


'''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			):
									snake_case_		:							Union[str, Any]			       =						{
									    """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},
									    }
									}
									snake_case_		:							Optional[Any]			       =						{
									    """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		:Any			):
									snake_case_		:							Optional[Any]			       =						np.random.randn(3  ,4			)
									self.assertTrue(np.allclose(transpose(_UpperCamelCase			)  ,x.transpose()			)			)

									snake_case_		:							Tuple			       =						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		:Union[str, Any]			):
									snake_case_		:							Dict			       =						np.random.randn(3  ,4			)
									snake_case_		:							str			       =						torch.tensor(_UpperCamelCase			)
									self.assertTrue(np.allclose(transpose(_UpperCamelCase			)  ,transpose(_UpperCamelCase			).numpy()			)			)

									snake_case_		:							List[Any]			       =						np.random.randn(3  ,4  ,5			)
									snake_case_		:							Optional[Any]			       =						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		:Tuple			):
									snake_case_		:							Optional[Any]			       =						np.random.randn(3  ,4			)
									snake_case_		:							Optional[int]			       =						tf.constant(_UpperCamelCase			)
									self.assertTrue(np.allclose(transpose(_UpperCamelCase			)  ,transpose(_UpperCamelCase			).numpy()			)			)

									snake_case_		:							Dict			       =						np.random.randn(3  ,4  ,5			)
									snake_case_		:							List[str]			       =						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		:Optional[int]			):
									snake_case_		:							Any			       =						np.random.randn(3  ,4			)
									snake_case_		:							Tuple			       =						jnp.array(_UpperCamelCase			)
									self.assertTrue(np.allclose(transpose(_UpperCamelCase			)  ,np.asarray(transpose(_UpperCamelCase			)			)			)			)

									snake_case_		:							int			       =						np.random.randn(3  ,4  ,5			)
									snake_case_		:							Union[str, Any]			       =						jnp.array(_UpperCamelCase			)
									self.assertTrue(np.allclose(transpose(_UpperCamelCase  ,axes=(1, 2, 0)			)  ,np.asarray(transpose(_UpperCamelCase  ,axes=(1, 2, 0)			)			)			)			)





							def 		a__   (  self		:str			):
									snake_case_		:							List[str]			       =						np.random.randn(3  ,4			)
									self.assertTrue(np.allclose(reshape(_UpperCamelCase  ,(4, 3)			)  ,np.reshape(_UpperCamelCase  ,(4, 3)			)			)			)

									snake_case_		:							Optional[int]			       =						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		:Optional[Any]			):
									snake_case_		:							List[Any]			       =						np.random.randn(3  ,4			)
									snake_case_		:							Dict			       =						torch.tensor(_UpperCamelCase			)
									self.assertTrue(np.allclose(reshape(_UpperCamelCase  ,(4, 3)			)  ,reshape(_UpperCamelCase  ,(4, 3)			).numpy()			)			)

									snake_case_		:							Optional[Any]			       =						np.random.randn(3  ,4  ,5			)
									snake_case_		:							Any			       =						torch.tensor(_UpperCamelCase			)
									self.assertTrue(np.allclose(reshape(_UpperCamelCase  ,(1_2, 5)			)  ,reshape(_UpperCamelCase  ,(1_2, 5)			).numpy()			)			)





							@require_tf
							def 		a__   (  self		:Optional[Any]			):
									snake_case_		:							Dict			       =						np.random.randn(3  ,4			)
									snake_case_		:							List[Any]			       =						tf.constant(_UpperCamelCase			)
									self.assertTrue(np.allclose(reshape(_UpperCamelCase  ,(4, 3)			)  ,reshape(_UpperCamelCase  ,(4, 3)			).numpy()			)			)

									snake_case_		:							List[str]			       =						np.random.randn(3  ,4  ,5			)
									snake_case_		:							str			       =						tf.constant(_UpperCamelCase			)
									self.assertTrue(np.allclose(reshape(_UpperCamelCase  ,(1_2, 5)			)  ,reshape(_UpperCamelCase  ,(1_2, 5)			).numpy()			)			)





							@require_flax
							def 		a__   (  self		:List[str]			):
									snake_case_		:							Union[str, Any]			       =						np.random.randn(3  ,4			)
									snake_case_		:							Any			       =						jnp.array(_UpperCamelCase			)
									self.assertTrue(np.allclose(reshape(_UpperCamelCase  ,(4, 3)			)  ,np.asarray(reshape(_UpperCamelCase  ,(4, 3)			)			)			)			)

									snake_case_		:							int			       =						np.random.randn(3  ,4  ,5			)
									snake_case_		:							Optional[Any]			       =						jnp.array(_UpperCamelCase			)
									self.assertTrue(np.allclose(reshape(_UpperCamelCase  ,(1_2, 5)			)  ,np.asarray(reshape(_UpperCamelCase  ,(1_2, 5)			)			)			)			)





							def 		a__   (  self		:Optional[Any]			):
									snake_case_		:							str			       =						np.random.randn(1  ,3  ,4			)
									self.assertTrue(np.allclose(squeeze(_UpperCamelCase			)  ,np.squeeze(_UpperCamelCase			)			)			)

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

									snake_case_		:							Optional[int]			       =						np.random.randn(1  ,4  ,1  ,5			)
									snake_case_		:							Union[str, Any]			       =						torch.tensor(_UpperCamelCase			)
									self.assertTrue(np.allclose(squeeze(_UpperCamelCase  ,axis=2			)  ,squeeze(_UpperCamelCase  ,axis=2			).numpy()			)			)





							@require_tf
							def 		a__   (  self		:int			):
									snake_case_		:							Dict			       =						np.random.randn(1  ,3  ,4			)
									snake_case_		:							Any			       =						tf.constant(_UpperCamelCase			)
									self.assertTrue(np.allclose(squeeze(_UpperCamelCase			)  ,squeeze(_UpperCamelCase			).numpy()			)			)

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





							@require_flax
							def 		a__   (  self		:Tuple			):
									snake_case_		:							Optional[int]			       =						np.random.randn(1  ,3  ,4			)
									snake_case_		:							Tuple			       =						jnp.array(_UpperCamelCase			)
									self.assertTrue(np.allclose(squeeze(_UpperCamelCase			)  ,np.asarray(squeeze(_UpperCamelCase			)			)			)			)

									snake_case_		:							Optional[int]			       =						np.random.randn(1  ,4  ,1  ,5			)
									snake_case_		:							Any			       =						jnp.array(_UpperCamelCase			)
									self.assertTrue(np.allclose(squeeze(_UpperCamelCase  ,axis=2			)  ,np.asarray(squeeze(_UpperCamelCase  ,axis=2			)			)			)			)





							def 		a__   (  self		:Any			):
									snake_case_		:							List[str]			       =						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]			):
									snake_case_		:							Dict			       =						np.random.randn(3  ,4			)
									snake_case_		:							Optional[Any]			       =						torch.tensor(_UpperCamelCase			)
									self.assertTrue(np.allclose(expand_dims(_UpperCamelCase  ,axis=1			)  ,expand_dims(_UpperCamelCase  ,axis=1			).numpy()			)			)





							@require_tf
							def 		a__   (  self		:Dict			):
									snake_case_		:							int			       =						np.random.randn(3  ,4			)
									snake_case_		:							Any			       =						tf.constant(_UpperCamelCase			)
									self.assertTrue(np.allclose(expand_dims(_UpperCamelCase  ,axis=1			)  ,expand_dims(_UpperCamelCase  ,axis=1			).numpy()			)			)





							@require_flax
							def 		a__   (  self		:int			):
									snake_case_		:							Optional[Any]			       =						np.random.randn(3  ,4			)
									snake_case_		:							List[Any]			       =						jnp.array(_UpperCamelCase			)
									self.assertTrue(np.allclose(expand_dims(_UpperCamelCase  ,axis=1			)  ,np.asarray(expand_dims(_UpperCamelCase  ,axis=1			)			)			)			)



'''simple docstring'''






import random
from typing import Any





def      UpperCAmelCase      (		lowerCamelCase_  :list ):







		'''simple docstring'''
		for _ in range(len(lowerCamelCase_ ) ):
				snake_case_		:							Union[str, Any]			       =						random.randint(0				,      len(lowerCamelCase_ ) - 1 )
				snake_case_		:							Any			       =						random.randint(0				,      len(lowerCamelCase_ ) - 1 )
				snake_case_       ,      snake_case_		:							List[str]			       =						data[b], data[a]
		return data


if __name__ == "__main__":
							__A	:	Optional[int]     =     [0, 1, 2, 3, 4, 5, 6, 7]
							__A	:	Optional[int]     =     ['python', 'says', 'hello', '!']
							print('Fisher-Yates Shuffle:')
							print('List', integers, strings)
							print('FY Shuffle', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))