
"""simple docstring"""







import copy
from typing import Dict, List, Optional

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


lowerCamelCase__        =		{
    '''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
}

lowerCamelCase__        =		logging.get_logger(__name__)




class 			A__	(     A__):
		A_						:	Optional[int]           =     'mask2former'
		A_						:	List[str]           =     ['swin']
		A_						:	Optional[int]           =     {'hidden_size': 'hidden_dim'}

		def __init__(				self	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = 2_56	,		_SCREAMING_SNAKE_CASE = 2_56	,		_SCREAMING_SNAKE_CASE = 2_56	,		_SCREAMING_SNAKE_CASE = 10_24	,		_SCREAMING_SNAKE_CASE = "relu"	,		_SCREAMING_SNAKE_CASE = 6	,		_SCREAMING_SNAKE_CASE = 10	,		_SCREAMING_SNAKE_CASE = 8	,		_SCREAMING_SNAKE_CASE = 0.0	,		_SCREAMING_SNAKE_CASE = 20_48	,		_SCREAMING_SNAKE_CASE = False	,		_SCREAMING_SNAKE_CASE = False	,		_SCREAMING_SNAKE_CASE = 4	,		_SCREAMING_SNAKE_CASE = 2_55	,		_SCREAMING_SNAKE_CASE = 1_00	,		_SCREAMING_SNAKE_CASE = 0.1	,		_SCREAMING_SNAKE_CASE = 2.0	,		_SCREAMING_SNAKE_CASE = 5.0	,		_SCREAMING_SNAKE_CASE = 5.0	,		_SCREAMING_SNAKE_CASE = 1_25_44	,		_SCREAMING_SNAKE_CASE = 3.0	,		_SCREAMING_SNAKE_CASE = 0.75	,		_SCREAMING_SNAKE_CASE = 0.02	,		_SCREAMING_SNAKE_CASE = 1.0	,		_SCREAMING_SNAKE_CASE = True	,		_SCREAMING_SNAKE_CASE = [4, 8, 16, 32]	,		_SCREAMING_SNAKE_CASE = None	,		**_SCREAMING_SNAKE_CASE	,		):
			if backbone_config is None:
				logger.info('`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.'			)
				__lowerCAmelCase      :						Dict						      =				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__			):
				__lowerCAmelCase      :						Dict						      =				backbone_config.pop('model_type'			)
				__lowerCAmelCase      :						int						      =				CONFIG_MAPPING[backbone_model_type]
				__lowerCAmelCase      :						Dict						      =				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			)}"			)

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

			super().__init__(**lowerCamelCase__			)

		@classmethod
		def 							__lowerCamelCase							(				cls	,		_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			):
			return cls(
			    backbone_config=lowerCamelCase__	,		**lowerCamelCase__	,		)







		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						str						      =				copy.deepcopy(self.__dict__			)
			__lowerCAmelCase      :						Optional[Any]						      =				self.backbone_config.to_dict()
			__lowerCAmelCase      :						Tuple						      =				self.__class__.model_type
			return output



"""simple docstring"""







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

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




@dataclass(frozen=_lowerCamelCase)
class 			A__	(     _lowerCamelCase):
		# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
		A_						:	str           =     field(default='text-classification'	,					metadata={'include_in_asdict_even_if_is_default': True})
		A_						:	ClassVar[Features]           =     Features({'text': Value('string')})
		A_						:	ClassVar[Features]           =     Features({'labels': ClassLabel})
		A_						:	str           =     "text"
		A_						:	str           =     "labels"

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
			if self.label_column not in features:
				raise ValueError(f"Column {self.label_column} is not present in features."			)
			if not isinstance(features[self.label_column]	,		_SCREAMING_SNAKE_CASE			):
				raise ValueError(f"Column {self.label_column} is not a ClassLabel."			)
			__lowerCAmelCase      :						Any						      =				copy.deepcopy(self			)
			__lowerCAmelCase      :						Dict						      =				self.label_schema.copy()
			__lowerCAmelCase      :						List[Any]						      =				features[self.label_column]
			__lowerCAmelCase      :						Dict						      =				label_schema
			return task_template







		@property
		def 							__lowerCamelCase							(				self			):
			return {
			    self.text_column: "text",
			    self.label_column: "labels",
			}