


"""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 snake_case   :







		def __init__(       self   :							List[str]     , a__   :							List[Any]     , a__   :							List[str]=13     , a__   :							str=32     , a__   :							List[Any]=2     , a__   :							Optional[Any]=3     , a__   :							List[str]=16     , a__   :							int=[1, 2, 1]     , a__   :							int=[2, 2, 4]     , a__   :							Tuple=2     , a__   :							Any=2.0     , a__   :							List[Any]=True     , a__   :							Tuple=0.0     , a__   :							Optional[Any]=0.0     , a__   :							List[Any]=0.1     , a__   :							Tuple="gelu"     , a__   :							Any=False     , a__   :							Optional[Any]=True     , a__   :							Optional[int]=0.0_2     , a__   :							Union[str, Any]=1E-5     , a__   :							int=True     , a__   :							List[str]=None     , a__   :							Dict=True     , a__   :							Optional[int]=10     , a__   :							str=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   :							Any		)							->    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   :							str		)							->    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   :							int     , a__   :							Any     , a__   :							Dict     , a__   :							Dict		)							->    Union[str, Any]:





						'''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   :							Tuple		)							->    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 snake_case   (	_UpperCamelCase  ,					_UpperCamelCase  ,					unittest.TestCase):
		__UpperCamelCase							     =      (DonutSwinModel,) if is_torch_available() else ()
		__UpperCamelCase							     =      {'feature-extraction': DonutSwinModel} if is_torch_available() else {}
		__UpperCamelCase							     =      True

		__UpperCamelCase							     =      False
		__UpperCamelCase							     =      False
		__UpperCamelCase							     =      False







		def 			a_			(       self   :							Optional[Any]		)							->    Optional[int]:





						'''simple docstring'''







						_A       	=      DonutSwinModelTester(self		)
						_A       	=      ConfigTester(self     , config_class=a__     , embed_dim=37		)







		def 			a_			(       self   :							Optional[Any]		)							->    str:





						'''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   :							Optional[int]		)							->    Union[str, Any]:





						'''simple docstring'''







						return







		def 			a_			(       self   :							Optional[int]		)							->    List[Any]:





						'''simple docstring'''







						_A       	=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*a__		)







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





						'''simple docstring'''







						pass







		def 			a_			(       self   :							Optional[int]		)							->    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__		)
										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   :							Optional[int]		)							->    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   :							Any		)							->    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   :							List[Any]     , a__   :							Optional[Any]     , a__   :							Dict     , a__   :							Optional[int]     , a__   :							int		)							->    Tuple:





						'''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   :							Union[str, Any]		)							->    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   :							Union[str, Any]		)							->    Union[str, Any]:





						'''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   :							Tuple		)							->    List[Any]:





						'''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		)							->    Optional[Any]:





						'''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"""


def   a__			(					__lowercase  ,      __lowercase )						->						Tuple:
				_A       	=      1
				while b > 0:
								if b & 1:
												res *= a

								a *= a
								b >>= 1

				return res






def   a__			(					__lowercase  ,      __lowercase  ,      __lowercase )						->						Optional[Any]:
				_A       	=      1
				while b > 0:
								if b & 1:
												_A       	=      ((res % c) * (a % c)) % c

								a *= a
								b >>= 1

				return res