
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 		UpperCamelCase__  (       unittest.TestCase    ):
   def __init__(self :					int					,				snake_case_ :					List[str]					,				snake_case_ :					int=1_3					,				snake_case_ :					List[Any]=7					,				snake_case_ :					Dict=True					,				snake_case_ :					Dict=True					,				snake_case_ :					Tuple=True					,				snake_case_ :					Union[str, Any]=True					,				snake_case_ :					Optional[Any]=9_9					,				snake_case_ :					Dict=3_2					,				snake_case_ :					List[str]=5					,				snake_case_ :					int=4					,				snake_case_ :					Union[str, Any]=3_7					,				snake_case_ :					Tuple="gelu"					,				snake_case_ :					Any=0.1					,				snake_case_ :					int=0.1					,				snake_case_ :					str=5_1_2					,				snake_case_ :					Optional[Any]=1_6					,				snake_case_ :					Dict=2					,				snake_case_ :					Any=0.02					,				snake_case_ :					Union[str, Any]=4					,				):
    __a :  Optional[Any]						=  parent
    __a :  Optional[int]						=  batch_size
    __a :  Optional[Any]						=  seq_length
    __a :  str						=  is_training
    __a :  List[Any]						=  use_attention_mask
    __a :  Union[str, Any]						=  use_token_type_ids
    __a :  int						=  use_labels
    __a :  Any						=  vocab_size
    __a :  Optional[int]						=  hidden_size
    __a :  List[str]						=  num_hidden_layers
    __a :  Optional[Any]						=  num_attention_heads
    __a :  List[Any]						=  intermediate_size
    __a :  int						=  hidden_act
    __a :  Tuple						=  hidden_dropout_prob
    __a :  Optional[Any]						=  attention_probs_dropout_prob
    __a :  Optional[int]						=  max_position_embeddings
    __a :  Optional[int]						=  type_vocab_size
    __a :  List[Any]						=  type_sequence_label_size
    __a :  Dict						=  initializer_range
    __a :  Optional[int]						=  num_choices
   def       lowerCAmelCase			(self :					Dict   ):
    __a :  List[Any]						=  ids_tensor([self.batch_size, self.seq_length]					,				self.vocab_size   )

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

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

    __a :  Optional[int]						=  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=snake_case_					,				initializer_range=self.initializer_range					,				)

    return config, input_ids, token_type_ids, attention_mask
   def       lowerCAmelCase			(self :					str   ):
    __a :  int						=  self.prepare_config_and_inputs()
    __a				,       __a				,       __a				,       __a :  List[str]						=  config_and_inputs
    __a :  Optional[Any]						=  {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
    return config, inputs_dict







   def       lowerCAmelCase			(self :					Optional[Any]   ):
    __a :  List[str]						=  self.prepare_config_and_inputs()
    __a				,       __a				,       __a				,       __a :  Dict						=  config_and_inputs

    __a :  str						=  True
    __a :  Any						=  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
    __a :  Optional[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 		UpperCamelCase__  (       __lowercase    ,unittest.TestCase    ):
   _SCREAMING_SNAKE_CASE      :							Dict	       =      True

   _SCREAMING_SNAKE_CASE      :							Union[str, Any]	       =      (
       (
           FlaxRobertaModel,
           FlaxRobertaForCausalLM,
           FlaxRobertaForMaskedLM,
           FlaxRobertaForSequenceClassification,
           FlaxRobertaForTokenClassification,
           FlaxRobertaForMultipleChoice,
           FlaxRobertaForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )
   def       lowerCAmelCase			(self :					str   ):
    __a :  List[str]						=  FlaxRobertaModelTester(self   )







   @slow
   def       lowerCAmelCase			(self :					str   ):
    for model_class_name in self.all_model_classes:
     __a :  Tuple						=  model_class_name.from_pretrained('''roberta-base'''					,				from_pt=snake_case_   )
     __a :  Optional[int]						=  model(np.ones((1, 1)   )   )
     self.assertIsNotNone(snake_case_   )


def    __UpperCamelCase     (				lowerCAmelCase__							:				str  ,   lowerCAmelCase__							:				str  ):
 __a :  str						=  len(lowerCAmelCase__  )
 __a :  Optional[int]						=  []
 for i in range(len(lowerCAmelCase__  ) - pat_len + 1  ):
  __a :  str						=  True
  for j in range(lowerCAmelCase__  ):
   if s[i + j] != pattern[j]:
    __a :  Tuple						=  False
    break
  if match_found:
   position.append(lowerCAmelCase__  )
 return position


if __name__ == "__main__":
      assert naive_pattern_search('ABCDEFG', 'DE') == [3]
      print(naive_pattern_search('ABAAABCDBBABCDDEBCABC', 'ABC'))
