



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"""



     _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				,			):






           '''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=UpperCAmelCase_				,			out_features=["stage1", "stage2", "stage3", "stage4"]				,			)

           if isinstance(UpperCAmelCase_				,			UpperCAmelCase_    ):
                 lowerCamelCase								=		backbone_config.pop("model_type"    )
                 lowerCamelCase								=		CONFIG_MAPPING[backbone_model_type]
                 lowerCamelCase								=		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    )}"""    )

           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__(**UpperCAmelCase_    )







     @classmethod
     def 						_a      (cls				,			__a				,			**__a    ):






           '''simple docstring'''



           return cls(
               backbone_config=UpperCAmelCase_				,			**UpperCAmelCase_				,			)







     def 						_a      (self    ):






           '''simple docstring'''



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




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_						:     Tuple	     =		{
    'configuration_xmod': [
        'XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XmodConfig',
        'XmodOnnxConfig',
    ],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_						:     List[Any]	     =		[
         'XMOD_PRETRAINED_MODEL_ARCHIVE_LIST',
         'XmodForCausalLM',
         'XmodForMaskedLM',
         'XmodForMultipleChoice',
         'XmodForQuestionAnswering',
         'XmodForSequenceClassification',
         'XmodForTokenClassification',
         'XmodModel',
         'XmodPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_xmod import (
              XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
              XmodForCausalLM,
              XmodForMaskedLM,
              XmodForMultipleChoice,
              XmodForQuestionAnswering,
              XmodForSequenceClassification,
              XmodForTokenClassification,
              XmodModel,
              XmodPreTrainedModel,
          )

else:
     import sys

     a_						:     str	     =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)