


"""simple docstring"""







import copy
from typing import Dict, Optional

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


__a			 =   logging.get_logger(__name__)

__a			 =   {
    "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 							lowerCamelCase       (			__SCREAMING_SNAKE_CASE ):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE										=      """oneformer"""
							SCREAMING_SNAKE_CASE										=      {"""hidden_size""": """hidden_dim"""}






							def __init__(						self :     str		, lowerCamelCase_ :     Optional[Dict] = None		, lowerCamelCase_ :     int = 255		, lowerCamelCase_ :     int = 150		, lowerCamelCase_ :     int = 0.1		, lowerCamelCase_ :     float = 2.0		, lowerCamelCase_ :     float = 5.0		, lowerCamelCase_ :     float = 5.0		, lowerCamelCase_ :     float = 0.5		, lowerCamelCase_ :     float = 0.07		, lowerCamelCase_ :     int = 12544		, lowerCamelCase_ :     float = 3.0		, lowerCamelCase_ :     float = 0.75		, lowerCamelCase_ :     float = 0.02		, lowerCamelCase_ :     float = 1.0		, lowerCamelCase_ :     float = 1E-05		, lowerCamelCase_ :     bool = False		, lowerCamelCase_ :     bool = True		, lowerCamelCase_ :     bool = True		, lowerCamelCase_ :     Optional[list] = [4, 8, 16, 32]		, lowerCamelCase_ :     int = 77		, lowerCamelCase_ :     int = 256		, lowerCamelCase_ :     int = 77		, lowerCamelCase_ :     int = 6		, lowerCamelCase_ :     int = 49408		, lowerCamelCase_ :     int = 2		, lowerCamelCase_ :     int = 16		, lowerCamelCase_ :     int = 256		, lowerCamelCase_ :     int = 256		, lowerCamelCase_ :     int = 256		, lowerCamelCase_ :     int = 1024		, lowerCamelCase_ :     str = "GN"		, lowerCamelCase_ :     int = 6		, lowerCamelCase_ :     int = 10		, lowerCamelCase_ :     bool = True		, lowerCamelCase_ :     int = 8		, lowerCamelCase_ :     float = 0.1		, lowerCamelCase_ :     int = 2048		, lowerCamelCase_ :     bool = False		, lowerCamelCase_ :     bool = False		, lowerCamelCase_ :     int = 2		, lowerCamelCase_ :     int = 4		, **lowerCamelCase_ :     Optional[int]		, ):
													if backbone_config is None:
																			logger.info('''`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'''				)
																			UpperCAmelCase__		=    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__		=    backbone_config.get('''model_type'''				)
																			UpperCAmelCase__		=    CONFIG_MAPPING[backbone_model_type]
																			UpperCAmelCase__		=    config_class.from_dict(lowerCamelCase_				)

													UpperCAmelCase__		=    backbone_config

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

													super().__init__(**lowerCamelCase_				)






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



"""simple docstring"""







from __future__ import annotations

from collections.abc import Callable






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase = 100	,			):





						"""simple docstring"""

						UpperCAmelCase__		=    x_start
						UpperCAmelCase__		=    fnc(_UpperCamelCase )
						UpperCAmelCase__		=    0.0

						for _ in range(_UpperCamelCase ):
												# Approximates small segments of curve as linear and solve
												# for trapezoidal area
												UpperCAmelCase__		=    (x_end - x_start) / steps + xa
												UpperCAmelCase__		=    fnc(_UpperCamelCase )
												area += abs(fxa + fxa ) * (xa - xa) / 2

												# Increment step
												UpperCAmelCase__		=    xa
												UpperCAmelCase__		=    fxa
						return area


if __name__ == "__main__":






						def 					a__ (       _UpperCamelCase ):





												"""simple docstring"""

												return x**3

						print("f(x) = x^3")
						print("The area between the curve, x = -10, x = 10 and the x axis is:")
						__a			 =   10
						while i <= 10_00_00:
												__a			 =   trapezoidal_area(f, -5, 5, i)
												print(f'''with {i} steps: {area}''')
												i *= 10