code: 
  
  
  
  
  """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 							lowerCamelCase		:
  
  
  
  
  
  
  
     def __init__(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=13    ,      SCREAMING_SNAKE_CASE_=32    ,      SCREAMING_SNAKE_CASE_=2    ,      SCREAMING_SNAKE_CASE_=3    ,      SCREAMING_SNAKE_CASE_=16    ,      SCREAMING_SNAKE_CASE_=[1, 2, 1]    ,      SCREAMING_SNAKE_CASE_=[2, 2, 4]    ,      SCREAMING_SNAKE_CASE_=2    ,      SCREAMING_SNAKE_CASE_=2.0    ,      SCREAMING_SNAKE_CASE_=True    ,      SCREAMING_SNAKE_CASE_=0.0    ,      SCREAMING_SNAKE_CASE_=0.0    ,      SCREAMING_SNAKE_CASE_=0.1    ,      SCREAMING_SNAKE_CASE_="gelu"    ,      SCREAMING_SNAKE_CASE_=False    ,      SCREAMING_SNAKE_CASE_=True    ,      SCREAMING_SNAKE_CASE_=0.02    ,      SCREAMING_SNAKE_CASE_=1e-5    ,      SCREAMING_SNAKE_CASE_=True    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=True    ,      SCREAMING_SNAKE_CASE_=10    ,      SCREAMING_SNAKE_CASE_=8    ,      ):
         UpperCamelCase				:   List[Any]          =							parent
         UpperCamelCase				:   Dict          =							batch_size
         UpperCamelCase				:   Union[str, Any]          =							image_size
         UpperCamelCase				:   Optional[Any]          =							patch_size
         UpperCamelCase				:   str          =							num_channels
         UpperCamelCase				:   Tuple          =							embed_dim
         UpperCamelCase				:   List[Any]          =							depths
         UpperCamelCase				:   Union[str, Any]          =							num_heads
         UpperCamelCase				:   List[Any]          =							window_size
         UpperCamelCase				:   str          =							mlp_ratio
         UpperCamelCase				:   Any          =							qkv_bias
         UpperCamelCase				:   List[Any]          =							hidden_dropout_prob
         UpperCamelCase				:   Tuple          =							attention_probs_dropout_prob
         UpperCamelCase				:   Any          =							drop_path_rate
         UpperCamelCase				:   Dict          =							hidden_act
         UpperCamelCase				:   List[Any]          =							use_absolute_embeddings
         UpperCamelCase				:   Union[str, Any]          =							patch_norm
         UpperCamelCase				:   List[str]          =							layer_norm_eps
         UpperCamelCase				:   Optional[Any]          =							initializer_range
         UpperCamelCase				:   Tuple          =							is_training
         UpperCamelCase				:   Optional[Any]          =							scope
         UpperCamelCase				:   Union[str, Any]          =							use_labels
         UpperCamelCase				:   Union[str, Any]          =							type_sequence_label_size
         UpperCamelCase				:   Union[str, Any]          =							encoder_stride
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase				:   Dict          =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
  
         UpperCamelCase				:   Optional[Any]          =							None
         if self.use_labels:
             UpperCamelCase				:   str          =							ids_tensor([self.batch_size]    ,      self.type_sequence_label_size )
  
         UpperCamelCase				:   Optional[int]          =							self.get_config()
  
         return config, pixel_values, labels
  
  
  
  
  
  
  
     def       a_							(							self ):
         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    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   Optional[int]          =							DonutSwinModel(config=SCREAMING_SNAKE_CASE_ )
         model.to(SCREAMING_SNAKE_CASE_ )
         model.eval()
         UpperCamelCase				:   Union[str, Any]          =							model(SCREAMING_SNAKE_CASE_ )
  
         UpperCamelCase				:   str          =							((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
         UpperCamelCase				:   int          =							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 ):
         UpperCamelCase				:   str          =							self.prepare_config_and_inputs()
         (
             (
             UpperCamelCase
         )       ,							(
             UpperCamelCase
         )       ,							(
             UpperCamelCase
         )       ,							
         )				:   Dict          =							config_and_inputs
         UpperCamelCase				:   List[Any]          =							{"""pixel_values""": pixel_values}
         return config, inputs_dict
  
  
  
  
  
  
  
  @require_torch
  class 							lowerCamelCase		( _UpperCAmelCase	,  _UpperCAmelCase	,  unittest.TestCase				):
     lowercase       :  str      =				(DonutSwinModel,) if is_torch_available() else ()
     lowercase       :  Optional[Any]      =				{'feature-extraction': DonutSwinModel} if is_torch_available() else {}
     lowercase       :  Dict      =				True
  
     lowercase       :  Optional[Any]      =				False
     lowercase       :  Dict      =				False
     lowercase       :  List[Any]      =				False
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase				:   Union[str, Any]          =							DonutSwinModelTester(self )
         UpperCamelCase				:   Union[str, Any]          =							ConfigTester(self    ,      config_class=SCREAMING_SNAKE_CASE_    ,      embed_dim=37 )
  
  
  
  
  
  
  
     def       a_							(							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       a_							(							self ):
         return
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase				:   Optional[int]          =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self ):
         # DonutSwin does not use inputs_embeds
         pass
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase       ,							UpperCamelCase				:   Optional[int]          =							self.model_tester.prepare_config_and_inputs_for_common()
  
         for model_class in self.all_model_classes:
             UpperCamelCase				:   List[str]          =							model_class(SCREAMING_SNAKE_CASE_ )
             self.assertIsInstance(model.get_input_embeddings()    ,      (nn.Module) )
             UpperCamelCase				:   Tuple          =							model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE_    ,      nn.Linear ) )
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase       ,							UpperCamelCase				:   List[Any]          =							self.model_tester.prepare_config_and_inputs_for_common()
  
         for model_class in self.all_model_classes:
             UpperCamelCase				:   Dict          =							model_class(SCREAMING_SNAKE_CASE_ )
             UpperCamelCase				:   Dict          =							inspect.signature(model.forward )
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             UpperCamelCase				:   Optional[int]          =							[*signature.parameters.keys()]
  
             UpperCamelCase				:   Optional[int]          =							["""pixel_values"""]
             self.assertListEqual(arg_names[:1]    ,      SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase       ,							UpperCamelCase				:   int          =							self.model_tester.prepare_config_and_inputs_for_common()
         UpperCamelCase				:   Dict          =							True
  
         for model_class in self.all_model_classes:
             UpperCamelCase				:   Tuple          =							True
             UpperCamelCase				:   Dict          =							False
             UpperCamelCase				:   List[Any]          =							True
             UpperCamelCase				:   List[Any]          =							model_class(SCREAMING_SNAKE_CASE_ )
             model.to(SCREAMING_SNAKE_CASE_ )
             model.eval()
             with torch.no_grad():
                 UpperCamelCase				:   Tuple          =							model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ) )
             UpperCamelCase				:   List[str]          =							outputs.attentions
             UpperCamelCase				:   List[str]          =							len(self.model_tester.depths )
             self.assertEqual(len(SCREAMING_SNAKE_CASE_ )    ,      SCREAMING_SNAKE_CASE_ )
  
             # check that output_attentions also work using config
             del inputs_dict["output_attentions"]
             UpperCamelCase				:   Tuple          =							True
             UpperCamelCase				:   List[str]          =							config.window_size**2
             UpperCamelCase				:   str          =							model_class(SCREAMING_SNAKE_CASE_ )
             model.to(SCREAMING_SNAKE_CASE_ )
             model.eval()
             with torch.no_grad():
                 UpperCamelCase				:   List[Any]          =							model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ) )
             UpperCamelCase				:   Dict          =							outputs.attentions
             self.assertEqual(len(SCREAMING_SNAKE_CASE_ )    ,      SCREAMING_SNAKE_CASE_ )
  
             self.assertListEqual(
                 list(attentions[0].shape[-3:] )    ,      [self.model_tester.num_heads[0], window_size_squared, window_size_squared]    ,      )
             UpperCamelCase				:   Optional[Any]          =							len(SCREAMING_SNAKE_CASE_ )
  
             # Check attention is always last and order is fine
             UpperCamelCase				:   Any          =							True
             UpperCamelCase				:   int          =							True
             UpperCamelCase				:   Optional[Any]          =							model_class(SCREAMING_SNAKE_CASE_ )
             model.to(SCREAMING_SNAKE_CASE_ )
             model.eval()
             with torch.no_grad():
                 UpperCamelCase				:   Dict          =							model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ) )
  
             if hasattr(self.model_tester    ,      """num_hidden_states_types""" ):
                 UpperCamelCase				:   List[Any]          =							self.model_tester.num_hidden_states_types
             else:
                 # also another +1 for reshaped_hidden_states
                 UpperCamelCase				:   List[Any]          =							2
             self.assertEqual(out_len + added_hidden_states    ,      len(SCREAMING_SNAKE_CASE_ ) )
  
             UpperCamelCase				:   int          =							outputs.attentions
  
             self.assertEqual(len(SCREAMING_SNAKE_CASE_ )    ,      SCREAMING_SNAKE_CASE_ )
  
             self.assertListEqual(
                 list(self_attentions[0].shape[-3:] )    ,      [self.model_tester.num_heads[0], window_size_squared, window_size_squared]    ,      )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   Dict          =							model_class(SCREAMING_SNAKE_CASE_ )
         model.to(SCREAMING_SNAKE_CASE_ )
         model.eval()
  
         with torch.no_grad():
             UpperCamelCase				:   Any          =							model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ) )
  
         UpperCamelCase				:   Tuple          =							outputs.hidden_states
  
         UpperCamelCase				:   List[str]          =							getattr(
             self.model_tester    ,      """expected_num_hidden_layers"""    ,      len(self.model_tester.depths ) + 1 )
         self.assertEqual(len(SCREAMING_SNAKE_CASE_ )    ,      SCREAMING_SNAKE_CASE_ )
  
         # DonutSwin has a different seq_length
         UpperCamelCase				:   Optional[Any]          =							(
             config.patch_size
             if isinstance(config.patch_size    ,      collections.abc.Iterable )
             else (config.patch_size, config.patch_size)
         )
  
         UpperCamelCase				:   int          =							(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]    ,      )
  
         UpperCamelCase				:   Optional[Any]          =							outputs.reshaped_hidden_states
         self.assertEqual(len(SCREAMING_SNAKE_CASE_ )    ,      SCREAMING_SNAKE_CASE_ )
  
         UpperCamelCase       ,							UpperCamelCase       ,							UpperCamelCase       ,							UpperCamelCase				:   Any          =							reshaped_hidden_states[0].shape
         UpperCamelCase				:   Optional[Any]          =							(
             reshaped_hidden_states[0].view(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      height * width ).permute(0    ,      2    ,      1 )
         )
         self.assertListEqual(
             list(reshaped_hidden_states.shape[-2:] )    ,      [num_patches, self.model_tester.embed_dim]    ,      )
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase       ,							UpperCamelCase				:   Optional[int]          =							self.model_tester.prepare_config_and_inputs_for_common()
  
         UpperCamelCase				:   Union[str, Any]          =							(
             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:
             UpperCamelCase				:   List[Any]          =							True
             self.check_hidden_states_output(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )
  
             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             UpperCamelCase				:   List[str]          =							True
  
             self.check_hidden_states_output(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase       ,							UpperCamelCase				:   List[Any]          =							self.model_tester.prepare_config_and_inputs_for_common()
         UpperCamelCase				:   Any          =							3
  
         UpperCamelCase				:   List[Any]          =							(
             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)
         )
         UpperCamelCase				:   int          =							(
             config.patch_size
             if isinstance(config.patch_size    ,      collections.abc.Iterable )
             else (config.patch_size, config.patch_size)
         )
  
         UpperCamelCase				:   int          =							image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
         UpperCamelCase				:   str          =							image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
  
         for model_class in self.all_model_classes:
             UpperCamelCase				:   str          =							True
             self.check_hidden_states_output(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      (padded_height, padded_width) )
  
             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             UpperCamelCase				:   int          =							True
             self.check_hidden_states_output(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      (padded_height, padded_width) )
  
  
  
  
  
  
  
     @slow
     def       a_							(							self ):
         for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCamelCase				:   str          =							DonutSwinModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
             self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase       ,							UpperCamelCase				:   str          =							self.model_tester.prepare_config_and_inputs_for_common()
  
         UpperCamelCase				:   str          =							_config_zero_init(SCREAMING_SNAKE_CASE_ )
         for model_class in self.all_model_classes:
             UpperCamelCase				:   Any          =							model_class(config=SCREAMING_SNAKE_CASE_ )
             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'    ,      )
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  import numpy as np
  import PIL
  import torch
  import torchvision.transforms as T
  import torchvision.transforms.functional as TF
  from PIL import Image
  def 					A_			(		snake_case_     :       List[str]						,snake_case_     :       Union[str, Any]=2_5_6    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Optional[int]          =							min(img.size    )
  
      if s < target_image_size:
          raise ValueError(f'min dim for image {s} < {target_image_size}'    )
  
      UpperCamelCase				:   Dict          =							target_image_size / s
      UpperCamelCase				:   Any          =							(round(r * img.size[1]    ), round(r * img.size[0]    ))
      UpperCamelCase				:   Union[str, Any]          =							TF.resize(snake_case_						,snake_case_						,interpolation=PIL.Image.LANCZOS    )
      UpperCamelCase				:   Optional[int]          =							TF.center_crop(snake_case_						,output_size=2 * [target_image_size]    )
      UpperCamelCase				:   Optional[Any]          =							torch.unsqueeze(T.ToTensor()(snake_case_    )						,0    )
      return img
  def 					A_			(		snake_case_     :       List[Any]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Any          =							2.0 * x - 1.0
      return x
  def 					A_			(		snake_case_     :       Tuple						,snake_case_     :       Optional[Any]=True						,snake_case_     :       Dict="RGB"    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Dict          =							x.detach().cpu()
      if process:
          UpperCamelCase				:   str          =							post_process_tensor(snake_case_    )
      UpperCamelCase				:   List[Any]          =							x.numpy()
      if process:
          UpperCamelCase				:   Optional[int]          =							(2_5_5 * x).astype(np.uinta    )
      UpperCamelCase				:   List[Any]          =							Image.fromarray(snake_case_    )
      if not x.mode == mode:
          UpperCamelCase				:   Any          =							x.convert(snake_case_    )
      return x
  def 					A_			(		snake_case_     :       int    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   int          =							torch.clamp(snake_case_						,-1.0						,1.0    )
      UpperCamelCase				:   Optional[Any]          =							(x + 1.0) / 2.0
      UpperCamelCase				:   Dict          =							x.permute(1						,2						,0    )
      return x
  
  
  def 					A_			(		snake_case_     :       int    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Optional[Any]          =							post_process_tensor(x.squeeze()    )
      return x.permute(2						,0						,1    ).unsqueeze(0    )
  
  
  
  
  