


from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


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

lowerCAmelCase__    :Tuple       =				{
    '''EleutherAI/gpt-j-6B''': '''https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json''',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}

class 						__a (							UpperCAmelCase     ):
		_a      :    int           =      'gptj'
		_a      :    Dict           =      {
		    'max_position_embeddings': 'n_positions',
		    'hidden_size': 'n_embd',
		    'num_attention_heads': 'n_head',
		    'num_hidden_layers': 'n_layer',
		}
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE=50400     ,       _SCREAMING_SNAKE_CASE=2048     ,       _SCREAMING_SNAKE_CASE=4096     ,       _SCREAMING_SNAKE_CASE=28     ,       _SCREAMING_SNAKE_CASE=16     ,       _SCREAMING_SNAKE_CASE=64     ,       _SCREAMING_SNAKE_CASE=None     ,       _SCREAMING_SNAKE_CASE="gelu_new"     ,       _SCREAMING_SNAKE_CASE=0.0     ,       _SCREAMING_SNAKE_CASE=0.0     ,       _SCREAMING_SNAKE_CASE=0.0     ,       _SCREAMING_SNAKE_CASE=1e-5     ,       _SCREAMING_SNAKE_CASE=0.02     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=50256     ,       _SCREAMING_SNAKE_CASE=50256     ,       _SCREAMING_SNAKE_CASE=False     ,       **_SCREAMING_SNAKE_CASE     ,       )					->			Tuple:




									"""simple docstring"""





									_UpperCAmelCase											=     vocab_size
									_UpperCAmelCase											=     n_positions
									_UpperCAmelCase											=     n_embd
									_UpperCAmelCase											=     n_layer
									_UpperCAmelCase											=     n_head
									_UpperCAmelCase											=     n_inner
									_UpperCAmelCase											=     rotary_dim
									_UpperCAmelCase											=     activation_function
									_UpperCAmelCase											=     resid_pdrop
									_UpperCAmelCase											=     embd_pdrop
									_UpperCAmelCase											=     attn_pdrop
									_UpperCAmelCase											=     layer_norm_epsilon
									_UpperCAmelCase											=     initializer_range
									_UpperCAmelCase											=     use_cache

									_UpperCAmelCase											=     bos_token_id
									_UpperCAmelCase											=     eos_token_id

									super().__init__(
									    bos_token_id=_SCREAMING_SNAKE_CASE     ,       eos_token_id=_SCREAMING_SNAKE_CASE     ,       tie_word_embeddings=_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE		)



class 						__a (							UpperCAmelCase     ):
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = "default"     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE = False     ,       )					->			Optional[int]:




									"""simple docstring"""





									super().__init__(_SCREAMING_SNAKE_CASE     ,       task=_SCREAMING_SNAKE_CASE     ,       patching_specs=_SCREAMING_SNAKE_CASE     ,       use_past=_SCREAMING_SNAKE_CASE		)
									if not getattr(self._config     ,       'pad_token_id'     ,       _SCREAMING_SNAKE_CASE		):
																# TODO: how to do that better?
																_UpperCAmelCase											=     0
		@property
		def       UpperCAmelCase__							(     self		)					->			Mapping[str, Mapping[int, str]]:




									"""simple docstring"""





									_UpperCAmelCase											=     OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}		)
									if self.use_past:
																self.fill_with_past_key_values_(_SCREAMING_SNAKE_CASE     ,       direction='inputs'		)
																_UpperCAmelCase											=     {0: 'batch', 1: 'past_sequence + sequence'}
									else:
																_UpperCAmelCase											=     {0: 'batch', 1: 'sequence'}

									return common_inputs
		@property
		def       UpperCAmelCase__							(     self		)					->			int:




									"""simple docstring"""





									return self._config.n_layer
		@property
		def       UpperCAmelCase__							(     self		)					->			int:




									"""simple docstring"""





									return self._config.n_head
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = False     ,       _SCREAMING_SNAKE_CASE = None     ,       )					->			Mapping[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     super(_SCREAMING_SNAKE_CASE     ,       self		).generate_dummy_inputs(
									    _SCREAMING_SNAKE_CASE     ,       batch_size=_SCREAMING_SNAKE_CASE     ,       seq_length=_SCREAMING_SNAKE_CASE     ,       is_pair=_SCREAMING_SNAKE_CASE     ,       framework=_SCREAMING_SNAKE_CASE		)

									# We need to order the input in the way they appears in the forward()
									_UpperCAmelCase											=     OrderedDict({'input_ids': common_inputs['input_ids']}		)

									# Need to add the past_keys
									if self.use_past:
																if not is_torch_available():
																							raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'		)
																else:
																							import torch

																							_UpperCAmelCase						,			_UpperCAmelCase											=     common_inputs['input_ids'].shape
																							# Not using the same length for past_key_values
																							_UpperCAmelCase											=     seqlen + 2
																							_UpperCAmelCase											=     (
																							    batch,
																							    self.num_attention_heads,
																							    past_key_values_length,
																							    self._config.hidden_size // self.num_attention_heads,
																							)
																							_UpperCAmelCase											=     [
																							    (torch.zeros(_SCREAMING_SNAKE_CASE		), torch.zeros(_SCREAMING_SNAKE_CASE		)) for _ in range(self.num_layers		)
																							]

									_UpperCAmelCase											=     common_inputs['attention_mask']
									if self.use_past:
																_UpperCAmelCase											=     ordered_inputs['attention_mask'].dtype
																_UpperCAmelCase											=     torch.cat(
																    [ordered_inputs['attention_mask'], torch.ones(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       dtype=_SCREAMING_SNAKE_CASE		)]     ,       dim=1		)

									return ordered_inputs






		@property
		def       UpperCAmelCase__							(     self		)					->			int:




									"""simple docstring"""





									return 13




import math

import qiskit

def 			lowerCAmelCase__					(     a__:						int = 1     ,    a__:						int = 1     ,    a__:						int = 1			)    ->						qiskit.result.counts.Counts:


							'''simple docstring'''





							if (
							    isinstance(a__     ,    a__			)
							    or isinstance(a__     ,    a__			)
							    or isinstance(a__     ,    a__			)
							):
														raise TypeError('inputs must be integers.'			)

							if (input_a < 0) or (input_a < 0) or (carry_in < 0):
														raise ValueError('inputs must be positive.'			)

							if (
							    (math.floor(a__			) != input_a)
							    or (math.floor(a__			) != input_a)
							    or (math.floor(a__			) != carry_in)
							):
														raise ValueError('inputs must be exact integers.'			)

							if (input_a > 2) or (input_a > 2) or (carry_in > 2):
														raise ValueError('inputs must be less or equal to 2.'			)

							# build registers
							_UpperCAmelCase											=     qiskit.QuantumRegister(4     ,    'qr'			)
							_UpperCAmelCase											=     qiskit.ClassicalRegister(2     ,    'cr'			)
							# list the entries
							_UpperCAmelCase											=     [input_a, input_a, carry_in]

							_UpperCAmelCase											=     qiskit.QuantumCircuit(a__     ,    a__			)

							for i in range(0     ,    3			):
														if entry[i] == 2:
																					quantum_circuit.h(a__			)  # for hadamard entries
														elif entry[i] == 1:
																					quantum_circuit.x(a__			)  # for 1 entries
														elif entry[i] == 0:
																					quantum_circuit.i(a__			)  # for 0 entries

    # build the circuit
							quantum_circuit.ccx(0     ,    1     ,    3			)  # ccx = toffoli gate
							quantum_circuit.cx(0     ,    1			)
							quantum_circuit.ccx(1     ,    2     ,    3			)
							quantum_circuit.cx(1     ,    2			)
							quantum_circuit.cx(0     ,    1			)

							quantum_circuit.measure([2, 3]     ,    a__			)  # measure the last two qbits

							_UpperCAmelCase											=     qiskit.Aer.get_backend('aer_simulator'			)
							_UpperCAmelCase											=     qiskit.execute(a__     ,    a__     ,    shots=1_0_0_0			)

							return job.result().get_counts(a__			)


if __name__ == "__main__":
		print(f'''Total sum count for state is: {quantum_full_adder(1, 1, 1)}''')
