"""
    #################################################################################################################################################################################################
    # 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 fairseq.data import Dictionary
from sew_asapp import tasks
from sapiens_transformers import (SEWDConfig, SEWDForCTC, SEWDModel, Wav2Vec2CTCTokenizer, Wav2Vec2FeatureExtractor, Wav2Vec2Processor, logging)
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
MAPPING = {'post_extract_proj': 'feature_projection', 'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv', 'attention.self.query_proj': 'encoder.encoder.layer.*.attention.self.query_proj',
'attention.self.key_proj': 'encoder.encoder.layer.*.attention.self.key_proj', 'attention.self.value_proj': 'encoder.encoder.layer.*.attention.self.value_proj',
'attention.output.dense': 'encoder.encoder.layer.*.attention.output.dense', 'attention.output.LayerNorm': 'encoder.encoder.layer.*.attention.output.LayerNorm',
'intermediate.dense': 'encoder.encoder.layer.*.intermediate.dense', 'output.dense': 'encoder.encoder.layer.*.output.dense', 'output.LayerNorm': 'encoder.encoder.layer.*.output.LayerNorm',
'encoder.encoder.rel_embeddings': 'encoder.encoder.rel_embeddings', 'encoder.encoder.LayerNorm': 'encoder.encoder.LayerNorm', 'encoder.upsample.0': 'encoder.upsample.projection',
'encoder.layer_norm': 'encoder.layer_norm', 'w2v_model.layer_norm': 'layer_norm', 'w2v_encoder.proj': 'lm_head', 'mask_emb': 'masked_spec_embed'}
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(fairseq_model, hf_model, is_finetuned):
    unused_weights = []
    fairseq_dict = fairseq_model.state_dict()
    feature_extractor = hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor
    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
        else:
            for key, mapped_key in MAPPING.items():
                mapped_key = "sew_d." + mapped_key if (is_finetuned and mapped_key != "lm_head") else mapped_key
                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]
                        if not layer_index.isnumeric(): continue
                        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 "weight" in name: weight_type = "weight"
                    elif "bias" in name: weight_type = "bias"
                    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:
            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 convert_config(model, is_finetuned):
    config = SEWDConfig()
    if is_finetuned: fs_config = model.w2v_encoder.w2v_model.cfg
    else: fs_config = model.cfg
    config.conv_bias = fs_config.conv_bias
    conv_layers = eval(fs_config.conv_feature_layers)
    config.conv_dim = [x[0] for x in conv_layers]
    config.conv_kernel = [x[1] for x in conv_layers]
    config.conv_stride = [x[2] for x in conv_layers]
    config.feat_extract_activation = "gelu"
    config.feat_extract_norm = "layer" if fs_config.extractor_mode == "layer_norm" else "group"
    config.final_dropout = 0.0
    config.hidden_act = fs_config.activation_fn.name
    config.hidden_size = fs_config.encoder_embed_dim
    config.initializer_range = 0.02
    config.intermediate_size = fs_config.encoder_ffn_embed_dim
    config.layer_norm_eps = 1e-5
    config.layerdrop = fs_config.encoder_layerdrop
    config.num_attention_heads = fs_config.encoder_attention_heads
    config.num_conv_pos_embedding_groups = fs_config.conv_pos_groups
    config.num_conv_pos_embeddings = fs_config.conv_pos
    config.num_feat_extract_layers = len(conv_layers)
    config.num_hidden_layers = fs_config.encoder_layers
    config.squeeze_factor = fs_config.squeeze_factor
    config.max_position_embeddings = fs_config.max_position_embeddings
    config.position_buckets = fs_config.position_buckets
    config.share_att_key = fs_config.share_att_key
    config.relative_attention = fs_config.relative_attention
    config.position_biased_input = fs_config.position_biased_input
    config.pos_att_type = tuple(fs_config.pos_att_type.split("|"))
    config.norm_rel_ebd = fs_config.norm_rel_ebd
    if is_finetuned:
        fs_config = model.cfg
        config.final_dropout = fs_config.final_dropout
        config.layerdrop = fs_config.layerdrop
    config.activation_dropout = fs_config.activation_dropout
    config.apply_spec_augment = fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
    config.attention_dropout = fs_config.attention_dropout
    config.feat_proj_dropout = fs_config.dropout_input
    config.hidden_dropout = fs_config.dropout
    config.mask_feature_length = fs_config.mask_channel_length
    config.mask_feature_prob = fs_config.mask_channel_prob
    config.mask_time_length = fs_config.mask_length
    config.mask_time_prob = fs_config.mask_prob
    config.feature_extractor_type = "Wav2Vec2FeatureExtractor"
    config.tokenizer_class = "Wav2Vec2CTCTokenizer"
    return config
@torch.no_grad()
def convert_sew_checkpoint(checkpoint_path, pytorch_dump_folder_path, config_path=None, dict_path=None, is_finetuned=True):
    if is_finetuned: model, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path], arg_overrides={"data": "/".join(dict_path.split("/")[:-1])})
    else: model, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path])
    if config_path is not None: config = SEWDConfig.from_pretrained(config_path)
    else: config = convert_config(model[0], is_finetuned)
    model = model[0].eval()
    return_attention_mask = True if config.feat_extract_norm == "layer" else False
    feature_extractor = Wav2Vec2FeatureExtractor(feature_size=1, sampling_rate=16000, padding_value=0, do_normalize=True, return_attention_mask=return_attention_mask)
    if is_finetuned:
        if dict_path:
            target_dict = Dictionary.load(dict_path)
            target_dict.indices[target_dict.bos_word] = target_dict.pad_index
            target_dict.indices[target_dict.pad_word] = target_dict.bos_index
            config.bos_token_id = target_dict.pad_index
            config.pad_token_id = target_dict.bos_index
            config.eos_token_id = target_dict.eos_index
            config.vocab_size = len(target_dict.symbols)
            vocab_path = os.path.join(pytorch_dump_folder_path, "vocab.json")
            if not os.path.isdir(pytorch_dump_folder_path):
                logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(pytorch_dump_folder_path))
                return
            os.makedirs(pytorch_dump_folder_path, exist_ok=True)
            with open(vocab_path, "w", encoding="utf-8") as vocab_handle: json.dump(target_dict.indices, vocab_handle)
            tokenizer = Wav2Vec2CTCTokenizer(vocab_path, unk_token=target_dict.unk_word, pad_token=target_dict.pad_word, bos_token=target_dict.bos_word, eos_token=target_dict.eos_word,
            word_delimiter_token="|", do_lower_case=False)
            processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)
            processor.save_pretrained(pytorch_dump_folder_path)
        hf_model = SEWDForCTC(config)
    else:
        hf_model = SEWDModel(config)
        feature_extractor.save_pretrained(pytorch_dump_folder_path)
    recursively_load_weights(model, hf_model, is_finetuned)
    hf_model.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("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
    parser.add_argument("--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not")
    args = parser.parse_args()
    convert_sew_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned)
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
