



import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
     import os

     # The slow tests are often failing with OOM error on GPU
     # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
     # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
     a_    :  Dict						  =		'platform'

     import jax
     import jax.numpy as jnp

     from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
         FlaxBlenderbotSmallForConditionalGeneration,
         FlaxBlenderbotSmallModel,
         shift_tokens_right,
     )

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__=None , UpperCAmelCase__=None , UpperCAmelCase__=None , UpperCAmelCase__=None , UpperCAmelCase__=None , UpperCAmelCase__=None , ):


      """simple docstring"""



      if attention_mask is None:
            lowerCamelCase             =		np.where(input_ids != config.pad_token_id , 1 , 0			)
      if decoder_attention_mask is None:
            lowerCamelCase             =		np.where(decoder_input_ids != config.pad_token_id , 1 , 0			)
      if head_mask is None:
            lowerCamelCase             =		np.ones((config.encoder_layers, config.encoder_attention_heads)			)
      if decoder_head_mask is None:
            lowerCamelCase             =		np.ones((config.decoder_layers, config.decoder_attention_heads)			)
      if cross_attn_head_mask is None:
            lowerCamelCase             =		np.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": attention_mask,
      }





class  lowerCamelCase__	:
     """simple docstring"""



     def __init__(self					,      __a					,      __a=13					,      __a=7					,      __a=True					,      __a=False					,      __a=99					,      __a=16					,      __a=2					,      __a=4					,      __a=4					,      __a="gelu"					,      __a=0.1					,      __a=0.1					,      __a=32					,      __a=2					,      __a=1					,      __a=0					,      __a=0.02					,      ):






           '''simple docstring'''



           lowerCamelCase             =		parent
           lowerCamelCase             =		batch_size
           lowerCamelCase             =		seq_length
           lowerCamelCase             =		is_training
           lowerCamelCase             =		use_labels
           lowerCamelCase             =		vocab_size
           lowerCamelCase             =		hidden_size
           lowerCamelCase             =		num_hidden_layers
           lowerCamelCase             =		num_attention_heads
           lowerCamelCase             =		intermediate_size
           lowerCamelCase             =		hidden_act
           lowerCamelCase             =		hidden_dropout_prob
           lowerCamelCase             =		attention_probs_dropout_prob
           lowerCamelCase             =		max_position_embeddings
           lowerCamelCase             =		eos_token_id
           lowerCamelCase             =		pad_token_id
           lowerCamelCase             =		bos_token_id
           lowerCamelCase             =		initializer_range







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		np.clip(ids_tensor([self.batch_size, self.seq_length - 1]					,      self.vocab_size     )					,      3					,      self.vocab_size     )
           lowerCamelCase             =		np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)					,      dtype=np.intaa     ))					,      -1     )

           lowerCamelCase             =		shift_tokens_right(__a					,      1					,      2     )

           lowerCamelCase             =		BlenderbotSmallConfig(
               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_id=self.eos_token_id					,      bos_token_id=self.bos_token_id					,      pad_token_id=self.pad_token_id					,      initializer_range=self.initializer_range					,      use_cache=__a					,      )
           lowerCamelCase             =		prepare_blenderbot_inputs_dict(__a					,      __a					,      __a     )
           return config, inputs_dict







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase      ,				lowerCamelCase             =		self.prepare_config_and_inputs()
           return config, inputs_dict







     def     _a   (self					,      __a					,      __a					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		20
           lowerCamelCase             =		model_class_name(__a     )

           lowerCamelCase             =		model.encode(inputs_dict["input_ids"]     )

           lowerCamelCase      ,				lowerCamelCase             =		(
               inputs_dict["decoder_input_ids"],
               inputs_dict["decoder_attention_mask"],
           )

           lowerCamelCase             =		model.init_cache(decoder_input_ids.shape[0]					,      __a					,      __a     )
           lowerCamelCase             =		jnp.ones((decoder_input_ids.shape[0], max_decoder_length)					,      dtype="i4"     )

           lowerCamelCase             =		jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]					,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,      )
           lowerCamelCase             =		model.decode(
               decoder_input_ids[:, :-1]					,      __a					,      decoder_attention_mask=__a					,      past_key_values=__a					,      decoder_position_ids=__a					,      )

           lowerCamelCase             =		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,      dtype="i4"     )
           lowerCamelCase             =		model.decode(
               decoder_input_ids[:, -1:]					,      __a					,      decoder_attention_mask=__a					,      past_key_values=outputs_cache.past_key_values					,      decoder_position_ids=__a					,      )

           lowerCamelCase             =		model.decode(__a					,      __a     )

           lowerCamelCase             =		np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
           self.parent.assertTrue(diff < 1E-3					,      msg=F"""Max diff is {diff}"""     )







     def     _a   (self					,      __a					,      __a					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		20
           lowerCamelCase             =		model_class_name(__a     )

           lowerCamelCase             =		model.encode(inputs_dict["input_ids"]     )

           lowerCamelCase      ,				lowerCamelCase             =		(
               inputs_dict["decoder_input_ids"],
               inputs_dict["decoder_attention_mask"],
           )

           lowerCamelCase             =		jnp.concatenate(
               [
                   decoder_attention_mask,
                   jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])     ),
               ]					,      axis=-1					,      )

           lowerCamelCase             =		model.init_cache(decoder_input_ids.shape[0]					,      __a					,      __a     )
           lowerCamelCase             =		jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]					,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,      )

           lowerCamelCase             =		model.decode(
               decoder_input_ids[:, :-1]					,      __a					,      decoder_attention_mask=__a					,      past_key_values=__a					,      decoder_position_ids=__a					,      )
           lowerCamelCase             =		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,      dtype="i4"     )
           lowerCamelCase             =		model.decode(
               decoder_input_ids[:, -1:]					,      __a					,      past_key_values=outputs_cache.past_key_values					,      decoder_attention_mask=__a					,      decoder_position_ids=__a					,      )

           lowerCamelCase             =		model.decode(__a					,      __a					,      decoder_attention_mask=__a     )

           lowerCamelCase             =		np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
           self.parent.assertTrue(diff < 1E-3					,      msg=F"""Max diff is {diff}"""     )





@require_flax
class  lowerCamelCase__	(    unittest.TestCase):
     """simple docstring"""



     _A      =      99







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		np.array(
               [
                   [71, 82, 18, 33, 46, 91, 2],
                   [68, 34, 26, 58, 30, 82, 2],
                   [5, 97, 17, 39, 94, 40, 2],
                   [76, 83, 94, 25, 70, 78, 2],
                   [87, 59, 41, 35, 48, 66, 2],
                   [55, 13, 16, 58, 5, 2, 1],  # note padding
                   [64, 27, 31, 51, 12, 75, 2],
                   [52, 64, 86, 17, 83, 39, 2],
                   [48, 61, 9, 24, 71, 82, 2],
                   [26, 1, 60, 48, 22, 13, 2],
                   [21, 5, 62, 28, 14, 76, 2],
                   [45, 98, 37, 86, 59, 48, 2],
                   [70, 70, 50, 9, 28, 0, 2],
               ]					,      dtype=np.intaa					,      )

           lowerCamelCase             =		input_ids.shape[0]
           lowerCamelCase             =		BlenderbotSmallConfig(
               vocab_size=self.vocab_size					,      d_model=24					,      encoder_layers=2					,      decoder_layers=2					,      encoder_attention_heads=2					,      decoder_attention_heads=2					,      encoder_ffn_dim=32					,      decoder_ffn_dim=32					,      max_position_embeddings=48					,      eos_token_id=2					,      pad_token_id=1					,      bos_token_id=0					,      )
           return config, input_ids, batch_size







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase      ,				lowerCamelCase      ,				lowerCamelCase             =		self._get_config_and_data()
           lowerCamelCase             =		FlaxBlenderbotSmallForConditionalGeneration(__a     )
           lowerCamelCase             =		lm_model(input_ids=__a     )
           lowerCamelCase             =		(batch_size, input_ids.shape[1], config.vocab_size)
           self.assertEqual(outputs["logits"].shape					,      __a     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		BlenderbotSmallConfig(
               vocab_size=self.vocab_size					,      d_model=14					,      encoder_layers=2					,      decoder_layers=2					,      encoder_attention_heads=2					,      decoder_attention_heads=2					,      encoder_ffn_dim=8					,      decoder_ffn_dim=8					,      max_position_embeddings=48					,      )
           lowerCamelCase             =		FlaxBlenderbotSmallForConditionalGeneration(__a     )
           lowerCamelCase             =		np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]					,      dtype=np.intaa     )
           lowerCamelCase             =		np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]					,      dtype=np.intaa     )
           lowerCamelCase             =		lm_model(input_ids=__a					,      decoder_input_ids=__a     )
           lowerCamelCase             =		(*summary.shape, config.vocab_size)
           self.assertEqual(outputs["logits"].shape					,      __a     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]					,      dtype=np.intaa     )
           lowerCamelCase             =		shift_tokens_right(__a					,      1					,      2     )
           lowerCamelCase             =		np.equal(__a					,      1     ).astype(np.floataa     ).sum()
           lowerCamelCase             =		np.equal(__a					,      1     ).astype(np.floataa     ).sum()
           self.assertEqual(shifted.shape					,      input_ids.shape     )
           self.assertEqual(__a					,      n_pad_before - 1     )
           self.assertTrue(np.equal(shifted[:, 0]					,      2     ).all()     )





@require_flax
class  lowerCamelCase__	(    UpperCAmelCase_							,     unittest.TestCase							,     UpperCAmelCase_):
     """simple docstring"""



     _A      =      True
     _A      =      (
         (
             FlaxBlenderbotSmallModel,
             FlaxBlenderbotSmallForConditionalGeneration,
         )
         if is_flax_available()
         else ()
     )
     _A      =      (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		FlaxBlenderbotSmallModelTester(self     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase      ,				lowerCamelCase             =		self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward(__a					,      __a					,      __a     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase      ,				lowerCamelCase             =		self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward_with_attn_mask(__a					,      __a					,      __a     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase      ,				lowerCamelCase             =		self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__     ):
                       lowerCamelCase             =		self._prepare_for_class(__a					,      __a     )
                       lowerCamelCase             =		model_class(__a     )

                       @jax.jit
                       def encode_jitted(__a					,      __a=None					,      **__a     ):
                             return model.encode(input_ids=__a					,      attention_mask=__a     )

                       with self.subTest("JIT Enabled"     ):
                             lowerCamelCase             =		encode_jitted(**__a     ).to_tuple()

                       with self.subTest("JIT Disabled"     ):
                             with jax.disable_jit():
                                   lowerCamelCase             =		encode_jitted(**__a     ).to_tuple()

                       self.assertEqual(len(__a     )					,      len(__a     )     )
                       for jitted_output, output in zip(__a					,      __a     ):
                             self.assertEqual(jitted_output.shape					,      output.shape     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase      ,				lowerCamelCase             =		self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__     ):
                       lowerCamelCase             =		model_class(__a     )
                       lowerCamelCase             =		model.encode(inputs_dict["input_ids"]					,      inputs_dict["attention_mask"]     )

                       lowerCamelCase             =		{
                           "decoder_input_ids": inputs_dict["decoder_input_ids"],
                           "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
                           "encoder_outputs": encoder_outputs,
                       }

                       @jax.jit
                       def decode_jitted(__a					,      __a					,      __a     ):
                             return model.decode(
                                 decoder_input_ids=__a					,      decoder_attention_mask=__a					,      encoder_outputs=__a					,      )

                       with self.subTest("JIT Enabled"     ):
                             lowerCamelCase             =		decode_jitted(**__a     ).to_tuple()

                       with self.subTest("JIT Disabled"     ):
                             with jax.disable_jit():
                                   lowerCamelCase             =		decode_jitted(**__a     ).to_tuple()

                       self.assertEqual(len(__a     )					,      len(__a     )     )
                       for jitted_output, output in zip(__a					,      __a     ):
                             self.assertEqual(jitted_output.shape					,      output.shape     )







     @slow
     def     _a   (self     ):






           '''simple docstring'''



           for model_class_name in self.all_model_classes:
                 lowerCamelCase             =		model_class_name.from_pretrained("facebook/blenderbot_small-90M"     )
                 # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
                 lowerCamelCase             =		np.ones((1, 1)     ) * model.config.eos_token_id
                 lowerCamelCase             =		model(__a     )
                 self.assertIsNotNone(__a     )




def      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



      if n == 1 or not isinstance(UpperCAmelCase__ , UpperCAmelCase__			):
            return 0
      elif n == 2:
            return 1
      else:
            lowerCamelCase             =		[0, 1]
            for i in range(2 , n + 1			):
                  sequence.append(sequence[i - 1] + sequence[i - 2]			)

            return sequence[n]

def      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		0
      lowerCamelCase             =		2

      while digits < n:
            index += 1
            lowerCamelCase             =		len(str(fibonacci(UpperCAmelCase__			)			)			)

      return index

def      __lowercase(						UpperCAmelCase__ = 1000			):


      """simple docstring"""



      return fibonacci_digits_index(UpperCAmelCase__			)


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