"""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


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

UpperCamelCase_       :       List[str]							   =		{
    """facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json""",
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}


class 			_lowercase		(   __UpperCAmelCase  ):
				lowerCamelCase__						=       'blenderbot'
				lowerCamelCase__						=       ['past_key_values']
				lowerCamelCase__						=       {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}

				def __init__(     self       :    Tuple       ,    _lowercase       :    List[str]=8_0_0_8       ,    _lowercase       :    str=1_2_8       ,    _lowercase       :    Union[str, Any]=2       ,    _lowercase       :    Union[str, Any]=1_0_2_4_0       ,    _lowercase       :    str=3_2       ,    _lowercase       :    List[Any]=2_4       ,    _lowercase       :    List[Any]=1_0_2_4_0       ,    _lowercase       :    List[str]=3_2       ,    _lowercase       :    Dict=0.0       ,    _lowercase       :    str=0.0       ,    _lowercase       :    List[str]=True       ,    _lowercase       :    List[Any]=True       ,    _lowercase       :    Union[str, Any]="gelu"       ,    _lowercase       :    Dict=2_5_6_0       ,    _lowercase       :    Union[str, Any]=0.1       ,    _lowercase       :    int=0.0       ,    _lowercase       :    List[Any]=0.0       ,    _lowercase       :    int=0.02       ,    _lowercase       :    str=1       ,    _lowercase       :    Dict=False       ,    _lowercase       :    List[str]=0       ,    _lowercase       :    List[Any]=1       ,    _lowercase       :    int=2       ,    _lowercase       :    int=3       ,    _lowercase       :    List[str]=2       ,    **_lowercase       :    int       ,    ):


										'''simple docstring'''
										UpperCAmelCase__						=		vocab_size
										UpperCAmelCase__						=		max_position_embeddings
										UpperCAmelCase__						=		d_model
										UpperCAmelCase__						=		encoder_ffn_dim
										UpperCAmelCase__						=		encoder_layers
										UpperCAmelCase__						=		encoder_attention_heads
										UpperCAmelCase__						=		decoder_ffn_dim
										UpperCAmelCase__						=		decoder_layers
										UpperCAmelCase__						=		decoder_attention_heads
										UpperCAmelCase__						=		dropout
										UpperCAmelCase__						=		attention_dropout
										UpperCAmelCase__						=		activation_dropout
										UpperCAmelCase__						=		activation_function
										UpperCAmelCase__						=		init_std
										UpperCAmelCase__						=		encoder_layerdrop
										UpperCAmelCase__						=		decoder_layerdrop
										UpperCAmelCase__						=		use_cache
										UpperCAmelCase__						=		encoder_layers
										UpperCAmelCase__						=		scale_embedding  # scale factor will be sqrt(d_model) if True

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


class 			_lowercase		(   __UpperCAmelCase  ):

				@property
				def        A (     self       :    Any     ):


										'''simple docstring'''

										if self.task in ["default", "seq2seq-lm"]:
																UpperCAmelCase__						=		OrderedDict(
																    [
																        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
																        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
																    ]     )
																if self.use_past:
																						UpperCAmelCase__						=		{0: '''batch'''}
																						UpperCAmelCase__						=		{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
																else:
																						UpperCAmelCase__						=		{0: '''batch''', 1: '''decoder_sequence'''}
																						UpperCAmelCase__						=		{0: '''batch''', 1: '''decoder_sequence'''}
																if self.use_past:
																						self.fill_with_past_key_values_(lowercase_       ,    direction="""inputs"""     )
										elif self.task == "causal-lm":
																UpperCAmelCase__						=		OrderedDict(
																    [
																        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
																        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
																    ]     )
																if self.use_past:
																						UpperCAmelCase__						=		self.num_layers
																						for i in range(lowercase_     ):
																												UpperCAmelCase__						=		{0: '''batch''', 2: '''past_sequence + sequence'''}
																												UpperCAmelCase__						=		{0: '''batch''', 2: '''past_sequence + sequence'''}
										else:
																UpperCAmelCase__						=		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
				# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
				def        A (     self       :    Tuple     ):


										'''simple docstring'''

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

				def        A (     self       :    Any       ,    _lowercase       :    Tuple       ,    _lowercase       :    int = -1       ,    _lowercase       :    Optional[int] = -1       ,    _lowercase       :    Tuple = False       ,    _lowercase       :    List[Any] = None       ,    ):


										'''simple docstring'''
										UpperCAmelCase__						=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    lowercase_       ,    lowercase_       ,    lowercase_       ,    lowercase_       ,    lowercase_     )
										# Generate decoder inputs
										UpperCAmelCase__						=		seq_length if not self.use_past else 1
										UpperCAmelCase__						=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    lowercase_       ,    lowercase_       ,    lowercase_       ,    lowercase_       ,    lowercase_     )
										UpperCAmelCase__						=		{f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
										UpperCAmelCase__						=		dict(**lowercase_       ,    **lowercase_     )

										if self.use_past:
																if not is_torch_available():
																						raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""     )
																else:
																						import torch
																UpperCAmelCase__						=		common_inputs['''input_ids'''].shape
																UpperCAmelCase__						=		common_inputs['''decoder_input_ids'''].shape[1]
																UpperCAmelCase__						=		self.num_attention_heads
																UpperCAmelCase__						=		(
																    batch,
																    num_encoder_attention_heads,
																    encoder_seq_length,
																    self._config.hidden_size // num_encoder_attention_heads,
																)
																UpperCAmelCase__						=		decoder_seq_length
																UpperCAmelCase__						=		(
																    batch,
																    num_decoder_attention_heads,
																    decoder_past_length,
																    self._config.hidden_size // num_decoder_attention_heads,
																)
																UpperCAmelCase__						=		torch.cat(
																    [common_inputs["""decoder_attention_mask"""], torch.ones(lowercase_       ,    lowercase_     )]       ,    dim=1     )
																UpperCAmelCase__						=		[]
																UpperCAmelCase__						=		self.num_layers

																for _ in range(lowercase_     ):
																						common_inputs["past_key_values"].append(
																						    (
																						        torch.zeros(lowercase_     ),
																						        torch.zeros(lowercase_     ),
																						        torch.zeros(lowercase_     ),
																						        torch.zeros(lowercase_     ),
																						    )     )
										return common_inputs

				def        A (     self       :    List[str]       ,    _lowercase       :    Union[str, Any]       ,    _lowercase       :    Dict = -1       ,    _lowercase       :    Optional[Any] = -1       ,    _lowercase       :    Union[str, Any] = False       ,    _lowercase       :    Dict = None       ,    ):


										'''simple docstring'''
										UpperCAmelCase__						=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    lowercase_       ,    lowercase_       ,    lowercase_       ,    lowercase_       ,    lowercase_     )

										if self.use_past:
																if not is_torch_available():
																						raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""     )
																else:
																						import torch
																UpperCAmelCase__						=		common_inputs['''input_ids'''].shape
																UpperCAmelCase__						=		seqlen
																UpperCAmelCase__						=		self.num_layers
																UpperCAmelCase__						=		self.num_attention_heads
																UpperCAmelCase__						=		(
																    batch,
																    num_encoder_attention_heads,
																    past_key_values_length,
																    self._config.hidden_size // num_encoder_attention_heads,
																)
																UpperCAmelCase__						=		common_inputs['''attention_mask'''].dtype
																UpperCAmelCase__						=		torch.cat(
																    [common_inputs["""attention_mask"""], torch.ones(lowercase_       ,    lowercase_       ,    dtype=lowercase_     )]       ,    dim=1     )
																UpperCAmelCase__						=		[
																    (torch.zeros(lowercase_     ), torch.zeros(lowercase_     )) for _ in range(lowercase_     )
																]
										return common_inputs

				def        A (     self       :    Tuple       ,    _lowercase       :    Dict       ,    _lowercase       :    int = -1       ,    _lowercase       :    str = -1       ,    _lowercase       :    Optional[Any] = False       ,    _lowercase       :    int = None       ,    ):


										'''simple docstring'''
										UpperCAmelCase__						=		compute_effective_axis_dimension(
										    lowercase_       ,    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
										UpperCAmelCase__						=		tokenizer.num_special_tokens_to_add(lowercase_     )
										UpperCAmelCase__						=		compute_effective_axis_dimension(
										    lowercase_       ,    fixed_dimension=OnnxConfig.default_fixed_sequence       ,    num_token_to_add=lowercase_     )

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

				def        A (     self       :    Optional[int]       ,    _lowercase       :    Union[str, Any]       ,    _lowercase       :    Optional[int] = -1       ,    _lowercase       :    int = -1       ,    _lowercase       :    Dict = False       ,    _lowercase       :    Any = None       ,    ):


										'''simple docstring'''

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

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

										return common_inputs

				def        A (     self       :    Union[str, Any]       ,    _lowercase       :    Tuple       ,    _lowercase       :    Any       ,    _lowercase       :    Union[str, Any]       ,    _lowercase       :    Optional[Any]     ):


										'''simple docstring'''

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

				def        A (     self       :    List[Any]       ,    _lowercase       :    List[Any]       ,    _lowercase       :    int     ):


										'''simple docstring'''

										if direction not in ["inputs", "outputs"]:
																raise ValueError(f'direction must either be "inputs" or "outputs", but {direction} was given'     )

										UpperCAmelCase__						=		'''past_key_values''' if direction == '''inputs''' else '''present'''
										UpperCAmelCase__						=		self.num_layers

										UpperCAmelCase__						=		'''past_encoder_sequence'''
										UpperCAmelCase__						=		'''past_decoder_sequence''' if direction == '''inputs''' else '''past_decoder_sequence + sequence'''

										for i in range(lowercase_     ):
																UpperCAmelCase__						=		{0: '''batch''', 2: decoder_sequence}
																UpperCAmelCase__						=		{0: '''batch''', 2: decoder_sequence}
																UpperCAmelCase__						=		{0: '''batch''', 2: encoder_sequence}
																UpperCAmelCase__						=		{0: '''batch''', 2: encoder_sequence}





"""simple docstring"""







from __future__ import annotations

import sys


def   a__      (		UpperCAmelCase__       :			int			)->	int:
						if n < 0:
												raise ValueError("""Negative arguments are not supported"""			)
						return _fib(UpperCAmelCase__			)[0]


def   a__      (		UpperCAmelCase__       :			int			)->	tuple[int, int]:
						if n == 0:  # (F(0), F(1))
												return (0, 1)

						# F(2n) = F(n)[2F(n+1) − F(n)]
						# F(2n+1) = F(n+1)^2+F(n)^2
						UpperCAmelCase__	,      UpperCAmelCase__						=		_fib(n // 2			)
						UpperCAmelCase__						=		a * (b * 2 - a)
						UpperCAmelCase__						=		a * a + b * b
						return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
						UpperCamelCase_       :       int							   =		int(sys.argv[1])
						print(F"""fibonacci({n}) is {fibonacci(n)}""")

