

"""simple docstring"""




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

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


A_				:      List[Any]           =			logging.get_logger(__name__)

A_				:      int           =			{
    '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json''',
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}





class  lowerCamelCase    (A__      ):
			lowerCamelCase__ :		Dict       		=				'blenderbot-small'
			lowerCamelCase__ :		Tuple       		=				['past_key_values']
			lowerCamelCase__ :		List[str]       		=				{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}

			def __init__(       self					:    List[Any]     ,					__UpperCAmelCase					:    List[str]=5_0_2_6_5     ,					__UpperCAmelCase					:    int=5_1_2     ,					__UpperCAmelCase					:    Optional[int]=8     ,					__UpperCAmelCase					:    str=2_0_4_8     ,					__UpperCAmelCase					:    Optional[int]=1_6     ,					__UpperCAmelCase					:    Optional[Any]=8     ,					__UpperCAmelCase					:    str=2_0_4_8     ,					__UpperCAmelCase					:    Optional[Any]=1_6     ,					__UpperCAmelCase					:    Dict=0.0     ,					__UpperCAmelCase					:    str=0.0     ,					__UpperCAmelCase					:    List[str]=True     ,					__UpperCAmelCase					:    Tuple=True     ,					__UpperCAmelCase					:    str="gelu"     ,					__UpperCAmelCase					:    Optional[int]=5_1_2     ,					__UpperCAmelCase					:    List[Any]=0.1     ,					__UpperCAmelCase					:    List[Any]=0.0     ,					__UpperCAmelCase					:    Optional[Any]=0.0     ,					__UpperCAmelCase					:    Any=0.02     ,					__UpperCAmelCase					:    Optional[Any]=1     ,					__UpperCAmelCase					:    List[Any]=False     ,					__UpperCAmelCase					:    Optional[int]=0     ,					__UpperCAmelCase					:    Union[str, Any]=1     ,					__UpperCAmelCase					:    str=2     ,					__UpperCAmelCase					:    Dict=2     ,					**__UpperCAmelCase					:    Optional[int]     ,					) ->     Dict:
						SCREAMING_SNAKE_CASE__										=   vocab_size
						SCREAMING_SNAKE_CASE__										=   max_position_embeddings
						SCREAMING_SNAKE_CASE__										=   d_model
						SCREAMING_SNAKE_CASE__										=   encoder_ffn_dim
						SCREAMING_SNAKE_CASE__										=   encoder_layers
						SCREAMING_SNAKE_CASE__										=   encoder_attention_heads
						SCREAMING_SNAKE_CASE__										=   decoder_ffn_dim
						SCREAMING_SNAKE_CASE__										=   decoder_layers
						SCREAMING_SNAKE_CASE__										=   decoder_attention_heads
						SCREAMING_SNAKE_CASE__										=   dropout
						SCREAMING_SNAKE_CASE__										=   attention_dropout
						SCREAMING_SNAKE_CASE__										=   activation_dropout
						SCREAMING_SNAKE_CASE__										=   activation_function
						SCREAMING_SNAKE_CASE__										=   init_std
						SCREAMING_SNAKE_CASE__										=   encoder_layerdrop
						SCREAMING_SNAKE_CASE__										=   decoder_layerdrop
						SCREAMING_SNAKE_CASE__										=   use_cache
						SCREAMING_SNAKE_CASE__										=   encoder_layers
						SCREAMING_SNAKE_CASE__										=   scale_embedding  # scale factor will be sqrt(d_model) if True

						super().__init__(
						    pad_token_id=_SCREAMING_SNAKE_CASE     ,					bos_token_id=_SCREAMING_SNAKE_CASE     ,					eos_token_id=_SCREAMING_SNAKE_CASE     ,					is_encoder_decoder=_SCREAMING_SNAKE_CASE     ,					decoder_start_token_id=_SCREAMING_SNAKE_CASE     ,					forced_eos_token_id=_SCREAMING_SNAKE_CASE     ,					**_SCREAMING_SNAKE_CASE     ,					)





class  lowerCamelCase    (A__      ):

			@property
			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     Mapping[str, Mapping[int, str]]:





						if self.task in ["default", "seq2seq-lm"]:
									SCREAMING_SNAKE_CASE__										=   OrderedDict(
									    [
									        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
									        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
									    ]							)

									if self.use_past:
												SCREAMING_SNAKE_CASE__										=   {0: """batch"""}
												SCREAMING_SNAKE_CASE__										=   {0: """batch""", 1: """past_decoder_sequence + sequence"""}
									else:
												SCREAMING_SNAKE_CASE__										=   {0: """batch""", 1: """decoder_sequence"""}
												SCREAMING_SNAKE_CASE__										=   {0: """batch""", 1: """decoder_sequence"""}

									if self.use_past:
												self.fill_with_past_key_values_(_SCREAMING_SNAKE_CASE     ,					direction="""inputs"""							)
						elif self.task == "causal-lm":
									# TODO: figure this case out.
									SCREAMING_SNAKE_CASE__										=   OrderedDict(
									    [
									        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
									        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
									    ]							)
									if self.use_past:
												SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   self.num_layers
												for i in range(_SCREAMING_SNAKE_CASE							):
															SCREAMING_SNAKE_CASE__										=   {0: """batch""", 2: """past_sequence + sequence"""}
															SCREAMING_SNAKE_CASE__										=   {0: """batch""", 2: """past_sequence + sequence"""}
						else:
									SCREAMING_SNAKE_CASE__										=   OrderedDict(
									    [
									        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
									        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
									        ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
									        ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
									    ]							)

						return common_inputs

			@property
			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     Mapping[str, Mapping[int, str]]:





						if self.task in ["default", "seq2seq-lm"]:
									SCREAMING_SNAKE_CASE__										=   super().outputs
						else:
									SCREAMING_SNAKE_CASE__										=   super(_SCREAMING_SNAKE_CASE     ,					self							).outputs
									if self.use_past:
												SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   self.num_layers
												for i in range(_SCREAMING_SNAKE_CASE							):
															SCREAMING_SNAKE_CASE__										=   {0: """batch""", 2: """past_sequence + sequence"""}
															SCREAMING_SNAKE_CASE__										=   {0: """batch""", 2: """past_sequence + sequence"""}
						return common_outputs

			def        SCREAMING_SNAKE_CASE					(       self					:    int     ,					__UpperCAmelCase					:    Any     ,					__UpperCAmelCase					:    Union[str, Any] = -1     ,					__UpperCAmelCase					:    List[Any] = -1     ,					__UpperCAmelCase					:    List[str] = False     ,					__UpperCAmelCase					:    Optional[Any] = None     ,					) ->     Mapping[str, Any]:
						SCREAMING_SNAKE_CASE__										=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    _SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE							)

						# Generate decoder inputs
						SCREAMING_SNAKE_CASE__										=   seq_length if not self.use_past else 1
						SCREAMING_SNAKE_CASE__										=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    _SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE							)
						SCREAMING_SNAKE_CASE__										=   {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
						SCREAMING_SNAKE_CASE__										=   dict(**_SCREAMING_SNAKE_CASE     ,					**_SCREAMING_SNAKE_CASE							)

						if self.use_past:
									if not is_torch_available():
												raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""							)
									else:
												import torch
									SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   common_inputs["""input_ids"""].shape
									SCREAMING_SNAKE_CASE__										=   common_inputs["""decoder_input_ids"""].shape[1]
									SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   self.num_attention_heads
									SCREAMING_SNAKE_CASE__										=   (
									    batch,
									    num_encoder_attention_heads,
									    encoder_seq_length,
									    self._config.hidden_size // num_encoder_attention_heads,
									)
									SCREAMING_SNAKE_CASE__										=   decoder_seq_length + 3
									SCREAMING_SNAKE_CASE__										=   (
									    batch,
									    num_decoder_attention_heads,
									    decoder_past_length,
									    self._config.hidden_size // num_decoder_attention_heads,
									)

									SCREAMING_SNAKE_CASE__										=   torch.cat(
									    [common_inputs["""decoder_attention_mask"""], torch.ones(_SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE							)]     ,					dim=1							)

									SCREAMING_SNAKE_CASE__										=   []
									# If the number of encoder and decoder layers are present in the model configuration, both are considered
									SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   self.num_layers
									SCREAMING_SNAKE_CASE__										=   min(_SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE							)
									SCREAMING_SNAKE_CASE__										=   max(_SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE							) - min_num_layers
									SCREAMING_SNAKE_CASE__										=   """encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

									for _ in range(_SCREAMING_SNAKE_CASE							):
												common_inputs["past_key_values"].append(
												    (
												        torch.zeros(_SCREAMING_SNAKE_CASE							),
												        torch.zeros(_SCREAMING_SNAKE_CASE							),
												        torch.zeros(_SCREAMING_SNAKE_CASE							),
												        torch.zeros(_SCREAMING_SNAKE_CASE							),
												    )							)
									# TODO: test this.
									SCREAMING_SNAKE_CASE__										=   encoder_shape if remaining_side_name == """encoder""" else decoder_shape
									for _ in range(_SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE							):
												common_inputs["past_key_values"].append((torch.zeros(_SCREAMING_SNAKE_CASE							), torch.zeros(_SCREAMING_SNAKE_CASE							))							)
						return common_inputs

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]     ,					__UpperCAmelCase					:    int     ,					__UpperCAmelCase					:    List[Any] = -1     ,					__UpperCAmelCase					:    Dict = -1     ,					__UpperCAmelCase					:    str = False     ,					__UpperCAmelCase					:    Dict = None     ,					) ->     Mapping[str, Any]:
						SCREAMING_SNAKE_CASE__										=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    _SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE							)

						if self.use_past:
									if not is_torch_available():
												raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""							)
									else:
												import torch
									SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   common_inputs["""input_ids"""].shape
									# Not using the same length for past_key_values
									SCREAMING_SNAKE_CASE__										=   seqlen + 2
									SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   self.num_layers
									SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   self.num_attention_heads
									SCREAMING_SNAKE_CASE__										=   (
									    batch,
									    num_encoder_attention_heads,
									    past_key_values_length,
									    self._config.hidden_size // num_encoder_attention_heads,
									)

									SCREAMING_SNAKE_CASE__										=   common_inputs["""attention_mask"""].dtype
									SCREAMING_SNAKE_CASE__										=   torch.cat(
									    [common_inputs["""attention_mask"""], torch.ones(_SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE     ,					dtype=_SCREAMING_SNAKE_CASE							)]     ,					dim=1							)
									SCREAMING_SNAKE_CASE__										=   [
									    (torch.zeros(_SCREAMING_SNAKE_CASE							), torch.zeros(_SCREAMING_SNAKE_CASE							)) for _ in range(_SCREAMING_SNAKE_CASE							)
									]
						return common_inputs

			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]     ,					__UpperCAmelCase					:    List[str]     ,					__UpperCAmelCase					:    List[str] = -1     ,					__UpperCAmelCase					:    Optional[int] = -1     ,					__UpperCAmelCase					:    Optional[int] = False     ,					__UpperCAmelCase					:    Optional[int] = None     ,					) ->     Mapping[str, Any]:
						SCREAMING_SNAKE_CASE__										=   compute_effective_axis_dimension(
						    _SCREAMING_SNAKE_CASE     ,					fixed_dimension=OnnxConfig.default_fixed_batch     ,					num_token_to_add=0							)

						# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
						SCREAMING_SNAKE_CASE__										=   tokenizer.num_special_tokens_to_add(_SCREAMING_SNAKE_CASE							)
						SCREAMING_SNAKE_CASE__										=   compute_effective_axis_dimension(
						    _SCREAMING_SNAKE_CASE     ,					fixed_dimension=OnnxConfig.default_fixed_sequence     ,					num_token_to_add=_SCREAMING_SNAKE_CASE							)

						# Generate dummy inputs according to compute batch and sequence
						SCREAMING_SNAKE_CASE__										=   [""" """.join([tokenizer.unk_token]							) * seq_length] * batch_size
						SCREAMING_SNAKE_CASE__										=   dict(tokenizer(_SCREAMING_SNAKE_CASE     ,					return_tensors=_SCREAMING_SNAKE_CASE							)							)
						return common_inputs

			def        SCREAMING_SNAKE_CASE					(       self					:    Any     ,					__UpperCAmelCase					:    List[Any]     ,					__UpperCAmelCase					:    str = -1     ,					__UpperCAmelCase					:    Dict = -1     ,					__UpperCAmelCase					:    int = False     ,					__UpperCAmelCase					:    Tuple = None     ,					) ->     Mapping[str, Any]:





						if self.task in ["default", "seq2seq-lm"]:
									SCREAMING_SNAKE_CASE__										=   self._generate_dummy_inputs_for_default_and_seqaseq_lm(
									    _SCREAMING_SNAKE_CASE     ,					batch_size=_SCREAMING_SNAKE_CASE     ,					seq_length=_SCREAMING_SNAKE_CASE     ,					is_pair=_SCREAMING_SNAKE_CASE     ,					framework=_SCREAMING_SNAKE_CASE							)

						elif self.task == "causal-lm":
									SCREAMING_SNAKE_CASE__										=   self._generate_dummy_inputs_for_causal_lm(
									    _SCREAMING_SNAKE_CASE     ,					batch_size=_SCREAMING_SNAKE_CASE     ,					seq_length=_SCREAMING_SNAKE_CASE     ,					is_pair=_SCREAMING_SNAKE_CASE     ,					framework=_SCREAMING_SNAKE_CASE							)
						else:
									SCREAMING_SNAKE_CASE__										=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    _SCREAMING_SNAKE_CASE     ,					batch_size=_SCREAMING_SNAKE_CASE     ,					seq_length=_SCREAMING_SNAKE_CASE     ,					is_pair=_SCREAMING_SNAKE_CASE     ,					framework=_SCREAMING_SNAKE_CASE							)

						return common_inputs





			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]     ,					__UpperCAmelCase					:    Tuple     ,					__UpperCAmelCase					:    Optional[int]     ,					__UpperCAmelCase					:    Dict     ,					__UpperCAmelCase					:    Union[str, Any]							) ->     List[str]:





						if self.task in ["default", "seq2seq-lm"]:
									SCREAMING_SNAKE_CASE__										=   super()._flatten_past_key_values_(_SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE							)
						else:
									SCREAMING_SNAKE_CASE__										=   super(_SCREAMING_SNAKE_CASE     ,					self							)._flatten_past_key_values_(
									    _SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE							)






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)}''')
