





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


lowerCAmelCase_    =				logging.get_logger(__name__)

lowerCAmelCase_    =				{
    '''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''',
}



class 					__lowerCAmelCase       (			_a     ):
			lowerCamelCase_     :     int       			=      '''efficientnet'''






			def __init__(self					,							__magic_name__ = 3					,							__magic_name__ = 600					,							__magic_name__ = 2.0					,							__magic_name__ = 3.1					,							__magic_name__ = 8					,							__magic_name__ = [3, 3, 5, 3, 5, 5, 3]					,							__magic_name__ = [32, 16, 24, 40, 80, 112, 192]					,							__magic_name__ = [16, 24, 40, 80, 112, 192, 320]					,							__magic_name__ = []					,							__magic_name__ = [1, 2, 2, 2, 1, 2, 1]					,							__magic_name__ = [1, 2, 2, 3, 3, 4, 1]					,							__magic_name__ = [1, 6, 6, 6, 6, 6, 6]					,							__magic_name__ = 0.25					,							__magic_name__ = "swish"					,							__magic_name__ = 2560					,							__magic_name__ = "mean"					,							__magic_name__ = 0.02					,							__magic_name__ = 0.001					,							__magic_name__ = 0.99					,							__magic_name__ = 0.5					,							__magic_name__ = 0.2					,							**__magic_name__					,							)     ->   str:


							'''simple docstring'''



							super().__init__(**__magic_name__    )

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




class 					__lowerCAmelCase       (			_a     ):
			lowerCamelCase_     :     Optional[Any]       			=      version.parse('''1.11'''     )






			@property
			def 			lowerCamelCase  (self    )     ->   Mapping[str, Mapping[int, str]]:


							'''simple docstring'''



							return OrderedDict(
							    [
							        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
							    ]    )






			@property
			def 			lowerCamelCase  (self    )     ->   float:


							'''simple docstring'''



							return 1e-5







import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('''9.1.0'''):
	lowerCAmelCase_    =				{
	    '''linear''': PIL.Image.Resampling.BILINEAR,
	    '''bilinear''': PIL.Image.Resampling.BILINEAR,
	    '''bicubic''': PIL.Image.Resampling.BICUBIC,
	    '''lanczos''': PIL.Image.Resampling.LANCZOS,
	    '''nearest''': PIL.Image.Resampling.NEAREST,
	}
else:
	lowerCAmelCase_    =				{
	    '''linear''': PIL.Image.LINEAR,
	    '''bilinear''': PIL.Image.BILINEAR,
	    '''bicubic''': PIL.Image.BICUBIC,
	    '''lanczos''': PIL.Image.LANCZOS,
	    '''nearest''': PIL.Image.NEAREST,
	}






def      lowerCamelCase_						(				_UpperCamelCase )		->						Optional[int]:






				"""simple docstring"""


				snake_case_						:    Dict				=  (images / 2 + 0.5).clamp(0 ,				1 )
				snake_case_						:    Dict				=  images.cpu().permute(0 ,				2 ,				3 ,				1 ).float().numpy()
				snake_case_						:    int				=  numpy_to_pil(_UpperCamelCase )
				return images






def      lowerCamelCase_						(				_UpperCamelCase )		->						List[Any]:






				"""simple docstring"""


				if images.ndim == 3:
								snake_case_						:    Optional[Any]				=  images[None, ...]
				snake_case_						:    Any				=  (images * 255).round().astype('''uint8''' )
				if images.shape[-1] == 1:
								# special case for grayscale (single channel) images
								snake_case_						:    str				=  [Image.fromarray(image.squeeze() ,				mode='''L''' ) for image in images]
				else:
								snake_case_						:    List[Any]				=  [Image.fromarray(_UpperCamelCase ) for image in images]

				return pil_images
