



'''simple docstring'''





import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_UpperCamelCase											=						{
    'facebook/mask2former-swin-small-coco-instance': (
        'https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json'
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

_UpperCamelCase											=						logging.get_logger(__name__)






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  a_												="""mask2former"""
  a_												=["""swin"""]
  a_												={"""hidden_size""": """hidden_dim"""}




  def __init__( self      :	Dict			,	_a      :	Optional[Dict] = None			,	_a      :	int = 256			,	_a      :	int = 256			,	_a      :	int = 256			,	_a      :	int = 1024			,	_a      :	str = "relu"			,	_a      :	int = 6			,	_a      :	int = 10			,	_a      :	int = 8			,	_a      :	float = 0.0			,	_a      :	int = 2048			,	_a      :	bool = False			,	_a      :	bool = False			,	_a      :	int = 4			,	_a      :	int = 255			,	_a      :	int = 100			,	_a      :	float = 0.1			,	_a      :	float = 2.0			,	_a      :	float = 5.0			,	_a      :	float = 5.0			,	_a      :	int = 1_2544			,	_a      :	float = 3.0			,	_a      :	float = 0.75			,	_a      :	float = 0.02			,	_a      :	float = 1.0			,	_a      :	bool = True			,	_a      :	List[int] = [4, 8, 16, 32]			,	_a      :	bool = None			,	**_a      :	Union[str, Any]			,	)					->					Union[str, Any]:
    if backbone_config is None:
      logger.info('`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.'   )
      __lowerCamelCase       :       Optional[int]           = CONFIG_MAPPING['swin'](
          image_size=224			,	in_channels=3			,	patch_size=4			,	embed_dim=96			,	depths=[2, 2, 18, 2]			,	num_heads=[3, 6, 12, 24]			,	window_size=7			,	drop_path_rate=0.3			,	use_absolute_embeddings=_a			,	out_features=['stage1', 'stage2', 'stage3', 'stage4']			,	)

    if isinstance(_a			,	_a   ):
      __lowerCamelCase       :       str           = backbone_config.pop('model_type'   )
      __lowerCamelCase       :       Optional[int]           = CONFIG_MAPPING[backbone_model_type]
      __lowerCamelCase       :       Optional[Any]           = config_class.from_dict(_a   )

    # verify that the backbone is supported
    if backbone_config.model_type not in self.backbones_supported:
      logger.warning_once(
          f'Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. '
          f'Supported model types: {",".join(self.backbones_supported   )}'   )

    __lowerCamelCase       :       Tuple           = backbone_config
    __lowerCamelCase       :       Union[str, Any]           = feature_size
    __lowerCamelCase       :       Union[str, Any]           = mask_feature_size
    __lowerCamelCase       :       Union[str, Any]           = hidden_dim
    __lowerCamelCase       :       Dict           = encoder_feedforward_dim
    __lowerCamelCase       :       Dict           = activation_function
    __lowerCamelCase       :       List[str]           = encoder_layers
    __lowerCamelCase       :       Any           = decoder_layers
    __lowerCamelCase       :       Optional[int]           = num_attention_heads
    __lowerCamelCase       :       List[str]           = dropout
    __lowerCamelCase       :       int           = dim_feedforward
    __lowerCamelCase       :       Any           = pre_norm
    __lowerCamelCase       :       List[str]           = enforce_input_projection
    __lowerCamelCase       :       Dict           = common_stride
    __lowerCamelCase       :       List[Any]           = ignore_value
    __lowerCamelCase       :       List[Any]           = num_queries
    __lowerCamelCase       :       Tuple           = no_object_weight
    __lowerCamelCase       :       Any           = class_weight
    __lowerCamelCase       :       Dict           = mask_weight
    __lowerCamelCase       :       Any           = dice_weight
    __lowerCamelCase       :       Union[str, Any]           = train_num_points
    __lowerCamelCase       :       List[Any]           = oversample_ratio
    __lowerCamelCase       :       Dict           = importance_sample_ratio
    __lowerCamelCase       :       Optional[Any]           = init_std
    __lowerCamelCase       :       Optional[int]           = init_xavier_std
    __lowerCamelCase       :       str           = use_auxiliary_loss
    __lowerCamelCase       :       Union[str, Any]           = feature_strides
    __lowerCamelCase       :       Optional[Any]           = output_auxiliary_logits
    __lowerCamelCase       :       Union[str, Any]           = decoder_layers

    super().__init__(**_a   )




  @classmethod
  def 						_lowercase     ( cls      :	Optional[Any]			,	_a      :	PretrainedConfig			,	**_a      :	str   )					->					Dict:
    return cls(
        backbone_config=_a			,	**_a			,	)




  def 						_lowercase     ( self      :	Dict   )					->					Dict[str, any]:
    __lowerCamelCase       :       str           = copy.deepcopy(self.__dict__   )
    __lowerCamelCase       :       Union[str, Any]           = self.backbone_config.to_dict()
    __lowerCamelCase       :       Tuple           = self.__class__.model_type
    return output











'''simple docstring'''





from __future__ import annotations

import math
from collections.abc import Callable

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase = 100       ,)      ->      float:
  __lowerCamelCase       :       Dict           = x_start
  __lowerCamelCase       :       int           = fnc(_lowerCAmelCase       )
  __lowerCamelCase       :       Dict           = 0.0

  for _ in range(_lowerCAmelCase       ):
    # Approximates curve as a sequence of linear lines and sums their length
    __lowerCamelCase       :       List[str]           = (x_end - x_start) / steps + xa
    __lowerCamelCase       :       List[Any]           = fnc(_lowerCAmelCase       )
    length += math.hypot(xa - xa       ,fxa - fxa       )

    # Increment step
    __lowerCamelCase       :       Any           = xa
    __lowerCamelCase       :       Tuple           = fxa

  return length


if __name__ == "__main__":

   def        a_     (							_lowerCAmelCase       )      ->      Dict:
     return math.sin(10 * x       )

   print('f(x) = sin(10 * x)')
   print('The length of the curve from x = -10 to x = 10 is:')
   _UpperCamelCase											=						10
   while i <= 100000:
      print(f'''With {i} steps: {line_length(f, -10, 10, i)}''')
      i *= 10






