

'''simple docstring'''
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 							A__					:





  def __init__(	self   :			Tuple							,      _a   :			Tuple							,      _a   :			Optional[Any]=13							,      _a   :			Optional[Any]=32							,      _a   :			Dict=2							,      _a   :			Tuple=3							,      _a   :			Dict=16							,      _a   :			Any=[1, 2, 1]							,      _a   :			List[Any]=[2, 2, 4]							,      _a   :			str=2							,      _a   :			Optional[Any]=2.0							,      _a   :			Any=True							,      _a   :			str=0.0							,      _a   :			Union[str, Any]=0.0							,      _a   :			Optional[int]=0.1							,      _a   :			Any="gelu"							,      _a   :			Dict=False							,      _a   :			Optional[Any]=True							,      _a   :			str=0.02							,      _a   :			List[Any]=1e-5							,      _a   :			str=True							,      _a   :			str=None							,      _a   :			Tuple=True							,      _a   :			Optional[int]=10							,      _a   :			Any=8							,      _a   :			Dict=["stage1", "stage2"]							,      _a   :			List[Any]=[1, 2]							,      )			->					Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=parent
       _SCREAMING_SNAKE_CASE												=batch_size
       _SCREAMING_SNAKE_CASE												=image_size
       _SCREAMING_SNAKE_CASE												=patch_size
       _SCREAMING_SNAKE_CASE												=num_channels
       _SCREAMING_SNAKE_CASE												=embed_dim
       _SCREAMING_SNAKE_CASE												=depths
       _SCREAMING_SNAKE_CASE												=num_heads
       _SCREAMING_SNAKE_CASE												=window_size
       _SCREAMING_SNAKE_CASE												=mlp_ratio
       _SCREAMING_SNAKE_CASE												=qkv_bias
       _SCREAMING_SNAKE_CASE												=hidden_dropout_prob
       _SCREAMING_SNAKE_CASE												=attention_probs_dropout_prob
       _SCREAMING_SNAKE_CASE												=drop_path_rate
       _SCREAMING_SNAKE_CASE												=hidden_act
       _SCREAMING_SNAKE_CASE												=use_absolute_embeddings
       _SCREAMING_SNAKE_CASE												=patch_norm
       _SCREAMING_SNAKE_CASE												=layer_norm_eps
       _SCREAMING_SNAKE_CASE												=initializer_range
       _SCREAMING_SNAKE_CASE												=is_training
       _SCREAMING_SNAKE_CASE												=scope
       _SCREAMING_SNAKE_CASE												=use_labels
       _SCREAMING_SNAKE_CASE												=type_sequence_label_size
       _SCREAMING_SNAKE_CASE												=encoder_stride
       _SCREAMING_SNAKE_CASE												=out_features
       _SCREAMING_SNAKE_CASE												=out_indices





  def 		A						(	self   :			Dict				)			->					Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

       _SCREAMING_SNAKE_CASE												=None
       if self.use_labels:
            _SCREAMING_SNAKE_CASE												=ids_tensor([self.batch_size]							,      self.type_sequence_label_size				)

       _SCREAMING_SNAKE_CASE												=self.get_config()

       return config, pixel_values, labels





  def 		A						(	self   :			List[str]				)			->					List[str]:





       '''simple docstring'''






       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 		A						(	self   :			Union[str, Any]							,      _a   :			str							,      _a   :			List[Any]							,      _a   :			Optional[Any]				)			->					Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=SwinModel(config=snake_case_				)
       model.to(snake_case_				)
       model.eval()
       _SCREAMING_SNAKE_CASE												=model(snake_case_				)

       _SCREAMING_SNAKE_CASE												=((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths				) - 1))
       _SCREAMING_SNAKE_CASE												=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   :			Dict							,      _a   :			List[str]							,      _a   :			Any							,      _a   :			Tuple				)			->					Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=SwinBackbone(config=snake_case_				)
       model.to(snake_case_				)
       model.eval()
       _SCREAMING_SNAKE_CASE												=model(snake_case_				)

       # 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], 16, 16]				)

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

       # verify backbone works with out_features=None
       _SCREAMING_SNAKE_CASE												=None
       _SCREAMING_SNAKE_CASE												=SwinBackbone(config=snake_case_				)
       model.to(snake_case_				)
       model.eval()
       _SCREAMING_SNAKE_CASE												=model(snake_case_				)

       # 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 		A						(	self   :			Tuple							,      _a   :			Optional[int]							,      _a   :			Optional[Any]							,      _a   :			int				)			->					Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=SwinForMaskedImageModeling(config=snake_case_				)
       model.to(snake_case_				)
       model.eval()
       _SCREAMING_SNAKE_CASE												=model(snake_case_				)
       self.parent.assertEqual(
           result.logits.shape							,      (self.batch_size, self.num_channels, self.image_size, self.image_size)				)

       # test greyscale images
       _SCREAMING_SNAKE_CASE												=1
       _SCREAMING_SNAKE_CASE												=SwinForMaskedImageModeling(snake_case_				)
       model.to(snake_case_				)
       model.eval()

       _SCREAMING_SNAKE_CASE												=floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
       _SCREAMING_SNAKE_CASE												=model(snake_case_				)
       self.parent.assertEqual(result.logits.shape							,      (self.batch_size, 1, self.image_size, self.image_size)				)





  def 		A						(	self   :			Optional[int]							,      _a   :			Any							,      _a   :			Any							,      _a   :			List[Any]				)			->					Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=self.type_sequence_label_size
       _SCREAMING_SNAKE_CASE												=SwinForImageClassification(snake_case_				)
       model.to(snake_case_				)
       model.eval()
       _SCREAMING_SNAKE_CASE												=model(snake_case_							,      labels=snake_case_				)
       self.parent.assertEqual(result.logits.shape							,      (self.batch_size, self.type_sequence_label_size)				)

       # test greyscale images
       _SCREAMING_SNAKE_CASE												=1
       _SCREAMING_SNAKE_CASE												=SwinForImageClassification(snake_case_				)
       model.to(snake_case_				)
       model.eval()

       _SCREAMING_SNAKE_CASE												=floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
       _SCREAMING_SNAKE_CASE												=model(snake_case_				)
       self.parent.assertEqual(result.logits.shape							,      (self.batch_size, self.type_sequence_label_size)				)





  def 		A						(	self   :			int				)			->					List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=self.prepare_config_and_inputs()
       (
           _SCREAMING_SNAKE_CASE							
       )												=config_and_inputs
       _SCREAMING_SNAKE_CASE												={'''pixel_values''': pixel_values}
       return config, inputs_dict







@require_torch
class 							A__					( 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 		A						(	self   :			int				)			->					List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=SwinModelTester(self				)
       _SCREAMING_SNAKE_CASE												=ConfigTester(self							,      config_class=snake_case_							,      embed_dim=37				)





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





       '''simple docstring'''






       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   :			str				)			->					Any:





       '''simple docstring'''






       return





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*snake_case_				)





  @unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.'				)
  def 		A						(	self   :			int				)			->					int:





       '''simple docstring'''






       pass





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





       '''simple docstring'''






       super().test_training_gradient_checkpointing()





  def 		A						(	self   :			int				)			->					List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*snake_case_				)





  def 		A						(	self   :			str				)			->					str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*snake_case_				)





  def 		A						(	self   :			Tuple				)			->					int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*snake_case_				)





  @unittest.skip(reason='Swin does not use inputs_embeds'				)
  def 		A						(	self   :			int				)			->					Dict:





       '''simple docstring'''






       pass





  @unittest.skip(reason='Swin Transformer does not use feedforward chunking'				)
  def 		A						(	self   :			Union[str, Any]				)			->					List[str]:





       '''simple docstring'''






       pass





  def 		A						(	self   :			List[str]				)			->					List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            _SCREAMING_SNAKE_CASE												=model_class(snake_case_				)
            self.assertIsInstance(model.get_input_embeddings()							,      (nn.Module)				)
            _SCREAMING_SNAKE_CASE												=model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(snake_case_							,      nn.Linear				)				)





  def 		A						(	self   :			Tuple				)			->					int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=self.model_tester.prepare_config_and_inputs_for_common()

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

            _SCREAMING_SNAKE_CASE												=['''pixel_values''']
            self.assertListEqual(arg_names[:1]							,      snake_case_				)





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=self.model_tester.prepare_config_and_inputs_for_common()
       _SCREAMING_SNAKE_CASE												=True

       for model_class in self.all_model_classes:
            _SCREAMING_SNAKE_CASE												=True
            _SCREAMING_SNAKE_CASE												=False
            _SCREAMING_SNAKE_CASE												=True
            _SCREAMING_SNAKE_CASE												=model_class(snake_case_				)
            model.to(snake_case_				)
            model.eval()
            with torch.no_grad():
                 _SCREAMING_SNAKE_CASE												=model(**self._prepare_for_class(snake_case_							,      snake_case_				)				)
            _SCREAMING_SNAKE_CASE												=outputs.attentions
            _SCREAMING_SNAKE_CASE												=len(self.model_tester.depths				)
            self.assertEqual(len(snake_case_				)							,      snake_case_				)

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

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

            # Check attention is always last and order is fine
            _SCREAMING_SNAKE_CASE												=True
            _SCREAMING_SNAKE_CASE												=True
            _SCREAMING_SNAKE_CASE												=model_class(snake_case_				)
            model.to(snake_case_				)
            model.eval()
            with torch.no_grad():
                 _SCREAMING_SNAKE_CASE												=model(**self._prepare_for_class(snake_case_							,      snake_case_				)				)

            # also another +1 for reshaped_hidden_states
            _SCREAMING_SNAKE_CASE												=1 if model_class.__name__ == '''SwinBackbone''' else 2
            self.assertEqual(out_len + added_hidden_states							,      len(snake_case_				)				)

            _SCREAMING_SNAKE_CASE												=outputs.attentions

            self.assertEqual(len(snake_case_				)							,      snake_case_				)

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





  def 		A						(	self   :			Dict							,      _a   :			str							,      _a   :			str							,      _a   :			str							,      _a   :			str				)			->					List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=model_class(snake_case_				)
       model.to(snake_case_				)
       model.eval()

       with torch.no_grad():
            _SCREAMING_SNAKE_CASE												=model(**self._prepare_for_class(snake_case_							,      snake_case_				)				)

       _SCREAMING_SNAKE_CASE												=outputs.hidden_states

       _SCREAMING_SNAKE_CASE												=getattr(
           self.model_tester							,      'expected_num_hidden_layers'							,      len(self.model_tester.depths				) + 1				)
       self.assertEqual(len(snake_case_				)							,      snake_case_				)

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

       _SCREAMING_SNAKE_CASE												=(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":
            _SCREAMING_SNAKE_CASE												=outputs.reshaped_hidden_states
            self.assertEqual(len(snake_case_				)							,      snake_case_				)

            _SCREAMING_SNAKE_CASE												=reshaped_hidden_states[0].shape
            _SCREAMING_SNAKE_CASE												=(
                reshaped_hidden_states[0].view(snake_case_							,      snake_case_							,      height * width				).permute(0							,      2							,      1				)
            )
            self.assertListEqual(
                list(reshaped_hidden_states.shape[-2:]				)							,      [num_patches, self.model_tester.embed_dim]							,      )





  def 		A						(	self   :			Dict				)			->					Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=self.model_tester.prepare_config_and_inputs_for_common()

       _SCREAMING_SNAKE_CASE												=(
           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:
            _SCREAMING_SNAKE_CASE												=True
            self.check_hidden_states_output(snake_case_							,      snake_case_							,      snake_case_							,      snake_case_				)

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            _SCREAMING_SNAKE_CASE												=True

            self.check_hidden_states_output(snake_case_							,      snake_case_							,      snake_case_							,      snake_case_				)





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=self.model_tester.prepare_config_and_inputs_for_common()
       _SCREAMING_SNAKE_CASE												=3

       _SCREAMING_SNAKE_CASE												=(
           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)
       )
       _SCREAMING_SNAKE_CASE												=(
           config.patch_size
           if isinstance(config.patch_size							,      collections.abc.Iterable				)
           else (config.patch_size, config.patch_size)
       )

       _SCREAMING_SNAKE_CASE												=image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
       _SCREAMING_SNAKE_CASE												=image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

       for model_class in self.all_model_classes:
            _SCREAMING_SNAKE_CASE												=True
            self.check_hidden_states_output(snake_case_							,      snake_case_							,      snake_case_							,      (padded_height, padded_width)				)

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            _SCREAMING_SNAKE_CASE												=True
            self.check_hidden_states_output(snake_case_							,      snake_case_							,      snake_case_							,      (padded_height, padded_width)				)





  @slow
  def 		A						(	self   :			Tuple				)			->					List[Any]:





       '''simple docstring'''






       for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            _SCREAMING_SNAKE_CASE												=SwinModel.from_pretrained(snake_case_				)
            self.assertIsNotNone(snake_case_				)





  def 		A						(	self   :			Dict				)			->					str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=self.model_tester.prepare_config_and_inputs_for_common()

       _SCREAMING_SNAKE_CASE												=_config_zero_init(snake_case_				)
       for model_class in self.all_model_classes:
            _SCREAMING_SNAKE_CASE												=model_class(config=snake_case_				)
            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 							A__					( unittest.TestCase						):





  @cached_property
  def 		A						(	self   :			Optional[int]				)			->					List[Any]:





       '''simple docstring'''






       return (
           AutoImageProcessor.from_pretrained('microsoft/swin-tiny-patch4-window7-224'				)
           if is_vision_available()
           else None
       )





  @slow
  def 		A						(	self   :			Any				)			->					str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=SwinForImageClassification.from_pretrained('microsoft/swin-tiny-patch4-window7-224'				).to(snake_case_				)
       _SCREAMING_SNAKE_CASE												=self.default_image_processor

       _SCREAMING_SNAKE_CASE												=Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
       _SCREAMING_SNAKE_CASE												=image_processor(images=snake_case_							,      return_tensors='pt'				).to(snake_case_				)

       # forward pass
       with torch.no_grad():
            _SCREAMING_SNAKE_CASE												=model(**snake_case_				)

       # verify the logits
       _SCREAMING_SNAKE_CASE												=torch.Size((1, 1000)				)
       self.assertEqual(outputs.logits.shape							,      snake_case_				)
       _SCREAMING_SNAKE_CASE												=torch.tensor([-0.09_48, -0.64_54, -0.09_21]				).to(snake_case_				)
       self.assertTrue(torch.allclose(outputs.logits[0, :3]							,      snake_case_							,      atol=1e-4				)				)







@require_torch
class 							A__					( unittest.TestCase  ,     UpperCamelCase__						):
  A__					=       (SwinBackbone,) if is_torch_available() else ()
  A__					=       SwinConfig





  def 		A						(	self   :			Tuple				)			->					Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=SwinModelTester(self				)






'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase				:   Dict      				=  logging.get_logger(__name__)

lowerCamelCase				:   Dict      				=  {
    "uw-madison/nystromformer-512": "https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}







class 							A__					( A__						):
  A__					=       'nystromformer'





  def __init__(	self   :			List[str]							,      _a   :			Dict=3_0000							,      _a   :			Dict=768							,      _a   :			Union[str, Any]=12							,      _a   :			Optional[int]=12							,      _a   :			Union[str, Any]=3072							,      _a   :			Optional[Any]="gelu_new"							,      _a   :			Dict=0.1							,      _a   :			Dict=0.1							,      _a   :			Union[str, Any]=510							,      _a   :			Any=2							,      _a   :			List[Any]=64							,      _a   :			int=64							,      _a   :			Optional[Any]=65							,      _a   :			str=False							,      _a   :			List[Any]=0.02							,      _a   :			List[str]=1e-5							,      _a   :			Optional[Any]=1							,      _a   :			Optional[Any]=0							,      _a   :			List[Any]=2							,      **_a   :			List[Any]							,      )			->					List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=vocab_size
       _SCREAMING_SNAKE_CASE												=max_position_embeddings
       _SCREAMING_SNAKE_CASE												=hidden_size
       _SCREAMING_SNAKE_CASE												=num_hidden_layers
       _SCREAMING_SNAKE_CASE												=num_attention_heads
       _SCREAMING_SNAKE_CASE												=intermediate_size
       _SCREAMING_SNAKE_CASE												=hidden_act
       _SCREAMING_SNAKE_CASE												=hidden_dropout_prob
       _SCREAMING_SNAKE_CASE												=attention_probs_dropout_prob
       _SCREAMING_SNAKE_CASE												=initializer_range
       _SCREAMING_SNAKE_CASE												=type_vocab_size
       _SCREAMING_SNAKE_CASE												=segment_means_seq_len
       _SCREAMING_SNAKE_CASE												=num_landmarks
       _SCREAMING_SNAKE_CASE												=conv_kernel_size
       _SCREAMING_SNAKE_CASE												=inv_coeff_init_option
       _SCREAMING_SNAKE_CASE												=layer_norm_eps
       super().__init__(pad_token_id=_a							,      bos_token_id=_a							,      eos_token_id=_a							,      **_a				)
