




"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__A       :						Dict												=			{
    '''auto_factory''': ['''get_values'''],
    '''configuration_auto''': ['''ALL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CONFIG_MAPPING''', '''MODEL_NAMES_MAPPING''', '''AutoConfig'''],
    '''feature_extraction_auto''': ['''FEATURE_EXTRACTOR_MAPPING''', '''AutoFeatureExtractor'''],
    '''image_processing_auto''': ['''IMAGE_PROCESSOR_MAPPING''', '''AutoImageProcessor'''],
    '''processing_auto''': ['''PROCESSOR_MAPPING''', '''AutoProcessor'''],
    '''tokenization_auto''': ['''TOKENIZER_MAPPING''', '''AutoTokenizer'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A       :						Any												=			[
      '''MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING''',
      '''MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING''',
      '''MODEL_FOR_AUDIO_XVECTOR_MAPPING''',
      '''MODEL_FOR_BACKBONE_MAPPING''',
      '''MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING''',
      '''MODEL_FOR_CAUSAL_LM_MAPPING''',
      '''MODEL_FOR_CTC_MAPPING''',
      '''MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING''',
      '''MODEL_FOR_DEPTH_ESTIMATION_MAPPING''',
      '''MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING''',
      '''MODEL_FOR_IMAGE_SEGMENTATION_MAPPING''',
      '''MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING''',
      '''MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING''',
      '''MODEL_FOR_MASKED_LM_MAPPING''',
      '''MODEL_FOR_MASK_GENERATION_MAPPING''',
      '''MODEL_FOR_MULTIPLE_CHOICE_MAPPING''',
      '''MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING''',
      '''MODEL_FOR_OBJECT_DETECTION_MAPPING''',
      '''MODEL_FOR_PRETRAINING_MAPPING''',
      '''MODEL_FOR_QUESTION_ANSWERING_MAPPING''',
      '''MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING''',
      '''MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING''',
      '''MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING''',
      '''MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING''',
      '''MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING''',
      '''MODEL_FOR_TEXT_ENCODING_MAPPING''',
      '''MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING''',
      '''MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING''',
      '''MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING''',
      '''MODEL_FOR_VISION_2_SEQ_MAPPING''',
      '''MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING''',
      '''MODEL_MAPPING''',
      '''MODEL_WITH_LM_HEAD_MAPPING''',
      '''MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING''',
      '''MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING''',
      '''AutoModel''',
      '''AutoBackbone''',
      '''AutoModelForAudioClassification''',
      '''AutoModelForAudioFrameClassification''',
      '''AutoModelForAudioXVector''',
      '''AutoModelForCausalLM''',
      '''AutoModelForCTC''',
      '''AutoModelForDepthEstimation''',
      '''AutoModelForImageClassification''',
      '''AutoModelForImageSegmentation''',
      '''AutoModelForInstanceSegmentation''',
      '''AutoModelForMaskGeneration''',
      '''AutoModelForTextEncoding''',
      '''AutoModelForMaskedImageModeling''',
      '''AutoModelForMaskedLM''',
      '''AutoModelForMultipleChoice''',
      '''AutoModelForNextSentencePrediction''',
      '''AutoModelForObjectDetection''',
      '''AutoModelForPreTraining''',
      '''AutoModelForQuestionAnswering''',
      '''AutoModelForSemanticSegmentation''',
      '''AutoModelForSeq2SeqLM''',
      '''AutoModelForSequenceClassification''',
      '''AutoModelForSpeechSeq2Seq''',
      '''AutoModelForTableQuestionAnswering''',
      '''AutoModelForTokenClassification''',
      '''AutoModelForUniversalSegmentation''',
      '''AutoModelForVideoClassification''',
      '''AutoModelForVision2Seq''',
      '''AutoModelForVisualQuestionAnswering''',
      '''AutoModelForDocumentQuestionAnswering''',
      '''AutoModelWithLMHead''',
      '''AutoModelForZeroShotImageClassification''',
      '''AutoModelForZeroShotObjectDetection''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A       :						List[Any]												=			[
      '''TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING''',
      '''TF_MODEL_FOR_CAUSAL_LM_MAPPING''',
      '''TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING''',
      '''TF_MODEL_FOR_MASK_GENERATION_MAPPING''',
      '''TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING''',
      '''TF_MODEL_FOR_MASKED_LM_MAPPING''',
      '''TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING''',
      '''TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING''',
      '''TF_MODEL_FOR_PRETRAINING_MAPPING''',
      '''TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING''',
      '''TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING''',
      '''TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING''',
      '''TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING''',
      '''TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING''',
      '''TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING''',
      '''TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING''',
      '''TF_MODEL_FOR_TEXT_ENCODING_MAPPING''',
      '''TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING''',
      '''TF_MODEL_FOR_VISION_2_SEQ_MAPPING''',
      '''TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING''',
      '''TF_MODEL_MAPPING''',
      '''TF_MODEL_WITH_LM_HEAD_MAPPING''',
      '''TFAutoModel''',
      '''TFAutoModelForAudioClassification''',
      '''TFAutoModelForCausalLM''',
      '''TFAutoModelForImageClassification''',
      '''TFAutoModelForMaskedImageModeling''',
      '''TFAutoModelForMaskedLM''',
      '''TFAutoModelForMaskGeneration''',
      '''TFAutoModelForMultipleChoice''',
      '''TFAutoModelForNextSentencePrediction''',
      '''TFAutoModelForPreTraining''',
      '''TFAutoModelForDocumentQuestionAnswering''',
      '''TFAutoModelForQuestionAnswering''',
      '''TFAutoModelForSemanticSegmentation''',
      '''TFAutoModelForSeq2SeqLM''',
      '''TFAutoModelForSequenceClassification''',
      '''TFAutoModelForSpeechSeq2Seq''',
      '''TFAutoModelForTableQuestionAnswering''',
      '''TFAutoModelForTextEncoding''',
      '''TFAutoModelForTokenClassification''',
      '''TFAutoModelForVision2Seq''',
      '''TFAutoModelForZeroShotImageClassification''',
      '''TFAutoModelWithLMHead''',
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A       :						Any												=			[
      '''FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING''',
      '''FLAX_MODEL_FOR_CAUSAL_LM_MAPPING''',
      '''FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING''',
      '''FLAX_MODEL_FOR_MASKED_LM_MAPPING''',
      '''FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING''',
      '''FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING''',
      '''FLAX_MODEL_FOR_PRETRAINING_MAPPING''',
      '''FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING''',
      '''FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING''',
      '''FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING''',
      '''FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING''',
      '''FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING''',
      '''FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING''',
      '''FLAX_MODEL_MAPPING''',
      '''FlaxAutoModel''',
      '''FlaxAutoModelForCausalLM''',
      '''FlaxAutoModelForImageClassification''',
      '''FlaxAutoModelForMaskedLM''',
      '''FlaxAutoModelForMultipleChoice''',
      '''FlaxAutoModelForNextSentencePrediction''',
      '''FlaxAutoModelForPreTraining''',
      '''FlaxAutoModelForQuestionAnswering''',
      '''FlaxAutoModelForSeq2SeqLM''',
      '''FlaxAutoModelForSequenceClassification''',
      '''FlaxAutoModelForSpeechSeq2Seq''',
      '''FlaxAutoModelForTokenClassification''',
      '''FlaxAutoModelForVision2Seq''',
  ]


if TYPE_CHECKING:
  from .auto_factory import get_values
  from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, MODEL_NAMES_MAPPING, AutoConfig
  from .feature_extraction_auto import FEATURE_EXTRACTOR_MAPPING, AutoFeatureExtractor
  from .image_processing_auto import IMAGE_PROCESSOR_MAPPING, AutoImageProcessor
  from .processing_auto import PROCESSOR_MAPPING, AutoProcessor
  from .tokenization_auto import TOKENIZER_MAPPING, AutoTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_auto import (
        MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
        MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING,
        MODEL_FOR_AUDIO_XVECTOR_MAPPING,
        MODEL_FOR_BACKBONE_MAPPING,
        MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
        MODEL_FOR_CAUSAL_LM_MAPPING,
        MODEL_FOR_CTC_MAPPING,
        MODEL_FOR_DEPTH_ESTIMATION_MAPPING,
        MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
        MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
        MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
        MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
        MODEL_FOR_MASK_GENERATION_MAPPING,
        MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
        MODEL_FOR_MASKED_LM_MAPPING,
        MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
        MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
        MODEL_FOR_OBJECT_DETECTION_MAPPING,
        MODEL_FOR_PRETRAINING_MAPPING,
        MODEL_FOR_QUESTION_ANSWERING_MAPPING,
        MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
        MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
        MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
        MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
        MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
        MODEL_FOR_TEXT_ENCODING_MAPPING,
        MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
        MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
        MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
        MODEL_FOR_VISION_2_SEQ_MAPPING,
        MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING,
        MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
        MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING,
        MODEL_MAPPING,
        MODEL_WITH_LM_HEAD_MAPPING,
        AutoBackbone,
        AutoModel,
        AutoModelForAudioClassification,
        AutoModelForAudioFrameClassification,
        AutoModelForAudioXVector,
        AutoModelForCausalLM,
        AutoModelForCTC,
        AutoModelForDepthEstimation,
        AutoModelForDocumentQuestionAnswering,
        AutoModelForImageClassification,
        AutoModelForImageSegmentation,
        AutoModelForInstanceSegmentation,
        AutoModelForMaskedImageModeling,
        AutoModelForMaskedLM,
        AutoModelForMaskGeneration,
        AutoModelForMultipleChoice,
        AutoModelForNextSentencePrediction,
        AutoModelForObjectDetection,
        AutoModelForPreTraining,
        AutoModelForQuestionAnswering,
        AutoModelForSemanticSegmentation,
        AutoModelForSeqaSeqLM,
        AutoModelForSequenceClassification,
        AutoModelForSpeechSeqaSeq,
        AutoModelForTableQuestionAnswering,
        AutoModelForTextEncoding,
        AutoModelForTokenClassification,
        AutoModelForUniversalSegmentation,
        AutoModelForVideoClassification,
        AutoModelForVisionaSeq,
        AutoModelForVisualQuestionAnswering,
        AutoModelForZeroShotImageClassification,
        AutoModelForZeroShotObjectDetection,
        AutoModelWithLMHead,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_auto import (
        TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
        TF_MODEL_FOR_CAUSAL_LM_MAPPING,
        TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
        TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
        TF_MODEL_FOR_MASK_GENERATION_MAPPING,
        TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
        TF_MODEL_FOR_MASKED_LM_MAPPING,
        TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
        TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
        TF_MODEL_FOR_PRETRAINING_MAPPING,
        TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
        TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
        TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
        TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
        TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
        TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
        TF_MODEL_FOR_TEXT_ENCODING_MAPPING,
        TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
        TF_MODEL_FOR_VISION_2_SEQ_MAPPING,
        TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
        TF_MODEL_MAPPING,
        TF_MODEL_WITH_LM_HEAD_MAPPING,
        TFAutoModel,
        TFAutoModelForAudioClassification,
        TFAutoModelForCausalLM,
        TFAutoModelForDocumentQuestionAnswering,
        TFAutoModelForImageClassification,
        TFAutoModelForMaskedImageModeling,
        TFAutoModelForMaskedLM,
        TFAutoModelForMaskGeneration,
        TFAutoModelForMultipleChoice,
        TFAutoModelForNextSentencePrediction,
        TFAutoModelForPreTraining,
        TFAutoModelForQuestionAnswering,
        TFAutoModelForSemanticSegmentation,
        TFAutoModelForSeqaSeqLM,
        TFAutoModelForSequenceClassification,
        TFAutoModelForSpeechSeqaSeq,
        TFAutoModelForTableQuestionAnswering,
        TFAutoModelForTextEncoding,
        TFAutoModelForTokenClassification,
        TFAutoModelForVisionaSeq,
        TFAutoModelForZeroShotImageClassification,
        TFAutoModelWithLMHead,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_auto import (
        FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
        FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
        FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
        FLAX_MODEL_FOR_MASKED_LM_MAPPING,
        FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
        FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
        FLAX_MODEL_FOR_PRETRAINING_MAPPING,
        FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
        FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
        FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
        FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
        FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
        FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING,
        FLAX_MODEL_MAPPING,
        FlaxAutoModel,
        FlaxAutoModelForCausalLM,
        FlaxAutoModelForImageClassification,
        FlaxAutoModelForMaskedLM,
        FlaxAutoModelForMultipleChoice,
        FlaxAutoModelForNextSentencePrediction,
        FlaxAutoModelForPreTraining,
        FlaxAutoModelForQuestionAnswering,
        FlaxAutoModelForSeqaSeqLM,
        FlaxAutoModelForSequenceClassification,
        FlaxAutoModelForSpeechSeqaSeq,
        FlaxAutoModelForTokenClassification,
        FlaxAutoModelForVisionaSeq,
    )

else:
  import sys

  __A       :						List[Any]												=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)












"""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      UpperCamelCase_       :



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



         """simple docstring"""





         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 						UpperCamelCase_ (					self )	->	List[Any]:



         """simple docstring"""





         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 						UpperCamelCase_ (					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 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__ )	->	Optional[int]:



         """simple docstring"""





         UpperCAmelCase					=	DonutSwinModel(config=snake_case__ )
         model.to(snake_case__ )
         model.eval()
         UpperCAmelCase					=	model(snake_case__ )

         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 						UpperCamelCase_ (					self )	->	Optional[Any]:



         """simple docstring"""





         UpperCAmelCase					=	self.prepare_config_and_inputs()
         (
             (
             UpperCAmelCase
         )			,				(
             UpperCAmelCase
         )			,				(
             UpperCAmelCase
         )			,				
         )					=	config_and_inputs
         UpperCAmelCase					=	{"""pixel_values""": pixel_values}
         return config, inputs_dict





@require_torch
class      UpperCamelCase_       (				a_      ,    a_      ,    unittest.TestCase     ):
  _A				:    Tuple =		(DonutSwinModel,) if is_torch_available() else ()
  _A				:    Any =		{'feature-extraction': DonutSwinModel} if is_torch_available() else {}
  _A				:    Optional[Any] =		True

  _A				:    Dict =		False
  _A				:    Any =		False
  _A				:    Tuple =		False



  def 						UpperCamelCase_ (					self )	->	Any:



         """simple docstring"""





         UpperCAmelCase					=	DonutSwinModelTester(self )
         UpperCAmelCase					=	ConfigTester(self       ,						config_class=snake_case__       ,						embed_dim=37 )



  def 						UpperCamelCase_ (					self )	->	Dict:



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



         """simple docstring"""





         return



  def 						UpperCamelCase_ (					self )	->	Optional[Any]:



         """simple docstring"""





         UpperCAmelCase					=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*snake_case__ )



  def 						UpperCamelCase_ (					self )	->	List[str]:



         """simple docstring"""





         pass



  def 						UpperCamelCase_ (					self )	->	List[str]:



         """simple docstring"""





         UpperCAmelCase			,				UpperCAmelCase					=	self.model_tester.prepare_config_and_inputs_for_common()

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



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



         """simple docstring"""





         UpperCAmelCase			,				UpperCAmelCase					=	self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
                UpperCAmelCase					=	model_class(snake_case__ )
                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]       ,						snake_case__ )



  def 						UpperCamelCase_ (					self )	->	Tuple:



         """simple docstring"""





         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(snake_case__ )
                model.to(snake_case__ )
                model.eval()
                with torch.no_grad():
                       UpperCAmelCase					=	model(**self._prepare_for_class(snake_case__       ,						snake_case__ ) )
                UpperCAmelCase					=	outputs.attentions
                UpperCAmelCase					=	len(self.model_tester.depths )
                self.assertEqual(len(snake_case__ )       ,						snake_case__ )

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

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

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

                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(snake_case__ ) )

                UpperCAmelCase					=	outputs.attentions

                self.assertEqual(len(snake_case__ )       ,						snake_case__ )

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



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__ )	->	Optional[int]:



         """simple docstring"""





         UpperCAmelCase					=	model_class(snake_case__ )
         model.to(snake_case__ )
         model.eval()

         with torch.no_grad():
                UpperCAmelCase					=	model(**self._prepare_for_class(snake_case__       ,						snake_case__ ) )

         UpperCAmelCase					=	outputs.hidden_states

         UpperCAmelCase					=	getattr(
             self.model_tester       ,						"""expected_num_hidden_layers"""       ,						len(self.model_tester.depths ) + 1 )
         self.assertEqual(len(snake_case__ )       ,						snake_case__ )

         # 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(snake_case__ )       ,						snake_case__ )

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



  def 						UpperCamelCase_ (					self )	->	str:



         """simple docstring"""





         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(snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__ )

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

                self.check_hidden_states_output(snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__ )



  def 						UpperCamelCase_ (					self )	->	Dict:



         """simple docstring"""





         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(snake_case__       ,						snake_case__       ,						snake_case__       ,						(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(snake_case__       ,						snake_case__       ,						snake_case__       ,						(padded_height, padded_width) )



  @slow
  def 						UpperCamelCase_ (					self )	->	List[str]:



         """simple docstring"""





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



  def 						UpperCamelCase_ (					self )	->	Any:



         """simple docstring"""





         UpperCAmelCase			,				UpperCAmelCase					=	self.model_tester.prepare_config_and_inputs_for_common()

         UpperCAmelCase					=	_config_zero_init(snake_case__ )
         for model_class in self.all_model_classes:
                UpperCAmelCase					=	model_class(config=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'''       ,						)
