





'''simple docstring'''


import copy
from typing import Dict, List, Optional

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


_SCREAMING_SNAKE_CASE									=				{
    "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
}

_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				Union[str, Any]								=       "mask2former"
 __lowerCamelCase    :				Optional[Any]								=       ["swin"]
 __lowerCamelCase    :				str								=       {"hidden_size": "hidden_dim"}




 def __init__(						self      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = 256      ,  _lowerCAmelCase = 256      ,  _lowerCAmelCase = 256      ,  _lowerCAmelCase = 1024      ,  _lowerCAmelCase = "relu"      ,  _lowerCAmelCase = 6      ,  _lowerCAmelCase = 10      ,  _lowerCAmelCase = 8      ,  _lowerCAmelCase = 0.0      ,  _lowerCAmelCase = 2048      ,  _lowerCAmelCase = False      ,  _lowerCAmelCase = False      ,  _lowerCAmelCase = 4      ,  _lowerCAmelCase = 255      ,  _lowerCAmelCase = 100      ,  _lowerCAmelCase = 0.1      ,  _lowerCAmelCase = 2.0      ,  _lowerCAmelCase = 5.0      ,  _lowerCAmelCase = 5.0      ,  _lowerCAmelCase = 12544      ,  _lowerCAmelCase = 3.0      ,  _lowerCAmelCase = 0.75      ,  _lowerCAmelCase = 0.02      ,  _lowerCAmelCase = 1.0      ,  _lowerCAmelCase = True      ,  _lowerCAmelCase = [4, 8, 16, 32]      ,  _lowerCAmelCase = None      ,  **_lowerCAmelCase      ,  )     ->				List[Any]:
     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=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=_lowerCAmelCase      ,  out_features=["stage1", "stage2", "stage3", "stage4"]      ,  )

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

     _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__(**_lowerCAmelCase		)




 @classmethod
 def 				_snake_case (						cls      ,  _lowerCAmelCase      ,  **_lowerCAmelCase		)     ->				Dict:
     return cls(
         backbone_config=_lowerCAmelCase      ,  **_lowerCAmelCase      ,  )





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







import qiskit






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase = 2 )	->						qiskit.result.counts.Counts:
				a__	:							Any      						= qubits

				# Using Aer's simulator
				a__	:							Dict      						= qiskit.Aer.get_backend("aer_simulator" )

				# Creating a Quantum Circuit acting on the q register
				a__	:							Dict      						= qiskit.QuantumCircuit(__UpperCamelCase	,				__UpperCamelCase )

				# Adding a H gate on qubit 0 (now q0 in superposition)
				circuit.h(0 )

				for i in range(1	,				__UpperCamelCase ):
								# Adding CX (CNOT) gate
								circuit.cx(i - 1	,				__UpperCamelCase )

				# Mapping the quantum measurement to the classical bits
				circuit.measure(list(range(__UpperCamelCase ) )	,				list(range(__UpperCamelCase ) ) )

				# Now measuring any one qubit would affect other qubits to collapse
				# their super position and have same state as the measured one.

				# Executing the circuit on the simulator
				a__	:							Any      						= qiskit.execute(__UpperCamelCase	,				__UpperCamelCase	,				shots=10_00 )

				return job.result().get_counts(__UpperCamelCase )


if __name__ == "__main__":
						print(F'Total count for various states are: {quantum_entanglement(3)}')


