import copy
from typing import Dict, List, Optional

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


A     :  Tuple					     =       {
    '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
}

A     :  str					     =       logging.get_logger(__name__)


class   lowerCamelCase					(snake_case__					):
  """simple docstring"""







  lowerCamelCase__     =						"""mask2former"""
  lowerCamelCase__     =						["""swin"""]
  lowerCamelCase__     =						{"""hidden_size""": """hidden_dim"""}







  def __init__(							self :       str  ,     __magic_name__ :       Optional[Dict] = None  ,     __magic_name__ :       int = 256  ,     __magic_name__ :       int = 256  ,     __magic_name__ :       int = 256  ,     __magic_name__ :       int = 1_024  ,     __magic_name__ :       str = "relu"  ,     __magic_name__ :       int = 6  ,     __magic_name__ :       int = 10  ,     __magic_name__ :       int = 8  ,     __magic_name__ :       float = 0.0  ,     __magic_name__ :       int = 2_048  ,     __magic_name__ :       bool = False  ,     __magic_name__ :       bool = False  ,     __magic_name__ :       int = 4  ,     __magic_name__ :       int = 255  ,     __magic_name__ :       int = 100  ,     __magic_name__ :       float = 0.1  ,     __magic_name__ :       float = 2.0  ,     __magic_name__ :       float = 5.0  ,     __magic_name__ :       float = 5.0  ,     __magic_name__ :       int = 12_544  ,     __magic_name__ :       float = 3.0  ,     __magic_name__ :       float = 0.75  ,     __magic_name__ :       float = 0.02  ,     __magic_name__ :       float = 1.0  ,     __magic_name__ :       bool = True  ,     __magic_name__ :       List[int] = [4, 8, 16, 32]  ,     __magic_name__ :       bool = None  ,     **__magic_name__ :       Optional[Any]  ,     )  ->	Dict:



        if backbone_config is None:
              logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."   )
              SCREAMING_SNAKE_CASE_          =  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=UpperCAmelCase_  ,     out_features=["stage1", "stage2", "stage3", "stage4"]  ,     )

        if isinstance(UpperCAmelCase_  ,     UpperCAmelCase_   ):
              SCREAMING_SNAKE_CASE_          =  backbone_config.pop("model_type"   )
              SCREAMING_SNAKE_CASE_          =  CONFIG_MAPPING[backbone_model_type]
              SCREAMING_SNAKE_CASE_          =  config_class.from_dict(UpperCAmelCase_   )

        # 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   )}'''   )

        SCREAMING_SNAKE_CASE_          =  backbone_config
        SCREAMING_SNAKE_CASE_          =  feature_size
        SCREAMING_SNAKE_CASE_          =  mask_feature_size
        SCREAMING_SNAKE_CASE_          =  hidden_dim
        SCREAMING_SNAKE_CASE_          =  encoder_feedforward_dim
        SCREAMING_SNAKE_CASE_          =  activation_function
        SCREAMING_SNAKE_CASE_          =  encoder_layers
        SCREAMING_SNAKE_CASE_          =  decoder_layers
        SCREAMING_SNAKE_CASE_          =  num_attention_heads
        SCREAMING_SNAKE_CASE_          =  dropout
        SCREAMING_SNAKE_CASE_          =  dim_feedforward
        SCREAMING_SNAKE_CASE_          =  pre_norm
        SCREAMING_SNAKE_CASE_          =  enforce_input_projection
        SCREAMING_SNAKE_CASE_          =  common_stride
        SCREAMING_SNAKE_CASE_          =  ignore_value
        SCREAMING_SNAKE_CASE_          =  num_queries
        SCREAMING_SNAKE_CASE_          =  no_object_weight
        SCREAMING_SNAKE_CASE_          =  class_weight
        SCREAMING_SNAKE_CASE_          =  mask_weight
        SCREAMING_SNAKE_CASE_          =  dice_weight
        SCREAMING_SNAKE_CASE_          =  train_num_points
        SCREAMING_SNAKE_CASE_          =  oversample_ratio
        SCREAMING_SNAKE_CASE_          =  importance_sample_ratio
        SCREAMING_SNAKE_CASE_          =  init_std
        SCREAMING_SNAKE_CASE_          =  init_xavier_std
        SCREAMING_SNAKE_CASE_          =  use_auxiliary_loss
        SCREAMING_SNAKE_CASE_          =  feature_strides
        SCREAMING_SNAKE_CASE_          =  output_auxiliary_logits
        SCREAMING_SNAKE_CASE_          =  decoder_layers

        super().__init__(**UpperCAmelCase_   )







  @classmethod
  def    __A     (							cls :       Tuple  ,     __magic_name__ :       PretrainedConfig  ,     **__magic_name__ :       List[Any]   )  ->	int:
        return cls(
            backbone_config=UpperCAmelCase_  ,     **UpperCAmelCase_  ,     )







  def    __A     (							self :       int   )  ->	Dict[str, any]:
        SCREAMING_SNAKE_CASE_          =  copy.deepcopy(self.__dict__   )
        SCREAMING_SNAKE_CASE_          =  self.backbone_config.to_dict()
        SCREAMING_SNAKE_CASE_          =  self.__class__.model_type
        return output



import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


A     :  str					     =       logging.getLogger(__name__)


@dataclass
class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  lowerCamelCase__     =						field(
      default=0.0  ,      metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}					)
  lowerCamelCase__     =						field(default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''Whether to SortishSamler or not.'''}					)
  lowerCamelCase__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}					)
  lowerCamelCase__     =						field(default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''whether to use adafactor'''}					)
  lowerCamelCase__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}					)
  lowerCamelCase__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}					)
  lowerCamelCase__     =						field(default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''Dropout probability. Goes into model.config.'''}					)
  lowerCamelCase__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}					)
  lowerCamelCase__     =						field(
      default='''linear'''  ,      metadata={'''help''': f"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()					)}"}  ,      )


