

'''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_       						=							logging.get_logger(__name__)

A_       						=							{
    "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 (       _a						):
			_A       :     List[str]       =					'''mobilenet_v2'''
			def __init__( self			:      int     ,SCREAMING_SNAKE_CASE__			:      Tuple=3     ,SCREAMING_SNAKE_CASE__			:      int=224     ,SCREAMING_SNAKE_CASE__			:      str=1.0     ,SCREAMING_SNAKE_CASE__			:      Tuple=8     ,SCREAMING_SNAKE_CASE__			:      Any=8     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]=6     ,SCREAMING_SNAKE_CASE__			:      Any=32     ,SCREAMING_SNAKE_CASE__			:      int=True     ,SCREAMING_SNAKE_CASE__			:      Optional[int]=True     ,SCREAMING_SNAKE_CASE__			:      Tuple="relu6"     ,SCREAMING_SNAKE_CASE__			:      int=True     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]=0.8     ,SCREAMING_SNAKE_CASE__			:      Dict=0.02     ,SCREAMING_SNAKE_CASE__			:      str=0.001     ,SCREAMING_SNAKE_CASE__			:      int=255     ,**SCREAMING_SNAKE_CASE__			:      Optional[int]     ,):
					super().__init__(**SCREAMING_SNAKE_CASE__							)

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

					SCREAMING_SNAKE_CASE:Optional[Any]						      =					num_channels
					SCREAMING_SNAKE_CASE:str						      =					image_size
					SCREAMING_SNAKE_CASE:List[Any]						      =					depth_multiplier
					SCREAMING_SNAKE_CASE:Dict						      =					depth_divisible_by
					SCREAMING_SNAKE_CASE:Optional[int]						      =					min_depth
					SCREAMING_SNAKE_CASE:Dict						      =					expand_ratio
					SCREAMING_SNAKE_CASE:Tuple						      =					output_stride
					SCREAMING_SNAKE_CASE:Union[str, Any]						      =					first_layer_is_expansion
					SCREAMING_SNAKE_CASE:Tuple						      =					finegrained_output
					SCREAMING_SNAKE_CASE:Optional[int]						      =					hidden_act
					SCREAMING_SNAKE_CASE:List[str]						      =					tf_padding
					SCREAMING_SNAKE_CASE:int						      =					classifier_dropout_prob
					SCREAMING_SNAKE_CASE:int						      =					initializer_range
					SCREAMING_SNAKE_CASE:Any						      =					layer_norm_eps
					SCREAMING_SNAKE_CASE:Optional[int]						      =					semantic_loss_ignore_index





class 							_snake_case (       _a						):
			_A       :     Tuple       =					version.parse('''1.11'''						)
			@property
			def     __UpperCamelCase						( self			:      Any							):
					return OrderedDict([("pixel_values", {0: "batch"})]							)
			@property
			def     __UpperCamelCase						( self			:      Union[str, 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     __UpperCamelCase						( self			:      Dict							):
					return 1e-4









'''simple docstring'''

def    A_  ( snake_case      ,       snake_case ):
  SCREAMING_SNAKE_CASE:Optional[int]						      =					word.split()

  def justify(snake_case      ,       snake_case      ,       snake_case ) -> str:
    SCREAMING_SNAKE_CASE:str						      =					max_width - width
    SCREAMING_SNAKE_CASE:Dict						      =					len(snake_case )
    if len(snake_case ) == 1:
      # if there is only word in line
      # just insert overall_spaces_count for the remainder of line
      return line[0] + " " * overall_spaces_count
    else:
      SCREAMING_SNAKE_CASE:Optional[int]						      =					words_count - 1
      # num_spaces_between_words_list[i] : tells you to insert
      # num_spaces_between_words_list[i] spaces
      # after word on line[i]
      SCREAMING_SNAKE_CASE:List[Any]						      =					spaces_to_insert_between_words * [
          overall_spaces_count // spaces_to_insert_between_words
      ]
      SCREAMING_SNAKE_CASE:List[Any]						      =					(
          overall_spaces_count % spaces_to_insert_between_words
      )
      # distribute spaces via round robin to the left words
      for i in range(snake_case ):
        num_spaces_between_words_list[i] += 1
      SCREAMING_SNAKE_CASE:str						      =					[]
      for i in range(snake_case ):
        # add the word
        aligned_words_list.append(line[i] )
        # add the spaces to insert
        aligned_words_list.append(num_spaces_between_words_list[i] * " " )
      # just add the last word to the sentence
      aligned_words_list.append(line[-1] )
      # join the aligned words list to form a justified line
      return "".join(snake_case )

  SCREAMING_SNAKE_CASE:Any						      =					[]
  SCREAMING_SNAKE_CASE:list[str]						      =					[]
  SCREAMING_SNAKE_CASE:List[str]						      =					0
  for word in words:
    if width + len(snake_case ) + len(snake_case ) <= max_width:
      # keep adding words until we can fill out max_width
      # width = sum of length of all words (without overall_spaces_count)
      # len(word) = length of current word
      # len(line) = number of overall_spaces_count to insert between words
      line.append(snake_case )
      width += len(snake_case )
    else:
      # justify the line and add it to result
      answer.append(justify(snake_case      ,       snake_case      ,       snake_case ) )
      # reset new line and new width
      SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Tuple						      =					[word], len(snake_case )
  SCREAMING_SNAKE_CASE:Optional[int]						      =					max_width - width - len(snake_case )
  answer.append(" ".join(snake_case ) + (remaining_spaces + 1) * " " )
  return answer


if __name__ == "__main__":
    from doctest import testmod

    testmod()






