



'''simple docstring'''





import unittest

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

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


if is_torch_available():
       import torch

       from transformers import (
           OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
           OpenAIGPTConfig,
           OpenAIGPTDoubleHeadsModel,
           OpenAIGPTForSequenceClassification,
           OpenAIGPTLMHeadModel,
           OpenAIGPTModel,
       )







class        UpperCAmelCase_			:
    """simple docstring"""







    def __init__(       self   :	Dict					,	snake_case_   :	List[str]					,	snake_case_   :	Union[str, Any]=13					,	snake_case_   :	List[str]=7					,	snake_case_   :	Optional[int]=True					,	snake_case_   :	Optional[int]=True					,	snake_case_   :	Tuple=True					,	snake_case_   :	List[Any]=99					,	snake_case_   :	str=32					,	snake_case_   :	List[Any]=5					,	snake_case_   :	Optional[Any]=4					,	snake_case_   :	Dict=37					,	snake_case_   :	Any="gelu"					,	snake_case_   :	Dict=0.1					,	snake_case_   :	Tuple=0.1					,	snake_case_   :	str=512					,	snake_case_   :	Union[str, Any]=16					,	snake_case_   :	Union[str, Any]=2					,	snake_case_   :	Optional[Any]=0.02					,	snake_case_   :	Tuple=3					,	snake_case_   :	str=4					,	snake_case_   :	Any=None					,	):
          snake_case__    :       Tuple       		= parent
          snake_case__    :       Tuple       		= batch_size
          snake_case__    :       Tuple       		= seq_length
          snake_case__    :       Union[str, Any]       		= is_training
          snake_case__    :       List[Any]       		= use_token_type_ids
          snake_case__    :       Optional[int]       		= use_labels
          snake_case__    :       Any       		= vocab_size
          snake_case__    :       List[Any]       		= hidden_size
          snake_case__    :       List[Any]       		= num_hidden_layers
          snake_case__    :       Optional[Any]       		= num_attention_heads
          snake_case__    :       Optional[int]       		= intermediate_size
          snake_case__    :       List[Any]       		= hidden_act
          snake_case__    :       Dict       		= hidden_dropout_prob
          snake_case__    :       Optional[int]       		= attention_probs_dropout_prob
          snake_case__    :       List[str]       		= max_position_embeddings
          snake_case__    :       Union[str, Any]       		= type_vocab_size
          snake_case__    :       Optional[int]       		= type_sequence_label_size
          snake_case__    :       str       		= initializer_range
          snake_case__    :       Any       		= num_labels
          snake_case__    :       Optional[int]       		= num_choices
          snake_case__    :       List[str]       		= scope
          snake_case__    :       List[Any]       		= self.vocab_size - 1




    def lowerCamelCase			(       self   :	List[str]		):
          snake_case__    :       List[str]       		= ids_tensor([self.batch_size, self.seq_length]					,	self.vocab_size		)

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

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

          snake_case__    :       Any       		= OpenAIGPTConfig(
              vocab_size=self.vocab_size					,	n_embd=self.hidden_size					,	n_layer=self.num_hidden_layers					,	n_head=self.num_attention_heads					,	n_positions=self.max_position_embeddings					,	pad_token_id=self.pad_token_id					,	)

          snake_case__    :       Tuple       		= ids_tensor([self.num_hidden_layers, self.num_attention_heads]					,	2		)

          return (
              config,
              input_ids,
              head_mask,
              token_type_ids,
              sequence_labels,
              token_labels,
              choice_labels,
          )




    def lowerCamelCase			(       self   :	Optional[Any]					,	snake_case_   :	Optional[Any]					,	snake_case_   :	Optional[int]					,	snake_case_   :	List[Any]					,	snake_case_   :	Tuple					,	*snake_case_   :	List[str]		):
          snake_case__    :       List[Any]       		= OpenAIGPTModel(config=snake_case_		)
          model.to(snake_case_		)
          model.eval()

          snake_case__    :       Optional[int]       		= model(snake_case_					,	token_type_ids=snake_case_					,	head_mask=snake_case_		)
          snake_case__    :       str       		= model(snake_case_					,	token_type_ids=snake_case_		)
          snake_case__    :       List[Any]       		= model(snake_case_		)

          self.parent.assertEqual(result.last_hidden_state.shape					,	(self.batch_size, self.seq_length, self.hidden_size)		)




    def lowerCamelCase			(       self   :	Any					,	snake_case_   :	Any					,	snake_case_   :	List[str]					,	snake_case_   :	List[str]					,	snake_case_   :	Tuple					,	*snake_case_   :	Union[str, Any]		):
          snake_case__    :       Optional[int]       		= OpenAIGPTLMHeadModel(snake_case_		)
          model.to(snake_case_		)
          model.eval()

          snake_case__    :       Optional[Any]       		= model(snake_case_					,	token_type_ids=snake_case_					,	labels=snake_case_		)
          self.parent.assertEqual(result.loss.shape					,	()		)
          self.parent.assertEqual(result.logits.shape					,	(self.batch_size, self.seq_length, self.vocab_size)		)




    def lowerCamelCase			(       self   :	Union[str, Any]					,	snake_case_   :	List[str]					,	snake_case_   :	List[Any]					,	snake_case_   :	List[Any]					,	snake_case_   :	Dict					,	*snake_case_   :	Any		):
          snake_case__    :       Dict       		= OpenAIGPTDoubleHeadsModel(snake_case_		)
          model.to(snake_case_		)
          model.eval()

          snake_case__    :       Any       		= model(snake_case_					,	token_type_ids=snake_case_					,	labels=snake_case_		)
          self.parent.assertEqual(result.loss.shape					,	()		)
          self.parent.assertEqual(result.logits.shape					,	(self.batch_size, self.seq_length, self.vocab_size)		)




    def lowerCamelCase			(       self   :	str					,	snake_case_   :	Tuple					,	snake_case_   :	List[str]					,	snake_case_   :	int					,	snake_case_   :	Union[str, Any]					,	*snake_case_   :	Any		):
          snake_case__    :       Union[str, Any]       		= self.num_labels
          snake_case__    :       str       		= OpenAIGPTForSequenceClassification(snake_case_		)
          model.to(snake_case_		)
          model.eval()

          snake_case__    :       Union[str, Any]       		= ids_tensor([self.batch_size]					,	self.type_sequence_label_size		)
          snake_case__    :       List[str]       		= model(snake_case_					,	token_type_ids=snake_case_					,	labels=snake_case_		)
          self.parent.assertEqual(result.logits.shape					,	(self.batch_size, self.num_labels)		)




    def lowerCamelCase			(       self   :	str		):
          snake_case__    :       Optional[Any]       		= self.prepare_config_and_inputs()
          (
              (
              snake_case__
          )		,							(
              snake_case__
          )		,							(
              snake_case__
          )		,							(
              snake_case__
          )		,							(
              snake_case__
          )		,							(
              snake_case__
          )		,							(
              snake_case__
          )		,							
          )    :       List[Any]       		= config_and_inputs
          snake_case__    :       int       		= {
              """input_ids""": input_ids,
              """token_type_ids""": token_type_ids,
              """head_mask""": head_mask,
          }

          return config, inputs_dict







@require_torch
class        UpperCAmelCase_			(			_a						,						_a						,						_a						,						unittest.TestCase      ):
    """simple docstring"""







    lowercase   =						(
        (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
        if is_torch_available()
        else ()
    )
    lowercase   =						(
        (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
    )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
    lowercase   =						(
        {
            "feature-extraction": OpenAIGPTModel,
            "text-classification": OpenAIGPTForSequenceClassification,
            "text-generation": OpenAIGPTLMHeadModel,
            "zero-shot": OpenAIGPTForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )




    def lowerCamelCase			(       self   :	List[str]					,	snake_case_   :	List[str]					,	snake_case_   :	int					,	snake_case_   :	Any					,	snake_case_   :	Union[str, Any]					,	snake_case_   :	List[Any]		):
          if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
                # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
                # `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
                # tiny config could not be created.
                return True

          return False




    def lowerCamelCase			(       self   :	List[Any]					,	snake_case_   :	Tuple					,	snake_case_   :	Union[str, Any]					,	snake_case_   :	Dict=False		):
          snake_case__    :       Optional[Any]       		= super()._prepare_for_class(snake_case_					,	snake_case_					,	return_labels=snake_case_		)

          if return_labels:
                if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
                      snake_case__    :       str       		= torch.zeros(
                          (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)					,	dtype=torch.long					,	device=snake_case_					,	)
                      snake_case__    :       List[str]       		= inputs_dict["""labels"""]
                      snake_case__    :       int       		= inputs_dict["""labels"""]
                      snake_case__    :       int       		= torch.zeros(
                          (self.model_tester.batch_size, self.model_tester.num_choices)					,	dtype=torch.long					,	device=snake_case_					,	)
                      snake_case__    :       Dict       		= torch.zeros(
                          self.model_tester.batch_size					,	dtype=torch.long					,	device=snake_case_		)
          return inputs_dict




    def lowerCamelCase			(       self   :	Optional[Any]		):
          snake_case__    :       Union[str, Any]       		= OpenAIGPTModelTester(self		)
          snake_case__    :       Tuple       		= ConfigTester(self					,	config_class=snake_case_					,	n_embd=37		)




    def lowerCamelCase			(       self   :	Union[str, Any]		):
          self.config_tester.run_common_tests()




    def lowerCamelCase			(       self   :	Any		):
          snake_case__    :       List[Any]       		= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_openai_gpt_model(*snake_case_		)




    def lowerCamelCase			(       self   :	List[str]		):
          snake_case__    :       List[str]       		= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_lm_head_model(*snake_case_		)




    def lowerCamelCase			(       self   :	Union[str, Any]		):
          snake_case__    :       Optional[Any]       		= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_double_lm_head_model(*snake_case_		)




    def lowerCamelCase			(       self   :	str		):
          snake_case__    :       Any       		= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*snake_case_		)




    @slow
    def lowerCamelCase			(       self   :	Union[str, Any]		):
          for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                snake_case__    :       Any       		= OpenAIGPTModel.from_pretrained(snake_case_		)
                self.assertIsNotNone(snake_case_		)







@require_torch
class        UpperCAmelCase_			(			unittest.TestCase      ):
    """simple docstring"""







    @slow
    def lowerCamelCase			(       self   :	Optional[int]		):
          snake_case__    :       Optional[int]       		= OpenAIGPTLMHeadModel.from_pretrained("""openai-gpt"""		)
          model.to(snake_case_		)
          snake_case__    :       List[Any]       		= torch.tensor([[481, 4_735, 544]]					,	dtype=torch.long					,	device=snake_case_		)  # the president is
          snake_case__    :       Optional[int]       		= [
              481,
              4_735,
              544,
              246,
              963,
              870,
              762,
              239,
              244,
              40_477,
              244,
              249,
              719,
              881,
              487,
              544,
              240,
              244,
              603,
              481,
          ]  # the president is a very good man. " \n " i\'m sure he is, " said the

          snake_case__    :       int       		= model.generate(snake_case_					,	do_sample=snake_case_		)
          self.assertListEqual(output_ids[0].tolist()					,	snake_case_		)





'''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_			:
    """simple docstring"""







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




    def lowerCamelCase			(       self   :	Tuple		):
          snake_case__    :       List[Any]       		= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

          snake_case__    :       int       		= None
          if self.use_labels:
                snake_case__    :       List[Any]       		= ids_tensor([self.batch_size]					,	self.num_labels		)

          snake_case__    :       List[str]       		= self.get_config()

          return config, pixel_values, labels




    def lowerCamelCase			(       self   :	str		):
          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=snake_case_					,	layer_scale_init_value=1E-5					,	)




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




    def lowerCamelCase			(       self   :	List[Any]					,	snake_case_   :	Tuple					,	snake_case_   :	Optional[int]					,	snake_case_   :	Optional[int]		):
          snake_case__    :       Optional[int]       		= self.num_labels
          snake_case__    :       Union[str, Any]       		= SwiftFormerForImageClassification(snake_case_		)
          model.to(snake_case_		)
          model.eval()
          snake_case__    :       Optional[Any]       		= model(snake_case_					,	labels=snake_case_		)
          self.parent.assertEqual(result.logits.shape					,	(self.batch_size, self.num_labels)		)

          snake_case__    :       Union[str, Any]       		= SwiftFormerForImageClassification(snake_case_		)
          model.to(snake_case_		)
          model.eval()

          snake_case__    :       List[Any]       		= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)
          snake_case__    :       Any       		= model(snake_case_		)
          self.parent.assertEqual(result.logits.shape					,	(self.batch_size, self.num_labels)		)




    def lowerCamelCase			(       self   :	List[Any]		):
          ((snake_case__)		,							(snake_case__)		,							(snake_case__))    :       Union[str, Any]       		= self.prepare_config_and_inputs()
          snake_case__    :       int       		= {"""pixel_values""": pixel_values}
          return config, inputs_dict







@require_torch
class        UpperCAmelCase_			(			_a						,						_a						,						unittest.TestCase      ):
    """simple docstring"""







    lowercase   =						(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
    lowercase   =						(
        {"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
        if is_torch_available()
        else {}
    )

    lowercase   =						False
    lowercase   =						False
    lowercase   =						False
    lowercase   =						False
    lowercase   =						False




    def lowerCamelCase			(       self   :	Tuple		):
          snake_case__    :       Optional[Any]       		= SwiftFormerModelTester(self		)
          snake_case__    :       Union[str, Any]       		= ConfigTester(
              self					,	config_class=snake_case_					,	has_text_modality=snake_case_					,	hidden_size=37					,	num_attention_heads=12					,	num_hidden_layers=12					,	)




    def lowerCamelCase			(       self   :	Optional[int]		):
          self.config_tester.run_common_tests()




    @unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""		)
    def lowerCamelCase			(       self   :	Optional[Any]		):
          pass




    def lowerCamelCase			(       self   :	Union[str, Any]		):
          snake_case__		,							snake_case__    :       Any       		= self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                snake_case__    :       Optional[int]       		= model_class(snake_case_		)
                snake_case__    :       Any       		= model.get_output_embeddings()
                self.assertTrue(x is None or isinstance(snake_case_					,	nn.Linear		)		)




    def lowerCamelCase			(       self   :	List[str]		):
          snake_case__		,							snake_case__    :       Dict       		= self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                snake_case__    :       Optional[int]       		= model_class(snake_case_		)
                snake_case__    :       Any       		= inspect.signature(model.forward		)
                # signature.parameters is an OrderedDict => so arg_names order is deterministic
                snake_case__    :       List[str]       		= [*signature.parameters.keys()]

                snake_case__    :       Optional[int]       		= ["""pixel_values"""]
                self.assertListEqual(arg_names[:1]					,	snake_case_		)




    def lowerCamelCase			(       self   :	List[Any]		):
          snake_case__    :       List[str]       		= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case_		)




    def lowerCamelCase			(       self   :	Tuple		):
          snake_case__    :       Any       		= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*snake_case_		)




    @slow
    def lowerCamelCase			(       self   :	List[str]		):
          for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                snake_case__    :       Union[str, Any]       		= SwiftFormerModel.from_pretrained(snake_case_		)
                self.assertIsNotNone(snake_case_		)




    @unittest.skip(reason="""SwiftFormer does not output attentions"""		)
    def lowerCamelCase			(       self   :	Union[str, Any]		):
          pass




    def lowerCamelCase			(       self   :	List[Any]		):
          def check_hidden_states_output(snake_case_   :	str					,	snake_case_   :	Tuple					,	snake_case_   :	List[Any]		):
                snake_case__    :       Optional[Any]       		= model_class(snake_case_		)
                model.to(snake_case_		)
                model.eval()

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

                snake_case__    :       Optional[Any]       		= outputs.hidden_states

                snake_case__    :       List[Any]       		= 8
                self.assertEqual(len(snake_case_		)					,	snake_case_		)  # 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(snake_case_		)		):
                      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),
                              ]		)					,	)

          snake_case__		,							snake_case__    :       int       		= self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                snake_case__    :       Any       		= True
                check_hidden_states_output(snake_case_					,	snake_case_					,	snake_case_		)

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

                check_hidden_states_output(snake_case_					,	snake_case_					,	snake_case_		)




    def lowerCamelCase			(       self   :	Optional[Any]		):
          def _config_zero_init(snake_case_   :	Optional[Any]		):
                snake_case__    :       Union[str, Any]       		= copy.deepcopy(snake_case_		)
                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(snake_case_					,	snake_case_					,	1E-1_0		)
                      if isinstance(getattr(snake_case_					,	snake_case_					,	snake_case_		)					,	snake_case_		):
                            snake_case__    :       List[str]       		= _config_zero_init(getattr(snake_case_					,	snake_case_		)		)
                            setattr(snake_case_					,	snake_case_					,	snake_case_		)
                return configs_no_init

          snake_case__		,							snake_case__    :       Tuple       		= self.model_tester.prepare_config_and_inputs_for_common()

          snake_case__    :       List[str]       		= _config_zero_init(snake_case_		)
          for model_class in self.all_model_classes:
                snake_case__    :       Optional[int]       		= model_class(config=snake_case_		)
                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 lowerCamelCase			(       self   :	Tuple		):
          pass





def 		__snake_case(							)						->					Optional[int]:
      snake_case__    :       Optional[int]       		= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
      return image







@require_torch
@require_vision
class        UpperCAmelCase_			(			unittest.TestCase      ):
    """simple docstring"""







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




    @slow
    def lowerCamelCase			(       self   :	List[Any]		):
          snake_case__    :       str       		= SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""		).to(snake_case_		)

          snake_case__    :       Dict       		= self.default_image_processor
          snake_case__    :       Optional[Any]       		= prepare_img()
          snake_case__    :       Union[str, Any]       		= image_processor(images=snake_case_					,	return_tensors="""pt"""		).to(snake_case_		)

          # forward pass
          with torch.no_grad():
                snake_case__    :       Dict       		= model(**snake_case_		)

          # verify the logits
          snake_case__    :       int       		= torch.Size((1, 1_000)		)
          self.assertEqual(outputs.logits.shape					,	snake_case_		)

          snake_case__    :       int       		= torch.tensor([[-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0]]		).to(snake_case_		)
          self.assertTrue(torch.allclose(outputs.logits[0, :3]					,	snake_case_					,	atol=1E-4		)		)
