


import numpy as np







def   lowerCamelCase(      a__		,a__):
				_SCREAMING_SNAKE_CASE	     =np.array(a__)
				_SCREAMING_SNAKE_CASE	     =np.array(a__)

				_SCREAMING_SNAKE_CASE	     =abs(predict - actual)
				_SCREAMING_SNAKE_CASE	     =difference.mean()

				return score







def   lowerCamelCase(      a__		,a__):
				_SCREAMING_SNAKE_CASE	     =np.array(a__)
				_SCREAMING_SNAKE_CASE	     =np.array(a__)

				_SCREAMING_SNAKE_CASE	     =predict - actual
				_SCREAMING_SNAKE_CASE	     =np.square(a__)

				_SCREAMING_SNAKE_CASE	     =square_diff.mean()
				return score







def   lowerCamelCase(      a__		,a__):
				_SCREAMING_SNAKE_CASE	     =np.array(a__)
				_SCREAMING_SNAKE_CASE	     =np.array(a__)

				_SCREAMING_SNAKE_CASE	     =predict - actual
				_SCREAMING_SNAKE_CASE	     =np.square(a__)
				_SCREAMING_SNAKE_CASE	     =square_diff.mean()
				_SCREAMING_SNAKE_CASE	     =np.sqrt(a__)
				return score







def   lowerCamelCase(      a__		,a__):
				_SCREAMING_SNAKE_CASE	     =np.array(a__)
				_SCREAMING_SNAKE_CASE	     =np.array(a__)

				_SCREAMING_SNAKE_CASE	     =np.log(predict + 1)
				_SCREAMING_SNAKE_CASE	     =np.log(actual + 1)

				_SCREAMING_SNAKE_CASE	     =log_predict - log_actual
				_SCREAMING_SNAKE_CASE	     =np.square(a__)
				_SCREAMING_SNAKE_CASE	     =square_diff.mean()

				_SCREAMING_SNAKE_CASE	     =np.sqrt(a__)

				return score







def   lowerCamelCase(      a__		,a__):
				_SCREAMING_SNAKE_CASE	     =np.array(a__)
				_SCREAMING_SNAKE_CASE	     =np.array(a__)

				_SCREAMING_SNAKE_CASE	     =predict - actual
				_SCREAMING_SNAKE_CASE	     =np.sum(a__) / len(a__)
				_SCREAMING_SNAKE_CASE	     =np.sum(a__) / len(a__)
				# print(numerator, denumerator)
				_SCREAMING_SNAKE_CASE	     =float(a__) / denumerator * 100

				return score







def   lowerCamelCase(      a__		,a__):
				return np.mean(np.array(a__) == np.array(a__))



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_  :	Any							  =							logging.get_logger(__name__)

snake_case_  :	Any							  =							{
    '''apple/mobilevit-small''': '''https://huggingface.co/apple/mobilevit-small/resolve/main/config.json''',
    '''apple/mobilevit-x-small''': '''https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json''',
    '''apple/mobilevit-xx-small''': '''https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json''',
    '''apple/deeplabv3-mobilevit-small''': (
        '''https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json'''
    ),
    '''apple/deeplabv3-mobilevit-x-small''': (
        '''https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json'''
    ),
    '''apple/deeplabv3-mobilevit-xx-small''': (
        '''https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json'''
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}

class   A__							(						UpperCamelCase__     ):
						UpperCAmelCase									=  "mobilevit"




						def __init__(				self    :					Tuple ,			_a    :					Any=3 ,			_a    :					Any=256 ,			_a    :					Any=2 ,			_a    :					List[str]=[144, 192, 240] ,			_a    :					Optional[int]=[16, 32, 64, 96, 128, 160, 640] ,			_a    :					Union[str, Any]=4 ,			_a    :					Any=2.0 ,			_a    :					List[str]=4.0 ,			_a    :					Tuple="silu" ,			_a    :					Optional[Any]=3 ,			_a    :					List[str]=32 ,			_a    :					Union[str, Any]=0.1 ,			_a    :					Tuple=0.0 ,			_a    :					Union[str, Any]=0.1 ,			_a    :					Union[str, Any]=0.02 ,			_a    :					Tuple=1E-5 ,			_a    :					Any=True ,			_a    :					str=256 ,			_a    :					Tuple=[6, 12, 18] ,			_a    :					int=0.1 ,			_a    :					str=255 ,			**_a    :					Any ,			)							->   Optional[int]:


										"""simple docstring"""





										super().__init__(**_a     )

										_SCREAMING_SNAKE_CASE	     =num_channels
										_SCREAMING_SNAKE_CASE	     =image_size
										_SCREAMING_SNAKE_CASE	     =patch_size
										_SCREAMING_SNAKE_CASE	     =hidden_sizes
										_SCREAMING_SNAKE_CASE	     =neck_hidden_sizes
										_SCREAMING_SNAKE_CASE	     =num_attention_heads
										_SCREAMING_SNAKE_CASE	     =mlp_ratio
										_SCREAMING_SNAKE_CASE	     =expand_ratio
										_SCREAMING_SNAKE_CASE	     =hidden_act
										_SCREAMING_SNAKE_CASE	     =conv_kernel_size
										_SCREAMING_SNAKE_CASE	     =output_stride
										_SCREAMING_SNAKE_CASE	     =hidden_dropout_prob
										_SCREAMING_SNAKE_CASE	     =attention_probs_dropout_prob
										_SCREAMING_SNAKE_CASE	     =classifier_dropout_prob
										_SCREAMING_SNAKE_CASE	     =initializer_range
										_SCREAMING_SNAKE_CASE	     =layer_norm_eps
										_SCREAMING_SNAKE_CASE	     =qkv_bias

										# decode head attributes for semantic segmentation
										_SCREAMING_SNAKE_CASE	     =aspp_out_channels
										_SCREAMING_SNAKE_CASE	     =atrous_rates
										_SCREAMING_SNAKE_CASE	     =aspp_dropout_prob
										_SCREAMING_SNAKE_CASE	     =semantic_loss_ignore_index

class   A__							(						UpperCamelCase__     ):
						UpperCAmelCase									=  version.parse("1.11"     )




						@property
						def 							__UpperCamelCase (				self    :					Union[str, Any]     )							->   Mapping[str, Mapping[int, str]]:


										"""simple docstring"""





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




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


										"""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    :					List[Any]     )							->   float:


										"""simple docstring"""





										return 1E-4