};
					if (streamIndex !== undefined) {
						toolCallBlocksByIndex.set(streamIndex, block);
					}
					if (toolCall.id) {
						toolCallBlocksById.set(toolCall.id, block);
					}
					blocks.push(block);
					stream.push({
						type: "toolcall_start",
						contentIndex: getContentIndex(block),
						partial: output,
					});
				}
				if (streamIndex !== undefined && block.streamIndex === undefined) {
					block.streamIndex = streamIndex;
					toolCallBlocksByIndex.set(streamIndex, block);
				}
				if (toolCall.id) {
					toolCallBlocksById.set(toolCall.id, block);
				}
				return block;
			};

			for await (const chunk of openaiStream) {
				if (!chunk || typeof chunk !== "object") continue;

				// OpenAI documents ChatCompletionChunk.id as the unique chat completion identifier,
				// and each chunk in a streamed completion carries the same id.
				output.responseId ||= chunk.id;
				if (typeof chunk.model === "string" && chunk.model.length > 0 && chunk.model !== model.id) {
					output.responseModel ||= chunk.model;
				}
				if (chunk.usage) {
					output.usage = parseChunkUsage(chunk.usage, model);
				}

				const choice = Array.isArray(chunk.choices) ? chunk.choices[0] : undefined;
				if (!choice) continue;

				// Fallback: some providers (e.g., Moonshot) return usage
				// in choice.usage instead of the standard chunk.usage
				if (!chunk.usage && (choice as any).usage) {
					output.usage = parseChunkUsage((choice as any).usage, model);
				}

				if (choice.finish_reason) {
					const finishReasonResult = mapStopReason(choice.finish_reason);
					output.stopReason = finishReasonResult.stopReason;
					if (finishReasonResult.errorMessage) {
						output.errorMessage = finishReasonResult.errorMessage;
					}
				}

				if (choice.delta) {
					if (
						choice.delta.content !== null &&
						choice.delta.content !== undefined &&
						choice.delta.content.length > 0
					) {
						const block = ensureTextBlock();
						block.text += choice.delta.content;
						stream.push({
							type: "text_delta",
							contentIndex: getContentIndex(block),
							delta: choice.delta.content,
							partial: output,
						});
					}

					// Some endpoints return reasoning in reasoning_content (llama.cpp),
					// or reasoning (other openai compatible endpoints)
					// Use the first non-empty reasoning field to avoid duplication
					// (e.g., chutes.ai returns both reasoning_content and reasoning with same content)
					const reasoningFields = ["reasoning_content", "reasoning", "reasoning_text"];
					const deltaFields = choice.delta as Record<string, unknown>;
					let foundReasoningField: string | null = null;
					for (const field of reasoningFields) {
						const value = deltaFields[field];
						if (typeof value === "string" && value.length > 0) {
							foundReasoningField = field;
							break;
						}
					}

					if (foundReasoningField) {
						const delta = deltaFields[foundReasoningField];
						if (typeof delta === "string" && delta.length > 0) {
							const block = ensureThinkingBlock(foundReasoningField);
							block.thinking += delta;
							stream.push({
								type: "thinking_delta",
								contentIndex: getContentIndex(block),
								delta,
								partial: output,
							});
						}
					}

					if (choice?.delta?.tool_calls) {
						for (const toolCall of choice.delta.tool_calls) {
							const block = ensureToolCallBlock(toolCall);
							if (!block.id && toolCall.id) {
								block.id = toolCall.id;
								toolCallBlocksById.set(toolCall.id, block);
							}
							if (!block.name && toolCall.function?.name) {
								block.name = toolCall.function.name;
							}

							let delta = "";
							if (toolCall.function?.arguments) {
								delta = toolCall.function.arguments;
								block.partialArgs = (block.partialArgs ?? "") + toolCall.function.arguments;
								block.arguments = parseStreamingJson(block.partialArgs);
							}
							stream.push({
								type: "toolcall_delta",
								contentIndex: getContentIndex(block),
								delta,
								partial: output,
							});
						}
					}

					const reasoningDetails = (choice.delta as any).reasoning_details;
					if (reasoningDetails && Array.isArray(reasoningDetails)) {
						for (const detail of reasoningDetails) {
							if (detail.type === "reasoning.encrypted" && detail.id && detail.data) {
								const matchingToolCall = output.content.find(
									(b) => b.type === "toolCall" && b.id === detail.id,
								) as ToolCall | undefined;
								if (matchingToolCall) {
									matchingToolCall.thoughtSignature = JSON.stringify(detail);
								}
							}
						}
					}
				}
			}

			for (const block of blocks) {
				finishBlock(block);
			}
			if (options?.signal?.aborted) {
				throw new Error("Request was aborted");
			}

			if (output.stopReason === "aborted") {
				throw new Error("Request was aborted");
			}
			if (output.stopReason === "error") {
				throw new Error(output.errorMessage || "Provider returned an error stop reason");
			}

			stream.push({ type: "done", reason: output.stopReason, message: output });
			stream.end();
		} catch (error) {
			for (const block of output.content) {
				delete (block as { index?: number }).index;
				// Streaming scratch buffers are only used during parsing; never persist them.
				delete (block as { partialArgs?: string }).partialArgs;
				delete (block as { streamIndex?: number }).streamIndex;
			}
			output.stopReason = options?.signal?.aborted ? "aborted" : "error";
			output.errorMessage = error instanceof Error ? error.message : JSON.stringify(error);
			// Some providers via OpenRouter give additional information in this field.
			const rawMetadata = (error as any)?.error?.metadata?.raw;
			if (rawMetadata) output.errorMessage += `\n${rawMetadata}`;
			stream.push({ type: "error", reason: output.stopReason, error: output });
			stream.end();
		}
	})();

	return stream;
};

export const streamSimpleOpenAICompletions: StreamFunction<"openai-completions", SimpleStreamOptions> = (
	model: Model<"openai-completions">,
	context: Context,
	options?: SimpleStreamOptions,
): AssistantMessageEventStream => {
	const apiKey = options?.apiKey || getEnvApiKey(model.provider);
	if (!apiKey) {
		throw new Error(`No API key for provider: ${model.provider}`);
	}

	const base = buildBaseOptions(model, options, apiKey);
	const clampedReasoning = options?.reasoning ? clampThinkingLevel(model, options.reasoning) : undefined;
	const reasoningEffort = clampedReasoning === "off" ? undefined : clampedReasoning;
	const toolChoice = (options as OpenAICompletionsOptions | undefined)?.toolChoice;

	return streamOpenAICompletions(model, context, {
		...base,
		reasoningEffort,
		toolChoice,
	} satisfies OpenAICompletionsOptions);
};

function createClient(
	model: Model<"openai-completions">,
	context: Context,
	apiKey?: string,
	optionsHeaders?: Record<string, string>,
	sessionId?: string,
	compat: ResolvedOpenAICompletionsCompat = getCompat(model),
) {
	if (!apiKey) {
		if (!process.env.OPENAI_API_KEY) {
			throw new Error(
				"OpenAI API key is required. Set OPENAI_API_KEY environment variable or pass it as an argument.",
			);
		}
		apiKey = process.env.OPENAI_API_KEY;
	}

	const headers = { ...model.headers };
	if (model.provider === "github-copilot") {
		const hasImages = hasCopilotVisionInput(context.messages);
		const copilotHeaders = buildCopilotDynamicHeaders({
			messages: context.messages,
			hasImages,
		});
		Object.assign(headers, copilotHeaders);
	}

	if (sessionId && compat.sendSessionAffinityHeaders) {
		headers.session_id = sessionId;
		headers["x-client-request-id"] = sessionId;
		headers["x-session-affinity"] = sessionId;
	}

	// Merge options headers last so they can override defaults
	if (optionsHeaders) {
		Object.assign(headers, optionsHeaders);
	}

	const defaultHeaders =
		model.provider === "cloudflare-ai-gateway"
			? {
					...headers,
					Authorization: headers.Authorization ?? null,
					"cf-aig-authorization": `Bearer ${apiKey}`,
				}
			: headers;

	return new OpenAI({
		apiKey,
		baseURL: isCloudflareProvider(model.provider) ? resolveCloudflareBaseUrl(model) : model.baseUrl,
		dangerouslyAllowBrowser: true,
		defaultHeaders,
	});
}

function buildParams(
	model: Model<"openai-completions">,
	context: Context,
	options?: OpenAICompletionsOptions,
	compat: ResolvedOpenAICompletionsCompat = getCompat(model),
	cacheRetention: CacheRetention = resolveCacheRetention(options?.cacheRetention),
) {
	const messages = convertMessages(model, context, compat);
	const cacheControl = getCompatCacheControl(compat, cacheRetention);

	const params: OpenAI.Chat.Completions.ChatCompletionCreateParamsStreaming = {
		model: model.id,
		messages,
		stream: true,
		prompt_cache_key:
			(model.baseUrl.includes("api.openai.com") && cacheRetention !== "none") ||
			(cacheRetention === "long" && compat.supportsLongCacheRetention)
				? options?.sessionId
				: undefined,
		prompt_cache_retention: cacheRetention === "long" && compat.supportsLongCacheRetention ? "24h" : undefined,
	};

	if (compat.supportsUsageInStreaming !== false) {
		(params as any).stream_options = { include_usage: true };
	}

	if (compat.supportsStore) {
		params.store = false;
	}

	if (options?.maxTokens) {
		if (compat.maxTokensField === "max_tokens") {
			(params as any).max_tokens = options.maxTokens;
		} else {
			params.max_completion_tokens = options.maxTokens;
		}
	}

	if (options?.temperature !== undefined) {
		params.temperature = options.temperature;
	}

	if (context.tools && context.tools.length > 0) {
		params.tools = convertTools(context.tools, compat);
		if (compat.zaiToolStream) {
			(params as any).tool_stream = true;
		}
	} else if (hasToolHistory(context.messages)) {
		// Anthropic (via LiteLLM/proxy) requires tools param when conversation has tool_calls/tool_results
		params.tools = [];
	}

	if (cacheControl) {
		applyAnthropicCacheControl(messages, params.tools, cacheControl);
	}

	if (options?.toolChoice) {
		params.tool_choice = options.toolChoice;
	}

	if (compat.thinkingFormat === "zai" && model.reasoning) {
		(params as any).enable_thinking = !!options?.reasoningEffort;
	} else if (compat.thinkingFormat === "qwen" && model.reasoning) {
		(params as any).enable_thinking = !!options?.reasoningEffort;
	} else if (compat.thinkingFormat === "qwen-chat-template" && model.reasoning) {
		(params as any).chat_template_kwargs = {
			enable_thinking: !!options?.reasoningEffort,
			preserve_thinking: true,
		};
	} else if (compat.thinkingFormat === "deepseek" && model.reasoning) {
		(params as any).thinking = { type: options?.reasoningEffort ? "enabled" : "disabled" };
		if (options?.reasoningEffort) {
			(params as any).reasoning_effort =
				model.thinkingLevelMap?.[options.reasoningEffort] ?? options.reasoningEffort;
		}
	} else if (compat.thinkingFormat === "openrouter" && model.reasoning) {
		// OpenRouter normalizes reasoning across providers via a nested reasoning object.
		const openRouterParams = params as typeof params & { reasoning?: { effort?: string } };
		if (options?.reasoningEffort) {
			openRouterParams.reasoning = {
				effort: model.thinkingLevelMap?.[options.reasoningEffort] ?? options.reasoningEffort,
			};
		} else if (model.thinkingLevelMap?.off !== null) {
			openRouterParams.reasoning = { effort: model.thinkingLevelMap?.off ?? "none" };
		}
	} else if (compat.thinkingFormat === "together" && model.reasoning) {
		const togetherParams = params as Omit<typeof params, "reasoning_effort"> & {
			reasoning?: { enabled: boolean };
			reasoning_effort?: string;
		};
		togetherParams.reasoning = { enabled: !!options?.reasoningEffort };
		if (options?.reasoningEffort && compat.supportsReasoningEffort) {
			togetherParams.reasoning_effort = model.thinkingLevelMap?.[options.reasoningEffort] ?? options.reasoningEffort;
		}
	} else if (options?.reasoningEffort && model.reasoning && compat.supportsReasoningEffort) {
		// OpenAI-style reasoning_effort
		(params as any).reasoning_effort = model.thinkingLevelMap?.[options.reasoningEffort] ?? options.reasoningEffort;
	} else if (!options?.reasoningEffort && model.reasoning && compat.supportsReasoningEffort) {
		const offValue = model.thinkingLevelMap?.off;
		if (typeof offValue === "string") {
			(params as any).reasoning_effort = offValue;
		}
	}

	// OpenRouter provider routing preferences
	if (model.baseUrl.includes("openrouter.ai") && model.compat?.openRouterRouting) {
		(params as any).provider = model.compat.openRouterRouting;
	}

	// Vercel AI Gateway provider routing preferences
	if (model.baseUrl.includes("ai-gateway.vercel.sh") && model.compat?.vercelGatewayRouting) {
		const routing = model.compat.vercelGatewayRouting;
		if (routing.only || routing.order) {
			const gatewayOptions: Record<string, string[]> = {};
			if (routing.only) gatewayOptions.only = routing.only;
			if (routing.order) gatewayOptions.order = routing.order;
			(params as any).providerOptions = { gateway: gatewayOptions };
		}
	}

	return params;
}

function getCompatCacheControl(
	compat: ResolvedOpenAICompletionsCompat,
	cacheRetention: CacheRetention,
): OpenAICompatCacheControl | undefined {
	if (compat.cacheControlFormat !== "anthropic" || cacheRetention === "none") {
		return undefined;
	}

	const ttl = cacheRetention === "long" && compat.supportsLongCacheRetention ? "1h" : undefined;
	return { type: "ephemeral", ...(ttl ? { ttl } : {}) };
}

function applyAnthropicCacheControl(
	messages: ChatCompletionMessageParam[],
	tools: OpenAI.Chat.Completions.ChatCompletionTool[] | undefined,
	cacheControl: OpenAICompatCacheControl,
): void {
	addCacheControlToSystemPrompt(messages, cacheControl);
	addCacheControlToLastTool(tools, cacheControl);
	addCacheControlToLastConversationMessage(messages, cacheControl);
}

function addCacheControlToSystemPrompt(
	messages: ChatCompletionMessageParam[],
	cacheControl: OpenAICompatCacheControl,
): void {
	for (const message of messages) {
		if (message.role === "system" || message.role === "developer") {
			addCacheControlToInstructionMessage(message, cacheControl);
			return;
		}
	}
}

function addCacheControlToLastConversationMessage(
	messages: ChatCompletionMessageParam[],
	cacheControl: OpenAICompatCacheControl,
): void {
	for (let i = messages.length - 1; i >= 0; i--) {
		const message = messages[i];
		if (message.role === "user" || message.role === "assistant") {
			if (addCacheControlToMessage(message, cacheControl)) {
				return;
			}
		}
	}
}

function addCacheControlToLastTool(
	tools: OpenAI.Chat.Completions.ChatCompletionTool[] | undefined,
	cacheControl: OpenAICompatCacheControl,
): void {
	if (!tools || tools.length === 0) {
		return;
	}

	const lastTool = tools[tools.length - 1] as ChatCompletionToolWithCacheControl;
	lastTool.cache_control = cacheControl;
}

function addCacheControlToInstructionMessage(
	message: ChatCompletionInstructionMessageParam,
	cacheControl: OpenAICompatCacheControl,
): boolean {
	return addCacheControlToTextContent(message, cacheControl);
}

function addCacheControlToMessage(
	message: ChatCompletionMessageParam,
	cacheControl: OpenAICompatCacheControl,
): boolean {
	if (message.role === "user" || message.role === "assistant") {
		return addCacheControlToTextContent(message, cacheControl);
	}
	return false;
}

function addCacheControlToTextContent(
	message:
		| ChatCompletionInstructionMessageParam
		| ChatCompletionAssistantMessageParam
		| Extract<ChatCompletionMessageParam, { role: "user" }>,
	cacheControl: OpenAICompatCacheControl,
): boolean {
	const content = message.content;
	if (typeof content === "string") {
		if (content.length === 0) {
			return false;
		}
		message.content = [
			{
				type: "text",
				text: content,
				cache_control: cacheControl,
			},
		] as ChatCompletionTextPartWithCacheControl[];
		return true;
	}

	if (!Array.isArray(content)) {
		return false;
	}

	for (let i = content.length - 1; i >= 0; i--) {
		const part = content[i];
		if (part?.type === "text") {
			const textPart = part as ChatCompletionTextPartWithCacheControl;
			textPart.cache_control = cacheControl;
			return true;
		}
	}

	return false;
}

export function convertMessages(
	model: Model<"openai-completions">,
	context: Context,
	compat: ResolvedOpenAICompletionsCompat,
): ChatCompletionMessageParam[] {
	const params: ChatCompletionMessageParam[] = [];

	const normalizeToolCallId = (id: string): string => {
		// Handle pipe-separated IDs from OpenAI Responses API
		// Format: {call_id}|{id} where {id} can be 400+ chars with special chars (+, /, =)
		// These come from providers like github-copilot, openai-codex, opencode
		// Extract just the call_id part and normalize it
		if (id.includes("|")) {
			const [callId] = id.split("|");
			// Sanitize to allowed chars and truncate to 40 chars (OpenAI limit)
			return callId.replace(/[^a-zA-Z0-9_-]/g, "_").slice(0, 40);
		}

		if (model.provider === "openai") return id.length > 40 ? id.slice(0, 40) : id;
		return id;
	};

	const transformedMessages = transformMessages(context.messages, model, (id) => normalizeToolCallId(id));

	if (context.systemPrompt) {
		const useDeveloperRole = model.reasoning && compat.supportsDeveloperRole;
		const role = useDeveloperRole ? "developer" : "system";
		params.push({ role: role, content: sanitizeSurrogates(context.systemPrompt) });
	}

	let lastRole: string | null = null;

	for (let i = 0; i < transformedMessages.length; i++) {
		const msg = transformedMessages[i];
		// Some providers don't allow user messages directly after tool results
		// Insert a synthetic assistant message to bridge the gap
		if (compat.requiresAssistantAfterToolResult && lastRole === "toolResult" && msg.role === "user") {
			params.push({
				role: "assistant",
				content: "I have processed the tool results.",
			});
		}

		if (msg.role === "user") {
			if (typeof msg.content === "string") {
				params.push({
					role: "user",
					content: sanitizeSurrogates(msg.content),
				});
			} else {
				const content: ChatCompletionContentPart[] = msg.content.map((item): ChatCompletionContentPart => {
					if (item.type === "text") {
						return {
							type: "text",
							text: sanitizeSurrogates(item.text),
						} satisfies ChatCompletionContentPartText;
					} else {
						return {
							type: "image_url",
							image_url: {
								url: `data:${item.mimeType};base64,${item.data}`,
							},
						} satisfies ChatCompletionContentPartImage;
					}
				});
				if (content.length === 0) continue;
				params.push({
					role: "user",
					content,
				});
			}
		} else if (msg.role === "assistant") {
			// Some providers don't accept null content, use empty string instead
			const assistantMsg: ChatCompletionAssistantMessageParam = {
				role: "assistant",
				content: compat.requiresAssistantAfterToolResult ? "" : null,
			};

			const assistantTextParts = msg.content
				.filter(isTextContentBlock)
				.filter((block) => block.text.trim().length > 0)
				.map(
					(block) =>
						({
							type: "text",
							text: sanitizeSurrogates(block.text),
						}) satisfies ChatCompletionContentPartText,
				);
			const assistantText = assistantTextParts.map((part) => part.text).join("");

			const nonEmptyThinkingBlocks = msg.content
				.filter(isThinkingContentBlock)
				.filter((block) => block.thinking.trim().length > 0);
			if (nonEmptyThinkingBlocks.length > 0) {
				if (compat.requiresThinkingAsText) {
					// Convert thinking blocks to plain text (no tags to avoid model mimicking them)
					const thinkingText = nonEmptyThinkingBlocks
						.map((block) => sanitizeSurrogates(block.thinking))
						.join("\n\n");
					assistantMsg.content = [{ type: "text", text: thinkingText }, ...assistantTextParts];
				} else {
					// Always send assistant content as a plain string (OpenAI Chat Completions
					// API standard format). Sending as an array of {type:"text", text:"..."}
					// objects is non-standard and causes some models (e.g. DeepSeek V3.2 via
					// NVIDIA NIM) to mirror the content-block structure literally in their
					// output, producing recursive nesting like [{'type':'text','text':'[{...}]'}].
					if (assistantText.length > 0) {
						assistantMsg.content = assistantText;
					}

					// Use the signature from the first thinking block if available (for llama.cpp server + gpt-oss)
					const signature = nonEmptyThinkingBlocks[0].thinkingSignature;
					if (signature && signature.length > 0) {
						(assistantMsg as any)[signature] = nonEmptyThinkingBlocks.map((block) => block.thinking).join("\n");
					}
				}
			} else if (assistantText.length > 0) {
				// Always send assistant content as a plain string (OpenAI Chat Completions
				// API standard format). Sending as an array of {type:"text", text:"..."}
				// objects is non-standard and causes some models (e.g. DeepSeek V3.2 via
				// NVIDIA NIM) to mirror the content-block structure literally in their
				// output, producing recursive nesting like [{'type':'text','text':'[{...}]'}].
				assistantMsg.content = assistantText;
			}

			const toolCalls = msg.content.filter(isToolCallBlock);
			if (toolCalls.length > 0) {
				assistantMsg.tool_calls = toolCalls.map((tc) => ({
					id: tc.id,
					type: "function" as const,
					function: {
						name: tc.name,
						arguments: JSON.stringify(tc.arguments),
					},
				}));
				const reasoningDetails = toolCalls
					.filter((tc) => tc.thoughtSignature)
					.map((tc) => {
						try {
							return JSON.parse(tc.thoughtSignature!);
						} catch {
							return null;
						}
					})
					.filter(Boolean);
				if (reasoningDetails.length > 0) {
					(assistantMsg as any).reasoning_details = reasoningDetails;
				}
			}
			if (
				compat.requiresReasoningContentOnAssistantMessages &&
				model.reasoning &&
				(assistantMsg as { reasoning_content?: string }).reasoning_content === undefined
			) {
				(assistantMsg as { reasoning_content?: string }).reasoning_content = "";
			}
			// Skip assistant messages that have no content and no tool calls.
			// Some providers require "either content or tool_calls, but not none".
			// Other providers also don't accept empty assistant messages.
			// This handles aborted assistant responses that got no content.
			const content = assistantMsg.content;
			const hasContent =
				content !== null &&
				content !== undefined &&
				(typeof content === "string" ? content.length > 0 : content.length > 0);
			if (!hasContent && !assistantMsg.tool_calls) {
				continue;
			}
			params.push(assistantMsg);
		} else if (msg.role === "toolResult") {
			const imageBlocks: Array<{ type: "image_url"; image_url: { url: string } }> = [];
			let j = i;

			for (; j < transformedMessages.length && transformedMessages[j].role === "toolResult"; j++) {
				const toolMsg = transformedMessages[j] as ToolResultMessage;

				// Extract text and image content
				const textResult = toolMsg.content
					.filter(isTextContentBlock)
					.map((block) => block.text)
					.join("\n");
				const hasImages = toolMsg.content.some((c) => c.type === "image");

				// Always send tool result with text (or placeholder if only images)
				const hasText = textResult.length > 0;
				// Some providers require the 'name' field in tool results
				const toolResultMsg: ChatCompletionToolMessageParam = {
					role: "tool",
					content: sanitizeSurrogates(hasText ? textResult : "(see attached image)"),
					tool_call_id: toolMsg.toolCallId,
				};
				if (compat.requiresToolResultName && toolMsg.toolName) {
					(toolResultMsg as any).name = toolMsg.toolName;
				}
				params.push(toolResultMsg);

				if (hasImages && model.input.includes("image")) {
					for (const block of toolMsg.content) {
						if (isImageContentBlock(block)) {
							imageBlocks.push({
								type: "image_url",
								image_url: {
									url: `data:${block.mimeType};base64,${block.data}`,
								},
							});
						}
					}
				}
			}

			i = j - 1;

			if (imageBlocks.length > 0) {
				if (compat.requiresAssistantAfterToolResult) {
					params.push({
						role: "assistant",
						content: "I have processed the tool results.",
					});
				}

				params.push({
					role: "user",
					content: [
						{
							type: "text",
							text: "Attached image(s) from tool result:",
						},
						...imageBlocks,
					],
				});
				lastRole = "user";
			} else {
				lastRole = "toolResult";
			}
			continue;
		}

		lastRole = msg.role;
	}

	return params;
}

function convertTools(
	tools: Tool[],
	compat: ResolvedOpenAICompletionsCompat,
): OpenAI.Chat.Completions.ChatCompletionTool[] {
	return tools.map((tool) => ({
		type: "function",
		function: {
			name: tool.name,
			description: tool.description,
			parameters: tool.parameters as any, // TypeBox already generates JSON Schema
			// Only include strict if provider supports it. Some reject unknown fields.
			...(compat.supportsStrictMode !== false && { strict: false }),
		},
	}));
}

function parseChunkUsage(
	rawUsage: {
		prompt_tokens?: number;
		completion_tokens?: number;
		prompt_cache_hit_tokens?: number;
		prompt_tokens_details?: { cached_tokens?: number; cache_write_tokens?: number };
	},
	model: Model<"openai-completions">,
): AssistantMessage["usage"] {
	const promptTokens = rawUsage.prompt_tokens || 0;
	const reportedCachedTokens = rawUsage.prompt_tokens_details?.cached_tokens ?? rawUsage.prompt_cache_hit_tokens ?? 0;
	const cacheWriteTokens = rawUsage.prompt_tokens_details?.cache_write_tokens || 0;

	// Normalize to pi-ai semantics:
	// - cacheRead: hits from cache created by previous requests only
	// - cacheWrite: tokens written to cache in this request
	// Some OpenAI-compatible providers (observed on OpenRouter) report cached_tokens
	// as (previous hits + current writes). In that case, remove cacheWrite from cacheRead.
	const cacheReadTokens =
		cacheWriteTokens > 0 ? Math.max(0, reportedCachedTokens - cacheWriteTokens) : reportedCachedTokens;

	const input = Math.max(0, promptTokens - cacheReadTokens - cacheWriteTokens);
	// OpenAI completion_tokens already includes reasoning_tokens.
	const outputTokens = rawUsage.completion_tokens || 0;
	const usage: AssistantMessage["usage"] = {
		input,
		output: outputTokens,
		cacheRead: cacheReadTokens,
		cacheWrite: cacheWriteTokens,
		totalTokens: input + outputTokens + cacheReadTokens + cacheWriteTokens,
		cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0, total: 0 },
	};
	calculateCost(model, usage);
	return usage;
}

function mapStopReason(reason: ChatCompletionChunk.Choice["finish_reason"] | string): {
	stopReason: StopReason;
	errorMessage?: string;
} {
	if (reason === null) return { stopReason: "stop" };
	switch (reason) {
		case "stop":
		case "end":
			return { stopReason: "stop" };
		case "length":
			return { stopReason: "length" };
		case "function_call":
		case "tool_calls":
			return { stopReason: "toolUse" };
		case "content_filter":
			return { stopReason: "error", errorMessage: "Provider finish_reason: content_filter" };
		case "network_error":
			return { stopReason: "error", errorMessage: "Provider finish_reason: network_error" };
		default:
			return {
				stopReason: "error",
				errorMessage: `Provider finish_reason: ${reason}`,
			};
	}
}

/**
 * Detect compatibility settings from provider and baseUrl for known providers.
 * Provider takes precedence over URL-based detection since it's explicitly configured.
 * Returns a fully resolved OpenAICompletionsCompat object with all fields set.
 */
function detectCompat(model: Model<"openai-completions">): ResolvedOpenAICompletionsCompat {
	const provider = model.provider;
	const baseUrl = model.baseUrl;

	const isZai = provider === "zai" || baseUrl.includes("api.z.ai");
	const isTogether =
		provider === "together" || baseUrl.includes("api.together.ai") || baseUrl.includes("api.together.xyz");
	const isMoonshot = provider === "moonshotai" || provider === "moonshotai-cn" || baseUrl.includes("api.moonshot.");
	const isCloudflareWorkersAI = provider === "cloudflare-workers-ai" || baseUrl.includes("api.cloudflare.com");
	const isCloudflareAiGateway = provider === "cloudflare-ai-gateway" || baseUrl.includes("gateway.ai.cloudflare.com");

	const isNonStandard =
		provider === "cerebras" ||
		baseUrl.includes("cerebras.ai") ||
		provider === "xai" ||
		baseUrl.includes("api.x.ai") ||
		isTogether ||
		baseUrl.includes("chutes.ai") ||
		baseUrl.includes("deepseek.com") ||
		isZai ||
		isMoonshot ||
		provider === "opencode" ||
		baseUrl.includes("opencode.ai") ||
		isCloudflareWorkersAI ||
		isCloudflareAiGateway;

	const useMaxTokens = baseUrl.includes("chutes.ai") || isMoonshot || isCloudflareAiGateway || isTogether;

	const isGrok = provider === "xai" || baseUrl.includes("api.x.ai");
	const isDeepSeek = provider === "deepseek" || baseUrl.includes("deepseek.com");
	const cacheControlFormat = provider === "openrouter" && model.id.startsWith("anthropic/") ? "anthropic" : undefined;

	return {
		supportsStore: !isNonStandard,
		supportsDeveloperRole: !isNonStandard,
		supportsReasoningEffort: !isGrok && !isZai && !isMoonshot && !isTogether && !isCloudflareAiGateway,
		supportsUsageInStreaming: true,
		maxTokensField: useMaxTokens ? "max_tokens" : "max_completion_tokens",
		requiresToolResultName: false,
		requiresAssistantAfterToolResult: false,
		requiresThinkingAsText: false,
		requiresReasoningContentOnAssistantMessages: isDeepSeek,
		thinkingFormat: isDeepSeek
			? "deepseek"
			: isZai
				? "zai"
				: isTogether
					? "together"
					: provider === "openrouter" || baseUrl.includes("openrouter.ai")
						? "openrouter"
						: "openai",
		openRouterRouting: {},
		vercelGatewayRouting: {},
		zaiToolStream: false,
		supportsStrictMode: !isMoonshot && !isTogether && !isCloudflareAiGateway,
		cacheControlFormat,
		sendSessionAffinityHeaders: false,
		supportsLongCacheRetention: !(isTogether || isCloudflareWorkersAI || isCloudflareAiGateway),
	};
}

/**
 * Get resolved compatibility settings for a model.
 * Uses explicit model.compat if provided, otherwise auto-detects from provider/URL.
 */
function getCompat(model: Model<"openai-completions">): ResolvedOpenAICompletionsCompat {
	const detected = detectCompat(model);
	if (!model.compat) return detected;

	return {
		supportsStore: model.compat.supportsStore ?? detected.supportsStore,
		supportsDeveloperRole: model.compat.supportsDeveloperRole ?? detected.supportsDeveloperRole,
		supportsReasoningEffort: model.compat.supportsReasoningEffort ?? detected.supportsReasoningEffort,
		supportsUsageInStreaming: model.compat.supportsUsageInStreaming ?? detected.supportsUsageInStreaming,
		maxTokensField: model.compat.maxTokensField ?? detected.maxTokensField,
		requiresToolResultName: model.compat.requiresToolResultName ?? detected.requiresToolResultName,
		requiresAssistantAfterToolResult:
			model.compat.requiresAssistantAfterToolResult ?? detected.requiresAssistantAfterToolResult,
		requiresThinkingAsText: model.compat.requiresThinkingAsText ?? detected.requiresThinkingAsText,
		requiresReasoningContentOnAssistantMessages:
			model.compat.requiresReasoningContentOnAssistantMessages ??
			detected.requiresReasoningContentOnAssistantMessages,
		thinkingFormat: model.compat.thinkingFormat ?? detected.thinkingFormat,
		openRouterRouting: model.compat.openRouterRouting ?? {},
		vercelGatewayRouting: model.compat.vercelGatewayRouting ?? detected.vercelGatewayRouting,
		zaiToolStream: model.compat.zaiToolStream ?? detected.zaiToolStream,
		supportsStrictMode: model.compat.supportsStrictMode ?? detected.supportsStrictMode,
		cacheControlFormat: model.compat.cacheControlFormat ?? detected.cacheControlFormat,
		sendSessionAffinityHeaders: model.compat.sendSessionAffinityHeaders ?? detected.sendSessionAffinityHeaders,
		supportsLongCacheRetention: model.compat.supportsLongCacheRetention ?? detected.supportsLongCacheRetention,
	};
}
