
import inspect
import warnings

import torch

from .state import AcceleratorState, GradientState
from .utils import DistributedType, honor_type, is_tpu_available


if is_tpu_available(check_device=False):
       import torch_xla.core.xla_model as xm



def 	_a  (					__SCREAMING_SNAKE_CASE					:  int		,				__SCREAMING_SNAKE_CASE					:  Tuple    ):






  """simple docstring"""




  if isinstance(__SCREAMING_SNAKE_CASE		,				(list, tuple)    ):
    return honor_type(__SCREAMING_SNAKE_CASE		,				(move_to_device(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    ) for t in state)    )
  elif isinstance(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    ):
    return type(__SCREAMING_SNAKE_CASE    )({k: move_to_device(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    ) for k, v in state.items()}    )
  elif isinstance(__SCREAMING_SNAKE_CASE		,				torch.Tensor    ):
    return state.to(__SCREAMING_SNAKE_CASE    )
  return state





class 						__lowercase(     torch.optim.Optimizer   ):


       """simple docstring"""







       def __init__(     self					:			Optional[Any]  ,      _lowerCAmelCase					:			Tuple  ,      _lowerCAmelCase					:			Tuple=True  ,      _lowerCAmelCase					:			str=None       )					->   List[str]:
         _lowerCAmelCase						=		optimizer
         _lowerCAmelCase						=		scaler
         _lowerCAmelCase						=		AcceleratorState()
         _lowerCAmelCase						=		GradientState()
         _lowerCAmelCase						=		device_placement
         _lowerCAmelCase						=		False
         _lowerCAmelCase						=		None

         # Handle device placement
         if device_placement:
           _lowerCAmelCase						=		self.optimizer.state_dict()
           if self.accelerator_state.distributed_type == DistributedType.TPU:
             xm.send_cpu_data_to_device(_lowerCAmelCase  ,      self.accelerator_state.device       )
           else:
             _lowerCAmelCase						=		move_to_device(_lowerCAmelCase  ,      self.accelerator_state.device       )
           self.optimizer.load_state_dict(_lowerCAmelCase       )



       @property
       def SCREAMING_SNAKE_CASE_				(     self					:			List[Any]       )					->   str:
         return self.optimizer.state



       @state.setter
       def SCREAMING_SNAKE_CASE_				(     self					:			List[str]  ,      _lowerCAmelCase					:			Dict       )					->   Union[str, Any]:
         _lowerCAmelCase						=		state



       @property
       def SCREAMING_SNAKE_CASE_				(     self					:			Tuple       )					->   str:
         return self.optimizer.param_groups



       @param_groups.setter
       def SCREAMING_SNAKE_CASE_				(     self					:			Tuple  ,      _lowerCAmelCase					:			Any       )					->   str:
         _lowerCAmelCase						=		param_groups



       @property
       def SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   str:
         return self.optimizer.defaults



       @defaults.setter
       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]  ,      _lowerCAmelCase					:			Dict       )					->   Tuple:
         _lowerCAmelCase						=		defaults



       def SCREAMING_SNAKE_CASE_				(     self					:			str  ,      _lowerCAmelCase					:			Optional[int]       )					->   Any:
         self.optimizer.add_param_group(_lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]  ,      _lowerCAmelCase					:			Optional[int]       )					->   Any:
         if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
           xm.send_cpu_data_to_device(_lowerCAmelCase  ,      self.accelerator_state.device       )
         self.optimizer.load_state_dict(_lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			str       )					->   Union[str, Any]:
         return self.optimizer.state_dict()



       def SCREAMING_SNAKE_CASE_				(     self					:			int  ,      _lowerCAmelCase					:			List[str]=None       )					->   Union[str, Any]:
         if self.gradient_state.sync_gradients:
           _lowerCAmelCase						=		'set_to_none' in inspect.signature(self.optimizer.zero_grad       ).parameters
           if accept_arg:
             if set_to_none is None:
               _lowerCAmelCase						=		False
             self.optimizer.zero_grad(set_to_none=_lowerCAmelCase       )
           else:
             if set_to_none is not None:
               raise ValueError('`set_to_none` for Optimizer.zero_grad` is not supported by this optimizer.'       )
             self.optimizer.zero_grad()



       def SCREAMING_SNAKE_CASE_				(     self					:			int  ,      _lowerCAmelCase					:			str=None       )					->   Union[str, Any]:
         if self.gradient_state.sync_gradients:
           if self.accelerator_state.distributed_type == DistributedType.TPU:
             _lowerCAmelCase						=		{'closure': closure} if closure is not None else {}
             xm.optimizer_step(self.optimizer  ,      optimizer_args=_lowerCAmelCase       )
           elif self.scaler is not None:
             _lowerCAmelCase						=		False
             if self._last_scale is None:
               # `get_scale` is an async operation requiring full synchronization
               # on CPU and GPUs before finishing. As a result, we store away
               # the prior one to reduce the call overhead
               _lowerCAmelCase						=		self.scaler.get_scale()
               _lowerCAmelCase						=		True
             self.scaler.step(self.optimizer  ,      _lowerCAmelCase       )
             self.scaler.update()
             _lowerCAmelCase						=		self.scaler.get_scale()
             if not new_scale:
               # If we reduced the loss scale, it means the optimizer step was skipped because of gradient overflow.
               _lowerCAmelCase						=		scale_after < self._last_scale
             _lowerCAmelCase						=		scale_after

           else:
             self.optimizer.step(_lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Tuple  ,      _lowerCAmelCase					:			Optional[Any]       )					->   List[str]:
         for param_group in self.optimizer.param_groups:
           _lowerCAmelCase						=		[parameters_map.get(_lowerCAmelCase  ,      _lowerCAmelCase       ) for p in param_group['params']]



       @property
       def SCREAMING_SNAKE_CASE_				(     self					:			int       )					->   Optional[int]:
         warnings.warn(
             'The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use '
             '`optimizer.step_was_skipped` instead.'  ,      _lowerCAmelCase  ,      )
         return self._is_overflow



       @property
       def SCREAMING_SNAKE_CASE_				(     self					:			Any       )					->   Any:
         return self._is_overflow



       def __getstate__(     self					:			Optional[int]       )					->   Dict:
         return self.__dict__.copy()






       def __setstate__(     self					:			Tuple  ,      _lowerCAmelCase					:			Union[str, Any]       )					->   List[Any]:
         self.__dict__.update(_lowerCAmelCase       )


import copy
from typing import Dict, Optional

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


_UpperCamelCase:					str     =logging.get_logger(__name__)

_UpperCamelCase:					Optional[Any]     ={
    '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 						__lowercase(     SCREAMING_SNAKE_CASE   ):


       """simple docstring"""







       UpperCamelCase_				 =							'''oneformer'''
       UpperCamelCase_				 =							{'''hidden_size''': '''hidden_dim'''}



       def __init__(     self					:			Optional[Any]  ,      _lowerCAmelCase					:			Optional[Dict] = None  ,      _lowerCAmelCase					:			int = 255  ,      _lowerCAmelCase					:			int = 150  ,      _lowerCAmelCase					:			int = 0.1  ,      _lowerCAmelCase					:			float = 2.0  ,      _lowerCAmelCase					:			float = 5.0  ,      _lowerCAmelCase					:			float = 5.0  ,      _lowerCAmelCase					:			float = 0.5  ,      _lowerCAmelCase					:			float = 0.07  ,      _lowerCAmelCase					:			int = 1_2544  ,      _lowerCAmelCase					:			float = 3.0  ,      _lowerCAmelCase					:			float = 0.75  ,      _lowerCAmelCase					:			float = 0.02  ,      _lowerCAmelCase					:			float = 1.0  ,      _lowerCAmelCase					:			float = 1e-05  ,      _lowerCAmelCase					:			bool = False  ,      _lowerCAmelCase					:			bool = True  ,      _lowerCAmelCase					:			bool = True  ,      _lowerCAmelCase					:			Optional[list] = [4, 8, 16, 32]  ,      _lowerCAmelCase					:			int = 77  ,      _lowerCAmelCase					:			int = 256  ,      _lowerCAmelCase					:			int = 77  ,      _lowerCAmelCase					:			int = 6  ,      _lowerCAmelCase					:			int = 4_9408  ,      _lowerCAmelCase					:			int = 2  ,      _lowerCAmelCase					:			int = 16  ,      _lowerCAmelCase					:			int = 256  ,      _lowerCAmelCase					:			int = 256  ,      _lowerCAmelCase					:			int = 256  ,      _lowerCAmelCase					:			int = 1024  ,      _lowerCAmelCase					:			str = "GN"  ,      _lowerCAmelCase					:			int = 6  ,      _lowerCAmelCase					:			int = 10  ,      _lowerCAmelCase					:			bool = True  ,      _lowerCAmelCase					:			int = 8  ,      _lowerCAmelCase					:			float = 0.1  ,      _lowerCAmelCase					:			int = 2048  ,      _lowerCAmelCase					:			bool = False  ,      _lowerCAmelCase					:			bool = False  ,      _lowerCAmelCase					:			int = 2  ,      _lowerCAmelCase					:			int = 4  ,      **_lowerCAmelCase					:			Any  ,      )					->   Tuple:
         if backbone_config is None:
           logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'       )
           _lowerCAmelCase						=		CONFIG_MAPPING['swin'](
               image_size=224  ,      in_channels=3  ,      patch_size=4  ,      embed_dim=96  ,      depths=[2, 2, 6, 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']  ,      )
         elif isinstance(_lowerCAmelCase  ,      _lowerCAmelCase       ):
           _lowerCAmelCase						=		backbone_config.get('model_type'       )
           _lowerCAmelCase						=		CONFIG_MAPPING[backbone_model_type]
           _lowerCAmelCase						=		config_class.from_dict(_lowerCAmelCase       )

         _lowerCAmelCase						=		backbone_config

         _lowerCAmelCase						=		ignore_value
         _lowerCAmelCase						=		num_queries
         _lowerCAmelCase						=		no_object_weight
         _lowerCAmelCase						=		class_weight
         _lowerCAmelCase						=		mask_weight
         _lowerCAmelCase						=		dice_weight
         _lowerCAmelCase						=		contrastive_weight
         _lowerCAmelCase						=		contrastive_temperature
         _lowerCAmelCase						=		train_num_points
         _lowerCAmelCase						=		oversample_ratio
         _lowerCAmelCase						=		importance_sample_ratio
         _lowerCAmelCase						=		init_std
         _lowerCAmelCase						=		init_xavier_std
         _lowerCAmelCase						=		layer_norm_eps
         _lowerCAmelCase						=		is_training
         _lowerCAmelCase						=		use_auxiliary_loss
         _lowerCAmelCase						=		output_auxiliary_logits
         _lowerCAmelCase						=		strides
         _lowerCAmelCase						=		task_seq_len
         _lowerCAmelCase						=		text_encoder_width
         _lowerCAmelCase						=		text_encoder_context_length
         _lowerCAmelCase						=		text_encoder_num_layers
         _lowerCAmelCase						=		text_encoder_vocab_size
         _lowerCAmelCase						=		text_encoder_proj_layers
         _lowerCAmelCase						=		text_encoder_n_ctx
         _lowerCAmelCase						=		conv_dim
         _lowerCAmelCase						=		mask_dim
         _lowerCAmelCase						=		hidden_dim
         _lowerCAmelCase						=		encoder_feedforward_dim
         _lowerCAmelCase						=		norm
         _lowerCAmelCase						=		encoder_layers
         _lowerCAmelCase						=		decoder_layers
         _lowerCAmelCase						=		use_task_norm
         _lowerCAmelCase						=		num_attention_heads
         _lowerCAmelCase						=		dropout
         _lowerCAmelCase						=		dim_feedforward
         _lowerCAmelCase						=		pre_norm
         _lowerCAmelCase						=		enforce_input_proj
         _lowerCAmelCase						=		query_dec_layers
         _lowerCAmelCase						=		common_stride
         _lowerCAmelCase						=		decoder_layers

         super().__init__(**_lowerCAmelCase       )






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