

'''simple docstring'''







import copy
from typing import Dict, Optional

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


_lowerCamelCase    =  logging.get_logger(__name__)

_lowerCamelCase    =  {
    '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   (		_snake_case  ):
						__SCREAMING_SNAKE_CASE     :					str  =      'oneformer'
						__SCREAMING_SNAKE_CASE     :					Tuple  =      {'hidden_size': 'hidden_dim'}

						def __init__(		self	:						Tuple    ,			lowercase__	:						Optional[Dict] = None    ,			lowercase__	:						int = 2_55    ,			lowercase__	:						int = 1_50    ,			lowercase__	:						int = 0.1    ,			lowercase__	:						float = 2.0    ,			lowercase__	:						float = 5.0    ,			lowercase__	:						float = 5.0    ,			lowercase__	:						float = 0.5    ,			lowercase__	:						float = 0.07    ,			lowercase__	:						int = 1_25_44    ,			lowercase__	:						float = 3.0    ,			lowercase__	:						float = 0.75    ,			lowercase__	:						float = 0.02    ,			lowercase__	:						float = 1.0    ,			lowercase__	:						float = 1e-05    ,			lowercase__	:						bool = False    ,			lowercase__	:						bool = True    ,			lowercase__	:						bool = True    ,			lowercase__	:						Optional[list] = [4, 8, 16, 32]    ,			lowercase__	:						int = 77    ,			lowercase__	:						int = 2_56    ,			lowercase__	:						int = 77    ,			lowercase__	:						int = 6    ,			lowercase__	:						int = 4_94_08    ,			lowercase__	:						int = 2    ,			lowercase__	:						int = 16    ,			lowercase__	:						int = 2_56    ,			lowercase__	:						int = 2_56    ,			lowercase__	:						int = 2_56    ,			lowercase__	:						int = 10_24    ,			lowercase__	:						str = "GN"    ,			lowercase__	:						int = 6    ,			lowercase__	:						int = 10    ,			lowercase__	:						bool = True    ,			lowercase__	:						int = 8    ,			lowercase__	:						float = 0.1    ,			lowercase__	:						int = 20_48    ,			lowercase__	:						bool = False    ,			lowercase__	:						bool = False    ,			lowercase__	:						int = 2    ,			lowercase__	:						int = 4    ,			**lowercase__	:						Any    ,			)						->Optional[int]:

							"""simple docstring"""



							if backbone_config is None:
								logger.info("""`backbone_config` is unset. Initializing the config with the default `Swin` backbone.""")
								_lowercase							=     CONFIG_MAPPING["""swin"""](
								    image_size=2_24    ,			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=lowercase__    ,			out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]    ,			)
							elif isinstance(lowercase__    ,			lowercase__):
								_lowercase							=     backbone_config.get("""model_type""")
								_lowercase							=     CONFIG_MAPPING[backbone_model_type]
								_lowercase							=     config_class.from_dict(lowercase__)

							_lowercase							=     backbone_config

							_lowercase							=     ignore_value
							_lowercase							=     num_queries
							_lowercase							=     no_object_weight
							_lowercase							=     class_weight
							_lowercase							=     mask_weight
							_lowercase							=     dice_weight
							_lowercase							=     contrastive_weight
							_lowercase							=     contrastive_temperature
							_lowercase							=     train_num_points
							_lowercase							=     oversample_ratio
							_lowercase							=     importance_sample_ratio
							_lowercase							=     init_std
							_lowercase							=     init_xavier_std
							_lowercase							=     layer_norm_eps
							_lowercase							=     is_training
							_lowercase							=     use_auxiliary_loss
							_lowercase							=     output_auxiliary_logits
							_lowercase							=     strides
							_lowercase							=     task_seq_len
							_lowercase							=     text_encoder_width
							_lowercase							=     text_encoder_context_length
							_lowercase							=     text_encoder_num_layers
							_lowercase							=     text_encoder_vocab_size
							_lowercase							=     text_encoder_proj_layers
							_lowercase							=     text_encoder_n_ctx
							_lowercase							=     conv_dim
							_lowercase							=     mask_dim
							_lowercase							=     hidden_dim
							_lowercase							=     encoder_feedforward_dim
							_lowercase							=     norm
							_lowercase							=     encoder_layers
							_lowercase							=     decoder_layers
							_lowercase							=     use_task_norm
							_lowercase							=     num_attention_heads
							_lowercase							=     dropout
							_lowercase							=     dim_feedforward
							_lowercase							=     pre_norm
							_lowercase							=     enforce_input_proj
							_lowercase							=     query_dec_layers
							_lowercase							=     common_stride
							_lowercase							=     decoder_layers

							super().__init__(**lowercase__)


						def 	_UpperCAmelCase							(		self	:						str)						->Dict[str, any]:

							"""simple docstring"""



							_lowercase							=     copy.deepcopy(self.__dict__)
							_lowercase							=     self.backbone_config.to_dict()
							_lowercase							=     self.__class__.model_type
							return output







'''simple docstring'''







from __future__ import annotations

from pathlib import Path






def 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_	):
	return pointa[0] * pointa[1] - pointa[1] * pointa[0]






def 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_							,       snake_case_							,       snake_case_							,       snake_case_							,       snake_case_	):
	_lowercase							=     (xa, ya)
	_lowercase							=     (xa - xa, ya - ya)
	_lowercase							=     (xa - xa, ya - ya)
	_lowercase							=     -vector_product(snake_case_							,       snake_case_	) / vector_product(
	    snake_case_							,       snake_case_	)
	_lowercase							=     +vector_product(snake_case_							,       snake_case_	) / vector_product(
	    snake_case_							,       snake_case_	)

	return a > 0 and b > 0 and a + b < 1






def 					_SCREAMING_SNAKE_CASE		(						snake_case_ = "p102_triangles.txt"	):
	_lowercase							=     Path(snake_case_	).parent.joinpath(snake_case_	).read_text(encoding="""utf-8"""	)

	_lowercase							=     []
	for line in data.strip().split("""\n"""	):
		triangles.append([int(snake_case_	) for number in line.split(""","""	)]	)

	_lowercase							=     0
	_lowercase							=     42

	for triangle in triangles:
		ret += contains_origin(*snake_case_	)

	return ret


if __name__ == "__main__":
							print(F"""{solution() = }""")




