
'''simple docstring'''







import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


_snake_case				:   Optional[int]     =				logging.get_logger(__name__)

_snake_case				:   str     =				{
    """Intel/dpt-large""": """https://huggingface.co/Intel/dpt-large/resolve/main/config.json""",
    # See all DPT models at https://huggingface.co/models?filter=dpt
}


class 			lowerCAmelCase (				__UpperCAmelCase			):
		a							:					List[Any]       =					"""dpt"""




		def __init__(				self					,						UpperCamelCase=768					,						UpperCamelCase=12					,						UpperCamelCase=12					,						UpperCamelCase=3_072					,						UpperCamelCase="gelu"					,						UpperCamelCase=0.0					,						UpperCamelCase=0.0					,						UpperCamelCase=0.02					,						UpperCamelCase=1e-12					,						UpperCamelCase=384					,						UpperCamelCase=16					,						UpperCamelCase=3					,						UpperCamelCase=False					,						UpperCamelCase=True					,						UpperCamelCase=[2, 5, 8, 11]					,						UpperCamelCase="project"					,						UpperCamelCase=[4, 2, 1, 0.5]					,						UpperCamelCase=[96, 192, 384, 768]					,						UpperCamelCase=256					,						UpperCamelCase=-1					,						UpperCamelCase=False					,						UpperCamelCase=True					,						UpperCamelCase=0.4					,						UpperCamelCase=255					,						UpperCamelCase=0.1					,						UpperCamelCase=[1, 1_024, 24, 24]					,						UpperCamelCase=[0, 1]					,						UpperCamelCase=None					,						**UpperCamelCase					,						):
								super().__init__(**UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= hidden_size
								_SCREAMING_SNAKE_CASE				= is_hybrid

								if self.is_hybrid:
														if backbone_config is None:
																				logger.info("Initializing the config with a `BiT` backbone."      )
																				_SCREAMING_SNAKE_CASE				= {
																				    "global_padding": "same",
																				    "layer_type": "bottleneck",
																				    "depths": [3, 4, 9],
																				    "out_features": ["stage1", "stage2", "stage3"],
																				    "embedding_dynamic_padding": True,
																				}
																				_SCREAMING_SNAKE_CASE				= BitConfig(**UpperCamelCase      )
														elif isinstance(UpperCamelCase					,						UpperCamelCase      ):
																				logger.info("Initializing the config with a `BiT` backbone."      )
																				_SCREAMING_SNAKE_CASE				= BitConfig(**UpperCamelCase      )
														elif isinstance(UpperCamelCase					,						UpperCamelCase      ):
																				_SCREAMING_SNAKE_CASE				= backbone_config
														else:
																				raise ValueError(
																				    F'backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.'      )

														_SCREAMING_SNAKE_CASE				= backbone_featmap_shape
														_SCREAMING_SNAKE_CASE				= neck_ignore_stages

														if readout_type != "project":
																				raise ValueError("Readout type must be 'project' when using `DPT-hybrid` mode."      )
								else:
														_SCREAMING_SNAKE_CASE				= None
														_SCREAMING_SNAKE_CASE				= None
														_SCREAMING_SNAKE_CASE				= []

								_SCREAMING_SNAKE_CASE				= num_hidden_layers
								_SCREAMING_SNAKE_CASE				= num_attention_heads
								_SCREAMING_SNAKE_CASE				= intermediate_size
								_SCREAMING_SNAKE_CASE				= hidden_act
								_SCREAMING_SNAKE_CASE				= hidden_dropout_prob
								_SCREAMING_SNAKE_CASE				= attention_probs_dropout_prob
								_SCREAMING_SNAKE_CASE				= initializer_range
								_SCREAMING_SNAKE_CASE				= layer_norm_eps
								_SCREAMING_SNAKE_CASE				= image_size
								_SCREAMING_SNAKE_CASE				= patch_size
								_SCREAMING_SNAKE_CASE				= num_channels
								_SCREAMING_SNAKE_CASE				= qkv_bias
								_SCREAMING_SNAKE_CASE				= backbone_out_indices
								if readout_type not in ["ignore", "add", "project"]:
														raise ValueError("Readout_type must be one of ['ignore', 'add', 'project']"      )
								_SCREAMING_SNAKE_CASE				= readout_type
								_SCREAMING_SNAKE_CASE				= reassemble_factors
								_SCREAMING_SNAKE_CASE				= neck_hidden_sizes
								_SCREAMING_SNAKE_CASE				= fusion_hidden_size
								_SCREAMING_SNAKE_CASE				= head_in_index
								_SCREAMING_SNAKE_CASE				= use_batch_norm_in_fusion_residual
								# auxiliary head attributes (semantic segmentation)
								_SCREAMING_SNAKE_CASE				= use_auxiliary_head
								_SCREAMING_SNAKE_CASE				= auxiliary_loss_weight
								_SCREAMING_SNAKE_CASE				= semantic_loss_ignore_index
								_SCREAMING_SNAKE_CASE				= semantic_classifier_dropout




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= copy.deepcopy(self.__dict__      )

								if output["backbone_config"] is not None:
														_SCREAMING_SNAKE_CASE				= self.backbone_config.to_dict()

								_SCREAMING_SNAKE_CASE				= self.__class__.model_type
								return output

'''simple docstring'''







from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean



def 			_a		(	_SCREAMING_SNAKE_CASE		:   int  ):

						# A local function to see if a dot lands in the circle.
						def is_in_circle(_SCREAMING_SNAKE_CASE		:   float	,			_SCREAMING_SNAKE_CASE		:   float  ) -> bool:
												_SCREAMING_SNAKE_CASE				= sqrt((x**2) + (y**2)  )
												# Our circle has a radius of 1, so a distance
												# greater than 1 would land outside the circle.
												return distance_from_centre <= 1

						# The proportion of guesses that landed in the circle
						_SCREAMING_SNAKE_CASE				= mean(
						    int(is_in_circle(uniform(-1.0	,			1.0  )	,			uniform(-1.0	,			1.0  )  )  )
						    for _ in range(_SCREAMING_SNAKE_CASE  )  )
						# The ratio of the area for circle to square is pi/4.
						_SCREAMING_SNAKE_CASE				= proportion * 4
						print(F'The estimated value of pi is {pi_estimate}'  )
						print(F'The numpy value of pi is {pi}'  )
						print(F'The total error is {abs(pi - pi_estimate  )}'  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   int	,			_SCREAMING_SNAKE_CASE		:   Callable[[float], float]	,			_SCREAMING_SNAKE_CASE		:   float = 0.0	,			_SCREAMING_SNAKE_CASE		:   float = 1.0	,			):
						return mean(
						    function_to_integrate(uniform(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )  ) for _ in range(_SCREAMING_SNAKE_CASE  )  ) * (max_value - min_value)



def 			_a		(	_SCREAMING_SNAKE_CASE		:   int	,			_SCREAMING_SNAKE_CASE		:   float = 0.0	,			_SCREAMING_SNAKE_CASE		:   float = 1.0  ):

						def identity_function(_SCREAMING_SNAKE_CASE		:   float  ) -> float:
												return x

						_SCREAMING_SNAKE_CASE				= area_under_curve_estimator(
						    _SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= (max_value * max_value - min_value * min_value) / 2

						print("******************"  )
						print(F'Estimating area under y=x where x varies from {min_value} to {max_value}'  )
						print(F'Estimated value is {estimated_value}'  )
						print(F'Expected value is {expected_value}'  )
						print(F'Total error is {abs(estimated_value - expected_value  )}'  )
						print("******************"  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   int  ):

						def function_to_integrate(_SCREAMING_SNAKE_CASE		:   float  ) -> float:
												return sqrt(4.0 - x * x  )

						_SCREAMING_SNAKE_CASE				= area_under_curve_estimator(
						    _SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			0.0	,			2.0  )

						print("******************"  )
						print("Estimating pi using area_under_curve_estimator"  )
						print(F'Estimated value is {estimated_value}'  )
						print(F'Expected value is {pi}'  )
						print(F'Total error is {abs(estimated_value - pi  )}'  )
						print("******************"  )


if __name__ == "__main__":
						import doctest

						doctest.testmod()