





'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from packaging import version

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


a__					:			Optional[Any]          =     logging.get_logger(__name__)

a__					:			Dict          =     {
    '''google/mobilenet_v1_1.0_224''': '''https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v1_0.75_192''': '''https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json''',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}






class 				__snake_case     (					__magic_name__							):
	__lowerCAmelCase       						=  '''mobilenet_v1'''





	def __init__(		self      ,       UpperCamelCase_=3      ,       UpperCamelCase_=224      ,       UpperCamelCase_=1.0      ,       UpperCamelCase_=8      ,       UpperCamelCase_="relu6"      ,       UpperCamelCase_=True      ,       UpperCamelCase_=0.9_9_9      ,       UpperCamelCase_=0.0_2      ,       UpperCamelCase_=0.0_0_1      ,       **UpperCamelCase_      ,       )      ->		Dict:
		super().__init__(**UpperCamelCase_		)

		if depth_multiplier <= 0:
			raise ValueError('depth_multiplier must be greater than zero.'		)

		snake_case__	       =   num_channels
		snake_case__	       =   image_size
		snake_case__	       =   depth_multiplier
		snake_case__	       =   min_depth
		snake_case__	       =   hidden_act
		snake_case__	       =   tf_padding
		snake_case__	       =   classifier_dropout_prob
		snake_case__	       =   initializer_range
		snake_case__	       =   layer_norm_eps






class 				__snake_case     (					__magic_name__							):
	__lowerCAmelCase       						=  version.parse('''1.11'''							)





	@property
	def   _snake_case   (		self		)      ->		Mapping[str, Mapping[int, str]]:
		return OrderedDict([('pixel_values', {0: 'batch'})]		)





	@property
	def   _snake_case   (		self		)      ->		Mapping[str, Mapping[int, str]]:
		if self.task == "image-classification":
			return OrderedDict([('logits', {0: 'batch'})]		)
		else:
			return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]		)





	@property
	def   _snake_case   (		self		)      ->		float:
		return 1E-4










'''simple docstring'''




import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


a__					:			int          =     logging.get_logger(__name__)






class 				__snake_case     (					__magic_name__							):





	def __init__(		self      ,       *UpperCamelCase_      ,       **UpperCamelCase_		)      ->		None:
		warnings.warn(
		    'The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
		    ' Please use PerceiverImageProcessor instead.'      ,       UpperCamelCase_      ,       )
		super().__init__(*UpperCamelCase_      ,       **UpperCamelCase_		)



