

import copy
from typing import Dict, List, Optional

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


snake_case__				= {
    '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
}

snake_case__				= logging.get_logger(__name__)



class  UpperCamelCase				( _UpperCAmelCase ):

     '''simple docstring'''

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



     def __init__(					self				,				A_ = None				,				A_ = 2_56				,				A_ = 2_56				,				A_ = 2_56				,				A_ = 10_24				,				A_ = "relu"				,				A_ = 6				,				A_ = 10				,				A_ = 8				,				A_ = 0.0				,				A_ = 20_48				,				A_ = False				,				A_ = False				,				A_ = 4				,				A_ = 2_55				,				A_ = 1_00				,				A_ = 0.1				,				A_ = 2.0				,				A_ = 5.0				,				A_ = 5.0				,				A_ = 1_25_44				,				A_ = 3.0				,				A_ = 0.75				,				A_ = 0.02				,				A_ = 1.0				,				A_ = True				,				A_ = [4, 8, 16, 32]				,				A_ = None				,				**A_				,				) ->				Dict:






           """simple docstring"""
           if backbone_config is None:
                 logger.info('''`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.'''						)
                 _lowerCamelCase			    =				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=lowercase__				,				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 Mask2Former. '
                     F'Supported model types: {",".join(self.backbones_supported						)}'						)

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

           super().__init__(**lowercase__						)



     @classmethod
     def 		UpperCamelCase_						(					cls				,				A_				,				**A_						) ->				str:






           """simple docstring"""
           return cls(
               backbone_config=lowercase__				,				**lowercase__				,				)





     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				copy.deepcopy(self.__dict__						)
           _lowerCamelCase			    =				self.backbone_config.to_dict()
           _lowerCamelCase			    =				self.__class__.model_type
           return output
from typing import TYPE_CHECKING

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


snake_case__				= {
    'configuration_trajectory_transformer': [
        'TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TrajectoryTransformerConfig',
    ],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case__				= [
         'TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TrajectoryTransformerModel',
         'TrajectoryTransformerPreTrainedModel',
         'load_tf_weights_in_trajectory_transformer',
     ]


if TYPE_CHECKING:
     from .configuration_trajectory_transformer import (
         TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
         TrajectoryTransformerConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_trajectory_transformer import (
              TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TrajectoryTransformerModel,
              TrajectoryTransformerPreTrainedModel,
              load_tf_weights_in_trajectory_transformer,
          )


else:
     import sys

     snake_case__				= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)