


"""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


__UpperCamelCase						=      logging.get_logger(__name__)

__UpperCamelCase						=      {
    '''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 					lowerCAmelCase	:




		'''simple docstring'''







		def __init__(	self						,       lowerCAmelCase__=None						,       **lowerCAmelCase__				)     ->							Union[str, Any]:
								logger.info('`diffusers.OnnxRuntimeModel` is experimental and might change in the future.'				)
								SCREAMING_SNAKE_CASE													=					model
								SCREAMING_SNAKE_CASE													=					kwargs.get('model_save_dir'						,       lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					kwargs.get('latest_model_name'						,       lowerCAmelCase__				)







		def __call__(	self						,       **lowerCAmelCase__				)     ->							Any:
								SCREAMING_SNAKE_CASE													=					{k: np.array(lowerCAmelCase__				) for k, v in kwargs.items()}
								return self.model.run(lowerCAmelCase__						,       lowerCAmelCase__				)







		@staticmethod
		def 							__A						(	lowerCAmelCase__						,       lowerCAmelCase__=None						,       lowerCAmelCase__=None				)     ->							Tuple:
								if provider is None:
														logger.info('No onnxruntime provider specified, using CPUExecutionProvider'				)
														SCREAMING_SNAKE_CASE													=					'CPUExecutionProvider'

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







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__ = None						,       **lowerCAmelCase__				)     ->							str:
								SCREAMING_SNAKE_CASE													=					file_name if file_name is not None else ONNX_WEIGHTS_NAME

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

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







		def 							__A						(	self						,       lowerCAmelCase__						,       **lowerCAmelCase__						,       )     ->							Any:
								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__						,       )     ->							Union[str, Any]:
								SCREAMING_SNAKE_CASE													=					file_name if file_name is not None else ONNX_WEIGHTS_NAME
								# load model from local directory
								if os.path.isdir(lowerCAmelCase__				):
														SCREAMING_SNAKE_CASE													=					OnnxRuntimeModel.load_model(
														    os.path.join(lowerCAmelCase__						,       lowerCAmelCase__				)						,       provider=lowerCAmelCase__						,       sess_options=lowerCAmelCase__				)
														SCREAMING_SNAKE_CASE													=					Path(lowerCAmelCase__				)
								# load model from hub
								else:
														# download model
														SCREAMING_SNAKE_CASE													=					hf_hub_download(
														    repo_id=lowerCAmelCase__						,       filename=lowerCAmelCase__						,       use_auth_token=lowerCAmelCase__						,       revision=lowerCAmelCase__						,       cache_dir=lowerCAmelCase__						,       force_download=lowerCAmelCase__						,       )
														SCREAMING_SNAKE_CASE													=					Path(lowerCAmelCase__				).parent
														SCREAMING_SNAKE_CASE													=					Path(lowerCAmelCase__				).name
														SCREAMING_SNAKE_CASE													=					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__						,       )     ->							Dict:
								SCREAMING_SNAKE_CASE													=					None
								if len(str(lowerCAmelCase__				).split('@'				)				) == 2:
														SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					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

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__					=   {"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"]}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__					=   [
           "FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST",
           "FocalNetForImageClassification",
           "FocalNetForMaskedImageModeling",
           "FocalNetBackbone",
           "FocalNetModel",
           "FocalNetPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_focalnet import (
                  FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FocalNetBackbone,
                  FocalNetForImageClassification,
                  FocalNetForMaskedImageModeling,
                  FocalNetModel,
                  FocalNetPreTrainedModel,
              )

else:
       import sys

       SCREAMING_SNAKE_CASE__					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






