



'''simple docstring'''





import collections
import inspect
import unittest

from transformers import DonutSwinConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_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 DonutSwinModel
  from transformers.models.donut.modeling_donut_swin import DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST






class __A      :



 '''simple docstring'''







 def __init__(self			,       A			,       A=13			,       A=32			,       A=2			,       A=3			,       A=16			,       A=[1, 2, 1]			,       A=[2, 2, 4]			,       A=2			,       A=2.0			,       A=True			,       A=0.0			,       A=0.0			,       A=0.1			,       A="gelu"			,       A=False			,       A=True			,       A=0.02			,       A=1E-5			,       A=True			,       A=None			,       A=True			,       A=10			,       A=8			,       )   ->					Optional[int]:


      """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



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


      """simple docstring"""



      return DonutSwinConfig(
          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  a__  (self			,       A			,       A			,       A	)   ->					List[str]:


      """simple docstring"""



      _a     =  DonutSwinModel(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  a__  (self	)   ->					List[Any]:


      """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 __A      (  A       ,       A       ,       unittest.TestCase						):



 '''simple docstring'''







 __lowerCamelCase   :	Union[str, Any]										=	(DonutSwinModel,) if is_torch_available() else ()
 __lowerCamelCase   :	Union[str, Any]										=	{'feature-extraction': DonutSwinModel} if is_torch_available() else {}
 __lowerCamelCase   :	Optional[int]										=	True

 __lowerCamelCase   :	Optional[Any]										=	False
 __lowerCamelCase   :	List[str]										=	False
 __lowerCamelCase   :	Any										=	False



 def  a__  (self	)   ->					str:


      """simple docstring"""



      _a     =  DonutSwinModelTester(self	)
      _a     =  ConfigTester(self			,       config_class=A			,       embed_dim=37	)



 def  a__  (self	)   ->					Optional[int]:


      """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  a__  (self	)   ->					List[str]:


      """simple docstring"""



      return



 def  a__  (self	)   ->					Any:


      """simple docstring"""



      _a     =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*A	)



 def  a__  (self	)   ->					Optional[int]:


      """simple docstring"""



      pass



 def  a__  (self	)   ->					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  a__  (self	)   ->					List[Any]:


      """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  a__  (self	)   ->					Optional[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	)	)

           if hasattr(self.model_tester			,       '''num_hidden_states_types'''	):
                _a     =  self.model_tester.num_hidden_states_types
           else:
                # also another +1 for reshaped_hidden_states
                _a     =  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  a__  (self			,       A			,       A			,       A			,       A	)   ->					Any:


      """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	)

      # DonutSwin 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]			,       )

      _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  a__  (self	)   ->					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  a__  (self	)   ->					Tuple:


      """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  a__  (self	)   ->					str:


      """simple docstring"""



      for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _a     =  DonutSwinModel.from_pretrained(A	)
           self.assertIsNotNone(A	)



 def  a__  (self	)   ->					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'''			,       )








'''simple docstring'''





from math import gcd




def lowerCAmelCase  (__A = 12_000):



     """simple docstring"""



     _a     =  0
     for d in range(max_d + 1):
          for n in range(d // 3 + 1  ,     (d + 1) // 2):
               if gcd(__A  ,     __A) == 1:
                    fractions_number += 1
     return fractions_number


if __name__ == "__main__":
  print(F"""{solution() = }""")



