






import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A  			=					logging.get_logger(__name__)

__A  			=					{
    'BridgeTower/bridgetower-base': 'https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json',
    'BridgeTower/bridgetower-base-itm-mlm': (
        'https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json'
    ),
}





class     SCREAMING_SNAKE_CASE					(			snake_case       ):





							"""simple docstring"""

							A_    	=       "bridgetower_vision_model"

							def __init__(	self: str		,  __A: Tuple=7_68		,  __A: List[Any]=12		,  __A: Optional[int]=3		,  __A: Optional[int]=16		,  __A: List[str]=2_88		,  __A: List[str]=1		,  __A: Union[str, Any]=1e-05		,  __A: List[str]=False		,  __A: Union[str, Any]=True		,  __A: Any=False		,  **__A: Any		,  )     ->					int:
												super().__init__(**snake_case__ )
												_A										=			hidden_size
												_A										=			num_hidden_layers
												_A										=			num_channels
												_A										=			patch_size
												_A										=			image_size
												_A										=			initializer_factor
												_A										=			layer_norm_eps
												_A										=			stop_gradient
												_A										=			share_layernorm
												_A										=			remove_last_layer



							@classmethod
							def    __A			(	cls: List[Any]		,  __A: Union[str, os.PathLike]		,  **__A: Dict )     ->					Optional[int]:
												_A										=			cls.get_config_dict(snake_case__		,  **snake_case__ )

												if config_dict.get('''model_type''' ) == "bridgetower":
																	_A										=			config_dict["text_config"]

												if "model_type" in config_dict and hasattr(cls		,  '''model_type''' ) and config_dict["model_type"] != cls.model_type:
																	logger.warning(
																	    f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
																	    f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )

												return cls.from_dict(snake_case__		,  **snake_case__ )





class     SCREAMING_SNAKE_CASE					(			snake_case       ):





							"""simple docstring"""

							A_    	=       "bridgetower_text_model"

							def __init__(	self: Optional[Any]		,  __A: Union[str, Any]=5_02_65		,  __A: str=7_68		,  __A: Optional[Any]=12		,  __A: Optional[Any]=12		,  __A: Any=1		,  __A: Union[str, Any]=30_72		,  __A: Union[str, Any]="gelu"		,  __A: Union[str, Any]=0.1		,  __A: int=0.1		,  __A: Dict=5_14		,  __A: Tuple=1		,  __A: Dict=1e-05		,  __A: Any=1		,  __A: Any=0		,  __A: List[Any]=2		,  __A: str="absolute"		,  __A: int=True		,  **__A: Tuple		,  )     ->					str:
												super().__init__(**snake_case__ )

												_A										=			vocab_size
												_A										=			hidden_size
												_A										=			num_hidden_layers
												_A										=			num_attention_heads
												_A										=			hidden_act
												_A										=			initializer_factor
												_A										=			intermediate_size
												_A										=			hidden_dropout_prob
												_A										=			attention_probs_dropout_prob
												_A										=			max_position_embeddings
												_A										=			type_vocab_size
												_A										=			layer_norm_eps
												_A										=			position_embedding_type
												_A										=			use_cache
												_A										=			pad_token_id
												_A										=			bos_token_id
												_A										=			eos_token_id



							@classmethod
							def    __A			(	cls: Tuple		,  __A: Union[str, os.PathLike]		,  **__A: Union[str, Any] )     ->					Tuple:
												_A										=			cls.get_config_dict(snake_case__		,  **snake_case__ )

												if config_dict.get('''model_type''' ) == "bridgetower":
																	_A										=			config_dict["text_config"]

												if "model_type" in config_dict and hasattr(cls		,  '''model_type''' ) and config_dict["model_type"] != cls.model_type:
																	logger.warning(
																	    f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
																	    f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )

												return cls.from_dict(snake_case__		,  **snake_case__ )





class     SCREAMING_SNAKE_CASE					(			snake_case       ):





							"""simple docstring"""

							A_    	=       "bridgetower"

							def __init__(	self: Any		,  __A: Dict=True		,  __A: Dict="gelu"		,  __A: Optional[int]=7_68		,  __A: Optional[int]=1		,  __A: Union[str, Any]=1e-05		,  __A: List[str]=False		,  __A: int="add"		,  __A: Dict=12		,  __A: int=6		,  __A: List[str]=False		,  __A: Tuple=False		,  __A: Optional[Any]=None		,  __A: Any=None		,  **__A: Any		,  )     ->					Optional[Any]:
												_A										=			kwargs.pop('''text_config_dict'''		,  snake_case__ )
												_A										=			kwargs.pop('''vision_config_dict'''		,  snake_case__ )

												super().__init__(**snake_case__ )
												_A										=			share_cross_modal_transformer_layers
												_A										=			hidden_act
												_A										=			hidden_size
												_A										=			initializer_factor
												_A										=			layer_norm_eps
												_A										=			share_link_tower_layers
												_A										=			link_tower_type
												_A										=			num_attention_heads
												_A										=			num_hidden_layers
												_A										=			tie_word_embeddings
												_A										=			init_layernorm_from_vision_encoder

												if text_config is None:
																	_A										=			{}
																	logger.info('''`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values.''' )

												if vision_config is None:
																	_A										=			{}
																	logger.info('''`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values.''' )

												_A										=			BridgeTowerTextConfig(**snake_case__ )
												_A										=			BridgeTowerVisionConfig(**snake_case__ )

							@classmethod
							def    __A			(	cls: Tuple		,  __A: BridgeTowerTextConfig		,  __A: BridgeTowerVisionConfig		,  **__A: Union[str, Any] )     ->					List[str]:
												return cls(text_config=text_config.to_dict()		,  vision_config=vision_config.to_dict()		,  **snake_case__ )



							def    __A			(	self: Optional[int] )     ->					Any:
												_A										=			copy.deepcopy(self.__dict__ )
												_A										=			self.text_config.to_dict()
												_A										=			self.vision_config.to_dict()
												_A										=			self.__class__.model_type
												return output





'''simple docstring'''





import math

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







def snake_case_     (					lowercase__ = 3		):
  if isinstance(lowercase__   ,       lowercase__		):
    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(lowercase__		) != number_of_qubits:
    raise ValueError("number of qubits must be exact integer."		)
  if number_of_qubits > 1_0:
    raise ValueError("number of qubits too large to simulate(>10)."		)

  UpperCAmelCase__      :					Optional[int]				       =							QuantumRegister(lowercase__   ,       "qr"		)
  UpperCAmelCase__      :					List[Any]				       =							ClassicalRegister(lowercase__   ,       "cr"		)

  UpperCAmelCase__      :					List[str]				       =							QuantumCircuit(lowercase__   ,       lowercase__		)

  UpperCAmelCase__      :					Union[str, Any]				       =							number_of_qubits

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

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

  # measure all the qubits
  quantum_circuit.measure(lowercase__   ,       lowercase__		)
  # simulate with 10000 shots
  UpperCAmelCase__      :					Union[str, Any]				       =							Aer.get_backend("qasm_simulator"		)
  UpperCAmelCase__      :					Optional[int]				       =							execute(lowercase__   ,       lowercase__   ,       shots=1_0_0_0_0		)

  return job.result().get_counts(lowercase__		)


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



