




"""simple docstring"""







import unittest

from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
  import torch

  from transformers import (
      MraForMaskedLM,
      MraForMultipleChoice,
      MraForQuestionAnswering,
      MraForSequenceClassification,
      MraForTokenClassification,
      MraModel,
  )
  from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST

class 				snake_case_			:







   """simple docstring"""




   def __init__(		self       ,					lowerCamelCase_       ,					lowerCamelCase_=2       ,					lowerCamelCase_=8       ,					lowerCamelCase_=True       ,					lowerCamelCase_=True       ,					lowerCamelCase_=True       ,					lowerCamelCase_=True       ,					lowerCamelCase_=9_9       ,					lowerCamelCase_=1_6       ,					lowerCamelCase_=5       ,					lowerCamelCase_=2       ,					lowerCamelCase_=3_6       ,					lowerCamelCase_="gelu"       ,					lowerCamelCase_=0.0       ,					lowerCamelCase_=0.0       ,					lowerCamelCase_=5_1_2       ,					lowerCamelCase_=1_6       ,					lowerCamelCase_=2       ,					lowerCamelCase_=0.02       ,					lowerCamelCase_=3       ,					lowerCamelCase_=4       ,					lowerCamelCase_=None       ,					)				->   int:
       UpperCamelCase              =	parent
       UpperCamelCase              =	batch_size
       UpperCamelCase              =	seq_length
       UpperCamelCase              =	is_training
       UpperCamelCase              =	use_input_mask
       UpperCamelCase              =	use_token_type_ids
       UpperCamelCase              =	use_labels
       UpperCamelCase              =	vocab_size
       UpperCamelCase              =	hidden_size
       UpperCamelCase              =	num_hidden_layers
       UpperCamelCase              =	num_attention_heads
       UpperCamelCase              =	intermediate_size
       UpperCamelCase              =	hidden_act
       UpperCamelCase              =	hidden_dropout_prob
       UpperCamelCase              =	attention_probs_dropout_prob
       UpperCamelCase              =	max_position_embeddings
       UpperCamelCase              =	type_vocab_size
       UpperCamelCase              =	type_sequence_label_size
       UpperCamelCase              =	initializer_range
       UpperCamelCase              =	num_labels
       UpperCamelCase              =	num_choices
       UpperCamelCase              =	scope







   def   UpperCAmelCase__			(		self)				->   Union[str, Any]:
       UpperCamelCase              =	ids_tensor([self.batch_size, self.seq_length]       ,					self.vocab_size)

       UpperCamelCase              =	None
       if self.use_input_mask:
           UpperCamelCase              =	random_attention_mask([self.batch_size, self.seq_length])

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

       UpperCamelCase              =	None
       UpperCamelCase              =	None
       UpperCamelCase              =	None
       if self.use_labels:
           UpperCamelCase              =	ids_tensor([self.batch_size]       ,					self.type_sequence_label_size)
           UpperCamelCase              =	ids_tensor([self.batch_size, self.seq_length]       ,					self.num_labels)
           UpperCamelCase              =	ids_tensor([self.batch_size]       ,					self.num_choices)

       UpperCamelCase              =	self.get_config()

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







   def   UpperCAmelCase__			(		self)				->   Any:
       return MraConfig(
           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   UpperCAmelCase__			(		self)				->   List[Any]:
       UpperCamelCase              =	self.get_config()
       UpperCamelCase              =	3_0_0
       return config







   def   UpperCAmelCase__			(		self)				->   Optional[Any]:
       (
           UpperCamelCase      
       )              =	self.prepare_config_and_inputs()

       UpperCamelCase              =	True
       UpperCamelCase              =	floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
       UpperCamelCase              =	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   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_)				->   List[str]:
       UpperCamelCase              =	MraModel(config=lowerCamelCase_)
       model.to(lowerCamelCase_)
       model.eval()
       UpperCamelCase              =	model(lowerCamelCase_       ,					attention_mask=lowerCamelCase_       ,					token_type_ids=lowerCamelCase_)
       UpperCamelCase              =	model(lowerCamelCase_       ,					token_type_ids=lowerCamelCase_)
       UpperCamelCase              =	model(lowerCamelCase_)
       self.parent.assertEqual(result.last_hidden_state.shape       ,					(self.batch_size, self.seq_length, self.hidden_size))







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					)				->   int:
       UpperCamelCase              =	True
       UpperCamelCase              =	MraModel(lowerCamelCase_)
       model.to(lowerCamelCase_)
       model.eval()
       UpperCamelCase              =	model(
           lowerCamelCase_       ,					attention_mask=lowerCamelCase_       ,					token_type_ids=lowerCamelCase_       ,					encoder_hidden_states=lowerCamelCase_       ,					encoder_attention_mask=lowerCamelCase_       ,					)
       UpperCamelCase              =	model(
           lowerCamelCase_       ,					attention_mask=lowerCamelCase_       ,					token_type_ids=lowerCamelCase_       ,					encoder_hidden_states=lowerCamelCase_       ,					)
       UpperCamelCase              =	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))







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_)				->   Dict:
       UpperCamelCase              =	MraForMaskedLM(config=lowerCamelCase_)
       model.to(lowerCamelCase_)
       model.eval()
       UpperCamelCase              =	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   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_)				->   Optional[int]:
       UpperCamelCase              =	MraForQuestionAnswering(config=lowerCamelCase_)
       model.to(lowerCamelCase_)
       model.eval()
       UpperCamelCase              =	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   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_)				->   Optional[int]:
       UpperCamelCase              =	self.num_labels
       UpperCamelCase              =	MraForSequenceClassification(lowerCamelCase_)
       model.to(lowerCamelCase_)
       model.eval()
       UpperCamelCase              =	model(lowerCamelCase_       ,					attention_mask=lowerCamelCase_       ,					token_type_ids=lowerCamelCase_       ,					labels=lowerCamelCase_)
       self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.num_labels))







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_)				->   Dict:
       UpperCamelCase              =	self.num_labels
       UpperCamelCase              =	MraForTokenClassification(config=lowerCamelCase_)
       model.to(lowerCamelCase_)
       model.eval()
       UpperCamelCase              =	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   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_)				->   List[str]:
       UpperCamelCase              =	self.num_choices
       UpperCamelCase              =	MraForMultipleChoice(config=lowerCamelCase_)
       model.to(lowerCamelCase_)
       model.eval()
       UpperCamelCase              =	input_ids.unsqueeze(1).expand(-1       ,					self.num_choices       ,					-1).contiguous()
       UpperCamelCase              =	token_type_ids.unsqueeze(1).expand(-1       ,					self.num_choices       ,					-1).contiguous()
       UpperCamelCase              =	input_mask.unsqueeze(1).expand(-1       ,					self.num_choices       ,					-1).contiguous()
       UpperCamelCase              =	model(
           lowerCamelCase_       ,					attention_mask=lowerCamelCase_       ,					token_type_ids=lowerCamelCase_       ,					labels=lowerCamelCase_       ,					)
       self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.num_choices))







   def   UpperCAmelCase__			(		self)				->   List[str]:
       UpperCamelCase              =	self.prepare_config_and_inputs()
       (
           UpperCamelCase      
       )              =	config_and_inputs
       UpperCamelCase              =	{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
       return config, inputs_dict

@require_torch
class 				snake_case_			(					__snake_case	, unittest.TestCase			):







   """simple docstring"""




   A_				      =       (
       (
           MraModel,
           MraForMaskedLM,
           MraForMultipleChoice,
           MraForQuestionAnswering,
           MraForSequenceClassification,
           MraForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   A_				      =       False
   A_				      =       False
   A_				      =       False
   A_				      =       False

   A_				      =       ()







   def   UpperCAmelCase__			(		self)				->   Union[str, Any]:
       UpperCamelCase              =	MraModelTester(self)
       UpperCamelCase              =	ConfigTester(self       ,					config_class=lowerCamelCase_       ,					hidden_size=3_7)







   def   UpperCAmelCase__			(		self)				->   List[Any]:
       self.config_tester.run_common_tests()







   def   UpperCAmelCase__			(		self)				->   List[Any]:
       UpperCamelCase              =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   int:
       UpperCamelCase              =	self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
           UpperCamelCase              =	type
           self.model_tester.create_and_check_model(*lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   List[str]:
       UpperCamelCase              =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   Dict:
       UpperCamelCase              =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   int:
       UpperCamelCase              =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   Optional[int]:
       UpperCamelCase              =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   Tuple:
       UpperCamelCase              =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*lowerCamelCase_)







   @slow
   def   UpperCAmelCase__			(		self)				->   Optional[int]:
       for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCamelCase              =	MraModel.from_pretrained(lowerCamelCase_)
           self.assertIsNotNone(lowerCamelCase_)







   @unittest.skip(reason='''MRA does not output attentions''')
   def   UpperCAmelCase__			(		self)				->   Optional[Any]:
       return

@require_torch
class 				snake_case_			(					unittest.TestCase			):







   """simple docstring"""




   @slow
   def   UpperCAmelCase__			(		self)				->   Optional[int]:
       UpperCamelCase              =	MraModel.from_pretrained('''uw-madison/mra-base-512-4''')
       UpperCamelCase              =	torch.arange(2_5_6).unsqueeze(0)

       with torch.no_grad():
           UpperCamelCase              =	model(lowerCamelCase_)[0]

       UpperCamelCase              =	torch.Size((1, 2_5_6, 7_6_8))
       self.assertEqual(output.shape       ,					lowerCamelCase_)

       UpperCamelCase              =	torch.tensor(
           [[[-0.0140, 0.0830, -0.0381], [0.1546, 0.1402, 0.0220], [0.1162, 0.0851, 0.0165]]])

       self.assertTrue(torch.allclose(output[:, :3, :3]       ,					lowerCamelCase_       ,					atol=1e-4))







   @slow
   def   UpperCAmelCase__			(		self)				->   List[str]:
       UpperCamelCase              =	MraForMaskedLM.from_pretrained('''uw-madison/mra-base-512-4''')
       UpperCamelCase              =	torch.arange(2_5_6).unsqueeze(0)

       with torch.no_grad():
           UpperCamelCase              =	model(lowerCamelCase_)[0]

       UpperCamelCase              =	5_0_2_6_5

       UpperCamelCase              =	torch.Size((1, 2_5_6, vocab_size))
       self.assertEqual(output.shape       ,					lowerCamelCase_)

       UpperCamelCase              =	torch.tensor(
           [[[9.2595, -3.6038, 11.8819], [9.3869, -3.2693, 11.0956], [11.8524, -3.4938, 13.1210]]])

       self.assertTrue(torch.allclose(output[:, :3, :3]       ,					lowerCamelCase_       ,					atol=1e-4))







   @slow
   def   UpperCAmelCase__			(		self)				->   str:
       UpperCamelCase              =	MraForMaskedLM.from_pretrained('''uw-madison/mra-base-4096-8-d3''')
       UpperCamelCase              =	torch.arange(4_0_9_6).unsqueeze(0)

       with torch.no_grad():
           UpperCamelCase              =	model(lowerCamelCase_)[0]

       UpperCamelCase              =	5_0_2_6_5

       UpperCamelCase              =	torch.Size((1, 4_0_9_6, vocab_size))
       self.assertEqual(output.shape       ,					lowerCamelCase_)

       UpperCamelCase              =	torch.tensor(
           [[[5.4789, -2.3564, 7.5064], [7.9067, -1.3369, 9.9668], [9.0712, -1.8106, 7.0380]]])

       self.assertTrue(torch.allclose(output[:, :3, :3]       ,					lowerCamelCase_       ,					atol=1e-4))







"""simple docstring"""







import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch
				from torch import nn

				from transformers import SwiftFormerForImageClassification, SwiftFormerModel
				from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import ViTImageProcessor


class      UpperCamelCase		:






						def __init__(		self  :Union[str, Any]			, __magic_name__  :Any			, __magic_name__  :List[str]=13			, __magic_name__  :Any=3			, __magic_name__  :str=True			, __magic_name__  :int=True			, __magic_name__  :Dict=0.1			, __magic_name__  :Optional[int]=0.1			, __magic_name__  :List[str]=224			, __magic_name__  :Any=1_000			, __magic_name__  :int=[3, 3, 6, 4]			, __magic_name__  :int=[48, 56, 112, 220]			, )      ->int:
										lowercase    :      Dict       = parent
										lowercase    :      List[str]       = batch_size
										lowercase    :      Union[str, Any]       = num_channels
										lowercase    :      Dict       = is_training
										lowercase    :      Any       = use_labels
										lowercase    :      Dict       = hidden_dropout_prob
										lowercase    :      Union[str, Any]       = attention_probs_dropout_prob
										lowercase    :      Optional[Any]       = num_labels
										lowercase    :      Optional[Any]       = image_size
										lowercase    :      Optional[int]       = layer_depths
										lowercase    :      Optional[int]       = embed_dims






						def      __snake_case      (		self  :Tuple				)      ->str:
										lowercase    :      Dict       = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

										lowercase    :      Optional[Any]       = None
										if self.use_labels:
														lowercase    :      str       = ids_tensor([self.batch_size]			, self.num_labels				)

										lowercase    :      List[Any]       = self.get_config()

										return config, pixel_values, labels






						def      __snake_case      (		self  :Dict				)      ->Union[str, Any]:
										return SwiftFormerConfig(
										    depths=self.layer_depths			, embed_dims=self.embed_dims			, mlp_ratio=4			, downsamples=[True, True, True, True]			, hidden_act="""gelu"""			, num_labels=self.num_labels			, down_patch_size=3			, down_stride=2			, down_pad=1			, drop_rate=0.0			, drop_path_rate=0.0			, use_layer_scale=__magic_name__			, layer_scale_init_value=1E-5			, )






						def      __snake_case      (		self  :List[str]			, __magic_name__  :Union[str, Any]			, __magic_name__  :Dict			, __magic_name__  :Dict				)      ->Optional[Any]:
										lowercase    :      Optional[int]       = SwiftFormerModel(config=__magic_name__				)
										model.to(__magic_name__				)
										model.eval()
										lowercase    :      Union[str, Any]       = model(__magic_name__				)
										self.parent.assertEqual(result.last_hidden_state.shape			, (self.batch_size, self.embed_dims[-1], 7, 7)				)






						def      __snake_case      (		self  :Any			, __magic_name__  :Any			, __magic_name__  :Tuple			, __magic_name__  :Any				)      ->Union[str, Any]:
										lowercase    :      Optional[int]       = self.num_labels
										lowercase    :      List[str]       = SwiftFormerForImageClassification(__magic_name__				)
										model.to(__magic_name__				)
										model.eval()
										lowercase    :      List[Any]       = model(__magic_name__			, labels=__magic_name__				)
										self.parent.assertEqual(result.logits.shape			, (self.batch_size, self.num_labels)				)

										lowercase    :      Union[str, Any]       = SwiftFormerForImageClassification(__magic_name__				)
										model.to(__magic_name__				)
										model.eval()

										lowercase    :      Tuple       = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)
										lowercase    :      Union[str, Any]       = model(__magic_name__				)
										self.parent.assertEqual(result.logits.shape			, (self.batch_size, self.num_labels)				)






						def      __snake_case      (		self  :Union[str, Any]				)      ->Optional[int]:
										((lowercase)      ,      (lowercase)      ,      (lowercase))    :      Optional[Any]       = self.prepare_config_and_inputs()
										lowercase    :      Any       = {"""pixel_values""": pixel_values}
										return config, inputs_dict


@require_torch
class      UpperCamelCase		(__snake_case						,						__snake_case						,						unittest.TestCase     ):
						_SCREAMING_SNAKE_CASE  : List[str]												=		(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
						_SCREAMING_SNAKE_CASE  : Any												=		(
						    {"""feature-extraction""": SwiftFormerModel, """image-classification""": SwiftFormerForImageClassification}
						    if is_torch_available()
						    else {}
						)

						_SCREAMING_SNAKE_CASE  : Optional[Any]												=		False
						_SCREAMING_SNAKE_CASE  : Optional[Any]												=		False
						_SCREAMING_SNAKE_CASE  : List[str]												=		False
						_SCREAMING_SNAKE_CASE  : Tuple												=		False
						_SCREAMING_SNAKE_CASE  : List[Any]												=		False






						def      __snake_case      (		self  :Union[str, Any]				)      ->Optional[int]:
										lowercase    :      Optional[Any]       = SwiftFormerModelTester(self				)
										lowercase    :      List[str]       = ConfigTester(
										    self			, config_class=__magic_name__			, has_text_modality=__magic_name__			, hidden_size=37			, num_attention_heads=12			, num_hidden_layers=12			, )






						def      __snake_case      (		self  :Union[str, Any]				)      ->Union[str, Any]:
										self.config_tester.run_common_tests()






						@unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""				)
						def      __snake_case      (		self  :Any				)      ->Any:
										pass






						def      __snake_case      (		self  :Optional[int]				)      ->List[Any]:
										lowercase      ,      lowercase    :      Optional[Any]       = self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														lowercase    :      Dict       = model_class(__magic_name__				)
														lowercase    :      int       = model.get_output_embeddings()
														self.assertTrue(x is None or isinstance(__magic_name__			, nn.Linear				)				)






						def      __snake_case      (		self  :Any				)      ->List[str]:
										lowercase      ,      lowercase    :      int       = self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														lowercase    :      Union[str, Any]       = model_class(__magic_name__				)
														lowercase    :      Tuple       = inspect.signature(model.forward				)
														# signature.parameters is an OrderedDict => so arg_names order is deterministic
														lowercase    :      str       = [*signature.parameters.keys()]

														lowercase    :      Any       = ["""pixel_values"""]
														self.assertListEqual(arg_names[:1]			, __magic_name__				)






						def      __snake_case      (		self  :Union[str, Any]				)      ->Optional[int]:
										lowercase    :      Union[str, Any]       = self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__magic_name__				)






						def      __snake_case      (		self  :List[Any]				)      ->List[str]:
										lowercase    :      Any       = self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*__magic_name__				)






						@slow
						def      __snake_case      (		self  :Any				)      ->Dict:
										for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														lowercase    :      List[Any]       = SwiftFormerModel.from_pretrained(__magic_name__				)
														self.assertIsNotNone(__magic_name__				)






						@unittest.skip(reason="""SwiftFormer does not output attentions"""				)
						def      __snake_case      (		self  :List[str]				)      ->Optional[Any]:
										pass






						def      __snake_case      (		self  :List[str]				)      ->List[str]:
										def check_hidden_states_output(__magic_name__  :str			, __magic_name__  :Optional[Any]			, __magic_name__  :Tuple				):
														lowercase    :      Optional[Any]       = model_class(__magic_name__				)
														model.to(__magic_name__				)
														model.eval()

														with torch.no_grad():
																		lowercase    :      Dict       = model(**self._prepare_for_class(__magic_name__			, __magic_name__				)				)

														lowercase    :      Tuple       = outputs.hidden_states

														lowercase    :      List[str]       = 8
														self.assertEqual(len(__magic_name__				)			, __magic_name__				)  # TODO

														# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
														# with the width and height being successively divided by 2, after every 2 blocks
														for i in range(len(__magic_name__				)				):
																		self.assertEqual(
																		    hidden_states[i].shape			, torch.Size(
																		        [
																		            self.model_tester.batch_size,
																		            self.model_tester.embed_dims[i // 2],
																		            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																		            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																		        ]				)			, )

										lowercase      ,      lowercase    :      Optional[Any]       = self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														lowercase    :      Optional[int]       = True
														check_hidden_states_output(__magic_name__			, __magic_name__			, __magic_name__				)

														# check that output_hidden_states also work using config
														del inputs_dict["output_hidden_states"]
														lowercase    :      Any       = True

														check_hidden_states_output(__magic_name__			, __magic_name__			, __magic_name__				)






						def      __snake_case      (		self  :Any				)      ->Optional[int]:
										def _config_zero_init(__magic_name__  :List[Any]				):
														lowercase    :      Tuple       = copy.deepcopy(__magic_name__				)
														for key in configs_no_init.__dict__.keys():
																		if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
																						setattr(__magic_name__			, __magic_name__			, 1E-10				)
																		if isinstance(getattr(__magic_name__			, __magic_name__			, __magic_name__				)			, __magic_name__				):
																						lowercase    :      Any       = _config_zero_init(getattr(__magic_name__			, __magic_name__				)				)
																						setattr(__magic_name__			, __magic_name__			, __magic_name__				)
														return configs_no_init

										lowercase      ,      lowercase    :      Dict       = self.model_tester.prepare_config_and_inputs_for_common()

										lowercase    :      int       = _config_zero_init(__magic_name__				)
										for model_class in self.all_model_classes:
														lowercase    :      Union[str, Any]       = model_class(config=__magic_name__				)
														for name, param in model.named_parameters():
																		if param.requires_grad:
																						self.assertIn(
																						    ((param.data.mean() * 1E9) / 1E9).round().item()			, [0.0, 1.0]			, msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""			, )






						@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""				)
						def      __snake_case      (		self  :Any				)      ->Any:
										pass


def      UpperCamelCase       ( )     ->       Tuple:
				lowercase    :      int       = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
				return image


@require_torch
@require_vision
class      UpperCamelCase		(unittest.TestCase     ):






						@cached_property
						def      __snake_case      (		self  :Optional[Any]				)      ->List[str]:
										return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs"""				) if is_vision_available() else None






						@slow
						def      __snake_case      (		self  :Optional[int]				)      ->int:
										lowercase    :      Union[str, Any]       = SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""				).to(__magic_name__				)

										lowercase    :      Any       = self.default_image_processor
										lowercase    :      str       = prepare_img()
										lowercase    :      Tuple       = image_processor(images=__magic_name__			, return_tensors="""pt"""				).to(__magic_name__				)

										# forward pass
										with torch.no_grad():
														lowercase    :      Union[str, Any]       = model(**__magic_name__				)

										# verify the logits
										lowercase    :      Optional[int]       = torch.Size((1, 1_000)				)
										self.assertEqual(outputs.logits.shape			, __magic_name__				)

										lowercase    :      Optional[Any]       = torch.tensor([[-2.17_03E00, 2.11_07E00, -2.08_11E00]]				).to(__magic_name__				)
										self.assertTrue(torch.allclose(outputs.logits[0, :3]			, __magic_name__			, atol=1E-4				)				)
