





'''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 			SCREAMING_SNAKE_CASE   (unittest.TestCase							):


     def __init__(						self						,     _UpperCAmelCase						,     _UpperCAmelCase=13						,     _UpperCAmelCase=7						,     _UpperCAmelCase=True						,     _UpperCAmelCase=True						,     _UpperCAmelCase=True						,     _UpperCAmelCase=True						,     _UpperCAmelCase=99						,     _UpperCAmelCase=32						,     _UpperCAmelCase=5						,     _UpperCAmelCase=4						,     _UpperCAmelCase=37						,     _UpperCAmelCase="gelu"						,     _UpperCAmelCase=0.1						,     _UpperCAmelCase=0.1						,     _UpperCAmelCase=512						,     _UpperCAmelCase=16						,     _UpperCAmelCase=2						,     _UpperCAmelCase=0.02						,     _UpperCAmelCase=4						,     ):

          '''simple docstring'''






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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Any								=	ids_tensor([self.batch_size, self.seq_length]						,     self.vocab_size)

          __A						:			Dict								=	None
          if self.use_attention_mask:
               __A						:			List[str]								=	random_attention_mask([self.batch_size, self.seq_length])

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

          __A						:			Tuple								=	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=_UpperCAmelCase						,     initializer_range=self.initializer_range						,     )

          return config, input_ids, token_type_ids, attention_mask


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			int								=	self.prepare_config_and_inputs()
          __A			,__A			,__A			,__A						:			str								=	config_and_inputs
          __A						:			List[str]								=	{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
          return config, inputs_dict


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[Any]								=	self.prepare_config_and_inputs()
          __A			,__A			,__A			,__A						:			Optional[Any]								=	config_and_inputs

          __A						:			Union[str, Any]								=	True
          __A						:			Tuple								=	floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
          __A						:			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 			SCREAMING_SNAKE_CASE   (a__ ,		unittest.TestCase							):
     lowerCAmelCase		      =	True

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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Union[str, Any]								=	FlaxRobertaModelTester(self)


     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          for model_class_name in self.all_model_classes:
               __A						:			List[str]								=	model_class_name.from_pretrained('roberta-base'						,     from_pt=_UpperCAmelCase)
               __A						:			Optional[Any]								=	model(np.ones((1, 1)))
               self.assertIsNotNone(_UpperCAmelCase)






'''simple docstring'''




from __future__ import annotations






def 	_lowerCAmelCase      (  __snake_case  :			list[int | str]      )					->					None:
     create_state_space_tree(__snake_case			,							[]			,							0			,							[0 for i in range(len(__snake_case      )      )]      )






def 	_lowerCAmelCase      (  __snake_case  :			list[int | str]			,							__snake_case  :			list[int | str]			,							__snake_case  :			int			,							__snake_case  :			list[int]			,							)					->					None:

     if index == len(__snake_case      ):
          print(__snake_case      )
          return

     for i in range(len(__snake_case      )      ):
          if not index_used[i]:
               current_sequence.append(sequence[i]      )
               __A						:			Any								=	True
               create_state_space_tree(__snake_case			,							__snake_case			,							index + 1			,							__snake_case      )
               current_sequence.pop()
               __A						:			Any								=	False
lowercase__   :				list[int | str]      =   [3, 1, 2, 4]
generate_all_permutations(sequence)

lowercase__   :				list[int | str]      =   ["A", "B", "C"]







generate_all_permutations(sequence_a)