

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
       lowerCamelCase           =					'''platform'''

       import jax
       import jax.numpy as jnp

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




def 			UpperCAmelCase__				(			_A      : Optional[int]	,      _A      : Optional[int]	,      _A      : Dict=None	,      _A      : Union[str, Any]=None	,      _A      : List[str]=None	,      _A      : int=None	,      _A      : Dict=None	,      _A      : Optional[int]=None	,      ):





  '''simple docstring'''







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

   '''simple docstring'''




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






     """simple docstring"""






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



   def  _UpperCAmelCase (				self       )							->    Dict:






     """simple docstring"""






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

     a__  =shift_tokens_right(lowercase_,    1,    2       )

     a__  =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=lowercase_,    )
     a__  =prepare_blenderbot_inputs_dict(lowercase_,    lowercase_,    lowercase_       )
     return config, inputs_dict



   def  _UpperCAmelCase (				self       )							->    List[str]:






     """simple docstring"""






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



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_,    lowercase_       )							->    Tuple:






     """simple docstring"""






     a__  =20
     a__  =model_class_name(lowercase_       )

     a__  =model.encode(inputs_dict['''input_ids''']       )

     a__,      a__  =(
         inputs_dict['''decoder_input_ids'''],
         inputs_dict['''decoder_attention_mask'''],
     )

     a__  =model.init_cache(decoder_input_ids.shape[0],    lowercase_,    lowercase_       )
     a__  =jnp.ones((decoder_input_ids.shape[0], max_decoder_length),    dtype='''i4'''       )

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

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

     a__  =model.decode(lowercase_,    lowercase_       )

     a__  =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  _UpperCAmelCase (				self,    lowercase_,    lowercase_,    lowercase_       )							->    List[str]:






     """simple docstring"""






     a__  =20
     a__  =model_class_name(lowercase_       )

     a__  =model.encode(inputs_dict['''input_ids''']       )

     a__,      a__  =(
         inputs_dict['''decoder_input_ids'''],
         inputs_dict['''decoder_attention_mask'''],
     )

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

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

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

     a__  =model.decode(lowercase_,    lowercase_,    decoder_attention_mask=lowercase_       )

     a__  =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 						__magic_name__			(							unittest.TestCase							):

   '''simple docstring'''




   lowerCamelCase__ :	Optional[Any]    =   99



   def  _UpperCAmelCase (				self       )							->    Optional[Any]:






     """simple docstring"""






     a__  =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,    )

     a__  =input_ids.shape[0]
     a__  =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  _UpperCAmelCase (				self       )							->    Dict:






     """simple docstring"""






     a__,      a__,      a__  =self._get_config_and_data()
     a__  =FlaxBlenderbotSmallForConditionalGeneration(lowercase_       )
     a__  =lm_model(input_ids=lowercase_       )
     a__  =(batch_size, input_ids.shape[1], config.vocab_size)
     self.assertEqual(outputs['''logits'''].shape,    lowercase_       )



   def  _UpperCAmelCase (				self       )							->    Dict:






     """simple docstring"""






     a__  =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,    )
     a__  =FlaxBlenderbotSmallForConditionalGeneration(lowercase_       )
     a__  =np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]],    dtype=np.intaa       )
     a__  =np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]],    dtype=np.intaa       )
     a__  =lm_model(input_ids=lowercase_,    decoder_input_ids=lowercase_       )
     a__  =(*summary.shape, config.vocab_size)
     self.assertEqual(outputs['''logits'''].shape,    lowercase_       )



   def  _UpperCAmelCase (				self       )							->    str:






     """simple docstring"""






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







@require_flax
class 						__magic_name__			(							lowerCamelCase__		,       unittest.TestCase		,       lowerCamelCase__							):

   '''simple docstring'''




   lowerCamelCase__ :	Optional[Any]    =   True
   lowerCamelCase__ :	List[Any]    =   (
       (
           FlaxBlenderbotSmallModel,
           FlaxBlenderbotSmallForConditionalGeneration,
       )
       if is_flax_available()
       else ()
   )
   lowerCamelCase__ :	Optional[Any]    =   (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()



   def  _UpperCAmelCase (				self       )							->    Optional[int]:






     """simple docstring"""






     a__  =FlaxBlenderbotSmallModelTester(self       )



   def  _UpperCAmelCase (				self       )							->    Any:






     """simple docstring"""






     a__,      a__  =self.model_tester.prepare_config_and_inputs()
     for model_class in self.all_model_classes:
       self.model_tester.check_use_cache_forward(lowercase_,    lowercase_,    lowercase_       )



   def  _UpperCAmelCase (				self       )							->    Optional[int]:






     """simple docstring"""






     a__,      a__  =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(lowercase_,    lowercase_,    lowercase_       )



   def  _UpperCAmelCase (				self       )							->    Union[str, Any]:






     """simple docstring"""






     a__,      a__  =self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__       ):
         a__  =self._prepare_for_class(lowercase_,    lowercase_       )
         a__  =model_class(lowercase_       )

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

         with self.subTest('''JIT Enabled'''       ):
           a__  =encode_jitted(**lowercase_       ).to_tuple()

         with self.subTest('''JIT Disabled'''       ):
           with jax.disable_jit():
             a__  =encode_jitted(**lowercase_       ).to_tuple()

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



   def  _UpperCAmelCase (				self       )							->    Optional[Any]:






     """simple docstring"""






     a__,      a__  =self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__       ):
         a__  =model_class(lowercase_       )
         a__  =model.encode(inputs_dict['''input_ids'''],    inputs_dict['''attention_mask''']       )

         a__  ={
             '''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(lowercase_,    lowercase_,    lowercase_       ):
           return model.decode(
               decoder_input_ids=lowercase_,    decoder_attention_mask=lowercase_,    encoder_outputs=lowercase_,    )

         with self.subTest('''JIT Enabled'''       ):
           a__  =decode_jitted(**lowercase_       ).to_tuple()

         with self.subTest('''JIT Disabled'''       ):
           with jax.disable_jit():
             a__  =decode_jitted(**lowercase_       ).to_tuple()

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



   @slow
   def  _UpperCAmelCase (				self       )							->    Union[str, Any]:






     """simple docstring"""






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



from math import factorial




def 			UpperCAmelCase__				(			_A      : int = 1_00							):





  '''simple docstring'''







  return sum(int(_A							) for x in str(factorial(_A							)							)							)


if __name__ == "__main__":
       print(solution(int(input('''Enter the Number: ''').strip())))
