



import copy
from typing import Dict, List, Optional

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


__UpperCAmelCase	       =	{
    '''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
}

__UpperCAmelCase	       =	logging.get_logger(__name__)



class     lowerCAmelCase_		(  a__							):
       UpperCAmelCase__    : Optional[Any]       					=  "mask2former"
       UpperCAmelCase__    : List[Any]       					=  ["swin"]
       UpperCAmelCase__    : List[Any]       					=  {"hidden_size": "hidden_dim"}
       def __init__(      self,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = 256,      SCREAMING_SNAKE_CASE_ = 256,      SCREAMING_SNAKE_CASE_ = 256,      SCREAMING_SNAKE_CASE_ = 1024,      SCREAMING_SNAKE_CASE_ = "relu",      SCREAMING_SNAKE_CASE_ = 6,      SCREAMING_SNAKE_CASE_ = 10,      SCREAMING_SNAKE_CASE_ = 8,      SCREAMING_SNAKE_CASE_ = 0.0,      SCREAMING_SNAKE_CASE_ = 2048,      SCREAMING_SNAKE_CASE_ = False,      SCREAMING_SNAKE_CASE_ = False,      SCREAMING_SNAKE_CASE_ = 4,      SCREAMING_SNAKE_CASE_ = 255,      SCREAMING_SNAKE_CASE_ = 100,      SCREAMING_SNAKE_CASE_ = 0.1,      SCREAMING_SNAKE_CASE_ = 2.0,      SCREAMING_SNAKE_CASE_ = 5.0,      SCREAMING_SNAKE_CASE_ = 5.0,      SCREAMING_SNAKE_CASE_ = 1_2544,      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_,      )					->		Optional[int]:
          if backbone_config is None:
             logger.info('`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.'	)
             UpperCamelCase   :  Optional[Any]						     =     CONFIG_MAPPING['swin'](
                 image_size=224,      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=SCREAMING_SNAKE_CASE_,      out_features=['stage1', 'stage2', 'stage3', 'stage4'],      )

          if isinstance(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	):
             UpperCamelCase   :  str						     =     backbone_config.pop('model_type'	)
             UpperCamelCase   :  Dict						     =     CONFIG_MAPPING[backbone_model_type]
             UpperCamelCase   :  List[Any]						     =     config_class.from_dict(SCREAMING_SNAKE_CASE_	)

          # 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	)}"""	)

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

          super().__init__(**SCREAMING_SNAKE_CASE_	)
       @classmethod
       def  snake_case_	(      cls,      SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)					->		Union[str, Any]:
          return cls(
              backbone_config=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_,      )






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








from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader



class     lowerCAmelCase_		(  a__							):
       def __init__(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = True,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = False,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = True,      SCREAMING_SNAKE_CASE_ = "arrow",      **SCREAMING_SNAKE_CASE_,      )					->		int:
          super().__init__(
              split=SCREAMING_SNAKE_CASE_,      features=SCREAMING_SNAKE_CASE_,      cache_dir=SCREAMING_SNAKE_CASE_,      keep_in_memory=SCREAMING_SNAKE_CASE_,      streaming=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_,      )
          UpperCamelCase   :  Any						     =     load_from_cache_file
          UpperCamelCase   :  Any						     =     file_format
          UpperCamelCase   :  str						     =     Spark(
              df=SCREAMING_SNAKE_CASE_,      features=SCREAMING_SNAKE_CASE_,      cache_dir=SCREAMING_SNAKE_CASE_,      working_dir=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_,      )






       def  snake_case_	(      self	)					->		Tuple:
          if self.streaming:
             return self.builder.as_streaming_dataset(split=self.split	)
          UpperCamelCase   :  Dict						     =     None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
          self.builder.download_and_prepare(
              download_mode=SCREAMING_SNAKE_CASE_,      file_format=self._file_format,      )
          return self.builder.as_dataset(split=self.split	)



