gemini-rust 1.7.1

use reqwest::Url;
use serde::{Deserialize, Serialize};
use time::OffsetDateTime;

use crate::{
    safety::{SafetyRating, SafetySetting},
    Content, Modality, Part,
};

/// Reason why generation finished
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
#[serde(rename_all = "SCREAMING_SNAKE_CASE")]
pub enum FinishReason {
    /// Default value. This value is unused.
    FinishReasonUnspecified,
    /// Natural stop point of the model or provided stop sequence.
    Stop,
    /// The maximum number of tokens as specified in the request was reached.
    MaxTokens,
    /// The response candidate content was flagged for safety reasons.
    Safety,
    /// The response candidate content was flagged for recitation reasons.
    Recitation,
    /// The response candidate content was flagged for using an unsupported language.
    Language,
    /// Unknown reason.
    Other,
    /// Token generation stopped because the content contains forbidden terms.
    Blocklist,
    /// Token generation stopped for potentially containing prohibited content.
    ProhibitedContent,
    /// Token generation stopped because the content potentially contains Sensitive Personally Identifiable Information (SPII).
    Spii,
    /// The function call generated by the model is invalid.
    MalformedFunctionCall,
    /// Token generation stopped because generated images contain safety violations.
    ImageSafety,
    /// Model generated a tool call but no tools were enabled in the request.
    UnexpectedToolCall,
    /// Model called too many tools consecutively, thus the system exited execution.
    TooManyToolCalls,
}

/// Citation metadata for content
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
#[serde(rename_all = "camelCase")]
pub struct CitationMetadata {
    /// The citation sources
    pub citation_sources: Vec<CitationSource>,
}

/// Citation source
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
#[serde(rename_all = "camelCase")]
pub struct CitationSource {
    /// The URI of the citation source
    pub uri: Option<String>,
    /// The title of the citation source
    pub title: Option<String>,
    /// The start index of the citation in the response
    pub start_index: Option<i32>,
    /// The end index of the citation in the response
    pub end_index: Option<i32>,
    /// The license of the citation source
    pub license: Option<String>,
    /// The publication date of the citation source
    #[serde(default, with = "time::serde::rfc3339::option")]
    pub publication_date: Option<OffsetDateTime>,
}

/// A candidate response
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
#[serde(rename_all = "camelCase")]
pub struct Candidate {
    /// The content of the candidate
    #[serde(default)]
    pub content: Content,
    /// The safety ratings for the candidate
    #[serde(skip_serializing_if = "Option::is_none")]
    pub safety_ratings: Option<Vec<SafetyRating>>,
    /// The citation metadata for the candidate
    #[serde(skip_serializing_if = "Option::is_none")]
    pub citation_metadata: Option<CitationMetadata>,
    /// The grounding metadata for the candidate
    #[serde(skip_serializing_if = "Option::is_none")]
    pub grounding_metadata: Option<GroundingMetadata>,
    /// The finish reason for the candidate
    #[serde(skip_serializing_if = "Option::is_none")]
    pub finish_reason: Option<FinishReason>,
    /// The index of the candidate
    #[serde(skip_serializing_if = "Option::is_none")]
    pub index: Option<i32>,
}

/// Metadata about token usage
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
#[serde(rename_all = "camelCase")]
pub struct UsageMetadata {
    /// The number of prompt tokens (null if request processing failed)
    #[serde(skip_serializing_if = "Option::is_none")]
    pub prompt_token_count: Option<i32>,
    /// The number of response tokens (null if generation failed)
    #[serde(skip_serializing_if = "Option::is_none")]
    pub candidates_token_count: Option<i32>,
    /// The total number of tokens (null if individual counts unavailable)
    #[serde(skip_serializing_if = "Option::is_none")]
    pub total_token_count: Option<i32>,
    /// The number of thinking tokens (Gemini 2.5 series only)
    #[serde(skip_serializing_if = "Option::is_none")]
    pub thoughts_token_count: Option<i32>,
    /// Detailed prompt token information
    #[serde(skip_serializing_if = "Option::is_none")]
    pub prompt_tokens_details: Option<Vec<PromptTokenDetails>>,
    /// The number of cached content tokens (batch API)
    #[serde(skip_serializing_if = "Option::is_none")]
    pub cached_content_token_count: Option<i32>,
    /// Detailed cache token information (batch API)
    #[serde(skip_serializing_if = "Option::is_none")]
    pub cache_tokens_details: Option<Vec<PromptTokenDetails>>,
}

/// Details about prompt tokens by modality
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
#[serde(rename_all = "camelCase")]
pub struct PromptTokenDetails {
    /// The modality (e.g., "TEXT")
    pub modality: Modality,
    /// Token count for this modality
    pub token_count: i32,
}

/// Grounding metadata for responses that use grounding tools
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
#[serde(rename_all = "camelCase")]
pub struct GroundingMetadata {
    /// Grounding chunks containing source information
    #[serde(skip_serializing_if = "Option::is_none")]
    pub grounding_chunks: Option<Vec<GroundingChunk>>,
    /// Grounding supports connecting response text to sources
    #[serde(skip_serializing_if = "Option::is_none")]
    pub grounding_supports: Option<Vec<GroundingSupport>>,
    /// Web search queries used for grounding
    #[serde(skip_serializing_if = "Option::is_none")]
    pub web_search_queries: Option<Vec<String>>,
    /// Google Maps widget context token
    #[serde(skip_serializing_if = "Option::is_none")]
    pub google_maps_widget_context_token: Option<String>,
}

/// A chunk of grounding information from a source
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
#[serde(rename_all = "camelCase")]
pub struct GroundingChunk {
    /// Maps-specific grounding information
    #[serde(skip_serializing_if = "Option::is_none")]
    pub maps: Option<MapsGroundingChunk>,
    /// Web-specific grounding information
    #[serde(skip_serializing_if = "Option::is_none")]
    pub web: Option<WebGroundingChunk>,
}

/// Maps-specific grounding chunk information
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
#[serde(rename_all = "camelCase")]
pub struct MapsGroundingChunk {
    /// The URI of the Maps source
    pub uri: Url,
    /// The title of the Maps source
    pub title: String,
    /// The place ID from Google Maps
    #[serde(skip_serializing_if = "Option::is_none")]
    pub place_id: Option<String>,
}

/// Web-specific grounding chunk information
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
#[serde(rename_all = "camelCase")]
pub struct WebGroundingChunk {
    /// The URI of the web source
    pub uri: Url,
    /// The title of the web source
    pub title: String,
}

/// Support information connecting response text to grounding sources
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
#[serde(rename_all = "camelCase")]
pub struct GroundingSupport {
    /// Segment of the response text
    pub segment: GroundingSegment,
    /// Indices of grounding chunks that support this segment
    pub grounding_chunk_indices: Vec<u32>,
}

/// A segment of response text
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
#[serde(rename_all = "camelCase")]
pub struct GroundingSegment {
    /// Start index of the segment in the response text
    #[serde(skip_serializing_if = "Option::is_none")]
    pub start_index: Option<u32>,
    /// End index of the segment in the response text
    #[serde(skip_serializing_if = "Option::is_none")]
    pub end_index: Option<u32>,
    /// The text content of the segment
    #[serde(skip_serializing_if = "Option::is_none")]
    pub text: Option<String>,
}

/// Response from the Gemini API for content generation
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
#[serde(rename_all = "camelCase")]
pub struct GenerationResponse {
    /// The candidates generated
    #[serde(default, skip_serializing_if = "Vec::is_empty")]
    pub candidates: Vec<Candidate>,
    /// The prompt feedback
    #[serde(skip_serializing_if = "Option::is_none")]
    pub prompt_feedback: Option<PromptFeedback>,
    /// Usage metadata
    #[serde(skip_serializing_if = "Option::is_none")]
    pub usage_metadata: Option<UsageMetadata>,
    /// Model version used
    #[serde(skip_serializing_if = "Option::is_none")]
    pub model_version: Option<String>,
    /// Response ID
    #[serde(skip_serializing_if = "Option::is_none")]
    pub response_id: Option<String>,
}

/// Reason why content was blocked
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
#[serde(rename_all = "SCREAMING_SNAKE_CASE")]
pub enum BlockReason {
    /// Default value. This value is unused.
    BlockReasonUnspecified,
    /// Prompt was blocked due to safety reasons. Inspect safetyRatings to understand which safety category blocked it.
    Safety,
    /// Prompt was blocked due to unknown reasons.
    Other,
    /// Prompt was blocked due to the terms which are included from the terminology blocklist.
    Blocklist,
    /// Prompt was blocked due to prohibited content.
    ProhibitedContent,
    /// Candidates blocked due to unsafe image generation content.
    ImageSafety,
}

/// Feedback about the prompt
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
#[serde(rename_all = "camelCase")]
pub struct PromptFeedback {
    /// The safety ratings for the prompt
    #[serde(default, skip_serializing_if = "Vec::is_empty")]
    pub safety_ratings: Vec<SafetyRating>,
    /// The block reason if the prompt was blocked
    #[serde(skip_serializing_if = "Option::is_none")]
    pub block_reason: Option<BlockReason>,
}

impl GenerationResponse {
    /// Get the text of the first candidate
    pub fn text(&self) -> String {
        self.candidates
            .first()
            .and_then(|c| {
                c.content.parts.as_ref().and_then(|parts| {
                    parts.first().and_then(|p| match p {
                        Part::Text {
                            text,
                            thought: _,
                            thought_signature: _,
                        } => Some(text.clone()),
                        _ => None,
                    })
                })
            })
            .unwrap_or_default()
    }

    /// Get function calls from the response
    pub fn function_calls(&self) -> Vec<&crate::tools::FunctionCall> {
        self.candidates
            .iter()
            .flat_map(|c| {
                c.content
                    .parts
                    .as_ref()
                    .map(|parts| {
                        parts
                            .iter()
                            .filter_map(|p| match p {
                                Part::FunctionCall {
                                    function_call,
                                    thought_signature: _,
                                } => Some(function_call),
                                _ => None,
                            })
                            .collect::<Vec<_>>()
                    })
                    .unwrap_or_default()
            })
            .collect()
    }

    /// Get function calls with their thought signatures from the response
    pub fn function_calls_with_thoughts(
        &self,
    ) -> Vec<(&crate::tools::FunctionCall, Option<&String>)> {
        self.candidates
            .iter()
            .flat_map(|c| {
                c.content
                    .parts
                    .as_ref()
                    .map(|parts| {
                        parts
                            .iter()
                            .filter_map(|p| match p {
                                Part::FunctionCall {
                                    function_call,
                                    thought_signature,
                                } => Some((function_call, thought_signature.as_ref())),
                                _ => None,
                            })
                            .collect::<Vec<_>>()
                    })
                    .unwrap_or_default()
            })
            .collect()
    }

    /// Get thought summaries from the response
    pub fn thoughts(&self) -> Vec<String> {
        self.candidates
            .iter()
            .flat_map(|c| {
                c.content
                    .parts
                    .as_ref()
                    .map(|parts| {
                        parts
                            .iter()
                            .filter_map(|p| match p {
                                Part::Text {
                                    text,
                                    thought: Some(true),
                                    thought_signature: _,
                                } => Some(text.clone()),
                                _ => None,
                            })
                            .collect::<Vec<_>>()
                    })
                    .unwrap_or_default()
            })
            .collect()
    }

    /// Get all text parts (both regular text and thoughts)
    pub fn all_text(&self) -> Vec<(String, bool)> {
        self.candidates
            .iter()
            .flat_map(|c| {
                c.content
                    .parts
                    .as_ref()
                    .map(|parts| {
                        parts
                            .iter()
                            .filter_map(|p| match p {
                                Part::Text {
                                    text,
                                    thought,
                                    thought_signature: _,
                                } => Some((text.clone(), thought.unwrap_or(false))),
                                _ => None,
                            })
                            .collect::<Vec<_>>()
                    })
                    .unwrap_or_default()
            })
            .collect()
    }

    /// Get text parts with their thought signatures from the response
    pub fn text_with_thoughts(&self) -> Vec<(String, bool, Option<&String>)> {
        self.candidates
            .iter()
            .flat_map(|c| {
                c.content
                    .parts
                    .as_ref()
                    .map(|parts| {
                        parts
                            .iter()
                            .filter_map(|p| match p {
                                Part::Text {
                                    text,
                                    thought,
                                    thought_signature,
                                } => Some((
                                    text.clone(),
                                    thought.unwrap_or(false),
                                    thought_signature.as_ref(),
                                )),
                                _ => None,
                            })
                            .collect::<Vec<_>>()
                    })
                    .unwrap_or_default()
            })
            .collect()
    }
}

/// Request to generate content
#[derive(Debug, Clone, Serialize, Deserialize)]
#[serde(rename_all = "camelCase")]
pub struct GenerateContentRequest {
    /// The contents to generate content from
    pub contents: Vec<Content>,
    /// The generation config
    #[serde(skip_serializing_if = "Option::is_none")]
    pub generation_config: Option<GenerationConfig>,
    /// The safety settings
    #[serde(skip_serializing_if = "Option::is_none")]
    pub safety_settings: Option<Vec<SafetySetting>>,
    /// The tools that the model can use
    #[serde(skip_serializing_if = "Option::is_none")]
    pub tools: Option<Vec<crate::tools::Tool>>,
    /// The tool config
    #[serde(skip_serializing_if = "Option::is_none")]
    pub tool_config: Option<crate::tools::ToolConfig>,
    /// The system instruction
    #[serde(skip_serializing_if = "Option::is_none")]
    pub system_instruction: Option<Content>,
    /// The cached content to use
    #[serde(skip_serializing_if = "Option::is_none")]
    pub cached_content: Option<String>,
}

/// Thinking level for Gemini 3 series models
///
/// Controls the depth of reasoning and analysis that the model applies.
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
#[serde(rename_all = "SCREAMING_SNAKE_CASE")]
pub enum ThinkingLevel {
    /// Unspecified thinking level (uses model default)
    ThinkingLevelUnspecified,
    /// Minimal thinking level - fastest responses with minimal reasoning
    Minimal,
    /// Low thinking level - faster responses with less reasoning
    Low,
    /// Medium thinking level - balanced reasoning depth
    Medium,
    /// High thinking level - deeper analysis with more comprehensive reasoning
    High,
}

/// Media resolution level for images and PDFs
///
/// Controls the resolution used when processing inline images and PDF documents,
/// which affects both quality and token consumption.
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
#[serde(rename_all = "SCREAMING_SNAKE_CASE")]
pub enum MediaResolutionLevel {
    /// Unspecified resolution (uses model default)
    MediaResolutionUnspecified,
    /// Low resolution - uses fewer tokens, lower quality
    MediaResolutionLow,
    /// Medium resolution - balanced token usage and quality
    MediaResolutionMedium,
    /// High resolution - uses more tokens, higher quality
    MediaResolutionHigh,
}

/// Wrapper struct for per-part media resolution.
/// Allows fine-grained control over the resolution used for individual inline images and PDFs.
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub struct MediaResolution {
    /// The media resolution level to use
    pub level: MediaResolutionLevel,
}

/// Configuration for thinking (Gemini 2.5 and Gemini 3 series)
///
/// - For Gemini 2.5 models, use `thinking_budget` and `include_thoughts`.
/// - For Gemini 3 models, use `thinking_level` (mutually exclusive with `thinking_budget`).
#[derive(Debug, Clone, Serialize, Deserialize)]
#[serde(rename_all = "camelCase")]
pub struct ThinkingConfig {
    /// The thinking budget (number of thinking tokens)
    ///
    /// - Set to 0 to disable thinking
    /// - Set to -1 for dynamic thinking (model decides)
    /// - Set to a positive number for a specific token budget
    ///
    /// Model-specific ranges:
    /// - 2.5 Pro: 128 to 32768 (cannot disable thinking)
    /// - 2.5 Flash: 0 to 24576
    /// - 2.5 Flash Lite: 512 to 24576
    #[serde(skip_serializing_if = "Option::is_none")]
    pub thinking_budget: Option<i32>,

    /// Whether to include thought summaries in the response
    ///
    /// When enabled, the response will include synthesized versions of the model's
    /// raw thoughts, providing insights into the reasoning process.
    #[serde(skip_serializing_if = "Option::is_none")]
    pub include_thoughts: Option<bool>,

    /// The thinking level (Required for Gemini 3)
    ///
    /// Gemini 3 uses thinking_level (Low/High) which is mutually exclusive with thinking_budget
    #[serde(skip_serializing_if = "Option::is_none")]
    pub thinking_level: Option<ThinkingLevel>,
}

impl ThinkingConfig {
    /// Create a new thinking config with default settings
    pub fn new() -> Self {
        Self {
            thinking_budget: None,
            include_thoughts: None,
            thinking_level: None,
        }
    }

    /// Set the thinking budget
    pub fn with_thinking_budget(mut self, budget: i32) -> Self {
        self.thinking_budget = Some(budget);
        self
    }

    /// Enable dynamic thinking (model decides the budget)
    pub fn with_dynamic_thinking(mut self) -> Self {
        self.thinking_budget = Some(-1);
        self
    }

    /// Include thought summaries in the response
    pub fn with_thoughts_included(mut self, include: bool) -> Self {
        self.include_thoughts = Some(include);
        self
    }

    /// Set the thinking level (Required for Gemini 3)
    pub fn with_thinking_level(mut self, level: ThinkingLevel) -> Self {
        self.thinking_level = Some(level);
        self
    }

    /// Create a thinking config that enables dynamic thinking with thoughts included
    pub fn dynamic_thinking() -> Self {
        Self {
            thinking_budget: Some(-1),
            include_thoughts: Some(true),
            thinking_level: None,
        }
    }
}

impl Default for ThinkingConfig {
    fn default() -> Self {
        Self::new()
    }
}

/// Configuration for generation
#[derive(Debug, Default, Clone, Serialize, Deserialize)]
#[serde(rename_all = "camelCase")]
pub struct GenerationConfig {
    /// The temperature for the model (0.0 to 1.0)
    ///
    /// Controls the randomness of the output. Higher values (e.g., 0.9) make output
    /// more random, lower values (e.g., 0.1) make output more deterministic.
    #[serde(skip_serializing_if = "Option::is_none")]
    pub temperature: Option<f32>,

    /// The top-p value for the model (0.0 to 1.0)
    ///
    /// For each token generation step, the model considers the top_p percentage of
    /// probability mass for potential token choices. Lower values are more selective,
    /// higher values allow more variety.
    #[serde(skip_serializing_if = "Option::is_none")]
    pub top_p: Option<f32>,

    /// The top-k value for the model
    ///
    /// For each token generation step, the model considers the top_k most likely tokens.
    /// Lower values are more selective, higher values allow more variety.
    #[serde(skip_serializing_if = "Option::is_none")]
    pub top_k: Option<i32>,

    /// Seed used in decoding.
    ///
    /// By default, the model uses a random value for each request if a seed is not provided.
    /// Setting a specific seed, along with consistent values for other parameters like temperature, can make the model return the same response for repeated requests with the same input.
    /// Identical outputs are not guaranteed across all runs, due to backend infrastructure variations, but it provides a "best effort" for reproducibility.
    #[serde(skip_serializing_if = "Option::is_none")]
    pub seed: Option<i32>,

    /// The maximum number of tokens to generate
    ///
    /// Limits the length of the generated content. One token is roughly 4 characters.
    #[serde(skip_serializing_if = "Option::is_none")]
    pub max_output_tokens: Option<i32>,

    /// The candidate count
    ///
    /// Number of alternative responses to generate.
    #[serde(skip_serializing_if = "Option::is_none")]
    pub candidate_count: Option<i32>,

    /// Whether to stop on specific sequences
    ///
    /// The model will stop generating content when it encounters any of these sequences.
    #[serde(skip_serializing_if = "Option::is_none")]
    pub stop_sequences: Option<Vec<String>>,

    /// The response mime type
    ///
    /// Specifies the format of the model's response.
    #[serde(skip_serializing_if = "Option::is_none")]
    pub response_mime_type: Option<String>,
    /// The response schema
    ///
    /// Specifies the JSON schema for structured responses.
    #[serde(skip_serializing_if = "Option::is_none")]
    pub response_schema: Option<serde_json::Value>,

    /// Response modalities (for TTS and other multimodal outputs)
    #[serde(skip_serializing_if = "Option::is_none")]
    pub response_modalities: Option<Vec<String>>,

    /// Optional. Config for image generation. An error will be returned if this field is set for models
    /// that don't support these config options.
    #[serde(skip_serializing_if = "Option::is_none")]
    pub image_config: Option<ImageConfig>,

    /// Speech configuration for text-to-speech generation
    #[serde(skip_serializing_if = "Option::is_none")]
    pub speech_config: Option<SpeechConfig>,

    /// The thinking configuration
    ///
    /// Configuration for the model's thinking process (Gemini 2.5 and Gemini 3 series).
    #[serde(skip_serializing_if = "Option::is_none")]
    pub thinking_config: Option<ThinkingConfig>,

    /// Global media resolution for all images and PDFs.
    /// Controls the resolution used for inline image and PDF data, affecting token usage.
    /// Can be overridden per-part using the Part::InlineData media_resolution field.
    #[serde(skip_serializing_if = "Option::is_none", rename = "media_resolution")]
    pub media_resolution: Option<MediaResolutionLevel>,
}

/// Response from the Gemini API for token counting
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
#[serde(rename_all = "camelCase")]
pub struct CountTokensResponse {
    /// The total number of tokens counted across all instances.
    pub total_tokens: u32,
    /// The total number of tokens in the cached content.
    #[serde(skip_serializing_if = "Option::is_none")]
    pub cached_content_token_count: Option<u32>,
}

/// Config for image generation features.
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
#[serde(rename_all = "camelCase")]
pub struct ImageConfig {
    /// Optional. The aspect ratio of the image to generate. Supported aspect ratios: 1:1, 2:3, 3:2, 3:4,
    /// 4:3, 9:16, 16:9, 21:9.
    ///
    /// If not specified, the model will choose a default aspect ratio based on any reference images
    /// provided.
    #[serde(skip_serializing_if = "Option::is_none")]
    pub aspect_ratio: Option<String>,
    /// Optional. Specifies the size of generated images. Supported values are `1K`, `2K`, `4K`. If not
    /// specified, the model will use default value `1K`.
    #[serde(skip_serializing_if = "Option::is_none")]
    pub image_size: Option<String>,
}

/// Configuration for speech generation (text-to-speech)
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
#[serde(rename_all = "camelCase")]
pub struct SpeechConfig {
    /// Single voice configuration
    #[serde(skip_serializing_if = "Option::is_none")]
    pub voice_config: Option<VoiceConfig>,
    /// Multi-speaker voice configuration
    #[serde(skip_serializing_if = "Option::is_none")]
    pub multi_speaker_voice_config: Option<MultiSpeakerVoiceConfig>,
}

/// Voice configuration for text-to-speech
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
#[serde(rename_all = "camelCase")]
pub struct VoiceConfig {
    /// Prebuilt voice configuration
    #[serde(skip_serializing_if = "Option::is_none")]
    pub prebuilt_voice_config: Option<PrebuiltVoiceConfig>,
}

/// Prebuilt voice configuration
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
#[serde(rename_all = "camelCase")]
pub struct PrebuiltVoiceConfig {
    /// The name of the voice to use
    pub voice_name: String,
}

/// Multi-speaker voice configuration
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
#[serde(rename_all = "camelCase")]
pub struct MultiSpeakerVoiceConfig {
    /// Configuration for each speaker
    pub speaker_voice_configs: Vec<SpeakerVoiceConfig>,
}

/// Configuration for a specific speaker in multi-speaker TTS
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
#[serde(rename_all = "camelCase")]
pub struct SpeakerVoiceConfig {
    /// The name of the speaker (must match the name used in the prompt)
    pub speaker: String,
    /// Voice configuration for this speaker
    pub voice_config: VoiceConfig,
}

impl SpeechConfig {
    /// Create a new speech config with a single voice
    pub fn single_voice(voice_name: impl Into<String>) -> Self {
        Self {
            voice_config: Some(VoiceConfig {
                prebuilt_voice_config: Some(PrebuiltVoiceConfig {
                    voice_name: voice_name.into(),
                }),
            }),
            multi_speaker_voice_config: None,
        }
    }

    /// Create a new speech config with multiple speakers
    pub fn multi_speaker(speakers: Vec<SpeakerVoiceConfig>) -> Self {
        Self {
            voice_config: None,
            multi_speaker_voice_config: Some(MultiSpeakerVoiceConfig {
                speaker_voice_configs: speakers,
            }),
        }
    }
}

impl SpeakerVoiceConfig {
    /// Create a new speaker voice configuration
    pub fn new(speaker: impl Into<String>, voice_name: impl Into<String>) -> Self {
        Self {
            speaker: speaker.into(),
            voice_config: VoiceConfig {
                prebuilt_voice_config: Some(PrebuiltVoiceConfig {
                    voice_name: voice_name.into(),
                }),
            },
        }
    }
}