



import inspect
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import (
        MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
        MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
        MODEL_MAPPING,
        DeiTForImageClassification,
        DeiTForImageClassificationWithTeacher,
        DeiTForMaskedImageModeling,
        DeiTModel,
    )
    from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import DeiTImageProcessor



class     lowerCAmelCase_		:






       """simple docstring"""




       def __init__(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_=13,      SCREAMING_SNAKE_CASE_=30,      SCREAMING_SNAKE_CASE_=2,      SCREAMING_SNAKE_CASE_=3,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=32,      SCREAMING_SNAKE_CASE_=5,      SCREAMING_SNAKE_CASE_=4,      SCREAMING_SNAKE_CASE_=37,      SCREAMING_SNAKE_CASE_="gelu",      SCREAMING_SNAKE_CASE_=0.1,      SCREAMING_SNAKE_CASE_=0.1,      SCREAMING_SNAKE_CASE_=10,      SCREAMING_SNAKE_CASE_=0.02,      SCREAMING_SNAKE_CASE_=3,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=2,      )					->		int:
          UpperCamelCase   :  int						     =     parent
          UpperCamelCase   :  Tuple						     =     batch_size
          UpperCamelCase   :  Dict						     =     image_size
          UpperCamelCase   :  Tuple						     =     patch_size
          UpperCamelCase   :  int						     =     num_channels
          UpperCamelCase   :  Dict						     =     is_training
          UpperCamelCase   :  Union[str, Any]						     =     use_labels
          UpperCamelCase   :  List[Any]						     =     hidden_size
          UpperCamelCase   :  Any						     =     num_hidden_layers
          UpperCamelCase   :  Optional[int]						     =     num_attention_heads
          UpperCamelCase   :  int						     =     intermediate_size
          UpperCamelCase   :  List[str]						     =     hidden_act
          UpperCamelCase   :  str						     =     hidden_dropout_prob
          UpperCamelCase   :  Optional[Any]						     =     attention_probs_dropout_prob
          UpperCamelCase   :  str						     =     type_sequence_label_size
          UpperCamelCase   :  str						     =     initializer_range
          UpperCamelCase   :  int						     =     scope
          UpperCamelCase   :  Tuple						     =     encoder_stride

          # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
          UpperCamelCase   :  Dict						     =     (image_size // patch_size) ** 2
          UpperCamelCase   :  Optional[Any]						     =     num_patches + 2
       def  snake_case_	(      self	)					->		Tuple:
          UpperCamelCase   :  Optional[Any]						     =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

          UpperCamelCase   :  Tuple						     =     None
          if self.use_labels:
             UpperCamelCase   :  int						     =     ids_tensor([self.batch_size],      self.type_sequence_label_size	)

          UpperCamelCase   :  Union[str, Any]						     =     self.get_config()

          return config, pixel_values, labels
       def  snake_case_	(      self	)					->		Any:
          return DeiTConfig(
              image_size=self.image_size,      patch_size=self.patch_size,      num_channels=self.num_channels,      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,      is_decoder=a__,      initializer_range=self.initializer_range,      encoder_stride=self.encoder_stride,      )
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Tuple:
          UpperCamelCase   :  List[Any]						     =     DeiTModel(config=a__	)
          model.to(a__	)
          model.eval()
          UpperCamelCase   :  List[Any]						     =     model(a__	)
          self.parent.assertEqual(result.last_hidden_state.shape,      (self.batch_size, self.seq_length, self.hidden_size)	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Tuple:
          UpperCamelCase   :  Tuple						     =     DeiTForMaskedImageModeling(config=a__	)
          model.to(a__	)
          model.eval()
          UpperCamelCase   :  Optional[int]						     =     model(a__	)
          self.parent.assertEqual(
              result.reconstruction.shape,      (self.batch_size, self.num_channels, self.image_size, self.image_size)	)

          # test greyscale images
          UpperCamelCase   :  Any						     =     1
          UpperCamelCase   :  List[str]						     =     DeiTForMaskedImageModeling(a__	)
          model.to(a__	)
          model.eval()

          UpperCamelCase   :  Dict						     =     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
          UpperCamelCase   :  Union[str, Any]						     =     model(a__	)
          self.parent.assertEqual(result.reconstruction.shape,      (self.batch_size, 1, self.image_size, self.image_size)	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Optional[Any]:
          UpperCamelCase   :  Tuple						     =     self.type_sequence_label_size
          UpperCamelCase   :  int						     =     DeiTForImageClassification(a__	)
          model.to(a__	)
          model.eval()
          UpperCamelCase   :  str						     =     model(a__,      labels=a__	)
          self.parent.assertEqual(result.logits.shape,      (self.batch_size, self.type_sequence_label_size)	)

          # test greyscale images
          UpperCamelCase   :  List[str]						     =     1
          UpperCamelCase   :  List[str]						     =     DeiTForImageClassification(a__	)
          model.to(a__	)
          model.eval()

          UpperCamelCase   :  Tuple						     =     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
          UpperCamelCase   :  Optional[int]						     =     model(a__,      labels=a__	)
          self.parent.assertEqual(result.logits.shape,      (self.batch_size, self.type_sequence_label_size)	)






       def  snake_case_	(      self	)					->		Any:
          UpperCamelCase   :  Tuple						     =     self.prepare_config_and_inputs()
          (
              (
              UpperCamelCase  
          )     ,					(
              UpperCamelCase  
          )     ,					(
              UpperCamelCase  
          )     ,					  
          )   :  List[Any]						     =     config_and_inputs
          UpperCamelCase   :  str						     =     {'pixel_values': pixel_values}
          return config, inputs_dict






@require_torch
class     lowerCAmelCase_		(  __a	,   __a	,   unittest.TestCase							):






       """simple docstring"""




       UpperCAmelCase__    : Optional[Any]       					=  (
           (
               DeiTModel,
               DeiTForImageClassification,
               DeiTForImageClassificationWithTeacher,
               DeiTForMaskedImageModeling,
           )
           if is_torch_available()
           else ()
       )
       UpperCAmelCase__    : Optional[Any]       					=  (
           {
               """feature-extraction""": DeiTModel,
               """image-classification""": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
           }
           if is_torch_available()
           else {}
       )

       UpperCAmelCase__    : Tuple       					=  False
       UpperCAmelCase__    : str       					=  False
       UpperCAmelCase__    : Union[str, Any]       					=  False
       def  snake_case_	(      self	)					->		Union[str, Any]:
          UpperCamelCase   :  List[Any]						     =     DeiTModelTester(self	)
          UpperCamelCase   :  Any						     =     ConfigTester(self,      config_class=a__,      has_text_modality=a__,      hidden_size=37	)
       def  snake_case_	(      self	)					->		int:
          self.config_tester.run_common_tests()
       @unittest.skip(reason='DeiT does not use inputs_embeds'	)
       def  snake_case_	(      self	)					->		Optional[Any]:
          pass
       def  snake_case_	(      self	)					->		Dict:
          UpperCamelCase     ,					UpperCamelCase   :  List[Any]						     =     self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
             UpperCamelCase   :  int						     =     model_class(a__	)
             self.assertIsInstance(model.get_input_embeddings(),      (nn.Module)	)
             UpperCamelCase   :  Optional[Any]						     =     model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(a__,      nn.Linear	)	)
       def  snake_case_	(      self	)					->		Optional[int]:
          UpperCamelCase     ,					UpperCamelCase   :  Optional[Any]						     =     self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
             UpperCamelCase   :  int						     =     model_class(a__	)
             UpperCamelCase   :  int						     =     inspect.signature(model.forward	)
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             UpperCamelCase   :  Optional[Any]						     =     [*signature.parameters.keys()]

             UpperCamelCase   :  Optional[Any]						     =     ['pixel_values']
             self.assertListEqual(arg_names[:1],      a__	)
       def  snake_case_	(      self	)					->		List[Any]:
          UpperCamelCase   :  Optional[int]						     =     self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*a__	)
       def  snake_case_	(      self	)					->		Optional[Any]:
          UpperCamelCase   :  int						     =     self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_image_modeling(*a__	)
       def  snake_case_	(      self	)					->		Tuple:
          UpperCamelCase   :  str						     =     self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*a__	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_=False	)					->		Dict:
          UpperCamelCase   :  str						     =     super()._prepare_for_class(a__,      a__,      return_labels=a__	)

          if return_labels:
             if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
                del inputs_dict["labels"]

          return inputs_dict
       def  snake_case_	(      self	)					->		List[Any]:
          if not self.model_tester.is_training:
             return

          UpperCamelCase     ,					UpperCamelCase   :  str						     =     self.model_tester.prepare_config_and_inputs_for_common()
          UpperCamelCase   :  int						     =     True

          for model_class in self.all_model_classes:
             # DeiTForImageClassificationWithTeacher supports inference-only
             if (
                 model_class in get_values(a__	)
                 or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
             ):
                continue
             UpperCamelCase   :  List[Any]						     =     model_class(a__	)
             model.to(a__	)
             model.train()
             UpperCamelCase   :  str						     =     self._prepare_for_class(a__,      a__,      return_labels=a__	)
             UpperCamelCase   :  Tuple						     =     model(**a__	).loss
             loss.backward()
       def  snake_case_	(      self	)					->		Dict:
          UpperCamelCase     ,					UpperCamelCase   :  Union[str, Any]						     =     self.model_tester.prepare_config_and_inputs_for_common()
          if not self.model_tester.is_training:
             return

          UpperCamelCase   :  Optional[Any]						     =     False
          UpperCamelCase   :  Any						     =     True

          for model_class in self.all_model_classes:
             if model_class in get_values(a__	) or not model_class.supports_gradient_checkpointing:
                continue
             # DeiTForImageClassificationWithTeacher supports inference-only
             if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
                continue
             UpperCamelCase   :  str						     =     model_class(a__	)
             model.gradient_checkpointing_enable()
             model.to(a__	)
             model.train()
             UpperCamelCase   :  List[Any]						     =     self._prepare_for_class(a__,      a__,      return_labels=a__	)
             UpperCamelCase   :  Tuple						     =     model(**a__	).loss
             loss.backward()
       def  snake_case_	(      self	)					->		Tuple:
          UpperCamelCase     ,					UpperCamelCase   :  str						     =     self.model_tester.prepare_config_and_inputs_for_common()

          UpperCamelCase   :  Tuple						     =     [
              {'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
              {'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
              {'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
          ]

          for model_class in self.all_model_classes:
             if (
                 model_class
                 not in [
                     *get_values(a__	),
                     *get_values(a__	),
                 ]
                 or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
             ):
                continue

             for problem_type in problem_types:
                with self.subTest(msg=F"""Testing {model_class} with {problem_type["title"]}"""	):
                   UpperCamelCase   :  Optional[Any]						     =     problem_type['title']
                   UpperCamelCase   :  Tuple						     =     problem_type['num_labels']

                   UpperCamelCase   :  Any						     =     model_class(a__	)
                   model.to(a__	)
                   model.train()

                   UpperCamelCase   :  str						     =     self._prepare_for_class(a__,      a__,      return_labels=a__	)

                   if problem_type["num_labels"] > 1:
                      UpperCamelCase   :  int						     =     inputs['labels'].unsqueeze(1	).repeat(1,      problem_type['num_labels']	)

                   UpperCamelCase   :  Dict						     =     inputs['labels'].to(problem_type['dtype']	)

                   # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
                   # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
                   # they have the same size." which is a symptom something in wrong for the regression problem.
                   # See https://github.com/huggingface/transformers/issues/11780
                   with warnings.catch_warnings(record=a__	) as warning_list:
                      UpperCamelCase   :  Optional[Any]						     =     model(**a__	).loss
                   for w in warning_list:
                      if "Using a target size that is different to the input size" in str(w.message	):
                         raise ValueError(
                             F"""Something is going wrong in the regression problem: intercepted {w.message}"""	)

                   loss.backward()






       @slow
       def  snake_case_	(      self	)					->		Dict:
          for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCamelCase   :  Optional[Any]						     =     DeiTModel.from_pretrained(a__	)
             self.assertIsNotNone(a__	)






def 		UpperCamelCase     (       )     ->					Optional[int]:
   UpperCamelCase   :  Union[str, Any]						     =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
   return image



@require_torch
@require_vision
class     lowerCAmelCase_		(  unittest.TestCase							):






       """simple docstring"""




       @cached_property
       def  snake_case_	(      self	)					->		int:
          return (
              DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224'	)
              if is_vision_available()
              else None
          )
       @slow
       def  snake_case_	(      self	)					->		str:
          UpperCamelCase   :  Optional[int]						     =     DeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224'	).to(
              a__	)

          UpperCamelCase   :  str						     =     self.default_image_processor
          UpperCamelCase   :  Union[str, Any]						     =     prepare_img()
          UpperCamelCase   :  str						     =     image_processor(images=a__,      return_tensors='pt'	).to(a__	)

          # forward pass
          with torch.no_grad():
             UpperCamelCase   :  Any						     =     model(**a__	)

          # verify the logits
          UpperCamelCase   :  Tuple						     =     torch.Size((1, 1000)	)
          self.assertEqual(outputs.logits.shape,      a__	)

          UpperCamelCase   :  int						     =     torch.tensor([-1.02_66, 0.19_12, -1.28_61]	).to(a__	)

          self.assertTrue(torch.allclose(outputs.logits[0, :3],      a__,      atol=1e-4	)	)






       @slow
       @require_accelerate
       @require_torch_gpu
       def  snake_case_	(      self	)					->		Optional[int]:
          UpperCamelCase   :  int						     =     DeiTModel.from_pretrained(
              'facebook/deit-base-distilled-patch16-224',      torch_dtype=torch.floataa,      device_map='auto'	)
          UpperCamelCase   :  Any						     =     self.default_image_processor

          UpperCamelCase   :  List[str]						     =     prepare_img()
          UpperCamelCase   :  List[str]						     =     image_processor(images=a__,      return_tensors='pt'	)
          UpperCamelCase   :  Dict						     =     inputs.pixel_values.to(a__	)

          # forward pass to make sure inference works in fp16
          with torch.no_grad():
             UpperCamelCase   :  Union[str, Any]						     =     model(a__	)








import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, 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():
    import jax.numpy as jnp

    from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
        FlaxRobertaPreLayerNormForCausalLM,
        FlaxRobertaPreLayerNormForMaskedLM,
        FlaxRobertaPreLayerNormForMultipleChoice,
        FlaxRobertaPreLayerNormForQuestionAnswering,
        FlaxRobertaPreLayerNormForSequenceClassification,
        FlaxRobertaPreLayerNormForTokenClassification,
        FlaxRobertaPreLayerNormModel,
    )



class     lowerCAmelCase_		(  unittest.TestCase							):
       def __init__(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_=13,      SCREAMING_SNAKE_CASE_=7,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=99,      SCREAMING_SNAKE_CASE_=32,      SCREAMING_SNAKE_CASE_=5,      SCREAMING_SNAKE_CASE_=4,      SCREAMING_SNAKE_CASE_=37,      SCREAMING_SNAKE_CASE_="gelu",      SCREAMING_SNAKE_CASE_=0.1,      SCREAMING_SNAKE_CASE_=0.1,      SCREAMING_SNAKE_CASE_=512,      SCREAMING_SNAKE_CASE_=16,      SCREAMING_SNAKE_CASE_=2,      SCREAMING_SNAKE_CASE_=0.02,      SCREAMING_SNAKE_CASE_=4,      )					->		Dict:
          UpperCamelCase   :  Optional[int]						     =     parent
          UpperCamelCase   :  Optional[int]						     =     batch_size
          UpperCamelCase   :  Optional[int]						     =     seq_length
          UpperCamelCase   :  Any						     =     is_training
          UpperCamelCase   :  Tuple						     =     use_attention_mask
          UpperCamelCase   :  Dict						     =     use_token_type_ids
          UpperCamelCase   :  Union[str, Any]						     =     use_labels
          UpperCamelCase   :  Any						     =     vocab_size
          UpperCamelCase   :  Any						     =     hidden_size
          UpperCamelCase   :  str						     =     num_hidden_layers
          UpperCamelCase   :  Optional[int]						     =     num_attention_heads
          UpperCamelCase   :  Union[str, Any]						     =     intermediate_size
          UpperCamelCase   :  List[Any]						     =     hidden_act
          UpperCamelCase   :  Any						     =     hidden_dropout_prob
          UpperCamelCase   :  Dict						     =     attention_probs_dropout_prob
          UpperCamelCase   :  int						     =     max_position_embeddings
          UpperCamelCase   :  int						     =     type_vocab_size
          UpperCamelCase   :  Optional[int]						     =     type_sequence_label_size
          UpperCamelCase   :  str						     =     initializer_range
          UpperCamelCase   :  Tuple						     =     num_choices
       def  snake_case_	(      self	)					->		Tuple:
          UpperCamelCase   :  Tuple						     =     ids_tensor([self.batch_size, self.seq_length],      self.vocab_size	)

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

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

          UpperCamelCase   :  Optional[int]						     =     RobertaPreLayerNormConfig(
              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=SCREAMING_SNAKE_CASE_,      initializer_range=self.initializer_range,      )

          return config, input_ids, token_type_ids, attention_mask
       def  snake_case_	(      self	)					->		int:
          UpperCamelCase   :  List[str]						     =     self.prepare_config_and_inputs()
          UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase   :  Any						     =     config_and_inputs
          UpperCamelCase   :  Union[str, Any]						     =     {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
          return config, inputs_dict






       def  snake_case_	(      self	)					->		List[str]:
          UpperCamelCase   :  int						     =     self.prepare_config_and_inputs()
          UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase   :  Optional[int]						     =     config_and_inputs

          UpperCamelCase   :  Dict						     =     True
          UpperCamelCase   :  List[Any]						     =     floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
          UpperCamelCase   :  List[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
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class     lowerCAmelCase_		(  a__	,   unittest.TestCase							):
       UpperCAmelCase__    : Union[str, Any]       					=  True

       UpperCAmelCase__    : Optional[Any]       					=  (
           (
               FlaxRobertaPreLayerNormModel,
               FlaxRobertaPreLayerNormForCausalLM,
               FlaxRobertaPreLayerNormForMaskedLM,
               FlaxRobertaPreLayerNormForSequenceClassification,
               FlaxRobertaPreLayerNormForTokenClassification,
               FlaxRobertaPreLayerNormForMultipleChoice,
               FlaxRobertaPreLayerNormForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )
       def  snake_case_	(      self	)					->		Optional[int]:
          UpperCamelCase   :  int						     =     FlaxRobertaPreLayerNormModelTester(self	)






       @slow
       def  snake_case_	(      self	)					->		List[str]:
          for model_class_name in self.all_model_classes:
             UpperCamelCase   :  List[str]						     =     model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40',      from_pt=SCREAMING_SNAKE_CASE_	)
             UpperCamelCase   :  List[Any]						     =     model(np.ones((1, 1)	)	)
             self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)






@require_flax
class     lowerCAmelCase_		(  unittest.TestCase							):
       @slow
       def  snake_case_	(      self	)					->		Dict:
          UpperCamelCase   :  Union[str, Any]						     =     FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40',      from_pt=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Optional[Any]						     =     np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]],      dtype=jnp.intaa	)
          UpperCamelCase   :  Union[str, Any]						     =     model(SCREAMING_SNAKE_CASE_	)[0]
          UpperCamelCase   :  List[Any]						     =     [1, 11, 5_0265]
          self.assertEqual(list(output.shape	),      SCREAMING_SNAKE_CASE_	)
          # compare the actual values for a slice.
          UpperCamelCase   :  Optional[int]						     =     np.array(
              [[[40.48_80, 18.01_99, -5.23_67], [-1.88_77, -4.08_85, 10.70_85], [-2.26_13, -5.61_10, 7.26_65]]],      dtype=np.floataa	)
          self.assertTrue(np.allclose(output[:, :3, :3],      SCREAMING_SNAKE_CASE_,      atol=1e-4	)	)






       @slow
       def  snake_case_	(      self	)					->		List[Any]:
          UpperCamelCase   :  Optional[int]						     =     FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40',      from_pt=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  List[str]						     =     np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]],      dtype=jnp.intaa	)
          UpperCamelCase   :  Dict						     =     model(SCREAMING_SNAKE_CASE_	)[0]
          # compare the actual values for a slice.
          UpperCamelCase   :  Any						     =     np.array(
              [[[0.02_08, -0.03_56, 0.02_37], [-0.15_69, -0.04_11, -0.26_26], [0.18_79, 0.01_25, -0.00_89]]],      dtype=np.floataa	)
          self.assertTrue(np.allclose(output[:, :3, :3],      SCREAMING_SNAKE_CASE_,      atol=1e-4	)	)



