


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		=						logging.get_logger(__name__)

lowerCamelCase		=						{
    '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/config.json''',
    '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/config.json''',
    '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/config.json''',
    '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/config.json''',
    '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/config.json''',
}





class    _a				(							_lowercase):
	_a    :    Optional[int]        =				'''gpt2'''
	_a    :    List[Any]        =				['''past_key_values''']
	_a    :    str        =				{
	    '''hidden_size''': '''n_embd''',
	    '''max_position_embeddings''': '''n_positions''',
	    '''num_attention_heads''': '''n_head''',
	    '''num_hidden_layers''': '''n_layer''',
	}




	def __init__(   self		:						Any						,   _SCREAMING_SNAKE_CASE		:						Union[str, Any]=5_0257						,   _SCREAMING_SNAKE_CASE		:						Optional[Any]=1024						,   _SCREAMING_SNAKE_CASE		:						Tuple=768						,   _SCREAMING_SNAKE_CASE		:						Union[str, Any]=12						,   _SCREAMING_SNAKE_CASE		:						Dict=12						,   _SCREAMING_SNAKE_CASE		:						Optional[Any]=None						,   _SCREAMING_SNAKE_CASE		:						str="gelu_new"						,   _SCREAMING_SNAKE_CASE		:						Any=0.1						,   _SCREAMING_SNAKE_CASE		:						List[str]=0.1						,   _SCREAMING_SNAKE_CASE		:						Any=0.1						,   _SCREAMING_SNAKE_CASE		:						Optional[int]=1E-5						,   _SCREAMING_SNAKE_CASE		:						Any=0.02						,   _SCREAMING_SNAKE_CASE		:						Optional[Any]="cls_index"						,   _SCREAMING_SNAKE_CASE		:						Union[str, Any]=True						,   _SCREAMING_SNAKE_CASE		:						Union[str, Any]=None						,   _SCREAMING_SNAKE_CASE		:						Dict=True						,   _SCREAMING_SNAKE_CASE		:						Optional[Any]=0.1						,   _SCREAMING_SNAKE_CASE		:						int=True						,   _SCREAMING_SNAKE_CASE		:						Union[str, Any]=True						,   _SCREAMING_SNAKE_CASE		:						List[str]=5_0256						,   _SCREAMING_SNAKE_CASE		:						Optional[Any]=5_0256						,   _SCREAMING_SNAKE_CASE		:						List[Any]=False						,   _SCREAMING_SNAKE_CASE		:						Optional[int]=False						,   **_SCREAMING_SNAKE_CASE		:						Optional[int]						,   )->			int:
			lowerCAmelCase__  :     int       	=   vocab_size
			lowerCAmelCase__  :     List[Any]       	=   n_positions
			lowerCAmelCase__  :     List[str]       	=   n_embd
			lowerCAmelCase__  :     Any       	=   n_layer
			lowerCAmelCase__  :     Union[str, Any]       	=   n_head
			lowerCAmelCase__  :     List[Any]       	=   n_inner
			lowerCAmelCase__  :     Optional[Any]       	=   activation_function
			lowerCAmelCase__  :     Any       	=   resid_pdrop
			lowerCAmelCase__  :     Tuple       	=   embd_pdrop
			lowerCAmelCase__  :     str       	=   attn_pdrop
			lowerCAmelCase__  :     Dict       	=   layer_norm_epsilon
			lowerCAmelCase__  :     Dict       	=   initializer_range
			lowerCAmelCase__  :     List[Any]       	=   summary_type
			lowerCAmelCase__  :     str       	=   summary_use_proj
			lowerCAmelCase__  :     Tuple       	=   summary_activation
			lowerCAmelCase__  :     Tuple       	=   summary_first_dropout
			lowerCAmelCase__  :     int       	=   summary_proj_to_labels
			lowerCAmelCase__  :     Optional[Any]       	=   scale_attn_weights
			lowerCAmelCase__  :     Optional[Any]       	=   use_cache
			lowerCAmelCase__  :     List[str]       	=   scale_attn_by_inverse_layer_idx
			lowerCAmelCase__  :     Tuple       	=   reorder_and_upcast_attn

			lowerCAmelCase__  :     Tuple       	=   bos_token_id
			lowerCAmelCase__  :     Tuple       	=   eos_token_id

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





class    _a				(							_lowercase):




	def __init__(   self		:						str						,   _SCREAMING_SNAKE_CASE		:						PretrainedConfig						,   _SCREAMING_SNAKE_CASE		:						str = "default"						,   _SCREAMING_SNAKE_CASE		:						List[PatchingSpec] = None						,   _SCREAMING_SNAKE_CASE		:						bool = False						,   )->			Optional[int]:
			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?
					lowerCAmelCase__  :     List[str]       	=   0




	@property
	def 				UpperCAmelCase__(   self		:						Union[str, Any]						)->			Mapping[str, Mapping[int, str]]:
			lowerCAmelCase__  :     Optional[int]       	=   OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}						)
			if self.use_past:
					self.fill_with_past_key_values_(_SCREAMING_SNAKE_CASE						,   direction='''inputs'''						)
					lowerCAmelCase__  :     Any       	=   {0: '''batch''', 1: '''past_sequence + sequence'''}
			else:
					lowerCAmelCase__  :     Union[str, Any]       	=   {0: '''batch''', 1: '''sequence'''}

			return common_inputs




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




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




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

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

			lowerCAmelCase__  :     Dict       	=   common_inputs['''attention_mask''']
			if self.use_past:
					lowerCAmelCase__  :     str       	=   ordered_inputs['''attention_mask'''].dtype
					lowerCAmelCase__  :     str       	=   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						)->			int:
			return 13








from __future__ import annotations

import sys






def 	lowerCamelCase_       (			_a			):




		"""simple docstring"""






		if n < 0:
				raise ValueError('''Negative arguments are not supported'''			)
		return _fib(_a			)[0]






def 	lowerCamelCase_       (			_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__  :     Dict       	=   _fib(n // 2			)
		lowerCAmelCase__  :     Dict       	=   a * (b * 2 - a)
		lowerCAmelCase__  :     Union[str, Any]       	=   a * a + b * b
		return (d, c + d) if n % 2 else (c, d)


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




