# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team.
# Copyright (c) 2022, NVIDIA CORPORATION.  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 importlib
import inspect
import os
from collections import OrderedDict
from pathlib import Path
from typing import List, Optional, Union

import safetensors
import torch
from huggingface_hub.utils import EntryNotFoundError

from ..quantizers.quantization_config import QuantizationMethod
from ..utils import (
    SAFE_WEIGHTS_INDEX_NAME,
    SAFETENSORS_FILE_EXTENSION,
    WEIGHTS_INDEX_NAME,
    _add_variant,
    _get_model_file,
    deprecate,
    is_accelerate_available,
    is_torch_version,
    logging,
)


logger = logging.get_logger(__name__)

_CLASS_REMAPPING_DICT = {
    "Transformer2DModel": {
        "ada_norm_zero": "DiTTransformer2DModel",
        "ada_norm_single": "PixArtTransformer2DModel",
    }
}


if is_accelerate_available():
    from accelerate import infer_auto_device_map
    from accelerate.utils import get_balanced_memory, get_max_memory, set_module_tensor_to_device


# Adapted from `transformers` (see modeling_utils.py)
def _determine_device_map(
    model: torch.nn.Module, device_map, max_memory, torch_dtype, keep_in_fp32_modules=[], hf_quantizer=None
):
    if isinstance(device_map, str):
        special_dtypes = {}
        if hf_quantizer is not None:
            special_dtypes.update(hf_quantizer.get_special_dtypes_update(model, torch_dtype))
        special_dtypes.update(
            {
                name: torch.float32
                for name, _ in model.named_parameters()
                if any(m in name for m in keep_in_fp32_modules)
            }
        )

        target_dtype = torch_dtype
        if hf_quantizer is not None:
            target_dtype = hf_quantizer.adjust_target_dtype(target_dtype)

        no_split_modules = model._get_no_split_modules(device_map)
        device_map_kwargs = {"no_split_module_classes": no_split_modules}

        if "special_dtypes" in inspect.signature(infer_auto_device_map).parameters:
            device_map_kwargs["special_dtypes"] = special_dtypes
        elif len(special_dtypes) > 0:
            logger.warning(
                "This model has some weights that should be kept in higher precision, you need to upgrade "
                "`accelerate` to properly deal with them (`pip install --upgrade accelerate`)."
            )

        if device_map != "sequential":
            max_memory = get_balanced_memory(
                model,
                dtype=torch_dtype,
                low_zero=(device_map == "balanced_low_0"),
                max_memory=max_memory,
                **device_map_kwargs,
            )
        else:
            max_memory = get_max_memory(max_memory)

        if hf_quantizer is not None:
            max_memory = hf_quantizer.adjust_max_memory(max_memory)

        device_map_kwargs["max_memory"] = max_memory
        device_map = infer_auto_device_map(model, dtype=target_dtype, **device_map_kwargs)

        if hf_quantizer is not None:
            hf_quantizer.validate_environment(device_map=device_map)

    return device_map


def _fetch_remapped_cls_from_config(config, old_class):
    previous_class_name = old_class.__name__
    remapped_class_name = _CLASS_REMAPPING_DICT.get(previous_class_name).get(config["norm_type"], None)

    # Details:
    # https://github.com/huggingface/diffusers/pull/7647#discussion_r1621344818
    if remapped_class_name:
        # load diffusers library to import compatible and original scheduler
        diffusers_library = importlib.import_module(__name__.split(".")[0])
        remapped_class = getattr(diffusers_library, remapped_class_name)
        logger.info(
            f"Changing class object to be of `{remapped_class_name}` type from `{previous_class_name}` type."
            f"This is because `{previous_class_name}` is scheduled to be deprecated in a future version. Note that this"
            " DOESN'T affect the final results."
        )
        return remapped_class
    else:
        return old_class


def load_state_dict(checkpoint_file: Union[str, os.PathLike], variant: Optional[str] = None):
    """
    Reads a checkpoint file, returning properly formatted errors if they arise.
    """
    # TODO: We merge the sharded checkpoints in case we're doing quantization. We can revisit this change
    # when refactoring the _merge_sharded_checkpoints() method later.
    if isinstance(checkpoint_file, dict):
        return checkpoint_file
    try:
        file_extension = os.path.basename(checkpoint_file).split(".")[-1]
        if file_extension == SAFETENSORS_FILE_EXTENSION:
            return safetensors.torch.load_file(checkpoint_file, device="cpu")
        else:
            weights_only_kwarg = {"weights_only": True} if is_torch_version(">=", "1.13") else {}
            return torch.load(
                checkpoint_file,
                map_location="cpu",
                **weights_only_kwarg,
            )
    except Exception as e:
        try:
            with open(checkpoint_file) as f:
                if f.read().startswith("version"):
                    raise OSError(
                        "You seem to have cloned a repository without having git-lfs installed. Please install "
                        "git-lfs and run `git lfs install` followed by `git lfs pull` in the folder "
                        "you cloned."
                    )
                else:
                    raise ValueError(
                        f"Unable to locate the file {checkpoint_file} which is necessary to load this pretrained "
                        "model. Make sure you have saved the model properly."
                    ) from e
        except (UnicodeDecodeError, ValueError):
            raise OSError(
                f"Unable to load weights from checkpoint file for '{checkpoint_file}' " f"at '{checkpoint_file}'. "
            )


def load_model_dict_into_meta(
    model,
    state_dict: OrderedDict,
    device: Optional[Union[str, torch.device]] = None,
    dtype: Optional[Union[str, torch.dtype]] = None,
    model_name_or_path: Optional[str] = None,
    hf_quantizer=None,
    keep_in_fp32_modules=None,
) -> List[str]:
    if device is not None and not isinstance(device, (str, torch.device)):
        raise ValueError(f"Expected device to have type `str` or `torch.device`, but got {type(device)=}.")
    if hf_quantizer is None:
        device = device or torch.device("cpu")
    dtype = dtype or torch.float32
    is_quantized = hf_quantizer is not None
    is_quant_method_bnb = getattr(model, "quantization_method", None) == QuantizationMethod.BITS_AND_BYTES

    accepts_dtype = "dtype" in set(inspect.signature(set_module_tensor_to_device).parameters.keys())
    empty_state_dict = model.state_dict()
    unexpected_keys = [param_name for param_name in state_dict if param_name not in empty_state_dict]

    for param_name, param in state_dict.items():
        if param_name not in empty_state_dict:
            continue

        set_module_kwargs = {}
        # We convert floating dtypes to the `dtype` passed. We also want to keep the buffers/params
        # in int/uint/bool and not cast them.
        # TODO: revisit cases when param.dtype == torch.float8_e4m3fn
        if torch.is_floating_point(param):
            if (
                keep_in_fp32_modules is not None
                and any(
                    module_to_keep_in_fp32 in param_name.split(".") for module_to_keep_in_fp32 in keep_in_fp32_modules
                )
                and dtype == torch.float16
            ):
                param = param.to(torch.float32)
                if accepts_dtype:
                    set_module_kwargs["dtype"] = torch.float32
            else:
                param = param.to(dtype)
                if accepts_dtype:
                    set_module_kwargs["dtype"] = dtype

        # bnb params are flattened.
        if empty_state_dict[param_name].shape != param.shape:
            if (
                is_quant_method_bnb
                and hf_quantizer.pre_quantized
                and hf_quantizer.check_if_quantized_param(model, param, param_name, state_dict, param_device=device)
            ):
                hf_quantizer.check_quantized_param_shape(param_name, empty_state_dict[param_name].shape, param.shape)
            elif not is_quant_method_bnb:
                model_name_or_path_str = f"{model_name_or_path} " if model_name_or_path is not None else ""
                raise ValueError(
                    f"Cannot load {model_name_or_path_str} because {param_name} expected shape {empty_state_dict[param_name]}, but got {param.shape}. If you want to instead overwrite randomly initialized weights, please make sure to pass both `low_cpu_mem_usage=False` and `ignore_mismatched_sizes=True`. For more information, see also: https://github.com/huggingface/diffusers/issues/1619#issuecomment-1345604389 as an example."
                )

        if is_quantized and (
            hf_quantizer.check_if_quantized_param(model, param, param_name, state_dict, param_device=device)
        ):
            hf_quantizer.create_quantized_param(model, param, param_name, device, state_dict, unexpected_keys)
        else:
            if accepts_dtype:
                set_module_tensor_to_device(model, param_name, device, value=param, **set_module_kwargs)
            else:
                set_module_tensor_to_device(model, param_name, device, value=param)

    return unexpected_keys


def _load_state_dict_into_model(model_to_load, state_dict: OrderedDict) -> List[str]:
    # Convert old format to new format if needed from a PyTorch state_dict
    # copy state_dict so _load_from_state_dict can modify it
    state_dict = state_dict.copy()
    error_msgs = []

    # PyTorch's `_load_from_state_dict` does not copy parameters in a module's descendants
    # so we need to apply the function recursively.
    def load(module: torch.nn.Module, prefix: str = ""):
        args = (state_dict, prefix, {}, True, [], [], error_msgs)
        module._load_from_state_dict(*args)

        for name, child in module._modules.items():
            if child is not None:
                load(child, prefix + name + ".")

    load(model_to_load)

    return error_msgs


def _fetch_index_file(
    is_local,
    pretrained_model_name_or_path,
    subfolder,
    use_safetensors,
    cache_dir,
    variant,
    force_download,
    proxies,
    local_files_only,
    token,
    revision,
    user_agent,
    commit_hash,
):
    if is_local:
        index_file = Path(
            pretrained_model_name_or_path,
            subfolder or "",
            _add_variant(SAFE_WEIGHTS_INDEX_NAME if use_safetensors else WEIGHTS_INDEX_NAME, variant),
        )
    else:
        index_file_in_repo = Path(
            subfolder or "",
            _add_variant(SAFE_WEIGHTS_INDEX_NAME if use_safetensors else WEIGHTS_INDEX_NAME, variant),
        ).as_posix()
        try:
            index_file = _get_model_file(
                pretrained_model_name_or_path,
                weights_name=index_file_in_repo,
                cache_dir=cache_dir,
                force_download=force_download,
                proxies=proxies,
                local_files_only=local_files_only,
                token=token,
                revision=revision,
                subfolder=None,
                user_agent=user_agent,
                commit_hash=commit_hash,
            )
            index_file = Path(index_file)
        except (EntryNotFoundError, EnvironmentError):
            index_file = None

    return index_file


# Adapted from
# https://github.com/bghira/SimpleTuner/blob/cea2457ab063f6dedb9e697830ae68a96be90641/helpers/training/save_hooks.py#L64
def _merge_sharded_checkpoints(sharded_ckpt_cached_folder, sharded_metadata):
    weight_map = sharded_metadata.get("weight_map", None)
    if weight_map is None:
        raise KeyError("'weight_map' key not found in the shard index file.")

    # Collect all unique safetensors files from weight_map
    files_to_load = set(weight_map.values())
    is_safetensors = all(f.endswith(".safetensors") for f in files_to_load)
    merged_state_dict = {}

    # Load tensors from each unique file
    for file_name in files_to_load:
        part_file_path = os.path.join(sharded_ckpt_cached_folder, file_name)
        if not os.path.exists(part_file_path):
            raise FileNotFoundError(f"Part file {file_name} not found.")

        if is_safetensors:
            with safetensors.safe_open(part_file_path, framework="pt", device="cpu") as f:
                for tensor_key in f.keys():
                    if tensor_key in weight_map:
                        merged_state_dict[tensor_key] = f.get_tensor(tensor_key)
        else:
            merged_state_dict.update(torch.load(part_file_path, weights_only=True, map_location="cpu"))

    return merged_state_dict


def _fetch_index_file_legacy(
    is_local,
    pretrained_model_name_or_path,
    subfolder,
    use_safetensors,
    cache_dir,
    variant,
    force_download,
    proxies,
    local_files_only,
    token,
    revision,
    user_agent,
    commit_hash,
):
    if is_local:
        index_file = Path(
            pretrained_model_name_or_path,
            subfolder or "",
            SAFE_WEIGHTS_INDEX_NAME if use_safetensors else WEIGHTS_INDEX_NAME,
        ).as_posix()
        splits = index_file.split(".")
        split_index = -3 if ".cache" in index_file else -2
        splits = splits[:-split_index] + [variant] + splits[-split_index:]
        index_file = ".".join(splits)
        if os.path.exists(index_file):
            deprecation_message = f"This serialization format is now deprecated to standardize the serialization format between `transformers` and `diffusers`. We recommend you to remove the existing files associated with the current variant ({variant}) and re-obtain them by running a `save_pretrained()`."
            deprecate("legacy_sharded_ckpts_with_variant", "1.0.0", deprecation_message, standard_warn=False)
            index_file = Path(index_file)
        else:
            index_file = None
    else:
        if variant is not None:
            index_file_in_repo = Path(
                subfolder or "",
                SAFE_WEIGHTS_INDEX_NAME if use_safetensors else WEIGHTS_INDEX_NAME,
            ).as_posix()
            splits = index_file_in_repo.split(".")
            split_index = -2
            splits = splits[:-split_index] + [variant] + splits[-split_index:]
            index_file_in_repo = ".".join(splits)
            try:
                index_file = _get_model_file(
                    pretrained_model_name_or_path,
                    weights_name=index_file_in_repo,
                    cache_dir=cache_dir,
                    force_download=force_download,
                    proxies=proxies,
                    local_files_only=local_files_only,
                    token=token,
                    revision=revision,
                    subfolder=None,
                    user_agent=user_agent,
                    commit_hash=commit_hash,
                )
                index_file = Path(index_file)
                deprecation_message = f"This serialization format is now deprecated to standardize the serialization format between `transformers` and `diffusers`. We recommend you to remove the existing files associated with the current variant ({variant}) and re-obtain them by running a `save_pretrained()`."
                deprecate("legacy_sharded_ckpts_with_variant", "1.0.0", deprecation_message, standard_warn=False)
            except (EntryNotFoundError, EnvironmentError):
                index_file = None

    return index_file
