
'''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 			UpperCamelCase__	:






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







       '''simple docstring'''



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






 def 						lowercase_    (      self	:Tuple      )					->      List[Any]:







       '''simple docstring'''



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

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

       __A													=      self.get_config()

       return config, pixel_values, labels






 def 						lowercase_    (      self	: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 						lowercase_    (      self	:Tuple						,					_A	:Optional[int]						,					_A	:Dict						,					_A	:List[Any]      )					->      str:







       '''simple docstring'''



       __A													=      SwinModel(config=_A      )
       model.to(_A      )
       model.eval()
       __A													=      model(_A      )

       __A													=      ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths      ) - 1))
       __A													=      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[int]						,					_A	:Optional[Any]						,					_A	:List[str]						,					_A	:List[Any]      )					->      Dict:







       '''simple docstring'''



       __A													=      SwinBackbone(config=_A      )
       model.to(_A      )
       model.eval()
       __A													=      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
       __A													=      None
       __A													=      SwinBackbone(config=_A      )
       model.to(_A      )
       model.eval()
       __A													=      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 						lowercase_    (      self	:int						,					_A	:Optional[int]						,					_A	:int						,					_A	:Union[str, Any]      )					->      Optional[int]:







       '''simple docstring'''



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

       # test greyscale images
       __A													=      1
       __A													=      SwinForMaskedImageModeling(_A      )
       model.to(_A      )
       model.eval()

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






 def 						lowercase_    (      self	:List[Any]						,					_A	:Union[str, Any]						,					_A	:List[Any]						,					_A	:Tuple      )					->      Any:







       '''simple docstring'''



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

       # test greyscale images
       __A													=      1
       __A													=      SwinForImageClassification(_A      )
       model.to(_A      )
       model.eval()

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






 def 						lowercase_    (      self	:Dict      )					->      Dict:







       '''simple docstring'''



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


@require_torch
class 			UpperCamelCase__	(     SCREAMING_SNAKE_CASE   ,  SCREAMING_SNAKE_CASE   ,  unittest.TestCase):
 UpperCAmelCase__     :     str    					=       (
     (
         SwinModel,
         SwinBackbone,
         SwinForImageClassification,
         SwinForMaskedImageModeling,
     )
     if is_torch_available()
     else ()
 )
 UpperCAmelCase__     :     Optional[Any]    					=       (
     {'feature-extraction': SwinModel, 'image-classification': SwinForImageClassification}
     if is_torch_available()
     else {}
 )
 UpperCAmelCase__     :     Dict    					=       True

 UpperCAmelCase__     :     List[Any]    					=       False
 UpperCAmelCase__     :     Tuple    					=       False
 UpperCAmelCase__     :     str    					=       False






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







       '''simple docstring'''



       __A													=      SwinModelTester(self      )
       __A													=      ConfigTester(self						,					config_class=_A						,					embed_dim=37      )






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







       '''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	:Union[str, Any]      )					->      Optional[Any]:







       '''simple docstring'''



       return






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







       '''simple docstring'''



       __A													=      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 						lowercase_    (      self	:int      )					->      Any:







       '''simple docstring'''



       pass






 def 						lowercase_    (      self	:Dict      )					->      Dict:







       '''simple docstring'''



       super().test_training_gradient_checkpointing()






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







       '''simple docstring'''



       __A													=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*_A      )






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







       '''simple docstring'''



       __A													=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*_A      )






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







       '''simple docstring'''



       __A													=      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 						lowercase_    (      self	:Union[str, Any]      )					->      Dict:







       '''simple docstring'''



       pass






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







       '''simple docstring'''



       pass






 def 						lowercase_    (      self	:List[str]      )					->      Dict:







       '''simple docstring'''



       __A						,   __A													=      self.model_tester.prepare_config_and_inputs_for_common()

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






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







       '''simple docstring'''



       __A						,   __A													=      self.model_tester.prepare_config_and_inputs_for_common()

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

             __A													=      ['pixel_values']
             self.assertListEqual(arg_names[:1]						,					_A      )






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







       '''simple docstring'''



       __A						,   __A													=      self.model_tester.prepare_config_and_inputs_for_common()
       __A													=      True

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

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

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

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

             __A													=      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 						lowercase_    (      self	:int						,					_A	:int						,					_A	:Dict						,					_A	:Optional[Any]						,					_A	:Dict      )					->      Dict:







       '''simple docstring'''



       __A													=      model_class(_A      )
       model.to(_A      )
       model.eval()

       with torch.no_grad():
             __A													=      model(**self._prepare_for_class(_A						,					_A      )      )

       __A													=      outputs.hidden_states

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

       __A													=      (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":
             __A													=      outputs.reshaped_hidden_states
             self.assertEqual(len(_A      )						,					_A      )

             __A						,   __A						,   __A						,   __A													=      reshaped_hidden_states[0].shape
             __A													=      (
                 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 						lowercase_    (      self	:int      )					->      Optional[Any]:







       '''simple docstring'''



       __A						,   __A													=      self.model_tester.prepare_config_and_inputs_for_common()

       __A													=      (
           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:
             __A													=      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"]
             __A													=      True

             self.check_hidden_states_output(_A						,					_A						,					_A						,					_A      )






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







       '''simple docstring'''



       __A						,   __A													=      self.model_tester.prepare_config_and_inputs_for_common()
       __A													=      3

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

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

       for model_class in self.all_model_classes:
             __A													=      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"]
             __A													=      True
             self.check_hidden_states_output(_A						,					_A						,					_A						,					(padded_height, padded_width)      )






 @slow
 def 						lowercase_    (      self	:Optional[Any]      )					->      Dict:







       '''simple docstring'''



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






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







       '''simple docstring'''



       __A						,   __A													=      self.model_tester.prepare_config_and_inputs_for_common()

       __A													=      _config_zero_init(_A      )
       for model_class in self.all_model_classes:
             __A													=      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 			UpperCamelCase__	(     unittest.TestCase):






 @cached_property
 def 						lowercase_    (      self	:Any      )					->      Any:







       '''simple docstring'''



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






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







       '''simple docstring'''



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

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

       # forward pass
       with torch.no_grad():
             __A													=      model(**_A      )

       # verify the logits
       __A													=      torch.Size((1, 1_000)      )
       self.assertEqual(outputs.logits.shape						,					_A      )
       __A													=      torch.tensor([-0.0_948, -0.6_454, -0.0_921]      ).to(_A      )
       self.assertTrue(torch.allclose(outputs.logits[0, :3]						,					_A						,					atol=1E-4      )      )


@require_torch
class 			UpperCamelCase__	(     unittest.TestCase   ,  SCREAMING_SNAKE_CASE):
 UpperCAmelCase__     :     int    					=       (SwinBackbone,) if is_torch_available() else ()
 UpperCAmelCase__     :     Optional[int]    					=       SwinConfig






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







       '''simple docstring'''



       __A													=      SwinModelTester(self      )











from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
		import torch

__magic_name__       =     logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
		from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):





		def 		__snake_case						(    cls				):
							"""simple docstring"""






							return cls





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase = None       ,			_UpperCAmelCase = None       ,			_UpperCAmelCase = None       ,			_UpperCAmelCase = None       ,			):
					"""simple docstring"""






					lowercase						   =       device
					lowercase						   =       shape[0]

					lowercase						   =       layout or torch.strided
					lowercase						   =       device or torch.device('cpu'				)

					if generator is not None:
										lowercase						   =       generator.device.type if not isinstance(_UpperCAmelCase       ,			_UpperCAmelCase				) else generator[0].device.type
										if gen_device_type != device.type and gen_device_type == "cpu":
															lowercase						   =       'cpu'
															if device != "mps":
																				logger.info(
																				    f"""The passed generator was created on 'cpu' even though a tensor on {device} was expected."""
																				    f""" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"""
																				    f""" slighly speed up this function by passing a generator that was created on the {device} device."""				)
										elif gen_device_type != device.type and gen_device_type == "cuda":
															raise ValueError(f"""Cannot generate a {device} tensor from a generator of type {gen_device_type}."""				)

					if isinstance(_UpperCAmelCase       ,			_UpperCAmelCase				):
										lowercase						   =       (1,) + shape[1:]
										lowercase						   =       [
										    torch.randn(_UpperCAmelCase       ,			generator=generator[i]       ,			device=_UpperCAmelCase       ,			dtype=_UpperCAmelCase       ,			layout=_UpperCAmelCase				)
										    for i in range(_UpperCAmelCase				)
										]
										lowercase						   =       torch.cat(_UpperCAmelCase       ,			dim=0				).to(_UpperCAmelCase				)
					else:
										lowercase						   =       torch.randn(_UpperCAmelCase       ,			generator=_UpperCAmelCase       ,			device=_UpperCAmelCase       ,			dtype=_UpperCAmelCase       ,			layout=_UpperCAmelCase				).to(_UpperCAmelCase				)

					return latents





def 		__snake_case						(    _UpperCAmelCase				):
					"""simple docstring"""






					if is_torch_version('<'       ,			'2.0.0'				) or not hasattr(_UpperCAmelCase       ,			'_dynamo'				):
										return False
					return isinstance(_UpperCAmelCase       ,			torch._dynamo.eval_frame.OptimizedModule				)






