



"""simple docstring"""
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 					a	:



  def __init__(						self :					List[Any]       ,		lowerCAmelCase :					Optional[Any]       ,		lowerCAmelCase :					List[Any]=13       ,		lowerCAmelCase :					List[Any]=30       ,		lowerCAmelCase :					Union[str, Any]=2       ,		lowerCAmelCase :					Union[str, Any]=3       ,		lowerCAmelCase :					int=True       ,		lowerCAmelCase :					Union[str, Any]=True       ,		lowerCAmelCase :					Dict=32       ,		lowerCAmelCase :					Optional[int]=5       ,		lowerCAmelCase :					Optional[int]=4       ,		lowerCAmelCase :					Union[str, Any]=37       ,		lowerCAmelCase :					Union[str, Any]="gelu"       ,		lowerCAmelCase :					Union[str, Any]=0.1       ,		lowerCAmelCase :					Optional[int]=0.1       ,		lowerCAmelCase :					List[Any]=10       ,		lowerCAmelCase :					Optional[Any]=0.0_2       ,		lowerCAmelCase :					Optional[Any]=3       ,		lowerCAmelCase :					str=None       ,		lowerCAmelCase :					List[Any]=2       ,		)     ->					List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Optional[int]     			=parent
     SCREAMING_SNAKE_CASE_: List[Any]     			=batch_size
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=image_size
     SCREAMING_SNAKE_CASE_: List[str]     			=patch_size
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=num_channels
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=is_training
     SCREAMING_SNAKE_CASE_: Tuple     			=use_labels
     SCREAMING_SNAKE_CASE_: int     			=hidden_size
     SCREAMING_SNAKE_CASE_: Optional[int]     			=num_hidden_layers
     SCREAMING_SNAKE_CASE_: Dict     			=num_attention_heads
     SCREAMING_SNAKE_CASE_: Optional[int]     			=intermediate_size
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=hidden_act
     SCREAMING_SNAKE_CASE_: str     			=hidden_dropout_prob
     SCREAMING_SNAKE_CASE_: Tuple     			=attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE_: Dict     			=type_sequence_label_size
     SCREAMING_SNAKE_CASE_: List[str]     			=initializer_range
     SCREAMING_SNAKE_CASE_: str     			=scope
     SCREAMING_SNAKE_CASE_: Any     			=encoder_stride

     # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
     SCREAMING_SNAKE_CASE_: Tuple     			=(image_size // patch_size) ** 2
     SCREAMING_SNAKE_CASE_: Any     			=num_patches + 2



  def 	lowerCamelCase__       (						self :					List[Any]		)     ->					List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: str     			=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

     SCREAMING_SNAKE_CASE_: List[Any]     			=None
     if self.use_labels:
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=ids_tensor([self.batch_size]       ,		self.type_sequence_label_size		)

     SCREAMING_SNAKE_CASE_: Optional[int]     			=self.get_config()

     return config, pixel_values, labels



  def 	lowerCamelCase__       (						self :					List[Any]		)     ->					Union[str, Any]:


     '''simple docstring'''




     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=lowerCAmelCase       ,		initializer_range=self.initializer_range       ,		encoder_stride=self.encoder_stride       ,		)



  def 	lowerCamelCase__       (						self :					Tuple       ,		lowerCAmelCase :					List[Any]       ,		lowerCAmelCase :					List[str]       ,		lowerCAmelCase :					Dict		)     ->					Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Optional[int]     			=DeiTModel(config=lowerCAmelCase		)
     model.to(lowerCAmelCase		)
     model.eval()
     SCREAMING_SNAKE_CASE_: Any     			=model(lowerCAmelCase		)
     self.parent.assertEqual(result.last_hidden_state.shape       ,		(self.batch_size, self.seq_length, self.hidden_size)		)



  def 	lowerCamelCase__       (						self :					Dict       ,		lowerCAmelCase :					Any       ,		lowerCAmelCase :					Tuple       ,		lowerCAmelCase :					Optional[Any]		)     ->					int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Any     			=DeiTForMaskedImageModeling(config=lowerCAmelCase		)
     model.to(lowerCAmelCase		)
     model.eval()
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=model(lowerCAmelCase		)
     self.parent.assertEqual(
         result.reconstruction.shape       ,		(self.batch_size, self.num_channels, self.image_size, self.image_size)		)

     # test greyscale images
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=1
     SCREAMING_SNAKE_CASE_: List[Any]     			=DeiTForMaskedImageModeling(lowerCAmelCase		)
     model.to(lowerCAmelCase		)
     model.eval()

     SCREAMING_SNAKE_CASE_: int     			=floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
     SCREAMING_SNAKE_CASE_: List[Any]     			=model(lowerCAmelCase		)
     self.parent.assertEqual(result.reconstruction.shape       ,		(self.batch_size, 1, self.image_size, self.image_size)		)



  def 	lowerCamelCase__       (						self :					int       ,		lowerCAmelCase :					Optional[Any]       ,		lowerCAmelCase :					str       ,		lowerCAmelCase :					List[Any]		)     ->					List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[Any]     			=self.type_sequence_label_size
     SCREAMING_SNAKE_CASE_: Tuple     			=DeiTForImageClassification(lowerCAmelCase		)
     model.to(lowerCAmelCase		)
     model.eval()
     SCREAMING_SNAKE_CASE_: Dict     			=model(lowerCAmelCase       ,		labels=lowerCAmelCase		)
     self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.type_sequence_label_size)		)

     # test greyscale images
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=1
     SCREAMING_SNAKE_CASE_: Optional[int]     			=DeiTForImageClassification(lowerCAmelCase		)
     model.to(lowerCAmelCase		)
     model.eval()

     SCREAMING_SNAKE_CASE_: Dict     			=floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
     SCREAMING_SNAKE_CASE_: Tuple     			=model(lowerCAmelCase       ,		labels=lowerCAmelCase		)
     self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.type_sequence_label_size)		)





  def 	lowerCamelCase__       (						self :					Dict		)     ->					int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[Any]     			=self.prepare_config_and_inputs()
     (
         (
         SCREAMING_SNAKE_CASE_
     )			,		(
         SCREAMING_SNAKE_CASE_
     )			,		(
         SCREAMING_SNAKE_CASE_
     )			,		
     ): List[str]     			=config_and_inputs
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			={"""pixel_values""": pixel_values}
     return config, inputs_dict







@require_torch
class 					a	(  UpperCAmelCase__	,      UpperCAmelCase__	,      unittest.TestCase       ):
  UpperCamelCase	:				Dict     						=					(
      (
          DeiTModel,
          DeiTForImageClassification,
          DeiTForImageClassificationWithTeacher,
          DeiTForMaskedImageModeling,
      )
      if is_torch_available()
      else ()
  )
  UpperCamelCase	:				List[str]     						=					(
      {
          'feature-extraction': DeiTModel,
          'image-classification': (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
      }
      if is_torch_available()
      else {}
  )

  UpperCamelCase	:				List[Any]     						=					False
  UpperCamelCase	:				List[str]     						=					False
  UpperCamelCase	:				Union[str, Any]     						=					False



  def 	lowerCamelCase__       (						self :					Any		)     ->					Optional[int]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Tuple     			=DeiTModelTester(self		)
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=ConfigTester(self       ,		config_class=lowerCAmelCase       ,		has_text_modality=lowerCAmelCase       ,		hidden_size=37		)



  def 	lowerCamelCase__       (						self :					Tuple		)     ->					List[str]:


     '''simple docstring'''




     self.config_tester.run_common_tests()



  @unittest.skip(reason="""DeiT does not use inputs_embeds"""		)
  def 	lowerCamelCase__       (						self :					int		)     ->					str:


     '''simple docstring'''




     pass



  def 	lowerCamelCase__       (						self :					Optional[int]		)     ->					List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Union[str, Any]     			=self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=model_class(lowerCAmelCase		)
        self.assertIsInstance(model.get_input_embeddings()       ,		(nn.Module)		)
        SCREAMING_SNAKE_CASE_: Optional[int]     			=model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(lowerCAmelCase       ,		nn.Linear		)		)



  def 	lowerCamelCase__       (						self :					Tuple		)     ->					int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: List[str]     			=self.model_tester.prepare_config_and_inputs_for_common()

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

        SCREAMING_SNAKE_CASE_: Tuple     			=["""pixel_values"""]
        self.assertListEqual(arg_names[:1]       ,		lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					Optional[Any]		)     ->					List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Optional[int]     			=self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					Dict		)     ->					int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: str     			=self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					int		)     ->					List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: str     			=self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					Optional[int]       ,		lowerCAmelCase :					Any       ,		lowerCAmelCase :					Any       ,		lowerCAmelCase :					Dict=False		)     ->					Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=super()._prepare_for_class(lowerCAmelCase       ,		lowerCAmelCase       ,		return_labels=lowerCAmelCase		)

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

     return inputs_dict



  def 	lowerCamelCase__       (						self :					Optional[Any]		)     ->					Optional[Any]:


     '''simple docstring'''




     if not self.model_tester.is_training:
        return

     SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Tuple     			=self.model_tester.prepare_config_and_inputs_for_common()
     SCREAMING_SNAKE_CASE_: List[Any]     			=True

     for model_class in self.all_model_classes:
        # DeiTForImageClassificationWithTeacher supports inference-only
        if (
            model_class in get_values(lowerCAmelCase		)
            or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
        ):
           continue
        SCREAMING_SNAKE_CASE_: List[Any]     			=model_class(lowerCAmelCase		)
        model.to(lowerCAmelCase		)
        model.train()
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=self._prepare_for_class(lowerCAmelCase       ,		lowerCAmelCase       ,		return_labels=lowerCAmelCase		)
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=model(**lowerCAmelCase		).loss
        loss.backward()



  def 	lowerCamelCase__       (						self :					Tuple		)     ->					Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Optional[int]     			=self.model_tester.prepare_config_and_inputs_for_common()
     if not self.model_tester.is_training:
        return

     SCREAMING_SNAKE_CASE_: List[str]     			=False
     SCREAMING_SNAKE_CASE_: int     			=True

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



  def 	lowerCamelCase__       (						self :					Union[str, Any]		)     ->					int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Tuple     			=self.model_tester.prepare_config_and_inputs_for_common()

     SCREAMING_SNAKE_CASE_: Any     			=[
         {"""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(lowerCAmelCase		),
                *get_values(lowerCAmelCase		),
            ]
            or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
        ):
           continue

        for problem_type in problem_types:
           with self.subTest(msg=f'''Testing {model_class} with {problem_type["title"]}'''		):
              SCREAMING_SNAKE_CASE_: Dict     			=problem_type["""title"""]
              SCREAMING_SNAKE_CASE_: Any     			=problem_type["""num_labels"""]

              SCREAMING_SNAKE_CASE_: Any     			=model_class(lowerCAmelCase		)
              model.to(lowerCAmelCase		)
              model.train()

              SCREAMING_SNAKE_CASE_: Optional[int]     			=self._prepare_for_class(lowerCAmelCase       ,		lowerCAmelCase       ,		return_labels=lowerCAmelCase		)

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

              SCREAMING_SNAKE_CASE_: Optional[Any]     			=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=lowerCAmelCase		) as warning_list:
                 SCREAMING_SNAKE_CASE_: Dict     			=model(**lowerCAmelCase		).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 	lowerCamelCase__       (						self :					Union[str, Any]		)     ->					Union[str, Any]:


     '''simple docstring'''




     for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        SCREAMING_SNAKE_CASE_: str     			=DeiTModel.from_pretrained(lowerCAmelCase		)
        self.assertIsNotNone(lowerCAmelCase		)







def      __magic_name__				(							):
   SCREAMING_SNAKE_CASE_: Optional[int]     			=Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
   return image
@require_torch
@require_vision
class 					a	(  unittest.TestCase       ):



  @cached_property
  def 	lowerCamelCase__       (						self :					Any		)     ->					List[str]:


     '''simple docstring'''




     return (
         DeiTImageProcessor.from_pretrained("""facebook/deit-base-distilled-patch16-224"""		)
         if is_vision_available()
         else None
     )



  @slow
  def 	lowerCamelCase__       (						self :					int		)     ->					Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Any     			=DeiTForImageClassificationWithTeacher.from_pretrained("""facebook/deit-base-distilled-patch16-224"""		).to(
         lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: List[str]     			=self.default_image_processor
     SCREAMING_SNAKE_CASE_: Optional[int]     			=prepare_img()
     SCREAMING_SNAKE_CASE_: int     			=image_processor(images=lowerCAmelCase       ,		return_tensors="""pt"""		).to(lowerCAmelCase		)

     # forward pass
     with torch.no_grad():
        SCREAMING_SNAKE_CASE_: Any     			=model(**lowerCAmelCase		)

     # verify the logits
     SCREAMING_SNAKE_CASE_: List[str]     			=torch.Size((1, 1000)		)
     self.assertEqual(outputs.logits.shape       ,		lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: int     			=torch.tensor([-1.0_2_6_6, 0.1_9_1_2, -1.2_8_6_1]		).to(lowerCAmelCase		)

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





  @slow
  @require_accelerate
  @require_torch_gpu
  def 	lowerCamelCase__       (						self :					int		)     ->					Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: str     			=DeiTModel.from_pretrained(
         """facebook/deit-base-distilled-patch16-224"""       ,		torch_dtype=torch.floataa       ,		device_map="""auto"""		)
     SCREAMING_SNAKE_CASE_: Tuple     			=self.default_image_processor

     SCREAMING_SNAKE_CASE_: Optional[Any]     			=prepare_img()
     SCREAMING_SNAKE_CASE_: List[Any]     			=image_processor(images=lowerCAmelCase       ,		return_tensors="""pt"""		)
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=inputs.pixel_values.to(lowerCAmelCase		)

     # forward pass to make sure inference works in fp16
     with torch.no_grad():
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=model(lowerCAmelCase		)








"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


_UpperCAmelCase     =			{
    """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/config.json""",
    """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/config.json""",
    """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/config.json""",
    """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json""",
    """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/config.json""",
    """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/config.json""",
    """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/config.json""",
    """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json""",
}
class 					a	(  UpperCAmelCase__       ):
  UpperCamelCase	:				Any     						=					'albert'



  def __init__(						self :					Dict       ,		lowerCAmelCase :					List[str]=3_0000       ,		lowerCAmelCase :					List[Any]=128       ,		lowerCAmelCase :					List[str]=4096       ,		lowerCAmelCase :					str=12       ,		lowerCAmelCase :					str=1       ,		lowerCAmelCase :					Tuple=64       ,		lowerCAmelCase :					Dict=1_6384       ,		lowerCAmelCase :					int=1       ,		lowerCAmelCase :					str="gelu_new"       ,		lowerCAmelCase :					Dict=0       ,		lowerCAmelCase :					Optional[Any]=0       ,		lowerCAmelCase :					str=512       ,		lowerCAmelCase :					Optional[int]=2       ,		lowerCAmelCase :					List[Any]=0.0_2       ,		lowerCAmelCase :					Union[str, Any]=1E-12       ,		lowerCAmelCase :					Tuple=0.1       ,		lowerCAmelCase :					List[Any]="absolute"       ,		lowerCAmelCase :					List[Any]=0       ,		lowerCAmelCase :					int=2       ,		lowerCAmelCase :					Optional[int]=3       ,		**lowerCAmelCase :					int       ,		)     ->					Tuple:


     '''simple docstring'''




     super().__init__(pad_token_id=lowerCAmelCase       ,		bos_token_id=lowerCAmelCase       ,		eos_token_id=lowerCAmelCase       ,		**lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: List[str]     			=vocab_size
     SCREAMING_SNAKE_CASE_: Optional[int]     			=embedding_size
     SCREAMING_SNAKE_CASE_: Optional[int]     			=hidden_size
     SCREAMING_SNAKE_CASE_: Tuple     			=num_hidden_layers
     SCREAMING_SNAKE_CASE_: Any     			=num_hidden_groups
     SCREAMING_SNAKE_CASE_: List[Any]     			=num_attention_heads
     SCREAMING_SNAKE_CASE_: List[Any]     			=inner_group_num
     SCREAMING_SNAKE_CASE_: Optional[int]     			=hidden_act
     SCREAMING_SNAKE_CASE_: int     			=intermediate_size
     SCREAMING_SNAKE_CASE_: Any     			=hidden_dropout_prob
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE_: int     			=max_position_embeddings
     SCREAMING_SNAKE_CASE_: Any     			=type_vocab_size
     SCREAMING_SNAKE_CASE_: int     			=initializer_range
     SCREAMING_SNAKE_CASE_: List[Any]     			=layer_norm_eps
     SCREAMING_SNAKE_CASE_: Dict     			=classifier_dropout_prob
     SCREAMING_SNAKE_CASE_: int     			=position_embedding_type







class 					a	(  UpperCAmelCase__       ):



  @property
  def 	lowerCamelCase__       (						self :					Optional[int]		)     ->					Mapping[str, Mapping[int, str]]:


     '''simple docstring'''




     if self.task == "multiple-choice":
        SCREAMING_SNAKE_CASE_: str     			={0: """batch""", 1: """choice""", 2: """sequence"""}
     else:
        SCREAMING_SNAKE_CASE_: Dict     			={0: """batch""", 1: """sequence"""}
     return OrderedDict(
         [
             ("""input_ids""", dynamic_axis),
             ("""attention_mask""", dynamic_axis),
             ("""token_type_ids""", dynamic_axis),
         ]		)



