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# 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 ..utils import (

SAFE_WEIGHTS_INDEX_NAME,

SAFETENSORS_FILE_EXTENSION,

WEIGHTS_INDEX_NAME,

_add_variant,

_get_model_file,

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):

if isinstance(device_map, str):

no_split_modules = model._get_no_split_modules(device_map)

device_map_kwargs = {"no_split_module_classes": no_split_modules}

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)

device_map_kwargs["max_memory"] = max_memory

device_map = infer_auto_device_map(model, dtype=torch_dtype, **device_map_kwargs)

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.

"""

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,

) -> List[str]:

device = device or torch.device("cpu")

dtype = dtype or torch.float32

accepts_dtype = "dtype" in set(inspect.signature(set_module_tensor_to_device).parameters.keys())

unexpected_keys = []

empty_state_dict = model.state_dict()

for param_name, param in state_dict.items():

if param_name not in empty_state_dict:

unexpected_keys.append(param_name)

continue

if empty_state_dict[param_name].shape != param.shape:

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 accepts_dtype:

set_module_tensor_to_device(model, param_name, device, value=param, dtype=dtype)

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