




"""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 :		List[str]      							=    logging.get_logger(__name__)

__A :		Tuple      							=    {
    '''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 							lowerCamelCase		( _UpperCAmelCase				):
   lowercase       :  Union[str, Any]      =				'mobilevit'







   def __init__(							self    ,      SCREAMING_SNAKE_CASE_=3    ,      SCREAMING_SNAKE_CASE_=256    ,      SCREAMING_SNAKE_CASE_=2    ,      SCREAMING_SNAKE_CASE_=[144, 192, 240]    ,      SCREAMING_SNAKE_CASE_=[16, 32, 64, 96, 128, 160, 640]    ,      SCREAMING_SNAKE_CASE_=4    ,      SCREAMING_SNAKE_CASE_=2.0    ,      SCREAMING_SNAKE_CASE_=4.0    ,      SCREAMING_SNAKE_CASE_="silu"    ,      SCREAMING_SNAKE_CASE_=3    ,      SCREAMING_SNAKE_CASE_=32    ,      SCREAMING_SNAKE_CASE_=0.1    ,      SCREAMING_SNAKE_CASE_=0.0    ,      SCREAMING_SNAKE_CASE_=0.1    ,      SCREAMING_SNAKE_CASE_=0.02    ,      SCREAMING_SNAKE_CASE_=1e-5    ,      SCREAMING_SNAKE_CASE_=True    ,      SCREAMING_SNAKE_CASE_=256    ,      SCREAMING_SNAKE_CASE_=[6, 12, 18]    ,      SCREAMING_SNAKE_CASE_=0.1    ,      SCREAMING_SNAKE_CASE_=255    ,      **SCREAMING_SNAKE_CASE_    ,      ):
       super().__init__(**SCREAMING_SNAKE_CASE_ )

       UpperCamelCase				:   Union[str, Any]          =							num_channels
       UpperCamelCase				:   List[str]          =							image_size
       UpperCamelCase				:   Optional[Any]          =							patch_size
       UpperCamelCase				:   Dict          =							hidden_sizes
       UpperCamelCase				:   Optional[int]          =							neck_hidden_sizes
       UpperCamelCase				:   Dict          =							num_attention_heads
       UpperCamelCase				:   Any          =							mlp_ratio
       UpperCamelCase				:   Optional[int]          =							expand_ratio
       UpperCamelCase				:   Union[str, Any]          =							hidden_act
       UpperCamelCase				:   Tuple          =							conv_kernel_size
       UpperCamelCase				:   Tuple          =							output_stride
       UpperCamelCase				:   List[str]          =							hidden_dropout_prob
       UpperCamelCase				:   Optional[int]          =							attention_probs_dropout_prob
       UpperCamelCase				:   Optional[Any]          =							classifier_dropout_prob
       UpperCamelCase				:   Dict          =							initializer_range
       UpperCamelCase				:   Optional[Any]          =							layer_norm_eps
       UpperCamelCase				:   Any          =							qkv_bias

       # decode head attributes for semantic segmentation
       UpperCamelCase				:   int          =							aspp_out_channels
       UpperCamelCase				:   Optional[Any]          =							atrous_rates
       UpperCamelCase				:   Dict          =							aspp_dropout_prob
       UpperCamelCase				:   Any          =							semantic_loss_ignore_index







class 							lowerCamelCase		( _UpperCAmelCase				):
   lowercase       :  Optional[int]      =				version.parse('1.11'				)







   @property
   def       a_							(							self ):
       return OrderedDict([("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""})] )







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







   @property
   def       a_							(							self ):
       return 1e-4










"""simple docstring"""




class 							lowerCamelCase		:







   def __init__(							self    ,      SCREAMING_SNAKE_CASE_=None ):
       UpperCamelCase				:   Optional[Any]          =							data
       UpperCamelCase				:   str          =							None







   def __repr__(							self ):
       UpperCamelCase				:   str          =							""""""
       UpperCamelCase				:   Any          =							self
       while temp:
           string_rep += f'<{temp.data}> ---> '
           UpperCamelCase				:   Optional[int]          =							temp.next
       string_rep += "<END>"
       return string_rep







def 					A_			(		snake_case_     :       Tuple    ):







    '''simple docstring'''

    # if elements_list is empty
    if not elements_list:
        raise Exception("""The Elements List is empty"""    )

    # Set first element as Head
    UpperCamelCase				:   List[Any]          =							Node(elements_list[0]    )
    UpperCamelCase				:   str          =							head
    # Loop through elements from position 1
    for data in elements_list[1:]:
        UpperCamelCase				:   Optional[int]          =							Node(snake_case_    )
        UpperCamelCase				:   Optional[Any]          =							current.next
    return head


__A :		List[Any]      							=    [1, 3, 5, 32, 44, 12, 43]
print(F'''List: {list_data}''')
print('''Creating Linked List from List.''')
__A :		int      							=    make_linked_list(list_data)
print('''Linked List:''')


print(linked_list)




