"""
    #################################################################################################################################################################################################
    # 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 sapiens_transformers import (Wav2Vec2ConformerConfig, Wav2Vec2ConformerForCTC, Wav2Vec2ConformerForPreTraining, Wav2Vec2CTCTokenizer, Wav2Vec2FeatureExtractor, Wav2Vec2Processor, 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.linear_k': 'encoder.layers.*.self_attn.linear_k',
'self_attn.linear_v': 'encoder.layers.*.self_attn.linear_v', 'self_attn.linear_q': 'encoder.layers.*.self_attn.linear_q', 'self_attn.pos_bias_u': 'encoder.layers.*.self_attn.pos_bias_u',
'self_attn.pos_bias_v': 'encoder.layers.*.self_attn.pos_bias_v', 'self_attn.linear_out': 'encoder.layers.*.self_attn.linear_out', 'self_attn.linear_pos': 'encoder.layers.*.self_attn.linear_pos',
'self_attn.rotary_emb': 'encoder.embed_positions', 'self_attn_layer_norm': 'encoder.layers.*.self_attn_layer_norm', 'conv_module.pointwise_conv1': 'encoder.layers.*.conv_module.pointwise_conv1',
'conv_module.pointwise_conv2': 'encoder.layers.*.conv_module.pointwise_conv2', 'conv_module.depthwise_conv': 'encoder.layers.*.conv_module.depthwise_conv',
'conv_module.batch_norm': 'encoder.layers.*.conv_module.batch_norm', 'conv_module.layer_norm': 'encoder.layers.*.conv_module.layer_norm', 'ffn1.w_1': 'encoder.layers.*.ffn1.intermediate_dense',
'ffn1.w_2': 'encoder.layers.*.ffn1.output_dense', 'ffn1.layer_norm': 'encoder.layers.*.ffn1_layer_norm', 'ffn2.w_1': 'encoder.layers.*.ffn2.intermediate_dense', 'ffn2.w_2': 'encoder.layers.*.ffn2.output_dense',
'ffn2.layer_norm': 'encoder.layers.*.ffn2_layer_norm', '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
    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
    elif weight_type == "inv_freq": hf_pointer.inv_freq.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_headless):
    unused_weights = []
    fairseq_dict = fairseq_model.state_dict()
    feature_extractor = hf_model.wav2vec2_conformer.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 = "wav2vec2_conformer." + mapped_key if mapped_key not in TOP_LEVEL_KEYS 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]
                        mapped_key = mapped_key.replace("*", layer_index)
                    if "pos_bias_u" in name: weight_type = None
                    elif "pos_bias_v" in name: weight_type = None
                    elif "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 "inv_freq" in name: weight_type = "inv_freq"
                    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_wav2vec2_conformer_checkpoint(checkpoint_path, pytorch_dump_folder_path, config_path=None, dict_path=None, is_finetuned=True):
    if config_path is not None: config = Wav2Vec2ConformerConfig.from_pretrained(config_path, hidden_act="swish")
    else: config = Wav2Vec2ConformerConfig()
    if "rope" in checkpoint_path: config.position_embeddings_type = "rotary"
    if is_finetuned:
        if dict_path:
            target_dict = Dictionary.load(dict_path)
            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)
            vocab_dict = target_dict.indices
            vocab_dict["<pad>"] = 0
            vocab_dict["<s>"] = 1
            with open(vocab_path, "w", encoding="utf-8") as vocab_handle: json.dump(vocab_dict, 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)
            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)
            processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)
            processor.save_pretrained(pytorch_dump_folder_path)
        hf_wav2vec = Wav2Vec2ConformerForCTC(config)
    else: hf_wav2vec = Wav2Vec2ConformerForPreTraining(config)
    if is_finetuned: model, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path], arg_overrides={"data": "/".join(dict_path.split("/")[:-1])})
    else:
        task_arg = argparse.Namespace(task="audio_pretraining")
        task = fairseq.tasks.setup_task(task_arg)
        model, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path], task=task)
    model = model[0].eval()
    recursively_load_weights(model, hf_wav2vec, not is_finetuned)
    hf_wav2vec.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("--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not")
    args = parser.parse_args()
    convert_wav2vec2_conformer_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_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.                                                                 #
    #################################################################################################################################################################################################
"""
