from functools import partial

import torch
import torchaudio.models as tm

from ..registry import ModelAttribute, model_zoo

INPUT_DIM = 80
IN_FEATURES = 16
N_TIME = 20
KERNEL_SIZE = 5
HOP_LENGTH = 20
N_CLASSES = 10
N_FREQ = 16
N_MELS = 80


def conformer_data_gen_fn():
    lengths = torch.randint(1, 400, (4,))
    input = torch.rand(4, int(lengths.max()), INPUT_DIM)
    return dict(input=input, lengths=lengths)


transformer_output_transform_fn = lambda outputs: dict(frames=outputs[0], lengths=outputs[1])

model_zoo.register(name='torchaudio_conformer',
                   model_fn=lambda: tm.Conformer(
                       input_dim=INPUT_DIM, num_heads=4, ffn_dim=128, num_layers=4, depthwise_conv_kernel_size=31),
                   data_gen_fn=conformer_data_gen_fn,
                   output_transform_fn=transformer_output_transform_fn)

single_output_transform_fn = lambda output: dict(output=output)

model_zoo.register(name='torchaudio_convtasnet',
                   model_fn=tm.ConvTasNet,
                   data_gen_fn=lambda: dict(input=torch.rand(4, 1, 8)),
                   output_transform_fn=single_output_transform_fn,
                   model_attribute=ModelAttribute(has_control_flow=True))

model_zoo.register(name='torchaudio_deepspeech',
                   model_fn=lambda: tm.DeepSpeech(IN_FEATURES, n_hidden=128, n_class=4),
                   data_gen_fn=lambda: dict(x=torch.rand(4, 1, 10, IN_FEATURES)),
                   output_transform_fn=single_output_transform_fn)


def emformer_data_gen_fn():
    input = torch.rand(4, 400, IN_FEATURES)
    lengths = torch.randint(1, 200, (4,))
    return dict(input=input, lengths=lengths)


model_zoo.register(
    name='torchaudio_emformer',
    model_fn=lambda: tm.Emformer(input_dim=IN_FEATURES, num_heads=4, ffn_dim=128, num_layers=4, segment_length=4),
    data_gen_fn=emformer_data_gen_fn,
    output_transform_fn=transformer_output_transform_fn,
    model_attribute=ModelAttribute(has_control_flow=True))

model_zoo.register(name='torchaudio_wav2letter_waveform',
                   model_fn=lambda: tm.Wav2Letter(input_type='waveform', num_features=40),
                   data_gen_fn=lambda: dict(x=torch.rand(4, 40, 400)),
                   output_transform_fn=single_output_transform_fn)

model_zoo.register(name='torchaudio_wav2letter_mfcc',
                   model_fn=lambda: tm.Wav2Letter(input_type='mfcc', num_features=40),
                   data_gen_fn=lambda: dict(x=torch.rand(4, 40, 400)),
                   output_transform_fn=single_output_transform_fn)


def wavernn_data_gen_fn():
    waveform = torch.rand(4, 1, (N_TIME - KERNEL_SIZE + 1) * HOP_LENGTH)
    specgram = torch.rand(4, 1, N_FREQ, N_TIME)
    return dict(waveform=waveform, specgram=specgram)


model_zoo.register(name='torchaudio_wavernn',
                   model_fn=lambda: tm.WaveRNN(upsample_scales=[2, 2, 5],
                                               n_classes=N_CLASSES,
                                               hop_length=HOP_LENGTH,
                                               kernel_size=KERNEL_SIZE,
                                               n_freq=N_FREQ,
                                               n_res_block=2,
                                               n_rnn=64,
                                               n_fc=64,
                                               n_hidden=16,
                                               n_output=16),
                   data_gen_fn=wavernn_data_gen_fn,
                   output_transform_fn=single_output_transform_fn,
                   model_attribute=ModelAttribute(has_control_flow=True))


def tacotron_data_gen_fn():
    n_batch = 4
    max_text_length = 100
    max_mel_specgram_length = 300
    tokens = torch.randint(0, 148, (n_batch, max_text_length))
    token_lengths = max_text_length * torch.ones((n_batch,))
    mel_specgram = torch.rand(n_batch, N_MELS, max_mel_specgram_length)
    mel_specgram_lengths = max_mel_specgram_length * torch.ones((n_batch,))
    return dict(tokens=tokens,
                token_lengths=token_lengths,
                mel_specgram=mel_specgram,
                mel_specgram_lengths=mel_specgram_lengths)


model_zoo.register(name='torchaudio_tacotron',
                   model_fn=lambda: tm.Tacotron2(n_mels=N_MELS),
                   data_gen_fn=tacotron_data_gen_fn,
                   output_transform_fn=lambda outputs: dict(summed_output=sum(x.sum() for x in outputs)),
                   model_attribute=ModelAttribute(has_control_flow=True))


def wav2vec_data_gen_fn():
    batch_size, num_frames = 4, 400
    waveforms = torch.randn(batch_size, num_frames)
    lengths = torch.randint(0, num_frames, (batch_size,))
    return dict(waveforms=waveforms, lengths=lengths)


model_zoo.register(name='torchaudio_wav2vec2_base',
                   model_fn=partial(tm.wav2vec2_base, encoder_layer_drop=0.0),
                   data_gen_fn=wav2vec_data_gen_fn,
                   output_transform_fn=transformer_output_transform_fn,
                   model_attribute=ModelAttribute(has_control_flow=True))

model_zoo.register(name='torchaudio_hubert_base',
                   model_fn=tm.hubert_base,
                   data_gen_fn=wav2vec_data_gen_fn,
                   output_transform_fn=transformer_output_transform_fn,
                   model_attribute=ModelAttribute(has_control_flow=True))
