code: 
  
  
  
  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 	lowercase		:
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	def __init__(			self			:		List[str]		,						__UpperCAmelCase			:		Tuple		,						__UpperCAmelCase			:		Tuple=13		,						__UpperCAmelCase			:		Dict=32		,						__UpperCAmelCase			:		Union[str, Any]=2		,						__UpperCAmelCase			:		str=3		,						__UpperCAmelCase			:		List[str]=16		,						__UpperCAmelCase			:		List[Any]=[1, 2, 1]		,						__UpperCAmelCase			:		Dict=[2, 2, 4]		,						__UpperCAmelCase			:		int=2		,						__UpperCAmelCase			:		Any=2.0		,						__UpperCAmelCase			:		Tuple=True		,						__UpperCAmelCase			:		Any=0.0		,						__UpperCAmelCase			:		Optional[int]=0.0		,						__UpperCAmelCase			:		Dict=0.1		,						__UpperCAmelCase			:		Tuple="gelu"		,						__UpperCAmelCase			:		str=False		,						__UpperCAmelCase			:		str=True		,						__UpperCAmelCase			:		Optional[int]=0.02		,						__UpperCAmelCase			:		Union[str, Any]=1E-5		,						__UpperCAmelCase			:		Union[str, Any]=True		,						__UpperCAmelCase			:		Optional[Any]=None		,						__UpperCAmelCase			:		Optional[Any]=True		,						__UpperCAmelCase			:		Optional[Any]=10		,						__UpperCAmelCase			:		Optional[int]=8		,						)    ->				Dict:
  							UpperCAmelCase_=     parent
  							UpperCAmelCase_=     batch_size
  							UpperCAmelCase_=     image_size
  							UpperCAmelCase_=     patch_size
  							UpperCAmelCase_=     num_channels
  							UpperCAmelCase_=     embed_dim
  							UpperCAmelCase_=     depths
  							UpperCAmelCase_=     num_heads
  							UpperCAmelCase_=     window_size
  							UpperCAmelCase_=     mlp_ratio
  							UpperCAmelCase_=     qkv_bias
  							UpperCAmelCase_=     hidden_dropout_prob
  							UpperCAmelCase_=     attention_probs_dropout_prob
  							UpperCAmelCase_=     drop_path_rate
  							UpperCAmelCase_=     hidden_act
  							UpperCAmelCase_=     use_absolute_embeddings
  							UpperCAmelCase_=     patch_norm
  							UpperCAmelCase_=     layer_norm_eps
  							UpperCAmelCase_=     initializer_range
  							UpperCAmelCase_=     is_training
  							UpperCAmelCase_=     scope
  							UpperCAmelCase_=     use_labels
  							UpperCAmelCase_=     type_sequence_label_size
  							UpperCAmelCase_=     encoder_stride
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Any  )    ->				Dict:
  							UpperCAmelCase_=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )
  
  							UpperCAmelCase_=     None
  							if self.use_labels:
  													UpperCAmelCase_=     ids_tensor([self.batch_size]		,						self.type_sequence_label_size  )
  
  							UpperCAmelCase_=     self.get_config()
  
  							return config, pixel_values, labels
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]  )    ->				Dict:
  							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 						_SCREAMING_SNAKE_CASE	(			self			:		Dict		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		Optional[int]  )    ->				List[str]:
  							UpperCAmelCase_=     DonutSwinModel(config=__UpperCAmelCase  )
  							model.to(__UpperCAmelCase  )
  							model.eval()
  							UpperCAmelCase_=     model(__UpperCAmelCase  )
  
  							UpperCAmelCase_=     ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths  ) - 1))
  							UpperCAmelCase_=     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 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]  )    ->				Optional[int]:
  							UpperCAmelCase_=     self.prepare_config_and_inputs()
  							(
  							    (
  							    UpperCAmelCase_
  							),     (
  							    UpperCAmelCase_
  							),     (
  							    UpperCAmelCase_
  							),     
  							)=     config_and_inputs
  							UpperCAmelCase_=     {"""pixel_values""": pixel_values}
  							return config, inputs_dict
  
  
  
  
  
  
  @require_torch
  class 	lowercase		(				snake_case__				,					snake_case__				,					unittest.TestCase):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      Tuple	     =							(DonutSwinModel,) if is_torch_available() else ()
  	a__   :      Optional[int]	     =							{"feature-extraction": DonutSwinModel} if is_torch_available() else {}
  	a__   :      str	     =							True
  
  	a__   :      Tuple	     =							False
  	a__   :      Union[str, Any]	     =							False
  	a__   :      Optional[Any]	     =							False
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]  )    ->				List[Any]:
  							UpperCAmelCase_=     DonutSwinModelTester(self  )
  							UpperCAmelCase_=     ConfigTester(self		,						config_class=__UpperCAmelCase		,						embed_dim=37  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				Optional[int]:
  							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 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]  )    ->				int:
  							return
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				Union[str, Any]:
  							UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_model(*__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Tuple  )    ->				Any:
  							# DonutSwin does not use inputs_embeds
  							pass
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]  )    ->				Dict:
  							UpperCAmelCase_,     UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs_for_common()
  
  							for model_class in self.all_model_classes:
  													UpperCAmelCase_=     model_class(__UpperCAmelCase  )
  													self.assertIsInstance(model.get_input_embeddings()		,						(nn.Module)  )
  													UpperCAmelCase_=     model.get_output_embeddings()
  													self.assertTrue(x is None or isinstance(__UpperCAmelCase		,						nn.Linear  )  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[str]  )    ->				List[Any]:
  							UpperCAmelCase_,     UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs_for_common()
  
  							for model_class in self.all_model_classes:
  													UpperCAmelCase_=     model_class(__UpperCAmelCase  )
  													UpperCAmelCase_=     inspect.signature(model.forward  )
  													# signature.parameters is an OrderedDict => so arg_names order is deterministic
  													UpperCAmelCase_=     [*signature.parameters.keys()]
  
  													UpperCAmelCase_=     ["""pixel_values"""]
  													self.assertListEqual(arg_names[:1]		,						__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]  )    ->				Dict:
  							UpperCAmelCase_,     UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs_for_common()
  							UpperCAmelCase_=     True
  
  							for model_class in self.all_model_classes:
  													UpperCAmelCase_=     True
  													UpperCAmelCase_=     False
  													UpperCAmelCase_=     True
  													UpperCAmelCase_=     model_class(__UpperCAmelCase  )
  													model.to(__UpperCAmelCase  )
  													model.eval()
  													with torch.no_grad():
  																			UpperCAmelCase_=     model(**self._prepare_for_class(__UpperCAmelCase		,						__UpperCAmelCase  )  )
  													UpperCAmelCase_=     outputs.attentions
  													UpperCAmelCase_=     len(self.model_tester.depths  )
  													self.assertEqual(len(__UpperCAmelCase  )		,						__UpperCAmelCase  )
  
  													# check that output_attentions also work using config
  													del inputs_dict["output_attentions"]
  													UpperCAmelCase_=     True
  													UpperCAmelCase_=     config.window_size**2
  													UpperCAmelCase_=     model_class(__UpperCAmelCase  )
  													model.to(__UpperCAmelCase  )
  													model.eval()
  													with torch.no_grad():
  																			UpperCAmelCase_=     model(**self._prepare_for_class(__UpperCAmelCase		,						__UpperCAmelCase  )  )
  													UpperCAmelCase_=     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]		,						)
  													UpperCAmelCase_=     len(__UpperCAmelCase  )
  
  													# Check attention is always last and order is fine
  													UpperCAmelCase_=     True
  													UpperCAmelCase_=     True
  													UpperCAmelCase_=     model_class(__UpperCAmelCase  )
  													model.to(__UpperCAmelCase  )
  													model.eval()
  													with torch.no_grad():
  																			UpperCAmelCase_=     model(**self._prepare_for_class(__UpperCAmelCase		,						__UpperCAmelCase  )  )
  
  													if hasattr(self.model_tester		,						"""num_hidden_states_types"""  ):
  																			UpperCAmelCase_=     self.model_tester.num_hidden_states_types
  													else:
  																			# also another +1 for reshaped_hidden_states
  																			UpperCAmelCase_=     2
  													self.assertEqual(out_len + added_hidden_states		,						len(__UpperCAmelCase  )  )
  
  													UpperCAmelCase_=     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 						_SCREAMING_SNAKE_CASE	(			self			:		List[str]		,						__UpperCAmelCase			:		Dict		,						__UpperCAmelCase			:		Optional[int]		,						__UpperCAmelCase			:		Optional[int]		,						__UpperCAmelCase			:		Union[str, Any]  )    ->				str:
  							UpperCAmelCase_=     model_class(__UpperCAmelCase  )
  							model.to(__UpperCAmelCase  )
  							model.eval()
  
  							with torch.no_grad():
  													UpperCAmelCase_=     model(**self._prepare_for_class(__UpperCAmelCase		,						__UpperCAmelCase  )  )
  
  							UpperCAmelCase_=     outputs.hidden_states
  
  							UpperCAmelCase_=     getattr(
  							    self.model_tester		,						"""expected_num_hidden_layers"""		,						len(self.model_tester.depths  ) + 1  )
  							self.assertEqual(len(__UpperCAmelCase  )		,						__UpperCAmelCase  )
  
  							# DonutSwin has a different seq_length
  							UpperCAmelCase_=     (
  							    config.patch_size
  							    if isinstance(config.patch_size		,						collections.abc.Iterable  )
  							    else (config.patch_size, config.patch_size)
  							)
  
  							UpperCAmelCase_=     (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_=     outputs.reshaped_hidden_states
  							self.assertEqual(len(__UpperCAmelCase  )		,						__UpperCAmelCase  )
  
  							UpperCAmelCase_,     UpperCAmelCase_,     UpperCAmelCase_,     UpperCAmelCase_=     reshaped_hidden_states[0].shape
  							UpperCAmelCase_=     (
  							    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 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]  )    ->				List[Any]:
  							UpperCAmelCase_,     UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs_for_common()
  
  							UpperCAmelCase_=     (
  							    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_=     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"]
  													UpperCAmelCase_=     True
  
  													self.check_hidden_states_output(__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				Dict:
  							UpperCAmelCase_,     UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs_for_common()
  							UpperCAmelCase_=     3
  
  							UpperCAmelCase_=     (
  							    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_=     (
  							    config.patch_size
  							    if isinstance(config.patch_size		,						collections.abc.Iterable  )
  							    else (config.patch_size, config.patch_size)
  							)
  
  							UpperCAmelCase_=     image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
  							UpperCAmelCase_=     image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
  
  							for model_class in self.all_model_classes:
  													UpperCAmelCase_=     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"]
  													UpperCAmelCase_=     True
  													self.check_hidden_states_output(__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase		,						(padded_height, padded_width)  )
  
  
  
  
  	@slow
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]  )    ->				str:
  							for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  													UpperCAmelCase_=     DonutSwinModel.from_pretrained(__UpperCAmelCase  )
  													self.assertIsNotNone(__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				str:
  							UpperCAmelCase_,     UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs_for_common()
  
  							UpperCAmelCase_=     _config_zero_init(__UpperCAmelCase  )
  							for model_class in self.all_model_classes:
  													UpperCAmelCase_=     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"""		,						)
  
  
  
  
  
style_context: 
  
  
  
  from .prime_check import Test
  
  Test()
  
  
  
  
  