


import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


lowercase_							 =       logging.get_logger(__name__)

lowercase_							 =       {
    'google/vit-hybrid-base-bit-384': 'https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json',
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}







class       __a	(   a__       ):
				lowerCamelCase    :					Union[str, Any] 					="vit-hybrid"







				def __init__(   self			,  UpperCAmelCase=None			,  UpperCAmelCase=768			,  UpperCAmelCase=12			,  UpperCAmelCase=12			,  UpperCAmelCase=3072			,  UpperCAmelCase="gelu"			,  UpperCAmelCase=0.0			,  UpperCAmelCase=0.0			,  UpperCAmelCase=0.0_2			,  UpperCAmelCase=1E-1_2			,  UpperCAmelCase=224			,  UpperCAmelCase=1			,  UpperCAmelCase=3			,  UpperCAmelCase=[1, 1024, 24, 24]			,  UpperCAmelCase=True			,  **UpperCAmelCase			,  ):

					'''simple docstring'''






					super().__init__(**SCREAMING_SNAKE_CASE_      )

					if backbone_config is None:
						logger.info('''`backbone_config` is `None`. Initializing the config with a `BiT` backbone.'''      )
						lowerCAmelCase_			       = {
						    'global_padding': 'same',
						    'layer_type': 'bottleneck',
						    'depths': [3, 4, 9],
						    'out_features': ['stage3'],
						    'embedding_dynamic_padding': True,
						}

					if isinstance(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_      ):
						if "model_type" in backbone_config:
							lowerCAmelCase_			       = CONFIG_MAPPING[backbone_config['model_type']]
						else:
							logger.info(
							    '''`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.'''      )
							lowerCAmelCase_			       = BitConfig
						lowerCAmelCase_			       = backbone_config_class(**SCREAMING_SNAKE_CASE_      )

					lowerCAmelCase_			       = backbone_featmap_shape
					lowerCAmelCase_			       = backbone_config
					lowerCAmelCase_			       = hidden_size
					lowerCAmelCase_			       = num_hidden_layers
					lowerCAmelCase_			       = num_attention_heads
					lowerCAmelCase_			       = intermediate_size
					lowerCAmelCase_			       = hidden_act
					lowerCAmelCase_			       = hidden_dropout_prob
					lowerCAmelCase_			       = attention_probs_dropout_prob
					lowerCAmelCase_			       = initializer_range
					lowerCAmelCase_			       = layer_norm_eps
					lowerCAmelCase_			       = image_size
					lowerCAmelCase_			       = patch_size
					lowerCAmelCase_			       = num_channels
					lowerCAmelCase_			       = qkv_bias







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					lowerCAmelCase_			       = copy.deepcopy(self.__dict__      )
					lowerCAmelCase_			       = self.backbone_config.to_dict()
					lowerCAmelCase_			       = self.__class__.model_type
					return output



"""simple docstring"""



import doctest

import projectq
from projectq.ops import H, Measure
def     UpperCAmelCase    (					_lowercase   :      projectq.cengines._main.MainEngine  )	->   int:


	"""simple docstring"""




	lowerCAmelCase_			       = quantum_engine.allocate_qubit()
	H | qubit
	Measure | qubit
	return int(_lowercase  )


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

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

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

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

		print('Random numbers', random_numbers_list)