# Copyright 2022 EleutherAI and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import gc
import json
import os
import shutil
import warnings

import torch

from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer, PreTrainedTokenizerFast
from transformers.convert_slow_tokenizer import TikTokenConverter


try:
    from transformers import LlamaTokenizerFast
except ImportError as e:
    warnings.warn(e)
    warnings.warn(
        "The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion"
    )
    LlamaTokenizerFast = None

"""
Sample usage:

```
python src/transformers/models/llama/convert_llama_weights_to_hf.py \
    --input_dir /path/to/downloaded/llama/weights --model_size 7B --output_dir /output/path
```

Thereafter, models can be loaded via:

```py
from transformers import LlamaForCausalLM, LlamaTokenizer

model = LlamaForCausalLM.from_pretrained("/output/path")
tokenizer = LlamaTokenizer.from_pretrained("/output/path")
```

Important note: you need to be able to host the whole model in RAM to execute this script (even if the biggest versions
come in several checkpoints they each contain a part of each weight of the model, so we need to load them all in RAM).

If you want you tokenizer to add a bos automatically you should update the tokenizer._tokenizers.post_processor:

```py
from tokenizers import processors
bos = "<|begin_of_text|>"
tokenizer._tokenizers.post_processor = processors.Sequence(
    [
        processors.ByteLevel(trim_offsets=False),
        processors.TemplateProcessing(
            single=f"{bos}:0 $A:0",
            pair=f"{bos}:0 $A:0 {bos}:1 $B:1",
            special_tokens=[
                (bos, tokenizer.encode(bos)),
            ],
        ),
    ]
)
```
"""

NUM_SHARDS = {
    "7B": 1,
    "8B": 1,
    "8Bf": 1,
    "7Bf": 1,
    "13B": 2,
    "13Bf": 2,
    "34B": 4,
    "30B": 4,
    "65B": 8,
    "70B": 8,
    "70Bf": 8,
}


def compute_intermediate_size(n, ffn_dim_multiplier=1, multiple_of=256):
    return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3)) + multiple_of - 1) // multiple_of)


def read_json(path):
    with open(path, "r") as f:
        return json.load(f)


def write_json(text, path):
    with open(path, "w") as f:
        json.dump(text, f)


def write_model(
    model_path,
    input_base_path,
    model_size=None,
    safe_serialization=True,
    llama_version=1,
    vocab_size=None,
    num_shards=None,
):
    os.makedirs(model_path, exist_ok=True)
    tmp_model_path = os.path.join(model_path, "tmp")
    os.makedirs(tmp_model_path, exist_ok=True)

    params = read_json(os.path.join(input_base_path, "params.json"))
    num_shards = NUM_SHARDS[model_size] if num_shards is None else num_shards
    params = params.get("model", params)
    n_layers = params["n_layers"]
    n_heads = params["n_heads"]
    n_heads_per_shard = n_heads // num_shards
    dim = params["dim"]
    dims_per_head = dim // n_heads
    base = params.get("rope_theta", 10000.0)
    inv_freq = 1.0 / (base ** (torch.arange(0, dims_per_head, 2).float() / dims_per_head))
    if base > 10000.0 and llama_version != 3:
        max_position_embeddings = 16384
    else:
        # Depending on the Llama version, the default max_position_embeddings has different values.
        if llama_version == 1:
            max_position_embeddings = 2048
        elif llama_version == 2:
            max_position_embeddings = 4096
        elif llama_version == 3:
            max_position_embeddings = 8192

    vocab_size = vocab_size if vocab_size is not None else 32000
    if params.get("n_kv_heads", None) is not None:
        num_key_value_heads = params["n_kv_heads"]  # for GQA / MQA
        num_key_value_heads_per_shard = num_key_value_heads // num_shards
        key_value_dim = dims_per_head * num_key_value_heads
    else:  # compatibility with other checkpoints
        num_key_value_heads = n_heads
        num_key_value_heads_per_shard = n_heads_per_shard
        key_value_dim = dims_per_head * num_key_value_heads
    print(num_shards, num_key_value_heads, num_key_value_heads_per_shard, key_value_dim)

    # permute for sliced rotary
    def permute(w, n_heads, dim1=dim, dim2=dim):
        return w.view(n_heads, dim1 // n_heads // 2, 2, dim2).transpose(1, 2).reshape(dim1, dim2)

    print(f"Fetching all parameters from the checkpoint at {input_base_path}.")
    # Load weights
    if num_shards == 1:
        # Not sharded
        # (The sharded implementation would also work, but this is simpler.)
        loaded = torch.load(os.path.join(input_base_path, "consolidated.00.pth"), map_location="cpu")
    else:
        # Sharded
        loaded = [
            torch.load(os.path.join(input_base_path, file), map_location="cpu")
            for file in sorted(os.listdir(input_base_path))
            if file.endswith(".pth")
        ]
    param_count = 0
    index_dict = {"weight_map": {}}
    for layer_i in range(n_layers):
        filename = f"pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin"
        if num_shards == 1:
            # Unsharded
            state_dict = {
                f"model.layers.{layer_i}.self_attn.q_proj.weight": permute(
                    loaded[f"layers.{layer_i}.attention.wq.weight"], n_heads=n_heads
                ),
                f"model.layers.{layer_i}.self_attn.k_proj.weight": permute(
                    loaded[f"layers.{layer_i}.attention.wk.weight"],
                    n_heads=num_key_value_heads,
                    dim1=key_value_dim,
                ),
                f"model.layers.{layer_i}.self_attn.v_proj.weight": loaded[f"layers.{layer_i}.attention.wv.weight"],
                f"model.layers.{layer_i}.self_attn.o_proj.weight": loaded[f"layers.{layer_i}.attention.wo.weight"],
                f"model.layers.{layer_i}.mlp.gate_proj.weight": loaded[f"layers.{layer_i}.feed_forward.w1.weight"],
                f"model.layers.{layer_i}.mlp.down_proj.weight": loaded[f"layers.{layer_i}.feed_forward.w2.weight"],
                f"model.layers.{layer_i}.mlp.up_proj.weight": loaded[f"layers.{layer_i}.feed_forward.w3.weight"],
                f"model.layers.{layer_i}.input_layernorm.weight": loaded[f"layers.{layer_i}.attention_norm.weight"],
                f"model.layers.{layer_i}.post_attention_layernorm.weight": loaded[f"layers.{layer_i}.ffn_norm.weight"],
            }
        else:
            # Sharded
            # Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share
            # the same storage object, saving attention_norm and ffn_norm will save other weights too, which is
            # redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned.

            state_dict = {
                f"model.layers.{layer_i}.input_layernorm.weight": loaded[0][
                    f"layers.{layer_i}.attention_norm.weight"
                ].clone(),
                f"model.layers.{layer_i}.post_attention_layernorm.weight": loaded[0][
                    f"layers.{layer_i}.ffn_norm.weight"
                ].clone(),
            }
            state_dict[f"model.layers.{layer_i}.self_attn.q_proj.weight"] = permute(
                torch.cat(
                    [
                        loaded[i][f"layers.{layer_i}.attention.wq.weight"].view(n_heads_per_shard, dims_per_head, dim)
                        for i in range(len(loaded))
                    ],
                    dim=0,
                ).reshape(dim, dim),
                n_heads=n_heads,
            )
            state_dict[f"model.layers.{layer_i}.self_attn.k_proj.weight"] = permute(
                torch.cat(
                    [
                        loaded[i][f"layers.{layer_i}.attention.wk.weight"].view(
                            num_key_value_heads_per_shard, dims_per_head, dim
                        )
                        for i in range(len(loaded))
                    ],
                    dim=0,
                ).reshape(key_value_dim, dim),
                num_key_value_heads,
                key_value_dim,
                dim,
            )
            state_dict[f"model.layers.{layer_i}.self_attn.v_proj.weight"] = torch.cat(
                [
                    loaded[i][f"layers.{layer_i}.attention.wv.weight"].view(
                        num_key_value_heads_per_shard, dims_per_head, dim
                    )
                    for i in range(len(loaded))
                ],
                dim=0,
            ).reshape(key_value_dim, dim)

            state_dict[f"model.layers.{layer_i}.self_attn.o_proj.weight"] = torch.cat(
                [loaded[i][f"layers.{layer_i}.attention.wo.weight"] for i in range(len(loaded))], dim=1
            )
            state_dict[f"model.layers.{layer_i}.mlp.gate_proj.weight"] = torch.cat(
                [loaded[i][f"layers.{layer_i}.feed_forward.w1.weight"] for i in range(len(loaded))], dim=0
            )
            state_dict[f"model.layers.{layer_i}.mlp.down_proj.weight"] = torch.cat(
                [loaded[i][f"layers.{layer_i}.feed_forward.w2.weight"] for i in range(len(loaded))], dim=1
            )
            state_dict[f"model.layers.{layer_i}.mlp.up_proj.weight"] = torch.cat(
                [loaded[i][f"layers.{layer_i}.feed_forward.w3.weight"] for i in range(len(loaded))], dim=0
            )

        state_dict[f"model.layers.{layer_i}.self_attn.rotary_emb.inv_freq"] = inv_freq
        for k, v in state_dict.items():
            index_dict["weight_map"][k] = filename
            param_count += v.numel()
        torch.save(state_dict, os.path.join(tmp_model_path, filename))

    filename = f"pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin"
    if num_shards == 1:
        # Unsharded
        state_dict = {
            "model.embed_tokens.weight": loaded["tok_embeddings.weight"],
            "model.norm.weight": loaded["norm.weight"],
            "lm_head.weight": loaded["output.weight"],
        }
    else:
        concat_dim = 0 if llama_version == 3 else 1
        state_dict = {
            "model.norm.weight": loaded[0]["norm.weight"],
            "model.embed_tokens.weight": torch.cat(
                [loaded[i]["tok_embeddings.weight"] for i in range(len(loaded))], dim=concat_dim
            ),
            "lm_head.weight": torch.cat([loaded[i]["output.weight"] for i in range(len(loaded))], dim=0),
        }

    for k, v in state_dict.items():
        index_dict["weight_map"][k] = filename
        param_count += v.numel()
    torch.save(state_dict, os.path.join(tmp_model_path, filename))

    # Write configs
    index_dict["metadata"] = {"total_size": param_count * 2}
    write_json(index_dict, os.path.join(tmp_model_path, "pytorch_model.bin.index.json"))
    ffn_dim_multiplier = params["ffn_dim_multiplier"] if "ffn_dim_multiplier" in params else 1
    multiple_of = params["multiple_of"] if "multiple_of" in params else 256
    config = LlamaConfig(
        hidden_size=dim,
        intermediate_size=compute_intermediate_size(dim, ffn_dim_multiplier, multiple_of),
        num_attention_heads=params["n_heads"],
        num_hidden_layers=params["n_layers"],
        rms_norm_eps=params["norm_eps"],
        num_key_value_heads=num_key_value_heads,
        vocab_size=vocab_size,
        rope_theta=base,
        max_position_embeddings=max_position_embeddings,
        bos_token_id=128000 if llama_version == 3 else 1,
        eos_token_id=128001 if llama_version == 3 else 2,
    )
    config.save_pretrained(tmp_model_path)

    # Make space so we can load the model properly now.
    del state_dict
    del loaded
    gc.collect()

    print("Loading the checkpoint in a Llama model.")
    model = LlamaForCausalLM.from_pretrained(tmp_model_path, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True)
    # Avoid saving this as part of the config.
    del model.config._name_or_path
    model.config.torch_dtype = torch.float16
    print("Saving in the Transformers format.")
    model.save_pretrained(model_path, safe_serialization=safe_serialization)
    shutil.rmtree(tmp_model_path, ignore_errors=True)


class Llama3Converter(TikTokenConverter):
    def __init__(self, vocab_file, num_reserved_special_tokens=256, **kwargs):
        super().__init__(vocab_file, **kwargs)
        tokenizer = self.converted()
        chat_template = (
            "{% set loop_messages = messages %}"
            "{% for message in loop_messages %}"
            "{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}"
            "{% if loop.index0 == 0 %}"
            "{% set content = bos_token + content %}"
            "{% endif %}"
            "{{ content }}"
            "{% endfor %}"
            "{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}"
        )
        num_reserved_special_tokens = 256
        special_tokens = [
            "<|begin_of_text|>",
            "<|end_of_text|>",
            "<|reserved_special_token_0|>",
            "<|reserved_special_token_1|>",
            "<|reserved_special_token_2|>",
            "<|reserved_special_token_3|>",
            "<|start_header_id|>",
            "<|end_header_id|>",
            "<|reserved_special_token_4|>",
            "<|eot_id|>",  # end of turn
        ] + [f"<|reserved_special_token_{i}|>" for i in range(5, num_reserved_special_tokens - 5)]
        tokenizer.add_special_tokens(special_tokens)

        self.tokenizer = PreTrainedTokenizerFast(
            tokenizer_object=tokenizer,
            bos_token="<|begin_of_text|>",
            eos_token="<|end_of_text|>",
            chat_template=chat_template,
            model_input_names=["input_ids", "attention_mask"],
        )


def write_tokenizer(tokenizer_path, input_tokenizer_path, llama_version=2):
    tokenizer_class = LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
    if llama_version == 3:
        tokenizer = Llama3Converter(input_tokenizer_path).tokenizer
    else:
        tokenizer = tokenizer_class(input_tokenizer_path)
    print(f"Saving a {tokenizer_class.__name__} to {tokenizer_path}.")
    tokenizer.save_pretrained(tokenizer_path)
    return tokenizer


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--input_dir",
        help="Location of LLaMA weights, which contains tokenizer.model and model folders",
    )
    parser.add_argument(
        "--model_size",
        default=None,
        help="'f' Deprecated in favor of `num_shards`: models correspond to the finetuned versions, and are specific to the Llama2 official release. For more details on Llama2, checkout the original repo: https://huggingface.co/meta-llama",
    )
    parser.add_argument(
        "--output_dir",
        help="Location to write HF model and tokenizer",
    )
    parser.add_argument(
        "--safe_serialization", default=True, type=bool, help="Whether or not to save using `safetensors`."
    )
    # Different Llama versions used different default values for max_position_embeddings, hence the need to be able to specify which version is being used.
    parser.add_argument(
        "--llama_version",
        choices=[1, 2, 3],
        default=1,
        type=int,
        help="Version of the Llama model to convert. Currently supports Llama1 and Llama2. Controls the context size",
    )
    parser.add_argument(
        "--num_shards",
        default=None,
        type=int,
        help="The number of individual shards used for the model. Does not have to be the same as the number of consolidated_xx.pth",
    )
    args = parser.parse_args()
    if args.model_size is None and args.num_shards is None:
        raise ValueError("You have to set at least `num_shards` if you are not giving the `model_size`")
    spm_path = os.path.join(args.input_dir, "tokenizer.model")
    vocab_size = len(write_tokenizer(args.output_dir, spm_path, llama_version=args.llama_version))
    if args.model_size != "tokenizer_only":
        write_model(
            model_path=args.output_dir,
            input_base_path=args.input_dir,
            model_size=args.model_size,
            safe_serialization=args.safe_serialization,
            llama_version=args.llama_version,
            vocab_size=vocab_size,
            num_shards=args.num_shards,
        )


if __name__ == "__main__":
    main()
