

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


lowercase_       		=  logging.get_logger(__name__)

lowercase_       		=  {
    "tensor(bool)": np.bool_,
    "tensor(int8)": np.inta,
    "tensor(uint8)": np.uinta,
    "tensor(int16)": np.intaa,
    "tensor(uint16)": np.uintaa,
    "tensor(int32)": np.intaa,
    "tensor(uint32)": np.uintaa,
    "tensor(int64)": np.intaa,
    "tensor(uint64)": np.uintaa,
    "tensor(float16)": np.floataa,
    "tensor(float)": np.floataa,
    "tensor(double)": np.floataa,
}


class  A						:







    """simple docstring"""





    def __init__(      self :       Any,lowercase_ :       int=None,**lowercase_ :       Optional[Any]		)->    Tuple:





        '''simple docstring'''







        logger.info('`diffusers.OnnxRuntimeModel` is experimental and might change in the future.'		)
        A__          =   model
        A__          =   kwargs.get('model_save_dir',lowercase_		)
        A__          =   kwargs.get('latest_model_name',lowercase_		)


    def __call__(      self :       str,**lowercase_ :       Dict		)->    Any:





        '''simple docstring'''







        A__          =   {k: np.array(lowercase_		) for k, v in kwargs.items()}
        return self.model.run(lowercase_,lowercase_		)


    @staticmethod
    def 						snake_case__     (      lowercase_ :       Union[str, Path],lowercase_ :       Optional[Any]=None,lowercase_ :       Union[str, Any]=None		)->    str:





        '''simple docstring'''







        if provider is None:
            logger.info('No onnxruntime provider specified, using CPUExecutionProvider'		)
            A__          =   'CPUExecutionProvider'

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


    def 						snake_case__     (      self :       Dict,lowercase_ :       Union[str, Path],lowercase_ :       Optional[str] = None,**lowercase_ :       List[str]		)->    Any:





        '''simple docstring'''







        A__          =   file_name if file_name is not None else ONNX_WEIGHTS_NAME

        A__          =   self.model_save_dir.joinpath(self.latest_model_name		)
        A__          =   Path(lowercase_		).joinpath(lowercase_		)
        try:
            shutil.copyfile(lowercase_,lowercase_		)
        except shutil.SameFileError:
            pass

        # copy external weights (for models >2GB)
        A__          =   self.model_save_dir.joinpath(lowercase_		)
        if src_path.exists():
            A__          =   Path(lowercase_		).joinpath(lowercase_		)
            try:
                shutil.copyfile(lowercase_,lowercase_		)
            except shutil.SameFileError:
                pass


    def 						snake_case__     (      self :       Tuple,lowercase_ :       Union[str, os.PathLike],**lowercase_ :       Any,)->    List[str]:





        '''simple docstring'''







        if os.path.isfile(lowercase_		):
            logger.error(F'Provided path ({save_directory}) should be a directory, not a file'		)
            return

        os.makedirs(lowercase_,exist_ok=lowercase_		)

        # saving model weights/files
        self._save_pretrained(lowercase_,**lowercase_		)


    @classmethod
    def 						snake_case__     (      cls :       Union[str, Any],lowercase_ :       Union[str, Path],lowercase_ :       Optional[Union[bool, str, None]] = None,lowercase_ :       Optional[Union[str, None]] = None,lowercase_ :       bool = False,lowercase_ :       Optional[str] = None,lowercase_ :       Optional[str] = None,lowercase_ :       Optional[str] = None,lowercase_ :       Optional["ort.SessionOptions"] = None,**lowercase_ :       int,)->    List[str]:





        '''simple docstring'''







        A__          =   file_name if file_name is not None else ONNX_WEIGHTS_NAME
        # load model from local directory
        if os.path.isdir(lowercase_		):
            A__          =   OnnxRuntimeModel.load_model(
                os.path.join(lowercase_,lowercase_		),provider=lowercase_,sess_options=lowercase_		)
            A__          =   Path(lowercase_		)
        # load model from hub
        else:
            # download model
            A__          =   hf_hub_download(
                repo_id=lowercase_,filename=lowercase_,use_auth_token=lowercase_,revision=lowercase_,cache_dir=lowercase_,force_download=lowercase_,)
            A__          =   Path(lowercase_		).parent
            A__          =   Path(lowercase_		).name
            A__          =   OnnxRuntimeModel.load_model(lowercase_,provider=lowercase_,sess_options=lowercase_		)
        return cls(model=lowercase_,**lowercase_		)


    @classmethod
    def 						snake_case__     (      cls :       str,lowercase_ :       Union[str, Path],lowercase_ :       bool = True,lowercase_ :       Optional[str] = None,lowercase_ :       Optional[str] = None,**lowercase_ :       Tuple,)->    Tuple:





        '''simple docstring'''







        A__          =   None
        if len(str(lowercase_		).split('@'		)		) == 2:
            A__      ,						A__          =   model_id.split('@'		)

        return cls._from_pretrained(
            model_id=lowercase_,revision=lowercase_,cache_dir=lowercase_,force_download=lowercase_,use_auth_token=lowercase_,**lowercase_,)





"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase    :     Any   						=		{
    'configuration_blenderbot': [
        'BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BlenderbotConfig',
        'BlenderbotOnnxConfig',
    ],
    'tokenization_blenderbot': ['BlenderbotTokenizer'],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase    :     Optional[int]   						=		['BlenderbotTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase    :     Optional[Any]   						=		[
							    'BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'BlenderbotForCausalLM',
							    'BlenderbotForConditionalGeneration',
							    'BlenderbotModel',
							    'BlenderbotPreTrainedModel',
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase    :     Optional[int]   						=		[
							    'TFBlenderbotForConditionalGeneration',
							    'TFBlenderbotModel',
							    'TFBlenderbotPreTrainedModel',
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase    :     Optional[int]   						=		[
							    'FlaxBlenderbotForConditionalGeneration',
							    'FlaxBlenderbotModel',
							    'FlaxBlenderbotPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_blenderbot import (
							    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    BlenderbotConfig,
							    BlenderbotOnnxConfig,
							)
							from .tokenization_blenderbot import BlenderbotTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_blenderbot import (
														    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BlenderbotForCausalLM,
														    BlenderbotForConditionalGeneration,
														    BlenderbotModel,
														    BlenderbotPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_blenderbot import (
														    TFBlenderbotForConditionalGeneration,
														    TFBlenderbotModel,
														    TFBlenderbotPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_blenderbot import (
														    FlaxBlenderbotForConditionalGeneration,
														    FlaxBlenderbotModel,
														    FlaxBlenderbotPreTrainedModel,
														)

else:
							import sys

							UpperCAmelCase    :     Any   						=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
