

'''simple docstring'''



import warnings
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


_UpperCamelCase						:  Optional[Any]							       =				logging.get_logger(__name__)

_UpperCamelCase						:  Union[str, Any]							       =				{
    "nvidia/segformer-b0-finetuned-ade-512-512": (
        "https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}



class 				_snake_case					(   a_			):
  SCREAMING_SNAKE_CASE			:       int				=     '''segformer'''

  def __init__(				self      , _SCREAMING_SNAKE_CASE=3      , _SCREAMING_SNAKE_CASE=4      , _SCREAMING_SNAKE_CASE=[2, 2, 2, 2]      , _SCREAMING_SNAKE_CASE=[8, 4, 2, 1]      , _SCREAMING_SNAKE_CASE=[32, 64, 1_60, 2_56]      , _SCREAMING_SNAKE_CASE=[7, 3, 3, 3]      , _SCREAMING_SNAKE_CASE=[4, 2, 2, 2]      , _SCREAMING_SNAKE_CASE=[1, 2, 5, 8]      , _SCREAMING_SNAKE_CASE=[4, 4, 4, 4]      , _SCREAMING_SNAKE_CASE="gelu"      , _SCREAMING_SNAKE_CASE=0.0      , _SCREAMING_SNAKE_CASE=0.0      , _SCREAMING_SNAKE_CASE=0.1      , _SCREAMING_SNAKE_CASE=0.02      , _SCREAMING_SNAKE_CASE=0.1      , _SCREAMING_SNAKE_CASE=1e-6      , _SCREAMING_SNAKE_CASE=2_56      , _SCREAMING_SNAKE_CASE=2_55      , **_SCREAMING_SNAKE_CASE      , ):





      '''simple docstring'''

      super().__init__(**_SCREAMING_SNAKE_CASE	)

      if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
          warnings.warn(
              'Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be'
              ' removed, as the behaviour will default to that of reshape_last_stage = True.'      , _SCREAMING_SNAKE_CASE      , )

      lowerCAmelCase 						=							num_channels
      lowerCAmelCase 						=							num_encoder_blocks
      lowerCAmelCase 						=							depths
      lowerCAmelCase 						=							sr_ratios
      lowerCAmelCase 						=							hidden_sizes
      lowerCAmelCase 						=							patch_sizes
      lowerCAmelCase 						=							strides
      lowerCAmelCase 						=							mlp_ratios
      lowerCAmelCase 						=							num_attention_heads
      lowerCAmelCase 						=							hidden_act
      lowerCAmelCase 						=							hidden_dropout_prob
      lowerCAmelCase 						=							attention_probs_dropout_prob
      lowerCAmelCase 						=							classifier_dropout_prob
      lowerCAmelCase 						=							initializer_range
      lowerCAmelCase 						=							drop_path_rate
      lowerCAmelCase 						=							layer_norm_eps
      lowerCAmelCase 						=							decoder_hidden_size
      lowerCAmelCase 						=							kwargs.get('reshape_last_stage'      , _SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							semantic_loss_ignore_index



class 				_snake_case					(   a_			):
  SCREAMING_SNAKE_CASE			:       List[str]				=     version.parse('''1.11'''			)

  @property
  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

  @property
  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      return 1e-4


  @property
  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      return 12








'''simple docstring'''



def       snake_case   (	snake_case      : list			) ->  list:


    """simple docstring"""


    if any(not isinstance(snake_case					,		snake_case			) or x < 0 for x in sequence			):
        raise TypeError('Sequence must be list of non-negative integers'			)
    for _ in range(len(snake_case			)			):
        for i, (rod_upper, rod_lower) in enumerate(zip(snake_case					,		sequence[1:]			)			):
            if rod_upper > rod_lower:
                sequence[i] -= rod_upper - rod_lower
                sequence[i + 1] += rod_upper - rod_lower
    return sequence


if __name__ == "__main__":
     assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
     assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]





