

'''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


lowerCAmelCase__											=	logging.get_logger(__name__)

lowerCAmelCase__											=	{
    '''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 			lowercase_	:




  """simple docstring"""




  def __init__(      self   :     Union[str, Any]					,lowercase__   :     int=None					,**lowercase__   :     Tuple			):
   logger.info('''`diffusers.OnnxRuntimeModel` is experimental and might change in the future.'''			)
   __lowercase							  = model
   __lowercase							  = kwargs.get('''model_save_dir'''					,_UpperCamelCase			)
   __lowercase							  = kwargs.get('''latest_model_name'''					,_UpperCamelCase			)






  def __call__(      self   :     Union[str, Any]					,**lowercase__   :     Union[str, Any]			):
   __lowercase							  = {k: np.array(_UpperCamelCase			) for k, v in kwargs.items()}
   return self.model.run(_UpperCamelCase					,_UpperCamelCase			)






  @staticmethod
  def 					SCREAMING_SNAKE_CASE      (      lowercase__   :     str					,lowercase__   :     Optional[int]=None					,lowercase__   :     str=None			):
   if provider is None:
    logger.info('''No onnxruntime provider specified, using CPUExecutionProvider'''			)
    __lowercase							  = 'CPUExecutionProvider'

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






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any					,lowercase__   :     Union[str, Any]					,lowercase__   :     List[Any] = None					,**lowercase__   :     Dict			):
   __lowercase							  = file_name if file_name is not None else ONNX_WEIGHTS_NAME

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

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






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]					,lowercase__   :     str					,**lowercase__   :     Any					,):
   if os.path.isfile(_UpperCamelCase			):
    logger.error(F"Provided path ({save_directory}) should be a directory, not a file"			)
    return

   os.makedirs(_UpperCamelCase					,exist_ok=_UpperCamelCase			)

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






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






  @classmethod
  def 					SCREAMING_SNAKE_CASE      (      cls   :     Any					,lowercase__   :     Optional[Any]					,lowercase__   :     Dict = True					,lowercase__   :     Dict = None					,lowercase__   :     int = None					,**lowercase__   :     str					,):
   __lowercase							  = None
   if len(str(_UpperCamelCase			).split('''@'''			)			) == 2:
    __lowercase							  = model_id.split('''@'''			)

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









from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase								=						logging.get_logger(__name__)

__UpperCAmelCase								=						{
    'snap-research/efficientformer-l1-300': (
        'https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'
    ),
}



class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		_snake_case    :    Optional[int]       						=  '''efficientformer'''



		def __init__(							self				,		_UpperCamelCase = [3, 2, 6, 4]				,		_UpperCamelCase = [4_8, 9_6, 2_2_4, 4_4_8]				,		_UpperCamelCase = [True, True, True, True]				,		_UpperCamelCase = 4_4_8				,		_UpperCamelCase = 3_2				,		_UpperCamelCase = 4				,		_UpperCamelCase = 7				,		_UpperCamelCase = 5				,		_UpperCamelCase = 8				,		_UpperCamelCase = 4				,		_UpperCamelCase = 0.0				,		_UpperCamelCase = 1_6				,		_UpperCamelCase = 3				,		_UpperCamelCase = 3				,		_UpperCamelCase = 3				,		_UpperCamelCase = 2				,		_UpperCamelCase = 1				,		_UpperCamelCase = 0.0				,		_UpperCamelCase = 1				,		_UpperCamelCase = True				,		_UpperCamelCase = True				,		_UpperCamelCase = 1E-5				,		_UpperCamelCase = "gelu"				,		_UpperCamelCase = 0.02				,		_UpperCamelCase = 1E-12				,		_UpperCamelCase = 2_2_4				,		_UpperCamelCase = 1E-05				,		**_UpperCamelCase				,		)	->							None:
							super().__init__(**_UpperCamelCase   )

							UpperCAmelCase_    :      int            =						hidden_act
							UpperCAmelCase_    :      Union[str, Any]            =						hidden_dropout_prob
							UpperCAmelCase_    :      Tuple            =						hidden_sizes
							UpperCAmelCase_    :      Union[str, Any]            =						num_hidden_layers
							UpperCAmelCase_    :      List[str]            =						num_attention_heads
							UpperCAmelCase_    :      List[Any]            =						initializer_range
							UpperCAmelCase_    :      int            =						layer_norm_eps
							UpperCAmelCase_    :      List[str]            =						patch_size
							UpperCAmelCase_    :      Union[str, Any]            =						num_channels
							UpperCAmelCase_    :      Optional[Any]            =						depths
							UpperCAmelCase_    :      List[Any]            =						mlp_expansion_ratio
							UpperCAmelCase_    :      List[str]            =						downsamples
							UpperCAmelCase_    :      List[Any]            =						dim
							UpperCAmelCase_    :      Tuple            =						key_dim
							UpperCAmelCase_    :      Optional[int]            =						attention_ratio
							UpperCAmelCase_    :      str            =						resolution
							UpperCAmelCase_    :      Dict            =						pool_size
							UpperCAmelCase_    :      Union[str, Any]            =						downsample_patch_size
							UpperCAmelCase_    :      List[str]            =						downsample_stride
							UpperCAmelCase_    :      List[str]            =						downsample_pad
							UpperCAmelCase_    :      Any            =						drop_path_rate
							UpperCAmelCase_    :      Dict            =						num_metaad_blocks
							UpperCAmelCase_    :      Dict            =						distillation
							UpperCAmelCase_    :      int            =						use_layer_scale
							UpperCAmelCase_    :      Any            =						layer_scale_init_value
							UpperCAmelCase_    :      Any            =						image_size
							UpperCAmelCase_    :      Dict            =						batch_norm_eps






