

from collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A_	:			Dict  	=      logging.get_logger(__name__)

A_	:			Optional[int]  	=      {
    "google/efficientnet-b7": "https://huggingface.co/google/efficientnet-b7/resolve/main/config.json",
}



class    _a						(UpperCAmelCase__    ):


					'''simple docstring'''






					UpperCAmelCase__:       Union[str, Any]  							=	'efficientnet'






					def __init__(			self   ,	A__ = 3   ,	A__ = 600   ,	A__ = 2.0   ,	A__ = 3.1   ,	A__ = 8   ,	A__ = [3, 3, 5, 3, 5, 5, 3]   ,	A__ = [32, 16, 24, 40, 80, 112, 192]   ,	A__ = [16, 24, 40, 80, 112, 192, 320]   ,	A__ = []   ,	A__ = [1, 2, 2, 2, 1, 2, 1]   ,	A__ = [1, 2, 2, 3, 3, 4, 1]   ,	A__ = [1, 6, 6, 6, 6, 6, 6]   ,	A__ = 0.2_5   ,	A__ = "swish"   ,	A__ = 2560   ,	A__ = "mean"   ,	A__ = 0.0_2   ,	A__ = 0.0_0_1   ,	A__ = 0.9_9   ,	A__ = 0.5   ,	A__ = 0.2   ,	**A__   ,	):
						super().__init__(**_SCREAMING_SNAKE_CASE      )

						A__							:   List[str]					       =		num_channels
						A__							:   Union[str, Any]					       =		image_size
						A__							:   int					       =		width_coefficient
						A__							:   int					       =		depth_coefficient
						A__							:   Optional[Any]					       =		depth_divisor
						A__							:   Optional[int]					       =		kernel_sizes
						A__							:   str					       =		in_channels
						A__							:   Dict					       =		out_channels
						A__							:   str					       =		depthwise_padding
						A__							:   Tuple					       =		strides
						A__							:   List[str]					       =		num_block_repeats
						A__							:   Tuple					       =		expand_ratios
						A__							:   int					       =		squeeze_expansion_ratio
						A__							:   Tuple					       =		hidden_act
						A__							:   Optional[int]					       =		hidden_dim
						A__							:   int					       =		pooling_type
						A__							:   Optional[Any]					       =		initializer_range
						A__							:   int					       =		batch_norm_eps
						A__							:   Any					       =		batch_norm_momentum
						A__							:   List[Any]					       =		dropout_rate
						A__							:   Optional[int]					       =		drop_connect_rate
						A__							:   Optional[int]					       =		sum(_SCREAMING_SNAKE_CASE      ) * 4



class    _a						(UpperCAmelCase__    ):


					'''simple docstring'''






					UpperCAmelCase__:       Dict  							=	version.parse('''1.11'''    )






					@property
					def   __A      (			self      ):
						return OrderedDict(
						    [
						        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
						    ]      )






					@property
					def   __A      (			self      ):
						return 1e-5






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_	:			Union[str, 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    _a						:


					'''simple docstring'''






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






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






					@staticmethod
					def   __A      (			A__   ,	A__=None   ,	A__=None      ):
						if provider is None:
							logger.info("""No onnxruntime provider specified, using CPUExecutionProvider"""      )
							A__							:   List[Any]					       =		"""CPUExecutionProvider"""

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






					def   __A      (			self   ,	A__   ,	A__ = None   ,	**A__      ):
						A__							:   List[Any]					       =		file_name if file_name is not None else ONNX_WEIGHTS_NAME

						A__							:   List[Any]					       =		self.model_save_dir.joinpath(self.latest_model_name      )
						A__							:   Optional[int]					       =		Path(A__      ).joinpath(A__      )
						try:
							shutil.copyfile(A__   ,	A__      )
						except shutil.SameFileError:
							pass

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






					def   __A      (			self   ,	A__   ,	**A__   ,	):
						if os.path.isfile(A__      ):
							logger.error(F"""Provided path ({save_directory}) should be a directory, not a file"""      )
							return

						os.makedirs(A__   ,	exist_ok=A__      )

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






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






					@classmethod
					def   __A      (			cls   ,	A__   ,	A__ = True   ,	A__ = None   ,	A__ = None   ,	**A__   ,	):
						A__							:   Optional[Any]					       =		None
						if len(str(A__      ).split("""@"""      )      ) == 2:
							A__       ,       A__							:   Union[str, Any]					       =		model_id.split("""@"""      )

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



