



import copy
from typing import Dict, List, Optional

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


UpperCAmelCase_		 =					{
    """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
}

UpperCAmelCase_		 =					logging.get_logger(__name__)


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 a__					: Any =  "mask2former"
 a__					: Tuple =  ["swin"]
 a__					: Optional[int] =  {"hidden_size": "hidden_dim"}

 def __init__(    self					:					Any			,						__lowerCAmelCase					:					Optional[Dict] = None			,						__lowerCAmelCase					:					int = 2_56			,						__lowerCAmelCase					:					int = 2_56			,						__lowerCAmelCase					:					int = 2_56			,						__lowerCAmelCase					:					int = 10_24			,						__lowerCAmelCase					:					str = "relu"			,						__lowerCAmelCase					:					int = 6			,						__lowerCAmelCase					:					int = 10			,						__lowerCAmelCase					:					int = 8			,						__lowerCAmelCase					:					float = 0.0			,						__lowerCAmelCase					:					int = 20_48			,						__lowerCAmelCase					:					bool = False			,						__lowerCAmelCase					:					bool = False			,						__lowerCAmelCase					:					int = 4			,						__lowerCAmelCase					:					int = 2_55			,						__lowerCAmelCase					:					int = 1_00			,						__lowerCAmelCase					:					float = 0.1			,						__lowerCAmelCase					:					float = 2.0			,						__lowerCAmelCase					:					float = 5.0			,						__lowerCAmelCase					:					float = 5.0			,						__lowerCAmelCase					:					int = 1_25_44			,						__lowerCAmelCase					:					float = 3.0			,						__lowerCAmelCase					:					float = 0.75			,						__lowerCAmelCase					:					float = 0.02			,						__lowerCAmelCase					:					float = 1.0			,						__lowerCAmelCase					:					bool = True			,						__lowerCAmelCase					:					List[int] = [4, 8, 16, 32]			,						__lowerCAmelCase					:					bool = None			,						**__lowerCAmelCase					:					Any			,						)   ->	Dict:
    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=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=__lowerCAmelCase			,						out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']			,						)

    if isinstance(__lowerCAmelCase			,						__lowerCAmelCase      ):
       _A      =       backbone_config.pop('''model_type'''      )
       _A      =       CONFIG_MAPPING[backbone_model_type]
       _A      =       config_class.from_dict(__lowerCAmelCase      )

    # 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__(**__lowerCAmelCase      )

 @classmethod
 def     snake_case_      (    cls					:					str			,						__lowerCAmelCase					:					PretrainedConfig			,						**__lowerCAmelCase					:					str      )   ->	int:
    return cls(
        backbone_config=__lowerCAmelCase			,						**__lowerCAmelCase			,						)

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




'''simple docstring'''

import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available



def snake_case_      (					*__snake_case  :							Optional[int])			->					int:
   if not isinstance(__snake_case	,   __snake_case):
      lowerCAmelCase_			=      list(__snake_case)
   for i in range(len(__snake_case)):
      lowerCAmelCase_			=      None
   gc.collect()
   if is_xpu_available():
      torch.xpu.empty_cache()
   elif is_npu_available():
      torch.npu.empty_cache()
   else:
      torch.cuda.empty_cache()
   return objects



def snake_case_      (					__snake_case  :							Exception)			->					bool:
   lowerCAmelCase_			=      [
       '''CUDA out of memory.''',  # CUDA OOM
       '''cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.''',  # CUDNN SNAFU
       '''DefaultCPUAllocator: can\'t allocate memory''',  # CPU OOM
   ]
   if isinstance(__snake_case	,   __snake_case) and len(exception.args) == 1:
      return any(err in exception.args[0] for err in _statements)
   return False






def snake_case_      (					__snake_case  :							callable = None	,   __snake_case  :							int = 128)			->					List[Any]:
   if function is None:
      return functools.partial(__snake_case	,   starting_batch_size=__snake_case)

   lowerCAmelCase_			=      starting_batch_size

   def decorator(*__snake_case  :							List[str]	,   **__snake_case  :							Union[str, Any]):
      nonlocal batch_size
      gc.collect()
      if is_xpu_available():
         torch.xpu.empty_cache()
      elif is_npu_available():
         torch.npu.empty_cache()
      else:
         torch.cuda.empty_cache()
      lowerCAmelCase_			=      list(inspect.signature(__snake_case).parameters.keys())
      # Guard against user error
      if len(__snake_case) < (len(__snake_case) + 1):
         lowerCAmelCase_			=      ''', '''.join([F'''{arg}={value}''' for arg, value in zip(params[1:]	,   args[1:])])
         raise TypeError(
             F'''Batch size was passed into `{function.__name__}` as the first argument when called.'''
             F'''Remove this as the decorator already does so: `{function.__name__}({arg_str})`''')
      while True:
         if batch_size == 0:
            raise RuntimeError('''No executable batch size found, reached zero.''')
         try:
            return function(__snake_case	,   *__snake_case	,   **__snake_case)
         except Exception as e:
            if should_reduce_batch_size(__snake_case):
               gc.collect()
               if is_xpu_available():
                  torch.xpu.empty_cache()
               elif is_npu_available():
                  torch.npu.empty_cache()
               else:
                  torch.cuda.empty_cache()
               batch_size //= 2
            else:
               raise

   return decorator





