'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import DebertaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers import (
           TFDebertaForMaskedLM,
           TFDebertaForQuestionAnswering,
           TFDebertaForSequenceClassification,
           TFDebertaForTokenClassification,
           TFDebertaModel,
       )





class 				lowercase    :


   def __init__( self					:						List[str] ,		__lowerCAmelCase					:						Union[str, Any] ,		__lowerCAmelCase					:						Any=13 ,		__lowerCAmelCase					:						Any=7 ,		__lowerCAmelCase					:						List[str]=True ,		__lowerCAmelCase					:						List[Any]=True ,		__lowerCAmelCase					:						int=True ,		__lowerCAmelCase					:						str=True ,		__lowerCAmelCase					:						int=99 ,		__lowerCAmelCase					:						Optional[Any]=32 ,		__lowerCAmelCase					:						Dict=2 ,		__lowerCAmelCase					:						Any=4 ,		__lowerCAmelCase					:						Optional[Any]=37 ,		__lowerCAmelCase					:						Dict="gelu" ,		__lowerCAmelCase					:						Optional[int]=0.1 ,		__lowerCAmelCase					:						int=0.1 ,		__lowerCAmelCase					:						List[str]=512 ,		__lowerCAmelCase					:						Tuple=16 ,		__lowerCAmelCase					:						str=2 ,		__lowerCAmelCase					:						Dict=0.02 ,		__lowerCAmelCase					:						Union[str, Any]=3 ,		__lowerCAmelCase					:						Dict=4 ,		__lowerCAmelCase					:						Tuple=None ,		)   ->       Dict:
         lowercase_     			=   parent
         lowercase_     			=   13
         lowercase_     			=   7
         lowercase_     			=   True
         lowercase_     			=   True
         lowercase_     			=   True
         lowercase_     			=   True
         lowercase_     			=   99
         lowercase_     			=   32
         lowercase_     			=   2
         lowercase_     			=   4
         lowercase_     			=   37
         lowercase_     			=   "gelu"
         lowercase_     			=   0.1
         lowercase_     			=   0.1
         lowercase_     			=   512
         lowercase_     			=   16
         lowercase_     			=   False
         lowercase_     			=   -1
         lowercase_     			=   True
         lowercase_     			=   2
         lowercase_     			=   0.02
         lowercase_     			=   3
         lowercase_     			=   4
         lowercase_     			=   None


   def __UpperCAmelCase							( self					:						Tuple)   ->       Optional[Any]:
         lowercase_     			=   ids_tensor([self.batch_size, self.seq_length] ,		self.vocab_size)

         lowercase_     			=   None
         if self.use_input_mask:
               lowercase_     			=   random_attention_mask([self.batch_size, self.seq_length])

         lowercase_     			=   None
         if self.use_token_type_ids:
               lowercase_     			=   ids_tensor([self.batch_size, self.seq_length] ,		self.type_vocab_size)

         lowercase_     			=   None
         lowercase_     			=   None
         lowercase_     			=   None
         if self.use_labels:
               lowercase_     			=   ids_tensor([self.batch_size] ,		self.type_sequence_label_size)
               lowercase_     			=   ids_tensor([self.batch_size, self.seq_length] ,		self.num_labels)

         lowercase_     			=   DebertaConfig(
             vocab_size=self.vocab_size ,		hidden_size=self.hidden_size ,		num_hidden_layers=self.num_hidden_layers ,		num_attention_heads=self.num_attention_heads ,		intermediate_size=self.intermediate_size ,		hidden_act=self.hidden_act ,		hidden_dropout_prob=self.hidden_dropout_prob ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob ,		max_position_embeddings=self.max_position_embeddings ,		type_vocab_size=self.type_vocab_size ,		relative_attention=self.relative_attention ,		max_relative_positions=self.max_relative_positions ,		position_biased_input=self.position_biased_input ,		initializer_range=self.initializer_range ,		return_dict=__A ,		)

         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


   def __UpperCAmelCase							( self					:						Any ,		__lowerCAmelCase					:						int ,		__lowerCAmelCase					:						Tuple ,		__lowerCAmelCase					:						Any ,		__lowerCAmelCase					:						Dict ,		__lowerCAmelCase					:						List[Any] ,		__lowerCAmelCase					:						str ,		__lowerCAmelCase					:						Optional[int])   ->       Optional[Any]:
         lowercase_     			=   TFDebertaModel(config=__A)
         lowercase_     			=   {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

         lowercase_     			=   [input_ids, input_mask]
         lowercase_     			=   model(__A)

         lowercase_     			=   model(__A)

         self.parent.assertEqual(result.last_hidden_state.shape ,		(self.batch_size, self.seq_length, self.hidden_size))


   def __UpperCAmelCase							( self					:						List[str] ,		__lowerCAmelCase					:						int ,		__lowerCAmelCase					:						List[str] ,		__lowerCAmelCase					:						Optional[int] ,		__lowerCAmelCase					:						int ,		__lowerCAmelCase					:						Dict ,		__lowerCAmelCase					:						Optional[Any] ,		__lowerCAmelCase					:						Union[str, Any])   ->       Optional[int]:
         lowercase_     			=   TFDebertaForMaskedLM(config=__A)
         lowercase_     			=   {
             "input_ids": input_ids,
             "attention_mask": input_mask,
             "token_type_ids": token_type_ids,
         }
         lowercase_     			=   model(__A)
         self.parent.assertEqual(result.logits.shape ,		(self.batch_size, self.seq_length, self.vocab_size))


   def __UpperCAmelCase							( self					:						str ,		__lowerCAmelCase					:						List[Any] ,		__lowerCAmelCase					:						int ,		__lowerCAmelCase					:						Any ,		__lowerCAmelCase					:						Any ,		__lowerCAmelCase					:						Any ,		__lowerCAmelCase					:						Any ,		__lowerCAmelCase					:						Tuple)   ->       List[Any]:
         lowercase_     			=   self.num_labels
         lowercase_     			=   TFDebertaForSequenceClassification(config=__A)
         lowercase_     			=   {
             "input_ids": input_ids,
             "attention_mask": input_mask,
             "token_type_ids": token_type_ids,
         }

         lowercase_     			=   model(__A)
         self.parent.assertEqual(result.logits.shape ,		(self.batch_size, self.num_labels))


   def __UpperCAmelCase							( self					:						Union[str, Any] ,		__lowerCAmelCase					:						Optional[Any] ,		__lowerCAmelCase					:						int ,		__lowerCAmelCase					:						int ,		__lowerCAmelCase					:						Union[str, Any] ,		__lowerCAmelCase					:						Optional[Any] ,		__lowerCAmelCase					:						Dict ,		__lowerCAmelCase					:						int)   ->       Dict:
         lowercase_     			=   self.num_labels
         lowercase_     			=   TFDebertaForTokenClassification(config=__A)
         lowercase_     			=   {
             "input_ids": input_ids,
             "attention_mask": input_mask,
             "token_type_ids": token_type_ids,
         }
         lowercase_     			=   model(__A)
         self.parent.assertEqual(result.logits.shape ,		(self.batch_size, self.seq_length, self.num_labels))


   def __UpperCAmelCase							( self					:						Optional[int] ,		__lowerCAmelCase					:						Tuple ,		__lowerCAmelCase					:						int ,		__lowerCAmelCase					:						Dict ,		__lowerCAmelCase					:						Dict ,		__lowerCAmelCase					:						List[str] ,		__lowerCAmelCase					:						Optional[Any] ,		__lowerCAmelCase					:						int)   ->       Any:
         lowercase_     			=   TFDebertaForQuestionAnswering(config=__A)
         lowercase_     			=   {
             "input_ids": input_ids,
             "attention_mask": input_mask,
             "token_type_ids": token_type_ids,
         }

         lowercase_     			=   model(__A)
         self.parent.assertEqual(result.start_logits.shape ,		(self.batch_size, self.seq_length))
         self.parent.assertEqual(result.end_logits.shape ,		(self.batch_size, self.seq_length))


   def __UpperCAmelCase							( self					:						Any)   ->       int:
         lowercase_     			=   self.prepare_config_and_inputs()
         (
             lowercase_       
         )     			=   config_and_inputs
         lowercase_     			=   {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
         return config, inputs_dict





@require_tf
class 				lowercase    (    a__			,					a__			,					unittest.TestCase			):
   lowerCamelCase_            =(
       (
           TFDebertaModel,
           TFDebertaForMaskedLM,
           TFDebertaForQuestionAnswering,
           TFDebertaForSequenceClassification,
           TFDebertaForTokenClassification,
       )
       if is_tf_available()
       else ()
   )
   lowerCamelCase_            =(
       {
           'feature-extraction': TFDebertaModel,
           'fill-mask': TFDebertaForMaskedLM,
           'question-answering': TFDebertaForQuestionAnswering,
           'text-classification': TFDebertaForSequenceClassification,
           'token-classification': TFDebertaForTokenClassification,
           'zero-shot': TFDebertaForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )

   lowerCamelCase_            =False
   lowerCamelCase_            =False


   def __UpperCAmelCase							( self					:						int)   ->       List[str]:
         lowercase_     			=   TFDebertaModelTester(self)
         lowercase_     			=   ConfigTester(self ,		config_class=__A ,		hidden_size=37)


   def __UpperCAmelCase							( self					:						int)   ->       Any:
         self.config_tester.run_common_tests()


   def __UpperCAmelCase							( self					:						Optional[Any])   ->       int:
         lowercase_     			=   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__A)


   def __UpperCAmelCase							( self					:						Any)   ->       str:
         lowercase_     			=   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*__A)


   def __UpperCAmelCase							( self					:						Optional[Any])   ->       Union[str, Any]:
         lowercase_     			=   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*__A)


   def __UpperCAmelCase							( self					:						int)   ->       Optional[int]:
         lowercase_     			=   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*__A)


   def __UpperCAmelCase							( self					:						Optional[Any])   ->       Any:
         lowercase_     			=   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*__A)


   @slow
   def __UpperCAmelCase							( self					:						str)   ->       Any:
         lowercase_     			=   TFDebertaModel.from_pretrained("kamalkraj/deberta-base")
         self.assertIsNotNone(__A)





@require_tf
class 				lowercase    (    unittest.TestCase			):


   @unittest.skip(reason="Model not available yet")
   def __UpperCAmelCase							( self					:						Dict)   ->       Optional[Any]:
         pass


   @slow
   def __UpperCAmelCase							( self					:						int)   ->       int:
         lowercase_     			=   TFDebertaModel.from_pretrained("kamalkraj/deberta-base")
         lowercase_     			=   tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]])
         lowercase_     			=   tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
         lowercase_     			=   model(__A ,		attention_mask=__A)[0]

         lowercase_     			=   tf.constant(
             [
                 [
                     [-0.5985_5896, -0.8055_2566, -0.846_2135],
                     [1.448_4025, -0.9348_3794, -0.8059_3085],
                     [0.312_2741, 0.0031_6059, -1.413_1377],
                 ]
             ])
         tf.debugging.assert_near(output[:, 1:4, 1:4] ,		__A ,		atol=1e-4)










'''simple docstring'''




from collections import Counter
from functools import total_ordering

from data_structures.heap.heap import Heap





@total_ordering
class _snake_case :
							"""simple docstring"""





							def __init__( self      ,					__A      ,					__A						)  ->None:



											"""simple docstring"""







											__lowerCamelCase		:   List[Any]           =       word
											__lowerCamelCase		:   Optional[Any]           =       count







							def __eq__( self      ,					__A						)  ->bool:



											"""simple docstring"""







											if not isinstance(__A      ,					__A						):
															return NotImplemented
											return self.count == other.count







							def __lt__( self      ,					__A						)  ->bool:



											"""simple docstring"""







											if not isinstance(__A      ,					__A						):
															return NotImplemented
											return self.count < other.count





def 				a_							(    lowercase				,    lowercase     )      ->list[str]:






				'''simple docstring'''

				__lowerCamelCase		:   Heap[WordCount]           =       Heap()
				__lowerCamelCase		:   int           =       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()





