



import collections
import inspect
import unittest

from transformers import SwinConfig
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 SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
						from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor



class        __snake_case		:




							def __init__( self  : str       ,       A_  : List[str]       ,       A_  : List[Any]=1_3       ,       A_  : Union[str, Any]=3_2       ,       A_  : Union[str, Any]=2       ,       A_  : Optional[Any]=3       ,       A_  : List[str]=1_6       ,       A_  : Dict=[1, 2, 1]       ,       A_  : Optional[int]=[2, 2, 4]       ,       A_  : Dict=2       ,       A_  : int=2.0       ,       A_  : str=True       ,       A_  : str=0.0       ,       A_  : Any=0.0       ,       A_  : List[str]=0.1       ,       A_  : Dict="gelu"       ,       A_  : Optional[int]=False       ,       A_  : Union[str, Any]=True       ,       A_  : List[Any]=0.02       ,       A_  : List[Any]=1e-5       ,       A_  : Optional[int]=True       ,       A_  : Any=None       ,       A_  : Tuple=True       ,       A_  : Tuple=1_0       ,       A_  : Optional[int]=8       ,       A_  : Union[str, Any]=["stage1", "stage2"]       ,       A_  : List[Any]=[1, 2]       ,       ):
									lowerCAmelCase_    :  Optional[Any]             =				parent
									lowerCAmelCase_    :  Union[str, Any]             =				batch_size
									lowerCAmelCase_    :  List[Any]             =				image_size
									lowerCAmelCase_    :  List[str]             =				patch_size
									lowerCAmelCase_    :  str             =				num_channels
									lowerCAmelCase_    :  Optional[Any]             =				embed_dim
									lowerCAmelCase_    :  Dict             =				depths
									lowerCAmelCase_    :  Tuple             =				num_heads
									lowerCAmelCase_    :  Dict             =				window_size
									lowerCAmelCase_    :  int             =				mlp_ratio
									lowerCAmelCase_    :  Union[str, Any]             =				qkv_bias
									lowerCAmelCase_    :  Any             =				hidden_dropout_prob
									lowerCAmelCase_    :  Any             =				attention_probs_dropout_prob
									lowerCAmelCase_    :  Optional[Any]             =				drop_path_rate
									lowerCAmelCase_    :  str             =				hidden_act
									lowerCAmelCase_    :  List[Any]             =				use_absolute_embeddings
									lowerCAmelCase_    :  List[Any]             =				patch_norm
									lowerCAmelCase_    :  List[Any]             =				layer_norm_eps
									lowerCAmelCase_    :  Dict             =				initializer_range
									lowerCAmelCase_    :  str             =				is_training
									lowerCAmelCase_    :  List[str]             =				scope
									lowerCAmelCase_    :  Optional[Any]             =				use_labels
									lowerCAmelCase_    :  Tuple             =				type_sequence_label_size
									lowerCAmelCase_    :  int             =				encoder_stride
									lowerCAmelCase_    :  int             =				out_features
									lowerCAmelCase_    :  Any             =				out_indices




							def      UpperCAmelCase__    ( self  : List[Any]):
									lowerCAmelCase_    :  Any             =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

									lowerCAmelCase_    :  List[str]             =				self.get_config()

									return config, pixel_values, labels




							def      UpperCAmelCase__    ( self  : Tuple):
									return SwinConfig(
									    image_size=self.image_size       ,       patch_size=self.patch_size       ,       num_channels=self.num_channels       ,       embed_dim=self.embed_dim       ,       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      UpperCAmelCase__    ( self  : Optional[Any]       ,       A_  : str       ,       A_  : Tuple       ,       A_  : Optional[int]):
									lowerCAmelCase_    :  Any             =				SwinModel(config=A_)
									model.to(A_)
									model.eval()
									lowerCAmelCase_    :  Any             =				model(A_)

									lowerCAmelCase_    :  Dict             =				((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      UpperCAmelCase__    ( self  : Union[str, Any]       ,       A_  : Tuple       ,       A_  : Union[str, Any]       ,       A_  : Dict):
									lowerCAmelCase_    :  List[Any]             =				SwinBackbone(config=A_)
									model.to(A_)
									model.eval()
									lowerCAmelCase_    :  int             =				model(A_)

									# verify hidden states
									self.parent.assertEqual(len(result.feature_maps)       ,       len(config.out_features))
									self.parent.assertListEqual(list(result.feature_maps[0].shape)       ,       [self.batch_size, model.channels[0], 1_6, 1_6])

									# verify channels
									self.parent.assertEqual(len(model.channels)       ,       len(config.out_features))

									# verify backbone works with out_features=None
									lowerCAmelCase_    :  Union[str, Any]             =				None
									lowerCAmelCase_    :  int             =				SwinBackbone(config=A_)
									model.to(A_)
									model.eval()
									lowerCAmelCase_    :  Tuple             =				model(A_)

									# verify feature maps
									self.parent.assertEqual(len(result.feature_maps)       ,       1)
									self.parent.assertListEqual(list(result.feature_maps[0].shape)       ,       [self.batch_size, model.channels[-1], 4, 4])

									# verify channels
									self.parent.assertEqual(len(model.channels)       ,       1)




							def      UpperCAmelCase__    ( self  : List[Any]       ,       A_  : Tuple       ,       A_  : List[str]       ,       A_  : List[str]):
									lowerCAmelCase_    :  Optional[Any]             =				SwinForMaskedImageModeling(config=A_)
									model.to(A_)
									model.eval()
									lowerCAmelCase_    :  int             =				model(A_)
									self.parent.assertEqual(
									    result.logits.shape       ,       (self.batch_size, self.num_channels, self.image_size, self.image_size))

									# test greyscale images
									lowerCAmelCase_    :  Union[str, Any]             =				1
									lowerCAmelCase_    :  Tuple             =				SwinForMaskedImageModeling(A_)
									model.to(A_)
									model.eval()

									lowerCAmelCase_    :  List[str]             =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
									lowerCAmelCase_    :  Union[str, Any]             =				model(A_)
									self.parent.assertEqual(result.logits.shape       ,       (self.batch_size, 1, self.image_size, self.image_size))




							def      UpperCAmelCase__    ( self  : List[str]       ,       A_  : Any       ,       A_  : Union[str, Any]       ,       A_  : Optional[Any]):
									lowerCAmelCase_    :  Dict             =				self.type_sequence_label_size
									lowerCAmelCase_    :  Tuple             =				SwinForImageClassification(A_)
									model.to(A_)
									model.eval()
									lowerCAmelCase_    :  List[str]             =				model(A_       ,       labels=A_)
									self.parent.assertEqual(result.logits.shape       ,       (self.batch_size, self.type_sequence_label_size))

									# test greyscale images
									lowerCAmelCase_    :  Any             =				1
									lowerCAmelCase_    :  Tuple             =				SwinForImageClassification(A_)
									model.to(A_)
									model.eval()

									lowerCAmelCase_    :  Union[str, Any]             =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
									lowerCAmelCase_    :  Tuple             =				model(A_)
									self.parent.assertEqual(result.logits.shape       ,       (self.batch_size, self.type_sequence_label_size))







							def      UpperCAmelCase__    ( self  : str):
									lowerCAmelCase_    :  Optional[int]             =				self.prepare_config_and_inputs()
									(
									    (
									    lowerCAmelCase_
									)      ,						(
									    lowerCAmelCase_
									)      ,						(
									    lowerCAmelCase_
									)      ,						
									)    :  str             =				config_and_inputs
									lowerCAmelCase_    :  Optional[Any]             =				{'''pixel_values''': pixel_values}
									return config, inputs_dict





@require_torch
class        __snake_case		(  UpperCamelCase_		,UpperCamelCase_		,unittest.TestCase		):
							_a       =				(
							    (
							        SwinModel,
							        SwinBackbone,
							        SwinForImageClassification,
							        SwinForMaskedImageModeling,
							    )
							    if is_torch_available()
							    else ()
							)
							_a       =				(
							    {'''feature-extraction''': SwinModel, '''image-classification''': SwinForImageClassification}
							    if is_torch_available()
							    else {}
							)
							_a       =				True

							_a       =				False
							_a       =				False
							_a       =				False




							def      UpperCAmelCase__    ( self  : List[Any]):
									lowerCAmelCase_    :  Dict             =				SwinModelTester(self)
									lowerCAmelCase_    :  int             =				ConfigTester(self       ,       config_class=A_       ,       embed_dim=3_7)




							def      UpperCAmelCase__    ( self  : str):
									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      UpperCAmelCase__    ( self  : Union[str, Any]):
									return




							def      UpperCAmelCase__    ( self  : Optional[int]):
									lowerCAmelCase_    :  Union[str, Any]             =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*A_)




							@unittest.skip(reason='''Got `CUDA error: misaligned address` with PyTorch 2.0.0.''')
							def      UpperCAmelCase__    ( self  : Dict):
									pass




							def      UpperCAmelCase__    ( self  : int):
									super().test_training_gradient_checkpointing()




							def      UpperCAmelCase__    ( self  : int):
									lowerCAmelCase_    :  Tuple             =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_backbone(*A_)




							def      UpperCAmelCase__    ( self  : Any):
									lowerCAmelCase_    :  List[Any]             =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_image_modeling(*A_)




							def      UpperCAmelCase__    ( self  : int):
									lowerCAmelCase_    :  int             =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*A_)




							@unittest.skip(reason='''Swin does not use inputs_embeds''')
							def      UpperCAmelCase__    ( self  : int):
									pass




							@unittest.skip(reason='''Swin Transformer does not use feedforward chunking''')
							def      UpperCAmelCase__    ( self  : Any):
									pass




							def      UpperCAmelCase__    ( self  : Optional[int]):
									lowerCAmelCase_      ,						lowerCAmelCase_    :  Dict             =				self.model_tester.prepare_config_and_inputs_for_common()

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




							def      UpperCAmelCase__    ( self  : Tuple):
									lowerCAmelCase_      ,						lowerCAmelCase_    :  Optional[Any]             =				self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											lowerCAmelCase_    :  Optional[int]             =				model_class(A_)
											lowerCAmelCase_    :  List[str]             =				inspect.signature(model.forward)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											lowerCAmelCase_    :  Tuple             =				[*signature.parameters.keys()]

											lowerCAmelCase_    :  Union[str, Any]             =				['''pixel_values''']
											self.assertListEqual(arg_names[:1]       ,       A_)




							def      UpperCAmelCase__    ( self  : Optional[int]):
									lowerCAmelCase_      ,						lowerCAmelCase_    :  Dict             =				self.model_tester.prepare_config_and_inputs_for_common()
									lowerCAmelCase_    :  List[Any]             =				True

									for model_class in self.all_model_classes:
											lowerCAmelCase_    :  Any             =				True
											lowerCAmelCase_    :  Dict             =				False
											lowerCAmelCase_    :  Union[str, Any]             =				True
											lowerCAmelCase_    :  Tuple             =				model_class(A_)
											model.to(A_)
											model.eval()
											with torch.no_grad():
													lowerCAmelCase_    :  List[str]             =				model(**self._prepare_for_class(A_       ,       A_))
											lowerCAmelCase_    :  int             =				outputs.attentions
											lowerCAmelCase_    :  List[Any]             =				len(self.model_tester.depths)
											self.assertEqual(len(A_)       ,       A_)

											# check that output_attentions also work using config
											del inputs_dict["output_attentions"]
											lowerCAmelCase_    :  Tuple             =				True
											lowerCAmelCase_    :  List[Any]             =				config.window_size**2
											lowerCAmelCase_    :  Optional[Any]             =				model_class(A_)
											model.to(A_)
											model.eval()
											with torch.no_grad():
													lowerCAmelCase_    :  List[str]             =				model(**self._prepare_for_class(A_       ,       A_))
											lowerCAmelCase_    :  Tuple             =				outputs.attentions
											self.assertEqual(len(A_)       ,       A_)

											self.assertListEqual(
											    list(attentions[0].shape[-3:])       ,       [self.model_tester.num_heads[0], window_size_squared, window_size_squared]       ,       )
											lowerCAmelCase_    :  Tuple             =				len(A_)

											# Check attention is always last and order is fine
											lowerCAmelCase_    :  Any             =				True
											lowerCAmelCase_    :  Any             =				True
											lowerCAmelCase_    :  Tuple             =				model_class(A_)
											model.to(A_)
											model.eval()
											with torch.no_grad():
													lowerCAmelCase_    :  Dict             =				model(**self._prepare_for_class(A_       ,       A_))

											# also another +1 for reshaped_hidden_states
											lowerCAmelCase_    :  Optional[int]             =				1 if model_class.__name__ == '''SwinBackbone''' else 2
											self.assertEqual(out_len + added_hidden_states       ,       len(A_))

											lowerCAmelCase_    :  Union[str, Any]             =				outputs.attentions

											self.assertEqual(len(A_)       ,       A_)

											self.assertListEqual(
											    list(self_attentions[0].shape[-3:])       ,       [self.model_tester.num_heads[0], window_size_squared, window_size_squared]       ,       )




							def      UpperCAmelCase__    ( self  : List[Any]       ,       A_  : str       ,       A_  : Union[str, Any]       ,       A_  : Optional[Any]       ,       A_  : str):
									lowerCAmelCase_    :  int             =				model_class(A_)
									model.to(A_)
									model.eval()

									with torch.no_grad():
											lowerCAmelCase_    :  List[Any]             =				model(**self._prepare_for_class(A_       ,       A_))

									lowerCAmelCase_    :  Union[str, Any]             =				outputs.hidden_states

									lowerCAmelCase_    :  List[Any]             =				getattr(
									    self.model_tester       ,       '''expected_num_hidden_layers'''       ,       len(self.model_tester.depths) + 1)
									self.assertEqual(len(A_)       ,       A_)

									# Swin has a different seq_length
									lowerCAmelCase_    :  Tuple             =				(
									    config.patch_size
									    if isinstance(config.patch_size       ,       collections.abc.Iterable)
									    else (config.patch_size, config.patch_size)
									)

									lowerCAmelCase_    :  Tuple             =				(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]       ,       )

									if not model_class.__name__ == "SwinBackbone":
											lowerCAmelCase_    :  Optional[Any]             =				outputs.reshaped_hidden_states
											self.assertEqual(len(A_)       ,       A_)

											lowerCAmelCase_      ,						lowerCAmelCase_      ,						lowerCAmelCase_      ,						lowerCAmelCase_    :  Any             =				reshaped_hidden_states[0].shape
											lowerCAmelCase_    :  List[str]             =				(
											    reshaped_hidden_states[0].view(A_       ,       A_       ,       height * width).permute(0       ,       2       ,       1)
											)
											self.assertListEqual(
											    list(reshaped_hidden_states.shape[-2:])       ,       [num_patches, self.model_tester.embed_dim]       ,       )




							def      UpperCAmelCase__    ( self  : Any):
									lowerCAmelCase_      ,						lowerCAmelCase_    :  Any             =				self.model_tester.prepare_config_and_inputs_for_common()

									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)
									)

									for model_class in self.all_model_classes:
											lowerCAmelCase_    :  Union[str, Any]             =				True
											self.check_hidden_states_output(A_       ,       A_       ,       A_       ,       A_)

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											lowerCAmelCase_    :  str             =				True

											self.check_hidden_states_output(A_       ,       A_       ,       A_       ,       A_)




							def      UpperCAmelCase__    ( self  : int):
									lowerCAmelCase_      ,						lowerCAmelCase_    :  Dict             =				self.model_tester.prepare_config_and_inputs_for_common()
									lowerCAmelCase_    :  Optional[int]             =				3

									lowerCAmelCase_    :  List[str]             =				(
									    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_    :  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[0] + patch_size[0] - (image_size[0] % patch_size[0])
									lowerCAmelCase_    :  List[Any]             =				image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

									for model_class in self.all_model_classes:
											lowerCAmelCase_    :  Optional[Any]             =				True
											self.check_hidden_states_output(A_       ,       A_       ,       A_       ,       (padded_height, padded_width))

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											lowerCAmelCase_    :  Dict             =				True
											self.check_hidden_states_output(A_       ,       A_       ,       A_       ,       (padded_height, padded_width))




							@slow
							def      UpperCAmelCase__    ( self  : Dict):
									for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											lowerCAmelCase_    :  Union[str, Any]             =				SwinModel.from_pretrained(A_)
											self.assertIsNotNone(A_)







							def      UpperCAmelCase__    ( self  : Optional[Any]):
									lowerCAmelCase_      ,						lowerCAmelCase_    :  int             =				self.model_tester.prepare_config_and_inputs_for_common()

									lowerCAmelCase_    :  Dict             =				_config_zero_init(A_)
									for model_class in self.all_model_classes:
											lowerCAmelCase_    :  Dict             =				model_class(config=A_)
											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        __snake_case		(  unittest.TestCase		):




							@cached_property
							def      UpperCAmelCase__    ( self  : List[str]):
									return (
									    AutoImageProcessor.from_pretrained('''microsoft/swin-tiny-patch4-window7-224''')
									    if is_vision_available()
									    else None
									)







							@slow
							def      UpperCAmelCase__    ( self  : str):
									lowerCAmelCase_    :  Tuple             =				SwinForImageClassification.from_pretrained('''microsoft/swin-tiny-patch4-window7-224''').to(A_)
									lowerCAmelCase_    :  Optional[Any]             =				self.default_image_processor

									lowerCAmelCase_    :  Union[str, Any]             =				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
									lowerCAmelCase_    :  Optional[int]             =				image_processor(images=A_       ,       return_tensors='''pt''').to(A_)

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

									# verify the logits
									lowerCAmelCase_    :  List[Any]             =				torch.Size((1, 1_0_0_0))
									self.assertEqual(outputs.logits.shape       ,       A_)
									lowerCAmelCase_    :  Tuple             =				torch.tensor([-0.0948, -0.6454, -0.0921]).to(A_)
									self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,       A_       ,       atol=1e-4))





@require_torch
class        __snake_case		(  unittest.TestCase		,UpperCamelCase_		):
							_a       =				(SwinBackbone,) if is_torch_available() else ()
							_a       =				SwinConfig




							def      UpperCAmelCase__    ( self  : str):
									lowerCAmelCase_    :  Dict             =				SwinModelTester(self)










from collections import OrderedDict
from typing import Mapping

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


A__						:						Dict          =      logging.get_logger(__name__)

A__						:						List[str]          =      {
    '''google/electra-small-generator''': '''https://huggingface.co/google/electra-small-generator/resolve/main/config.json''',
    '''google/electra-base-generator''': '''https://huggingface.co/google/electra-base-generator/resolve/main/config.json''',
    '''google/electra-large-generator''': '''https://huggingface.co/google/electra-large-generator/resolve/main/config.json''',
    '''google/electra-small-discriminator''': (
        '''https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json'''
    ),
    '''google/electra-base-discriminator''': (
        '''https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json'''
    ),
    '''google/electra-large-discriminator''': (
        '''https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json'''
    ),
}



class        __snake_case		(  UpperCamelCase_		):
							_a       =				'''electra'''




							def __init__( self  : Dict       ,       A_  : Dict=3_0_5_2_2       ,       A_  : List[Any]=1_2_8       ,       A_  : Dict=2_5_6       ,       A_  : Any=1_2       ,       A_  : Optional[Any]=4       ,       A_  : Optional[int]=1_0_2_4       ,       A_  : Optional[int]="gelu"       ,       A_  : str=0.1       ,       A_  : Optional[int]=0.1       ,       A_  : str=5_1_2       ,       A_  : Optional[int]=2       ,       A_  : Dict=0.02       ,       A_  : Union[str, Any]=1e-12       ,       A_  : List[Any]="first"       ,       A_  : List[str]=True       ,       A_  : List[str]="gelu"       ,       A_  : Any=0.1       ,       A_  : int=0       ,       A_  : Optional[Any]="absolute"       ,       A_  : Optional[int]=True       ,       A_  : str=None       ,       **A_  : List[Any]       ,       ):
									super().__init__(pad_token_id=A_       ,       **A_)

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

									lowerCAmelCase_    :  Union[str, Any]             =				summary_type
									lowerCAmelCase_    :  str             =				summary_use_proj
									lowerCAmelCase_    :  str             =				summary_activation
									lowerCAmelCase_    :  List[Any]             =				summary_last_dropout
									lowerCAmelCase_    :  Optional[Any]             =				position_embedding_type
									lowerCAmelCase_    :  List[Any]             =				use_cache
									lowerCAmelCase_    :  Any             =				classifier_dropout





class        __snake_case		(  UpperCamelCase_		):




							@property
							def      UpperCAmelCase__    ( self  : Union[str, Any]):
									if self.task == "multiple-choice":
											lowerCAmelCase_    :  Any             =				{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
									else:
											lowerCAmelCase_    :  Union[str, Any]             =				{0: '''batch''', 1: '''sequence'''}
									return OrderedDict(
									    [
									        ('''input_ids''', dynamic_axis),
									        ('''attention_mask''', dynamic_axis),
									        ('''token_type_ids''', dynamic_axis),
									    ])





