"""
    #################################################################################################################################################################################################
    # 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 torch import nn
from sapiens_transformers import Speech2TextConfig, Speech2TextForConditionalGeneration
def remove_ignore_keys_(state_dict):
    ignore_keys = ["encoder.version", "decoder.version", "model.encoder.version", "model.decoder.version", "decoder.output_projection.weight", "_float_tensor",
    "encoder.embed_positions._float_tensor", "decoder.embed_positions._float_tensor"]
    for k in ignore_keys: state_dict.pop(k, None)
def rename_keys(s_dict):
    keys = list(s_dict.keys())
    for key in keys:
        if "transformer_layers" in key: s_dict[key.replace("transformer_layers", "layers")] = s_dict.pop(key)
        elif "subsample" in key: s_dict[key.replace("subsample", "conv")] = s_dict.pop(key)
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 convert_fairseq_s2t_checkpoint_to_tfms(checkpoint_path, pytorch_dump_folder_path):
    m2m_100 = torch.load(checkpoint_path, map_location="cpu")
    args = m2m_100["args"]
    state_dict = m2m_100["model"]
    lm_head_weights = state_dict["decoder.output_projection.weight"]
    remove_ignore_keys_(state_dict)
    rename_keys(state_dict)
    vocab_size = state_dict["decoder.embed_tokens.weight"].shape[0]
    tie_embeds = args.share_decoder_input_output_embed
    conv_kernel_sizes = [int(i) for i in args.conv_kernel_sizes.split(",")]
    config = Speech2TextConfig(vocab_size=vocab_size, max_source_positions=args.max_source_positions, max_target_positions=args.max_target_positions, encoder_layers=args.encoder_layers,
    decoder_layers=args.decoder_layers, encoder_attention_heads=args.encoder_attention_heads, decoder_attention_heads=args.decoder_attention_heads, encoder_ffn_dim=args.encoder_ffn_embed_dim,
    decoder_ffn_dim=args.decoder_ffn_embed_dim, d_model=args.encoder_embed_dim, dropout=args.dropout, attention_dropout=args.attention_dropout, activation_dropout=args.activation_dropout,
    activation_function="relu", num_conv_layers=len(conv_kernel_sizes), conv_channels=args.conv_channels, conv_kernel_sizes=conv_kernel_sizes, input_feat_per_channel=args.input_feat_per_channel,
    input_channels=args.input_channels, tie_word_embeddings=tie_embeds, num_beams=5, max_length=200, use_cache=True, decoder_start_token_id=2, early_stopping=True)
    model = Speech2TextForConditionalGeneration(config)
    missing, unexpected = model.model.load_state_dict(state_dict, strict=False)
    if len(missing) > 0 and not set(missing) <= {'encoder.embed_positions.weights', 'decoder.embed_positions.weights'}: raise ValueError(f"Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing, but all the following weights are missing {missing}")
    if tie_embeds: model.lm_head = make_linear_from_emb(model.model.decoder.embed_tokens)
    else: model.lm_head.weight.data = lm_head_weights
    model.save_pretrained(pytorch_dump_folder_path)
if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--fairseq_path", type=str, help="Path to the fairseq model (.pt) file.")
    parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
    args = parser.parse_args()
    convert_fairseq_s2t_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
