



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


_snake_case				=					logging.get_logger(__name__)

_snake_case				=					{
    '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 		lowerCAmelCase_ (			__snake_case ):







		"""simple docstring"""
		UpperCAmelCase__													=       "mobilenet_v2"
		def __init__(		self     ,    _SCREAMING_SNAKE_CASE=3     ,    _SCREAMING_SNAKE_CASE=224     ,    _SCREAMING_SNAKE_CASE=1.0     ,    _SCREAMING_SNAKE_CASE=8     ,    _SCREAMING_SNAKE_CASE=8     ,    _SCREAMING_SNAKE_CASE=6     ,    _SCREAMING_SNAKE_CASE=32     ,    _SCREAMING_SNAKE_CASE=True     ,    _SCREAMING_SNAKE_CASE=True     ,    _SCREAMING_SNAKE_CASE="relu6"     ,    _SCREAMING_SNAKE_CASE=True     ,    _SCREAMING_SNAKE_CASE=0.8     ,    _SCREAMING_SNAKE_CASE=0.0_2     ,    _SCREAMING_SNAKE_CASE=0.0_0_1     ,    _SCREAMING_SNAKE_CASE=255     ,    **_SCREAMING_SNAKE_CASE     ,    )     ->  str:
									super().__init__(**__UpperCamelCase			)

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

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






class 		lowerCAmelCase_ (			__snake_case ):







		"""simple docstring"""
		UpperCAmelCase__													=       version.parse("1.11" )
		@property
		def 						__lowercase(		self			)     ->  List[Any]:
									return OrderedDict([('pixel_values', {0: 'batch'})]			)
		@property
		def 						__lowercase(		self			)     ->  Optional[Any]:


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



		@property
		def 						__lowercase(		self			)     ->  List[Any]:
									return 1e-4






from itertools import count





def a(lowercase__ = 50						):





	'''simple docstring'''
	snake_case_   		=			[1] * min_block_length

	for n in count(lowercase__						):
		fill_count_functions.append(1						)

		for block_length in range(lowercase__      ,			n + 1						):
			for block_start in range(n - block_length						):
				fill_count_functions[n] += fill_count_functions[
				    n - block_start - block_length - 1
				]

			fill_count_functions[n] += 1

		if fill_count_functions[n] > 1000000:
			break

	return n


if __name__ == "__main__":
		print(f"""{solution() = }""")
