






import copy
from typing import Dict, Optional

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


A_			:List[str]	    =		logging.get_logger(__name__)

A_			:List[str]	    =		{
    '''shi-labs/oneformer_ade20k_swin_tiny''': (
        '''https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json'''
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}




class 							__A	(     a					):





				"""simple docstring"""







				UpperCamelCase__					: int     					="""oneformer"""
				UpperCamelCase__					: Tuple     					={"""hidden_size""": """hidden_dim"""}


				def __init__(					self				,    lowerCamelCase__ = None				,    lowerCamelCase__ = 255				,    lowerCamelCase__ = 150				,    lowerCamelCase__ = 0.1				,    lowerCamelCase__ = 2.0				,    lowerCamelCase__ = 5.0				,    lowerCamelCase__ = 5.0				,    lowerCamelCase__ = 0.5				,    lowerCamelCase__ = 0.07				,    lowerCamelCase__ = 12544				,    lowerCamelCase__ = 3.0				,    lowerCamelCase__ = 0.75				,    lowerCamelCase__ = 0.02				,    lowerCamelCase__ = 1.0				,    lowerCamelCase__ = 1E-05				,    lowerCamelCase__ = False				,    lowerCamelCase__ = True				,    lowerCamelCase__ = True				,    lowerCamelCase__ = [4, 8, 16, 32]				,    lowerCamelCase__ = 77				,    lowerCamelCase__ = 256				,    lowerCamelCase__ = 77				,    lowerCamelCase__ = 6				,    lowerCamelCase__ = 49408				,    lowerCamelCase__ = 2				,    lowerCamelCase__ = 16				,    lowerCamelCase__ = 256				,    lowerCamelCase__ = 256				,    lowerCamelCase__ = 256				,    lowerCamelCase__ = 1024				,    lowerCamelCase__ = "GN"				,    lowerCamelCase__ = 6				,    lowerCamelCase__ = 10				,    lowerCamelCase__ = True				,    lowerCamelCase__ = 8				,    lowerCamelCase__ = 0.1				,    lowerCamelCase__ = 2048				,    lowerCamelCase__ = False				,    lowerCamelCase__ = False				,    lowerCamelCase__ = 2				,    lowerCamelCase__ = 4				,    **lowerCamelCase__				,    ):




									"""simple docstring"""

									if backbone_config is None:
														logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'       )
														__UpperCamelCase :   List[Any]									=CONFIG_MAPPING['swin'](
														    image_size=224				,    in_channels=3				,    patch_size=4				,    embed_dim=96				,    depths=[2, 2, 6, 2]				,    num_heads=[3, 6, 12, 24]				,    window_size=7				,    drop_path_rate=0.3				,    use_absolute_embeddings=lowerCamelCase__				,    out_features=['stage1', 'stage2', 'stage3', 'stage4']				,    )
									elif isinstance(lowerCamelCase__				,    lowerCamelCase__       ):
														__UpperCamelCase :   Tuple									=backbone_config.get('model_type'       )
														__UpperCamelCase :   Union[str, Any]									=CONFIG_MAPPING[backbone_model_type]
														__UpperCamelCase :   Optional[int]									=config_class.from_dict(lowerCamelCase__       )

									__UpperCamelCase :   str									=backbone_config

									__UpperCamelCase :   int									=ignore_value
									__UpperCamelCase :   List[str]									=num_queries
									__UpperCamelCase :   List[Any]									=no_object_weight
									__UpperCamelCase :   int									=class_weight
									__UpperCamelCase :   Any									=mask_weight
									__UpperCamelCase :   str									=dice_weight
									__UpperCamelCase :   Optional[int]									=contrastive_weight
									__UpperCamelCase :   Tuple									=contrastive_temperature
									__UpperCamelCase :   Any									=train_num_points
									__UpperCamelCase :   str									=oversample_ratio
									__UpperCamelCase :   Union[str, Any]									=importance_sample_ratio
									__UpperCamelCase :   Optional[Any]									=init_std
									__UpperCamelCase :   List[Any]									=init_xavier_std
									__UpperCamelCase :   List[Any]									=layer_norm_eps
									__UpperCamelCase :   int									=is_training
									__UpperCamelCase :   Dict									=use_auxiliary_loss
									__UpperCamelCase :   Any									=output_auxiliary_logits
									__UpperCamelCase :   Optional[Any]									=strides
									__UpperCamelCase :   str									=task_seq_len
									__UpperCamelCase :   List[Any]									=text_encoder_width
									__UpperCamelCase :   Tuple									=text_encoder_context_length
									__UpperCamelCase :   Optional[Any]									=text_encoder_num_layers
									__UpperCamelCase :   Dict									=text_encoder_vocab_size
									__UpperCamelCase :   str									=text_encoder_proj_layers
									__UpperCamelCase :   Optional[Any]									=text_encoder_n_ctx
									__UpperCamelCase :   Dict									=conv_dim
									__UpperCamelCase :   int									=mask_dim
									__UpperCamelCase :   Dict									=hidden_dim
									__UpperCamelCase :   str									=encoder_feedforward_dim
									__UpperCamelCase :   Dict									=norm
									__UpperCamelCase :   Optional[Any]									=encoder_layers
									__UpperCamelCase :   Optional[Any]									=decoder_layers
									__UpperCamelCase :   Optional[int]									=use_task_norm
									__UpperCamelCase :   Dict									=num_attention_heads
									__UpperCamelCase :   str									=dropout
									__UpperCamelCase :   int									=dim_feedforward
									__UpperCamelCase :   Optional[int]									=pre_norm
									__UpperCamelCase :   Union[str, Any]									=enforce_input_proj
									__UpperCamelCase :   Tuple									=query_dec_layers
									__UpperCamelCase :   str									=common_stride
									__UpperCamelCase :   Tuple									=decoder_layers

									super().__init__(**lowerCamelCase__       )







				def  __lowercase				(					self       ):




									"""simple docstring"""

									__UpperCamelCase :   List[str]									=copy.deepcopy(self.__dict__       )
									__UpperCamelCase :   Dict									=self.backbone_config.to_dict()
									__UpperCamelCase :   List[str]									=self.__class__.model_type
									return output









import random




class 							__A	:





				"""simple docstring"""







				def __init__(					self				,    lowerCamelCase__				,    lowerCamelCase__       ):




									"""simple docstring"""

									__UpperCamelCase :   Optional[Any]									=x
									__UpperCamelCase :   int									=y


				def  __lowercase				(					self       ):




									"""simple docstring"""

									return (self.x**2 + self.y**2) <= 1







				@classmethod
				def  __lowercase				(					cls       ):




									"""simple docstring"""

									return cls(x=random.random()				,    y=random.random()       )






def 				A	(			a_   )	->		float:
					if number_of_simulations < 1:
										raise ValueError('At least one simulation is necessary to estimate PI.'   )

					__UpperCamelCase :   List[str]									=0
					for _ in range(a_   ):
										__UpperCamelCase :   Optional[int]									=Point.random_unit_square()

										if random_point.is_in_unit_circle():
															number_in_unit_circle += 1

					return 4 * number_in_unit_circle / number_of_simulations


if __name__ == "__main__":
							# import doctest

							# doctest.testmod()
							from math import pi

							A_			:Optional[int]	    =		'''Please enter the desired number of Monte Carlo simulations: '''
							A_			:int	    =		estimate_pi(int(input(prompt).strip()))
							print(f"An estimate of PI is {my_pi} with an error of {abs(my_pi - pi)}")

