


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 lowerCAmelCase_						:

 '''simple docstring'''

 def __init__(	self		,      _UpperCAmelCase		,      _UpperCAmelCase=13		,      _UpperCAmelCase=32		,      _UpperCAmelCase=2		,      _UpperCAmelCase=3		,      _UpperCAmelCase=16		,      _UpperCAmelCase=[1, 2, 1]		,      _UpperCAmelCase=[2, 2, 4]		,      _UpperCAmelCase=2		,      _UpperCAmelCase=2.0		,      _UpperCAmelCase=True		,      _UpperCAmelCase=0.0		,      _UpperCAmelCase=0.0		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase="gelu"		,      _UpperCAmelCase=False		,      _UpperCAmelCase=True		,      _UpperCAmelCase=0.02		,      _UpperCAmelCase=1E-5		,      _UpperCAmelCase=True		,      _UpperCAmelCase=None		,      _UpperCAmelCase=True		,      _UpperCAmelCase=10		,      _UpperCAmelCase=8		,      _UpperCAmelCase=["stage1", "stage2"]		,      _UpperCAmelCase=[1, 2]		,      ):
       snake_case_       =      parent
       snake_case_       =      batch_size
       snake_case_       =      image_size
       snake_case_       =      patch_size
       snake_case_       =      num_channels
       snake_case_       =      embed_dim
       snake_case_       =      depths
       snake_case_       =      num_heads
       snake_case_       =      window_size
       snake_case_       =      mlp_ratio
       snake_case_       =      qkv_bias
       snake_case_       =      hidden_dropout_prob
       snake_case_       =      attention_probs_dropout_prob
       snake_case_       =      drop_path_rate
       snake_case_       =      hidden_act
       snake_case_       =      use_absolute_embeddings
       snake_case_       =      patch_norm
       snake_case_       =      layer_norm_eps
       snake_case_       =      initializer_range
       snake_case_       =      is_training
       snake_case_       =      scope
       snake_case_       =      use_labels
       snake_case_       =      type_sequence_label_size
       snake_case_       =      encoder_stride
       snake_case_       =      out_features
       snake_case_       =      out_indices



 def 						UpperCamelCase__     (	self					):
       snake_case_       =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

       snake_case_       =      self.get_config()

       return config, pixel_values, labels



 def 						UpperCamelCase__     (	self					):
       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		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					):
       snake_case_       =      SwinModel(config=_UpperCAmelCase					)
       model.to(_UpperCAmelCase					)
       model.eval()
       snake_case_       =      model(_UpperCAmelCase					)

       snake_case_       =      ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths					) - 1))
       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		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					):
       snake_case_       =      SwinBackbone(config=_UpperCAmelCase					)
       model.to(_UpperCAmelCase					)
       model.eval()
       snake_case_       =      model(_UpperCAmelCase					)

       # 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
       snake_case_       =      None
       snake_case_       =      SwinBackbone(config=_UpperCAmelCase					)
       model.to(_UpperCAmelCase					)
       model.eval()
       snake_case_       =      model(_UpperCAmelCase					)

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

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

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



 def 						UpperCamelCase__     (	self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					):
       snake_case_       =      self.type_sequence_label_size
       snake_case_       =      SwinForImageClassification(_UpperCAmelCase					)
       model.to(_UpperCAmelCase					)
       model.eval()
       snake_case_       =      model(_UpperCAmelCase		,      labels=_UpperCAmelCase					)
       self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.type_sequence_label_size)					)

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

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





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







@require_torch
class lowerCAmelCase_						( lowerCamelCase__					,						lowerCamelCase__					,						unittest.TestCase       ):

 '''simple docstring'''

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

 __snake_case												=  False
 __snake_case												=  False
 __snake_case												=  False



 def 						UpperCamelCase__     (	self					):
       snake_case_       =      SwinModelTester(self					)
       snake_case_       =      ConfigTester(self		,      config_class=_UpperCAmelCase		,      embed_dim=37					)



 def 						UpperCamelCase__     (	self					):
       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					):
       return



 def 						UpperCamelCase__     (	self					):
       snake_case_       =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_UpperCAmelCase					)



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



 def 						UpperCamelCase__     (	self					):
       super().test_training_gradient_checkpointing()



 def 						UpperCamelCase__     (	self					):
       snake_case_       =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*_UpperCAmelCase					)



 def 						UpperCamelCase__     (	self					):
       snake_case_       =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*_UpperCAmelCase					)



 def 						UpperCamelCase__     (	self					):
       snake_case_       =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase					)



 @unittest.skip(reason='''Swin does not use inputs_embeds'''					)
 def 						UpperCamelCase__     (	self					):
       pass



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



 def 						UpperCamelCase__     (	self					):
       snake_case_       ,    snake_case_       =      self.model_tester.prepare_config_and_inputs_for_common()

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



 def 						UpperCamelCase__     (	self					):
       snake_case_       ,    snake_case_       =      self.model_tester.prepare_config_and_inputs_for_common()

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

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



 def 						UpperCamelCase__     (	self					):
       snake_case_       ,    snake_case_       =      self.model_tester.prepare_config_and_inputs_for_common()
       snake_case_       =      True

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

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

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

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

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

             snake_case_       =      outputs.attentions

             self.assertEqual(len(_UpperCAmelCase					)		,      _UpperCAmelCase					)

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



 def 						UpperCamelCase__     (	self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					):
       snake_case_       =      model_class(_UpperCAmelCase					)
       model.to(_UpperCAmelCase					)
       model.eval()

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

       snake_case_       =      outputs.hidden_states

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

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

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

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



 def 						UpperCamelCase__     (	self					):
       snake_case_       ,    snake_case_       =      self.model_tester.prepare_config_and_inputs_for_common()

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

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

             self.check_hidden_states_output(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)



 def 						UpperCamelCase__     (	self					):
       snake_case_       ,    snake_case_       =      self.model_tester.prepare_config_and_inputs_for_common()
       snake_case_       =      3

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

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

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

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



 @slow
 def 						UpperCamelCase__     (	self					):
       for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             snake_case_       =      SwinModel.from_pretrained(_UpperCAmelCase					)
             self.assertIsNotNone(_UpperCAmelCase					)





 def 						UpperCamelCase__     (	self					):
       snake_case_       ,    snake_case_       =      self.model_tester.prepare_config_and_inputs_for_common()

       snake_case_       =      _config_zero_init(_UpperCAmelCase					)
       for model_class in self.all_model_classes:
             snake_case_       =      model_class(config=_UpperCAmelCase					)
             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 lowerCAmelCase_						( unittest.TestCase       ):

 '''simple docstring'''

 @cached_property
 def 						UpperCamelCase__     (	self					):
       return (
           AutoImageProcessor.from_pretrained('''microsoft/swin-tiny-patch4-window7-224'''					)
           if is_vision_available()
           else None
       )





 @slow
 def 						UpperCamelCase__     (	self					):
       snake_case_       =      SwinForImageClassification.from_pretrained('''microsoft/swin-tiny-patch4-window7-224'''					).to(_UpperCAmelCase					)
       snake_case_       =      self.default_image_processor

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

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

       # verify the logits
       snake_case_       =      torch.Size((1, 10_00)					)
       self.assertEqual(outputs.logits.shape		,      _UpperCAmelCase					)
       snake_case_       =      torch.tensor([-0.0_948, -0.6_454, -0.0_921]					).to(_UpperCAmelCase					)
       self.assertTrue(torch.allclose(outputs.logits[0, :3]		,      _UpperCAmelCase		,      atol=1E-4					)					)







@require_torch
class lowerCAmelCase_						( unittest.TestCase					,						lowerCamelCase__       ):

 '''simple docstring'''

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



 def 						UpperCamelCase__     (	self					):
       snake_case_       =      SwinModelTester(self					)



UpperCAmelCase			=    10**8






def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					)->  bool:




      """simple docstring"""

      if n % 10 == 0:
            return False
      snake_case_       =      str(SCREAMING_SNAKE_CASE					)
      return s == s[::-1]






def        __lowerCAmelCase    ()->  int:




      """simple docstring"""

      snake_case_       =      set()
      snake_case_       =      1
      snake_case_       =      5
      while sum_squares < LIMIT:
            snake_case_       =      first_square + 1
            while sum_squares < LIMIT:
                  if is_palindrome(SCREAMING_SNAKE_CASE					):
                        answer.add(SCREAMING_SNAKE_CASE					)
                  last_square += 1
                  sum_squares += last_square**2
            first_square += 1
            snake_case_       =      first_square**2 + (first_square + 1) ** 2

      return sum(SCREAMING_SNAKE_CASE					)


if __name__ == "__main__":
   print(solution())