
'''simple docstring'''







import collections
import inspect
import unittest

from transformers import SwinvaConfig
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_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 SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
  from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor







class UpperCAmelCase__	:





  """simple docstring"""

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


     '''simple docstring'''






     _a				:	List[Any]			=   parent
     _a				:	List[str]			=   batch_size
     _a				:	str			=   image_size
     _a				:	Tuple			=   patch_size
     _a				:	Union[str, Any]			=   num_channels
     _a				:	Optional[int]			=   embed_dim
     _a				:	Any			=   depths
     _a				:	str			=   num_heads
     _a				:	str			=   window_size
     _a				:	Optional[int]			=   mlp_ratio
     _a				:	Dict			=   qkv_bias
     _a				:	Union[str, Any]			=   hidden_dropout_prob
     _a				:	Dict			=   attention_probs_dropout_prob
     _a				:	Union[str, Any]			=   drop_path_rate
     _a				:	Union[str, Any]			=   hidden_act
     _a				:	Any			=   use_absolute_embeddings
     _a				:	str			=   patch_norm
     _a				:	Optional[Any]			=   layer_norm_eps
     _a				:	Tuple			=   initializer_range
     _a				:	Dict			=   is_training
     _a				:	Tuple			=   scope
     _a				:	Dict			=   use_labels
     _a				:	Union[str, Any]			=   type_sequence_label_size
     _a				:	Optional[int]			=   encoder_stride


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


     '''simple docstring'''






     _a				:	Optional[Any]			=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

     _a				:	int			=   None
     if self.use_labels:
        _a				:	Union[str, Any]			=   ids_tensor([self.batch_size]							,self.type_sequence_label_size       )

     _a				:	Union[str, Any]			=   self.get_config()

     return config, pixel_values, labels


  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     return SwinvaConfig(
         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							,)


  def   __lowercase		(		self      :     Tuple							,_a      :     str							,_a      :     Tuple							,_a      :     Dict       ):


     '''simple docstring'''






     _a				:	str			=   SwinvaModel(config=_a       )
     model.to(_a       )
     model.eval()
     _a				:	str			=   model(_a       )

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


     '''simple docstring'''






     _a				:	Any			=   SwinvaForMaskedImageModeling(config=_a       )
     model.to(_a       )
     model.eval()
     _a				:	List[Any]			=   model(_a       )
     self.parent.assertEqual(
         result.logits.shape							,(self.batch_size, self.num_channels, self.image_size, self.image_size)       )

     # test greyscale images
     _a				:	int			=   1
     _a				:	Optional[int]			=   SwinvaForMaskedImageModeling(_a       )
     model.to(_a       )
     model.eval()

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


  def   __lowercase		(		self      :     Tuple							,_a      :     Optional[Any]							,_a      :     Optional[int]							,_a      :     str       ):


     '''simple docstring'''






     _a				:	Any			=   self.type_sequence_label_size
     _a				:	str			=   SwinvaForImageClassification(_a       )
     model.to(_a       )
     model.eval()
     _a				:	str			=   model(_a							,labels=_a       )
     self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.type_sequence_label_size)       )


  def   __lowercase		(		self      :     str       ):


     '''simple docstring'''






     _a				:	str			=   self.prepare_config_and_inputs()
     _a,  _a,  _a				:	Optional[Any]			=   config_and_inputs
     _a				:	Union[str, Any]			=   {'pixel_values': pixel_values}
     return config, inputs_dict







@require_torch
class UpperCAmelCase__	( lowercase__    ,				lowercase__    ,				unittest.TestCase	):





  """simple docstring"""

  __UpperCAmelCase				:				Optional[int]   =					(
      (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
  )
  __UpperCAmelCase				:				Tuple   =					(
      {'''feature-extraction''': SwinvaModel, '''image-classification''': SwinvaForImageClassification}
      if is_torch_available()
      else {}
  )

  __UpperCAmelCase				:				Optional[Any]   =					False
  __UpperCAmelCase				:				List[Any]   =					False
  __UpperCAmelCase				:				List[str]   =					False
  __UpperCAmelCase				:				Tuple   =					False


  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     _a				:	List[str]			=   SwinvaModelTester(self       )
     _a				:	List[Any]			=   ConfigTester(self							,config_class=_a							,embed_dim=37       )


  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     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'''






     _a				:	List[str]			=   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      :     Optional[int]       ):


     '''simple docstring'''






     pass


  @unittest.skip(reason='Swinv2 does not use inputs_embeds'       )
  def   __lowercase		(		self      :     Dict       ):


     '''simple docstring'''






     pass


  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     _a,  _a				:	int			=   self.model_tester.prepare_config_and_inputs_for_common()

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


  def   __lowercase		(		self      :     Any       ):


     '''simple docstring'''






     _a,  _a				:	List[str]			=   self.model_tester.prepare_config_and_inputs_for_common()

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

        _a				:	int			=   ['pixel_values']
        self.assertListEqual(arg_names[:1]							,_a       )


  def   __lowercase		(		self      :     List[Any]       ):


     '''simple docstring'''






     _a,  _a				:	Tuple			=   self.model_tester.prepare_config_and_inputs_for_common()
     _a				:	List[str]			=   True

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

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

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

        # Check attention is always last and order is fine
        _a				:	Union[str, Any]			=   True
        _a				:	Tuple			=   True
        _a				:	Optional[int]			=   model_class(_a       )
        model.to(_a       )
        model.eval()
        with torch.no_grad():
           _a				:	Optional[Any]			=   model(**self._prepare_for_class(_a							,_a       )       )

        if hasattr(self.model_tester							,'num_hidden_states_types'       ):
           _a				:	Optional[Any]			=   self.model_tester.num_hidden_states_types
        else:
           # also another +1 for reshaped_hidden_states
           _a				:	Dict			=   2
        self.assertEqual(out_len + added_hidden_states							,len(_a       )       )

        _a				:	Optional[int]			=   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      :     Dict							,_a      :     List[str]							,_a      :     Any							,_a      :     List[str]							,_a      :     Optional[int]       ):


     '''simple docstring'''






     _a				:	Union[str, Any]			=   model_class(_a       )
     model.to(_a       )
     model.eval()

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

     _a				:	str			=   outputs.hidden_states

     _a				:	int			=   getattr(
         self.model_tester							,'expected_num_hidden_layers'							,len(self.model_tester.depths       ) + 1       )
     self.assertEqual(len(_a       )							,_a       )

     # Swinv2 has a different seq_length
     _a				:	Optional[Any]			=   (
         config.patch_size
         if isinstance(config.patch_size							,collections.abc.Iterable       )
         else (config.patch_size, config.patch_size)
     )

     _a				:	Dict			=   (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]							,)

     _a				:	Optional[int]			=   outputs.reshaped_hidden_states
     self.assertEqual(len(_a       )							,_a       )

     _a,  _a,  _a,  _a				:	str			=   reshaped_hidden_states[0].shape
     _a				:	List[Any]			=   (
         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      :     Tuple       ):


     '''simple docstring'''






     _a,  _a				:	Optional[int]			=   self.model_tester.prepare_config_and_inputs_for_common()

     _a				:	Tuple			=   (
         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				:	Any			=   True
        self.check_hidden_states_output(_a							,_a							,_a							,_a       )

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

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


  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     _a,  _a				:	str			=   self.model_tester.prepare_config_and_inputs_for_common()
     _a				:	Union[str, Any]			=   3

     _a				:	Optional[int]			=   (
         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				:	str			=   (
         config.patch_size
         if isinstance(config.patch_size							,collections.abc.Iterable       )
         else (config.patch_size, config.patch_size)
     )

     _a				:	Optional[Any]			=   image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
     _a				:	int			=   image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

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


  def   __lowercase		(		self      :     Dict       ):


     '''simple docstring'''






     _a				:	Dict			=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_image_modeling(*_a       )


  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     _a				:	Optional[int]			=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*_a       )


  @slow
  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        _a				:	Union[str, Any]			=   SwinvaModel.from_pretrained(_a       )
        self.assertIsNotNone(_a       )


  def   __lowercase		(		self      :     Any       ):


     '''simple docstring'''






     _a,  _a				:	str			=   self.model_tester.prepare_config_and_inputs_for_common()

     _a				:	Dict			=   _config_zero_init(_a       )
     for model_class in self.all_model_classes:
        _a				:	Any			=   model_class(config=_a       )
        for name, param in model.named_parameters():
           if "embeddings" not in name and "logit_scale" 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      :     Dict       ):


     '''simple docstring'''






     return (
         AutoImageProcessor.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256'       )
         if is_vision_available()
         else None
     )


  @slow
  def   __lowercase		(		self      :     List[Any]       ):


     '''simple docstring'''






     _a				:	Dict			=   SwinvaForImageClassification.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256'       ).to(
         _a       )
     _a				:	Any			=   self.default_image_processor

     _a				:	List[Any]			=   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
     _a				:	int			=   image_processor(images=_a							,return_tensors='pt'       ).to(_a       )

     # forward pass
     with torch.no_grad():
        _a				:	Union[str, Any]			=   model(**_a       )

     # verify the logits
     _a				:	List[str]			=   torch.Size((1, 1000)       )
     self.assertEqual(outputs.logits.shape							,_a       )
     _a				:	Any			=   torch.tensor([-0.3947, -0.4306, 0.0026]       ).to(_a       )
     self.assertTrue(torch.allclose(outputs.logits[0, :3]							,_a							,atol=1E-4       )       )





'''simple docstring'''







from __future__ import annotations

from collections.abc import Iterator
from typing import Generic, TypeVar

__lowerCAmelCase         =    TypeVar("""T""")







class UpperCAmelCase__	( Generic[T]	):





  """simple docstring"""

  def __init__(		self      :     Tuple							,_a      :     T       ):


     '''simple docstring'''






     _a				:	List[str]			=   data
     _a				:	Node[T] | None			=   None


  def __str__(		self      :     Dict       ):


     '''simple docstring'''






     return F"""{self.data}"""







class UpperCAmelCase__	( Generic[T]	):





  """simple docstring"""

  def __init__(		self      :     Optional[int]       ):


     '''simple docstring'''






     _a				:	Node[T] | None			=   None


  def __iter__(		self      :     str       ):


     '''simple docstring'''






     _a				:	Tuple			=   self.top
     while node:
        yield node.data
        _a				:	int			=   node.next


  def __str__(		self      :     str       ):


     '''simple docstring'''






     return "->".join([str(_a       ) for item in self]       )


  def __len__(		self      :     Optional[Any]       ):


     '''simple docstring'''






     return len(tuple(iter(self       )       )       )


  def   __lowercase		(		self      :     str       ):


     '''simple docstring'''






     return self.top is None


  def   __lowercase		(		self      :     List[Any]							,_a      :     T       ):


     '''simple docstring'''






     _a				:	int			=   Node(_a       )
     if not self.is_empty():
        _a				:	Optional[Any]			=   self.top
     _a				:	List[str]			=   node


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     if self.is_empty():
        raise IndexError('pop from empty stack'       )
     assert isinstance(self.top							,_a       )
     _a				:	List[Any]			=   self.top
     _a				:	int			=   self.top.next
     return pop_node.data


  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     if self.is_empty():
        raise IndexError('peek from empty stack'       )

     assert self.top is not None
     return self.top.data


  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     _a				:	Optional[int]			=   None


if __name__ == "__main__":
  from doctest import testmod

  testmod()



