






"""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    __lowerCamelCase    (  unittest.TestCase  ):

   def __init__(					self ,				snake_case_ ,				snake_case_=13 ,				snake_case_=7 ,				snake_case_=True ,				snake_case_=True ,				snake_case_=True ,				snake_case_=True ,				snake_case_=99 ,				snake_case_=32 ,				snake_case_=5 ,				snake_case_=4 ,				snake_case_=37 ,				snake_case_="gelu" ,				snake_case_=0.1 ,				snake_case_=0.1 ,				snake_case_=512 ,				snake_case_=16 ,				snake_case_=2 ,				snake_case_=0.02 ,				snake_case_=4 ,				)		->       List[Any]:
       UpperCamelCase__         =    parent
       UpperCamelCase__         =    batch_size
       UpperCamelCase__         =    seq_length
       UpperCamelCase__         =    is_training
       UpperCamelCase__         =    use_attention_mask
       UpperCamelCase__         =    use_token_type_ids
       UpperCamelCase__         =    use_labels
       UpperCamelCase__         =    vocab_size
       UpperCamelCase__         =    hidden_size
       UpperCamelCase__         =    num_hidden_layers
       UpperCamelCase__         =    num_attention_heads
       UpperCamelCase__         =    intermediate_size
       UpperCamelCase__         =    hidden_act
       UpperCamelCase__         =    hidden_dropout_prob
       UpperCamelCase__         =    attention_probs_dropout_prob
       UpperCamelCase__         =    max_position_embeddings
       UpperCamelCase__         =    type_vocab_size
       UpperCamelCase__         =    type_sequence_label_size
       UpperCamelCase__         =    initializer_range
       UpperCamelCase__         =    num_choices

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       UpperCamelCase__         =    ids_tensor([self.batch_size, self.seq_length] ,				self.vocab_size      )

       UpperCamelCase__         =    None
       if self.use_attention_mask:
           UpperCamelCase__         =    random_attention_mask([self.batch_size, self.seq_length]      )

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

       UpperCamelCase__         =    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 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[str]:
       UpperCamelCase__         =    self.prepare_config_and_inputs()
       UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__         =    config_and_inputs
       UpperCamelCase__         =    {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
       return config, inputs_dict







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Tuple:
       UpperCamelCase__         =    self.prepare_config_and_inputs()
       UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__         =    config_and_inputs

       UpperCamelCase__         =    True
       UpperCamelCase__         =    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
       UpperCamelCase__         =    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    __lowerCamelCase    (  _a							,	unittest.TestCase  ):
   a      :				Tuple 						=True

   a      :				Tuple 						=(
       (
           FlaxRobertaModel,
           FlaxRobertaForCausalLM,
           FlaxRobertaForMaskedLM,
           FlaxRobertaForSequenceClassification,
           FlaxRobertaForTokenClassification,
           FlaxRobertaForMultipleChoice,
           FlaxRobertaForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       UpperCamelCase__         =    FlaxRobertaModelTester(self      )







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












"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__					: List[str]=	logging.get_logger(__name__)
class    __lowerCamelCase    (  _a  ):
   a      :				Optional[int] 						="""timm_backbone"""

   def __init__(					self ,				snake_case_=None ,				snake_case_=3 ,				snake_case_=True ,				snake_case_=True ,				snake_case_=None ,				**snake_case_ ,				)		->       Dict:
       super().__init__(**snake_case_      )
       UpperCamelCase__         =    backbone
       UpperCamelCase__         =    num_channels
       UpperCamelCase__         =    features_only
       UpperCamelCase__         =    use_pretrained_backbone
       UpperCamelCase__         =    True
       UpperCamelCase__         =    out_indices if out_indices is not None else (-1,)




