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# coding=utf-8

# 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 os

import shutil

from pathlib import Path

from typing import Optional, Union

import numpy as np

from huggingface_hub import hf_hub_download

from .utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging

if is_onnx_available():

import onnxruntime as ort

logger = logging.get_logger(__name__)

ORT_TO_NP_TYPE = {

"tensor(bool)": np.bool_,

"tensor(int8)": np.int8,

"tensor(uint8)": np.uint8,

"tensor(int16)": np.int16,

"tensor(uint16)": np.uint16,

"tensor(int32)": np.int32,

"tensor(uint32)": np.uint32,

"tensor(int64)": np.int64,

"tensor(uint64)": np.uint64,

"tensor(float16)": np.float16,

"tensor(float)": np.float32,

"tensor(double)": np.float64,

}

class OnnxRuntimeModel:

def __init__(self, model=None, **kwargs):

logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future.")

self.model = model

self.model_save_dir = kwargs.get("model_save_dir", None)

self.latest_model_name = kwargs.get("latest_model_name", ONNX_WEIGHTS_NAME)

def __call__(self, **kwargs):

inputs = {k: np.array(v) for k, v in kwargs.items()}

return self.model.run(None, inputs)

@staticmethod

def load_model(path: Union[str, Path], provider=None, sess_options=None):

"""

Loads an ONNX Inference session with an ExecutionProvider. Default provider is `CPUExecutionProvider`

Arguments:

path (`str` or `Path`):

Directory from which to load

provider(`str`, *optional*):

Onnxruntime execution provider to use for loading the model, defaults to `CPUExecutionProvider`

"""

if provider is None:

logger.info("No onnxruntime provider specified, using CPUExecutionProvider")

provider = "CPUExecutionProvider"

return ort.InferenceSession(path, providers=[provider], sess_options=sess_options)

def _save_pretrained(self, save_directory: Union[str, Path], file_name: Optional[str] = None, **kwargs):

"""

Save a model and its configuration file to a directory, so that it can be re-loaded using the

[`~optimum.onnxruntime.modeling_ort.ORTModel.from_pretrained`] class method. It will always save the

latest_model_name.

Arguments:

save_directory (`str` or `Path`):

Directory where to save the model file.

file_name(`str`, *optional*):

Overwrites the default model file name from `"model.onnx"` to `file_name`. This allows you to save the

model with a different name.

"""

model_file_name = file_name if file_name is not None else ONNX_WEIGHTS_NAME

src_path = self.model_save_dir.joinpath(self.latest_model_name)

dst_path = Path(save_directory).joinpath(model_file_name)

try:

shutil.copyfile(src_path, dst_path)

except shutil.SameFileError:

pass

# copy external weights (for models >2GB)

src_path = self.model_save_dir.joinpath(ONNX_EXTERNAL_WEIGHTS_NAME)

if src_path.exists():

dst_path = Path(save_directory).joinpath(ONNX_EXTERNAL_WEIGHTS_NAME)

try:

shutil.copyfile(src_path, dst_path)

except shutil.SameFileError:

pass

def save_pretrained(

self,

save_directory: Union[str, os.PathLike],

**kwargs,

"""

Save a model to a directory, so that it can be re-loaded using the [`~OnnxModel.from_pretrained`] class

method.:

Arguments:

save_directory (`str` or `os.PathLike`):

Directory to which to save. Will be created if it doesn't exist.

"""

if os.path.isfile(save_directory):

logger.error(f"Provided path ({save_directory}) should be a directory, not a file")

return

os.makedirs(save_directory, exist_ok=True)

# saving model weights/files

self._save_pretrained(save_directory, **kwargs)

@classmethod

def _from_pretrained(

cls,

model_id: Union[str, Path],

use_auth_token: Optional[Union[bool, str, None]] = None,

revision: Optional[Union[str, None]] = None,

force_download: bool = False,

cache_dir: Optional[str] = None,

file_name: Optional[str] = None,

provider: Optional[str] = None,

sess_options: Optional["ort.SessionOptions"] = None,

**kwargs,

"""

Load a model from a directory or the HF Hub.

Arguments:

model_id (`str` or `Path`):

Directory from which to load

use_auth_token (`str` or `bool`):

Is needed to load models from a private or gated repository

revision (`str`):

Revision is the specific model version to use. It can be a branch name, a tag name, or a commit id

cache_dir (`Union[str, Path]`, *optional*):

Path to a directory in which a downloaded pretrained model configuration should be cached if the

standard cache should not be used.

force_download (`bool`, *optional*, defaults to `False`):

Whether or not to force the (re-)download of the model weights and configuration files, overriding the

cached versions if they exist.

file_name(`str`):

Overwrites the default model file name from `"model.onnx"` to `file_name`. This allows you to load

different model files from the same repository or directory.

provider(`str`):

The ONNX runtime provider, e.g. `CPUExecutionProvider` or `CUDAExecutionProvider`.

kwargs (`Dict`, *optional*):

kwargs will be passed to the model during initialization

"""

model_file_name = file_name if file_name is not None else ONNX_WEIGHTS_NAME

# load model from local directory

if os.path.isdir(model_id):

model = OnnxRuntimeModel.load_model(

os.path.join(model_id, model_file_name), provider=provider, sess_options=sess_options

)

kwargs["model_save_dir"] = Path(model_id)

# load model from hub

else:

# download model

model_cache_path = hf_hub_download(

repo_id=model_id,

filename=model_file_name,

use_auth_token=use_auth_token,

revision=revision,

cache_dir=cache_dir,

force_download=force_download,

)

kwargs["model_save_dir"] = Path(model_cache_path).parent

kwargs["latest_model_name"] = Path(model_cache_path).name

model = OnnxRuntimeModel.load_model(model_cache_path, provider=provider, sess_options=sess_options)

return cls(model=model, **kwargs)

@classmethod

def from_pretrained(

cls,

model_id: Union[str, Path],

force_download: bool = True,

use_auth_token: Optional[str] = None,

cache_dir: Optional[str] = None,

**model_kwargs,

revision = None

if len(str(model_id).split("@")) == 2:

model_id, revision = model_id.split("@")

return cls._from_pretrained(

model_id=model_id,

revision=revision,

cache_dir=cache_dir,

force_download=force_download,

use_auth_token=use_auth_token,

**model_kwargs,

)

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