"""
    #################################################################################################################################################################################################
    # 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
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from sapiens_transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
INIT_COMMON = [("/", "."), ("layer_", "layers."), ("kernel", "weight"), ("beta", "bias"), ("gamma", "weight"), ("pegasus", "model")]
END_COMMON = [(".output.dense", ".fc2"), ("intermediate.LayerNorm", "final_layer_norm"), ("intermediate.dense", "fc1")]
DECODER_PATTERNS = (INIT_COMMON + [("attention.self.LayerNorm", "self_attn_layer_norm"), ("attention.output.dense", "self_attn.out_proj"), ("attention.self", "self_attn"), ("attention.encdec.LayerNorm", "encoder_attn_layer_norm"),
("attention.encdec_output.dense", "encoder_attn.out_proj"), ("attention.encdec", "encoder_attn"), ("key", "k_proj"), ("value", "v_proj"), ("query", "q_proj"), ("decoder.LayerNorm", "decoder.layernorm_embedding")] + END_COMMON)
REMAINING_PATTERNS = (INIT_COMMON + [("embeddings.word_embeddings", "shared.weight"), ("embeddings.position_embeddings", "embed_positions.weight"), ("attention.self.LayerNorm", "self_attn_layer_norm"),
("attention.output.dense", "self_attn.output"), ("attention.self", "self_attn.self"), ("encoder.LayerNorm", "encoder.layernorm_embedding")] + END_COMMON)
KEYS_TO_IGNORE = ["encdec/key/bias", "encdec/query/bias", "encdec/value/bias", "self/key/bias", "self/query/bias", "self/value/bias", "encdec_output/dense/bias", "attention/output/dense/bias"]
def rename_state_dict_key(k, patterns):
    for tf_name, hf_name in patterns: k = k.replace(tf_name, hf_name)
    return k
def convert_bigbird_pegasus(tf_weights: dict, config_update: dict) -> BigBirdPegasusForConditionalGeneration:
    cfg = BigBirdPegasusConfig(**config_update)
    torch_model = BigBirdPegasusForConditionalGeneration(cfg)
    state_dict = torch_model.state_dict()
    mapping = {}
    decoder_weights = {k: tf_weights[k] for k in tf_weights if k.startswith("pegasus/decoder")}
    remaining_weights = {k: tf_weights[k] for k in tf_weights if not k.startswith("pegasus/decoder")}
    for k, v in tqdm(decoder_weights.items(), "tf -> hf conversion"):
        conditions = [k.endswith(ending) for ending in KEYS_TO_IGNORE]
        if any(conditions): continue
        patterns = DECODER_PATTERNS
        new_k = rename_state_dict_key(k, patterns)
        if new_k not in state_dict: raise ValueError(f"could not find new key {new_k} in state dict. (converted from {k})")
        if any(True if i in k else False for i in ["dense", "query", "key", "value"]): v = v.T
        mapping[new_k] = torch.from_numpy(v)
        assert v.shape == state_dict[new_k].shape, f"{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"
    for k, v in tqdm(remaining_weights.items(), "tf -> hf conversion"):
        conditions = [k.endswith(ending) for ending in KEYS_TO_IGNORE]
        if any(conditions): continue
        patterns = REMAINING_PATTERNS
        new_k = rename_state_dict_key(k, patterns)
        if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings": raise ValueError(f"could not find new key {new_k} in state dict. (converted from {k})")
        if any(True if i in k else False for i in ["dense", "query", "key", "value"]): v = v.T
        mapping[new_k] = torch.from_numpy(v)
        if k != "pegasus/embeddings/position_embeddings": assert v.shape == state_dict[new_k].shape, f"{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"
    mapping["model.encoder.embed_positions.weight"] = mapping["model.embed_positions.weight"]
    mapping["model.decoder.embed_positions.weight"] = mapping.pop("model.embed_positions.weight")
    missing, extra = torch_model.load_state_dict(mapping, strict=False)
    unexpected_missing = [k for k in missing if k not in ["final_logits_bias", "model.encoder.embed_tokens.weight", "model.decoder.embed_tokens.weight", "lm_head.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) -> Dict:
    init_vars = tf.train.list_variables(path)
    tf_weights = {}
    ignore_name = ["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_bigbird_pegasus_ckpt_to_pytorch(ckpt_path: str, save_dir: str, config_update: dict):
    tf_weights = get_tf_weights_as_numpy(ckpt_path)
    torch_model = convert_bigbird_pegasus(tf_weights, config_update)
    torch_model.save_pretrained(save_dir)
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()
    config_update = {}
    convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
