package workflow

import (
	"bufio"
	"bytes"
	"context"
	"encoding/json"
	"fmt"
	"io"
	"net/http"
	"strings"
	"time"
)

// OpenAIAdapter implements LLMAdapter for OpenAI-compatible server
type OpenAIAdapter struct {
	baseURL       string
	apiKey        string
	model         string
	cacheControl  *bool
	requestAPIKey string
	httpClient    *http.Client
}

// OpenAIConfig holds configuration for OpenAI-compatible adapter
type OpenAIConfig struct {
	BaseURL       string        // OpenAI-compatible server URL (e.g., "http://localhost:4000")
	APIKey        string        // Optional API key for Authorization header
	Model         string        // Optional model override (overwrites model in LLM requests)
	CacheControl  *bool         // Optional cache_control override (overwrites cache_control in LLM requests)
	RequestAPIKey string        // Optional API key sent in request body as "api_key" (for BYOK passthrough)
	Timeout       time.Duration // HTTP timeout (default: 5 minutes)
}

// NewOpenAIAdapter creates a new OpenAI-compatible adapter
func NewOpenAIAdapter(config OpenAIConfig) *OpenAIAdapter {
	timeout := config.Timeout
	if timeout == 0 {
		timeout = 5 * time.Minute
	}

	baseURL := strings.TrimSuffix(config.BaseURL, "/")
	if baseURL == "" {
		baseURL = "http://localhost:4000"
	}

	return &OpenAIAdapter{
		baseURL:       baseURL,
		apiKey:        config.APIKey,
		model:         config.Model,
		cacheControl:  config.CacheControl,
		requestAPIKey: config.RequestAPIKey,
		httpClient: &http.Client{
			Timeout: timeout,
		},
	}
}

// ResponseFormat represents the response format configuration (OpenAI style)
type ResponseFormat struct {
	Type       string      `json:"type"`                  // "json_schema" or "text"
	JSONSchema *JSONSchema `json:"json_schema,omitempty"` // Only when type is "json_schema"
}

// JSONSchema represents the JSON Schema configuration
type JSONSchema struct {
	Name        string                 `json:"name"`                  // Schema name identifier
	Description string                 `json:"description,omitempty"` // Optional description
	Schema      map[string]interface{} `json:"schema"`                // JSON Schema object
	Strict      bool                   `json:"strict,omitempty"`      // Optional strict mode
}

// OutputConfig represents the output configuration (Anthropic style)
type OutputConfig struct {
	Format *OutputFormat `json:"format,omitempty"`
}

// OutputFormat represents the format configuration for Anthropic
type OutputFormat struct {
	Type   string                 `json:"type"`            // "json_schema" or "text"
	Schema map[string]interface{} `json:"schema,omitempty"` // JSON Schema object (when type is "json_schema")
}

// ChatCompletionRequest represents the OpenAI-compatible chat request
type ChatCompletionRequest struct {
	Model         string                   `json:"model"`
	Messages      []ChatMessage            `json:"messages"`
	Stream        bool                     `json:"stream,omitempty"`
	Temperature   *float64                 `json:"temperature,omitempty"`
	MaxTokens     *int                     `json:"max_tokens,omitempty"`
	TopP          *float64                 `json:"top_p,omitempty"`
	Stop          []string                 `json:"stop,omitempty"`
	Tools         []map[string]interface{} `json:"tools,omitempty"`
	APIKey        string                   `json:"api_key,omitempty"`
	ResponseFormat *ResponseFormat         `json:"response_format,omitempty"`  // OpenAI style
	OutputConfig   *OutputConfig           `json:"output_config,omitempty"`    // Anthropic style
}

// CacheControl represents Anthropic's cache control directive
type CacheControl struct {
	Type string `json:"type"`
}

// ChatMessage represents a message in the chat
type ChatMessage struct {
	Role         string        `json:"role"`
	Content      string        `json:"content"`
	CacheControl *CacheControl `json:"cache_control,omitempty"`
}

// ChatCompletionResponse represents the OpenAI-compatible chat response
type ChatCompletionResponse struct {
	ID      string `json:"id"`
	Object  string `json:"object"`
	Created int64  `json:"created"`
	Model   string `json:"model"`
	Choices []struct {
		Index   int `json:"index"`
		Message struct {
			Role    string `json:"role"`
			Content string `json:"content"`
		} `json:"message"`
		FinishReason string `json:"finish_reason"`
	} `json:"choices"`
	Usage struct {
		PromptTokens     int `json:"prompt_tokens"`
		CompletionTokens int `json:"completion_tokens"`
		TotalTokens      int `json:"total_tokens"`
	} `json:"usage"`
}

// ChatCompletionStreamResponse represents a streaming response chunk
type ChatCompletionStreamResponse struct {
	ID      string `json:"id"`
	Object  string `json:"object"`
	Created int64  `json:"created"`
	Model   string `json:"model"`
	Choices []struct {
		Index int `json:"index"`
		Delta struct {
			Role    string `json:"role,omitempty"`
			Content string `json:"content,omitempty"`
		} `json:"delta"`
		FinishReason *string `json:"finish_reason"`
	} `json:"choices"`
}

// Call implements LLMAdapter interface
func (a *OpenAIAdapter) Call(ctx context.Context, params map[string]interface{}, stream chan<- string) (map[string]interface{}, error) {
	// Build request from params
	req, err := a.buildRequest(params)
	if err != nil {
		return nil, fmt.Errorf("failed to build request: %w", err)
	}

	// Check if streaming is requested
	isStreaming := false
	if streamVal, ok := params["stream"].(bool); ok {
		isStreaming = streamVal
	}
	req.Stream = isStreaming

	// Make the request
	var result map[string]interface{}
	if isStreaming && stream != nil {
		result, err = a.callStreaming(ctx, req, stream)
	} else {
		result, err = a.callNonStreaming(ctx, req)
	}
	if err != nil {
		return nil, err
	}

	// Check if structured output (json_schema) was requested
	// Check both response_format (OpenAI) and output_config (Anthropic) styles
	shouldParseJSON := false

	// Check response_format (OpenAI style)
	if responseFormat, ok := params["response_format"].(map[string]interface{}); ok {
		if formatType, ok := responseFormat["type"].(string); ok && formatType == "json_schema" {
			shouldParseJSON = true
		}
	}

	// Check output_config (Anthropic style)
	if outputConfig, ok := params["output_config"].(map[string]interface{}); ok {
		if format, ok := outputConfig["format"].(map[string]interface{}); ok {
			if formatType, ok := format["type"].(string); ok && formatType == "json_schema" {
				shouldParseJSON = true
			}
		}
	}

	// Parse content as JSON if structured output was requested
	if shouldParseJSON {
		if content, ok := result["content"].(string); ok && content != "" {
			var parsed interface{}
			if err := json.Unmarshal([]byte(content), &parsed); err != nil {
				model, _ := result["model"].(string)
				return nil, &LLMError{
					Type:      "json_parse_error",
					Code:      "JSON_PARSE_ERROR",
					Message:   fmt.Sprintf("failed to parse structured output as JSON: %v", err),
					Retryable: false,
					Model:     model,
				}
			}
			// Replace the content string with the parsed JSON object
			result["content"] = parsed
		}
	}

	return result, nil
}

// buildRequest constructs a ChatCompletionRequest from params
func (a *OpenAIAdapter) buildRequest(params map[string]interface{}) (*ChatCompletionRequest, error) {
	req := &ChatCompletionRequest{}

	// API key in request body for BYOK passthrough
	if a.requestAPIKey != "" {
		req.APIKey = a.requestAPIKey
	}

	// Model: adapter-level override takes priority, then params, then default
	// Note: In v3.9+, model should not be specified in workflow JSON - it should be
	// injected at runtime via adapter configuration (a.model). The params["model"]
	// fallback is kept for backward compatibility with v3.6/v3.7/v3.8 workflows.
	if a.model != "" {
		req.Model = a.model
	} else if model, ok := params["model"].(string); ok {
		req.Model = model
	} else {
		req.Model = "gpt-4" // default model
	}

	// Cache control: adapter-level override takes priority, then params
	cacheControl := false
	if a.cacheControl != nil {
		cacheControl = *a.cacheControl
	} else if cc, ok := params["cache_control"].(bool); ok {
		cacheControl = cc
	}

	// Messages (required)
	if messages, ok := params["messages"].([]interface{}); ok {
		for _, m := range messages {
			msg, ok := m.(map[string]interface{})
			if !ok {
				continue
			}
			chatMsg := ChatMessage{}
			if role, ok := msg["role"].(string); ok {
				chatMsg.Role = role
			}
			if content, ok := msg["content"].(string); ok {
				chatMsg.Content = content
			}
			// Apply cache_control to system messages for Anthropic models
			if cacheControl && chatMsg.Role == "system" && isAnthropicModel(req.Model) {
				chatMsg.CacheControl = &CacheControl{Type: "ephemeral"}
			}
			req.Messages = append(req.Messages, chatMsg)
		}
	}

	// Optional parameters
	if temp, ok := params["temperature"].(float64); ok {
		req.Temperature = &temp
	}
	if maxTokens, ok := params["max_tokens"].(int); ok {
		req.MaxTokens = &maxTokens
	} else if maxTokens, ok := params["max_tokens"].(float64); ok {
		mt := int(maxTokens)
		req.MaxTokens = &mt
	}
	if topP, ok := params["top_p"].(float64); ok {
		req.TopP = &topP
	}
	if stop, ok := params["stop"].([]interface{}); ok {
		for _, s := range stop {
			if str, ok := s.(string); ok {
				req.Stop = append(req.Stop, str)
			}
		}
	}
	if tools, ok := params["tools"].([]interface{}); ok {
		for _, t := range tools {
			if tool, ok := t.(map[string]interface{}); ok {
				req.Tools = append(req.Tools, tool)
			}
		}
	}

	// Handle response_format (vendor-agnostic to vendor-specific mapping)
	if responseFormat, ok := params["response_format"].(map[string]interface{}); ok {
		if err := a.applyResponseFormat(req, responseFormat); err != nil {
			return nil, fmt.Errorf("failed to apply response_format: %w", err)
		}
	}

	// Handle output_config (Anthropic style - direct passthrough)
	// This takes precedence over response_format mapping if both are present
	if outputConfig, ok := params["output_config"].(map[string]interface{}); ok {
		if err := a.applyOutputConfig(req, outputConfig); err != nil {
			return nil, fmt.Errorf("failed to apply output_config: %w", err)
		}
	}

	return req, nil
}

// applyResponseFormat maps vendor-agnostic response_format to vendor-specific format
func (a *OpenAIAdapter) applyResponseFormat(req *ChatCompletionRequest, responseFormat map[string]interface{}) error {
	// Extract type field
	formatType, ok := responseFormat["type"].(string)
	if !ok {
		return fmt.Errorf("response_format.type must be a string")
	}

	// If type is "text", no need to set anything (default behavior)
	if formatType == "text" {
		return nil
	}

	// Handle "json_schema" type
	if formatType == "json_schema" {
		// Extract json_schema object
		jsonSchemaRaw, ok := responseFormat["json_schema"].(map[string]interface{})
		if !ok {
			return fmt.Errorf("response_format.json_schema must be an object when type is json_schema")
		}

		// Build JSONSchema struct
		jsonSchema := &JSONSchema{}

		if name, ok := jsonSchemaRaw["name"].(string); ok {
			jsonSchema.Name = name
		} else {
			return fmt.Errorf("response_format.json_schema.name is required")
		}

		if desc, ok := jsonSchemaRaw["description"].(string); ok {
			jsonSchema.Description = desc
		}

		if schema, ok := jsonSchemaRaw["schema"].(map[string]interface{}); ok {
			jsonSchema.Schema = schema
		} else {
			return fmt.Errorf("response_format.json_schema.schema is required")
		}

		if strict, ok := jsonSchemaRaw["strict"].(bool); ok {
			jsonSchema.Strict = strict
		}

		// Determine vendor and apply appropriate format
		if isAnthropicModel(req.Model) {
			// Anthropic uses output_config with schema directly in format
			req.OutputConfig = &OutputConfig{
				Format: &OutputFormat{
					Type:   "json_schema",
					Schema: jsonSchema.Schema,
				},
			}
		} else {
			// OpenAI and others use response_format with json_schema wrapper
			req.ResponseFormat = &ResponseFormat{
				Type:       "json_schema",
				JSONSchema: jsonSchema,
			}
		}
	}

	return nil
}

// applyOutputConfig applies output_config directly from params (Anthropic style)
// This is a direct passthrough - the output_config structure in the workflow JSON
// must match the target LLM vendor's API format exactly
func (a *OpenAIAdapter) applyOutputConfig(req *ChatCompletionRequest, outputConfig map[string]interface{}) error {
	// Extract format field
	format, ok := outputConfig["format"].(map[string]interface{})
	if !ok {
		return fmt.Errorf("output_config.format must be an object")
	}

	// Extract type field
	formatType, ok := format["type"].(string)
	if !ok {
		return fmt.Errorf("output_config.format.type must be a string")
	}

	// If type is "text", no need to set anything (default behavior)
	if formatType == "text" {
		return nil
	}

	// Handle "json_schema" type
	if formatType == "json_schema" {
		// Extract schema object
		schemaRaw, ok := format["schema"].(map[string]interface{})
		if !ok {
			// Provide more detailed error information
			schemaVal, exists := format["schema"]
			if !exists {
				return fmt.Errorf("output_config.format.schema is required when type is json_schema (field is missing)")
			}
			return fmt.Errorf("output_config.format.schema must be an object when type is json_schema (got %T: %v)", schemaVal, schemaVal)
		}

		// For Anthropic models, use output_config structure with schema directly
		req.OutputConfig = &OutputConfig{
			Format: &OutputFormat{
				Type:   "json_schema",
				Schema: schemaRaw,
			},
		}
	}

	return nil
}

// callNonStreaming makes a non-streaming request
func (a *OpenAIAdapter) callNonStreaming(ctx context.Context, req *ChatCompletionRequest) (map[string]interface{}, error) {
	req.Stream = false

	body, err := json.Marshal(req)
	if err != nil {
		return nil, fmt.Errorf("failed to marshal request: %w", err)
	}

	httpReq, err := http.NewRequestWithContext(ctx, "POST", a.baseURL+"/chat/completions", bytes.NewReader(body))
	if err != nil {
		return nil, fmt.Errorf("failed to create request: %w", err)
	}

	httpReq.Header.Set("Content-Type", "application/json")
	if a.apiKey != "" {
		httpReq.Header.Set("Authorization", "Bearer "+a.apiKey)
	}

	resp, err := a.httpClient.Do(httpReq)
	if err != nil {
		return nil, fmt.Errorf("failed to send request: %w", err)
	}
	defer resp.Body.Close()

	if resp.StatusCode != http.StatusOK {
		bodyBytes, _ := io.ReadAll(resp.Body)
		return nil, parseOpenAIError(resp.StatusCode, bodyBytes, req.Model)
	}

	var chatResp ChatCompletionResponse
	if err := json.NewDecoder(resp.Body).Decode(&chatResp); err != nil {
		return nil, fmt.Errorf("failed to decode response: %w", err)
	}

	// Extract content from response
	content := ""
	if len(chatResp.Choices) > 0 {
		content = chatResp.Choices[0].Message.Content
	}

	return map[string]interface{}{
		"content":       content,
		"model":         chatResp.Model,
		"finish_reason": getFinishReason(chatResp.Choices),
		"response_id":   chatResp.ID,
		"usage": map[string]interface{}{
			"prompt_tokens":     chatResp.Usage.PromptTokens,
			"completion_tokens": chatResp.Usage.CompletionTokens,
			"total_tokens":      chatResp.Usage.TotalTokens,
		},
	}, nil
}

// callStreaming makes a streaming request
func (a *OpenAIAdapter) callStreaming(ctx context.Context, req *ChatCompletionRequest, stream chan<- string) (map[string]interface{}, error) {
	req.Stream = true

	body, err := json.Marshal(req)
	if err != nil {
		return nil, fmt.Errorf("failed to marshal request: %w", err)
	}

	httpReq, err := http.NewRequestWithContext(ctx, "POST", a.baseURL+"/chat/completions", bytes.NewReader(body))
	if err != nil {
		return nil, fmt.Errorf("failed to create request: %w", err)
	}

	httpReq.Header.Set("Content-Type", "application/json")
	httpReq.Header.Set("Accept", "text/event-stream")
	if a.apiKey != "" {
		httpReq.Header.Set("Authorization", "Bearer "+a.apiKey)
	}

	resp, err := a.httpClient.Do(httpReq)
	if err != nil {
		return nil, fmt.Errorf("failed to send request: %w", err)
	}
	defer resp.Body.Close()

	if resp.StatusCode != http.StatusOK {
		bodyBytes, _ := io.ReadAll(resp.Body)
		return nil, parseOpenAIError(resp.StatusCode, bodyBytes, req.Model)
	}

	// Process SSE stream
	var fullContent strings.Builder
	var finishReason string
	var model string
	var responseID string

	scanner := bufio.NewScanner(resp.Body)
	// Increase buffer size for large SSE chunks (1MB max)
	scanner.Buffer(make([]byte, 64*1024), 1024*1024)
	for scanner.Scan() {
		line := scanner.Text()

		// Skip empty lines and comments
		if line == "" || strings.HasPrefix(line, ":") {
			continue
		}

		// Parse SSE data
		if !strings.HasPrefix(line, "data: ") {
			continue
		}

		data := strings.TrimPrefix(line, "data: ")

		// Check for end of stream
		if data == "[DONE]" {
			break
		}

		var chunk ChatCompletionStreamResponse
		if err := json.Unmarshal([]byte(data), &chunk); err != nil {
			continue // Skip malformed chunks
		}

		if model == "" && chunk.Model != "" {
			model = chunk.Model
		}
		if responseID == "" && chunk.ID != "" {
			responseID = chunk.ID
		}

		// Extract content from chunk
		if len(chunk.Choices) > 0 {
			delta := chunk.Choices[0].Delta
			if delta.Content != "" {
				fullContent.WriteString(delta.Content)
				// Send chunk to stream channel
				select {
				case stream <- delta.Content:
				case <-ctx.Done():
					return nil, ctx.Err()
				}
			}
			if chunk.Choices[0].FinishReason != nil {
				finishReason = *chunk.Choices[0].FinishReason
			}
		}
	}

	if err := scanner.Err(); err != nil {
		return nil, fmt.Errorf("error reading stream: %w", err)
	}

	return map[string]interface{}{
		"content":       fullContent.String(),
		"model":         model,
		"finish_reason": finishReason,
		"response_id":   responseID,
	}, nil
}

// isAnthropicModel checks if the model string refers to an Anthropic model
func isAnthropicModel(model string) bool {
	m := strings.ToLower(model)
	return strings.HasPrefix(m, "claude") ||
		strings.HasPrefix(m, "anthropic/") ||
		strings.Contains(m, "anthropic")
}

// parseOpenAIError constructs a structured LLMError from an HTTP error response
func parseOpenAIError(statusCode int, body []byte, model string) *LLMError {
	llmErr := &LLMError{
		StatusCode: statusCode,
		Model:      model,
		Message:    string(body),
	}

	// Try to parse body as JSON for richer error info
	var errResp map[string]interface{}
	if json.Unmarshal(body, &errResp) == nil {
		llmErr.Raw = errResp
		if msg, ok := errResp["message"].(string); ok {
			llmErr.Message = msg
		}
		if errObj, ok := errResp["error"].(map[string]interface{}); ok {
			if msg, ok := errObj["message"].(string); ok {
				llmErr.Message = msg
			}
			if code, ok := errObj["code"].(string); ok {
				llmErr.Code = code
			}
			if errType, ok := errObj["type"].(string); ok {
				llmErr.ProviderError = errType
			}
		}
	}

	// Classify error type based on status code
	switch {
	case statusCode == 401 || statusCode == 403:
		llmErr.Type = "auth_error"
		llmErr.Code = "AUTH_ERROR"
		llmErr.Retryable = false
	case statusCode == 400:
		llmErr.Type = "bad_request"
		llmErr.Code = "BAD_REQUEST"
		llmErr.Retryable = false
	case statusCode == 429:
		llmErr.Type = "rate_limit"
		llmErr.Code = "RATE_LIMIT"
		llmErr.Retryable = true
	case statusCode == 408:
		llmErr.Type = "timeout"
		llmErr.Code = "TIMEOUT"
		llmErr.Retryable = true
	case statusCode >= 500:
		llmErr.Type = "service_unavailable"
		llmErr.Code = "SERVICE_UNAVAILABLE"
		llmErr.Retryable = true
	default:
		llmErr.Type = "unknown_error"
		llmErr.Code = "UNKNOWN_ERROR"
		llmErr.Retryable = false
	}

	// Refine classification based on error message content
	msgLower := strings.ToLower(llmErr.Message)
	if strings.Contains(msgLower, "context length") || strings.Contains(msgLower, "token limit") {
		llmErr.Type = "context_length_exceeded"
		llmErr.Code = "CONTEXT_LENGTH_EXCEEDED"
		llmErr.Retryable = false
	}
	if strings.Contains(msgLower, "content policy") || strings.Contains(msgLower, "content_filter") {
		llmErr.Type = "content_policy_violation"
		llmErr.Code = "CONTENT_POLICY_VIOLATION"
		llmErr.Retryable = false
	}

	// Infer provider from model name
	llmErr.Provider = inferProvider(model)

	return llmErr
}

func getFinishReason(choices []struct {
	Index   int `json:"index"`
	Message struct {
		Role    string `json:"role"`
		Content string `json:"content"`
	} `json:"message"`
	FinishReason string `json:"finish_reason"`
}) string {
	if len(choices) > 0 {
		return choices[0].FinishReason
	}
	return ""
}