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 		SCREAMING_SNAKE_CASE_		(				self    :					str     )    ->				List[Any]:
         A						:     List[str]         ={
             'task_specific_params': {
                 'summarization': {'length_penalty': 1.0, 'max_length': 1_28, 'min_length': 12, 'num_beams': 4},
                 'summarization_cnn': {'length_penalty': 2.0, 'max_length': 1_42, 'min_length': 56, 'num_beams': 4},
                 'summarization_xsum': {'length_penalty': 1.0, 'max_length': 62, 'min_length': 11, 'num_beams': 6},
             }
         }
         A						:     List[str]         ={
             'task_specific_params.summarization.length_penalty': 1.0,
             'task_specific_params.summarization.max_length': 1_28,
             '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': 1_42,
             '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(SCREAMING_SNAKE_CASE__     )      ,	SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple     )    ->				Any:
         A						:     str         =np.random.randn(3      ,	4     )
         self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__     )      ,	x.transpose()     )     )

         A						:     Optional[int]         =np.random.randn(3      ,	4      ,	5     )
         self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__      ,	axes=(1, 2, 0)     )      ,	x.transpose((1, 2, 0)     )     )     )







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

         A						:     Any         =np.random.randn(3      ,	4      ,	5     )
         A						:     Optional[int]         =torch.tensor(SCREAMING_SNAKE_CASE__     )
         self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__      ,	axes=(1, 2, 0)     )      ,	transpose(SCREAMING_SNAKE_CASE__      ,	axes=(1, 2, 0)     ).numpy()     )     )







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

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







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

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







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

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







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

         A						:     Optional[int]         =np.random.randn(3      ,	4      ,	5     )
         A						:     Any         =torch.tensor(SCREAMING_SNAKE_CASE__     )
         self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__      ,	(12, 5)     )      ,	reshape(SCREAMING_SNAKE_CASE__      ,	(12, 5)     ).numpy()     )     )







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

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







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

         A						:     str         =np.random.randn(3      ,	4      ,	5     )
         A						:     Tuple         =jnp.array(SCREAMING_SNAKE_CASE__     )
         self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__      ,	(12, 5)     )      ,	np.asarray(reshape(SCREAMING_SNAKE_CASE__      ,	(12, 5)     )     )     )     )







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

         A						:     Dict         =np.random.randn(1      ,	4      ,	1      ,	5     )
         self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__      ,	axis=2     )      ,	np.squeeze(SCREAMING_SNAKE_CASE__      ,	axis=2     )     )     )







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

         A						:     Tuple         =np.random.randn(1      ,	4      ,	1      ,	5     )
         A						:     Any         =torch.tensor(SCREAMING_SNAKE_CASE__     )
         self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__      ,	axis=2     )      ,	squeeze(SCREAMING_SNAKE_CASE__      ,	axis=2     ).numpy()     )     )







  @require_tf
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple     )    ->				Optional[Any]:
         A						:     Optional[Any]         =np.random.randn(1      ,	3      ,	4     )
         A						:     Union[str, Any]         =tf.constant(SCREAMING_SNAKE_CASE__     )
         self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__     )      ,	squeeze(SCREAMING_SNAKE_CASE__     ).numpy()     )     )

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







  @require_flax
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple     )    ->				int:
         A						:     List[str]         =np.random.randn(1      ,	3      ,	4     )
         A						:     int         =jnp.array(SCREAMING_SNAKE_CASE__     )
         self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__     )      ,	np.asarray(squeeze(SCREAMING_SNAKE_CASE__     )     )     )     )

         A						:     List[Any]         =np.random.randn(1      ,	4      ,	1      ,	5     )
         A						:     Any         =jnp.array(SCREAMING_SNAKE_CASE__     )
         self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__      ,	axis=2     )      ,	np.asarray(squeeze(SCREAMING_SNAKE_CASE__      ,	axis=2     )     )     )     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				int:
         A						:     Tuple         =np.random.randn(3      ,	4     )
         self.assertTrue(np.allclose(expand_dims(SCREAMING_SNAKE_CASE__      ,	axis=1     )      ,	np.expand_dims(SCREAMING_SNAKE_CASE__      ,	axis=1     )     )     )







  @require_torch
  def 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				Tuple:
         A						:     Union[str, Any]         =np.random.randn(3      ,	4     )
         A						:     Optional[Any]         =torch.tensor(SCREAMING_SNAKE_CASE__     )
         self.assertTrue(np.allclose(expand_dims(SCREAMING_SNAKE_CASE__      ,	axis=1     )      ,	expand_dims(SCREAMING_SNAKE_CASE__      ,	axis=1     ).numpy()     )     )







  @require_tf
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				str:
         A						:     str         =np.random.randn(3      ,	4     )
         A						:     int         =tf.constant(SCREAMING_SNAKE_CASE__     )
         self.assertTrue(np.allclose(expand_dims(SCREAMING_SNAKE_CASE__      ,	axis=1     )      ,	expand_dims(SCREAMING_SNAKE_CASE__      ,	axis=1     ).numpy()     )     )







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







import heapq



def A__  (			lowercase:     dict					)				->		set[int]:
       A						:     list[list]         =[]

       # for each node and his adjacency list add them and the rank of the node to queue
       # using heapq module the queue will be filled like a Priority Queue
       # heapq works with a min priority queue, so I used -1*len(v) to build it
       for key, value in graph.items():
              # O(log(n))
              heapq.heappush(lowercase,		[-1 * len(lowercase					), (key, value)]					)

       # chosen_vertices = set of chosen vertices
       A						:     Dict         =set()

       # while queue isn't empty and there are still edges
       #   (queue[0][0] is the rank of the node with max rank)
       while queue and queue[0][0] != 0:
              # extract vertex with max rank from queue and add it to chosen_vertices
              A						:     List[str]         =heapq.heappop(lowercase					)[1][0]
              chosen_vertices.add(lowercase					)

              # Remove all arcs adjacent to argmax
              for elem in queue:
                     # if v haven't adjacent node, skip
                     if elem[0] == 0:
                            continue
                     # if argmax is reachable from elem
                     # remove argmax from elem's adjacent list and update his rank
                     if argmax in elem[1][1]:
                            A						:     str         =elem[1][1].index(lowercase					)
                            del elem[1][1][index]
                            elem[0] += 1
        # re-order the queue
              heapq.heapify(lowercase					)
       return chosen_vertices


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    _lowercase							:      List[Any]      							={0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
    print(f'''Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}''')






