






'''simple docstring'''

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


UpperCAmelCase__						:Union[str, Any]          =    {
    '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
}

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







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):
       snake_case__    :     Optional[Any]   		=			'maskformer'
       snake_case__    :     Optional[int]   		=			{'hidden_size': 'mask_feature_size'}
       snake_case__    :     List[Any]   		=			['resnet', 'swin']
       snake_case__    :     List[str]   		=			['detr']




       def __init__(					self      : str	,    A__      : int = 256	,    A__      : int = 256	,    A__      : float = 0.1	,    A__      : bool = False	,    A__      : Optional[Dict] = None	,    A__      : Optional[Dict] = None	,    A__      : float = 0.02	,    A__      : float = 1.0	,    A__      : float = 1.0	,    A__      : float = 1.0	,    A__      : float = 20.0	,    A__      : Optional[bool] = None	,    **A__      : List[str]	,    ):


            """simple docstring"""







            if backbone_config is None:
                 # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
                 __lowerCamelCase						:       int				   =				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					):
                 __lowerCamelCase						:       Optional[Any]				   =				backbone_config.pop("""model_type"""					)
                 __lowerCamelCase						:       str				   =				CONFIG_MAPPING[backbone_model_type]
                 __lowerCamelCase						:       Union[str, Any]				   =				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
                 __lowerCamelCase						:       Optional[Any]				   =				DetrConfig()
            else:
                 # verify that the decoder is supported
                 __lowerCamelCase						:       Dict				   =				(
                     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					):
                      __lowerCamelCase						:       str				   =				CONFIG_MAPPING[decoder_type]
                      __lowerCamelCase						:       List[str]				   =				config_class.from_dict(__A					)

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

            __lowerCamelCase						:       int				   =				self.decoder_config.encoder_attention_heads
            __lowerCamelCase						:       Optional[Any]				   =				self.decoder_config.num_hidden_layers
            super().__init__(**__A					)




       @classmethod
       def 					a_					(					cls      : Any	,    A__      : PretrainedConfig	,    A__      : PretrainedConfig	,    **A__      : Optional[int]					):


            """simple docstring"""







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




       def 					a_					(					self      : Any					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				copy.deepcopy(self.__dict__					)
            __lowerCamelCase						:       Dict				   =				self.backbone_config.to_dict()
            __lowerCamelCase						:       Union[str, Any]				   =				self.decoder_config.to_dict()
            __lowerCamelCase						:       Tuple				   =				self.__class__.model_type
            return output











'''simple docstring'''

from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

UpperCAmelCase__						:List[str]          =			typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
UpperCAmelCase__						:Optional[int]          =			typing.Union[np.floataa, int, float]  # noqa: UP007
def     __lowercase  (_lowercase,						_lowercase     )   ->     VectorOut:


     """simple docstring"""
     return np.sqrt(np.sum((np.asarray(_lowercase     ) - np.asarray(_lowercase     )) ** 2     )     )
def     __lowercase  (_lowercase,						_lowercase     )   ->     VectorOut:


     """simple docstring"""
     return sum((va - va) ** 2 for va, va in zip(_lowercase,						_lowercase     )     ) ** (1 / 2)


if __name__ == "__main__":
   def     __lowercase  ()   ->     None:


        """simple docstring"""
        from timeit import timeit

        print("""Without Numpy"""     )
        print(
            timeit(
                """euclidean_distance_no_np([1, 2, 3], [4, 5, 6])""",						number=10_000,						globals=globals(),						)     )
        print("""With Numpy"""     )
        print(
            timeit(
                """euclidean_distance([1, 2, 3], [4, 5, 6])""",						number=10_000,						globals=globals(),						)     )
   benchmark()



