import copy
from typing import Dict, Optional

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


UpperCAmelCase     						=			logging.get_logger(__name__)

UpperCAmelCase     						=			{
    '''shi-labs/oneformer_ade20k_swin_tiny''': (
        '''https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json'''
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}




class    __lowerCAmelCase					(    a_  ):



 '''simple docstring'''
 _lowerCamelCase  :		Optional[Any]			      =	'oneformer'
 _lowerCamelCase  :		Dict			      =	{'hidden_size': 'hidden_dim'}

 def __init__(     self    :						List[str]    ,     lowerCamelCase    :						Optional[Dict] = None    ,     lowerCamelCase    :						int = 2_5_5    ,     lowerCamelCase    :						int = 1_5_0    ,     lowerCamelCase    :						int = 0.1    ,     lowerCamelCase    :						float = 2.0    ,     lowerCamelCase    :						float = 5.0    ,     lowerCamelCase    :						float = 5.0    ,     lowerCamelCase    :						float = 0.5    ,     lowerCamelCase    :						float = 0.0_7    ,     lowerCamelCase    :						int = 1_2_5_4_4    ,     lowerCamelCase    :						float = 3.0    ,     lowerCamelCase    :						float = 0.7_5    ,     lowerCamelCase    :						float = 0.0_2    ,     lowerCamelCase    :						float = 1.0    ,     lowerCamelCase    :						float = 1e-05    ,     lowerCamelCase    :						bool = False    ,     lowerCamelCase    :						bool = True    ,     lowerCamelCase    :						bool = True    ,     lowerCamelCase    :						Optional[list] = [4, 8, 1_6, 3_2]    ,     lowerCamelCase    :						int = 7_7    ,     lowerCamelCase    :						int = 2_5_6    ,     lowerCamelCase    :						int = 7_7    ,     lowerCamelCase    :						int = 6    ,     lowerCamelCase    :						int = 4_9_4_0_8    ,     lowerCamelCase    :						int = 2    ,     lowerCamelCase    :						int = 1_6    ,     lowerCamelCase    :						int = 2_5_6    ,     lowerCamelCase    :						int = 2_5_6    ,     lowerCamelCase    :						int = 2_5_6    ,     lowerCamelCase    :						int = 1_0_2_4    ,     lowerCamelCase    :						str = "GN"    ,     lowerCamelCase    :						int = 6    ,     lowerCamelCase    :						int = 1_0    ,     lowerCamelCase    :						bool = True    ,     lowerCamelCase    :						int = 8    ,     lowerCamelCase    :						float = 0.1    ,     lowerCamelCase    :						int = 2_0_4_8    ,     lowerCamelCase    :						bool = False    ,     lowerCamelCase    :						bool = False    ,     lowerCamelCase    :						int = 2    ,     lowerCamelCase    :						int = 4    ,     **lowerCamelCase    :						Dict    ,     )	->							Optional[Any]:
        if backbone_config is None:
               logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'					)
               _UpperCamelCase       :    List[str]					  =					CONFIG_MAPPING['swin'](
                   image_size=2_2_4    ,     in_channels=3    ,     patch_size=4    ,     embed_dim=9_6    ,     depths=[2, 2, 6, 2]    ,     num_heads=[3, 6, 1_2, 2_4]    ,     window_size=7    ,     drop_path_rate=0.3    ,     use_absolute_embeddings=lowerCamelCase    ,     out_features=['stage1', 'stage2', 'stage3', 'stage4']    ,     )
        elif isinstance(lowerCamelCase    ,     lowerCamelCase					):
               _UpperCamelCase       :    List[str]					  =					backbone_config.get('model_type'					)
               _UpperCamelCase       :    Any					  =					CONFIG_MAPPING[backbone_model_type]
               _UpperCamelCase       :    Dict					  =					config_class.from_dict(lowerCamelCase					)

        _UpperCamelCase       :    Optional[Any]					  =					backbone_config

        _UpperCamelCase       :    Any					  =					ignore_value
        _UpperCamelCase       :    str					  =					num_queries
        _UpperCamelCase       :    Dict					  =					no_object_weight
        _UpperCamelCase       :    Dict					  =					class_weight
        _UpperCamelCase       :    Optional[Any]					  =					mask_weight
        _UpperCamelCase       :    Tuple					  =					dice_weight
        _UpperCamelCase       :    Tuple					  =					contrastive_weight
        _UpperCamelCase       :    Optional[int]					  =					contrastive_temperature
        _UpperCamelCase       :    Union[str, Any]					  =					train_num_points
        _UpperCamelCase       :    Optional[Any]					  =					oversample_ratio
        _UpperCamelCase       :    int					  =					importance_sample_ratio
        _UpperCamelCase       :    Union[str, Any]					  =					init_std
        _UpperCamelCase       :    str					  =					init_xavier_std
        _UpperCamelCase       :    Union[str, Any]					  =					layer_norm_eps
        _UpperCamelCase       :    Tuple					  =					is_training
        _UpperCamelCase       :    int					  =					use_auxiliary_loss
        _UpperCamelCase       :    Tuple					  =					output_auxiliary_logits
        _UpperCamelCase       :    str					  =					strides
        _UpperCamelCase       :    Optional[int]					  =					task_seq_len
        _UpperCamelCase       :    Optional[Any]					  =					text_encoder_width
        _UpperCamelCase       :    Any					  =					text_encoder_context_length
        _UpperCamelCase       :    Optional[Any]					  =					text_encoder_num_layers
        _UpperCamelCase       :    Optional[Any]					  =					text_encoder_vocab_size
        _UpperCamelCase       :    int					  =					text_encoder_proj_layers
        _UpperCamelCase       :    List[str]					  =					text_encoder_n_ctx
        _UpperCamelCase       :    Union[str, Any]					  =					conv_dim
        _UpperCamelCase       :    Tuple					  =					mask_dim
        _UpperCamelCase       :    Tuple					  =					hidden_dim
        _UpperCamelCase       :    str					  =					encoder_feedforward_dim
        _UpperCamelCase       :    Tuple					  =					norm
        _UpperCamelCase       :    Optional[int]					  =					encoder_layers
        _UpperCamelCase       :    List[str]					  =					decoder_layers
        _UpperCamelCase       :    List[Any]					  =					use_task_norm
        _UpperCamelCase       :    Tuple					  =					num_attention_heads
        _UpperCamelCase       :    Any					  =					dropout
        _UpperCamelCase       :    List[Any]					  =					dim_feedforward
        _UpperCamelCase       :    Union[str, Any]					  =					pre_norm
        _UpperCamelCase       :    List[Any]					  =					enforce_input_proj
        _UpperCamelCase       :    Dict					  =					query_dec_layers
        _UpperCamelCase       :    Union[str, Any]					  =					common_stride
        _UpperCamelCase       :    Dict					  =					decoder_layers

        super().__init__(**lowerCamelCase					)

 def    __snake_case   (     self    :						Tuple					)	->							Dict[str, any]:
        _UpperCamelCase       :    Union[str, Any]					  =					copy.deepcopy(self.__dict__					)
        _UpperCamelCase       :    List[Any]					  =					self.backbone_config.to_dict()
        _UpperCamelCase       :    Any					  =					self.__class__.model_type
        return output




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase     						=			{
    '''configuration_mpnet''': ['''MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MPNetConfig'''],
    '''tokenization_mpnet''': ['''MPNetTokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase     						=			['''MPNetTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase     						=			[
     '''MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''MPNetForMaskedLM''',
     '''MPNetForMultipleChoice''',
     '''MPNetForQuestionAnswering''',
     '''MPNetForSequenceClassification''',
     '''MPNetForTokenClassification''',
     '''MPNetLayer''',
     '''MPNetModel''',
     '''MPNetPreTrainedModel''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase     						=			[
     '''TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFMPNetEmbeddings''',
     '''TFMPNetForMaskedLM''',
     '''TFMPNetForMultipleChoice''',
     '''TFMPNetForQuestionAnswering''',
     '''TFMPNetForSequenceClassification''',
     '''TFMPNetForTokenClassification''',
     '''TFMPNetMainLayer''',
     '''TFMPNetModel''',
     '''TFMPNetPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
 from .tokenization_mpnet import MPNetTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_mpnet_fast import MPNetTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mpnet import (
      MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      MPNetForMaskedLM,
      MPNetForMultipleChoice,
      MPNetForQuestionAnswering,
      MPNetForSequenceClassification,
      MPNetForTokenClassification,
      MPNetLayer,
      MPNetModel,
      MPNetPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_mpnet import (
      TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFMPNetEmbeddings,
      TFMPNetForMaskedLM,
      TFMPNetForMultipleChoice,
      TFMPNetForQuestionAnswering,
      TFMPNetForSequenceClassification,
      TFMPNetForTokenClassification,
      TFMPNetMainLayer,
      TFMPNetModel,
      TFMPNetPreTrainedModel,
  )

else:
 import sys

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



