



from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel

@require_tf
class  snake_case__:

					"""simple docstring"""






					lowercase_								=						MBartConfig
					lowercase_								=						{}
					lowercase_								=						"""gelu"""







					def __init__(				self   :      List[Any] ,  SCREAMING_SNAKE_CASE   :      Tuple ,  SCREAMING_SNAKE_CASE   :      Optional[int]=13 ,  SCREAMING_SNAKE_CASE   :      Any=7 ,  SCREAMING_SNAKE_CASE   :      Optional[int]=True ,  SCREAMING_SNAKE_CASE   :      Optional[int]=False ,  SCREAMING_SNAKE_CASE   :      List[str]=99 ,  SCREAMING_SNAKE_CASE   :      str=32 ,  SCREAMING_SNAKE_CASE   :      List[Any]=2 ,  SCREAMING_SNAKE_CASE   :      Dict=4 ,  SCREAMING_SNAKE_CASE   :      int=37 ,  SCREAMING_SNAKE_CASE   :      Dict=0.1 ,  SCREAMING_SNAKE_CASE   :      List[str]=0.1 ,  SCREAMING_SNAKE_CASE   :      List[Any]=20 ,  SCREAMING_SNAKE_CASE   :      int=2 ,  SCREAMING_SNAKE_CASE   :      int=1 ,  SCREAMING_SNAKE_CASE   :      int=0 ,  ):
									lowercase__	:       str						=	parent
									lowercase__	:       str						=	batch_size
									lowercase__	:       Union[str, Any]						=	seq_length
									lowercase__	:       Dict						=	is_training
									lowercase__	:       str						=	use_labels
									lowercase__	:       List[Any]						=	vocab_size
									lowercase__	:       Dict						=	hidden_size
									lowercase__	:       Tuple						=	num_hidden_layers
									lowercase__	:       int						=	num_attention_heads
									lowercase__	:       int						=	intermediate_size
									lowercase__	:       Tuple						=	hidden_dropout_prob
									lowercase__	:       int						=	attention_probs_dropout_prob
									lowercase__	:       Union[str, Any]						=	max_position_embeddings
									lowercase__	:       Optional[Any]						=	eos_token_id
									lowercase__	:       Optional[int]						=	pad_token_id
									lowercase__	:       Any						=	bos_token_id







					def 					snake_case	(				self   :      Dict	):
									lowercase__	:       Optional[Any]						=	ids_tensor([self.batch_size, self.seq_length - 1] ,  self.vocab_size	)
									lowercase__	:       List[str]						=	tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size	) ,  1	)
									lowercase__	:       Optional[int]						=	tf.concat([input_ids, eos_tensor] ,  axis=1	)

									lowercase__	:       Optional[int]						=	ids_tensor([self.batch_size, self.seq_length] ,  self.vocab_size	)

									lowercase__	:       Dict						=	self.config_cls(
									    vocab_size=self.vocab_size ,  d_model=self.hidden_size ,  encoder_layers=self.num_hidden_layers ,  decoder_layers=self.num_hidden_layers ,  encoder_attention_heads=self.num_attention_heads ,  decoder_attention_heads=self.num_attention_heads ,  encoder_ffn_dim=self.intermediate_size ,  decoder_ffn_dim=self.intermediate_size ,  dropout=self.hidden_dropout_prob ,  attention_dropout=self.attention_probs_dropout_prob ,  max_position_embeddings=self.max_position_embeddings ,  eos_token_ids=[2] ,  bos_token_id=self.bos_token_id ,  pad_token_id=self.pad_token_id ,  decoder_start_token_id=self.pad_token_id ,  **self.config_updates ,  )
									lowercase__	:       str						=	prepare_mbart_inputs_dict(SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE	)
									return config, inputs_dict







					def 					snake_case	(				self   :      Optional[int] ,  SCREAMING_SNAKE_CASE   :      Dict ,  SCREAMING_SNAKE_CASE   :      Any	):
									lowercase__	:       str						=	TFMBartModel(config=SCREAMING_SNAKE_CASE	).get_decoder()
									lowercase__	:       Union[str, Any]						=	inputs_dict['input_ids']

									lowercase__	:       str						=	input_ids[:1, :]
									lowercase__	:       Optional[int]						=	inputs_dict['attention_mask'][:1, :]
									lowercase__	:       Tuple						=	inputs_dict['head_mask']
									lowercase__	:       Tuple						=	1

									# first forward pass
									lowercase__	:       Union[str, Any]						=	model(SCREAMING_SNAKE_CASE ,  attention_mask=SCREAMING_SNAKE_CASE ,  head_mask=SCREAMING_SNAKE_CASE ,  use_cache=SCREAMING_SNAKE_CASE	)

									lowercase__	:       Union[str, Any]						=	outputs.to_tuple()
									lowercase__	:       Optional[int]						=	past_key_values[1]






def    __lowerCamelCase   (  lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__=None	,					lowerCamelCase__=None	,					lowerCamelCase__=None	,					lowerCamelCase__=None	,					lowerCamelCase__=None	,					):

				"""simple docstring"""


				if attention_mask is None:
								lowercase__	:       Dict						=	tf.cast(tf.math.not_equal(lowerCamelCase_	,					config.pad_token_id		)	,					tf.inta		)
				if decoder_attention_mask is None:
								lowercase__	:       List[Any]						=	tf.concat(
								    [
								        tf.ones(decoder_input_ids[:, :1].shape	,					dtype=tf.inta		),
								        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]	,					config.pad_token_id		)	,					tf.inta		),
								    ]	,					axis=-1	,					)
				if head_mask is None:
								lowercase__	:       List[Any]						=	tf.ones((config.encoder_layers, config.encoder_attention_heads)		)
				if decoder_head_mask is None:
								lowercase__	:       int						=	tf.ones((config.decoder_layers, config.decoder_attention_heads)		)
				if cross_attn_head_mask is None:
								lowercase__	:       Tuple						=	tf.ones((config.decoder_layers, config.decoder_attention_heads)		)
				return {
				    "input_ids": input_ids,
				    "decoder_input_ids": decoder_input_ids,
				    "attention_mask": attention_mask,
				    "decoder_attention_mask": decoder_attention_mask,
				    "head_mask": head_mask,
				    "decoder_head_mask": decoder_head_mask,
				    "cross_attn_head_mask": cross_attn_head_mask,
				}

@require_tf
class  snake_case__(_a							,     _a							,     unittest.TestCase     ):

					"""simple docstring"""






					lowercase_								=						(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
					lowercase_								=						(TFMBartForConditionalGeneration,) if is_tf_available() else ()
					lowercase_								=						(
					    {
					        """conversational""": TFMBartForConditionalGeneration,
					        """feature-extraction""": TFMBartModel,
					        """summarization""": TFMBartForConditionalGeneration,
					        """text2text-generation""": TFMBartForConditionalGeneration,
					        """translation""": TFMBartForConditionalGeneration,
					    }
					    if is_tf_available()
					    else {}
					)
					lowercase_								=						True
					lowercase_								=						False
					lowercase_								=						False







					def 					snake_case	(				self   :      List[Any] ,  SCREAMING_SNAKE_CASE   :      Union[str, Any] ,  SCREAMING_SNAKE_CASE   :      Optional[Any] ,  SCREAMING_SNAKE_CASE   :      List[str] ,  SCREAMING_SNAKE_CASE   :      Optional[int] ,  SCREAMING_SNAKE_CASE   :      Tuple	):

									if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
													# Exception encountered when calling layer '...'
													return True

									return False







					def 					snake_case	(				self   :      int	):
									lowercase__	:       List[str]						=	TFMBartModelTester(self	)
									lowercase__	:       List[Any]						=	ConfigTester(self ,  config_class=SCREAMING_SNAKE_CASE	)







					def 					snake_case	(				self   :      Optional[int]	):
									self.config_tester.run_common_tests()







					def 					snake_case	(				self   :      Dict	):
									lowercase__	:       Optional[int]						=	self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.check_decoder_model_past_large_inputs(*SCREAMING_SNAKE_CASE	)






@require_sentencepiece
@require_tokenizers
@require_tf
class  snake_case__(unittest.TestCase     ):

					"""simple docstring"""






					lowercase_								=						[
					    """ UN Chief Says There Is No Military Solution in Syria""",
					]
					lowercase_								=						[
					    """Şeful ONU declară că nu există o soluţie militară în Siria""",
					]
					lowercase_								=						"""facebook/mbart-large-en-ro"""







					@cached_property
					def 					snake_case	(				self   :      Union[str, Any]	):
									return AutoTokenizer.from_pretrained(self.model_name	)







					@cached_property
					def 					snake_case	(				self   :      Any	):
									lowercase__	:       Union[str, Any]						=	TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name	)
									return model







					def 					snake_case	(				self   :      Optional[int] ,  **SCREAMING_SNAKE_CASE   :      List[Any]	):
									lowercase__	:       str						=	self.translate_src_text(**SCREAMING_SNAKE_CASE	)
									self.assertListEqual(self.expected_text ,  SCREAMING_SNAKE_CASE	)







					def 					snake_case	(				self   :      int ,  **SCREAMING_SNAKE_CASE   :      Optional[int]	):
									lowercase__	:       List[Any]						=	self.tokenizer(self.src_text ,  **SCREAMING_SNAKE_CASE ,  return_tensors="tf"	)
									lowercase__	:       int						=	self.model.generate(
									    model_inputs.input_ids ,  attention_mask=model_inputs.attention_mask ,  num_beams=2	)
									lowercase__	:       List[Any]						=	self.tokenizer.batch_decode(SCREAMING_SNAKE_CASE ,  skip_special_tokens=SCREAMING_SNAKE_CASE	)
									return generated_words







					@slow
					def 					snake_case	(				self   :      str	):
									self._assert_generated_batch_equal_expected()








def 							UpperCamelCase_( lowerCamelCase_					)       ->		int:
    if n == 1 or not isinstance(lowerCamelCase_				,					lowerCamelCase_					):
        return 0
    elif n == 2:
        return 1
    else:
        _lowercase			:				List[str] 						=						[0, 1]
        for i in range(2				,					n + 1					):
            sequence.append(sequence[i - 1] + sequence[i - 2]					)

        return sequence[n]



def 							UpperCamelCase_( lowerCamelCase_					)       ->		int:
    _lowercase			:				Tuple 						=						0
    _lowercase			:				List[str] 						=						2

    while digits < n:
        index += 1
        _lowercase			:				Optional[int] 						=						len(str(fibonacci(lowerCamelCase_					)					)					)

    return index



def 							UpperCamelCase_( lowerCamelCase_ = 1000					)       ->		int:
    return fibonacci_digits_index(lowerCamelCase_					)


if __name__ == "__main__":
 print(solution(int(str(input()).strip())))


