




from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


UpperCAmelCase_						=logging.get_logger(__name__)

UpperCAmelCase_						={
    """t5-small""": """https://huggingface.co/t5-small/resolve/main/config.json""",
    """t5-base""": """https://huggingface.co/t5-base/resolve/main/config.json""",
    """t5-large""": """https://huggingface.co/t5-large/resolve/main/config.json""",
    """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/config.json""",
    """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/config.json""",
}


class 							__UpperCamelCase			( __UpperCAmelCase  ):





				'''simple docstring'''



				__a							: str										="""t5"""
				__a							: List[Any]										=["""past_key_values"""]
				__a							: Optional[Any]										={"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}



				def __init__( self       , UpperCAmelCase_=3_21_28       , UpperCAmelCase_=5_12       , UpperCAmelCase_=64       , UpperCAmelCase_=20_48       , UpperCAmelCase_=6       , UpperCAmelCase_=None       , UpperCAmelCase_=8       , UpperCAmelCase_=32       , UpperCAmelCase_=1_28       , UpperCAmelCase_=0.1       , UpperCAmelCase_=1E-6       , UpperCAmelCase_=1.0       , UpperCAmelCase_="relu"       , UpperCAmelCase_=True       , UpperCAmelCase_=True       , UpperCAmelCase_=0       , UpperCAmelCase_=1       , **UpperCAmelCase_       , ):
											lowerCAmelCase   =		vocab_size
											lowerCAmelCase   =		d_model
											lowerCAmelCase   =		d_kv
											lowerCAmelCase   =		d_ff
											lowerCAmelCase   =		num_layers
											lowerCAmelCase   =		(
											    num_decoder_layers if num_decoder_layers is not None else self.num_layers
											)  # default = symmetry
											lowerCAmelCase   =		num_heads
											lowerCAmelCase   =		relative_attention_num_buckets
											lowerCAmelCase   =		relative_attention_max_distance
											lowerCAmelCase   =		dropout_rate
											lowerCAmelCase   =		layer_norm_epsilon
											lowerCAmelCase   =		initializer_factor
											lowerCAmelCase   =		feed_forward_proj
											lowerCAmelCase   =		use_cache

											lowerCAmelCase   =		self.feed_forward_proj.split('''-'''						)
											lowerCAmelCase   =		act_info[-1]
											lowerCAmelCase   =		act_info[0] == '''gated'''

											if len(UpperCAmelCase_						) > 1 and act_info[0] != "gated" or len(UpperCAmelCase_						) > 2:
																		raise ValueError(
																		    F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
																		    '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
																		    '''\'gated-gelu\' or \'relu\''''						)

											# for backwards compatibility
											if feed_forward_proj == "gated-gelu":
																		lowerCAmelCase   =		'''gelu_new'''

											super().__init__(
											    pad_token_id=UpperCAmelCase_       , eos_token_id=UpperCAmelCase_       , is_encoder_decoder=UpperCAmelCase_       , **UpperCAmelCase_       , )




class 							__UpperCamelCase			( __UpperCAmelCase  ):





				'''simple docstring'''



				@property
				def       __snake_case		( self						):
											lowerCAmelCase   =		{
											    '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
											    '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
											}
											if self.use_past:
																		lowerCAmelCase   =		'''past_encoder_sequence + sequence'''
																		lowerCAmelCase   =		{0: '''batch'''}
																		lowerCAmelCase   =		{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
											else:
																		lowerCAmelCase   =		{0: '''batch''', 1: '''decoder_sequence'''}
																		lowerCAmelCase   =		{0: '''batch''', 1: '''decoder_sequence'''}

											if self.use_past:
																		self.fill_with_past_key_values_(UpperCAmelCase_       , direction='''inputs'''						)

											return common_inputs






				@property
				def       __snake_case		( self						):
											return 13









import math

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



def       UpperCAmelCase       (     _snake_case = 3							):
							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).'''							)

							lowerCAmelCase   =		QuantumRegister(_snake_case						, '''qr'''							)
							lowerCAmelCase   =		ClassicalRegister(_snake_case						, '''cr'''							)

							lowerCAmelCase   =		QuantumCircuit(_snake_case						, _snake_case							)

							lowerCAmelCase   =		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
							lowerCAmelCase   =		Aer.get_backend('''qasm_simulator'''							)
							lowerCAmelCase   =		execute(_snake_case						, _snake_case						, shots=10000							)

							return job.result().get_counts(_snake_case							)


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



