"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
import argparse
import torch
from sapiens_transformers import (SpeechT5Config, SpeechT5FeatureExtractor, SpeechT5ForSpeechToSpeech, SpeechT5ForSpeechToText, SpeechT5ForTextToSpeech, SpeechT5Processor, SpeechT5Tokenizer, logging)
from sapiens_transformers.tokenization_utils import AddedToken
logging.set_verbosity_info()
logger = logging.get_logger("sapiens_transformers.models.speecht5")
MAPPING_SPEECH_ENCODER_PRENET = {'speech_encoder_prenet.layer_norm': 'speecht5.encoder.prenet.feature_projection.layer_norm', 'speech_encoder_prenet.post_extract_proj': 'speecht5.encoder.prenet.feature_projection.projection',
'speech_encoder_prenet.pos_conv.0': 'speecht5.encoder.prenet.pos_conv_embed.conv', 'speech_encoder_prenet.mask_emb': 'speecht5.encoder.prenet.masked_spec_embed'}
MAPPING_TEXT_ENCODER_PRENET = {'text_encoder_prenet.encoder_prenet.0': 'speecht5.encoder.prenet.embed_tokens', 'text_encoder_prenet.encoder_prenet.1.alpha': 'speecht5.encoder.prenet.encode_positions.alpha'}
MAPPING_SPEECH_DECODER_PRENET = {'speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0': 'speecht5.decoder.prenet.layers.0', 'speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0': 'speecht5.decoder.prenet.layers.1',
'speech_decoder_prenet.decoder_prenet.0.1': 'speecht5.decoder.prenet.final_layer', 'speech_decoder_prenet.decoder_prenet.1.alpha': 'speecht5.decoder.prenet.encode_positions.alpha', 'speech_decoder_prenet.spkembs_layer.0': 'speecht5.decoder.prenet.speaker_embeds_layer'}
MAPPING_SPEECH_DECODER_POSTNET = {'speech_decoder_postnet.feat_out': 'speech_decoder_postnet.feat_out', 'speech_decoder_postnet.prob_out': 'speech_decoder_postnet.prob_out', 'speech_decoder_postnet.postnet.postnet.0.0': 'speech_decoder_postnet.layers.0.conv',
'speech_decoder_postnet.postnet.postnet.0.1': 'speech_decoder_postnet.layers.0.batch_norm', 'speech_decoder_postnet.postnet.postnet.1.0': 'speech_decoder_postnet.layers.1.conv', 'speech_decoder_postnet.postnet.postnet.1.1': 'speech_decoder_postnet.layers.1.batch_norm',
'speech_decoder_postnet.postnet.postnet.2.0': 'speech_decoder_postnet.layers.2.conv', 'speech_decoder_postnet.postnet.postnet.2.1': 'speech_decoder_postnet.layers.2.batch_norm', 'speech_decoder_postnet.postnet.postnet.3.0': 'speech_decoder_postnet.layers.3.conv',
'speech_decoder_postnet.postnet.postnet.3.1': 'speech_decoder_postnet.layers.3.batch_norm', 'speech_decoder_postnet.postnet.postnet.4.0': 'speech_decoder_postnet.layers.4.conv', 'speech_decoder_postnet.postnet.postnet.4.1': 'speech_decoder_postnet.layers.4.batch_norm'}
MAPPING_TEXT_DECODER_PRENET = {'text_decoder_prenet.embed_tokens': 'speecht5.decoder.prenet.embed_tokens'}
MAPPING_TEXT_DECODER_POSTNET = {'text_decoder_postnet.output_projection': 'text_decoder_postnet.lm_head'}
MAPPING_ENCODER = {'encoder.layers.*.self_attn.k_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj', 'encoder.layers.*.self_attn.v_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj',
'encoder.layers.*.self_attn.q_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj', 'encoder.layers.*.self_attn.out_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj',
'encoder.layers.*.self_attn_layer_norm': 'speecht5.encoder.wrapped_encoder.layers.*.layer_norm', 'encoder.layers.*.fc1': 'speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense',
'encoder.layers.*.fc2': 'speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense', 'encoder.layers.*.final_layer_norm': 'speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm',
'encoder.layer_norm': 'speecht5.encoder.wrapped_encoder.layer_norm', 'encoder.pos_emb.pe_k': 'speecht5.encoder.wrapped_encoder.embed_positions.pe_k'}
MAPPING_DECODER = {'decoder.layers.*.self_attn.k_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj', 'decoder.layers.*.self_attn.v_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj',
'decoder.layers.*.self_attn.q_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj', 'decoder.layers.*.self_attn.out_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj',
'decoder.layers.*.self_attn_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm', 'decoder.layers.*.encoder_attn.k_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj',
'decoder.layers.*.encoder_attn.v_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj', 'decoder.layers.*.encoder_attn.q_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj',
'decoder.layers.*.encoder_attn.out_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj', 'decoder.layers.*.encoder_attn_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm',
'decoder.layers.*.fc1': 'speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense', 'decoder.layers.*.fc2': 'speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense',
'decoder.layers.*.final_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm'}
MAPPING_S2T = {**MAPPING_SPEECH_ENCODER_PRENET, **MAPPING_ENCODER, **MAPPING_DECODER, **MAPPING_TEXT_DECODER_PRENET, **MAPPING_TEXT_DECODER_POSTNET}
MAPPING_T2S = {**MAPPING_TEXT_ENCODER_PRENET, **MAPPING_ENCODER, **MAPPING_DECODER, **MAPPING_SPEECH_DECODER_PRENET, **MAPPING_SPEECH_DECODER_POSTNET}
MAPPING_S2S = {**MAPPING_SPEECH_ENCODER_PRENET, **MAPPING_ENCODER, **MAPPING_DECODER, **MAPPING_SPEECH_DECODER_PRENET, **MAPPING_SPEECH_DECODER_POSTNET}
TOP_LEVEL_KEYS = []
IGNORE_KEYS = ["encoder.version", "encoder.layers.*.norm_k.weight", "encoder.layers.*.norm_k.bias", "decoder.version", "decoder.layers.*.norm_k.weight",
"decoder.layers.*.norm_k.bias", "decoder.pos_emb.pe_k", "speech_encoder_prenet.embed_positions._float_tensor", "text_decoder_prenet.embed_positions._float_tensor"]
IGNORE_KEYS_S2T = IGNORE_KEYS + ["encoder.proj", "text_encoder_prenet.*", "speech_decoder_prenet.*", "speech_decoder_postnet.*"]
IGNORE_KEYS_T2S = IGNORE_KEYS + ["encoder.proj", "speech_encoder_prenet.*", "text_decoder_prenet.*", "text_decoder_postnet.*"]
IGNORE_KEYS_S2S = IGNORE_KEYS + ["encoder.proj", "text_encoder_prenet.*", "text_decoder_prenet.*", "text_decoder_postnet.*"]
def set_recursively(hf_pointer, key, value, full_name, weight_type):
    for attribute in key.split("."): hf_pointer = getattr(hf_pointer, attribute)
    if weight_type is not None: hf_shape = getattr(hf_pointer, weight_type).shape
    else: hf_shape = hf_pointer.shape
    if hf_shape != value.shape: raise ValueError(f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be {value.shape} for {full_name}")
    if weight_type == "weight": hf_pointer.weight.data = value
    elif weight_type == "weight_g": hf_pointer.weight_g.data = value
    elif weight_type == "weight_v": hf_pointer.weight_v.data = value
    elif weight_type == "bias": hf_pointer.bias.data = value
    elif weight_type == "running_mean": hf_pointer.running_mean.data = value
    elif weight_type == "running_var": hf_pointer.running_var.data = value
    elif weight_type == "num_batches_tracked": hf_pointer.num_batches_tracked.data = value
    else: hf_pointer.data = value
    logger.info(f"{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}.")
def should_ignore(name, ignore_keys):
    for key in ignore_keys:
        if key.endswith(".*"):
            if name.startswith(key[:-1]): return True
        elif ".*." in key:
            prefix, suffix = key.split(".*.")
            if prefix in name and suffix in name: return True
        elif key in name: return True
    return False
def recursively_load_weights(fairseq_dict, hf_model, task):
    unused_weights = []
    if task == "s2t":
        feature_encoder = hf_model.speecht5.encoder.prenet.feature_encoder
        MAPPING = MAPPING_S2T
        IGNORE_KEYS = IGNORE_KEYS_S2T
    elif task == "t2s":
        feature_encoder = None
        MAPPING = MAPPING_T2S
        IGNORE_KEYS = IGNORE_KEYS_T2S
    elif task == "s2s":
        feature_encoder = hf_model.speecht5.encoder.prenet.feature_encoder
        MAPPING = MAPPING_S2S
        IGNORE_KEYS = IGNORE_KEYS_S2S
    else: raise ValueError(f"Unsupported task: {task}")
    for name, value in fairseq_dict.items():
        if should_ignore(name, IGNORE_KEYS):
            logger.info(f"{name} was ignored")
            continue
        is_used = False
        if "conv_layers" in name:
            load_conv_layer(name, value, feature_encoder, unused_weights, hf_model.config.feat_extract_norm == "group")
            is_used = True
        else:
            for key, mapped_key in MAPPING.items():
                if "*" in key:
                    prefix, suffix = key.split(".*.")
                    if prefix in name and suffix in name: key = suffix
                if key in name:
                    is_used = True
                    if "*" in mapped_key:
                        layer_index = name.split(key)[0].split(".")[-2]
                        mapped_key = mapped_key.replace("*", layer_index)
                    if "weight_g" in name: weight_type = "weight_g"
                    elif "weight_v" in name: weight_type = "weight_v"
                    elif "bias" in name: weight_type = "bias"
                    elif "weight" in name: weight_type = "weight"
                    elif "running_mean" in name: weight_type = "running_mean"
                    elif "running_var" in name: weight_type = "running_var"
                    elif "num_batches_tracked" in name: weight_type = "num_batches_tracked"
                    else: weight_type = None
                    set_recursively(hf_model, mapped_key, value, name, weight_type)
                continue
        if not is_used: unused_weights.append(name)
    logger.warning(f"Unused weights: {unused_weights}")
def load_conv_layer(full_name, value, feature_extractor, unused_weights, use_group_norm):
    name = full_name.split("conv_layers.")[-1]
    items = name.split(".")
    layer_id = int(items[0])
    type_id = int(items[1])
    if type_id == 0:
        if "bias" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape: raise ValueError(f"{full_name} has size {value.shape}, but {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.")
            feature_extractor.conv_layers[layer_id].conv.bias.data = value
            logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}.")
        elif "weight" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape: raise ValueError(f"{full_name} has size {value.shape}, but {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.")
            feature_extractor.conv_layers[layer_id].conv.weight.data = value
            logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}.")
    elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
        if "bias" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape: raise ValueError(f"{full_name} has size {value.shape}, but {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.")
            feature_extractor.conv_layers[layer_id].layer_norm.bias.data = value
            logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.")
        elif "weight" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape: raise ValueError(f"{full_name} has size {value.shape}, but {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.")
            feature_extractor.conv_layers[layer_id].layer_norm.weight.data = value
            logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.")
    else: unused_weights.append(full_name)
@torch.no_grad()
def convert_speecht5_checkpoint(task, checkpoint_path, pytorch_dump_folder_path, config_path=None, vocab_path=None, repo_id=None):
    if config_path is not None: config = SpeechT5Config.from_pretrained(config_path)
    else: config = SpeechT5Config()
    if task == "s2t":
        config.max_length = config.max_text_positions
        model = SpeechT5ForSpeechToText(config)
    elif task == "t2s":
        config.max_speech_positions = 1876
        config.max_text_positions = 600
        config.max_length = config.max_speech_positions
        model = SpeechT5ForTextToSpeech(config)
    elif task == "s2s":
        config.max_speech_positions = 1876
        config.max_length = config.max_speech_positions
        model = SpeechT5ForSpeechToSpeech(config)
    else: raise ValueError(f"Unknown task name: {task}")
    if vocab_path:
        tokenizer = SpeechT5Tokenizer(vocab_path, model_max_length=config.max_text_positions)
        mask_token = AddedToken("<mask>", lstrip=True, rstrip=False)
        tokenizer.mask_token = mask_token
        tokenizer.add_special_tokens({"mask_token": mask_token})
        tokenizer.add_tokens(["<ctc_blank>"])
    feature_extractor = SpeechT5FeatureExtractor()
    processor = SpeechT5Processor(tokenizer=tokenizer, feature_extractor=feature_extractor)
    processor.save_pretrained(pytorch_dump_folder_path)
    fairseq_checkpoint = torch.load(checkpoint_path)
    recursively_load_weights(fairseq_checkpoint["model"], model, task)
    model.save_pretrained(pytorch_dump_folder_path)
    if repo_id:
        print("Pushing to the hub...")
        processor.push_to_hub(repo_id)
        model.push_to_hub(repo_id)
if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--task", default="s2t", type=str, help="Type of the SpeechT5 model you'd like to convert. Should be one of 's2t', 't2s', 's2s'.")
    parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to fairseq checkpoint")
    parser.add_argument("--vocab_path", default=None, type=str, help="Path to SentencePiece model")
    parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
    parser.add_argument("--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model.")
    parser.add_argument("--push_to_hub", default=None, type=str, help="Where to upload the converted model on the HF hub.")
    args = parser.parse_args()
    convert_speecht5_checkpoint(args.task, args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.vocab_path, args.push_to_hub)
"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
