





'''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    UpperCamelCase__	(		unittest.TestCase  ):







	def     __a (					self      : int		):




				'''simple docstring'''
				a__	 =			{
				    "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},
				    }
				}
				a__	 =			{
				    "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(__UpperCAmelCase		)			,			__UpperCAmelCase		)







	def     __a (					self      : Tuple		):




				'''simple docstring'''
				a__	 =			np.random.randn(3			,			4		)
				self.assertTrue(np.allclose(transpose(__UpperCAmelCase		)			,			x.transpose()		)		)

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







	@require_torch
	def     __a (					self      : List[Any]		):




				'''simple docstring'''
				a__	 =			np.random.randn(3			,			4		)
				a__	 =			torch.tensor(__UpperCAmelCase		)
				self.assertTrue(np.allclose(transpose(__UpperCAmelCase		)			,			transpose(__UpperCAmelCase		).numpy()		)		)

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







	@require_tf
	def     __a (					self      : Union[str, Any]		):




				'''simple docstring'''
				a__	 =			np.random.randn(3			,			4		)
				a__	 =			tf.constant(__UpperCAmelCase		)
				self.assertTrue(np.allclose(transpose(__UpperCAmelCase		)			,			transpose(__UpperCAmelCase		).numpy()		)		)

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







	@require_flax
	def     __a (					self      : Dict		):




				'''simple docstring'''
				a__	 =			np.random.randn(3			,			4		)
				a__	 =			jnp.array(__UpperCAmelCase		)
				self.assertTrue(np.allclose(transpose(__UpperCAmelCase		)			,			np.asarray(transpose(__UpperCAmelCase		)		)		)		)

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







	def     __a (					self      : List[Any]		):




				'''simple docstring'''
				a__	 =			np.random.randn(3			,			4		)
				self.assertTrue(np.allclose(reshape(__UpperCAmelCase			,			(4, 3)		)			,			np.reshape(__UpperCAmelCase			,			(4, 3)		)		)		)

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







	@require_torch
	def     __a (					self      : Any		):




				'''simple docstring'''
				a__	 =			np.random.randn(3			,			4		)
				a__	 =			torch.tensor(__UpperCAmelCase		)
				self.assertTrue(np.allclose(reshape(__UpperCAmelCase			,			(4, 3)		)			,			reshape(__UpperCAmelCase			,			(4, 3)		).numpy()		)		)

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







	@require_tf
	def     __a (					self      : Tuple		):




				'''simple docstring'''
				a__	 =			np.random.randn(3			,			4		)
				a__	 =			tf.constant(__UpperCAmelCase		)
				self.assertTrue(np.allclose(reshape(__UpperCAmelCase			,			(4, 3)		)			,			reshape(__UpperCAmelCase			,			(4, 3)		).numpy()		)		)

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







	@require_flax
	def     __a (					self      : List[Any]		):




				'''simple docstring'''
				a__	 =			np.random.randn(3			,			4		)
				a__	 =			jnp.array(__UpperCAmelCase		)
				self.assertTrue(np.allclose(reshape(__UpperCAmelCase			,			(4, 3)		)			,			np.asarray(reshape(__UpperCAmelCase			,			(4, 3)		)		)		)		)

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







	def     __a (					self      : Tuple		):




				'''simple docstring'''
				a__	 =			np.random.randn(1			,			3			,			4		)
				self.assertTrue(np.allclose(squeeze(__UpperCAmelCase		)			,			np.squeeze(__UpperCAmelCase		)		)		)

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







	@require_torch
	def     __a (					self      : Dict		):




				'''simple docstring'''
				a__	 =			np.random.randn(1			,			3			,			4		)
				a__	 =			torch.tensor(__UpperCAmelCase		)
				self.assertTrue(np.allclose(squeeze(__UpperCAmelCase		)			,			squeeze(__UpperCAmelCase		).numpy()		)		)

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







	@require_tf
	def     __a (					self      : Optional[int]		):




				'''simple docstring'''
				a__	 =			np.random.randn(1			,			3			,			4		)
				a__	 =			tf.constant(__UpperCAmelCase		)
				self.assertTrue(np.allclose(squeeze(__UpperCAmelCase		)			,			squeeze(__UpperCAmelCase		).numpy()		)		)

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







	@require_flax
	def     __a (					self      : Union[str, Any]		):




				'''simple docstring'''
				a__	 =			np.random.randn(1			,			3			,			4		)
				a__	 =			jnp.array(__UpperCAmelCase		)
				self.assertTrue(np.allclose(squeeze(__UpperCAmelCase		)			,			np.asarray(squeeze(__UpperCAmelCase		)		)		)		)

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







	def     __a (					self      : Dict		):




				'''simple docstring'''
				a__	 =			np.random.randn(3			,			4		)
				self.assertTrue(np.allclose(expand_dims(__UpperCAmelCase			,			axis=1		)			,			np.expand_dims(__UpperCAmelCase			,			axis=1		)		)		)







	@require_torch
	def     __a (					self      : List[Any]		):




				'''simple docstring'''
				a__	 =			np.random.randn(3			,			4		)
				a__	 =			torch.tensor(__UpperCAmelCase		)
				self.assertTrue(np.allclose(expand_dims(__UpperCAmelCase			,			axis=1		)			,			expand_dims(__UpperCAmelCase			,			axis=1		).numpy()		)		)







	@require_tf
	def     __a (					self      : Union[str, Any]		):




				'''simple docstring'''
				a__	 =			np.random.randn(3			,			4		)
				a__	 =			tf.constant(__UpperCAmelCase		)
				self.assertTrue(np.allclose(expand_dims(__UpperCAmelCase			,			axis=1		)			,			expand_dims(__UpperCAmelCase			,			axis=1		).numpy()		)		)







	@require_flax
	def     __a (					self      : str		):




				'''simple docstring'''
				a__	 =			np.random.randn(3			,			4		)
				a__	 =			jnp.array(__UpperCAmelCase		)
				self.assertTrue(np.allclose(expand_dims(__UpperCAmelCase			,			axis=1		)			,			np.asarray(expand_dims(__UpperCAmelCase			,			axis=1		)		)		)		)








from __future__ import annotations





def 		a__      (		_UpperCamelCase      :			list[float]				):
   if len(_UpperCamelCase				) < 2:
      raise ValueError('''Monogons and Digons are not polygons in the Euclidean space'''				)
   if any(i <= 0 for i in nums				):
      raise ValueError('''All values must be greater than 0'''				)
   __lowerCamelCase    				=     nums.copy()
   copy_nums.sort()
   return copy_nums[-1] < sum(copy_nums[:-1]				)


if __name__ == "__main__":
  import doctest

  doctest.testmod()






