





'''simple docstring'''

import copy

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


__lowerCamelCase : Union[str, Any]											=			logging.get_logger(__name__)

__lowerCamelCase : str											=			{
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}

class       UpperCAmelCase (						_lowercase  ):
  UpperCAmelCase					:	Tuple 			=		'''deta'''
  UpperCAmelCase					:	str 			=		{
      '''hidden_size''': '''d_model''',
      '''num_attention_heads''': '''encoder_attention_heads''',
  }




  def __init__(self      :	Tuple							,			A__      :	Optional[int]=None							,			A__      :	Any=9_0_0							,			A__      :	Tuple=2_0_4_8							,			A__      :	Optional[int]=6							,			A__      :	int=2_0_4_8							,			A__      :	str=8							,			A__      :	Union[str, Any]=6							,			A__      :	Dict=1_0_2_4							,			A__      :	Optional[int]=8							,			A__      :	Union[str, Any]=0.0							,			A__      :	Dict=True							,			A__      :	Dict="relu"							,			A__      :	Dict=2_5_6							,			A__      :	int=0.1							,			A__      :	str=0.0							,			A__      :	List[str]=0.0							,			A__      :	str=0.0_2							,			A__      :	Any=1.0							,			A__      :	Dict=True							,			A__      :	str=False							,			A__      :	Tuple="sine"							,			A__      :	Tuple=5							,			A__      :	Any=4							,			A__      :	Tuple=4							,			A__      :	Optional[int]=True							,			A__      :	List[Any]=3_0_0							,			A__      :	List[Any]=True							,			A__      :	Optional[int]=True							,			A__      :	str=1							,			A__      :	str=5							,			A__      :	int=2							,			A__      :	Tuple=1							,			A__      :	List[str]=1							,			A__      :	List[str]=5							,			A__      :	List[Any]=2							,			A__      :	Any=0.1							,			A__      :	Dict=0.2_5							,			**A__      :	Dict							,			)					-> int:
      if backbone_config is None:
          logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."				)
          lowercase         = CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"]				)
      else:
          if isinstance(A__							,			A__				):
              lowercase         = backbone_config.pop("model_type"				)
              lowercase         = CONFIG_MAPPING[backbone_model_type]
              lowercase         = config_class.from_dict(A__				)

      lowercase         = backbone_config
      lowercase         = num_queries
      lowercase         = max_position_embeddings
      lowercase         = d_model
      lowercase         = encoder_ffn_dim
      lowercase         = encoder_layers
      lowercase         = encoder_attention_heads
      lowercase         = decoder_ffn_dim
      lowercase         = decoder_layers
      lowercase         = decoder_attention_heads
      lowercase         = dropout
      lowercase         = attention_dropout
      lowercase         = activation_dropout
      lowercase         = activation_function
      lowercase         = init_std
      lowercase         = init_xavier_std
      lowercase         = encoder_layerdrop
      lowercase         = auxiliary_loss
      lowercase         = position_embedding_type
      # deformable attributes
      lowercase         = num_feature_levels
      lowercase         = encoder_n_points
      lowercase         = decoder_n_points
      lowercase         = two_stage
      lowercase         = two_stage_num_proposals
      lowercase         = with_box_refine
      lowercase         = assign_first_stage
      if two_stage is True and with_box_refine is False:
          raise ValueError("If two_stage is True, with_box_refine must be True."				)
      # Hungarian matcher
      lowercase         = class_cost
      lowercase         = bbox_cost
      lowercase         = giou_cost
      # Loss coefficients
      lowercase         = mask_loss_coefficient
      lowercase         = dice_loss_coefficient
      lowercase         = bbox_loss_coefficient
      lowercase         = giou_loss_coefficient
      lowercase         = eos_coefficient
      lowercase         = focal_alpha
      super().__init__(is_encoder_decoder=A__							,			**A__				)




  @property
  def 				UpperCAmelCase__   (self      :	List[str]				)					-> int:
      return self.encoder_attention_heads




  @property
  def 				UpperCAmelCase__   (self      :	Any				)					-> int:
      return self.d_model





  def 				UpperCAmelCase__   (self      :	Any				)					-> Optional[Any]:
      lowercase         = copy.deepcopy(self.__dict__				)
      lowercase         = self.backbone_config.to_dict()
      lowercase         = self.__class__.model_type
      return output











'''simple docstring'''

from __future__ import annotations

class       UpperCAmelCase :




		def __init__(self      :	Dict							,			A__      :	str							,			A__      :	str				)					-> str:
						lowercase   ,							lowercase         = text, pattern
						lowercase   ,							lowercase         = len(A__				), len(A__				)




		def 				UpperCAmelCase__   (self      :	Any							,			A__      :	str				)					-> int:

						for i in range(self.patLen - 1							,			-1							,			-1				):
										if char == self.pattern[i]:
														return i
						return -1




		def 				UpperCAmelCase__   (self      :	List[Any]							,			A__      :	int				)					-> int:

						for i in range(self.patLen - 1							,			-1							,			-1				):
										if self.pattern[i] != self.text[current_pos + i]:
														return current_pos + i
						return -1





		def 				UpperCAmelCase__   (self      :	Optional[Any]				)					-> list[int]:
						# searches pattern in text and returns index positions
						lowercase         = []
						for i in range(self.textLen - self.patLen + 1				):
										lowercase         = self.mismatch_in_text(A__				)
										if mismatch_index == -1:
														positions.append(A__				)
										else:
														lowercase         = self.match_in_pattern(self.text[mismatch_index]				)
														lowercase         = (
														    mismatch_index - match_index
														)  # shifting index lgtm [py/multiple-definition]
						return positions




__lowerCamelCase : List[str]											=			"ABAABA"
__lowerCamelCase : List[str]											=			"AB"
__lowerCamelCase : Union[str, Any]											=			BoyerMooreSearch(text, pattern)
__lowerCamelCase : List[str]											=			bms.bad_character_heuristic()

if len(positions) == 0:
			print("No match found")
else:
			print("Pattern found in following positions: ")
			print(positions)




