






"""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


SCREAMING_SNAKE_CASE						:						List[Any]					   =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE						:						Any					   =       {
    '''google/mobilenet_v2_1.4_224''': '''https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json''',
    '''google/mobilenet_v2_1.0_224''': '''https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v2_0.75_160''': '''https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json''',
    '''google/mobilenet_v2_0.35_96''': '''https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json''',
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}





class       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							Optional[int]   			=					"""mobilenet_v2"""


	def __init__(      self  ,					__a=3  ,					__a=224  ,					__a=1.0  ,					__a=8  ,					__a=8  ,					__a=6  ,					__a=32  ,					__a=True  ,					__a=True  ,					__a="relu6"  ,					__a=True  ,					__a=0.8  ,					__a=0.02  ,					__a=0.001  ,					__a=255  ,					**__a  ,					):


					"""simple docstring"""

					super().__init__(**__a						)

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

					A__							 =       num_channels
					A__							 =       image_size
					A__							 =       depth_multiplier
					A__							 =       depth_divisible_by
					A__							 =       min_depth
					A__							 =       expand_ratio
					A__							 =       output_stride
					A__							 =       first_layer_is_expansion
					A__							 =       finegrained_output
					A__							 =       hidden_act
					A__							 =       tf_padding
					A__							 =       classifier_dropout_prob
					A__							 =       initializer_range
					A__							 =       layer_norm_eps
					A__							 =       semantic_loss_ignore_index





class       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							Dict   			=					version.parse("""1.11"""       )


	@property
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					return OrderedDict([('pixel_values', {0: 'batch'})]						)


	@property
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					if self.task == "image-classification":
									return OrderedDict([('logits', {0: 'batch'})]						)
					else:
									return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]						)






	@property
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					return 1E-4










"""simple docstring"""






from math import ceil



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       list(range(0       ,lowerCAmelCase__				)				)

				A__							 =       [item for sublist in list(device_map.values()				) for item in sublist]

				# Duplicate check
				A__							 =       []
				for i in device_map_blocks:
								if device_map_blocks.count(lowerCAmelCase__				) > 1 and i not in duplicate_blocks:
												duplicate_blocks.append(lowerCAmelCase__				)
    # Missing blocks
				A__							 =       [i for i in blocks if i not in device_map_blocks]
				A__							 =       [i for i in device_map_blocks if i not in blocks]

				if len(lowerCAmelCase__				) != 0:
								raise ValueError(
								    'Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device.'
								    ' These attention blocks were specified more than once: ' + str(lowerCAmelCase__				)				)
				if len(lowerCAmelCase__				) != 0:
								raise ValueError(
								    'There are attention blocks for this model that are not specified in the device_map. Add these attention '
								    'blocks to a device on the device_map: ' + str(lowerCAmelCase__				)				)
				if len(lowerCAmelCase__				) != 0:
								raise ValueError(
								    'The device_map contains more attention blocks than this model has. Remove these from the device_map:'
								    + str(lowerCAmelCase__				)				)







def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       list(range(lowerCAmelCase__				)				)
				A__							 =       int(ceil(n_layers / len(lowerCAmelCase__				)				)				)
				A__							 =       [layers[i : i + n_blocks] for i in range(0       ,lowerCAmelCase__       ,lowerCAmelCase__				)]

				return dict(zip(lowerCAmelCase__       ,lowerCAmelCase__				)				)


