



'''simple docstring'''






from __future__ import annotations


def  snake_case_     (					_lowerCAmelCase  :						list[int | float]   ,      _lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						int  )      ->   int | float:
							if len(_lowerCAmelCase  ) == 0:
														raise ValueError('''find_max() arg is an empty sequence'''  )
							if (
							    left >= len(_lowerCAmelCase  )
							    or left < -len(_lowerCAmelCase  )
							    or right >= len(_lowerCAmelCase  )
							    or right < -len(_lowerCAmelCase  )
							):
														raise IndexError('''list index out of range'''  )
							if left == right:
														return nums[left]
							UpperCAmelCase :       List[Any] 							=		(left + right) >> 1  # the middle
							UpperCAmelCase :       Optional[Any] 							=		find_max(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )  # find max in range[left, mid]
							UpperCAmelCase :       Dict 							=		find_max(_lowerCAmelCase   ,      mid + 1   ,      _lowerCAmelCase  )  # find max in range[mid + 1, right]

							return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
						import doctest

						doctest.testmod(verbose=True)










'''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 				SCREAMING_SNAKE_CASE(							unittest.TestCase			):







	"""simple docstring"""






	def      A	(						self			:     Any		)			->		str:
								UpperCAmelCase :       Any 							=		{
								    '''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},
								    }
								}
								UpperCAmelCase :       int 							=		{
								    '''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(__snake_case		)		,			__snake_case		)







	def      A	(						self			:     int		)			->		str:
								UpperCAmelCase :       Any 							=		np.random.randn(3		,			4		)
								self.assertTrue(np.allclose(transpose(__snake_case		)		,			x.transpose()		)		)

								UpperCAmelCase :       str 							=		np.random.randn(3		,			4		,			5		)
								self.assertTrue(np.allclose(transpose(__snake_case		,			axes=(1, 2, 0)		)		,			x.transpose((1, 2, 0)		)		)		)







	@require_torch
	def      A	(						self			:     str		)			->		Union[str, Any]:
								UpperCAmelCase :       Any 							=		np.random.randn(3		,			4		)
								UpperCAmelCase :       List[Any] 							=		torch.tensor(__snake_case		)
								self.assertTrue(np.allclose(transpose(__snake_case		)		,			transpose(__snake_case		).numpy()		)		)

								UpperCAmelCase :       Tuple 							=		np.random.randn(3		,			4		,			5		)
								UpperCAmelCase :       Any 							=		torch.tensor(__snake_case		)
								self.assertTrue(np.allclose(transpose(__snake_case		,			axes=(1, 2, 0)		)		,			transpose(__snake_case		,			axes=(1, 2, 0)		).numpy()		)		)







	@require_tf
	def      A	(						self			:     List[str]		)			->		Optional[Any]:
								UpperCAmelCase :       int 							=		np.random.randn(3		,			4		)
								UpperCAmelCase :       Optional[int] 							=		tf.constant(__snake_case		)
								self.assertTrue(np.allclose(transpose(__snake_case		)		,			transpose(__snake_case		).numpy()		)		)

								UpperCAmelCase :       Union[str, Any] 							=		np.random.randn(3		,			4		,			5		)
								UpperCAmelCase :       str 							=		tf.constant(__snake_case		)
								self.assertTrue(np.allclose(transpose(__snake_case		,			axes=(1, 2, 0)		)		,			transpose(__snake_case		,			axes=(1, 2, 0)		).numpy()		)		)







	@require_flax
	def      A	(						self			:     Tuple		)			->		Any:
								UpperCAmelCase :       List[Any] 							=		np.random.randn(3		,			4		)
								UpperCAmelCase :       List[str] 							=		jnp.array(__snake_case		)
								self.assertTrue(np.allclose(transpose(__snake_case		)		,			np.asarray(transpose(__snake_case		)		)		)		)

								UpperCAmelCase :       Dict 							=		np.random.randn(3		,			4		,			5		)
								UpperCAmelCase :       int 							=		jnp.array(__snake_case		)
								self.assertTrue(np.allclose(transpose(__snake_case		,			axes=(1, 2, 0)		)		,			np.asarray(transpose(__snake_case		,			axes=(1, 2, 0)		)		)		)		)







	def      A	(						self			:     Optional[Any]		)			->		Any:
								UpperCAmelCase :       Union[str, Any] 							=		np.random.randn(3		,			4		)
								self.assertTrue(np.allclose(reshape(__snake_case		,			(4, 3)		)		,			np.reshape(__snake_case		,			(4, 3)		)		)		)

								UpperCAmelCase :       Union[str, Any] 							=		np.random.randn(3		,			4		,			5		)
								self.assertTrue(np.allclose(reshape(__snake_case		,			(12, 5)		)		,			np.reshape(__snake_case		,			(12, 5)		)		)		)







	@require_torch
	def      A	(						self			:     Union[str, Any]		)			->		int:
								UpperCAmelCase :       Dict 							=		np.random.randn(3		,			4		)
								UpperCAmelCase :       Optional[Any] 							=		torch.tensor(__snake_case		)
								self.assertTrue(np.allclose(reshape(__snake_case		,			(4, 3)		)		,			reshape(__snake_case		,			(4, 3)		).numpy()		)		)

								UpperCAmelCase :       List[str] 							=		np.random.randn(3		,			4		,			5		)
								UpperCAmelCase :       List[Any] 							=		torch.tensor(__snake_case		)
								self.assertTrue(np.allclose(reshape(__snake_case		,			(12, 5)		)		,			reshape(__snake_case		,			(12, 5)		).numpy()		)		)







	@require_tf
	def      A	(						self			:     int		)			->		List[str]:
								UpperCAmelCase :       List[Any] 							=		np.random.randn(3		,			4		)
								UpperCAmelCase :       List[str] 							=		tf.constant(__snake_case		)
								self.assertTrue(np.allclose(reshape(__snake_case		,			(4, 3)		)		,			reshape(__snake_case		,			(4, 3)		).numpy()		)		)

								UpperCAmelCase :       List[Any] 							=		np.random.randn(3		,			4		,			5		)
								UpperCAmelCase :       Optional[Any] 							=		tf.constant(__snake_case		)
								self.assertTrue(np.allclose(reshape(__snake_case		,			(12, 5)		)		,			reshape(__snake_case		,			(12, 5)		).numpy()		)		)







	@require_flax
	def      A	(						self			:     Any		)			->		Dict:
								UpperCAmelCase :       Tuple 							=		np.random.randn(3		,			4		)
								UpperCAmelCase :       Union[str, Any] 							=		jnp.array(__snake_case		)
								self.assertTrue(np.allclose(reshape(__snake_case		,			(4, 3)		)		,			np.asarray(reshape(__snake_case		,			(4, 3)		)		)		)		)

								UpperCAmelCase :       Any 							=		np.random.randn(3		,			4		,			5		)
								UpperCAmelCase :       Optional[Any] 							=		jnp.array(__snake_case		)
								self.assertTrue(np.allclose(reshape(__snake_case		,			(12, 5)		)		,			np.asarray(reshape(__snake_case		,			(12, 5)		)		)		)		)







	def      A	(						self			:     List[Any]		)			->		List[Any]:
								UpperCAmelCase :       Union[str, Any] 							=		np.random.randn(1		,			3		,			4		)
								self.assertTrue(np.allclose(squeeze(__snake_case		)		,			np.squeeze(__snake_case		)		)		)

								UpperCAmelCase :       str 							=		np.random.randn(1		,			4		,			1		,			5		)
								self.assertTrue(np.allclose(squeeze(__snake_case		,			axis=2		)		,			np.squeeze(__snake_case		,			axis=2		)		)		)







	@require_torch
	def      A	(						self			:     Optional[int]		)			->		Union[str, Any]:
								UpperCAmelCase :       Optional[int] 							=		np.random.randn(1		,			3		,			4		)
								UpperCAmelCase :       List[str] 							=		torch.tensor(__snake_case		)
								self.assertTrue(np.allclose(squeeze(__snake_case		)		,			squeeze(__snake_case		).numpy()		)		)

								UpperCAmelCase :       Any 							=		np.random.randn(1		,			4		,			1		,			5		)
								UpperCAmelCase :       str 							=		torch.tensor(__snake_case		)
								self.assertTrue(np.allclose(squeeze(__snake_case		,			axis=2		)		,			squeeze(__snake_case		,			axis=2		).numpy()		)		)







	@require_tf
	def      A	(						self			:     Optional[Any]		)			->		Dict:
								UpperCAmelCase :       int 							=		np.random.randn(1		,			3		,			4		)
								UpperCAmelCase :       Optional[int] 							=		tf.constant(__snake_case		)
								self.assertTrue(np.allclose(squeeze(__snake_case		)		,			squeeze(__snake_case		).numpy()		)		)

								UpperCAmelCase :       List[str] 							=		np.random.randn(1		,			4		,			1		,			5		)
								UpperCAmelCase :       Optional[int] 							=		tf.constant(__snake_case		)
								self.assertTrue(np.allclose(squeeze(__snake_case		,			axis=2		)		,			squeeze(__snake_case		,			axis=2		).numpy()		)		)







	@require_flax
	def      A	(						self			:     List[Any]		)			->		Dict:
								UpperCAmelCase :       Optional[int] 							=		np.random.randn(1		,			3		,			4		)
								UpperCAmelCase :       int 							=		jnp.array(__snake_case		)
								self.assertTrue(np.allclose(squeeze(__snake_case		)		,			np.asarray(squeeze(__snake_case		)		)		)		)

								UpperCAmelCase :       str 							=		np.random.randn(1		,			4		,			1		,			5		)
								UpperCAmelCase :       int 							=		jnp.array(__snake_case		)
								self.assertTrue(np.allclose(squeeze(__snake_case		,			axis=2		)		,			np.asarray(squeeze(__snake_case		,			axis=2		)		)		)		)







	def      A	(						self			:     Optional[Any]		)			->		int:
								UpperCAmelCase :       Optional[Any] 							=		np.random.randn(3		,			4		)
								self.assertTrue(np.allclose(expand_dims(__snake_case		,			axis=1		)		,			np.expand_dims(__snake_case		,			axis=1		)		)		)







	@require_torch
	def      A	(						self			:     List[str]		)			->		Tuple:
								UpperCAmelCase :       Tuple 							=		np.random.randn(3		,			4		)
								UpperCAmelCase :       Tuple 							=		torch.tensor(__snake_case		)
								self.assertTrue(np.allclose(expand_dims(__snake_case		,			axis=1		)		,			expand_dims(__snake_case		,			axis=1		).numpy()		)		)







	@require_tf
	def      A	(						self			:     List[str]		)			->		Union[str, Any]:
								UpperCAmelCase :       Union[str, Any] 							=		np.random.randn(3		,			4		)
								UpperCAmelCase :       Any 							=		tf.constant(__snake_case		)
								self.assertTrue(np.allclose(expand_dims(__snake_case		,			axis=1		)		,			expand_dims(__snake_case		,			axis=1		).numpy()		)		)







	@require_flax
	def      A	(						self			:     Any		)			->		List[Any]:
								UpperCAmelCase :       List[str] 							=		np.random.randn(3		,			4		)
								UpperCAmelCase :       str 							=		jnp.array(__snake_case		)
								self.assertTrue(np.allclose(expand_dims(__snake_case		,			axis=1		)		,			np.asarray(expand_dims(__snake_case		,			axis=1		)		)		)		)





