# coding=utf-8
# Copyright 2025 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.

from __future__ import annotations

import os
import shutil
from pathlib import Path

import numpy as np
from huggingface_hub import hf_hub_download
from huggingface_hub.utils import validate_hf_hub_args

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: str | Path, provider=None, sess_options=None, provider_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"

        if provider_options is None:
            provider_options = []
        elif not isinstance(provider_options, list):
            provider_options = [provider_options]

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

    def _save_pretrained(self, save_directory: str | Path, file_name: str | None = 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: 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
    @validate_hf_hub_args
    def _from_pretrained(
        cls,
        model_id: str | Path,
        token: bool | str | None | None = None,
        revision: str | None | None = None,
        force_download: bool = False,
        cache_dir: str | None = None,
        file_name: str | None = None,
        provider: str | None = None,
        sess_options: "ort.SessionOptions" | None = None,
        **kwargs,
    ):
        """
        Load a model from a directory or the HF Hub.

        Arguments:
            model_id (`str` or `Path`):
                Directory from which to load
            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 (`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(
                Path(model_id, model_file_name).as_posix(),
                provider=provider,
                sess_options=sess_options,
                provider_options=kwargs.pop("provider_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,
                token=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,
                provider_options=kwargs.pop("provider_options"),
            )
        return cls(model=model, **kwargs)

    @classmethod
    @validate_hf_hub_args
    def from_pretrained(
        cls,
        model_id: str | Path,
        force_download: bool = True,
        token: str | None = None,
        cache_dir: str | None = 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,
            token=token,
            **model_kwargs,
        )
