"""simple docstring"""


def 					UpperCAmelCase    (		UpperCAmelCase					)				->      int:
     snake_case_					       =					[[0 for _ in range(UpperCAmelCase					)] for _ in range(m + 1					)]
     for i in range(m + 1					):
          snake_case_					       =					1

     for n in range(m + 1					):
          for k in range(1						,					UpperCAmelCase					):
               memo[n][k] += memo[n][k - 1]
               if n - k > 0:
                    memo[n][k] += memo[n - k - 1][k]

     return memo[m][m - 1]


if __name__ == "__main__":
    import sys

    if len(sys.argv) == 1:
        try:
            __UpperCamelCase									=			int(input('''Enter a number: ''').strip())
            print(partition(n))
        except ValueError:
            print('''Please enter a number.''')
    else:
        try:
            __UpperCamelCase									=			int(sys.argv[1])
            print(partition(n))
        except ValueError:
            print('''Please pass a number.''')



"""simple docstring"""





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		       =							'platform'

    import jax
    import jax.numpy as jnp

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







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase=None,		__lowerCamelCase=None,		__lowerCamelCase=None,		__lowerCamelCase=None,		__lowerCamelCase=None,		__lowerCamelCase=None,		):
   if attention_mask is None:
      UpperCAmelCase_					:					Union[str, Any]			     =    np.where(input_ids != config.pad_token_id,		1,		0  )
   if decoder_attention_mask is None:
      UpperCAmelCase_					:					Optional[int]			     =    np.where(decoder_input_ids != config.pad_token_id,		1,		0  )
   if head_mask is None:
      UpperCAmelCase_					:					int			     =    np.ones((config.encoder_layers, config.encoder_attention_heads)  )
   if decoder_head_mask is None:
      UpperCAmelCase_					:					Union[str, Any]			     =    np.ones((config.decoder_layers, config.decoder_attention_heads)  )
   if cross_attn_head_mask is None:
      UpperCAmelCase_					:					List[Any]			     =    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 A_	:




  '''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				,   ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    parent
     UpperCAmelCase_					:					Tuple			     =    batch_size
     UpperCAmelCase_					:					str			     =    seq_length
     UpperCAmelCase_					:					Dict			     =    is_training
     UpperCAmelCase_					:					List[Any]			     =    use_labels
     UpperCAmelCase_					:					Optional[int]			     =    vocab_size
     UpperCAmelCase_					:					int			     =    hidden_size
     UpperCAmelCase_					:					Optional[Any]			     =    num_hidden_layers
     UpperCAmelCase_					:					Dict			     =    num_attention_heads
     UpperCAmelCase_					:					List[str]			     =    intermediate_size
     UpperCAmelCase_					:					Optional[int]			     =    hidden_act
     UpperCAmelCase_					:					str			     =    hidden_dropout_prob
     UpperCAmelCase_					:					int			     =    attention_probs_dropout_prob
     UpperCAmelCase_					:					Optional[Any]			     =    max_position_embeddings
     UpperCAmelCase_					:					str			     =    eos_token_id
     UpperCAmelCase_					:					str			     =    pad_token_id
     UpperCAmelCase_					:					str			     =    bos_token_id
     UpperCAmelCase_					:					List[Any]			     =    initializer_range







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[Any]			     =    np.clip(ids_tensor([self.batch_size, self.seq_length - 1]				,   self.vocab_size  )				,   3				,   self.vocab_size  )
     UpperCAmelCase_					:					Any			     =    np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)				,   dtype=np.intaa  ))				,   -1  )

     UpperCAmelCase_					:					str			     =    shift_tokens_right(lowercase_				,   1				,   2  )

     UpperCAmelCase_					:					str			     =    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_				,   )
     UpperCAmelCase_					:					Optional[int]			     =    prepare_blenderbot_inputs_dict(lowercase_				,   lowercase_				,   lowercase_  )
     return config, inputs_dict







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_     ,   UpperCAmelCase_					:					Optional[int]			     =    self.prepare_config_and_inputs()
     return config, inputs_dict







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    20
     UpperCAmelCase_					:					int			     =    model_class_name(lowercase_  )

     UpperCAmelCase_					:					Optional[int]			     =    model.encode(inputs_dict["input_ids"]  )

     UpperCAmelCase_     ,   UpperCAmelCase_					:					Any			     =    (
         inputs_dict["decoder_input_ids"],
         inputs_dict["decoder_attention_mask"],
     )

     UpperCAmelCase_					:					Any			     =    model.init_cache(decoder_input_ids.shape[0]				,   lowercase_				,   lowercase_  )
     UpperCAmelCase_					:					Tuple			     =    jnp.ones((decoder_input_ids.shape[0], max_decoder_length)				,   dtype="i4"  )

     UpperCAmelCase_					:					Union[str, Any]			     =    jnp.broadcast_to(
         jnp.arange(decoder_input_ids.shape[-1] - 1  )[None, :]				,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,   )
     UpperCAmelCase_					:					int			     =    model.decode(
         decoder_input_ids[:, :-1]				,   lowercase_				,   decoder_attention_mask=lowercase_				,   past_key_values=lowercase_				,   decoder_position_ids=lowercase_				,   )

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

     UpperCAmelCase_					:					Optional[Any]			     =    model.decode(lowercase_				,   lowercase_  )

     UpperCAmelCase_					:					Tuple			     =    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_  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    20
     UpperCAmelCase_					:					Any			     =    model_class_name(lowercase_  )

     UpperCAmelCase_					:					Tuple			     =    model.encode(inputs_dict["input_ids"]  )

     UpperCAmelCase_     ,   UpperCAmelCase_					:					List[Any]			     =    (
         inputs_dict["decoder_input_ids"],
         inputs_dict["decoder_attention_mask"],
     )

     UpperCAmelCase_					:					Optional[Any]			     =    jnp.concatenate(
         [
             decoder_attention_mask,
             jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])  ),
         ]				,   axis=-1				,   )

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

     UpperCAmelCase_					:					List[str]			     =    model.decode(
         decoder_input_ids[:, :-1]				,   lowercase_				,   decoder_attention_mask=lowercase_				,   past_key_values=lowercase_				,   decoder_position_ids=lowercase_				,   )
     UpperCAmelCase_					:					Any			     =    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,   dtype="i4"  )
     UpperCAmelCase_					:					Dict			     =    model.decode(
         decoder_input_ids[:, -1:]				,   lowercase_				,   past_key_values=outputs_cache.past_key_values				,   decoder_attention_mask=lowercase_				,   decoder_position_ids=lowercase_				,   )

     UpperCAmelCase_					:					Dict			     =    model.decode(lowercase_				,   lowercase_				,   decoder_attention_mask=lowercase_  )

     UpperCAmelCase_					:					Optional[Any]			     =    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 A_	(unittest.TestCase      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Tuple       =		99







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[Any]			     =    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				,   )

     UpperCAmelCase_					:					Any			     =    input_ids.shape[0]
     UpperCAmelCase_					:					Dict			     =    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  ):

     """simple docstring"""
     UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_					:					Tuple			     =    self._get_config_and_data()
     UpperCAmelCase_					:					List[str]			     =    FlaxBlenderbotSmallForConditionalGeneration(lowercase_  )
     UpperCAmelCase_					:					Optional[int]			     =    lm_model(input_ids=lowercase_  )
     UpperCAmelCase_					:					Optional[int]			     =    (batch_size, input_ids.shape[1], config.vocab_size)
     self.assertEqual(outputs["logits"].shape				,   lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    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				,   )
     UpperCAmelCase_					:					Optional[int]			     =    FlaxBlenderbotSmallForConditionalGeneration(lowercase_  )
     UpperCAmelCase_					:					str			     =    np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]				,   dtype=np.intaa  )
     UpperCAmelCase_					:					str			     =    np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]				,   dtype=np.intaa  )
     UpperCAmelCase_					:					Tuple			     =    lm_model(input_ids=lowercase_				,   decoder_input_ids=lowercase_  )
     UpperCAmelCase_					:					Tuple			     =    (*summary.shape, config.vocab_size)
     self.assertEqual(outputs["logits"].shape				,   lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[Any]			     =    np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]				,   dtype=np.intaa  )
     UpperCAmelCase_					:					Dict			     =    shift_tokens_right(lowercase_				,   1				,   2  )
     UpperCAmelCase_					:					Tuple			     =    np.equal(lowercase_				,   1  ).astype(np.floataa  ).sum()
     UpperCAmelCase_					:					Optional[Any]			     =    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 A_	(lowercase__				,unittest.TestCase				,lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						str       =		True
  SCREAMING_SNAKE_CASE__			:						Union[str, Any]       =		(
      (
          FlaxBlenderbotSmallModel,
          FlaxBlenderbotSmallForConditionalGeneration,
      )
      if is_flax_available()
      else ()
  )
  SCREAMING_SNAKE_CASE__			:						List[Any]       =		(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Dict			     =    FlaxBlenderbotSmallModelTester(self  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_     ,   UpperCAmelCase_					:					Tuple			     =    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  ):

     """simple docstring"""
     UpperCAmelCase_     ,   UpperCAmelCase_					:					Optional[Any]			     =    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  ):

     """simple docstring"""
     UpperCAmelCase_     ,   UpperCAmelCase_					:					Any			     =    self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__  ):
           UpperCAmelCase_					:					List[Any]			     =    self._prepare_for_class(lowercase_				,   lowercase_  )
           UpperCAmelCase_					:					Dict			     =    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"  ):
              UpperCAmelCase_					:					List[Any]			     =    encode_jitted(**lowercase_  ).to_tuple()

           with self.subTest("JIT Disabled"  ):
              with jax.disable_jit():
                 UpperCAmelCase_					:					Optional[Any]			     =    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  ):

     """simple docstring"""
     UpperCAmelCase_     ,   UpperCAmelCase_					:					Union[str, Any]			     =    self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__  ):
           UpperCAmelCase_					:					Optional[int]			     =    model_class(lowercase_  )
           UpperCAmelCase_					:					Tuple			     =    model.encode(inputs_dict["input_ids"]				,   inputs_dict["attention_mask"]  )

           UpperCAmelCase_					:					int			     =    {
               "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"  ):
              UpperCAmelCase_					:					str			     =    decode_jitted(**lowercase_  ).to_tuple()

           with self.subTest("JIT Disabled"  ):
              with jax.disable_jit():
                 UpperCAmelCase_					:					List[Any]			     =    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  ):

     """simple docstring"""
     for model_class_name in self.all_model_classes:
        UpperCAmelCase_					:					Optional[Any]			     =    model_class_name.from_pretrained("facebook/blenderbot_small-90M"  )
        # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
        UpperCAmelCase_					:					List[str]			     =    np.ones((1, 1)  ) * model.config.eos_token_id
        UpperCAmelCase_					:					Optional[int]			     =    model(lowercase_  )
        self.assertIsNotNone(lowercase_  )


