





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    lowerCAmelCase							(					unittest.TestCase			):
 def      A_	(      self  :      List[str]	)			->	int:
     lowerCamelCase__    :      Union[str, 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},
         }
     }
     lowerCamelCase__    :      Tuple					 =		{
         '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(UpperCAmelCase	)			,     UpperCAmelCase	)
 def      A_	(      self  :      List[str]	)			->	Optional[int]:
     lowerCamelCase__    :      Union[str, Any]					 =		np.random.randn(3			,     4	)
     self.assertTrue(np.allclose(transpose(UpperCAmelCase	)			,     x.transpose()	)	)

     lowerCamelCase__    :      str					 =		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  :      Any	)			->	int:
     lowerCamelCase__    :      List[Any]					 =		np.random.randn(3			,     4	)
     lowerCamelCase__    :      Dict					 =		torch.tensor(UpperCAmelCase	)
     self.assertTrue(np.allclose(transpose(UpperCAmelCase	)			,     transpose(UpperCAmelCase	).numpy()	)	)

     lowerCamelCase__    :      Union[str, Any]					 =		np.random.randn(3			,     4			,     5	)
     lowerCamelCase__    :      str					 =		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  :      int	)			->	Union[str, Any]:
     lowerCamelCase__    :      int					 =		np.random.randn(3			,     4	)
     lowerCamelCase__    :      Any					 =		tf.constant(UpperCAmelCase	)
     self.assertTrue(np.allclose(transpose(UpperCAmelCase	)			,     transpose(UpperCAmelCase	).numpy()	)	)

     lowerCamelCase__    :      Any					 =		np.random.randn(3			,     4			,     5	)
     lowerCamelCase__    :      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  :      Any	)			->	Optional[Any]:
     lowerCamelCase__    :      List[str]					 =		np.random.randn(3			,     4	)
     lowerCamelCase__    :      Optional[int]					 =		jnp.array(UpperCAmelCase	)
     self.assertTrue(np.allclose(transpose(UpperCAmelCase	)			,     np.asarray(transpose(UpperCAmelCase	)	)	)	)

     lowerCamelCase__    :      Any					 =		np.random.randn(3			,     4			,     5	)
     lowerCamelCase__    :      List[str]					 =		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[str]	)			->	Dict:
     lowerCamelCase__    :      Tuple					 =		np.random.randn(3			,     4	)
     self.assertTrue(np.allclose(reshape(UpperCAmelCase			,     (4, 3)	)			,     np.reshape(UpperCAmelCase			,     (4, 3)	)	)	)

     lowerCamelCase__    :      List[Any]					 =		np.random.randn(3			,     4			,     5	)
     self.assertTrue(np.allclose(reshape(UpperCAmelCase			,     (12, 5)	)			,     np.reshape(UpperCAmelCase			,     (12, 5)	)	)	)
 @require_torch
 def      A_	(      self  :      List[Any]	)			->	int:
     lowerCamelCase__    :      Dict					 =		np.random.randn(3			,     4	)
     lowerCamelCase__    :      Dict					 =		torch.tensor(UpperCAmelCase	)
     self.assertTrue(np.allclose(reshape(UpperCAmelCase			,     (4, 3)	)			,     reshape(UpperCAmelCase			,     (4, 3)	).numpy()	)	)

     lowerCamelCase__    :      Union[str, Any]					 =		np.random.randn(3			,     4			,     5	)
     lowerCamelCase__    :      Union[str, Any]					 =		torch.tensor(UpperCAmelCase	)
     self.assertTrue(np.allclose(reshape(UpperCAmelCase			,     (12, 5)	)			,     reshape(UpperCAmelCase			,     (12, 5)	).numpy()	)	)
 @require_tf
 def      A_	(      self  :      Tuple	)			->	List[Any]:
     lowerCamelCase__    :      int					 =		np.random.randn(3			,     4	)
     lowerCamelCase__    :      Dict					 =		tf.constant(UpperCAmelCase	)
     self.assertTrue(np.allclose(reshape(UpperCAmelCase			,     (4, 3)	)			,     reshape(UpperCAmelCase			,     (4, 3)	).numpy()	)	)

     lowerCamelCase__    :      List[str]					 =		np.random.randn(3			,     4			,     5	)
     lowerCamelCase__    :      Dict					 =		tf.constant(UpperCAmelCase	)
     self.assertTrue(np.allclose(reshape(UpperCAmelCase			,     (12, 5)	)			,     reshape(UpperCAmelCase			,     (12, 5)	).numpy()	)	)
 @require_flax
 def      A_	(      self  :      Optional[int]	)			->	str:
     lowerCamelCase__    :      Optional[Any]					 =		np.random.randn(3			,     4	)
     lowerCamelCase__    :      int					 =		jnp.array(UpperCAmelCase	)
     self.assertTrue(np.allclose(reshape(UpperCAmelCase			,     (4, 3)	)			,     np.asarray(reshape(UpperCAmelCase			,     (4, 3)	)	)	)	)

     lowerCamelCase__    :      str					 =		np.random.randn(3			,     4			,     5	)
     lowerCamelCase__    :      Any					 =		jnp.array(UpperCAmelCase	)
     self.assertTrue(np.allclose(reshape(UpperCAmelCase			,     (12, 5)	)			,     np.asarray(reshape(UpperCAmelCase			,     (12, 5)	)	)	)	)
 def      A_	(      self  :      str	)			->	List[str]:
     lowerCamelCase__    :      Union[str, Any]					 =		np.random.randn(1			,     3			,     4	)
     self.assertTrue(np.allclose(squeeze(UpperCAmelCase	)			,     np.squeeze(UpperCAmelCase	)	)	)

     lowerCamelCase__    :      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  :      List[str]	)			->	List[str]:
     lowerCamelCase__    :      Tuple					 =		np.random.randn(1			,     3			,     4	)
     lowerCamelCase__    :      Union[str, Any]					 =		torch.tensor(UpperCAmelCase	)
     self.assertTrue(np.allclose(squeeze(UpperCAmelCase	)			,     squeeze(UpperCAmelCase	).numpy()	)	)

     lowerCamelCase__    :      str					 =		np.random.randn(1			,     4			,     1			,     5	)
     lowerCamelCase__    :      Optional[Any]					 =		torch.tensor(UpperCAmelCase	)
     self.assertTrue(np.allclose(squeeze(UpperCAmelCase			,     axis=2	)			,     squeeze(UpperCAmelCase			,     axis=2	).numpy()	)	)
 @require_tf
 def      A_	(      self  :      Optional[int]	)			->	List[str]:
     lowerCamelCase__    :      str					 =		np.random.randn(1			,     3			,     4	)
     lowerCamelCase__    :      Optional[int]					 =		tf.constant(UpperCAmelCase	)
     self.assertTrue(np.allclose(squeeze(UpperCAmelCase	)			,     squeeze(UpperCAmelCase	).numpy()	)	)

     lowerCamelCase__    :      List[str]					 =		np.random.randn(1			,     4			,     1			,     5	)
     lowerCamelCase__    :      Optional[Any]					 =		tf.constant(UpperCAmelCase	)
     self.assertTrue(np.allclose(squeeze(UpperCAmelCase			,     axis=2	)			,     squeeze(UpperCAmelCase			,     axis=2	).numpy()	)	)
 @require_flax
 def      A_	(      self  :      Optional[Any]	)			->	List[Any]:
     lowerCamelCase__    :      List[str]					 =		np.random.randn(1			,     3			,     4	)
     lowerCamelCase__    :      Optional[Any]					 =		jnp.array(UpperCAmelCase	)
     self.assertTrue(np.allclose(squeeze(UpperCAmelCase	)			,     np.asarray(squeeze(UpperCAmelCase	)	)	)	)

     lowerCamelCase__    :      Optional[Any]					 =		np.random.randn(1			,     4			,     1			,     5	)
     lowerCamelCase__    :      Tuple					 =		jnp.array(UpperCAmelCase	)
     self.assertTrue(np.allclose(squeeze(UpperCAmelCase			,     axis=2	)			,     np.asarray(squeeze(UpperCAmelCase			,     axis=2	)	)	)	)
 def      A_	(      self  :      Dict	)			->	Optional[Any]:
     lowerCamelCase__    :      Any					 =		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  :      Optional[Any]	)			->	List[Any]:
     lowerCamelCase__    :      Any					 =		np.random.randn(3			,     4	)
     lowerCamelCase__    :      Optional[int]					 =		torch.tensor(UpperCAmelCase	)
     self.assertTrue(np.allclose(expand_dims(UpperCAmelCase			,     axis=1	)			,     expand_dims(UpperCAmelCase			,     axis=1	).numpy()	)	)
 @require_tf
 def      A_	(      self  :      int	)			->	Optional[int]:
     lowerCamelCase__    :      Optional[int]					 =		np.random.randn(3			,     4	)
     lowerCamelCase__    :      Any					 =		tf.constant(UpperCAmelCase	)
     self.assertTrue(np.allclose(expand_dims(UpperCAmelCase			,     axis=1	)			,     expand_dims(UpperCAmelCase			,     axis=1	).numpy()	)	)







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












def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     set:
    lowerCamelCase__    :      Optional[Any]					 =		set()
    # edges = list of graph's edges
    lowerCamelCase__    :      List[str]					 =		get_edges(_UpperCAmelCase )

    # While there are still elements in edges list, take an arbitrary edge
    # (from_node, to_node) and add his extremity to chosen_vertices and then
    # remove all arcs adjacent to the from_node and to_node
    while edges:
        lowerCamelCase__ ,							lowerCamelCase__    :      str					 =		edges.pop()
        chosen_vertices.add(_UpperCAmelCase )
        chosen_vertices.add(_UpperCAmelCase )
        for edge in edges.copy():
            if from_node in edge or to_node in edge:
                edges.discard(_UpperCAmelCase )
    return chosen_vertices


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     set:
    lowerCamelCase__    :      Union[str, Any]					 =		set()
    for from_node, to_nodes in graph.items():
        for to_node in to_nodes:
            edges.add((from_node, to_node) )
    return edges


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
    # print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")





