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


lowerCAmelCase      :				Tuple     			=logging.get_logger(__name__)

lowerCAmelCase      :				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 						__snake_case						(			__lowerCAmelCase		):





      '''simple docstring'''






      _snake_case     		=							'blenderbot'
      _snake_case     		=							['past_key_values']
      _snake_case     		=							{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}



      def __init__(       self  :					Any     ,	_UpperCamelCase  :					Optional[int]=8008     ,	_UpperCamelCase  :					Optional[int]=128     ,	_UpperCamelCase  :					Optional[int]=2     ,	_UpperCamelCase  :					Tuple=1_0240     ,	_UpperCamelCase  :					Dict=32     ,	_UpperCamelCase  :					Tuple=24     ,	_UpperCamelCase  :					Tuple=1_0240     ,	_UpperCamelCase  :					List[str]=32     ,	_UpperCamelCase  :					List[str]=0.0     ,	_UpperCamelCase  :					str=0.0     ,	_UpperCamelCase  :					Tuple=True     ,	_UpperCamelCase  :					str=True     ,	_UpperCamelCase  :					int="gelu"     ,	_UpperCamelCase  :					List[str]=2560     ,	_UpperCamelCase  :					List[str]=0.1     ,	_UpperCamelCase  :					Tuple=0.0     ,	_UpperCamelCase  :					List[Any]=0.0     ,	_UpperCamelCase  :					Any=0.0_2     ,	_UpperCamelCase  :					List[str]=1     ,	_UpperCamelCase  :					List[str]=False     ,	_UpperCamelCase  :					Any=0     ,	_UpperCamelCase  :					List[str]=1     ,	_UpperCamelCase  :					Optional[Any]=2     ,	_UpperCamelCase  :					int=3     ,	_UpperCamelCase  :					Optional[Any]=2     ,	**_UpperCamelCase  :					Tuple     ,	)					->str:


          """simple docstring"""


          _lowerCamelCase  :		Union[str, Any]			    =    vocab_size
          _lowerCamelCase  :		Union[str, Any]			    =    max_position_embeddings
          _lowerCamelCase  :		List[str]			    =    d_model
          _lowerCamelCase  :		Dict			    =    encoder_ffn_dim
          _lowerCamelCase  :		Optional[Any]			    =    encoder_layers
          _lowerCamelCase  :		str			    =    encoder_attention_heads
          _lowerCamelCase  :		List[Any]			    =    decoder_ffn_dim
          _lowerCamelCase  :		Optional[int]			    =    decoder_layers
          _lowerCamelCase  :		Any			    =    decoder_attention_heads
          _lowerCamelCase  :		Union[str, Any]			    =    dropout
          _lowerCamelCase  :		List[Any]			    =    attention_dropout
          _lowerCamelCase  :		Union[str, Any]			    =    activation_dropout
          _lowerCamelCase  :		Optional[int]			    =    activation_function
          _lowerCamelCase  :		Union[str, Any]			    =    init_std
          _lowerCamelCase  :		Dict			    =    encoder_layerdrop
          _lowerCamelCase  :		str			    =    decoder_layerdrop
          _lowerCamelCase  :		Dict			    =    use_cache
          _lowerCamelCase  :		Any			    =    encoder_layers
          _lowerCamelCase  :		str			    =    scale_embedding  # scale factor will be sqrt(d_model) if True

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





class 						__snake_case						(			__lowerCAmelCase		):





      '''simple docstring'''






      @property
      def 						_SCREAMING_SNAKE_CASE     (       self  :					List[str])					->Mapping[str, Mapping[int, str]]:


          """simple docstring"""


          if self.task in ["default", "seq2seq-lm"]:
              _lowerCamelCase  :		Union[str, Any]			    =    OrderedDict(
                  [
                      ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                      ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                  ])
              if self.use_past:
                  _lowerCamelCase  :		int			    =    {0: """batch"""}
                  _lowerCamelCase  :		int			    =    {0: """batch""", 1: """past_decoder_sequence + sequence"""}
              else:
                  _lowerCamelCase  :		str			    =    {0: """batch""", 1: """decoder_sequence"""}
                  _lowerCamelCase  :		Tuple			    =    {0: """batch""", 1: """decoder_sequence"""}
              if self.use_past:
                  self.fill_with_past_key_values_(_UpperCamelCase     ,	direction="""inputs""")
          elif self.task == "causal-lm":
              _lowerCamelCase  :		Any			    =    OrderedDict(
                  [
                      ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                      ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                  ])
              if self.use_past:
                  _lowerCamelCase    ,				_lowerCamelCase  :		str			    =    self.num_layers
                  for i in range(_UpperCamelCase):
                      _lowerCamelCase  :		str			    =    {0: """batch""", 2: """past_sequence + sequence"""}
                      _lowerCamelCase  :		List[str]			    =    {0: """batch""", 2: """past_sequence + sequence"""}
          else:
              _lowerCamelCase  :		Any			    =    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 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[int])					->Mapping[str, Mapping[int, str]]:


          """simple docstring"""


          if self.task in ["default", "seq2seq-lm"]:
              _lowerCamelCase  :		Union[str, Any]			    =    super().outputs
          else:
              _lowerCamelCase  :		str			    =    super(_UpperCamelCase     ,	self).outputs
              if self.use_past:
                  _lowerCamelCase    ,				_lowerCamelCase  :		Dict			    =    self.num_layers
                  for i in range(_UpperCamelCase):
                      _lowerCamelCase  :		Any			    =    {0: """batch""", 2: """past_sequence + sequence"""}
                      _lowerCamelCase  :		Tuple			    =    {0: """batch""", 2: """past_sequence + sequence"""}
          return common_outputs



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Dict     ,	_UpperCamelCase  :					PreTrainedTokenizer     ,	_UpperCamelCase  :					int = -1     ,	_UpperCamelCase  :					int = -1     ,	_UpperCamelCase  :					bool = False     ,	_UpperCamelCase  :					Optional[TensorType] = None     ,	)					->Mapping[str, Any]:


          """simple docstring"""


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

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

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



      def 						_SCREAMING_SNAKE_CASE     (       self  :					List[Any]     ,	_UpperCamelCase  :					PreTrainedTokenizer     ,	_UpperCamelCase  :					int = -1     ,	_UpperCamelCase  :					int = -1     ,	_UpperCamelCase  :					bool = False     ,	_UpperCamelCase  :					Optional[TensorType] = None     ,	)					->Mapping[str, Any]:


          """simple docstring"""


          _lowerCamelCase  :		List[Any]			    =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              _UpperCamelCase     ,	_UpperCamelCase     ,	_UpperCamelCase     ,	_UpperCamelCase     ,	_UpperCamelCase)

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



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[int]     ,	_UpperCamelCase  :					PreTrainedTokenizer     ,	_UpperCamelCase  :					int = -1     ,	_UpperCamelCase  :					int = -1     ,	_UpperCamelCase  :					bool = False     ,	_UpperCamelCase  :					Optional[TensorType] = None     ,	)					->Mapping[str, Any]:


          """simple docstring"""


          _lowerCamelCase  :		List[str]			    =    compute_effective_axis_dimension(
              _UpperCamelCase     ,	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
          _lowerCamelCase  :		Dict			    =    tokenizer.num_special_tokens_to_add(_UpperCamelCase)
          _lowerCamelCase  :		Optional[int]			    =    compute_effective_axis_dimension(
              _UpperCamelCase     ,	fixed_dimension=OnnxConfig.default_fixed_sequence     ,	num_token_to_add=_UpperCamelCase)

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



      def 						_SCREAMING_SNAKE_CASE     (       self  :					str     ,	_UpperCamelCase  :					PreTrainedTokenizer     ,	_UpperCamelCase  :					int = -1     ,	_UpperCamelCase  :					int = -1     ,	_UpperCamelCase  :					bool = False     ,	_UpperCamelCase  :					Optional[TensorType] = None     ,	)					->Mapping[str, Any]:


          """simple docstring"""


          if self.task in ["default", "seq2seq-lm"]:
              _lowerCamelCase  :		Optional[Any]			    =    self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                  _UpperCamelCase     ,	batch_size=_UpperCamelCase     ,	seq_length=_UpperCamelCase     ,	is_pair=_UpperCamelCase     ,	framework=_UpperCamelCase)

          elif self.task == "causal-lm":
              _lowerCamelCase  :		Any			    =    self._generate_dummy_inputs_for_causal_lm(
                  _UpperCamelCase     ,	batch_size=_UpperCamelCase     ,	seq_length=_UpperCamelCase     ,	is_pair=_UpperCamelCase     ,	framework=_UpperCamelCase)
          else:
              _lowerCamelCase  :		Union[str, Any]			    =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                  _UpperCamelCase     ,	batch_size=_UpperCamelCase     ,	seq_length=_UpperCamelCase     ,	is_pair=_UpperCamelCase     ,	framework=_UpperCamelCase)

          return common_inputs



      def 						_SCREAMING_SNAKE_CASE     (       self  :					int     ,	_UpperCamelCase  :					Dict     ,	_UpperCamelCase  :					Optional[Any]     ,	_UpperCamelCase  :					str     ,	_UpperCamelCase  :					Optional[Any])					->List[str]:


          """simple docstring"""


          if self.task in ["default", "seq2seq-lm"]:
              _lowerCamelCase  :		List[Any]			    =    super()._flatten_past_key_values_(_UpperCamelCase     ,	_UpperCamelCase     ,	_UpperCamelCase     ,	_UpperCamelCase)
          else:
              _lowerCamelCase  :		Tuple			    =    super(_UpperCamelCase     ,	self)._flatten_past_key_values_(
                  _UpperCamelCase     ,	_UpperCamelCase     ,	_UpperCamelCase     ,	_UpperCamelCase)




      def 						_SCREAMING_SNAKE_CASE     (       self  :					Any     ,	_UpperCamelCase  :					Mapping[str, Mapping[int, str]]     ,	_UpperCamelCase  :					str)					->Optional[int]:


          """simple docstring"""


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

          _lowerCamelCase  :		Optional[int]			    =    """past_key_values""" if direction == """inputs""" else """present"""
          _lowerCamelCase    ,				_lowerCamelCase  :		str			    =    self.num_layers

          _lowerCamelCase  :		List[str]			    =    """past_encoder_sequence"""
          _lowerCamelCase  :		str			    =    """past_decoder_sequence""" if direction == """inputs""" else """past_decoder_sequence + sequence"""

          for i in range(_UpperCamelCase):
              _lowerCamelCase  :		List[Any]			    =    {0: """batch""", 2: decoder_sequence}
              _lowerCamelCase  :		int			    =    {0: """batch""", 2: decoder_sequence}
              _lowerCamelCase  :		Tuple			    =    {0: """batch""", 2: encoder_sequence}
              _lowerCamelCase  :		Any			    =    {0: """batch""", 2: encoder_sequence}





from __future__ import annotations

import sys







def     A__	(       __A						):







    '''simple docstring'''
    if n < 0:
        raise ValueError("""Negative arguments are not supported"""						)
    return _fib(__A						)[0]







def     A__	(       __A						):







    '''simple docstring'''
    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
    _lowerCamelCase    ,				_lowerCamelCase  :		List[Any]			    =    _fib(n // 2						)
    _lowerCamelCase  :		List[Any]			    =    a * (b * 2 - a)
    _lowerCamelCase  :		Optional[int]			    =    a * a + b * b
    return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
    lowerCAmelCase      :				Union[str, Any]     			=int(sys.argv[1])
    print(F"""fibonacci({n}) is {fibonacci(n)}""")




