


'''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				: Optional[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				: str							    =				logging.get_logger(__name__)


class       a__				( __A      ):







				"""simple docstring"""



				__UpperCamelCase    :		List[str]	=	'maskformer'
				__UpperCamelCase    :		Dict	=	{'hidden_size': 'mask_feature_size'}
				__UpperCamelCase    :		Dict	=	['resnet', 'swin']
				__UpperCamelCase    :		Tuple	=	['detr']




				def __init__(self       ,			__lowercase = 2_56       ,			__lowercase = 2_56       ,			__lowercase = 0.1       ,			__lowercase = False       ,			__lowercase = None       ,			__lowercase = None       ,			__lowercase = 0.0_2       ,			__lowercase = 1.0       ,			__lowercase = 1.0       ,			__lowercase = 1.0       ,			__lowercase = 2_0.0       ,			__lowercase = None       ,			**__lowercase       ,			):
										if backbone_config is None:
																# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
																__lowerCAmelCase							       =				SwinConfig(
																    image_size=3_84       ,			in_channels=3       ,			patch_size=4       ,			embed_dim=1_28       ,			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(__lowercase       ,			__lowercase		):
																__lowerCAmelCase							       =				backbone_config.pop('''model_type'''		)
																__lowerCAmelCase							       =				CONFIG_MAPPING[backbone_model_type]
																__lowerCAmelCase							       =				config_class.from_dict(__lowercase		)

										# 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							       =				DetrConfig()
										else:
																# verify that the decoder is supported
																__lowerCAmelCase							       =				(
																    decoder_config.pop('''model_type'''		) if isinstance(__lowercase       ,			__lowercase		) 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(__lowercase       ,			__lowercase		):
																						__lowerCAmelCase							       =				CONFIG_MAPPING[decoder_type]
																						__lowerCAmelCase							       =				config_class.from_dict(__lowercase		)

										__lowerCAmelCase							       =				backbone_config
										__lowerCAmelCase							       =				decoder_config
										# main feature dimension for the model
										__lowerCAmelCase							       =				fpn_feature_size
										__lowerCAmelCase							       =				mask_feature_size
										# initializer
										__lowerCAmelCase							       =				init_std
										__lowerCAmelCase							       =				init_xavier_std
										# Hungarian matcher && loss
										__lowerCAmelCase							       =				cross_entropy_weight
										__lowerCAmelCase							       =				dice_weight
										__lowerCAmelCase							       =				mask_weight
										__lowerCAmelCase							       =				use_auxiliary_loss
										__lowerCAmelCase							       =				no_object_weight
										__lowerCAmelCase							       =				output_auxiliary_logits

										__lowerCAmelCase							       =				self.decoder_config.encoder_attention_heads
										__lowerCAmelCase							       =				self.decoder_config.num_hidden_layers
										super().__init__(**__lowercase		)




				@classmethod
				def 					_snake_case  (cls       ,			__lowercase       ,			__lowercase       ,			**__lowercase		):
										return cls(
										    backbone_config=__lowercase       ,			decoder_config=__lowercase       ,			**__lowercase       ,			)




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				copy.deepcopy(self.__dict__		)
										__lowerCAmelCase							       =				self.backbone_config.to_dict()
										__lowerCAmelCase							       =				self.decoder_config.to_dict()
										__lowerCAmelCase							       =				self.__class__.model_type
										return output





'''simple docstring'''

from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


_UpperCAmelCase				: str							    =				{
    """configuration_gpt_neox_japanese""": ["""GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXJapaneseConfig"""],
    """tokenization_gpt_neox_japanese""": ["""GPTNeoXJapaneseTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase				: Union[str, Any]							    =				[
	    """GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GPTNeoXJapaneseForCausalLM""",
	    """GPTNeoXJapaneseLayer""",
	    """GPTNeoXJapaneseModel""",
	    """GPTNeoXJapanesePreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
	from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox_japanese import (
		    GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXJapaneseForCausalLM,
		    GPTNeoXJapaneseLayer,
		    GPTNeoXJapaneseModel,
		    GPTNeoXJapanesePreTrainedModel,
		)


else:
	import sys

	_UpperCAmelCase				: int							    =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

