






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 				_UpperCAmelCase :

				"""simple docstring"""




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

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




				def     lowercase__       (					self						:   List[Any]  ):

								'''simple docstring'''
								lowercase__       			=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

								lowercase__       			=      self.get_config()

								return config, pixel_values, labels




				def     lowercase__       (					self						:   Any  ):

								'''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     lowercase__       (					self						:   Optional[Any],  lowerCamelCase						:   List[Any],  lowerCamelCase						:   Any,  lowerCamelCase						:   Optional[Any]  ):

								'''simple docstring'''
								lowercase__       			=      SwinModel(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__       			=      model(lowerCamelCase  )

								lowercase__       			=      ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths  ) - 1))
								lowercase__       			=      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     lowercase__       (					self						:   Optional[Any],  lowerCamelCase						:   str,  lowerCamelCase						:   Union[str, Any],  lowerCamelCase						:   Union[str, Any]  ):

								'''simple docstring'''
								lowercase__       			=      SwinBackbone(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__       			=      model(lowerCamelCase  )

								# 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
								lowercase__       			=      None
								lowercase__       			=      SwinBackbone(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__       			=      model(lowerCamelCase  )

								# 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     lowercase__       (					self						:   Optional[int],  lowerCamelCase						:   int,  lowerCamelCase						:   Dict,  lowerCamelCase						:   Any  ):

								'''simple docstring'''
								lowercase__       			=      SwinForMaskedImageModeling(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__       			=      model(lowerCamelCase  )
								self.parent.assertEqual(
								    result.logits.shape,  (self.batch_size, self.num_channels, self.image_size, self.image_size)  )

								# test greyscale images
								lowercase__       			=      1
								lowercase__       			=      SwinForMaskedImageModeling(lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()

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




				def     lowercase__       (					self						:   Dict,  lowerCamelCase						:   List[str],  lowerCamelCase						:   str,  lowerCamelCase						:   List[str]  ):

								'''simple docstring'''
								lowercase__       			=      self.type_sequence_label_size
								lowercase__       			=      SwinForImageClassification(lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__       			=      model(lowerCamelCase,  labels=lowerCamelCase  )
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.type_sequence_label_size)  )

								# test greyscale images
								lowercase__       			=      1
								lowercase__       			=      SwinForImageClassification(lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()

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




				def     lowercase__       (					self						:   List[str]  ):

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



@require_torch
class 				_UpperCAmelCase (	A__		,A__		,unittest.TestCase     ):

				"""simple docstring"""




				lowercase__    							=	(
				    (
				        SwinModel,
				        SwinBackbone,
				        SwinForImageClassification,
				        SwinForMaskedImageModeling,
				    )
				    if is_torch_available()
				    else ()
				)
				lowercase__    							=	(
				    {"""feature-extraction""": SwinModel, """image-classification""": SwinForImageClassification}
				    if is_torch_available()
				    else {}
				)
				lowercase__    							=	True

				lowercase__    							=	False
				lowercase__    							=	False
				lowercase__    							=	False




				def     lowercase__       (					self						:   Optional[Any]  ):

								'''simple docstring'''
								lowercase__       			=      SwinModelTester(self  )
								lowercase__       			=      ConfigTester(self,  config_class=lowerCamelCase,  embed_dim=37  )




				def     lowercase__       (					self						:   Union[str, 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     lowercase__       (					self						:   Tuple  ):

								'''simple docstring'''
								return




				def     lowercase__       (					self						:   Union[str, Any]  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCamelCase  )




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

								'''simple docstring'''
								pass




				def     lowercase__       (					self						:   Dict  ):

								'''simple docstring'''
								super().test_training_gradient_checkpointing()




				def     lowercase__       (					self						:   Dict  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_backbone(*lowerCamelCase  )




				def     lowercase__       (					self						:   Optional[Any]  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_image_modeling(*lowerCamelCase  )




				def     lowercase__       (					self						:   Optional[Any]  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*lowerCamelCase  )




				@unittest.skip(reason='''Swin does not use inputs_embeds'''  )
				def     lowercase__       (					self						:   Union[str, Any]  ):

								'''simple docstring'''
								pass




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

								'''simple docstring'''
								pass




				def     lowercase__       (					self						:   Tuple  ):

								'''simple docstring'''
								lowercase__			,						lowercase__       			=      self.model_tester.prepare_config_and_inputs_for_common()

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




				def     lowercase__       (					self						:   List[str]  ):

								'''simple docstring'''
								lowercase__			,						lowercase__       			=      self.model_tester.prepare_config_and_inputs_for_common()

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

												lowercase__       			=      ['''pixel_values''']
												self.assertListEqual(arg_names[:1],  lowerCamelCase  )




				def     lowercase__       (					self						:   Optional[int]  ):

								'''simple docstring'''
								lowercase__			,						lowercase__       			=      self.model_tester.prepare_config_and_inputs_for_common()
								lowercase__       			=      True

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

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

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

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

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

												lowercase__       			=      outputs.attentions

												self.assertEqual(len(lowerCamelCase  ),  lowerCamelCase  )

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




				def     lowercase__       (					self						:   Union[str, Any],  lowerCamelCase						:   Dict,  lowerCamelCase						:   Dict,  lowerCamelCase						:   List[str],  lowerCamelCase						:   List[Any]  ):

								'''simple docstring'''
								lowercase__       			=      model_class(lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()

								with torch.no_grad():
												lowercase__       			=      model(**self._prepare_for_class(lowerCamelCase,  lowerCamelCase  )  )

								lowercase__       			=      outputs.hidden_states

								lowercase__       			=      getattr(
								    self.model_tester,  '''expected_num_hidden_layers''',  len(self.model_tester.depths  ) + 1  )
								self.assertEqual(len(lowerCamelCase  ),  lowerCamelCase  )

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

								lowercase__       			=      (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":
												lowercase__       			=      outputs.reshaped_hidden_states
												self.assertEqual(len(lowerCamelCase  ),  lowerCamelCase  )

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




				def     lowercase__       (					self						:   Union[str, Any]  ):

								'''simple docstring'''
								lowercase__			,						lowercase__       			=      self.model_tester.prepare_config_and_inputs_for_common()

								lowercase__       			=      (
								    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:
												lowercase__       			=      True
												self.check_hidden_states_output(lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase  )

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

												self.check_hidden_states_output(lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase  )




				def     lowercase__       (					self						:   List[Any]  ):

								'''simple docstring'''
								lowercase__			,						lowercase__       			=      self.model_tester.prepare_config_and_inputs_for_common()
								lowercase__       			=      3

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

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

								for model_class in self.all_model_classes:
												lowercase__       			=      True
												self.check_hidden_states_output(lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  (padded_height, padded_width)  )

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




				@slow
				def     lowercase__       (					self						:   int  ):

								'''simple docstring'''
								for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												lowercase__       			=      SwinModel.from_pretrained(lowerCamelCase  )
												self.assertIsNotNone(lowerCamelCase  )




				def     lowercase__       (					self						:   List[Any]  ):

								'''simple docstring'''
								lowercase__			,						lowercase__       			=      self.model_tester.prepare_config_and_inputs_for_common()

								lowercase__       			=      _config_zero_init(lowerCamelCase  )
								for model_class in self.all_model_classes:
												lowercase__       			=      model_class(config=lowerCamelCase  )
												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 				_UpperCAmelCase (	unittest.TestCase     ):

				"""simple docstring"""




				@cached_property
				def     lowercase__       (					self						:   List[str]  ):

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




				@slow
				def     lowercase__       (					self						:   List[str]  ):

								'''simple docstring'''
								lowercase__       			=      SwinForImageClassification.from_pretrained('''microsoft/swin-tiny-patch4-window7-224'''  ).to(lowerCamelCase  )
								lowercase__       			=      self.default_image_processor

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

								# forward pass
								with torch.no_grad():
												lowercase__       			=      model(**lowerCamelCase  )

								# verify the logits
								lowercase__       			=      torch.Size((1, 1_000)  )
								self.assertEqual(outputs.logits.shape,  lowerCamelCase  )
								lowercase__       			=      torch.tensor([-0.0948, -0.6454, -0.0921]  ).to(lowerCamelCase  )
								self.assertTrue(torch.allclose(outputs.logits[0, :3],  lowerCamelCase,  atol=1E-4  )  )



@require_torch
class 				_UpperCAmelCase (	unittest.TestCase		,A__     ):

				"""simple docstring"""




				lowercase__    							=	(SwinBackbone,) if is_torch_available() else ()
				lowercase__    							=	SwinConfig




				def     lowercase__       (					self						:   int  ):

								'''simple docstring'''
								lowercase__       			=      SwinModelTester(self  )












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A__     :      Dict             =			{'configuration_sew_d': ['SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWDConfig']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__     :      Union[str, Any]             =			[
							    'SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'SEWDForCTC',
							    'SEWDForSequenceClassification',
							    'SEWDModel',
							    'SEWDPreTrainedModel',
							]

if TYPE_CHECKING:
							from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_sew_d import (
														    SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SEWDForCTC,
														    SEWDForSequenceClassification,
														    SEWDModel,
														    SEWDPreTrainedModel,
														)


else:
							import sys

							A__     :      Tuple             =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




