
'''simple docstring'''



import copy
from typing import Dict, Optional

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


UpperCamelCase__					:						Optional[int]											=     logging.get_logger(__name__)

UpperCamelCase__					:						str											=     {
    "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       _a (_lowerCamelCase):





       """simple docstring"""




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






       def __init__(							self				,							A__ = None				,							A__ = 2_55				,							A__ = 1_50				,							A__ = 0.1				,							A__ = 2.0				,							A__ = 5.0				,							A__ = 5.0				,							A__ = 0.5				,							A__ = 0.07				,							A__ = 1_25_44				,							A__ = 3.0				,							A__ = 0.75				,							A__ = 0.02				,							A__ = 1.0				,							A__ = 1E-05				,							A__ = False				,							A__ = True				,							A__ = True				,							A__ = [4, 8, 16, 32]				,							A__ = 77				,							A__ = 2_56				,							A__ = 77				,							A__ = 6				,							A__ = 4_94_08				,							A__ = 2				,							A__ = 16				,							A__ = 2_56				,							A__ = 2_56				,							A__ = 2_56				,							A__ = 10_24				,							A__ = "GN"				,							A__ = 6				,							A__ = 10				,							A__ = True				,							A__ = 8				,							A__ = 0.1				,							A__ = 20_48				,							A__ = False				,							A__ = False				,							A__ = 2				,							A__ = 4				,							**A__				,							)			->	List[Any]:
          if backbone_config is None:
             logger.info("""`backbone_config` is unset. Initializing the config with the default `Swin` backbone."""					)
             _SCREAMING_SNAKE_CASE          = CONFIG_MAPPING["""swin"""](
                 image_size=2_24				,							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=A__				,							out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]				,							)
          elif isinstance(A__				,							A__					):
             _SCREAMING_SNAKE_CASE          = backbone_config.get("""model_type"""					)
             _SCREAMING_SNAKE_CASE          = CONFIG_MAPPING[backbone_model_type]
             _SCREAMING_SNAKE_CASE          = config_class.from_dict(A__					)

          _SCREAMING_SNAKE_CASE          = backbone_config

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

          super().__init__(**A__					)






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








"""simple docstring"""

import doctest

import projectq
from projectq.ops import H, Measure




def 			lowercase__  (    lowerCAmelCase :    projectq.cengines._main.MainEngine       )       ->					int:






    """simple docstring"""




    UpperCAmelCase  	=    quantum_engine.allocate_qubit()
    H | qubit
    Measure | qubit
    return int(lowerCAmelCase       )


if __name__ == "__main__":
   doctest.testmod()

   # initialises a new quantum backend
   SCREAMING_SNAKE_CASE_			    =  projectq.MainEngine()

   # Generate a list of 10 random numbers
   SCREAMING_SNAKE_CASE_			    =  [get_random_number(quantum_engine) for _ in range(10)]

   # Flushes the quantum engine from memory
   quantum_engine.flush()

   print('''Random numbers''', random_numbers_list)



