



import copy
from typing import Dict, List, Optional

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


__snake_case						:Optional[Any]						=					{
    '''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						:Optional[int]						=					logging.get_logger(__name__)

class    _A	(     __UpperCAmelCase   ):
       UpperCamelCase__  :   Optional[int]						 =						'''mask2former'''
       UpperCamelCase__  :   Optional[int]						 =						['''swin''']
       UpperCamelCase__  :   Dict						 =						{'''hidden_size''': '''hidden_dim'''}



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


           '''simple docstring'''

           if backbone_config is None:
               logger.info('''`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.''')
               __a							=    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):
               __a							=    backbone_config.pop('''model_type''')
               __a							=    CONFIG_MAPPING[backbone_model_type]
               __a							=    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)}')

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

           super().__init__(**__SCREAMING_SNAKE_CASE)



       @classmethod
       def 		_lowerCamelCase      (		cls							:					Any					,		__SCREAMING_SNAKE_CASE							:					PretrainedConfig					,		**__SCREAMING_SNAKE_CASE							:					Optional[int]):


           '''simple docstring'''

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





       def 		_lowerCamelCase      (		self							:					Tuple):


           '''simple docstring'''

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










from typing import TYPE_CHECKING

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


__snake_case						:Tuple						=					{
    '''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case						:List[Any]						=					['''BloomTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case						:Any						=					[
     '''BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''BloomForCausalLM''',
     '''BloomModel''',
     '''BloomPreTrainedModel''',
     '''BloomForSequenceClassification''',
     '''BloomForTokenClassification''',
     '''BloomForQuestionAnswering''',
 ]

if TYPE_CHECKING:
 from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_bloom_fast import BloomTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bloom import (
      BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
      BloomForCausalLM,
      BloomForQuestionAnswering,
      BloomForSequenceClassification,
      BloomForTokenClassification,
      BloomModel,
      BloomPreTrainedModel,
  )

else:
 import sys

 __snake_case						:Any						=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





