# coding=utf-8
# Copyright 2025 The HuggingFace Inc. team.
#
# 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 os
import re
import warnings
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Union

import requests
import torch
from huggingface_hub import DDUFEntry, ModelCard, model_info, snapshot_download
from huggingface_hub.utils import OfflineModeIsEnabled, validate_hf_hub_args
from packaging import version
from requests.exceptions import HTTPError

from .. import __version__
from ..utils import (
    FLAX_WEIGHTS_NAME,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    WEIGHTS_NAME,
    deprecate,
    get_class_from_dynamic_module,
    is_accelerate_available,
    is_peft_available,
    is_transformers_available,
    is_transformers_version,
    logging,
)
from ..utils.torch_utils import is_compiled_module
from .transformers_loading_utils import _load_tokenizer_from_dduf, _load_transformers_model_from_dduf


if is_transformers_available():
    import transformers
    from transformers import PreTrainedModel, PreTrainedTokenizerBase
    from transformers.utils import FLAX_WEIGHTS_NAME as TRANSFORMERS_FLAX_WEIGHTS_NAME
    from transformers.utils import SAFE_WEIGHTS_NAME as TRANSFORMERS_SAFE_WEIGHTS_NAME
    from transformers.utils import WEIGHTS_NAME as TRANSFORMERS_WEIGHTS_NAME

if is_accelerate_available():
    import accelerate
    from accelerate import dispatch_model
    from accelerate.hooks import remove_hook_from_module
    from accelerate.utils import compute_module_sizes, get_max_memory


INDEX_FILE = "diffusion_pytorch_model.bin"
CUSTOM_PIPELINE_FILE_NAME = "pipeline.py"
DUMMY_MODULES_FOLDER = "diffusers.utils"
TRANSFORMERS_DUMMY_MODULES_FOLDER = "transformers.utils"
CONNECTED_PIPES_KEYS = ["prior"]

logger = logging.get_logger(__name__)

LOADABLE_CLASSES = {
    "diffusers": {
        "ModelMixin": ["save_pretrained", "from_pretrained"],
        "SchedulerMixin": ["save_pretrained", "from_pretrained"],
        "DiffusionPipeline": ["save_pretrained", "from_pretrained"],
        "OnnxRuntimeModel": ["save_pretrained", "from_pretrained"],
    },
    "transformers": {
        "PreTrainedTokenizer": ["save_pretrained", "from_pretrained"],
        "PreTrainedTokenizerFast": ["save_pretrained", "from_pretrained"],
        "PreTrainedModel": ["save_pretrained", "from_pretrained"],
        "FeatureExtractionMixin": ["save_pretrained", "from_pretrained"],
        "ProcessorMixin": ["save_pretrained", "from_pretrained"],
        "ImageProcessingMixin": ["save_pretrained", "from_pretrained"],
    },
    "onnxruntime.training": {
        "ORTModule": ["save_pretrained", "from_pretrained"],
    },
}

ALL_IMPORTABLE_CLASSES = {}
for library in LOADABLE_CLASSES:
    ALL_IMPORTABLE_CLASSES.update(LOADABLE_CLASSES[library])


def is_safetensors_compatible(filenames, passed_components=None, folder_names=None) -> bool:
    """
    Checking for safetensors compatibility:
    - The model is safetensors compatible only if there is a safetensors file for each model component present in
      filenames.

    Converting default pytorch serialized filenames to safetensors serialized filenames:
    - For models from the diffusers library, just replace the ".bin" extension with ".safetensors"
    - For models from the transformers library, the filename changes from "pytorch_model" to "model", and the ".bin"
      extension is replaced with ".safetensors"
    """
    passed_components = passed_components or []
    if folder_names:
        filenames = {f for f in filenames if os.path.split(f)[0] in folder_names}

    # extract all components of the pipeline and their associated files
    components = {}
    for filename in filenames:
        if not len(filename.split("/")) == 2:
            continue

        component, component_filename = filename.split("/")
        if component in passed_components:
            continue

        components.setdefault(component, [])
        components[component].append(component_filename)

    # If there are no component folders check the main directory for safetensors files
    if not components:
        return any(".safetensors" in filename for filename in filenames)

    # iterate over all files of a component
    # check if safetensor files exist for that component
    # if variant is provided check if the variant of the safetensors exists
    for component, component_filenames in components.items():
        matches = []
        for component_filename in component_filenames:
            filename, extension = os.path.splitext(component_filename)

            match_exists = extension == ".safetensors"
            matches.append(match_exists)

        if not any(matches):
            return False

    return True


def filter_model_files(filenames):
    """Filter model repo files for just files/folders that contain model weights"""
    weight_names = [
        WEIGHTS_NAME,
        SAFETENSORS_WEIGHTS_NAME,
        FLAX_WEIGHTS_NAME,
        ONNX_WEIGHTS_NAME,
        ONNX_EXTERNAL_WEIGHTS_NAME,
    ]

    if is_transformers_available():
        weight_names += [TRANSFORMERS_WEIGHTS_NAME, TRANSFORMERS_SAFE_WEIGHTS_NAME, TRANSFORMERS_FLAX_WEIGHTS_NAME]

    allowed_extensions = [wn.split(".")[-1] for wn in weight_names]

    return [f for f in filenames if any(f.endswith(extension) for extension in allowed_extensions)]


def variant_compatible_siblings(filenames, variant=None, ignore_patterns=None) -> Union[List[os.PathLike], str]:
    weight_names = [
        WEIGHTS_NAME,
        SAFETENSORS_WEIGHTS_NAME,
        FLAX_WEIGHTS_NAME,
        ONNX_WEIGHTS_NAME,
        ONNX_EXTERNAL_WEIGHTS_NAME,
    ]

    if is_transformers_available():
        weight_names += [TRANSFORMERS_WEIGHTS_NAME, TRANSFORMERS_SAFE_WEIGHTS_NAME, TRANSFORMERS_FLAX_WEIGHTS_NAME]

    # model_pytorch, diffusion_model_pytorch, ...
    weight_prefixes = [w.split(".")[0] for w in weight_names]
    # .bin, .safetensors, ...
    weight_suffixs = [w.split(".")[-1] for w in weight_names]
    # -00001-of-00002
    transformers_index_format = r"\d{5}-of-\d{5}"

    if variant is not None:
        # `diffusion_pytorch_model.fp16.bin` as well as `model.fp16-00001-of-00002.safetensors`
        variant_file_re = re.compile(
            rf"({'|'.join(weight_prefixes)})\.({variant}|{variant}-{transformers_index_format})\.({'|'.join(weight_suffixs)})$"
        )
        # `text_encoder/pytorch_model.bin.index.fp16.json`
        variant_index_re = re.compile(
            rf"({'|'.join(weight_prefixes)})\.({'|'.join(weight_suffixs)})\.index\.{variant}\.json$"
        )
        legacy_variant_file_re = re.compile(rf".*-{transformers_index_format}\.{variant}\.[a-z]+$")
        legacy_variant_index_re = re.compile(
            rf"({'|'.join(weight_prefixes)})\.({'|'.join(weight_suffixs)})\.{variant}\.index\.json$"
        )

    # `diffusion_pytorch_model.bin` as well as `model-00001-of-00002.safetensors`
    non_variant_file_re = re.compile(
        rf"({'|'.join(weight_prefixes)})(-{transformers_index_format})?\.({'|'.join(weight_suffixs)})$"
    )
    # `text_encoder/pytorch_model.bin.index.json`
    non_variant_index_re = re.compile(rf"({'|'.join(weight_prefixes)})\.({'|'.join(weight_suffixs)})\.index\.json")

    def filter_for_compatible_extensions(filenames, ignore_patterns=None):
        if not ignore_patterns:
            return filenames

        # ignore patterns uses glob style patterns e.g *.safetensors but we're only
        # interested in the extension name
        return {f for f in filenames if not any(f.endswith(pat.lstrip("*.")) for pat in ignore_patterns)}

    def filter_with_regex(filenames, pattern_re):
        return {f for f in filenames if pattern_re.match(f.split("/")[-1]) is not None}

    # Group files by component
    components = {}
    for filename in filenames:
        if not len(filename.split("/")) == 2:
            components.setdefault("", []).append(filename)
            continue

        component, _ = filename.split("/")
        components.setdefault(component, []).append(filename)

    usable_filenames = set()
    variant_filenames = set()
    for component, component_filenames in components.items():
        component_filenames = filter_for_compatible_extensions(component_filenames, ignore_patterns=ignore_patterns)

        component_variants = set()
        component_legacy_variants = set()
        component_non_variants = set()
        if variant is not None:
            component_variants = filter_with_regex(component_filenames, variant_file_re)
            component_variant_index_files = filter_with_regex(component_filenames, variant_index_re)

            component_legacy_variants = filter_with_regex(component_filenames, legacy_variant_file_re)
            component_legacy_variant_index_files = filter_with_regex(component_filenames, legacy_variant_index_re)

        if component_variants or component_legacy_variants:
            variant_filenames.update(
                component_variants | component_variant_index_files
                if component_variants
                else component_legacy_variants | component_legacy_variant_index_files
            )

        else:
            component_non_variants = filter_with_regex(component_filenames, non_variant_file_re)
            component_variant_index_files = filter_with_regex(component_filenames, non_variant_index_re)

            usable_filenames.update(component_non_variants | component_variant_index_files)

    usable_filenames.update(variant_filenames)

    if len(variant_filenames) == 0 and variant is not None:
        error_message = f"You are trying to load model files of the `variant={variant}`, but no such modeling files are available. "
        raise ValueError(error_message)

    if len(variant_filenames) > 0 and usable_filenames != variant_filenames:
        logger.warning(
            f"\nA mixture of {variant} and non-{variant} filenames will be loaded.\nLoaded {variant} filenames:\n"
            f"[{', '.join(variant_filenames)}]\nLoaded non-{variant} filenames:\n"
            f"[{', '.join(usable_filenames - variant_filenames)}\nIf this behavior is not "
            f"expected, please check your folder structure."
        )

    return usable_filenames, variant_filenames


@validate_hf_hub_args
def warn_deprecated_model_variant(pretrained_model_name_or_path, token, variant, revision, model_filenames):
    info = model_info(
        pretrained_model_name_or_path,
        token=token,
        revision=None,
    )
    filenames = {sibling.rfilename for sibling in info.siblings}
    comp_model_filenames, _ = variant_compatible_siblings(filenames, variant=revision)
    comp_model_filenames = [".".join(f.split(".")[:1] + f.split(".")[2:]) for f in comp_model_filenames]

    if set(model_filenames).issubset(set(comp_model_filenames)):
        warnings.warn(
            f"You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'` even though you can load it via `variant=`{revision}`. Loading model variants via `revision='{revision}'` is deprecated and will be removed in diffusers v1. Please use `variant='{revision}'` instead.",
            FutureWarning,
        )
    else:
        warnings.warn(
            f"You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant='{revision}'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have the required variant filenames in the 'main' branch. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title '{pretrained_model_name_or_path} is missing {revision} files' so that the correct variant file can be added.",
            FutureWarning,
        )


def _unwrap_model(model):
    """Unwraps a model."""
    if is_compiled_module(model):
        model = model._orig_mod

    if is_peft_available():
        from peft import PeftModel

        if isinstance(model, PeftModel):
            model = model.base_model.model

    return model


def maybe_raise_or_warn(
    library_name, library, class_name, importable_classes, passed_class_obj, name, is_pipeline_module
):
    """Simple helper method to raise or warn in case incorrect module has been passed"""
    if not is_pipeline_module:
        library = importlib.import_module(library_name)
        class_obj = getattr(library, class_name)
        class_candidates = {c: getattr(library, c, None) for c in importable_classes.keys()}

        expected_class_obj = None
        for class_name, class_candidate in class_candidates.items():
            if class_candidate is not None and issubclass(class_obj, class_candidate):
                expected_class_obj = class_candidate

        # Dynamo wraps the original model in a private class.
        # I didn't find a public API to get the original class.
        sub_model = passed_class_obj[name]
        unwrapped_sub_model = _unwrap_model(sub_model)
        model_cls = unwrapped_sub_model.__class__

        if not issubclass(model_cls, expected_class_obj):
            raise ValueError(
                f"{passed_class_obj[name]} is of type: {model_cls}, but should be" f" {expected_class_obj}"
            )
    else:
        logger.warning(
            f"You have passed a non-standard module {passed_class_obj[name]}. We cannot verify whether it"
            " has the correct type"
        )


def get_class_obj_and_candidates(
    library_name, class_name, importable_classes, pipelines, is_pipeline_module, component_name=None, cache_dir=None
):
    """Simple helper method to retrieve class object of module as well as potential parent class objects"""
    component_folder = os.path.join(cache_dir, component_name)

    if is_pipeline_module:
        pipeline_module = getattr(pipelines, library_name)

        class_obj = getattr(pipeline_module, class_name)
        class_candidates = {c: class_obj for c in importable_classes.keys()}
    elif os.path.isfile(os.path.join(component_folder, library_name + ".py")):
        # load custom component
        class_obj = get_class_from_dynamic_module(
            component_folder, module_file=library_name + ".py", class_name=class_name
        )
        class_candidates = {c: class_obj for c in importable_classes.keys()}
    else:
        # else we just import it from the library.
        library = importlib.import_module(library_name)

        class_obj = getattr(library, class_name)
        class_candidates = {c: getattr(library, c, None) for c in importable_classes.keys()}

    return class_obj, class_candidates


def _get_custom_pipeline_class(
    custom_pipeline,
    repo_id=None,
    hub_revision=None,
    class_name=None,
    cache_dir=None,
    revision=None,
):
    if custom_pipeline.endswith(".py"):
        path = Path(custom_pipeline)
        # decompose into folder & file
        file_name = path.name
        custom_pipeline = path.parent.absolute()
    elif repo_id is not None:
        file_name = f"{custom_pipeline}.py"
        custom_pipeline = repo_id
    else:
        file_name = CUSTOM_PIPELINE_FILE_NAME

    if repo_id is not None and hub_revision is not None:
        # if we load the pipeline code from the Hub
        # make sure to overwrite the `revision`
        revision = hub_revision

    return get_class_from_dynamic_module(
        custom_pipeline,
        module_file=file_name,
        class_name=class_name,
        cache_dir=cache_dir,
        revision=revision,
    )


def _get_pipeline_class(
    class_obj,
    config=None,
    load_connected_pipeline=False,
    custom_pipeline=None,
    repo_id=None,
    hub_revision=None,
    class_name=None,
    cache_dir=None,
    revision=None,
):
    if custom_pipeline is not None:
        return _get_custom_pipeline_class(
            custom_pipeline,
            repo_id=repo_id,
            hub_revision=hub_revision,
            class_name=class_name,
            cache_dir=cache_dir,
            revision=revision,
        )

    if class_obj.__name__ != "DiffusionPipeline":
        return class_obj

    diffusers_module = importlib.import_module(class_obj.__module__.split(".")[0])
    class_name = class_name or config["_class_name"]
    if not class_name:
        raise ValueError(
            "The class name could not be found in the configuration file. Please make sure to pass the correct `class_name`."
        )

    class_name = class_name[4:] if class_name.startswith("Flax") else class_name

    pipeline_cls = getattr(diffusers_module, class_name)

    if load_connected_pipeline:
        from .auto_pipeline import _get_connected_pipeline

        connected_pipeline_cls = _get_connected_pipeline(pipeline_cls)
        if connected_pipeline_cls is not None:
            logger.info(
                f"Loading connected pipeline {connected_pipeline_cls.__name__} instead of {pipeline_cls.__name__} as specified via `load_connected_pipeline=True`"
            )
        else:
            logger.info(f"{pipeline_cls.__name__} has no connected pipeline class. Loading {pipeline_cls.__name__}.")

        pipeline_cls = connected_pipeline_cls or pipeline_cls

    return pipeline_cls


def _load_empty_model(
    library_name: str,
    class_name: str,
    importable_classes: List[Any],
    pipelines: Any,
    is_pipeline_module: bool,
    name: str,
    torch_dtype: Union[str, torch.dtype],
    cached_folder: Union[str, os.PathLike],
    **kwargs,
):
    # retrieve class objects.
    class_obj, _ = get_class_obj_and_candidates(
        library_name,
        class_name,
        importable_classes,
        pipelines,
        is_pipeline_module,
        component_name=name,
        cache_dir=cached_folder,
    )

    if is_transformers_available():
        transformers_version = version.parse(version.parse(transformers.__version__).base_version)
    else:
        transformers_version = "N/A"

    # Determine library.
    is_transformers_model = (
        is_transformers_available()
        and issubclass(class_obj, PreTrainedModel)
        and transformers_version >= version.parse("4.20.0")
    )
    diffusers_module = importlib.import_module(__name__.split(".")[0])
    is_diffusers_model = issubclass(class_obj, diffusers_module.ModelMixin)

    model = None
    config_path = cached_folder
    user_agent = {
        "diffusers": __version__,
        "file_type": "model",
        "framework": "pytorch",
    }

    if is_diffusers_model:
        # Load config and then the model on meta.
        config, unused_kwargs, commit_hash = class_obj.load_config(
            os.path.join(config_path, name),
            cache_dir=cached_folder,
            return_unused_kwargs=True,
            return_commit_hash=True,
            force_download=kwargs.pop("force_download", False),
            proxies=kwargs.pop("proxies", None),
            local_files_only=kwargs.pop("local_files_only", False),
            token=kwargs.pop("token", None),
            revision=kwargs.pop("revision", None),
            subfolder=kwargs.pop("subfolder", None),
            user_agent=user_agent,
        )
        with accelerate.init_empty_weights():
            model = class_obj.from_config(config, **unused_kwargs)
    elif is_transformers_model:
        config_class = getattr(class_obj, "config_class", None)
        if config_class is None:
            raise ValueError("`config_class` cannot be None. Please double-check the model.")

        config = config_class.from_pretrained(
            cached_folder,
            subfolder=name,
            force_download=kwargs.pop("force_download", False),
            proxies=kwargs.pop("proxies", None),
            local_files_only=kwargs.pop("local_files_only", False),
            token=kwargs.pop("token", None),
            revision=kwargs.pop("revision", None),
            user_agent=user_agent,
        )
        with accelerate.init_empty_weights():
            model = class_obj(config)

    if model is not None:
        model = model.to(dtype=torch_dtype)
    return model


def _assign_components_to_devices(
    module_sizes: Dict[str, float], device_memory: Dict[str, float], device_mapping_strategy: str = "balanced"
):
    device_ids = list(device_memory.keys())
    device_cycle = device_ids + device_ids[::-1]
    device_memory = device_memory.copy()

    device_id_component_mapping = {}
    current_device_index = 0
    for component in module_sizes:
        device_id = device_cycle[current_device_index % len(device_cycle)]
        component_memory = module_sizes[component]
        curr_device_memory = device_memory[device_id]

        # If the GPU doesn't fit the current component offload to the CPU.
        if component_memory > curr_device_memory:
            device_id_component_mapping["cpu"] = [component]
        else:
            if device_id not in device_id_component_mapping:
                device_id_component_mapping[device_id] = [component]
            else:
                device_id_component_mapping[device_id].append(component)

            # Update the device memory.
            device_memory[device_id] -= component_memory
            current_device_index += 1

    return device_id_component_mapping


def _get_final_device_map(device_map, pipeline_class, passed_class_obj, init_dict, library, max_memory, **kwargs):
    # To avoid circular import problem.
    from diffusers import pipelines

    torch_dtype = kwargs.get("torch_dtype", torch.float32)

    # Load each module in the pipeline on a meta device so that we can derive the device map.
    init_empty_modules = {}
    for name, (library_name, class_name) in init_dict.items():
        if class_name.startswith("Flax"):
            raise ValueError("Flax pipelines are not supported with `device_map`.")

        # Define all importable classes
        is_pipeline_module = hasattr(pipelines, library_name)
        importable_classes = ALL_IMPORTABLE_CLASSES
        loaded_sub_model = None

        # Use passed sub model or load class_name from library_name
        if name in passed_class_obj:
            # if the model is in a pipeline module, then we load it from the pipeline
            # check that passed_class_obj has correct parent class
            maybe_raise_or_warn(
                library_name,
                library,
                class_name,
                importable_classes,
                passed_class_obj,
                name,
                is_pipeline_module,
            )
            with accelerate.init_empty_weights():
                loaded_sub_model = passed_class_obj[name]

        else:
            sub_model_dtype = (
                torch_dtype.get(name, torch_dtype.get("default", torch.float32))
                if isinstance(torch_dtype, dict)
                else torch_dtype
            )
            loaded_sub_model = _load_empty_model(
                library_name=library_name,
                class_name=class_name,
                importable_classes=importable_classes,
                pipelines=pipelines,
                is_pipeline_module=is_pipeline_module,
                pipeline_class=pipeline_class,
                name=name,
                torch_dtype=sub_model_dtype,
                cached_folder=kwargs.get("cached_folder", None),
                force_download=kwargs.get("force_download", None),
                proxies=kwargs.get("proxies", None),
                local_files_only=kwargs.get("local_files_only", None),
                token=kwargs.get("token", None),
                revision=kwargs.get("revision", None),
            )

        if loaded_sub_model is not None:
            init_empty_modules[name] = loaded_sub_model

    # determine device map
    # Obtain a sorted dictionary for mapping the model-level components
    # to their sizes.
    module_sizes = {
        module_name: compute_module_sizes(
            module,
            dtype=torch_dtype.get(module_name, torch_dtype.get("default", torch.float32))
            if isinstance(torch_dtype, dict)
            else torch_dtype,
        )[""]
        for module_name, module in init_empty_modules.items()
        if isinstance(module, torch.nn.Module)
    }
    module_sizes = dict(sorted(module_sizes.items(), key=lambda item: item[1], reverse=True))

    # Obtain maximum memory available per device (GPUs only).
    max_memory = get_max_memory(max_memory)
    max_memory = dict(sorted(max_memory.items(), key=lambda item: item[1], reverse=True))
    max_memory = {k: v for k, v in max_memory.items() if k != "cpu"}

    # Obtain a dictionary mapping the model-level components to the available
    # devices based on the maximum memory and the model sizes.
    final_device_map = None
    if len(max_memory) > 0:
        device_id_component_mapping = _assign_components_to_devices(
            module_sizes, max_memory, device_mapping_strategy=device_map
        )

        # Obtain the final device map, e.g., `{"unet": 0, "text_encoder": 1, "vae": 1, ...}`
        final_device_map = {}
        for device_id, components in device_id_component_mapping.items():
            for component in components:
                final_device_map[component] = device_id

    return final_device_map


def load_sub_model(
    library_name: str,
    class_name: str,
    importable_classes: List[Any],
    pipelines: Any,
    is_pipeline_module: bool,
    pipeline_class: Any,
    torch_dtype: torch.dtype,
    provider: Any,
    sess_options: Any,
    device_map: Optional[Union[Dict[str, torch.device], str]],
    max_memory: Optional[Dict[Union[int, str], Union[int, str]]],
    offload_folder: Optional[Union[str, os.PathLike]],
    offload_state_dict: bool,
    model_variants: Dict[str, str],
    name: str,
    from_flax: bool,
    variant: str,
    low_cpu_mem_usage: bool,
    cached_folder: Union[str, os.PathLike],
    use_safetensors: bool,
    dduf_entries: Optional[Dict[str, DDUFEntry]],
    provider_options: Any,
):
    """Helper method to load the module `name` from `library_name` and `class_name`"""

    # retrieve class candidates

    class_obj, class_candidates = get_class_obj_and_candidates(
        library_name,
        class_name,
        importable_classes,
        pipelines,
        is_pipeline_module,
        component_name=name,
        cache_dir=cached_folder,
    )

    load_method_name = None
    # retrieve load method name
    for class_name, class_candidate in class_candidates.items():
        if class_candidate is not None and issubclass(class_obj, class_candidate):
            load_method_name = importable_classes[class_name][1]

    # if load method name is None, then we have a dummy module -> raise Error
    if load_method_name is None:
        none_module = class_obj.__module__
        is_dummy_path = none_module.startswith(DUMMY_MODULES_FOLDER) or none_module.startswith(
            TRANSFORMERS_DUMMY_MODULES_FOLDER
        )
        if is_dummy_path and "dummy" in none_module:
            # call class_obj for nice error message of missing requirements
            class_obj()

        raise ValueError(
            f"The component {class_obj} of {pipeline_class} cannot be loaded as it does not seem to have"
            f" any of the loading methods defined in {ALL_IMPORTABLE_CLASSES}."
        )

    load_method = _get_load_method(class_obj, load_method_name, is_dduf=dduf_entries is not None)

    # add kwargs to loading method
    diffusers_module = importlib.import_module(__name__.split(".")[0])
    loading_kwargs = {}
    if issubclass(class_obj, torch.nn.Module):
        loading_kwargs["torch_dtype"] = torch_dtype
    if issubclass(class_obj, diffusers_module.OnnxRuntimeModel):
        loading_kwargs["provider"] = provider
        loading_kwargs["sess_options"] = sess_options
        loading_kwargs["provider_options"] = provider_options

    is_diffusers_model = issubclass(class_obj, diffusers_module.ModelMixin)

    if is_transformers_available():
        transformers_version = version.parse(version.parse(transformers.__version__).base_version)
    else:
        transformers_version = "N/A"

    is_transformers_model = (
        is_transformers_available()
        and issubclass(class_obj, PreTrainedModel)
        and transformers_version >= version.parse("4.20.0")
    )

    # When loading a transformers model, if the device_map is None, the weights will be initialized as opposed to diffusers.
    # To make default loading faster we set the `low_cpu_mem_usage=low_cpu_mem_usage` flag which is `True` by default.
    # This makes sure that the weights won't be initialized which significantly speeds up loading.
    if is_diffusers_model or is_transformers_model:
        loading_kwargs["device_map"] = device_map
        loading_kwargs["max_memory"] = max_memory
        loading_kwargs["offload_folder"] = offload_folder
        loading_kwargs["offload_state_dict"] = offload_state_dict
        loading_kwargs["variant"] = model_variants.pop(name, None)
        loading_kwargs["use_safetensors"] = use_safetensors

        if from_flax:
            loading_kwargs["from_flax"] = True

        # the following can be deleted once the minimum required `transformers` version
        # is higher than 4.27
        if (
            is_transformers_model
            and loading_kwargs["variant"] is not None
            and transformers_version < version.parse("4.27.0")
        ):
            raise ImportError(
                f"When passing `variant='{variant}'`, please make sure to upgrade your `transformers` version to at least 4.27.0.dev0"
            )
        elif is_transformers_model and loading_kwargs["variant"] is None:
            loading_kwargs.pop("variant")

        # if `from_flax` and model is transformer model, can currently not load with `low_cpu_mem_usage`
        if not (from_flax and is_transformers_model):
            loading_kwargs["low_cpu_mem_usage"] = low_cpu_mem_usage
        else:
            loading_kwargs["low_cpu_mem_usage"] = False

    # check if the module is in a subdirectory
    if dduf_entries:
        loading_kwargs["dduf_entries"] = dduf_entries
        loaded_sub_model = load_method(name, **loading_kwargs)
    elif os.path.isdir(os.path.join(cached_folder, name)):
        loaded_sub_model = load_method(os.path.join(cached_folder, name), **loading_kwargs)
    else:
        # else load from the root directory
        loaded_sub_model = load_method(cached_folder, **loading_kwargs)

    if isinstance(loaded_sub_model, torch.nn.Module) and isinstance(device_map, dict):
        # remove hooks
        remove_hook_from_module(loaded_sub_model, recurse=True)
        needs_offloading_to_cpu = device_map[""] == "cpu"

        if needs_offloading_to_cpu:
            dispatch_model(
                loaded_sub_model,
                state_dict=loaded_sub_model.state_dict(),
                device_map=device_map,
                force_hooks=True,
                main_device=0,
            )
        else:
            dispatch_model(loaded_sub_model, device_map=device_map, force_hooks=True)

    return loaded_sub_model


def _get_load_method(class_obj: object, load_method_name: str, is_dduf: bool) -> Callable:
    """
    Return the method to load the sub model.

    In practice, this method will return the `"from_pretrained"` (or `load_method_name`) method of the class object
    except if loading from a DDUF checkpoint. In that case, transformers models and tokenizers have a specific loading
    method that we need to use.
    """
    if is_dduf:
        if issubclass(class_obj, PreTrainedTokenizerBase):
            return lambda *args, **kwargs: _load_tokenizer_from_dduf(class_obj, *args, **kwargs)
        if issubclass(class_obj, PreTrainedModel):
            return lambda *args, **kwargs: _load_transformers_model_from_dduf(class_obj, *args, **kwargs)
    return getattr(class_obj, load_method_name)


def _fetch_class_library_tuple(module):
    # import it here to avoid circular import
    diffusers_module = importlib.import_module(__name__.split(".")[0])
    pipelines = getattr(diffusers_module, "pipelines")

    # register the config from the original module, not the dynamo compiled one
    not_compiled_module = _unwrap_model(module)
    library = not_compiled_module.__module__.split(".")[0]

    # check if the module is a pipeline module
    module_path_items = not_compiled_module.__module__.split(".")
    pipeline_dir = module_path_items[-2] if len(module_path_items) > 2 else None

    path = not_compiled_module.__module__.split(".")
    is_pipeline_module = pipeline_dir in path and hasattr(pipelines, pipeline_dir)

    # if library is not in LOADABLE_CLASSES, then it is a custom module.
    # Or if it's a pipeline module, then the module is inside the pipeline
    # folder so we set the library to module name.
    if is_pipeline_module:
        library = pipeline_dir
    elif library not in LOADABLE_CLASSES:
        library = not_compiled_module.__module__

    # retrieve class_name
    class_name = not_compiled_module.__class__.__name__

    return (library, class_name)


def _identify_model_variants(folder: str, variant: str, config: dict) -> dict:
    model_variants = {}
    if variant is not None:
        for sub_folder in os.listdir(folder):
            folder_path = os.path.join(folder, sub_folder)
            is_folder = os.path.isdir(folder_path) and sub_folder in config
            variant_exists = is_folder and any(p.split(".")[1].startswith(variant) for p in os.listdir(folder_path))
            if variant_exists:
                model_variants[sub_folder] = variant
    return model_variants


def _resolve_custom_pipeline_and_cls(folder, config, custom_pipeline):
    custom_class_name = None
    if os.path.isfile(os.path.join(folder, f"{custom_pipeline}.py")):
        custom_pipeline = os.path.join(folder, f"{custom_pipeline}.py")
    elif isinstance(config["_class_name"], (list, tuple)) and os.path.isfile(
        os.path.join(folder, f"{config['_class_name'][0]}.py")
    ):
        custom_pipeline = os.path.join(folder, f"{config['_class_name'][0]}.py")
        custom_class_name = config["_class_name"][1]

    return custom_pipeline, custom_class_name


def _maybe_raise_warning_for_inpainting(pipeline_class, pretrained_model_name_or_path: str, config: dict):
    if pipeline_class.__name__ == "StableDiffusionInpaintPipeline" and version.parse(
        version.parse(config["_diffusers_version"]).base_version
    ) <= version.parse("0.5.1"):
        from diffusers import StableDiffusionInpaintPipeline, StableDiffusionInpaintPipelineLegacy

        pipeline_class = StableDiffusionInpaintPipelineLegacy

        deprecation_message = (
            "You are using a legacy checkpoint for inpainting with Stable Diffusion, therefore we are loading the"
            f" {StableDiffusionInpaintPipelineLegacy} class instead of {StableDiffusionInpaintPipeline}. For"
            " better inpainting results, we strongly suggest using Stable Diffusion's official inpainting"
            " checkpoint: https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting instead or adapting your"
            f" checkpoint {pretrained_model_name_or_path} to the format of"
            " https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting. Note that we do not actively maintain"
            " the {StableDiffusionInpaintPipelineLegacy} class and will likely remove it in version 1.0.0."
        )
        deprecate("StableDiffusionInpaintPipelineLegacy", "1.0.0", deprecation_message, standard_warn=False)


def _update_init_kwargs_with_connected_pipeline(
    init_kwargs: dict, passed_pipe_kwargs: dict, passed_class_objs: dict, folder: str, **pipeline_loading_kwargs
) -> dict:
    from .pipeline_utils import DiffusionPipeline

    modelcard = ModelCard.load(os.path.join(folder, "README.md"))
    connected_pipes = {prefix: getattr(modelcard.data, prefix, [None])[0] for prefix in CONNECTED_PIPES_KEYS}

    # We don't scheduler argument to match the existing logic:
    # https://github.com/huggingface/diffusers/blob/867e0c919e1aa7ef8b03c8eb1460f4f875a683ae/src/diffusers/pipelines/pipeline_utils.py#L906C13-L925C14
    pipeline_loading_kwargs_cp = pipeline_loading_kwargs.copy()
    if pipeline_loading_kwargs_cp is not None and len(pipeline_loading_kwargs_cp) >= 1:
        for k in pipeline_loading_kwargs:
            if "scheduler" in k:
                _ = pipeline_loading_kwargs_cp.pop(k)

    def get_connected_passed_kwargs(prefix):
        connected_passed_class_obj = {
            k.replace(f"{prefix}_", ""): w for k, w in passed_class_objs.items() if k.split("_")[0] == prefix
        }
        connected_passed_pipe_kwargs = {
            k.replace(f"{prefix}_", ""): w for k, w in passed_pipe_kwargs.items() if k.split("_")[0] == prefix
        }

        connected_passed_kwargs = {**connected_passed_class_obj, **connected_passed_pipe_kwargs}
        return connected_passed_kwargs

    connected_pipes = {
        prefix: DiffusionPipeline.from_pretrained(
            repo_id, **pipeline_loading_kwargs_cp, **get_connected_passed_kwargs(prefix)
        )
        for prefix, repo_id in connected_pipes.items()
        if repo_id is not None
    }

    for prefix, connected_pipe in connected_pipes.items():
        # add connected pipes to `init_kwargs` with <prefix>_<component_name>, e.g. "prior_text_encoder"
        init_kwargs.update(
            {"_".join([prefix, name]): component for name, component in connected_pipe.components.items()}
        )

    return init_kwargs


def _get_custom_components_and_folders(
    pretrained_model_name: str,
    config_dict: Dict[str, Any],
    filenames: Optional[List[str]] = None,
    variant_filenames: Optional[List[str]] = None,
    variant: Optional[str] = None,
):
    config_dict = config_dict.copy()

    # retrieve all folder_names that contain relevant files
    folder_names = [k for k, v in config_dict.items() if isinstance(v, list) and k != "_class_name"]

    diffusers_module = importlib.import_module(__name__.split(".")[0])
    pipelines = getattr(diffusers_module, "pipelines")

    # optionally create a custom component <> custom file mapping
    custom_components = {}
    for component in folder_names:
        module_candidate = config_dict[component][0]

        if module_candidate is None or not isinstance(module_candidate, str):
            continue

        # We compute candidate file path on the Hub. Do not use `os.path.join`.
        candidate_file = f"{component}/{module_candidate}.py"

        if candidate_file in filenames:
            custom_components[component] = module_candidate
        elif module_candidate not in LOADABLE_CLASSES and not hasattr(pipelines, module_candidate):
            raise ValueError(
                f"{candidate_file} as defined in `model_index.json` does not exist in {pretrained_model_name} and is not a module in 'diffusers/pipelines'."
            )

    return custom_components, folder_names


def _get_ignore_patterns(
    passed_components,
    model_folder_names: List[str],
    model_filenames: List[str],
    use_safetensors: bool,
    from_flax: bool,
    allow_pickle: bool,
    use_onnx: bool,
    is_onnx: bool,
    variant: Optional[str] = None,
) -> List[str]:
    if (
        use_safetensors
        and not allow_pickle
        and not is_safetensors_compatible(
            model_filenames, passed_components=passed_components, folder_names=model_folder_names
        )
    ):
        raise EnvironmentError(
            f"Could not find the necessary `safetensors` weights in {model_filenames} (variant={variant})"
        )

    if from_flax:
        ignore_patterns = ["*.bin", "*.safetensors", "*.onnx", "*.pb"]

    elif use_safetensors and is_safetensors_compatible(
        model_filenames, passed_components=passed_components, folder_names=model_folder_names
    ):
        ignore_patterns = ["*.bin", "*.msgpack"]

        use_onnx = use_onnx if use_onnx is not None else is_onnx
        if not use_onnx:
            ignore_patterns += ["*.onnx", "*.pb"]

    else:
        ignore_patterns = ["*.safetensors", "*.msgpack"]

        use_onnx = use_onnx if use_onnx is not None else is_onnx
        if not use_onnx:
            ignore_patterns += ["*.onnx", "*.pb"]

    return ignore_patterns


def _download_dduf_file(
    pretrained_model_name: str,
    dduf_file: str,
    pipeline_class_name: str,
    cache_dir: str,
    proxies: str,
    local_files_only: bool,
    token: str,
    revision: str,
):
    model_info_call_error = None
    if not local_files_only:
        try:
            info = model_info(pretrained_model_name, token=token, revision=revision)
        except (HTTPError, OfflineModeIsEnabled, requests.ConnectionError) as e:
            logger.warning(f"Couldn't connect to the Hub: {e}.\nWill try to load from local cache.")
            local_files_only = True
            model_info_call_error = e  # save error to reraise it if model is not cached locally

    if (
        not local_files_only
        and dduf_file is not None
        and dduf_file not in (sibling.rfilename for sibling in info.siblings)
    ):
        raise ValueError(f"Requested {dduf_file} file is not available in {pretrained_model_name}.")

    try:
        user_agent = {"pipeline_class": pipeline_class_name, "dduf": True}
        cached_folder = snapshot_download(
            pretrained_model_name,
            cache_dir=cache_dir,
            proxies=proxies,
            local_files_only=local_files_only,
            token=token,
            revision=revision,
            allow_patterns=[dduf_file],
            user_agent=user_agent,
        )
        return cached_folder
    except FileNotFoundError:
        # Means we tried to load pipeline with `local_files_only=True` but the files have not been found in local cache.
        # This can happen in two cases:
        # 1. If the user passed `local_files_only=True`                    => we raise the error directly
        # 2. If we forced `local_files_only=True` when `model_info` failed => we raise the initial error
        if model_info_call_error is None:
            # 1. user passed `local_files_only=True`
            raise
        else:
            # 2. we forced `local_files_only=True` when `model_info` failed
            raise EnvironmentError(
                f"Cannot load model {pretrained_model_name}: model is not cached locally and an error occurred"
                " while trying to fetch metadata from the Hub. Please check out the root cause in the stacktrace"
                " above."
            ) from model_info_call_error


def _maybe_raise_error_for_incorrect_transformers(config_dict):
    has_transformers_component = False
    for k in config_dict:
        if isinstance(config_dict[k], list):
            has_transformers_component = config_dict[k][0] == "transformers"
            if has_transformers_component:
                break
    if has_transformers_component and not is_transformers_version(">", "4.47.1"):
        raise ValueError("Please upgrade your `transformers` installation to the latest version to use DDUF.")
