"""
    #################################################################################################################################################################################################
    # 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 os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from sapiens_transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from sapiens_transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
PATTERNS = [["memory_attention", "encoder_attn"], ["attention", "attn"], ["/", "."], [".LayerNorm.gamma", "_layer_norm.weight"], [".LayerNorm.beta", "_layer_norm.bias"],
["r.layer_", "r.layers."], ["output_proj", "out_proj"], ["ffn.dense_1.", "fc2."], ["ffn.dense.", "fc1."], ["ffn_layer_norm", "final_layer_norm"], ["kernel", "weight"],
["encoder_layer_norm.", "encoder.layer_norm."], ["decoder_layer_norm.", "decoder.layer_norm."], ["embeddings.weights", "shared.weight"]]
def rename_state_dict_key(k):
    for pegasus_name, hf_name in PATTERNS: k = k.replace(pegasus_name, hf_name)
    return k
def convert_pegasus(tf_weights: dict, cfg_updates: dict) -> PegasusForConditionalGeneration:
    cfg_kwargs = DEFAULTS.copy()
    cfg_kwargs.update(cfg_updates)
    cfg = PegasusConfig(**cfg_kwargs)
    torch_model = PegasusForConditionalGeneration(cfg)
    sd = torch_model.model.state_dict()
    mapping = {}
    for k, v in tf_weights.items():
        new_k = rename_state_dict_key(k)
        if new_k not in sd: raise ValueError(f"could not find new key {new_k} in state dict. (converted from {k})")
        if "dense" in k or "proj" in new_k: v = v.T
        mapping[new_k] = torch.tensor(v, dtype=sd[new_k].dtype)
        assert v.shape == sd[new_k].shape, f"{new_k}, {k}, {v.shape}, {sd[new_k].shape}"
    mapping["shared.weight"][cfg.pad_token_id] = torch.zeros_like(mapping["shared.weight"][cfg.pad_token_id + 1])
    mapping["encoder.embed_tokens.weight"] = mapping["shared.weight"]
    mapping["decoder.embed_tokens.weight"] = mapping["shared.weight"]
    empty_biases = {k: torch.zeros_like(v) for k, v in sd.items() if k.endswith("bias") and k not in mapping}
    mapping.update(**empty_biases)
    missing, extra = torch_model.model.load_state_dict(mapping, strict=False)
    unexpected_missing = [k for k in missing if k not in ["encoder.embed_positions.weight", "decoder.embed_positions.weight"]]
    assert unexpected_missing == [], f"no matches found for the following torch keys {unexpected_missing}"
    assert extra == [], f"no matches found for the following tf keys {extra}"
    return torch_model
def get_tf_weights_as_numpy(path="./ckpt/aeslc/model.ckpt-32000") -> Dict:
    init_vars = tf.train.list_variables(path)
    tf_weights = {}
    ignore_name = ["Adafactor", "global_step"]
    for name, shape in tqdm(init_vars, desc="converting tf checkpoint to dict"):
        skip_key = any(pat in name for pat in ignore_name)
        if skip_key: continue
        array = tf.train.load_variable(path, name)
        tf_weights[name] = array
    return tf_weights
def convert_pegasus_ckpt_to_pytorch(ckpt_path: str, save_dir: str):
    dataset = Path(ckpt_path).parent.name
    desired_max_model_length = task_specific_params[f"summarization_{dataset}"]["max_position_embeddings"]
    tok = PegasusTokenizer.from_pretrained("sshleifer/pegasus", model_max_length=desired_max_model_length)
    assert tok.model_max_length == desired_max_model_length
    tok.save_pretrained(save_dir)
    tf_weights = get_tf_weights_as_numpy(ckpt_path)
    cfg_updates = task_specific_params[f"summarization_{dataset}"]
    if dataset == "large": cfg_updates["task_specific_params"] = task_specific_params
    torch_model = convert_pegasus(tf_weights, cfg_updates)
    torch_model.save_pretrained(save_dir)
    sd = torch_model.state_dict()
    sd.pop("model.decoder.embed_positions.weight")
    sd.pop("model.encoder.embed_positions.weight")
    torch.save(sd, Path(save_dir) / "pytorch_model.bin")
if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("tf_ckpt_path", type=str, help="passed to tf.train.list_variables")
    parser.add_argument("save_dir", default=None, type=str, help="Path to the output PyTorch model.")
    args = parser.parse_args()
    if args.save_dir is None:
        dataset = Path(args.tf_ckpt_path).parent.name
        args.save_dir = os.path.join("pegasus", dataset)
    convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
