import argparse
import json
import os
import shutil
import re
from collections import defaultdict
from inspect import signature
from tempfile import TemporaryDirectory
from typing import Dict, List, Optional, Set, Tuple

import torch

from huggingface_hub import CommitInfo, CommitOperationAdd, Discussion, HfApi, hf_hub_download
from huggingface_hub.file_download import repo_folder_name
from safetensors.torch import load_file, save_file
from transformers import AutoConfig


COMMIT_DESCRIPTION = """
This is an automated PR created with https://huggingface.co/spaces/safetensors/convert

This new file is equivalent to `pytorch_model.bin` but safe in the sense that
no arbitrary code can be put into it.

These files also happen to load much faster than their pytorch counterpart:
https://colab.research.google.com/github/huggingface/notebooks/blob/main/safetensors_doc/en/speed.ipynb

The widgets on your model page will run using this model even if this is not merged
making sure the file actually works.

If you find any issues: please report here: https://huggingface.co/spaces/safetensors/convert/discussions

Feel free to ignore this PR.
"""

ConversionResult = Tuple[List["CommitOperationAdd"], List[Tuple[str, "Exception"]]]


class AlreadyExists(Exception):
    pass


def shared_pointers(tensors):
    ptrs = defaultdict(list)
    for k, v in tensors.items():
        ptrs[v.data_ptr()].append(k)
    failing = []
    for ptr, names in ptrs.items():
        if len(names) > 1:
            failing.append(names)
    return failing


def check_file_size(sf_filename: str, pt_filename: str):
    sf_size = os.stat(sf_filename).st_size
    pt_size = os.stat(pt_filename).st_size

    if (sf_size - pt_size) / pt_size > 0.01:
        raise RuntimeError(
            f"""The file size different is more than 1%:
         - {sf_filename}: {sf_size}
         - {pt_filename}: {pt_size}
         """
        )


def rename(pt_filename: str) -> str:
    filename, ext = os.path.splitext(pt_filename)
    local = f"{filename}.safetensors"
    local = local.replace("pytorch_model", "model")
    return local


def convert_multi(model_id: str, folder: str, token: Optional[str]) -> ConversionResult:
    filename = hf_hub_download(repo_id=model_id, filename="pytorch_model.bin.index.json", token=token, cache_dir=folder)
    with open(filename, "r") as f:
        data = json.load(f)

    filenames = set(data["weight_map"].values())
    local_filenames = []
    for filename in filenames:
        pt_filename = hf_hub_download(repo_id=model_id, filename=filename, token=token, cache_dir=folder)

        sf_filename = rename(pt_filename)
        sf_filename = os.path.join(folder, sf_filename)
        convert_file(pt_filename, sf_filename)
        local_filenames.append(sf_filename)

    index = os.path.join(folder, "model.safetensors.index.json")
    with open(index, "w") as f:
        newdata = {k: v for k, v in data.items()}
        newmap = {k: rename(v) for k, v in data["weight_map"].items()}
        newdata["weight_map"] = newmap
        json.dump(newdata, f, indent=4)
    local_filenames.append(index)

    operations = [
        CommitOperationAdd(path_in_repo=local.split("/")[-1], path_or_fileobj=local) for local in local_filenames
    ]
    errors: List[Tuple[str, "Exception"]] = []

    return operations, errors


def convert_single(model_id: str, folder: str, token: Optional[str]) -> ConversionResult:
    pt_filename = hf_hub_download(repo_id=model_id, filename="pytorch_model.bin", token=token, cache_dir=folder)

    sf_name = "model.safetensors"
    sf_filename = os.path.join(folder, sf_name)
    convert_file(pt_filename, sf_filename)
    operations = [CommitOperationAdd(path_in_repo=sf_name, path_or_fileobj=sf_filename)]
    errors: List[Tuple[str, "Exception"]] = []
    return operations, errors


def convert_file(
    pt_filename: str,
    sf_filename: str,
):
    loaded = torch.load(pt_filename, map_location="cpu")
    if "state_dict" in loaded:
        loaded = loaded["state_dict"]
    shared = shared_pointers(loaded)
    for shared_weights in shared:
        for name in shared_weights[1:]:
            loaded.pop(name)

    # For tensors to be contiguous
    loaded = {k: v.contiguous() for k, v in loaded.items()}

    dirname = os.path.dirname(sf_filename)
    #os.makedirs(dirname, exist_ok=True)
    save_file(loaded, sf_filename, metadata={"format": "pt"})
    check_file_size(sf_filename, pt_filename)
    reloaded = load_file(sf_filename)
    for k in loaded:
        pt_tensor = loaded[k]
        sf_tensor = reloaded[k]
        if not torch.equal(pt_tensor, sf_tensor):
            raise RuntimeError(f"The output tensors do not match for key {k}")


def create_diff(pt_infos: Dict[str, List[str]], sf_infos: Dict[str, List[str]]) -> str:
    errors = []
    for key in ["missing_keys", "mismatched_keys", "unexpected_keys"]:
        pt_set = set(pt_infos[key])
        sf_set = set(sf_infos[key])

        pt_only = pt_set - sf_set
        sf_only = sf_set - pt_set

        if pt_only:
            errors.append(f"{key} : PT warnings contain {pt_only} which are not present in SF warnings")
        if sf_only:
            errors.append(f"{key} : SF warnings contain {sf_only} which are not present in PT warnings")
    return "\n".join(errors)


def check_final_model(model_id: str, folder: str, token: Optional[str]):
    config = hf_hub_download(repo_id=model_id, filename="config.json", token=token, cache_dir=folder)
    shutil.copy(config, os.path.join(folder, "config.json"))
    config = AutoConfig.from_pretrained(folder)

    import transformers

    class_ = getattr(transformers, config.architectures[0])
    (pt_model, pt_infos) = class_.from_pretrained(folder, output_loading_info=True)
    (sf_model, sf_infos) = class_.from_pretrained(folder, output_loading_info=True)

    if pt_infos != sf_infos:
        error_string = create_diff(pt_infos, sf_infos)
        raise ValueError(f"Different infos when reloading the model: {error_string}")

    pt_params = pt_model.state_dict()
    sf_params = sf_model.state_dict()

    pt_shared = shared_pointers(pt_params)
    sf_shared = shared_pointers(sf_params)
    if pt_shared != sf_shared:
        raise RuntimeError("The reconstructed model is wrong, shared tensors are different {shared_pt} != {shared_tf}")

    sig = signature(pt_model.forward)
    input_ids = torch.arange(10).unsqueeze(0)
    pixel_values = torch.randn(1, 3, 224, 224)
    input_values = torch.arange(1000).float().unsqueeze(0)
    # Hardcoded for whisper basically
    input_features = torch.zeros((1, 80, 3000))
    kwargs = {}
    if "input_ids" in sig.parameters:
        kwargs["input_ids"] = input_ids
    if "input_features" in sig.parameters:
        kwargs["input_features"] = input_features
    if "decoder_input_ids" in sig.parameters:
        kwargs["decoder_input_ids"] = input_ids
    if "pixel_values" in sig.parameters:
        kwargs["pixel_values"] = pixel_values
    if "input_values" in sig.parameters:
        kwargs["input_values"] = input_values
    if "bbox" in sig.parameters:
        kwargs["bbox"] = torch.zeros((1, 10, 4)).long()
    if "image" in sig.parameters:
        kwargs["image"] = pixel_values

    if torch.cuda.is_available():
        pt_model = pt_model.cuda()
        sf_model = sf_model.cuda()
        kwargs = {k: v.cuda() for k, v in kwargs.items()}

    try:
        pt_logits = pt_model(**kwargs)[0]
    except Exception as e:
        try:
            # Musicgen special exception.
            decoder_input_ids = torch.ones((input_ids.shape[0] * pt_model.decoder.num_codebooks, 1), dtype=torch.long)
            if torch.cuda.is_available():
                decoder_input_ids = decoder_input_ids.cuda()

            kwargs["decoder_input_ids"] = decoder_input_ids
            pt_logits = pt_model(**kwargs)[0]
        except Exception:
            raise e
    sf_logits = sf_model(**kwargs)[0]

    torch.testing.assert_close(sf_logits, pt_logits)
    print(f"Model {model_id} is ok !")


def previous_pr(api: "HfApi", model_id: str, pr_title: str) -> Optional["Discussion"]:
    try:
        main_commit = api.list_repo_commits(model_id)[0].commit_id
        discussions = api.get_repo_discussions(repo_id=model_id)
    except Exception:
        return None
    for discussion in discussions:
        if discussion.status == "open" and discussion.is_pull_request and discussion.title == pr_title:
            commits = api.list_repo_commits(model_id, revision=discussion.git_reference)

            if main_commit == commits[1].commit_id:
                return discussion
    return None


def convert_generic(model_id: str, folder: str, filenames: Set[str], token: Optional[str]) -> ConversionResult:
    operations = []
    errors = []

    extensions = set([".bin", ".ckpt"])
    for filename in filenames:
        prefix, ext = os.path.splitext(filename)
        if ext in extensions:
            pt_filename = hf_hub_download(model_id, filename=filename, token=token, cache_dir=folder)
            dirname, raw_filename = os.path.split(filename)
            if raw_filename == "pytorch_model.bin":
                # XXX: This is a special case to handle `transformers` and the
                # `transformers` part of the model which is actually loaded by `transformers`.
                sf_in_repo = os.path.join(dirname, "model.safetensors")
            else:
                sf_in_repo = f"{prefix}.safetensors"
            sf_filename = os.path.join(folder, sf_in_repo)
            try:
                convert_file(pt_filename, sf_filename)
                operations.append(CommitOperationAdd(path_in_repo=sf_in_repo, path_or_fileobj=sf_filename))
            except Exception as e:
                errors.append((pt_filename, e))
    return operations, errors


def convert(api: "HfApi", model_id: str, force: bool = False) -> Tuple["CommitInfo", List[Tuple[str, "Exception"]]]:
    pr_title = "Adding `safetensors` variant of this model"
    info = api.model_info(model_id)
    filenames = set(s.rfilename for s in info.siblings)

    with TemporaryDirectory() as d:
        folder = os.path.join(d, repo_folder_name(repo_id=model_id, repo_type="models"))
        os.makedirs(folder)
        new_pr = None
        try:
            operations = None
            pr = previous_pr(api, model_id, pr_title)

            library_name = getattr(info, "library_name", None)
            if any(filename.endswith(".safetensors") for filename in filenames) and not force:
                raise AlreadyExists(f"Model {model_id} is already converted, skipping..")
            elif pr is not None and not force:
                url = f"https://huggingface.co/{model_id}/discussions/{pr.num}"
                new_pr = pr
                raise AlreadyExists(f"Model {model_id} already has an open PR check out {url}")
            elif library_name == "transformers":
                if "pytorch_model.bin" in filenames:
                    operations, errors = convert_single(model_id, folder, token=api.token)
                elif "pytorch_model.bin.index.json" in filenames:
                    operations, errors = convert_multi(model_id, folder, token=api.token)
                else:
                    raise RuntimeError(f"Model {model_id} doesn't seem to be a valid pytorch model. Cannot convert")
                # check_final_model(model_id, folder, token=api.token)
            else:
                operations, errors = convert_generic(model_id, folder, filenames, token=api.token)

            if operations:
                new_pr = api.create_commit(
                    repo_id=model_id,
                    operations=operations,
                    commit_message=pr_title,
                    commit_description=COMMIT_DESCRIPTION,
                    create_pr=True,
                )
                print(f"Pr created at {new_pr.pr_url}")
            else:
                print("No files to convert")
        finally:
            shutil.rmtree(folder)
        return new_pr, errors


def main(input_directory, output_directory):
    # Get a list of all files in the input directory
    files = os.listdir(input_directory)
    
    # Filter the list to get only the relevant files
    model_files = [file for file in files if re.match(r'pytorch_model-\d{5}-of-\d{5}\.bin', file)]
    
    # Determine the range for the loop based on the number of model files
    num_models = len(model_files)
    
    if num_models == 0:
        print("No model files found in the input directory.")
        return
    
    # Extract yyyyy from the first model filename
    match = re.search(r'pytorch_model-\d{5}-of-(\d{5})\.bin', model_files[0])
    if match:
        yyyyy = int(match.group(1))
    else:
        print("Unable to determine the number of shards from the filename.")
        return
    
    if num_models != yyyyy:
        print("Error: Number of shards mismatch.")
        return
    
    # Copy *.json files (except pytorch_model.bin.index.json) from input to output directory
    for file in files:
        if file.endswith('.json') and not file == 'pytorch_model.bin.index.json':
            src = os.path.join(input_directory, file)
            dest = os.path.join(output_directory, file)
            shutil.copy2(src, dest)
            print(f"Copied {src} to {dest}")
    
    # Copy *.model files from input to output directory
    for file in files:
        if file.endswith('.model'):
            src = os.path.join(input_directory, file)
            dest = os.path.join(output_directory, file)
            shutil.copy2(src, dest)
            print(f"Copied {src} to {dest}")
    
    # Convert and rename model files
    for i in range(1, num_models + 1):
        input_filename = os.path.join(input_directory, f"pytorch_model-{i:05d}-of-{yyyyy:05d}.bin")
        output_filename = os.path.join(output_directory, f"model-{i:05d}-of-{yyyyy:05d}.safetensors")
        convert_file(input_filename, output_filename)
        print(f"Converted {input_filename} to {output_filename}")
    
if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Convert pytorch_model model to safetensor and copy JSON and .model files.")
    parser.add_argument("input_directory", help="Path to the input directory containing pytorch_model files")
    parser.add_argument("output_directory", help="Path to the output directory for converted safetensor files")
    args = parser.parse_args()
    
    main(args.input_directory, args.output_directory)