


"""simple docstring"""






import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
  from transformers.models.roberta.modeling_flax_roberta import (
      FlaxRobertaForCausalLM,
      FlaxRobertaForMaskedLM,
      FlaxRobertaForMultipleChoice,
      FlaxRobertaForQuestionAnswering,
      FlaxRobertaForSequenceClassification,
      FlaxRobertaForTokenClassification,
      FlaxRobertaModel,
  )




class 							A_      (       unittest.TestCase ):


  def __init__(			self:						List[Any]							,__lowerCAmelCase:						int							,__lowerCAmelCase:						List[Any]=13							,__lowerCAmelCase:						Dict=7							,__lowerCAmelCase:						str=True							,__lowerCAmelCase:						Dict=True							,__lowerCAmelCase:						Any=True							,__lowerCAmelCase:						List[Any]=True							,__lowerCAmelCase:						int=99							,__lowerCAmelCase:						str=32							,__lowerCAmelCase:						Optional[int]=5							,__lowerCAmelCase:						Optional[Any]=4							,__lowerCAmelCase:						List[Any]=37							,__lowerCAmelCase:						Dict="gelu"							,__lowerCAmelCase:						List[str]=0.1							,__lowerCAmelCase:						List[str]=0.1							,__lowerCAmelCase:						str=512							,__lowerCAmelCase:						List[str]=16							,__lowerCAmelCase:						Union[str, Any]=2							,__lowerCAmelCase:						List[Any]=0.02							,__lowerCAmelCase:						Optional[int]=4							,):







     '''simple docstring'''







     _lowerCamelCase   :				List[str]							=     parent
     _lowerCamelCase   :				Optional[Any]							=     batch_size
     _lowerCamelCase   :				Union[str, Any]							=     seq_length
     _lowerCamelCase   :				Optional[int]							=     is_training
     _lowerCamelCase   :				Tuple							=     use_attention_mask
     _lowerCamelCase   :				List[Any]							=     use_token_type_ids
     _lowerCamelCase   :				Optional[Any]							=     use_labels
     _lowerCamelCase   :				int							=     vocab_size
     _lowerCamelCase   :				int							=     hidden_size
     _lowerCamelCase   :				List[Any]							=     num_hidden_layers
     _lowerCamelCase   :				str							=     num_attention_heads
     _lowerCamelCase   :				Optional[int]							=     intermediate_size
     _lowerCamelCase   :				Tuple							=     hidden_act
     _lowerCamelCase   :				Union[str, Any]							=     hidden_dropout_prob
     _lowerCamelCase   :				int							=     attention_probs_dropout_prob
     _lowerCamelCase   :				str							=     max_position_embeddings
     _lowerCamelCase   :				List[Any]							=     type_vocab_size
     _lowerCamelCase   :				Optional[int]							=     type_sequence_label_size
     _lowerCamelCase   :				Union[str, Any]							=     initializer_range
     _lowerCamelCase   :				Any							=     num_choices


  def 							_lowercase					(			self:						str    ):







     '''simple docstring'''







     _lowerCamelCase   :				List[str]							=     ids_tensor([self.batch_size, self.seq_length]							,self.vocab_size    )

     _lowerCamelCase   :				int							=     None
     if self.use_attention_mask:
        _lowerCamelCase   :				Dict							=     random_attention_mask([self.batch_size, self.seq_length]    )

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

     _lowerCamelCase   :				Any							=     RobertaConfig(
         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							,is_decoder=__lowerCAmelCase							,initializer_range=self.initializer_range							,)

     return config, input_ids, token_type_ids, attention_mask


  def 							_lowercase					(			self:						Optional[int]    ):







     '''simple docstring'''







     _lowerCamelCase   :				Dict							=     self.prepare_config_and_inputs()
     _lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase   :				str							=     config_and_inputs
     _lowerCamelCase   :				Any							=     {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
     return config, inputs_dict






  def 							_lowercase					(			self:						List[str]    ):







     '''simple docstring'''







     _lowerCamelCase   :				Dict							=     self.prepare_config_and_inputs()
     _lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase   :				List[str]							=     config_and_inputs

     _lowerCamelCase   :				int							=     True
     _lowerCamelCase   :				Optional[int]							=     floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
     _lowerCamelCase   :				Dict							=     ids_tensor([self.batch_size, self.seq_length]							,vocab_size=2    )

     return (
         config,
         input_ids,
         token_type_ids,
         encoder_hidden_states,
         encoder_attention_mask,
     )




@require_flax
class 							A_      (       _a     ,	unittest.TestCase ):
  lowerCAmelCase__      			=	True

  lowerCAmelCase__      			=	(
      (
          FlaxRobertaModel,
          FlaxRobertaForCausalLM,
          FlaxRobertaForMaskedLM,
          FlaxRobertaForSequenceClassification,
          FlaxRobertaForTokenClassification,
          FlaxRobertaForMultipleChoice,
          FlaxRobertaForQuestionAnswering,
      )
      if is_flax_available()
      else ()
  )


  def 							_lowercase					(			self:						Dict    ):







     '''simple docstring'''







     _lowerCamelCase   :				Optional[int]							=     FlaxRobertaModelTester(self    )






  @slow
  def 							_lowercase					(			self:						Optional[Any]    ):







     '''simple docstring'''







     for model_class_name in self.all_model_classes:
        _lowerCamelCase   :				List[Any]							=     model_class_name.from_pretrained("roberta-base"							,from_pt=__lowerCAmelCase    )
        _lowerCamelCase   :				int							=     model(np.ones((1, 1)    )    )
        self.assertIsNotNone(__lowerCAmelCase    )


def 			_A	(      SCREAMING_SNAKE_CASE      ):
	stooge(SCREAMING_SNAKE_CASE	,0	,len(SCREAMING_SNAKE_CASE      ) - 1      )
	return arr



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	if i >= h:
		return

	# If first element is smaller than the last then swap them
	if arr[i] > arr[h]:
		UpperCAmelCase__							,						UpperCAmelCase__:	int									= arr[h], arr[i]

	# If there are more than 2 elements in the array
	if h - i + 1 > 2:
		UpperCAmelCase__:	str									= (int)((h - i + 1) / 3      )

		# Recursively sort first 2/3 elements
		stooge(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,(h - t)      )

		# Recursively sort last 2/3 elements
		stooge(SCREAMING_SNAKE_CASE	,i + t	,(SCREAMING_SNAKE_CASE)      )

		# Recursively sort first 2/3 elements
		stooge(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,(h - t)      )


if __name__ == "__main__":
					_lowerCAmelCase  :      List[str]			 =input("""Enter numbers separated by a comma:\n""").strip()
					_lowerCAmelCase  :      Any			 =[int(item) for item in user_input.split(""",""")]
					print(stooge_sort(unsorted))