



import copy
from typing import Dict, List, Optional

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


__SCREAMING_SNAKE_CASE           =  {
    """facebook/mask2former-swin-small-coco-instance""": (
        """https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"""
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

__SCREAMING_SNAKE_CASE           =  logging.get_logger(__name__)

class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''
						a__   =  "mask2former"
						a__   =  ["swin"]
						a__   =  {"hidden_size": "hidden_dim"}







						def __init__(	self      :			List[str]	,	__lowerCamelCase      :			Optional[Dict] = None	,	__lowerCamelCase      :			int = 2_56	,	__lowerCamelCase      :			int = 2_56	,	__lowerCamelCase      :			int = 2_56	,	__lowerCamelCase      :			int = 10_24	,	__lowerCamelCase      :			str = "relu"	,	__lowerCamelCase      :			int = 6	,	__lowerCamelCase      :			int = 10	,	__lowerCamelCase      :			int = 8	,	__lowerCamelCase      :			float = 0.0	,	__lowerCamelCase      :			int = 20_48	,	__lowerCamelCase      :			bool = False	,	__lowerCamelCase      :			bool = False	,	__lowerCamelCase      :			int = 4	,	__lowerCamelCase      :			int = 2_55	,	__lowerCamelCase      :			int = 1_00	,	__lowerCamelCase      :			float = 0.1	,	__lowerCamelCase      :			float = 2.0	,	__lowerCamelCase      :			float = 5.0	,	__lowerCamelCase      :			float = 5.0	,	__lowerCamelCase      :			int = 1_25_44	,	__lowerCamelCase      :			float = 3.0	,	__lowerCamelCase      :			float = 0.75	,	__lowerCamelCase      :			float = 0.02	,	__lowerCamelCase      :			float = 1.0	,	__lowerCamelCase      :			bool = True	,	__lowerCamelCase      :			List[int] = [4, 8, 16, 32]	,	__lowerCamelCase      :			bool = None	,	**__lowerCamelCase      :			int	,	)      ->				List[str]:
									if backbone_config is None:
												logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone." )
												A							:					List[Any]              =						CONFIG_MAPPING["swin"](
												    image_size=2_24	,	in_channels=3	,	patch_size=4	,	embed_dim=96	,	depths=[2, 2, 18, 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"]	,	)

									if isinstance(__lowerCamelCase	,	__lowerCamelCase ):
												A							:					List[str]              =						backbone_config.pop("model_type" )
												A							:					str              =						CONFIG_MAPPING[backbone_model_type]
												A							:					List[str]              =						config_class.from_dict(__lowerCamelCase )

									# 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 Mask2Former. """
												    F"""Supported model types: {",".join(self.backbones_supported )}""" )

									A							:					int              =						backbone_config
									A							:					Optional[Any]              =						feature_size
									A							:					int              =						mask_feature_size
									A							:					str              =						hidden_dim
									A							:					List[Any]              =						encoder_feedforward_dim
									A							:					int              =						activation_function
									A							:					Optional[int]              =						encoder_layers
									A							:					Tuple              =						decoder_layers
									A							:					Dict              =						num_attention_heads
									A							:					int              =						dropout
									A							:					List[str]              =						dim_feedforward
									A							:					Optional[int]              =						pre_norm
									A							:					List[str]              =						enforce_input_projection
									A							:					int              =						common_stride
									A							:					Dict              =						ignore_value
									A							:					Tuple              =						num_queries
									A							:					Union[str, Any]              =						no_object_weight
									A							:					Union[str, Any]              =						class_weight
									A							:					int              =						mask_weight
									A							:					int              =						dice_weight
									A							:					Optional[int]              =						train_num_points
									A							:					int              =						oversample_ratio
									A							:					Dict              =						importance_sample_ratio
									A							:					Tuple              =						init_std
									A							:					List[str]              =						init_xavier_std
									A							:					int              =						use_auxiliary_loss
									A							:					Union[str, Any]              =						feature_strides
									A							:					int              =						output_auxiliary_logits
									A							:					Union[str, Any]              =						decoder_layers

									super().__init__(**__lowerCamelCase )







						@classmethod
						def 			SCREAMING_SNAKE_CASE__		(	cls      :			Optional[int]	,	__lowerCamelCase      :			PretrainedConfig	,	**__lowerCamelCase      :			Any )      ->				Optional[int]:
									return cls(
									    backbone_config=__lowerCamelCase	,	**__lowerCamelCase	,	)







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Dict[str, any]:
									A							:					Optional[Any]              =						copy.deepcopy(self.__dict__ )
									A							:					List[Any]              =						self.backbone_config.to_dict()
									A							:					List[Any]              =						self.__class__.model_type
									return output




from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Sequence, Value
from .base import TaskTemplate

@dataclass(frozen=_A						)
class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''
						# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
						a__   =  field(default="question-answering-extractive"						,metadata={"include_in_asdict_even_if_is_default": True}						)
						a__   =  Features({"question": Value("string"						), "context": Value("string"						)}						)
						a__   =  Features(
						    {
						        "answers": Sequence(
						            {
						                "text": Value("string"						),
						                "answer_start": Value("int32"						),
						            }						)
						    }						)
						a__   =  "question"
						a__   =  "context"
						a__   =  "answers"







						@property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				Dict[str, str]:
									return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}