






"""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  lowercase		(   self:     int    ):

            '''simple docstring'''

            a__          =  {
                '''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},
                }
            }
            a__          =  {
                '''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(__A    )				,		__A    )




       def  lowercase		(   self:     Optional[int]    ):

            '''simple docstring'''

            a__          =  np.random.randn(3				,		4    )
            self.assertTrue(np.allclose(transpose(__A    )				,		x.transpose()    )    )

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




       @require_torch
       def  lowercase		(   self:     Dict    ):

            '''simple docstring'''

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

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




       @require_tf
       def  lowercase		(   self:     int    ):

            '''simple docstring'''

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

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




       @require_flax
       def  lowercase		(   self:     Dict    ):

            '''simple docstring'''

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

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




       def  lowercase		(   self:     List[Any]    ):

            '''simple docstring'''

            a__          =  np.random.randn(3				,		4    )
            self.assertTrue(np.allclose(reshape(__A				,		(4, 3)    )				,		np.reshape(__A				,		(4, 3)    )    )    )

            a__          =  np.random.randn(3				,		4				,		5    )
            self.assertTrue(np.allclose(reshape(__A				,		(12, 5)    )				,		np.reshape(__A				,		(12, 5)    )    )    )




       @require_torch
       def  lowercase		(   self:     Dict    ):

            '''simple docstring'''

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

            a__          =  np.random.randn(3				,		4				,		5    )
            a__          =  torch.tensor(__A    )
            self.assertTrue(np.allclose(reshape(__A				,		(12, 5)    )				,		reshape(__A				,		(12, 5)    ).numpy()    )    )




       @require_tf
       def  lowercase		(   self:     int    ):

            '''simple docstring'''

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

            a__          =  np.random.randn(3				,		4				,		5    )
            a__          =  tf.constant(__A    )
            self.assertTrue(np.allclose(reshape(__A				,		(12, 5)    )				,		reshape(__A				,		(12, 5)    ).numpy()    )    )




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

            '''simple docstring'''

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

            a__          =  np.random.randn(3				,		4				,		5    )
            a__          =  jnp.array(__A    )
            self.assertTrue(np.allclose(reshape(__A				,		(12, 5)    )				,		np.asarray(reshape(__A				,		(12, 5)    )    )    )    )




       def  lowercase		(   self:     List[Any]    ):

            '''simple docstring'''

            a__          =  np.random.randn(1				,		3				,		4    )
            self.assertTrue(np.allclose(squeeze(__A    )				,		np.squeeze(__A    )    )    )

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




       @require_torch
       def  lowercase		(   self:     int    ):

            '''simple docstring'''

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

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




       @require_tf
       def  lowercase		(   self:     str    ):

            '''simple docstring'''

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

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




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

            '''simple docstring'''

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

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




       def  lowercase		(   self:     List[Any]    ):

            '''simple docstring'''

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




       @require_torch
       def  lowercase		(   self:     List[str]    ):

            '''simple docstring'''

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




       @require_tf
       def  lowercase		(   self:     List[Any]    ):

            '''simple docstring'''

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





       @require_flax
       def  lowercase		(   self:     str    ):

            '''simple docstring'''

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










"""simple docstring"""







class        _SCREAMING_SNAKE_CASE						:





							"""simple docstring"""





							def __init__(   self:     Any    ):

												'''simple docstring'''

												a__          =  {}




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												print(self.vertex    )
												for i in self.vertex:
																	print(__A				,		''' -> '''				,		''' -> '''.join([str(__A    ) for j in self.vertex[i]]    )    )




							def  lowercase		(   self:     Union[str, Any]				,		__A:     int				,		__A:     int    ):

												'''simple docstring'''

												if from_vertex in self.vertex:
																	self.vertex[from_vertex].append(__A    )
												else:
																	# else make a new vertex
																	a__          =  [to_vertex]




							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												a__          =  [False] * len(self.vertex    )

												# call the recursive helper function
												for i in range(len(self.vertex    )    ):
																	if not visited[i]:
																						self.dfs_recursive(__A				,		__A    )





							def  lowercase		(   self:     Optional[int]				,		__A:     int				,		__A:     list    ):

												'''simple docstring'''

												a__          =  True

												print(__A				,		end=''' '''    )

												# Recur for all the vertices that are adjacent to this node
												for i in self.vertex:
																	if not visited[i]:
																						self.dfs_recursive(__A				,		__A    )


if __name__ == "__main__":
	__a       :	Any             = Graph()
	g.add_edge(0, 1)
	g.add_edge(0, 2)
	g.add_edge(1, 2)
	g.add_edge(2, 0)
	g.add_edge(2, 3)
	g.add_edge(3, 3)

	g.print_graph()
	print('DFS:')
	g.dfs()

	# OUTPUT:
	# 0  ->  1 -> 2
	# 1  ->  2
	# 2  ->  0 -> 3
	# 3  ->  3
	# DFS:
	#  0 1 2 3


