






from collections import Counter
from functools import total_ordering

from data_structures.heap.heap import Heap





@total_ordering
class   SCREAMING_SNAKE_CASE :





							"""simple docstring"""

							def __init__(     self:      Optional[Any]						,   __A:      str						,   __A:      int      )			-> None:
												_A									=       word
												_A									=       count

							def __eq__(     self:      int						,   __A:      object      )			-> bool:
												if not isinstance(__A						,   __A      ):
																	return NotImplemented
												return self.count == other.count



							def __lt__(     self:      Union[str, Any]						,   __A:      object      )			-> bool:
												if not isinstance(__A						,   __A      ):
																	return NotImplemented
												return self.count < other.count




def    __A    (   _lowercase    ,	_lowercase       ):

					'''simple docstring'''







					_A									=       Heap()
					_A									=       Counter(_lowercase       )
					heap.build_max_heap(
					    [WordCount(_lowercase    ,	_lowercase       ) for word, count in count_by_word.items()]       )
					return [heap.extract_max().word for _ in range(min(_lowercase    ,	len(_lowercase       )       )       )]


if __name__ == "__main__":
				import doctest

				doctest.testmod()









import unittest

import numpy as np

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
				import tensorflow as tf

				from transformers.activations_tf import get_tf_activation





@require_tf
class 		A__				(   unittest.TestCase ):






	def  lowercase      (	self						)      ->   Dict:




						"""simple docstring"""






						__magic_name__					:							int		   =							tf.constant([-100, -1.0, -0.1, 0, 0.1, 1.0, 1_0_0.0]						)
						__magic_name__					:							List[str]		   =							get_tf_activation('''gelu'''						)
						__magic_name__					:							str		   =							get_tf_activation('''gelu_10'''						)

						__magic_name__					:							List[str]		   =							gelu(lowerCamelCase						)
						__magic_name__					:							Optional[Any]		   =							geluaa(lowerCamelCase						)

						__magic_name__					:							Optional[int]		   =							tf.where(y_gelu_aa < 1_0.0				,     1.0				,     0.0						)

						self.assertEqual(tf.math.reduce_max(lowerCamelCase						).numpy().item()				,     1_0.0						)
						self.assertTrue(np.allclose(y_gelu * clipped_mask				,     y_gelu_aa * clipped_mask						)						)






	def  lowercase      (	self						)      ->   Dict:




						"""simple docstring"""






						get_tf_activation('''gelu'''						)
						get_tf_activation('''gelu_10'''						)
						get_tf_activation('''gelu_fast'''						)
						get_tf_activation('''gelu_new'''						)
						get_tf_activation('''glu'''						)
						get_tf_activation('''mish'''						)
						get_tf_activation('''quick_gelu'''						)
						get_tf_activation('''relu'''						)
						get_tf_activation('''sigmoid'''						)
						get_tf_activation('''silu'''						)
						get_tf_activation('''swish'''						)
						get_tf_activation('''tanh'''						)
						with self.assertRaises(lowerCamelCase						):
											get_tf_activation('''bogus'''						)
						with self.assertRaises(lowerCamelCase						):
											get_tf_activation(lowerCamelCase						)



