

"""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   __lowercase      (     unittest.TestCase       ):

  def __init__(	self		:      Optional[Any]       ,A		:      str       ,A		:      str=13       ,A		:      Optional[int]=7       ,A		:      str=True       ,A		:      Any=True       ,A		:      Union[str, Any]=True       ,A		:      List[Any]=True       ,A		:      Union[str, Any]=99       ,A		:      Optional[Any]=32       ,A		:      List[str]=5       ,A		:      Union[str, Any]=4       ,A		:      Tuple=37       ,A		:      List[Any]="gelu"       ,A		:      Tuple=0.1       ,A		:      Optional[int]=0.1       ,A		:      Optional[int]=512       ,A		:      List[str]=16       ,A		:      Any=2       ,A		:      int=0.0_2       ,A		:      Any=4       ,):






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

  def  __lowercase							(	self		:      List[Any]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Union[str, Any]        =       ids_tensor([self.batch_size, self.seq_length]       ,self.vocab_size     )

   UpperCAmelCase__       :	Any        =       None
   if self.use_attention_mask:
    UpperCAmelCase__       :	str        =       random_attention_mask([self.batch_size, self.seq_length]     )

   UpperCAmelCase__       :	List[Any]        =       None
   if self.use_token_type_ids:
    UpperCAmelCase__       :	str        =       ids_tensor([self.batch_size, self.seq_length]       ,self.type_vocab_size     )

   UpperCAmelCase__       :	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=A       ,initializer_range=self.initializer_range       ,)

   return config, input_ids, token_type_ids, attention_mask

  def  __lowercase							(	self		:      Optional[Any]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Optional[Any]        =       self.prepare_config_and_inputs()
   UpperCAmelCase__ ,   UpperCAmelCase__ ,   UpperCAmelCase__ ,   UpperCAmelCase__       :	Tuple        =       config_and_inputs
   UpperCAmelCase__       :	List[str]        =       {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
   return config, inputs_dict




  def  __lowercase							(	self		:      Optional[Any]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Optional[Any]        =       self.prepare_config_and_inputs()
   UpperCAmelCase__ ,   UpperCAmelCase__ ,   UpperCAmelCase__ ,   UpperCAmelCase__       :	Tuple        =       config_and_inputs

   UpperCAmelCase__       :	List[Any]        =       True
   UpperCAmelCase__       :	int        =       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
   UpperCAmelCase__       :	Any        =       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   __lowercase      (     __lowerCamelCase						,      unittest.TestCase       ):
  snake_case_												=							True

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

  def  __lowercase							(	self		:      Optional[Any]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Dict        =       FlaxRobertaModelTester(self     )




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






   '''simple docstring'''
   for model_class_name in self.all_model_classes:
    UpperCAmelCase__       :	Any        =       model_class_name.from_pretrained("""roberta-base"""       ,from_pt=A     )
    UpperCAmelCase__       :	int        =       model(np.ones((1, 1)     )     )
    self.assertIsNotNone(A     )







from __future__ import annotations

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				if len(__magic_name__						) == 0:
								return []
				lowercase     ,	lowercase         =      min(__magic_name__						), max(__magic_name__						)
				lowercase         =      int(max_value - min_value						) + 1
				lowercase         =      [[] for _ in range(__magic_name__						)]

				for i in my_list:
								buckets[int(i - min_value						)].append(__magic_name__						)

				return [v for bucket in buckets for v in sorted(__magic_name__						)]


if __name__ == "__main__":
						from doctest import testmod

						testmod()
						assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
						assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]
