"""
    #################################################################################################################################################################################################
    # 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 json
import os
import fairseq
import torch
from torch import nn
from sapiens_transformers import (Speech2Text2Config, Speech2Text2ForCausalLM, Speech2Text2Tokenizer, SpeechEncoderDecoderConfig, SpeechEncoderDecoderModel, Wav2Vec2Config,
Wav2Vec2FeatureExtractor, Wav2Vec2Model, logging)
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
MAPPING = {'post_extract_proj': 'feature_projection.projection', 'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv', 'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
'self_attn.v_proj': 'encoder.layers.*.attention.v_proj', 'self_attn.q_proj': 'encoder.layers.*.attention.q_proj', 'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
'self_attn_layer_norm': 'encoder.layers.*.layer_norm', 'fc1': 'encoder.layers.*.feed_forward.intermediate_dense', 'fc2': 'encoder.layers.*.feed_forward.output_dense',
'final_layer_norm': 'encoder.layers.*.final_layer_norm', 'encoder.layer_norm': 'encoder.layer_norm', 'w2v_model.layer_norm': 'feature_projection.layer_norm',
'quantizer.weight_proj': 'quantizer.weight_proj', 'quantizer.vars': 'quantizer.codevectors', 'project_q': 'project_q', 'final_proj': 'project_hid', 'w2v_encoder.proj': 'lm_head', 'mask_emb': 'masked_spec_embed'}
TOP_LEVEL_KEYS = ["lm_head", "quantizer.weight_proj", "quantizer.codevectors", "project_q", "project_hid"]
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
    assert hf_shape == value.shape, (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
    else: hf_pointer.data = value
    logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.")
def recursively_load_weights_wav2vec2(fairseq_model, hf_model):
    unused_weights = []
    fairseq_dict = fairseq_model.state_dict()
    feature_extractor = hf_model.feature_extractor
    proj_weight = None
    for name, value in fairseq_dict.items():
        is_used = False
        if "conv_layers" in name:
            load_conv_layer(name, value, feature_extractor, unused_weights, hf_model.config.feat_extract_norm == "group")
            is_used = True
        elif name.split(".")[0] == "proj":
            proj_weight = fairseq_model.proj
            is_used = True
        else:
            for key, mapped_key in MAPPING.items():
                if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
                    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"
                    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}")
    return proj_weight
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:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (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:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (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:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (f"{full_name} has size {value.shape}, but {feature_extractor[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:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (f"{full_name} has size {value.shape}, but {feature_extractor[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)
def make_linear_from_emb(emb):
    vocab_size, emb_size = emb.weight.shape
    lin_layer = nn.Linear(vocab_size, emb_size, bias=False)
    lin_layer.weight.data = emb.weight.data
    return lin_layer
def create_vocab_dict(dict_path):
    with open(dict_path, "r", encoding="utf-8") as f:
        lines = f.readlines()
        words = [line.split(" ")[0] for line in lines]
    num_words = len(words)
    vocab_dict = {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}
    vocab_dict.update(dict(zip(words, range(4, num_words + 4))))
    return vocab_dict
@torch.no_grad()
def convert_wav2vec2_checkpoint(checkpoint_path, pytorch_dump_folder_path, dict_path, encoder_config_path, decoder_config_path, vocab_size, num_decoder_layers):
    encoder_config = Wav2Vec2Config.from_pretrained(encoder_config_path)
    decoder_config = Speech2Text2Config.from_pretrained(decoder_config_path, vocab_size=vocab_size, decoder_layers=num_decoder_layers, do_stable_layer_norm=True)
    feature_extractor = Wav2Vec2FeatureExtractor(feature_size=1, sampling_rate=16000, padding_value=0, do_normalize=True, return_attention_mask=True)
    model, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path], arg_overrides={"data": "/".join(dict_path.split("/")[:-1])})
    model = model[0].eval()
    hf_encoder = Wav2Vec2Model(encoder_config)
    projection_layer = recursively_load_weights_wav2vec2(model.encoder, hf_encoder)
    hf_decoder = Speech2Text2ForCausalLM(decoder_config)
    missing_keys, unexpected_keys = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict(), strict=False)
    unexpected_keys.remove("embed_out")
    hf_decoder.lm_head.weight = nn.Parameter(model.decoder.embed_out.detach())
    logger.warning(f"The following keys are missing when loading the decoder weights: {missing_keys}")
    logger.warning(f"The following keys are unexpected when loading the decoder weights: {unexpected_keys}")
    hf_wav2vec = SpeechEncoderDecoderModel(encoder=hf_encoder, decoder=hf_decoder)
    hf_wav2vec.config.tie_word_embeddings = False
    hf_wav2vec.enc_to_dec_proj.weight = nn.Parameter(projection_layer.weight)
    hf_wav2vec.enc_to_dec_proj.bias = nn.Parameter(projection_layer.bias)
    vocab_dict = create_vocab_dict(dict_path)
    with open(os.path.join(pytorch_dump_folder_path, "vocab.json"), "w") as fp: json.dump(vocab_dict, fp)
    tokenizer = Speech2Text2Tokenizer(os.path.join(pytorch_dump_folder_path, "vocab.json"))
    tokenizer.save_pretrained(pytorch_dump_folder_path)
    config = hf_wav2vec.config.to_dict()
    config["pad_token_id"] = tokenizer.pad_token_id
    config["bos_token_id"] = tokenizer.bos_token_id
    config["eos_token_id"] = tokenizer.eos_token_id
    config["tokenizer_class"] = "speech_to_text_2"
    config["feature_extractor_type"] = "wav2vec2"
    hf_wav2vec.config = SpeechEncoderDecoderConfig.from_dict(config)
    hf_wav2vec.save_pretrained(pytorch_dump_folder_path)
    feature_extractor.save_pretrained(pytorch_dump_folder_path)
if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
    parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
    parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
    parser.add_argument("--encoder_config_path", default="facebook/wav2vec2-large-lv60", type=str, help="Path to hf encoder wav2vec2 checkpoint config")
    parser.add_argument("--decoder_config_path", default="facebook/s2t-small-mustc-en-fr-st", type=str, help="Path to hf decoder s2t checkpoint config")
    parser.add_argument("--vocab_size", default=10224, type=int, help="Vocab size of decoder")
    parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")
    args = parser.parse_args()
    convert_wav2vec2_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.dict_path, encoder_config_path=args.encoder_config_path, decoder_config_path=args.decoder_config_path,
    vocab_size=args.vocab_size, num_decoder_layers=args.num_decoder_layers)
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
