






"""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


lowerCAmelCase_	:    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
}

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





class   UpperCamelCase_ (       a_  ):
  _A       :					Optional[Any]				=	'maskformer'
  _A       :					List[Any]				=	{'hidden_size': 'mask_feature_size'}
  _A       :					Optional[int]				=	['resnet', 'swin']
  _A       :					List[Any]				=	['detr']



  def __init__(      self ,		snake_case__ = 2_56 ,		snake_case__ = 2_56 ,		snake_case__ = 0.1 ,		snake_case__ = False ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = 0.02 ,		snake_case__ = 1.0 ,		snake_case__ = 1.0 ,		snake_case__ = 1.0 ,		snake_case__ = 20.0 ,		snake_case__ = None ,		**snake_case__ ,		)			->			Optional[Any]:



         """simple docstring"""





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

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

         UpperCAmelCase	    =			self.decoder_config.encoder_attention_heads
         UpperCAmelCase	    =			self.decoder_config.num_hidden_layers
         super().__init__(**__A				)



  @classmethod
  def 		UpperCamelCase_				(      cls ,		snake_case__ ,		snake_case__ ,		**snake_case__				)			->			List[Any]:



         """simple docstring"""





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



  def 		UpperCamelCase_				(      self				)			->			Dict:



         """simple docstring"""





         UpperCAmelCase	    =			copy.deepcopy(self.__dict__				)
         UpperCAmelCase	    =			self.backbone_config.to_dict()
         UpperCAmelCase	    =			self.decoder_config.to_dict()
         UpperCAmelCase	    =			self.__class__.model_type
         return output








"""simple docstring"""







from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)
