





"""simple docstring"""




import os
import tempfile
import unittest

from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    MODEL_FOR_PRETRAINING_MAPPING,
		    NezhaForMaskedLM,
		    NezhaForMultipleChoice,
		    NezhaForNextSentencePrediction,
		    NezhaForPreTraining,
		    NezhaForQuestionAnswering,
		    NezhaForSequenceClassification,
		    NezhaForTokenClassification,
		    NezhaModel,
		)
		from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST


class 			__UpperCamelCase							:






							def __init__(  self ,   lowerCAmelCase__ ,   lowerCAmelCase__=13 ,   lowerCAmelCase__=7 ,   lowerCAmelCase__=True ,   lowerCAmelCase__=True ,   lowerCAmelCase__=True ,   lowerCAmelCase__=True ,   lowerCAmelCase__=99 ,   lowerCAmelCase__=32 ,   lowerCAmelCase__=5 ,   lowerCAmelCase__=4 ,   lowerCAmelCase__=37 ,   lowerCAmelCase__="gelu" ,   lowerCAmelCase__=0.1 ,   lowerCAmelCase__=0.1 ,   lowerCAmelCase__=128 ,   lowerCAmelCase__=32 ,   lowerCAmelCase__=16 ,   lowerCAmelCase__=2 ,   lowerCAmelCase__=0.02 ,   lowerCAmelCase__=3 ,   lowerCAmelCase__=4 ,   lowerCAmelCase__=None ,   )      ->					str:
								a :						Union[str, Any]      =							parent
								a :						List[Any]      =							batch_size
								a :						Union[str, Any]      =							seq_length
								a :						Any      =							is_training
								a :						Optional[Any]      =							use_input_mask
								a :						Dict      =							use_token_type_ids
								a :						str      =							use_labels
								a :						List[str]      =							vocab_size
								a :						Any      =							hidden_size
								a :						str      =							num_hidden_layers
								a :						Dict      =							num_attention_heads
								a :						Any      =							intermediate_size
								a :						List[Any]      =							hidden_act
								a :						Optional[Any]      =							hidden_dropout_prob
								a :						List[Any]      =							attention_probs_dropout_prob
								a :						Tuple      =							max_position_embeddings
								a :						Union[str, Any]      =							type_vocab_size
								a :						str      =							type_sequence_label_size
								a :						Any      =							initializer_range
								a :						Tuple      =							num_labels
								a :						Any      =							num_choices
								a :						Optional[Any]      =							scope






							def        __a  (  self )      ->					Optional[int]:
								a :						Optional[Any]      =							ids_tensor([self.batch_size, self.seq_length] ,   self.vocab_size )

								a :						str      =							None
								if self.use_input_mask:
									a :						Any      =							random_attention_mask([self.batch_size, self.seq_length] )

								a :						Tuple      =							None
								if self.use_token_type_ids:
									a :						Any      =							ids_tensor([self.batch_size, self.seq_length] ,   self.type_vocab_size )

								a :						Optional[int]      =							None
								a :						Optional[int]      =							None
								a :						Dict      =							None
								if self.use_labels:
									a :						List[Any]      =							ids_tensor([self.batch_size] ,   self.type_sequence_label_size )
									a :						Any      =							ids_tensor([self.batch_size, self.seq_length] ,   self.num_labels )
									a :						List[str]      =							ids_tensor([self.batch_size] ,   self.num_choices )

								a :						Dict      =							self.get_config()

								return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






							def        __a  (  self )      ->					Dict:
								return NezhaConfig(
								    vocab_size=self.vocab_size ,   hidden_size=self.hidden_size ,   num_hidden_layers=self.num_hidden_layers ,   num_attention_heads=self.num_attention_heads ,   intermediate_size=self.intermediate_size ,   hidden_act=self.hidden_act ,   hidden_dropout_prob=self.hidden_dropout_prob ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob ,   max_position_embeddings=self.max_position_embeddings ,   type_vocab_size=self.type_vocab_size ,   is_decoder=_lowerCAmelCase ,   initializer_range=self.initializer_range ,   )






							def        __a  (  self )      ->					str:
								(
								    (
								    a      
								),			(
								    a      
								),			(
								    a      
								),			(
								    a      
								),			(
								    a      
								),			(
								    a      
								),			(
								    a      
								),			      
								) :						List[Any]      =							self.prepare_config_and_inputs()

								a :						Optional[Any]      =							True
								a :						int      =							floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
								a :						str      =							ids_tensor([self.batch_size, self.seq_length] ,   vocab_size=2 )

								return (
								    config,
								    input_ids,
								    token_type_ids,
								    input_mask,
								    sequence_labels,
								    token_labels,
								    choice_labels,
								    encoder_hidden_states,
								    encoder_attention_mask,
								)






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					Dict:
								a :						Optional[int]      =							NezhaModel(config=_lowerCAmelCase )
								model.to(_lowerCAmelCase )
								model.eval()
								a :						List[str]      =							model(_lowerCAmelCase ,   attention_mask=_lowerCAmelCase ,   token_type_ids=_lowerCAmelCase )
								a :						List[Any]      =							model(_lowerCAmelCase ,   token_type_ids=_lowerCAmelCase )
								a :						List[Any]      =							model(_lowerCAmelCase )
								self.parent.assertEqual(result.last_hidden_state.shape ,   (self.batch_size, self.seq_length, self.hidden_size) )
								self.parent.assertEqual(result.pooler_output.shape ,   (self.batch_size, self.hidden_size) )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   )      ->					Optional[int]:
								a :						Dict      =							True
								a :						Union[str, Any]      =							NezhaModel(_lowerCAmelCase )
								model.to(_lowerCAmelCase )
								model.eval()
								a :						Dict      =							model(
								    _lowerCAmelCase ,   attention_mask=_lowerCAmelCase ,   token_type_ids=_lowerCAmelCase ,   encoder_hidden_states=_lowerCAmelCase ,   encoder_attention_mask=_lowerCAmelCase ,   )
								a :						Union[str, Any]      =							model(
								    _lowerCAmelCase ,   attention_mask=_lowerCAmelCase ,   token_type_ids=_lowerCAmelCase ,   encoder_hidden_states=_lowerCAmelCase ,   )
								a :						Union[str, Any]      =							model(_lowerCAmelCase ,   attention_mask=_lowerCAmelCase ,   token_type_ids=_lowerCAmelCase )
								self.parent.assertEqual(result.last_hidden_state.shape ,   (self.batch_size, self.seq_length, self.hidden_size) )
								self.parent.assertEqual(result.pooler_output.shape ,   (self.batch_size, self.hidden_size) )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					Any:
								a :						List[str]      =							NezhaForMaskedLM(config=_lowerCAmelCase )
								model.to(_lowerCAmelCase )
								model.eval()
								a :						Dict      =							model(_lowerCAmelCase ,   attention_mask=_lowerCAmelCase ,   token_type_ids=_lowerCAmelCase ,   labels=_lowerCAmelCase )
								self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.seq_length, self.vocab_size) )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					str:
								a :						str      =							NezhaForNextSentencePrediction(config=_lowerCAmelCase )
								model.to(_lowerCAmelCase )
								model.eval()
								a :						str      =							model(
								    _lowerCAmelCase ,   attention_mask=_lowerCAmelCase ,   token_type_ids=_lowerCAmelCase ,   labels=_lowerCAmelCase ,   )
								self.parent.assertEqual(result.logits.shape ,   (self.batch_size, 2) )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					str:
								a :						Optional[int]      =							NezhaForPreTraining(config=_lowerCAmelCase )
								model.to(_lowerCAmelCase )
								model.eval()
								a :						int      =							model(
								    _lowerCAmelCase ,   attention_mask=_lowerCAmelCase ,   token_type_ids=_lowerCAmelCase ,   labels=_lowerCAmelCase ,   next_sentence_label=_lowerCAmelCase ,   )
								self.parent.assertEqual(result.prediction_logits.shape ,   (self.batch_size, self.seq_length, self.vocab_size) )
								self.parent.assertEqual(result.seq_relationship_logits.shape ,   (self.batch_size, 2) )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					str:
								a :						Optional[Any]      =							NezhaForQuestionAnswering(config=_lowerCAmelCase )
								model.to(_lowerCAmelCase )
								model.eval()
								a :						str      =							model(
								    _lowerCAmelCase ,   attention_mask=_lowerCAmelCase ,   token_type_ids=_lowerCAmelCase ,   start_positions=_lowerCAmelCase ,   end_positions=_lowerCAmelCase ,   )
								self.parent.assertEqual(result.start_logits.shape ,   (self.batch_size, self.seq_length) )
								self.parent.assertEqual(result.end_logits.shape ,   (self.batch_size, self.seq_length) )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					Dict:
								a :						Tuple      =							self.num_labels
								a :						List[str]      =							NezhaForSequenceClassification(_lowerCAmelCase )
								model.to(_lowerCAmelCase )
								model.eval()
								a :						List[str]      =							model(_lowerCAmelCase ,   attention_mask=_lowerCAmelCase ,   token_type_ids=_lowerCAmelCase ,   labels=_lowerCAmelCase )
								self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.num_labels) )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					str:
								a :						str      =							self.num_labels
								a :						Optional[int]      =							NezhaForTokenClassification(config=_lowerCAmelCase )
								model.to(_lowerCAmelCase )
								model.eval()
								a :						str      =							model(_lowerCAmelCase ,   attention_mask=_lowerCAmelCase ,   token_type_ids=_lowerCAmelCase ,   labels=_lowerCAmelCase )
								self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.seq_length, self.num_labels) )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					Tuple:
								a :						int      =							self.num_choices
								a :						int      =							NezhaForMultipleChoice(config=_lowerCAmelCase )
								model.to(_lowerCAmelCase )
								model.eval()
								a :						str      =							input_ids.unsqueeze(1 ).expand(-1 ,   self.num_choices ,   -1 ).contiguous()
								a :						List[str]      =							token_type_ids.unsqueeze(1 ).expand(-1 ,   self.num_choices ,   -1 ).contiguous()
								a :						int      =							input_mask.unsqueeze(1 ).expand(-1 ,   self.num_choices ,   -1 ).contiguous()
								a :						str      =							model(
								    _lowerCAmelCase ,   attention_mask=_lowerCAmelCase ,   token_type_ids=_lowerCAmelCase ,   labels=_lowerCAmelCase ,   )
								self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.num_choices) )






							def        __a  (  self )      ->					Tuple:
								a :						List[Any]      =							self.prepare_config_and_inputs()
								(
								    (
								    a      
								),			(
								    a      
								),			(
								    a      
								),			(
								    a      
								),			(
								    a      
								),			(
								    a      
								),			(
								    a      
								),			      
								) :						Dict      =							config_and_inputs
								a :						Tuple      =							{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
								return config, inputs_dict


@require_torch
class 			__UpperCamelCase							(		a__						,			a__						,			a__						,			unittest.TestCase		):
							lowerCamelCase  :   Dict    =(
							    (
							        NezhaModel,
							        NezhaForMaskedLM,
							        NezhaForMultipleChoice,
							        NezhaForNextSentencePrediction,
							        NezhaForPreTraining,
							        NezhaForQuestionAnswering,
							        NezhaForSequenceClassification,
							        NezhaForTokenClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							lowerCamelCase  :   str    =(
							    {
							        "feature-extraction": NezhaModel,
							        "fill-mask": NezhaForMaskedLM,
							        "question-answering": NezhaForQuestionAnswering,
							        "text-classification": NezhaForSequenceClassification,
							        "token-classification": NezhaForTokenClassification,
							        "zero-shot": NezhaForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							lowerCamelCase  :   str    =True






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__=False )      ->					Any:
								a :						Any      =							super()._prepare_for_class(_lowerCAmelCase ,   _lowerCAmelCase ,   return_labels=_lowerCAmelCase )

								if return_labels:
									if model_class in get_values(_lowerCAmelCase ):
										a :						List[str]      =							torch.zeros(
										    (self.model_tester.batch_size, self.model_tester.seq_length) ,   dtype=torch.long ,   device=_lowerCAmelCase )
										a :						Union[str, Any]      =							torch.zeros(
										    self.model_tester.batch_size ,   dtype=torch.long ,   device=_lowerCAmelCase )
								return inputs_dict






							def        __a  (  self )      ->					str:
								a :						List[str]      =							NezhaModelTester(self )
								a :						int      =							ConfigTester(self ,   config_class=_lowerCAmelCase ,   hidden_size=37 )






							def        __a  (  self )      ->					Optional[Any]:
								self.config_tester.run_common_tests()






							def        __a  (  self )      ->					List[str]:
								a :						str      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_lowerCAmelCase )






							def        __a  (  self )      ->					Dict:
								a :						Optional[Any]      =							self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_model_as_decoder(*_lowerCAmelCase )






							def        __a  (  self )      ->					str:
								# This regression test was failing with PyTorch < 1.3
								(
								    (
								    a      
								),			(
								    a      
								),			(
								    a      
								),			(
								    a      
								),			(
								    a      
								),			(
								    a      
								),			(
								    a      
								),			(
								    a      
								),			(
								    a      
								),			      
								) :						Union[str, Any]      =							self.model_tester.prepare_config_and_inputs_for_decoder()

								a :						List[str]      =							None

								self.model_tester.create_and_check_model_as_decoder(
								    _lowerCAmelCase ,   _lowerCAmelCase ,   _lowerCAmelCase ,   _lowerCAmelCase ,   _lowerCAmelCase ,   _lowerCAmelCase ,   _lowerCAmelCase ,   _lowerCAmelCase ,   _lowerCAmelCase ,   )






							def        __a  (  self )      ->					Dict:
								a :						Union[str, Any]      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*_lowerCAmelCase )






							def        __a  (  self )      ->					Optional[int]:
								a :						Tuple      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_multiple_choice(*_lowerCAmelCase )






							def        __a  (  self )      ->					Union[str, Any]:
								a :						Dict      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_next_sequence_prediction(*_lowerCAmelCase )






							def        __a  (  self )      ->					Union[str, Any]:
								a :						Optional[Any]      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_pretraining(*_lowerCAmelCase )






							def        __a  (  self )      ->					Any:
								a :						List[Any]      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*_lowerCAmelCase )






							def        __a  (  self )      ->					str:
								a :						str      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*_lowerCAmelCase )






							def        __a  (  self )      ->					Dict:
								a :						Any      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*_lowerCAmelCase )






							@slow
							def        __a  (  self )      ->					Optional[Any]:
								for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									a :						Any      =							NezhaModel.from_pretrained(_lowerCAmelCase )
									self.assertIsNotNone(_lowerCAmelCase )






							@slow
							@require_torch_gpu
							def        __a  (  self )      ->					Union[str, Any]:
								a,			a :						Tuple      =							self.model_tester.prepare_config_and_inputs_for_common()
								for model_class in self.all_model_classes:
									# NezhaForMultipleChoice behaves incorrectly in JIT environments.
									if model_class == NezhaForMultipleChoice:
										return

									a :						Optional[int]      =							True
									a :						str      =							model_class(config=_lowerCAmelCase )

									a :						Optional[Any]      =							self._prepare_for_class(_lowerCAmelCase ,   _lowerCAmelCase )
									a :						Optional[int]      =							torch.jit.trace(
									    _lowerCAmelCase ,   (inputs_dict["input_ids"].to("cpu" ), inputs_dict["attention_mask"].to("cpu" )) )

									with tempfile.TemporaryDirectory() as tmp:
										torch.jit.save(_lowerCAmelCase ,   os.path.join(_lowerCAmelCase ,   "bert.pt" ) )
										a :						Tuple      =							torch.jit.load(os.path.join(_lowerCAmelCase ,   "bert.pt" ) ,   map_location=_lowerCAmelCase )
										loaded(inputs_dict["input_ids"].to(_lowerCAmelCase ) ,   inputs_dict["attention_mask"].to(_lowerCAmelCase ) )


@require_torch
class 			__UpperCamelCase							(		unittest.TestCase		):






							@slow
							def        __a  (  self )      ->					Tuple:
								a :						Dict      =							NezhaModel.from_pretrained("sijunhe/nezha-cn-base" )
								a :						Any      =							torch.tensor([[0, 1, 2, 3, 4, 5]] )
								a :						List[Any]      =							torch.tensor([[0, 1, 1, 1, 1, 1]] )
								with torch.no_grad():
									a :						Optional[int]      =							model(_lowerCAmelCase ,   attention_mask=_lowerCAmelCase )[0]
								a :						List[str]      =							torch.Size((1, 6, 768) )
								self.assertEqual(output.shape ,   _lowerCAmelCase )
								a :						Tuple      =							torch.tensor([[[0.0_685, 0.2_441, 0.1_102], [0.0_600, 0.1_906, 0.1_349], [0.0_221, 0.0_819, 0.0_586]]] )

								self.assertTrue(torch.allclose(output[:, 1:4, 1:4] ,   _lowerCAmelCase ,   atol=1E-4 ) )






							@slow
							def        __a  (  self )      ->					Union[str, Any]:
								a :						str      =							NezhaForMaskedLM.from_pretrained("sijunhe/nezha-cn-base" )
								a :						Union[str, Any]      =							torch.tensor([[0, 1, 2, 3, 4, 5]] )
								a :						Any      =							torch.tensor([[1, 1, 1, 1, 1, 1]] )
								with torch.no_grad():
									a :						List[Any]      =							model(_lowerCAmelCase ,   attention_mask=_lowerCAmelCase )[0]
								a :						Tuple      =							torch.Size((1, 6, 2_1128) )
								self.assertEqual(output.shape ,   _lowerCAmelCase )
								a :						int      =							torch.tensor(
								    [[-2.7_939, -1.7_902, -2.2_189], [-2.8_585, -1.8_908, -2.3_723], [-2.6_499, -1.7_750, -2.2_558]] )

								self.assertTrue(torch.allclose(output[:, 1:4, 1:4] ,   _lowerCAmelCase ,   atol=1E-4 ) )









'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE									=				{
    "naver-clova-ix/donut-base": "https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json",
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				Union[str, Any]								=       "donut-swin"

 __lowerCamelCase    :				int								=       {
     "num_attention_heads": "num_heads",
     "num_hidden_layers": "num_layers",
 }




 def __init__(						self      ,  _lowerCAmelCase=224      ,  _lowerCAmelCase=4      ,  _lowerCAmelCase=3      ,  _lowerCAmelCase=96      ,  _lowerCAmelCase=[2, 2, 6, 2]      ,  _lowerCAmelCase=[3, 6, 12, 24]      ,  _lowerCAmelCase=7      ,  _lowerCAmelCase=4.0      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase="gelu"      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=1E-5      ,  **_lowerCAmelCase      ,  )     ->				Optional[Any]:
     super().__init__(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   image_size
     _lowerCAmelCase						      =   patch_size
     _lowerCAmelCase						      =   num_channels
     _lowerCAmelCase						      =   embed_dim
     _lowerCAmelCase						      =   depths
     _lowerCAmelCase						      =   len(_lowerCAmelCase		)
     _lowerCAmelCase						      =   num_heads
     _lowerCAmelCase						      =   window_size
     _lowerCAmelCase						      =   mlp_ratio
     _lowerCAmelCase						      =   qkv_bias
     _lowerCAmelCase						      =   hidden_dropout_prob
     _lowerCAmelCase						      =   attention_probs_dropout_prob
     _lowerCAmelCase						      =   drop_path_rate
     _lowerCAmelCase						      =   hidden_act
     _lowerCAmelCase						      =   use_absolute_embeddings
     _lowerCAmelCase						      =   layer_norm_eps
     _lowerCAmelCase						      =   initializer_range
     # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
     # this indicates the channel dimension after the last stage of the model
     _lowerCAmelCase						      =   int(embed_dim * 2 ** (len(_lowerCAmelCase		) - 1)		)


