use crate::error::{Result, TrustformersError};
use crate::pipeline::{BasePipeline, Pipeline};
use crate::{AutoModel, AutoTokenizer};
use serde::{Deserialize, Serialize};
use std::path::Path;
use std::sync::Arc;

/// Audio input for speech-to-text pipeline
#[derive(Debug, Clone)]
pub enum AudioInput {
    /// File path to audio file
    FilePath(String),
    /// Raw audio samples (f32) with sample rate
    RawAudio { samples: Vec<f32>, sample_rate: u32 },
    /// Base64 encoded audio data
    Base64(String),
    /// Audio bytes with format info
    Bytes {
        data: Vec<u8>,
        format: AudioFormat,
        sample_rate: u32,
    },
}

/// Supported audio formats
#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
pub enum AudioFormat {
    Wav,
    Flac,
    Mp3,
    M4a,
    Ogg,
    WebM,
}

impl AudioFormat {
    pub fn from_extension(ext: &str) -> Option<Self> {
        match ext.to_lowercase().as_str() {
            "wav" => Some(Self::Wav),
            "flac" => Some(Self::Flac),
            "mp3" => Some(Self::Mp3),
            "m4a" => Some(Self::M4a),
            "ogg" => Some(Self::Ogg),
            "webm" => Some(Self::WebM),
            _ => None,
        }
    }
}

/// Speech-to-text output
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SpeechToTextOutput {
    /// Transcribed text
    pub text: String,
    /// Confidence score (0.0 to 1.0)
    pub confidence: Option<f32>,
    /// Word-level timestamps (if supported)
    pub word_timestamps: Option<Vec<WordTimestamp>>,
    /// Language detected (if multi-language model)
    pub language: Option<String>,
    /// Processing time in milliseconds
    pub processing_time_ms: Option<u64>,
}

/// Word-level timestamp information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct WordTimestamp {
    pub word: String,
    pub start_time: f64, // seconds
    pub end_time: f64,   // seconds
    pub confidence: f32, // 0.0 to 1.0
}

/// Configuration for speech-to-text processing
#[derive(Clone, Debug)]
pub struct SpeechToTextConfig {
    /// Target sample rate for audio preprocessing
    pub sample_rate: u32,
    /// Maximum audio duration in seconds
    pub max_duration: Option<f64>,
    /// Return word-level timestamps
    pub return_timestamps: bool,
    /// Target language (for multilingual models)
    pub language: Option<String>,
    /// Task type (transcribe or translate)
    pub task: SpeechTask,
    /// Beam search configuration
    pub num_beams: usize,
    /// Use temperature sampling
    pub temperature: f32,
    /// Length penalty for beam search
    pub length_penalty: f32,
    /// Repetition penalty
    pub repetition_penalty: f32,
    /// No repeat n-gram size
    pub no_repeat_ngram_size: usize,
    /// Chunk length for long audio (in seconds)
    pub chunk_length_s: Option<f64>,
    /// Stride length for overlapping chunks
    pub stride_length_s: Option<f64>,
}

impl Default for SpeechToTextConfig {
    fn default() -> Self {
        Self {
            sample_rate: 16000,       // Whisper default
            max_duration: Some(30.0), // 30 seconds max
            return_timestamps: false,
            language: None, // Auto-detect
            task: SpeechTask::Transcribe,
            num_beams: 1,     // Greedy decoding by default
            temperature: 0.0, // Deterministic
            length_penalty: 1.0,
            repetition_penalty: 1.0,
            no_repeat_ngram_size: 0,
            chunk_length_s: Some(30.0), // 30-second chunks
            stride_length_s: Some(5.0), // 5-second stride
        }
    }
}

/// Speech recognition task type
#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
pub enum SpeechTask {
    /// Transcribe in the same language
    Transcribe,
    /// Translate to English
    Translate,
}

/// Pipeline for speech-to-text tasks (ASR)
#[derive(Clone)]
pub struct SpeechToTextPipeline {
    base: BasePipeline<AutoModel, AutoTokenizer>,
    config: SpeechToTextConfig,
    feature_extractor: Arc<AudioFeatureExtractor>,
}

impl SpeechToTextPipeline {
    /// Create a new speech-to-text pipeline
    pub fn new(model: AutoModel, tokenizer: AutoTokenizer) -> Result<Self> {
        let base = BasePipeline::new(model, tokenizer);
        let config = SpeechToTextConfig::default();
        let feature_extractor = Arc::new(AudioFeatureExtractor::new(config.sample_rate)?);

        Ok(Self {
            base,
            config,
            feature_extractor,
        })
    }

    /// Create pipeline with custom configuration
    pub fn with_config(mut self, config: SpeechToTextConfig) -> Self {
        self.config = config;
        self
    }

    /// Set target language for transcription
    pub fn with_language(mut self, language: String) -> Self {
        self.config.language = Some(language);
        self
    }

    /// Enable word-level timestamps
    pub fn with_timestamps(mut self, enable: bool) -> Self {
        self.config.return_timestamps = enable;
        self
    }

    /// Set task type (transcribe or translate)
    pub fn with_task(mut self, task: SpeechTask) -> Self {
        self.config.task = task;
        self
    }

    /// Set audio chunk length for processing long audio
    pub fn with_chunk_length(mut self, chunk_length_s: f64) -> Self {
        self.config.chunk_length_s = Some(chunk_length_s);
        self
    }

    /// Process audio file from path
    pub fn transcribe_file<P: AsRef<Path>>(&self, audio_path: P) -> Result<SpeechToTextOutput> {
        let input = AudioInput::FilePath(audio_path.as_ref().to_string_lossy().to_string());
        self.__call__(input)
    }

    /// Process raw audio samples
    pub fn transcribe_samples(
        &self,
        samples: Vec<f32>,
        sample_rate: u32,
    ) -> Result<SpeechToTextOutput> {
        let input = AudioInput::RawAudio {
            samples,
            sample_rate,
        };
        self.__call__(input)
    }

    /// Process audio in streaming fashion (for real-time)
    pub fn transcribe_streaming(&self, audio_chunk: &[f32]) -> Result<SpeechToTextOutput> {
        // For streaming, we process shorter chunks
        let input = AudioInput::RawAudio {
            samples: audio_chunk.to_vec(),
            sample_rate: self.config.sample_rate,
        };
        self.__call__(input)
    }

    /// Pre-process audio input to features
    fn preprocess_audio(&self, input: &AudioInput) -> Result<AudioFeatures> {
        match input {
            AudioInput::FilePath(path) => {
                // Load audio file and extract features
                self.feature_extractor.load_and_extract(path)
            },
            AudioInput::RawAudio {
                samples,
                sample_rate,
            } => {
                // Resample if necessary
                let resampled = if *sample_rate != self.config.sample_rate {
                    self.feature_extractor.resample(
                        samples,
                        *sample_rate,
                        self.config.sample_rate,
                    )?
                } else {
                    samples.clone()
                };

                // Extract features
                self.feature_extractor.extract_features(&resampled)
            },
            AudioInput::Base64(encoded) => {
                // Decode base64 and process
                let decoded = base64::decode(encoded).map_err(|e| {
                    TrustformersError::invalid_input_simple(format!(
                        "Failed to decode base64 audio: {}",
                        e
                    ))
                })?;

                // Assume WAV format for base64 input
                self.feature_extractor.decode_and_extract(&decoded, AudioFormat::Wav)
            },
            AudioInput::Bytes {
                data,
                format,
                sample_rate,
            } => {
                // Decode bytes and extract features
                self.feature_extractor
                    .decode_and_extract(data, *format)?
                    .resample_to(self.config.sample_rate)
            },
        }
    }

    /// Post-process model output to speech-to-text result
    fn postprocess_output(
        &self,
        model_output: &crate::core::tensor::Tensor,
        audio_duration: f64,
    ) -> Result<SpeechToTextOutput> {
        // This is a simplified implementation
        // In a real implementation, this would:
        // 1. Decode token IDs to text using the tokenizer
        // 2. Extract timestamps if requested
        // 3. Calculate confidence scores
        // 4. Handle language detection

        let text = "Transcribed text placeholder".to_string(); // Simplified
        let confidence = Some(0.95); // Placeholder confidence

        let word_timestamps = if self.config.return_timestamps {
            Some(vec![
                WordTimestamp {
                    word: "Transcribed".to_string(),
                    start_time: 0.0,
                    end_time: 0.5,
                    confidence: 0.95,
                },
                WordTimestamp {
                    word: "text".to_string(),
                    start_time: 0.5,
                    end_time: 1.0,
                    confidence: 0.90,
                },
            ])
        } else {
            None
        };

        Ok(SpeechToTextOutput {
            text,
            confidence,
            word_timestamps,
            language: self.config.language.clone(),
            processing_time_ms: Some(100), // Placeholder
        })
    }
}

impl Pipeline for SpeechToTextPipeline {
    type Input = AudioInput;
    type Output = SpeechToTextOutput;

    fn __call__(&self, input: Self::Input) -> Result<Self::Output> {
        let start_time = std::time::Instant::now();

        // 1. Preprocess audio to features
        let audio_features = self.preprocess_audio(&input)?;
        let audio_duration = audio_features.duration();

        // 2. Check audio duration limits
        if let Some(max_duration) = self.config.max_duration {
            if audio_duration > max_duration {
                return Err(TrustformersError::invalid_input_simple(format!(
                    "Audio duration ({:.2}s) exceeds maximum allowed ({:.2}s)",
                    audio_duration, max_duration
                )));
            }
        }

        // 3. Convert features to tensor for model input
        let input_tensor = audio_features.to_tensor()?;

        // 4. Run model inference (simplified for demonstration)
        // let model_output = self.base.model.forward(input_tensor)?;
        let model_output = input_tensor; // Placeholder

        // 5. Post-process output to final result
        let mut result = self.postprocess_output(&model_output, audio_duration)?;

        // 6. Add processing time
        result.processing_time_ms = Some(start_time.elapsed().as_millis() as u64);

        Ok(result)
    }
}

/// Audio feature extractor for speech models
pub struct AudioFeatureExtractor {
    sample_rate: u32,
    n_fft: usize,
    hop_length: usize,
    n_mels: usize,
}

impl AudioFeatureExtractor {
    pub fn new(sample_rate: u32) -> Result<Self> {
        Ok(Self {
            sample_rate,
            n_fft: 400,      // Whisper default
            hop_length: 160, // Whisper default
            n_mels: 80,      // Whisper default
        })
    }

    pub fn load_and_extract(&self, path: &str) -> Result<AudioFeatures> {
        // Placeholder implementation
        // In a real implementation, this would use a library like `symphonia` or `rodio`
        // to load audio files and extract features

        Ok(AudioFeatures {
            features: vec![vec![0.0; self.n_mels]; 100], // Placeholder mel spectrogram
            sample_rate: self.sample_rate,
            duration_s: 5.0, // Placeholder duration
        })
    }

    pub fn extract_features(&self, samples: &[f32]) -> Result<AudioFeatures> {
        // Placeholder for mel spectrogram extraction
        // In a real implementation, this would:
        // 1. Apply pre-emphasis filter
        // 2. Compute STFT
        // 3. Convert to mel scale
        // 4. Apply log compression

        let duration_s = samples.len() as f64 / self.sample_rate as f64;
        let n_frames = (samples.len() / self.hop_length) + 1;

        Ok(AudioFeatures {
            features: vec![vec![0.0; self.n_mels]; n_frames], // Placeholder
            sample_rate: self.sample_rate,
            duration_s,
        })
    }

    pub fn resample(&self, samples: &[f32], from_rate: u32, to_rate: u32) -> Result<Vec<f32>> {
        if from_rate == to_rate {
            return Ok(samples.to_vec());
        }

        // Simplified resampling (in reality would use proper resampling algorithm)
        let ratio = to_rate as f64 / from_rate as f64;
        let new_len = (samples.len() as f64 * ratio) as usize;

        let mut resampled = Vec::with_capacity(new_len);
        for i in 0..new_len {
            let original_idx = (i as f64 / ratio) as usize;
            if original_idx < samples.len() {
                resampled.push(samples[original_idx]);
            } else {
                resampled.push(0.0);
            }
        }

        Ok(resampled)
    }

    pub fn decode_and_extract(&self, data: &[u8], format: AudioFormat) -> Result<AudioFeatures> {
        // Placeholder for audio decoding
        // In a real implementation, this would decode various audio formats

        match format {
            AudioFormat::Wav => {
                // Decode WAV format
                self.extract_features(&[0.0; 16000]) // Placeholder
            },
            _ => {
                // For other formats, would use appropriate decoder
                self.extract_features(&[0.0; 16000]) // Placeholder
            },
        }
    }
}

/// Audio features (typically mel spectrogram)
#[derive(Debug)]
pub struct AudioFeatures {
    pub features: Vec<Vec<f32>>, // [time_frames, n_mels]
    pub sample_rate: u32,
    pub duration_s: f64,
}

impl AudioFeatures {
    pub fn duration(&self) -> f64 {
        self.duration_s
    }

    pub fn to_tensor(&self) -> Result<crate::core::tensor::Tensor> {
        // Convert features to tensor format expected by model
        // This is a placeholder implementation
        use crate::core::tensor::Tensor;

        // Flatten features for tensor creation
        let flat_features: Vec<f32> = self.features.iter().flatten().cloned().collect();
        let shape = vec![1, self.features.len(), self.features[0].len()]; // [batch, time, features]

        Tensor::from_vec(flat_features, &shape).map_err(Into::into)
    }

    pub fn resample_to(self, target_rate: u32) -> Result<Self> {
        if self.sample_rate == target_rate {
            return Ok(self);
        }

        // Placeholder resampling
        Ok(self)
    }
}

// Import base64 crate (would be added to dependencies)
mod base64 {
    pub fn decode(_input: &str) -> Result<Vec<u8>, String> {
        // Placeholder implementation
        Ok(vec![])
    }
}
