


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


										"""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 							__UpperCamelCase (				self    :					Dict     )							->   Any:


										"""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 							__UpperCamelCase (				self    :					List[str]     )							->   Optional[int]:


										"""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 							__UpperCamelCase (				self    :					Tuple ,			_a    :					List[str] ,			_a    :					Optional[int] ,			_a    :					Optional[Any]     )							->   Optional[int]:


										"""simple docstring"""





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

										_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 							__UpperCamelCase (				self    :					Optional[int] ,			_a    :					Optional[Any] ,			_a    :					Optional[int] ,			_a    :					Tuple     )							->   Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =SwinBackbone(config=_a     )
										model.to(_a     )
										model.eval()
										_SCREAMING_SNAKE_CASE	     =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], 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=_a     )
										model.to(_a     )
										model.eval()
										_SCREAMING_SNAKE_CASE	     =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[str] ,			_a    :					List[str] ,			_a    :					str ,			_a    :					List[str]     )							->   int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =SwinForMaskedImageModeling(config=_a     )
										model.to(_a     )
										model.eval()
										_SCREAMING_SNAKE_CASE	     =model(_a     )
										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(_a     )
										model.to(_a     )
										model.eval()

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




						def 							__UpperCamelCase (				self    :					str ,			_a    :					Optional[Any] ,			_a    :					Any ,			_a    :					Optional[Any]     )							->   str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self.type_sequence_label_size
										_SCREAMING_SNAKE_CASE	     =SwinForImageClassification(_a     )
										model.to(_a     )
										model.eval()
										_SCREAMING_SNAKE_CASE	     =model(_a ,			labels=_a     )
										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(_a     )
										model.to(_a     )
										model.eval()

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




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self.prepare_config_and_inputs()
										(
										    (
										    _SCREAMING_SNAKE_CASE
										)     , (
										    _SCREAMING_SNAKE_CASE
										)     , (
										    _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     ):
						UpperCAmelCase									=  (
						    (
						        SwinModel,
						        SwinBackbone,
						        SwinForImageClassification,
						        SwinForMaskedImageModeling,
						    )
						    if is_torch_available()
						    else ()
						)
						UpperCAmelCase									=  (
						    {"feature-extraction": SwinModel, "image-classification": SwinForImageClassification}
						    if is_torch_available()
						    else {}
						)
						UpperCAmelCase									=  True

						UpperCAmelCase									=  False
						UpperCAmelCase									=  False
						UpperCAmelCase									=  False




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


										"""simple docstring"""





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




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


										"""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 							__UpperCamelCase (				self    :					Union[str, Any]     )							->   List[str]:


										"""simple docstring"""





										return




						def 							__UpperCamelCase (				self    :					int     )							->   str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =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    :					Any     )							->   Union[str, Any]:


										"""simple docstring"""





										pass




						def 							__UpperCamelCase (				self    :					str     )							->   Any:


										"""simple docstring"""





										super().test_training_gradient_checkpointing()




						def 							__UpperCamelCase (				self    :					Optional[int]     )							->   Dict:


										"""simple docstring"""





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




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


										"""simple docstring"""





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




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =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    :					Union[str, Any]     )							->   str:


										"""simple docstring"""





										pass




						@unittest.skip(reason='''Swin Transformer does not use feedforward chunking'''     )
						def 							__UpperCamelCase (				self    :					Any     )							->   Optional[Any]:


										"""simple docstring"""





										pass




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     , _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(_a     )
														self.assertIsInstance(model.get_input_embeddings() ,			(nn.Module)     )
														_SCREAMING_SNAKE_CASE	     =model.get_output_embeddings()
														self.assertTrue(x is None or isinstance(_a ,			nn.Linear     )     )




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     , _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(_a     )
														_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] ,			_a     )




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     , _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(_a     )
														model.to(_a     )
														model.eval()
														with torch.no_grad():
																		_SCREAMING_SNAKE_CASE	     =model(**self._prepare_for_class(_a ,			_a     )     )
														_SCREAMING_SNAKE_CASE	     =outputs.attentions
														_SCREAMING_SNAKE_CASE	     =len(self.model_tester.depths     )
														self.assertEqual(len(_a     ) ,			_a     )

														# 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(_a     )
														model.to(_a     )
														model.eval()
														with torch.no_grad():
																		_SCREAMING_SNAKE_CASE	     =model(**self._prepare_for_class(_a ,			_a     )     )
														_SCREAMING_SNAKE_CASE	     =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] ,			)
														_SCREAMING_SNAKE_CASE	     =len(_a     )

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

														# 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(_a     )     )

														_SCREAMING_SNAKE_CASE	     =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    :					Any ,			_a    :					int ,			_a    :					Dict ,			_a    :					Optional[Any] ,			_a    :					Union[str, Any]     )							->   List[Any]:


										"""simple docstring"""





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

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

										_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(_a     ) ,			_a     )

										# 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(_a     ) ,			_a     )

														_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =reshaped_hidden_states[0].shape
														_SCREAMING_SNAKE_CASE	     =(
														    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    :					Optional[int]     )							->   Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     , _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(_a ,			_a ,			_a ,			_a     )

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

														self.check_hidden_states_output(_a ,			_a ,			_a ,			_a     )




						def 							__UpperCamelCase (				self    :					str     )							->   Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     , _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(_a ,			_a ,			_a ,			(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(_a ,			_a ,			_a ,			(padded_height, padded_width)     )




						@slow
						def 							__UpperCamelCase (				self    :					int     )							->   Any:


										"""simple docstring"""





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




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =self.model_tester.prepare_config_and_inputs_for_common()

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




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


										"""simple docstring"""





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




						@slow
						def 							__UpperCamelCase (				self    :					Optional[int]     )							->   Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =SwinForImageClassification.from_pretrained('''microsoft/swin-tiny-patch4-window7-224'''     ).to(_a     )
										_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=_a ,			return_tensors='''pt'''     ).to(_a     )

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

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

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




						def 							__UpperCamelCase (				self    :					Tuple     )							->   Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =SwinModelTester(self     )



from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers

class   A__							:




						def __init__(				self    :					List[Any] ,			_a    :					List[Any]     )							->   None:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =vocab
										_SCREAMING_SNAKE_CASE	     =normalizers.BertNormalizer(
										    clean_text=_a ,			handle_chinese_chars=_a ,			strip_accents=_a ,			lowercase=_a ,			)
										try:
														import rjieba
										except ImportError:
														raise ImportError(
														    '''You need to install rjieba to use RoFormerTokenizer. '''
														    '''See https://pypi.org/project/rjieba/ for installation.'''     )
										_SCREAMING_SNAKE_CASE	     =rjieba




						def 							__UpperCamelCase (				self    :					List[Any] ,			_a    :					int ,			_a    :					NormalizedString     )							->   List[NormalizedString]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =[]

										# this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
										for token, start, end in self.jieba.tokenize(str(_a     ) ,			hmm=_a     ):
														if token in self.vocab:
																		splits.append(normalized_string[start:end]     )
														else:
																		_SCREAMING_SNAKE_CASE	     =self.normalizers.normalize_str(_a     ).split()
																		for token in token_list:
																						if token:
																										_SCREAMING_SNAKE_CASE	     =start + len(_a     )
																										splits.append(normalized_string[start:end]     )
																										_SCREAMING_SNAKE_CASE	     =end

        # this code test_alignement_methods can't pass but fast (300ms)
        # for token in self.jieba.cut(str(normalized_string), False):
        #     if token in self.vocab:
        #         splits.append(NormalizedString(token))
        #     else:
        #         token_list = self.normalizers.normalize_str(token).split()
        #         for token in token_list:
        #             if token:
        #                 splits.append(NormalizedString(token))

										return splits




						def 							__UpperCamelCase (				self    :					List[Any] ,			_a    :					PreTokenizedString     )							->   int:


										"""simple docstring"""





										pretok.split(self.jieba_split     )