

import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


SCREAMING_SNAKE_CASE_            = {
    'facebook/maskformer-swin-base-ade': (
        'https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)

class 			a					(		UpperCAmelCase			):
						_lowercase        =     "maskformer"
						_lowercase        =     {"hidden_size": "mask_feature_size"}
						_lowercase        =     ["resnet", "swin"]
						_lowercase        =     ["detr"]


						def __init__(    self  ,   A_ = 256  ,   A_ = 256  ,   A_ = 0.1  ,   A_ = False  ,   A_ = None  ,   A_ = None  ,   A_ = 0.02  ,   A_ = 1.0  ,   A_ = 1.0  ,   A_ = 1.0  ,   A_ = 20.0  ,   A_ = None  ,   **A_  ,   ):







										'''simple docstring'''


										if backbone_config is None:
														# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
														_UpperCAmelCase    :						List[str]							=      SwinConfig(
														    image_size=384  ,   in_channels=3  ,   patch_size=4  ,   embed_dim=128  ,   depths=[2, 2, 18, 2]  ,   num_heads=[4, 8, 16, 32]  ,   window_size=12  ,   drop_path_rate=0.3  ,   out_features=["stage1", "stage2", "stage3", "stage4"]  ,   )

										if isinstance(A_  ,   A_		):
														_UpperCAmelCase    :						Any							=      backbone_config.pop("model_type"		)
														_UpperCAmelCase    :						Dict							=      CONFIG_MAPPING[backbone_model_type]
														_UpperCAmelCase    :						int							=      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 MaskFormer. '
														    f'Supported model types: {",".join(self.backbones_supported		)}'		)

										if decoder_config is None:
														# fall back to https://huggingface.co/facebook/detr-resnet-50
														_UpperCAmelCase    :						Union[str, Any]							=      DetrConfig()
										else:
														# verify that the decoder is supported
														_UpperCAmelCase    :						List[str]							=      (
														    decoder_config.pop("model_type"		) if isinstance(A_  ,   A_		) else decoder_config.model_type
														)
														if decoder_type not in self.decoders_supported:
																		raise ValueError(
																		    f'Transformer Decoder {decoder_type} not supported, please use one of'
																		    f' {",".join(self.decoders_supported		)}'		)
														if isinstance(A_  ,   A_		):
																		_UpperCAmelCase    :						Union[str, Any]							=      CONFIG_MAPPING[decoder_type]
																		_UpperCAmelCase    :						str							=      config_class.from_dict(A_		)

										_UpperCAmelCase    :						Union[str, Any]							=      backbone_config
										_UpperCAmelCase    :						Union[str, Any]							=      decoder_config
										# main feature dimension for the model
										_UpperCAmelCase    :						Optional[int]							=      fpn_feature_size
										_UpperCAmelCase    :						str							=      mask_feature_size
										# initializer
										_UpperCAmelCase    :						Union[str, Any]							=      init_std
										_UpperCAmelCase    :						Optional[int]							=      init_xavier_std
										# Hungarian matcher && loss
										_UpperCAmelCase    :						Tuple							=      cross_entropy_weight
										_UpperCAmelCase    :						Dict							=      dice_weight
										_UpperCAmelCase    :						Dict							=      mask_weight
										_UpperCAmelCase    :						List[str]							=      use_auxiliary_loss
										_UpperCAmelCase    :						str							=      no_object_weight
										_UpperCAmelCase    :						Any							=      output_auxiliary_logits

										_UpperCAmelCase    :						int							=      self.decoder_config.encoder_attention_heads
										_UpperCAmelCase    :						Optional[int]							=      self.decoder_config.num_hidden_layers
										super().__init__(**A_		)


						@classmethod
						def     _UpperCAmelCase			(    cls  ,   A_  ,   A_  ,   **A_		):







										'''simple docstring'''


										return cls(
										    backbone_config=A_  ,   decoder_config=A_  ,   **A_  ,   )





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						int							=      copy.deepcopy(self.__dict__		)
										_UpperCAmelCase    :						Optional[int]							=      self.backbone_config.to_dict()
										_UpperCAmelCase    :						Optional[int]							=      self.decoder_config.to_dict()
										_UpperCAmelCase    :						List[Any]							=      self.__class__.model_type
										return output





def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					str					, lowerCAmelCase:					str	)				->    bool:
				_UpperCAmelCase    :						Optional[Any]							=      len(lowerCAmelCase	) + 1
				_UpperCAmelCase    :						Optional[int]							=      len(lowerCAmelCase	) + 1

				# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
				# length i of input_string matches with prefix string of length j of
				# given pattern.
				# "dp" stands for dynamic programming.
				_UpperCAmelCase    :						List[str]							=      [[0 for i in range(lowerCAmelCase	)] for j in range(lowerCAmelCase	)]

				# since string of zero length match pattern of zero length
				_UpperCAmelCase    :						List[Any]							=      1

				# since pattern of zero length will never match with string of non-zero length
				for i in range(1					, lowerCAmelCase	):
								_UpperCAmelCase    :						Dict							=      0

				# since string of zero length will match with pattern where there
				# is at least one * alternatively
				for j in range(1					, lowerCAmelCase	):
								_UpperCAmelCase    :						Tuple							=      dp[0][j - 2] if pattern[j - 1] == "*" else 0

				# now using bottom-up approach to find for all remaining lengths
				for i in range(1					, lowerCAmelCase	):
								for j in range(1					, lowerCAmelCase	):
												if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
																_UpperCAmelCase    :						Optional[Any]							=      dp[i - 1][j - 1]

												elif pattern[j - 1] == "*":
																if dp[i][j - 2] == 1:
																				_UpperCAmelCase    :						List[str]							=      1
																elif pattern[j - 2] in (input_string[i - 1], "."):
																				_UpperCAmelCase    :						str							=      dp[i - 1][j]
																else:
																				_UpperCAmelCase    :						int							=      0
												else:
																_UpperCAmelCase    :						List[Any]							=      0

				return bool(dp[-1][-1]	)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					# inputing the strings
					# input_string = input("input a string :")
					# pattern = input("input a pattern :")

					SCREAMING_SNAKE_CASE_            = 'aab'
					SCREAMING_SNAKE_CASE_            = 'c*a*b'

					# using function to check whether given string matches the given pattern
					if match_pattern(input_string, pattern):
										print(F'''{input_string} matches the given pattern {pattern}''')
					else:
										print(F'''{input_string} does not match with the given pattern {pattern}''')


