




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       a__   ( unittest.TestCase				):






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	{
											    "task_specific_params": {
											        "summarization": {"length_penalty": 1.0, "max_length": 1_2_8, "min_length": 1_2, "num_beams": 4},
											        "summarization_cnn": {"length_penalty": 2.0, "max_length": 1_4_2, "min_length": 5_6, "num_beams": 4},
											        "summarization_xsum": {"length_penalty": 1.0, "max_length": 6_2, "min_length": 1_1, "num_beams": 6},
											    }
											}
											__lowerCAmelCase           =	{
											    "task_specific_params.summarization.length_penalty": 1.0,
											    "task_specific_params.summarization.max_length": 1_2_8,
											    "task_specific_params.summarization.min_length": 1_2,
											    "task_specific_params.summarization.num_beams": 4,
											    "task_specific_params.summarization_cnn.length_penalty": 2.0,
											    "task_specific_params.summarization_cnn.max_length": 1_4_2,
											    "task_specific_params.summarization_cnn.min_length": 5_6,
											    "task_specific_params.summarization_cnn.num_beams": 4,
											    "task_specific_params.summarization_xsum.length_penalty": 1.0,
											    "task_specific_params.summarization_xsum.max_length": 6_2,
											    "task_specific_params.summarization_xsum.min_length": 1_1,
											    "task_specific_params.summarization_xsum.num_beams": 6,
											}

											self.assertEqual(flatten_dict(_A      )		,      _A      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	np.random.randn(3		,      4      )
											self.assertTrue(np.allclose(transpose(_A      )		,      x.transpose()      )      )

											__lowerCAmelCase           =	np.random.randn(3		,      4		,      5      )
											self.assertTrue(np.allclose(transpose(_A		,      axes=(1, 2, 0)      )		,      x.transpose((1, 2, 0)      )      )      )






						@require_torch
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	np.random.randn(3		,      4      )
											__lowerCAmelCase           =	torch.tensor(_A      )
											self.assertTrue(np.allclose(transpose(_A      )		,      transpose(_A      ).numpy()      )      )

											__lowerCAmelCase           =	np.random.randn(3		,      4		,      5      )
											__lowerCAmelCase           =	torch.tensor(_A      )
											self.assertTrue(np.allclose(transpose(_A		,      axes=(1, 2, 0)      )		,      transpose(_A		,      axes=(1, 2, 0)      ).numpy()      )      )






						@require_tf
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	np.random.randn(3		,      4      )
											__lowerCAmelCase           =	tf.constant(_A      )
											self.assertTrue(np.allclose(transpose(_A      )		,      transpose(_A      ).numpy()      )      )

											__lowerCAmelCase           =	np.random.randn(3		,      4		,      5      )
											__lowerCAmelCase           =	tf.constant(_A      )
											self.assertTrue(np.allclose(transpose(_A		,      axes=(1, 2, 0)      )		,      transpose(_A		,      axes=(1, 2, 0)      ).numpy()      )      )






						@require_flax
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	np.random.randn(3		,      4      )
											__lowerCAmelCase           =	jnp.array(_A      )
											self.assertTrue(np.allclose(transpose(_A      )		,      np.asarray(transpose(_A      )      )      )      )

											__lowerCAmelCase           =	np.random.randn(3		,      4		,      5      )
											__lowerCAmelCase           =	jnp.array(_A      )
											self.assertTrue(np.allclose(transpose(_A		,      axes=(1, 2, 0)      )		,      np.asarray(transpose(_A		,      axes=(1, 2, 0)      )      )      )      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	np.random.randn(3		,      4      )
											self.assertTrue(np.allclose(reshape(_A		,      (4, 3)      )		,      np.reshape(_A		,      (4, 3)      )      )      )

											__lowerCAmelCase           =	np.random.randn(3		,      4		,      5      )
											self.assertTrue(np.allclose(reshape(_A		,      (1_2, 5)      )		,      np.reshape(_A		,      (1_2, 5)      )      )      )






						@require_torch
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	np.random.randn(3		,      4      )
											__lowerCAmelCase           =	torch.tensor(_A      )
											self.assertTrue(np.allclose(reshape(_A		,      (4, 3)      )		,      reshape(_A		,      (4, 3)      ).numpy()      )      )

											__lowerCAmelCase           =	np.random.randn(3		,      4		,      5      )
											__lowerCAmelCase           =	torch.tensor(_A      )
											self.assertTrue(np.allclose(reshape(_A		,      (1_2, 5)      )		,      reshape(_A		,      (1_2, 5)      ).numpy()      )      )






						@require_tf
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	np.random.randn(3		,      4      )
											__lowerCAmelCase           =	tf.constant(_A      )
											self.assertTrue(np.allclose(reshape(_A		,      (4, 3)      )		,      reshape(_A		,      (4, 3)      ).numpy()      )      )

											__lowerCAmelCase           =	np.random.randn(3		,      4		,      5      )
											__lowerCAmelCase           =	tf.constant(_A      )
											self.assertTrue(np.allclose(reshape(_A		,      (1_2, 5)      )		,      reshape(_A		,      (1_2, 5)      ).numpy()      )      )






						@require_flax
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	np.random.randn(3		,      4      )
											__lowerCAmelCase           =	jnp.array(_A      )
											self.assertTrue(np.allclose(reshape(_A		,      (4, 3)      )		,      np.asarray(reshape(_A		,      (4, 3)      )      )      )      )

											__lowerCAmelCase           =	np.random.randn(3		,      4		,      5      )
											__lowerCAmelCase           =	jnp.array(_A      )
											self.assertTrue(np.allclose(reshape(_A		,      (1_2, 5)      )		,      np.asarray(reshape(_A		,      (1_2, 5)      )      )      )      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	np.random.randn(1		,      3		,      4      )
											self.assertTrue(np.allclose(squeeze(_A      )		,      np.squeeze(_A      )      )      )

											__lowerCAmelCase           =	np.random.randn(1		,      4		,      1		,      5      )
											self.assertTrue(np.allclose(squeeze(_A		,      axis=2      )		,      np.squeeze(_A		,      axis=2      )      )      )






						@require_torch
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	np.random.randn(1		,      3		,      4      )
											__lowerCAmelCase           =	torch.tensor(_A      )
											self.assertTrue(np.allclose(squeeze(_A      )		,      squeeze(_A      ).numpy()      )      )

											__lowerCAmelCase           =	np.random.randn(1		,      4		,      1		,      5      )
											__lowerCAmelCase           =	torch.tensor(_A      )
											self.assertTrue(np.allclose(squeeze(_A		,      axis=2      )		,      squeeze(_A		,      axis=2      ).numpy()      )      )






						@require_tf
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	np.random.randn(1		,      3		,      4      )
											__lowerCAmelCase           =	tf.constant(_A      )
											self.assertTrue(np.allclose(squeeze(_A      )		,      squeeze(_A      ).numpy()      )      )

											__lowerCAmelCase           =	np.random.randn(1		,      4		,      1		,      5      )
											__lowerCAmelCase           =	tf.constant(_A      )
											self.assertTrue(np.allclose(squeeze(_A		,      axis=2      )		,      squeeze(_A		,      axis=2      ).numpy()      )      )






						@require_flax
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	np.random.randn(1		,      3		,      4      )
											__lowerCAmelCase           =	jnp.array(_A      )
											self.assertTrue(np.allclose(squeeze(_A      )		,      np.asarray(squeeze(_A      )      )      )      )

											__lowerCAmelCase           =	np.random.randn(1		,      4		,      1		,      5      )
											__lowerCAmelCase           =	jnp.array(_A      )
											self.assertTrue(np.allclose(squeeze(_A		,      axis=2      )		,      np.asarray(squeeze(_A		,      axis=2      )      )      )      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	np.random.randn(3		,      4      )
											self.assertTrue(np.allclose(expand_dims(_A		,      axis=1      )		,      np.expand_dims(_A		,      axis=1      )      )      )






						@require_torch
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	np.random.randn(3		,      4      )
											__lowerCAmelCase           =	torch.tensor(_A      )
											self.assertTrue(np.allclose(expand_dims(_A		,      axis=1      )		,      expand_dims(_A		,      axis=1      ).numpy()      )      )






						@require_tf
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	np.random.randn(3		,      4      )
											__lowerCAmelCase           =	tf.constant(_A      )
											self.assertTrue(np.allclose(expand_dims(_A		,      axis=1      )		,      expand_dims(_A		,      axis=1      ).numpy()      )      )






						@require_flax
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	np.random.randn(3		,      4      )
											__lowerCAmelCase           =	jnp.array(_A      )
											self.assertTrue(np.allclose(expand_dims(_A		,      axis=1      )		,      np.asarray(expand_dims(_A		,      axis=1      )      )      )      )










def      _a				(						SCREAMING_SNAKE_CASE_							:    List[Any]					):
					__lowerCAmelCase       ,							__lowerCAmelCase           =	[], []
					while len(SCREAMING_SNAKE_CASE_					) > 1:
										__lowerCAmelCase       ,							__lowerCAmelCase           =	min(SCREAMING_SNAKE_CASE_					), max(SCREAMING_SNAKE_CASE_					)
										start.append(SCREAMING_SNAKE_CASE_					)
										end.append(SCREAMING_SNAKE_CASE_					)
										collection.remove(SCREAMING_SNAKE_CASE_					)
										collection.remove(SCREAMING_SNAKE_CASE_					)
					end.reverse()
					return start + collection + end


if __name__ == "__main__":
					UpperCamelCase__         =      input("""Enter numbers separated by a comma:\n""").strip()
					UpperCamelCase__         =      [int(item) for item in user_input.split(""",""")]
					print(*merge_sort(unsorted), sep=""",""")




