






"""simple docstring"""






import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
  import jax.numpy as jnp

if is_tf_available():
  import tensorflow as tf

if is_torch_available():
  import torch




class 							lowerCAmelCase__				(			unittest.TestCase ):






      '''simple docstring'''

      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  str    						=		{
              '''task_specific_params''': {
                  '''summarization''': {'''length_penalty''': 1.0, '''max_length''': 128, '''min_length''': 12, '''num_beams''': 4},
                  '''summarization_cnn''': {'''length_penalty''': 2.0, '''max_length''': 142, '''min_length''': 56, '''num_beams''': 4},
                  '''summarization_xsum''': {'''length_penalty''': 1.0, '''max_length''': 62, '''min_length''': 11, '''num_beams''': 6},
              }
          }
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		{
              '''task_specific_params.summarization.length_penalty''': 1.0,
              '''task_specific_params.summarization.max_length''': 128,
              '''task_specific_params.summarization.min_length''': 12,
              '''task_specific_params.summarization.num_beams''': 4,
              '''task_specific_params.summarization_cnn.length_penalty''': 2.0,
              '''task_specific_params.summarization_cnn.max_length''': 142,
              '''task_specific_params.summarization_cnn.min_length''': 56,
              '''task_specific_params.summarization_cnn.num_beams''': 4,
              '''task_specific_params.summarization_xsum.length_penalty''': 1.0,
              '''task_specific_params.summarization_xsum.max_length''': 62,
              '''task_specific_params.summarization_xsum.min_length''': 11,
              '''task_specific_params.summarization_xsum.num_beams''': 6,
          }

          self.assertEqual(flatten_dict(lowercase_)						,	lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		np.random.randn(3						,	4)
          self.assertTrue(np.allclose(transpose(lowercase_)						,	x.transpose()))

          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		np.random.randn(3						,	4						,	5)
          self.assertTrue(np.allclose(transpose(lowercase_						,	axes=(1, 2, 0))						,	x.transpose((1, 2, 0))))
      @require_torch
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  int    						=		np.random.randn(3						,	4)
          SCREAMING_SNAKE_CASE_      :  Dict    						=		torch.tensor(lowercase_)
          self.assertTrue(np.allclose(transpose(lowercase_)						,	transpose(lowercase_).numpy()))

          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		np.random.randn(3						,	4						,	5)
          SCREAMING_SNAKE_CASE_      :  str    						=		torch.tensor(lowercase_)
          self.assertTrue(np.allclose(transpose(lowercase_						,	axes=(1, 2, 0))						,	transpose(lowercase_						,	axes=(1, 2, 0)).numpy()))
      @require_tf
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  int    						=		np.random.randn(3						,	4)
          SCREAMING_SNAKE_CASE_      :  int    						=		tf.constant(lowercase_)
          self.assertTrue(np.allclose(transpose(lowercase_)						,	transpose(lowercase_).numpy()))

          SCREAMING_SNAKE_CASE_      :  int    						=		np.random.randn(3						,	4						,	5)
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		tf.constant(lowercase_)
          self.assertTrue(np.allclose(transpose(lowercase_						,	axes=(1, 2, 0))						,	transpose(lowercase_						,	axes=(1, 2, 0)).numpy()))
      @require_flax
      def _SCREAMING_SNAKE_CASE (						self     :  List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		np.random.randn(3						,	4)
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		jnp.array(lowercase_)
          self.assertTrue(np.allclose(transpose(lowercase_)						,	np.asarray(transpose(lowercase_))))

          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		np.random.randn(3						,	4						,	5)
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		jnp.array(lowercase_)
          self.assertTrue(np.allclose(transpose(lowercase_						,	axes=(1, 2, 0))						,	np.asarray(transpose(lowercase_						,	axes=(1, 2, 0)))))
      def _SCREAMING_SNAKE_CASE (						self     :  Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		np.random.randn(3						,	4)
          self.assertTrue(np.allclose(reshape(lowercase_						,	(4, 3))						,	np.reshape(lowercase_						,	(4, 3))))

          SCREAMING_SNAKE_CASE_      :  Any    						=		np.random.randn(3						,	4						,	5)
          self.assertTrue(np.allclose(reshape(lowercase_						,	(12, 5))						,	np.reshape(lowercase_						,	(12, 5))))
      @require_torch
      def _SCREAMING_SNAKE_CASE (						self     :  Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		np.random.randn(3						,	4)
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		torch.tensor(lowercase_)
          self.assertTrue(np.allclose(reshape(lowercase_						,	(4, 3))						,	reshape(lowercase_						,	(4, 3)).numpy()))

          SCREAMING_SNAKE_CASE_      :  Dict    						=		np.random.randn(3						,	4						,	5)
          SCREAMING_SNAKE_CASE_      :  Dict    						=		torch.tensor(lowercase_)
          self.assertTrue(np.allclose(reshape(lowercase_						,	(12, 5))						,	reshape(lowercase_						,	(12, 5)).numpy()))
      @require_tf
      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  int    						=		np.random.randn(3						,	4)
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		tf.constant(lowercase_)
          self.assertTrue(np.allclose(reshape(lowercase_						,	(4, 3))						,	reshape(lowercase_						,	(4, 3)).numpy()))

          SCREAMING_SNAKE_CASE_      :  int    						=		np.random.randn(3						,	4						,	5)
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		tf.constant(lowercase_)
          self.assertTrue(np.allclose(reshape(lowercase_						,	(12, 5))						,	reshape(lowercase_						,	(12, 5)).numpy()))
      @require_flax
      def _SCREAMING_SNAKE_CASE (						self     :  int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Any    						=		np.random.randn(3						,	4)
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		jnp.array(lowercase_)
          self.assertTrue(np.allclose(reshape(lowercase_						,	(4, 3))						,	np.asarray(reshape(lowercase_						,	(4, 3)))))

          SCREAMING_SNAKE_CASE_      :  List[str]    						=		np.random.randn(3						,	4						,	5)
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		jnp.array(lowercase_)
          self.assertTrue(np.allclose(reshape(lowercase_						,	(12, 5))						,	np.asarray(reshape(lowercase_						,	(12, 5)))))
      def _SCREAMING_SNAKE_CASE (						self     :  List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		np.random.randn(1						,	3						,	4)
          self.assertTrue(np.allclose(squeeze(lowercase_)						,	np.squeeze(lowercase_)))

          SCREAMING_SNAKE_CASE_      :  Any    						=		np.random.randn(1						,	4						,	1						,	5)
          self.assertTrue(np.allclose(squeeze(lowercase_						,	axis=2)						,	np.squeeze(lowercase_						,	axis=2)))
      @require_torch
      def _SCREAMING_SNAKE_CASE (						self     :  Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		np.random.randn(1						,	3						,	4)
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		torch.tensor(lowercase_)
          self.assertTrue(np.allclose(squeeze(lowercase_)						,	squeeze(lowercase_).numpy()))

          SCREAMING_SNAKE_CASE_      :  int    						=		np.random.randn(1						,	4						,	1						,	5)
          SCREAMING_SNAKE_CASE_      :  Any    						=		torch.tensor(lowercase_)
          self.assertTrue(np.allclose(squeeze(lowercase_						,	axis=2)						,	squeeze(lowercase_						,	axis=2).numpy()))
      @require_tf
      def _SCREAMING_SNAKE_CASE (						self     :  Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		np.random.randn(1						,	3						,	4)
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		tf.constant(lowercase_)
          self.assertTrue(np.allclose(squeeze(lowercase_)						,	squeeze(lowercase_).numpy()))

          SCREAMING_SNAKE_CASE_      :  Any    						=		np.random.randn(1						,	4						,	1						,	5)
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		tf.constant(lowercase_)
          self.assertTrue(np.allclose(squeeze(lowercase_						,	axis=2)						,	squeeze(lowercase_						,	axis=2).numpy()))
      @require_flax
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		np.random.randn(1						,	3						,	4)
          SCREAMING_SNAKE_CASE_      :  int    						=		jnp.array(lowercase_)
          self.assertTrue(np.allclose(squeeze(lowercase_)						,	np.asarray(squeeze(lowercase_))))

          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		np.random.randn(1						,	4						,	1						,	5)
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		jnp.array(lowercase_)
          self.assertTrue(np.allclose(squeeze(lowercase_						,	axis=2)						,	np.asarray(squeeze(lowercase_						,	axis=2))))
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  int    						=		np.random.randn(3						,	4)
          self.assertTrue(np.allclose(expand_dims(lowercase_						,	axis=1)						,	np.expand_dims(lowercase_						,	axis=1)))
      @require_torch
      def _SCREAMING_SNAKE_CASE (						self     :  Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Dict    						=		np.random.randn(3						,	4)
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		torch.tensor(lowercase_)
          self.assertTrue(np.allclose(expand_dims(lowercase_						,	axis=1)						,	expand_dims(lowercase_						,	axis=1).numpy()))
      @require_tf
      def _SCREAMING_SNAKE_CASE (						self     :  List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		np.random.randn(3						,	4)
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		tf.constant(lowercase_)
          self.assertTrue(np.allclose(expand_dims(lowercase_						,	axis=1)						,	expand_dims(lowercase_						,	axis=1).numpy()))



      @require_flax
      def _SCREAMING_SNAKE_CASE (						self     :  List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Dict    						=		np.random.randn(3						,	4)
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		jnp.array(lowercase_)
          self.assertTrue(np.allclose(expand_dims(lowercase_						,	axis=1)						,	np.asarray(expand_dims(lowercase_						,	axis=1))))










"""simple docstring"""






def 					_A			(__a = 50							) ->       int:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Tuple    						=		[[0] * 3 for _ in range(length + 1							)]

    for row_length in range(length + 1							):
        for tile_length in range(2    ,				5							):
            for tile_start in range(row_length - tile_length + 1							):
                different_colour_ways_number[row_length][tile_length - 2] += (
                    different_colour_ways_number[row_length - tile_start - tile_length][
                        tile_length - 2
                    ]
                    + 1
                )

    return sum(different_colour_ways_number[length]							)


if __name__ == "__main__":
  print(f'''{solution() = }''')


