





import collections
import inspect
import unittest

from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
    import torch
    from torch import nn

    from transformers import (
        FocalNetBackbone,
        FocalNetForImageClassification,
        FocalNetForMaskedImageModeling,
        FocalNetModel,
    )
    from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor

class    lowerCAmelCase							:
 def __init__(      self  :      Optional[int]			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Any=13			,     UpperCAmelCase  :      List[str]=32			,     UpperCAmelCase  :      Any=2			,     UpperCAmelCase  :      Union[str, Any]=3			,     UpperCAmelCase  :      Union[str, Any]=16			,     UpperCAmelCase  :      Tuple=[32, 64, 128]			,     UpperCAmelCase  :      Optional[Any]=[1, 2, 1]			,     UpperCAmelCase  :      Dict=[2, 2, 4]			,     UpperCAmelCase  :      Union[str, Any]=2			,     UpperCAmelCase  :      str=2.0			,     UpperCAmelCase  :      int=True			,     UpperCAmelCase  :      Tuple=0.0			,     UpperCAmelCase  :      Dict=0.0			,     UpperCAmelCase  :      int=0.1			,     UpperCAmelCase  :      Tuple="gelu"			,     UpperCAmelCase  :      Dict=False			,     UpperCAmelCase  :      Union[str, Any]=True			,     UpperCAmelCase  :      Union[str, Any]=0.0_2			,     UpperCAmelCase  :      Tuple=1e-5			,     UpperCAmelCase  :      Optional[int]=True			,     UpperCAmelCase  :      Optional[Any]=None			,     UpperCAmelCase  :      List[str]=True			,     UpperCAmelCase  :      Union[str, Any]=10			,     UpperCAmelCase  :      Union[str, Any]=8			,     UpperCAmelCase  :      Dict=["stage1", "stage2"]			,     UpperCAmelCase  :      List[Any]=[1, 2]			,     )			->	Any:
     lowerCamelCase__    :      Dict					 =		parent
     lowerCamelCase__    :      str					 =		batch_size
     lowerCamelCase__    :      List[Any]					 =		image_size
     lowerCamelCase__    :      Optional[Any]					 =		patch_size
     lowerCamelCase__    :      Any					 =		num_channels
     lowerCamelCase__    :      List[Any]					 =		embed_dim
     lowerCamelCase__    :      Optional[Any]					 =		hidden_sizes
     lowerCamelCase__    :      Any					 =		depths
     lowerCamelCase__    :      List[str]					 =		num_heads
     lowerCamelCase__    :      Any					 =		window_size
     lowerCamelCase__    :      int					 =		mlp_ratio
     lowerCamelCase__    :      str					 =		qkv_bias
     lowerCamelCase__    :      Any					 =		hidden_dropout_prob
     lowerCamelCase__    :      Tuple					 =		attention_probs_dropout_prob
     lowerCamelCase__    :      str					 =		drop_path_rate
     lowerCamelCase__    :      int					 =		hidden_act
     lowerCamelCase__    :      Any					 =		use_absolute_embeddings
     lowerCamelCase__    :      List[str]					 =		patch_norm
     lowerCamelCase__    :      Dict					 =		layer_norm_eps
     lowerCamelCase__    :      str					 =		initializer_range
     lowerCamelCase__    :      str					 =		is_training
     lowerCamelCase__    :      Tuple					 =		scope
     lowerCamelCase__    :      int					 =		use_labels
     lowerCamelCase__    :      Optional[int]					 =		type_sequence_label_size
     lowerCamelCase__    :      str					 =		encoder_stride
     lowerCamelCase__    :      Union[str, Any]					 =		out_features
     lowerCamelCase__    :      Dict					 =		out_indices
 def      A_	(      self  :      List[Any]	)			->	Dict:
     lowerCamelCase__    :      Union[str, Any]					 =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

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

     return config, pixel_values, labels
 def      A_	(      self  :      Optional[int]	)			->	Any:
     return FocalNetConfig(
         image_size=self.image_size			,     patch_size=self.patch_size			,     num_channels=self.num_channels			,     embed_dim=self.embed_dim			,     hidden_sizes=self.hidden_sizes			,     depths=self.depths			,     num_heads=self.num_heads			,     window_size=self.window_size			,     mlp_ratio=self.mlp_ratio			,     qkv_bias=self.qkv_bias			,     hidden_dropout_prob=self.hidden_dropout_prob			,     attention_probs_dropout_prob=self.attention_probs_dropout_prob			,     drop_path_rate=self.drop_path_rate			,     hidden_act=self.hidden_act			,     use_absolute_embeddings=self.use_absolute_embeddings			,     path_norm=self.patch_norm			,     layer_norm_eps=self.layer_norm_eps			,     initializer_range=self.initializer_range			,     encoder_stride=self.encoder_stride			,     out_features=self.out_features			,     out_indices=self.out_indices			,     )
 def      A_	(      self  :      List[Any]			,     UpperCAmelCase  :      Union[str, Any]			,     UpperCAmelCase  :      int			,     UpperCAmelCase  :      Tuple	)			->	int:
     lowerCamelCase__    :      str					 =		FocalNetModel(config=UpperCAmelCase	)
     model.to(UpperCAmelCase	)
     model.eval()
     lowerCamelCase__    :      Union[str, Any]					 =		model(UpperCAmelCase	)

     lowerCamelCase__    :      Optional[Any]					 =		((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths	) - 1))
     lowerCamelCase__    :      int					 =		int(config.embed_dim * 2 ** (len(config.depths	) - 1)	)

     self.parent.assertEqual(result.last_hidden_state.shape			,     (self.batch_size, expected_seq_len, expected_dim)	)
 def      A_	(      self  :      Tuple			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      List[str]			,     UpperCAmelCase  :      int	)			->	str:
     lowerCamelCase__    :      Tuple					 =		FocalNetBackbone(config=UpperCAmelCase	)
     model.to(UpperCAmelCase	)
     model.eval()
     lowerCamelCase__    :      Union[str, Any]					 =		model(UpperCAmelCase	)

     # verify feature maps
     self.parent.assertEqual(len(result.feature_maps	)			,     len(config.out_features	)	)
     self.parent.assertListEqual(list(result.feature_maps[0].shape	)			,     [self.batch_size, self.image_size, 8, 8]	)

     # verify channels
     self.parent.assertEqual(len(model.channels	)			,     len(config.out_features	)	)
     self.parent.assertListEqual(model.channels			,     config.hidden_sizes[:-1]	)

     # verify backbone works with out_features=None
     lowerCamelCase__    :      Any					 =		None
     lowerCamelCase__    :      int					 =		FocalNetBackbone(config=UpperCAmelCase	)
     model.to(UpperCAmelCase	)
     model.eval()
     lowerCamelCase__    :      Optional[int]					 =		model(UpperCAmelCase	)

     # verify feature maps
     self.parent.assertEqual(len(result.feature_maps	)			,     1	)
     self.parent.assertListEqual(list(result.feature_maps[0].shape	)			,     [self.batch_size, self.image_size * 2, 4, 4]	)

     # verify channels
     self.parent.assertEqual(len(model.channels	)			,     1	)
     self.parent.assertListEqual(model.channels			,     [config.hidden_sizes[-1]]	)
 def      A_	(      self  :      List[Any]			,     UpperCAmelCase  :      Tuple			,     UpperCAmelCase  :      List[Any]			,     UpperCAmelCase  :      List[Any]	)			->	List[Any]:
     lowerCamelCase__    :      Dict					 =		FocalNetForMaskedImageModeling(config=UpperCAmelCase	)
     model.to(UpperCAmelCase	)
     model.eval()
     lowerCamelCase__    :      Optional[int]					 =		model(UpperCAmelCase	)
     self.parent.assertEqual(
         result.reconstruction.shape			,     (self.batch_size, self.num_channels, self.image_size, self.image_size)	)

     # test greyscale images
     lowerCamelCase__    :      int					 =		1
     lowerCamelCase__    :      Optional[Any]					 =		FocalNetForMaskedImageModeling(UpperCAmelCase	)
     model.to(UpperCAmelCase	)
     model.eval()

     lowerCamelCase__    :      List[str]					 =		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
     lowerCamelCase__    :      Tuple					 =		model(UpperCAmelCase	)
     self.parent.assertEqual(result.reconstruction.shape			,     (self.batch_size, 1, self.image_size, self.image_size)	)
 def      A_	(      self  :      int			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Dict			,     UpperCAmelCase  :      Dict	)			->	int:
     lowerCamelCase__    :      Dict					 =		self.type_sequence_label_size
     lowerCamelCase__    :      int					 =		FocalNetForImageClassification(UpperCAmelCase	)
     model.to(UpperCAmelCase	)
     model.eval()
     lowerCamelCase__    :      Dict					 =		model(UpperCAmelCase			,     labels=UpperCAmelCase	)
     self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.type_sequence_label_size)	)

     # test greyscale images
     lowerCamelCase__    :      Optional[Any]					 =		1
     lowerCamelCase__    :      Optional[Any]					 =		FocalNetForImageClassification(UpperCAmelCase	)
     model.to(UpperCAmelCase	)
     model.eval()

     lowerCamelCase__    :      int					 =		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
     lowerCamelCase__    :      str					 =		model(UpperCAmelCase	)
     self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.type_sequence_label_size)	)







 def      A_	(      self  :      Union[str, Any]	)			->	int:
     lowerCamelCase__    :      Any					 =		self.prepare_config_and_inputs()

     lowerCamelCase__ ,							lowerCamelCase__ ,							lowerCamelCase__    :      Optional[Any]					 =		config_and_inputs
     lowerCamelCase__    :      List[Any]					 =		{'pixel_values': pixel_values}
     return config, inputs_dict


@require_torch
class    lowerCAmelCase							(					__UpperCamelCase,  __UpperCamelCase,  unittest.TestCase			):
 UpperCAmelCase__													=		(
     (
         FocalNetModel,
         FocalNetForImageClassification,
         FocalNetForMaskedImageModeling,
         FocalNetBackbone,
     )
     if is_torch_available()
     else ()
 )
 UpperCAmelCase__													=		(
     {"""feature-extraction""": FocalNetModel, """image-classification""": FocalNetForImageClassification}
     if is_torch_available()
     else {}
 )
 UpperCAmelCase__													=		False

 UpperCAmelCase__													=		False
 UpperCAmelCase__													=		False
 UpperCAmelCase__													=		False
 UpperCAmelCase__													=		False
 def      A_	(      self  :      Any	)			->	List[str]:
     lowerCamelCase__    :      Tuple					 =		FocalNetModelTester(self	)
     lowerCamelCase__    :      Union[str, Any]					 =		ConfigTester(self			,     config_class=UpperCAmelCase			,     embed_dim=37			,     has_text_modality=UpperCAmelCase	)
 def      A_	(      self  :      str	)			->	Tuple:
     self.create_and_test_config_common_properties()
     self.config_tester.create_and_test_config_to_json_string()
     self.config_tester.create_and_test_config_to_json_file()
     self.config_tester.create_and_test_config_from_and_save_pretrained()
     self.config_tester.create_and_test_config_with_num_labels()
     self.config_tester.check_config_can_be_init_without_params()
     self.config_tester.check_config_arguments_init()
 def      A_	(      self  :      Any	)			->	Dict:
     return
 def      A_	(      self  :      List[Any]	)			->	str:
     lowerCamelCase__    :      Any					 =		self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*UpperCAmelCase	)
 def      A_	(      self  :      Optional[Any]	)			->	List[str]:
     lowerCamelCase__    :      Dict					 =		self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_backbone(*UpperCAmelCase	)
 def      A_	(      self  :      Optional[int]	)			->	int:
     lowerCamelCase__    :      Union[str, Any]					 =		self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_image_modeling(*UpperCAmelCase	)
 def      A_	(      self  :      str	)			->	str:
     lowerCamelCase__    :      str					 =		self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase	)
 @unittest.skip(reason='FocalNet does not use inputs_embeds'	)
 def      A_	(      self  :      Any	)			->	List[str]:
     pass
 @unittest.skip(reason='FocalNet does not use feedforward chunking'	)
 def      A_	(      self  :      Tuple	)			->	Any:
     pass
 def      A_	(      self  :      List[Any]	)			->	List[Any]:
     lowerCamelCase__ ,							lowerCamelCase__    :      Optional[Any]					 =		self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes[:-1]:
         lowerCamelCase__    :      List[str]					 =		model_class(UpperCAmelCase	)
         self.assertIsInstance(model.get_input_embeddings()			,     (nn.Module)	)
         lowerCamelCase__    :      Optional[int]					 =		model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(UpperCAmelCase			,     nn.Linear	)	)
 def      A_	(      self  :      Any	)			->	str:
     lowerCamelCase__ ,							lowerCamelCase__    :      str					 =		self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes[:-1]:
         lowerCamelCase__    :      List[Any]					 =		model_class(UpperCAmelCase	)
         lowerCamelCase__    :      List[Any]					 =		inspect.signature(model.forward	)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         lowerCamelCase__    :      Union[str, Any]					 =		[*signature.parameters.keys()]

         lowerCamelCase__    :      Tuple					 =		['pixel_values']
         self.assertListEqual(arg_names[:1]			,     UpperCAmelCase	)
 def      A_	(      self  :      Tuple			,     UpperCAmelCase  :      Dict			,     UpperCAmelCase  :      Tuple			,     UpperCAmelCase  :      Union[str, Any]			,     UpperCAmelCase  :      Optional[int]	)			->	List[Any]:
     lowerCamelCase__    :      Tuple					 =		model_class(UpperCAmelCase	)
     model.to(UpperCAmelCase	)
     model.eval()

     with torch.no_grad():
         lowerCamelCase__    :      Tuple					 =		model(**self._prepare_for_class(UpperCAmelCase			,     UpperCAmelCase	)	)

     lowerCamelCase__    :      int					 =		outputs.hidden_states

     lowerCamelCase__    :      Any					 =		getattr(
         self.model_tester			,     'expected_num_hidden_layers'			,     len(self.model_tester.depths	) + 1	)
     self.assertEqual(len(UpperCAmelCase	)			,     UpperCAmelCase	)

     # FocalNet has a different seq_length
     lowerCamelCase__    :      Union[str, Any]					 =		(
         config.patch_size
         if isinstance(config.patch_size			,     collections.abc.Iterable	)
         else (config.patch_size, config.patch_size)
     )

     lowerCamelCase__    :      List[str]					 =		(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

     self.assertListEqual(
         list(hidden_states[0].shape[-2:]	)			,     [num_patches, self.model_tester.embed_dim]			,     )

     lowerCamelCase__    :      Tuple					 =		outputs.reshaped_hidden_states
     self.assertEqual(len(UpperCAmelCase	)			,     UpperCAmelCase	)

     lowerCamelCase__ ,							lowerCamelCase__ ,							lowerCamelCase__ ,							lowerCamelCase__    :      str					 =		reshaped_hidden_states[0].shape
     lowerCamelCase__    :      str					 =		(
         reshaped_hidden_states[0].view(UpperCAmelCase			,     UpperCAmelCase			,     height * width	).permute(0			,     2			,     1	)
     )
     self.assertListEqual(
         list(reshaped_hidden_states.shape[-2:]	)			,     [num_patches, self.model_tester.embed_dim]			,     )
 def      A_	(      self  :      int	)			->	List[str]:
     lowerCamelCase__ ,							lowerCamelCase__    :      str					 =		self.model_tester.prepare_config_and_inputs_for_common()

     lowerCamelCase__    :      Tuple					 =		(
         self.model_tester.image_size
         if isinstance(self.model_tester.image_size			,     collections.abc.Iterable	)
         else (self.model_tester.image_size, self.model_tester.image_size)
     )

     for model_class in self.all_model_classes[:-1]:
         lowerCamelCase__    :      Optional[int]					 =		True
         self.check_hidden_states_output(UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase	)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         lowerCamelCase__    :      Optional[Any]					 =		True

         self.check_hidden_states_output(UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase	)
 def      A_	(      self  :      List[str]	)			->	Dict:
     lowerCamelCase__ ,							lowerCamelCase__    :      str					 =		self.model_tester.prepare_config_and_inputs_for_common()
     lowerCamelCase__    :      int					 =		3

     lowerCamelCase__    :      Any					 =		(
         self.model_tester.image_size
         if isinstance(self.model_tester.image_size			,     collections.abc.Iterable	)
         else (self.model_tester.image_size, self.model_tester.image_size)
     )
     lowerCamelCase__    :      Any					 =		(
         config.patch_size
         if isinstance(config.patch_size			,     collections.abc.Iterable	)
         else (config.patch_size, config.patch_size)
     )

     lowerCamelCase__    :      List[str]					 =		image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
     lowerCamelCase__    :      Union[str, Any]					 =		image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

     for model_class in self.all_model_classes[:-1]:
         lowerCamelCase__    :      Optional[Any]					 =		True
         self.check_hidden_states_output(UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase			,     (padded_height, padded_width)	)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         lowerCamelCase__    :      List[Any]					 =		True
         self.check_hidden_states_output(UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase			,     (padded_height, padded_width)	)
 @slow
 def      A_	(      self  :      Dict	)			->	Union[str, Any]:
     for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         lowerCamelCase__    :      Tuple					 =		FocalNetModel.from_pretrained(UpperCAmelCase	)
         self.assertIsNotNone(UpperCAmelCase	)







 def      A_	(      self  :      List[Any]	)			->	Dict:
     lowerCamelCase__ ,							lowerCamelCase__    :      Union[str, Any]					 =		self.model_tester.prepare_config_and_inputs_for_common()

     lowerCamelCase__    :      Optional[int]					 =		_config_zero_init(UpperCAmelCase	)
     for model_class in self.all_model_classes:
         lowerCamelCase__    :      Union[str, Any]					 =		model_class(config=UpperCAmelCase	)
         for name, param in model.named_parameters():
             if "embeddings" not in name and param.requires_grad:
                 self.assertIn(
                     ((param.data.mean() * 1e9).round() / 1e9).item()			,     [0.0, 1.0]			,     msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""			,     )


@require_vision
@require_torch
class    lowerCAmelCase							(					unittest.TestCase			):
 @cached_property
 def      A_	(      self  :      str	)			->	List[str]:
     # TODO update organization
     return AutoImageProcessor.from_pretrained('microsoft/focalnet-tiny'	) if is_vision_available() else None







 @slow
 def      A_	(      self  :      List[Any]	)			->	int:
     lowerCamelCase__    :      List[str]					 =		FocalNetForImageClassification.from_pretrained('microsoft/focalnet-tiny'	).to(UpperCAmelCase	)
     lowerCamelCase__    :      Optional[int]					 =		self.default_image_processor

     lowerCamelCase__    :      List[str]					 =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
     lowerCamelCase__    :      List[str]					 =		image_processor(images=UpperCAmelCase			,     return_tensors='pt'	).to(UpperCAmelCase	)

     # forward pass
     with torch.no_grad():
         lowerCamelCase__    :      List[Any]					 =		model(**UpperCAmelCase	)

     # verify the logits
     lowerCamelCase__    :      List[str]					 =		torch.Size((1, 1000)	)
     self.assertEqual(outputs.logits.shape			,     UpperCAmelCase	)
     lowerCamelCase__    :      Optional[Any]					 =		torch.tensor([0.2_1_6_6, -0.4_3_6_8, 0.2_1_9_1]	).to(UpperCAmelCase	)
     self.assertTrue(torch.allclose(outputs.logits[0, :3]			,     UpperCAmelCase			,     atol=1e-4	)	)
     self.assertTrue(outputs.logits.argmax(dim=-1	).item()			,     281	)


@require_torch
class    lowerCAmelCase							(					__UpperCamelCase,  unittest.TestCase			):
 UpperCAmelCase__													=		(FocalNetBackbone,) if is_torch_available() else ()
 UpperCAmelCase__													=		FocalNetConfig

 UpperCAmelCase__													=		False
 def      A_	(      self  :      Any	)			->	Optional[Any]:
     lowerCamelCase__    :      Any					 =		FocalNetModelTester(self	)










'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCAmelCase      						=	logging.get_logger(__name__)

_lowerCAmelCase      						=	{
    '''facebook/xlm-roberta-xl''': '''https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json''',
    '''facebook/xlm-roberta-xxl''': '''https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json''',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}






class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  __lowercase							:      int				=      '''xlm-roberta-xl'''




  def __init__(    self  ,__UpperCAmelCase=25_0880  ,__UpperCAmelCase=2560  ,__UpperCAmelCase=36  ,__UpperCAmelCase=32  ,__UpperCAmelCase=1_0240  ,__UpperCAmelCase="gelu"  ,__UpperCAmelCase=0.1  ,__UpperCAmelCase=0.1  ,__UpperCAmelCase=514  ,__UpperCAmelCase=1  ,__UpperCAmelCase=0.0_2  ,__UpperCAmelCase=1E-05  ,__UpperCAmelCase=1  ,__UpperCAmelCase=0  ,__UpperCAmelCase=2  ,__UpperCAmelCase="absolute"  ,__UpperCAmelCase=True  ,__UpperCAmelCase=None  ,**__UpperCAmelCase  ,)    ->		str:
      super().__init__(pad_token_id=__UpperCAmelCase  ,bos_token_id=__UpperCAmelCase  ,eos_token_id=__UpperCAmelCase  ,**__UpperCAmelCase      )
      lowerCAmelCase__ :  List[Any]							      =					vocab_size
      lowerCAmelCase__ :  int							      =					hidden_size
      lowerCAmelCase__ :  int							      =					num_hidden_layers
      lowerCAmelCase__ :  str							      =					num_attention_heads
      lowerCAmelCase__ :  int							      =					hidden_act
      lowerCAmelCase__ :  Dict							      =					intermediate_size
      lowerCAmelCase__ :  List[Any]							      =					hidden_dropout_prob
      lowerCAmelCase__ :  str							      =					attention_probs_dropout_prob
      lowerCAmelCase__ :  Optional[int]							      =					max_position_embeddings
      lowerCAmelCase__ :  List[str]							      =					type_vocab_size
      lowerCAmelCase__ :  List[Any]							      =					initializer_range
      lowerCAmelCase__ :  Tuple							      =					layer_norm_eps
      lowerCAmelCase__ :  int							      =					position_embedding_type
      lowerCAmelCase__ :  Optional[Any]							      =					use_cache
      lowerCAmelCase__ :  Optional[Any]							      =					classifier_dropout







class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  @property
  def 							UpperCAmelCase_	(    self      )    ->		Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
          lowerCAmelCase__ :  Dict							      =					{0: """batch""", 1: """choice""", 2: """sequence"""}
      else:
          lowerCAmelCase__ :  Any							      =					{0: """batch""", 1: """sequence"""}
      return OrderedDict(
          [
              ("""input_ids""", dynamic_axis),
              ("""attention_mask""", dynamic_axis),
          ]      )


