


import copy
from typing import Dict, List, Optional

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


__lowerCAmelCase			=							{
    '''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
}

__lowerCAmelCase			=							logging.get_logger(__name__)





class   __a      (   __UpperCamelCase				):
 __lowercase			:							Optional[Any]       					=			'mask2former'
 __lowercase			:							Any       					=			['swin']
 __lowercase			:							Union[str, Any]       					=			{'hidden_size': 'hidden_dim'}
 def __init__(       self    ,			lowerCAmelCase__ = None    ,			lowerCAmelCase__ = 256    ,			lowerCAmelCase__ = 256    ,			lowerCAmelCase__ = 256    ,			lowerCAmelCase__ = 1_024    ,			lowerCAmelCase__ = "relu"    ,			lowerCAmelCase__ = 6    ,			lowerCAmelCase__ = 10    ,			lowerCAmelCase__ = 8    ,			lowerCAmelCase__ = 0.0    ,			lowerCAmelCase__ = 2_048    ,			lowerCAmelCase__ = False    ,			lowerCAmelCase__ = False    ,			lowerCAmelCase__ = 4    ,			lowerCAmelCase__ = 255    ,			lowerCAmelCase__ = 100    ,			lowerCAmelCase__ = 0.1    ,			lowerCAmelCase__ = 2.0    ,			lowerCAmelCase__ = 5.0    ,			lowerCAmelCase__ = 5.0    ,			lowerCAmelCase__ = 12_544    ,			lowerCAmelCase__ = 3.0    ,			lowerCAmelCase__ = 0.7_5    ,			lowerCAmelCase__ = 0.0_2    ,			lowerCAmelCase__ = 1.0    ,			lowerCAmelCase__ = True    ,			lowerCAmelCase__ = [4, 8, 16, 32]    ,			lowerCAmelCase__ = None    ,			**lowerCAmelCase__    ,			) ->    Optional[Any]:




        '''simple docstring'''
        if backbone_config is None:
               logger.info('`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.'			)
               lowercase__:       Dict        =       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__			):
               lowercase__:       Any        =       backbone_config.pop('model_type'			)
               lowercase__:       Optional[Any]        =       CONFIG_MAPPING[backbone_model_type]
               lowercase__:       Tuple        =       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			)}'			)

        lowercase__:       Optional[Any]        =       backbone_config
        lowercase__:       str        =       feature_size
        lowercase__:       Optional[int]        =       mask_feature_size
        lowercase__:       Union[str, Any]        =       hidden_dim
        lowercase__:       List[Any]        =       encoder_feedforward_dim
        lowercase__:       Optional[Any]        =       activation_function
        lowercase__:       Tuple        =       encoder_layers
        lowercase__:       Optional[int]        =       decoder_layers
        lowercase__:       Optional[Any]        =       num_attention_heads
        lowercase__:       str        =       dropout
        lowercase__:       List[str]        =       dim_feedforward
        lowercase__:       Any        =       pre_norm
        lowercase__:       Optional[Any]        =       enforce_input_projection
        lowercase__:       int        =       common_stride
        lowercase__:       List[Any]        =       ignore_value
        lowercase__:       Union[str, Any]        =       num_queries
        lowercase__:       Union[str, Any]        =       no_object_weight
        lowercase__:       List[Any]        =       class_weight
        lowercase__:       str        =       mask_weight
        lowercase__:       Optional[int]        =       dice_weight
        lowercase__:       Any        =       train_num_points
        lowercase__:       Union[str, Any]        =       oversample_ratio
        lowercase__:       int        =       importance_sample_ratio
        lowercase__:       int        =       init_std
        lowercase__:       Optional[Any]        =       init_xavier_std
        lowercase__:       Tuple        =       use_auxiliary_loss
        lowercase__:       Any        =       feature_strides
        lowercase__:       str        =       output_auxiliary_logits
        lowercase__:       Union[str, Any]        =       decoder_layers

        super().__init__(**lowerCAmelCase__			)
 @classmethod
 def 		SCREAMING_SNAKE_CASE__       (       cls    ,			lowerCAmelCase__    ,			**lowerCAmelCase__			) ->    List[Any]:




        '''simple docstring'''
        return cls(
            backbone_config=lowerCAmelCase__    ,			**lowerCAmelCase__    ,			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict[str, any]:




        '''simple docstring'''
        lowercase__:       int        =       copy.deepcopy(self.__dict__			)
        lowercase__:       Optional[int]        =       self.backbone_config.to_dict()
        lowercase__:       Union[str, Any]        =       self.__class__.model_type
        return output








import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute




def        snake_case_					(  snake_case = 3					)  ->    qiskit.result.counts.Counts:
       if isinstance(snake_case      ,							snake_case					):
              raise TypeError('number of qubits must be a integer.'					)
       if number_of_qubits <= 0:
              raise ValueError('number of qubits must be > 0.'					)
       if math.floor(snake_case					) != number_of_qubits:
              raise ValueError('number of qubits must be exact integer.'					)
       if number_of_qubits > 10:
              raise ValueError('number of qubits too large to simulate(>10).'					)

       lowercase__:       str        =       QuantumRegister(snake_case      ,							'qr'					)
       lowercase__:       str        =       ClassicalRegister(snake_case      ,							'cr'					)

       lowercase__:       List[Any]        =       QuantumCircuit(snake_case      ,							snake_case					)

       lowercase__:       int        =       number_of_qubits

       for i in range(snake_case					):
              quantum_circuit.h(number_of_qubits - i - 1					)
              counter -= 1
              for j in range(snake_case					):
                     quantum_circuit.cp(np.pi / 2 ** (counter - j)      ,							snake_case      ,							snake_case					)

       for k in range(number_of_qubits // 2					):
              quantum_circuit.swap(snake_case      ,							number_of_qubits - k - 1					)

       # measure all the qubits
       quantum_circuit.measure(snake_case      ,							snake_case					)
       # simulate with 10000 shots
       lowercase__:       str        =       Aer.get_backend('qasm_simulator'					)
       lowercase__:       Union[str, Any]        =       execute(snake_case      ,							snake_case      ,							shots=1_00_00					)

       return job.result().get_counts(snake_case					)


if __name__ == "__main__":
 print(
     F'''Total count for quantum fourier transform state is: \
    {quantum_fourier_transform(3)}'''
 )


