





"""simple docstring"""




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


a		:					Any	      =     logging.get_logger(__name__)

a		:					str	      =     {
    '''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 			__UpperCamelCase							:






							def __init__(  self ,   lowerCAmelCase__=None ,   **lowerCAmelCase__ )      ->					Optional[Any]:
								logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future." )
								a :						Tuple      =							model
								a :						int      =							kwargs.get("model_save_dir" ,   lowerCAmelCase__ )
								a :						int      =							kwargs.get("latest_model_name" ,   lowerCAmelCase__ )






							def __call__(  self ,   **lowerCAmelCase__ )      ->					Union[str, Any]:
								a :						List[Any]      =							{k: np.array(lowerCAmelCase__ ) for k, v in kwargs.items()}
								return self.model.run(lowerCAmelCase__ ,   lowerCAmelCase__ )






							@staticmethod
							def        __a  (  lowerCAmelCase__ ,   lowerCAmelCase__=None ,   lowerCAmelCase__=None )      ->					List[Any]:
								if provider is None:
									logger.info("No onnxruntime provider specified, using CPUExecutionProvider" )
									a :						List[Any]      =							"CPUExecutionProvider"

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






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ = None ,   **lowerCAmelCase__ )      ->					Dict:
								a :						int      =							file_name if file_name is not None else ONNX_WEIGHTS_NAME

								a :						Any      =							self.model_save_dir.joinpath(self.latest_model_name )
								a :						str      =							Path(lowerCAmelCase__ ).joinpath(lowerCAmelCase__ )
								try:
									shutil.copyfile(lowerCAmelCase__ ,   lowerCAmelCase__ )
								except shutil.SameFileError:
									pass

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






							def        __a  (  self ,   lowerCAmelCase__ ,   **lowerCAmelCase__ ,   )      ->					List[str]:
								if os.path.isfile(lowerCAmelCase__ ):
									logger.error(f"""Provided path ({save_directory}) should be a directory, not a file""" )
									return

								os.makedirs(lowerCAmelCase__ ,   exist_ok=lowerCAmelCase__ )

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






							@classmethod
							def        __a  (  cls ,   lowerCAmelCase__ ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = False ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   **lowerCAmelCase__ ,   )      ->					Any:
								a :						Tuple      =							file_name if file_name is not None else ONNX_WEIGHTS_NAME
								# load model from local directory
								if os.path.isdir(lowerCAmelCase__ ):
									a :						Tuple      =							OnnxRuntimeModel.load_model(
									    os.path.join(lowerCAmelCase__ ,   lowerCAmelCase__ ) ,   provider=lowerCAmelCase__ ,   sess_options=lowerCAmelCase__ )
									a :						Union[str, Any]      =							Path(lowerCAmelCase__ )
								# load model from hub
								else:
									# download model
									a :						List[str]      =							hf_hub_download(
									    repo_id=lowerCAmelCase__ ,   filename=lowerCAmelCase__ ,   use_auth_token=lowerCAmelCase__ ,   revision=lowerCAmelCase__ ,   cache_dir=lowerCAmelCase__ ,   force_download=lowerCAmelCase__ ,   )
									a :						int      =							Path(lowerCAmelCase__ ).parent
									a :						Union[str, Any]      =							Path(lowerCAmelCase__ ).name
									a :						Optional[Any]      =							OnnxRuntimeModel.load_model(lowerCAmelCase__ ,   provider=lowerCAmelCase__ ,   sess_options=lowerCAmelCase__ )
								return cls(model=lowerCAmelCase__ ,   **lowerCAmelCase__ )






							@classmethod
							def        __a  (  cls ,   lowerCAmelCase__ ,   lowerCAmelCase__ = True ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   **lowerCAmelCase__ ,   )      ->					Optional[Any]:
								a :						Optional[Any]      =							None
								if len(str(lowerCAmelCase__ ).split("@" ) ) == 2:
									a,			a :						Optional[Any]      =							model_id.split("@" )

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









"""simple docstring"""




from typing import TYPE_CHECKING

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


a		:					Optional[Any]	      =     {
    '''configuration_owlvit''': [
        '''OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''OwlViTConfig''',
        '''OwlViTOnnxConfig''',
        '''OwlViTTextConfig''',
        '''OwlViTVisionConfig''',
    ],
    '''processing_owlvit''': ['''OwlViTProcessor'''],
}


try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a		:					str	      =     ['''OwlViTFeatureExtractor''']
		a		:					List[Any]	      =     ['''OwlViTImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a		:					List[Any]	      =     [
		    '''OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''OwlViTModel''',
		    '''OwlViTPreTrainedModel''',
		    '''OwlViTTextModel''',
		    '''OwlViTVisionModel''',
		    '''OwlViTForObjectDetection''',
		]

if TYPE_CHECKING:
		from .configuration_owlvit import (
		    OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    OwlViTConfig,
		    OwlViTOnnxConfig,
		    OwlViTTextConfig,
		    OwlViTVisionConfig,
		)
		from .processing_owlvit import OwlViTProcessor

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_owlvit import OwlViTFeatureExtractor
				from .image_processing_owlvit import OwlViTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_owlvit import (
				    OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    OwlViTForObjectDetection,
				    OwlViTModel,
				    OwlViTPreTrainedModel,
				    OwlViTTextModel,
				    OwlViTVisionModel,
				)

else:
		import sys

		a		:					List[str]	      =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


