


import random

import torch
from huggingface_hub import HfApi

from diffusers import UNetaDModel


_A		:				Optional[int]									=       HfApi()

_A		:				Union[str, Any]									=       {}
# fmt: off
_A		:				Optional[Any]									=       torch.tensor([
    -0.75_15, -1.68_83, 0.24_20, 0.03_00, 0.63_47, 1.34_33, -1.17_43, -3.74_67,
    1.23_42, -2.24_85, 0.46_36, 0.80_76, -0.79_91, 0.39_69, 0.84_98, 0.91_89,
    -1.88_87, -3.35_22, 0.76_39, 0.20_40, 0.62_71, -2.71_48, -1.63_16, 3.08_39,
    0.31_86, 0.27_21, -0.97_59, -1.24_61, 2.62_57, 1.35_57
])
_A		:				int									=       torch.tensor([
    -2.36_39, -2.53_44, 0.00_54, -0.66_74, 1.59_90, 1.01_58, 0.31_24, -2.14_36,
    1.87_95, -2.54_29, -0.15_66, -0.39_73, 1.24_90, 2.64_47, 1.22_83, -0.52_08,
    -2.81_54, -3.51_19, 2.38_38, 1.20_33, 1.72_01, -2.12_56, -1.45_76, 2.79_48,
    2.42_04, -0.97_52, -1.25_46, 0.80_27, 3.27_58, 3.13_65
])
_A		:				Union[str, Any]									=       torch.tensor([
    -0.65_31, -0.68_91, -0.31_72, -0.53_75, -0.91_40, -0.53_67, -0.11_75, -0.78_69,
    -0.38_08, -0.45_13, -0.20_98, -0.00_83, 0.31_83, 0.51_40, 0.22_47, -0.13_04,
    -0.13_02, -0.28_02, -0.20_84, -0.20_25, -0.49_67, -0.48_73, -0.08_61, 0.69_25,
    0.02_50, 0.12_90, -0.15_43, 0.63_16, 1.04_60, 1.49_43
])
_A		:				Optional[Any]									=       torch.tensor([
    0.09_11, 0.11_07, 0.01_82, 0.04_35, -0.08_05, -0.06_08, 0.03_81, 0.21_72,
    -0.02_80, 0.13_27, -0.02_99, -0.02_55, -0.00_50, -0.11_70, -0.10_46, 0.03_09,
    0.13_67, 0.17_28, -0.05_33, -0.07_48, -0.05_34, 0.16_24, 0.03_84, -0.18_05,
    -0.07_07, 0.06_42, 0.02_20, -0.01_34, -0.13_33, -0.15_05
])
_A		:				Optional[int]									=       torch.tensor([
    0.13_21, 0.13_37, 0.04_40, 0.06_22, -0.05_91, -0.03_70, 0.05_03, 0.21_33,
    -0.01_77, 0.14_15, -0.01_16, -0.01_12, 0.00_44, -0.09_80, -0.07_89, 0.03_95,
    0.15_02, 0.17_85, -0.04_88, -0.05_14, -0.04_04, 0.15_39, 0.04_54, -0.15_59,
    -0.06_65, 0.06_59, 0.03_83, -0.00_05, -0.12_66, -0.13_86
])
_A		:				str									=       torch.tensor([
    0.11_54, 0.12_18, 0.03_07, 0.05_26, -0.07_11, -0.05_41, 0.03_66, 0.20_78,
    -0.02_67, 0.13_17, -0.02_26, -0.01_93, -0.00_14, -0.10_55, -0.09_02, 0.03_30,
    0.13_91, 0.17_09, -0.05_62, -0.06_93, -0.05_60, 0.14_82, 0.03_81, -0.16_83,
    -0.06_81, 0.06_61, 0.03_31, -0.00_46, -0.12_68, -0.14_31
])
_A		:				List[Any]									=       torch.tensor([
    0.11_92, 0.12_40, 0.04_14, 0.06_06, -0.05_57, -0.04_12, 0.04_30, 0.20_42,
    -0.02_00, 0.13_85, -0.01_15, -0.01_32, 0.00_17, -0.09_65, -0.08_02, 0.03_98,
    0.14_33, 0.17_47, -0.04_58, -0.05_33, -0.04_07, 0.15_45, 0.04_19, -0.15_74,
    -0.06_45, 0.06_26, 0.03_41, -0.00_10, -0.11_99, -0.13_90
])
_A		:				Tuple									=       torch.tensor([
    0.10_75, 0.10_74, 0.02_05, 0.04_31, -0.07_74, -0.06_07, 0.02_98, 0.20_42,
    -0.03_20, 0.12_67, -0.02_81, -0.02_50, -0.00_64, -0.10_91, -0.09_46, 0.02_90,
    0.13_28, 0.16_50, -0.05_80, -0.07_38, -0.05_86, 0.14_40, 0.03_37, -0.17_46,
    -0.07_12, 0.06_05, 0.02_50, -0.00_99, -0.13_16, -0.14_73
])
_A		:				str									=       torch.tensor([
    -1.45_72, -2.04_81, -0.04_14, -0.60_05, 1.41_36, 0.58_48, 0.40_28, -2.73_30,
    1.22_12, -2.12_28, 0.21_55, 0.40_39, 0.76_62, 2.05_35, 0.74_77, -0.32_43,
    -2.17_58, -2.76_48, 1.69_47, 0.70_26, 1.23_38, -1.60_78, -0.86_82, 2.28_10,
    1.85_74, -0.57_18, -0.55_86, -0.01_86, 2.34_15, 2.12_51])
_A		:				List[str]									=       torch.tensor([
    -1.36_90, -1.97_20, -0.40_90, -0.69_66, 1.46_60, 0.99_38, -0.13_85, -2.73_24,
    0.77_36, -1.89_17, 0.29_23, 0.42_93, 0.16_93, 1.41_12, 1.18_87, -0.31_81,
    -2.21_60, -2.63_81, 1.31_70, 0.81_63, 0.92_40, -1.65_44, -0.60_99, 2.52_59,
    1.64_30, -0.90_90, -0.93_92, -0.01_26, 2.42_68, 2.32_66
])
_A		:				Tuple									=       torch.tensor([
    -1.35_25, -1.96_28, -0.39_56, -0.68_60, 1.46_64, 1.00_14, -0.12_59, -2.72_12,
    0.77_72, -1.88_11, 0.29_96, 0.43_88, 0.17_04, 1.40_29, 1.17_01, -0.30_27,
    -2.20_53, -2.62_87, 1.33_50, 0.81_31, 0.92_74, -1.62_92, -0.60_98, 2.51_31,
    1.65_05, -0.89_58, -0.92_98, -0.01_51, 2.42_57, 2.33_55
])
_A		:				Optional[Any]									=       torch.tensor([
    -2.05_85, -2.78_97, -0.28_50, -0.89_40, 1.90_52, 0.57_02, 0.63_45, -3.89_59,
    1.59_32, -3.23_19, 0.19_74, 0.02_87, 1.75_66, 2.65_43, 0.83_87, -0.53_51,
    -3.27_36, -4.33_75, 2.90_29, 1.63_90, 1.46_40, -2.17_01, -1.90_13, 2.93_41,
    3.49_81, -0.62_55, -1.16_44, -0.15_91, 3.70_97, 3.20_66
])
_A		:				Optional[int]									=       torch.tensor([
    -2.31_39, -2.55_94, -0.01_97, -0.67_85, 1.70_01, 1.16_06, 0.30_75, -2.17_40,
    1.80_71, -2.56_30, -0.09_26, -0.38_11, 1.21_16, 2.62_46, 1.27_31, -0.53_98,
    -2.81_53, -3.61_40, 2.38_93, 1.32_62, 1.62_58, -2.18_56, -1.32_67, 2.83_95,
    2.37_79, -1.06_23, -1.24_68, 0.89_59, 3.33_67, 3.22_43
])
_A		:				List[Any]									=       torch.tensor([
    -2.06_28, -2.76_67, -0.20_89, -0.82_63, 2.05_39, 0.59_92, 0.64_95, -3.83_36,
    1.60_25, -3.28_17, 0.17_21, -0.06_33, 1.75_16, 2.70_39, 0.81_00, -0.59_08,
    -3.21_13, -4.43_43, 2.92_57, 1.36_32, 1.55_62, -2.14_89, -1.98_94, 3.05_60,
    3.33_96, -0.73_28, -1.04_17, 0.03_83, 3.70_93, 3.23_43
])
_A		:				Dict									=       torch.tensor([
    -1.45_74, -2.05_69, -0.04_73, -0.61_17, 1.40_18, 0.57_69, 0.41_29, -2.73_44,
    1.22_41, -2.13_97, 0.20_00, 0.39_37, 0.76_16, 2.04_53, 0.73_24, -0.33_91,
    -2.17_46, -2.77_44, 1.69_63, 0.69_21, 1.21_87, -1.61_72, -0.88_77, 2.24_39,
    1.84_71, -0.58_39, -0.56_05, -0.04_64, 2.32_50, 2.12_19
])
# fmt: on

_A		:				Tuple									=       api.list_models(filter='''diffusers''')
for mod in models:
   if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
      _A		:				int									=       '''/home/patrick/google_checkpoints/''' + mod.modelId.split('''/''')[-1]

      print(F"""Started running {mod.modelId}!!!""")

      if mod.modelId.startswith('''CompVis'''):
         _A		:				List[str]									=       UNetaDModel.from_pretrained(local_checkpoint, subfolder='''unet''')
      else:
         _A		:				Union[str, Any]									=       UNetaDModel.from_pretrained(local_checkpoint)

      torch.manual_seed(0)
      random.seed(0)

      _A		:				Optional[Any]									=       torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
      _A		:				Tuple									=       torch.tensor([10] * noise.shape[0])
      with torch.no_grad():
         _A		:				Union[str, Any]									=       model(noise, time_step).sample

      assert torch.allclose(
          logits[0, 0, 0, :30], results['''_'''.join('''_'''.join(mod.modelId.split('''/''')).split('''-'''))], atol=1e-3
      )
      print(F"""{mod.modelId} has passed successfully!!!""")






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        a					:





     def __init__(    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Tuple=13    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=3    ,	SCREAMING_SNAKE_CASE_					:				Any=True    ,	SCREAMING_SNAKE_CASE_					:				List[Any]=True    ,	SCREAMING_SNAKE_CASE_					:				Dict=0.1    ,	SCREAMING_SNAKE_CASE_					:				Tuple=0.1    ,	SCREAMING_SNAKE_CASE_					:				int=224    ,	SCREAMING_SNAKE_CASE_					:				Tuple=1000    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]=[3, 3, 6, 4]    ,	SCREAMING_SNAKE_CASE_					:				List[Any]=[48, 56, 112, 220]    ,	):
        __lowerCamelCase:				Optional[Any]     =	parent
        __lowerCamelCase:				Any     =	batch_size
        __lowerCamelCase:				str     =	num_channels
        __lowerCamelCase:				str     =	is_training
        __lowerCamelCase:				List[str]     =	use_labels
        __lowerCamelCase:				List[Any]     =	hidden_dropout_prob
        __lowerCamelCase:				int     =	attention_probs_dropout_prob
        __lowerCamelCase:				Union[str, Any]     =	num_labels
        __lowerCamelCase:				int     =	image_size
        __lowerCamelCase:				Optional[Any]     =	layer_depths
        __lowerCamelCase:				Dict     =	embed_dims





     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        __lowerCamelCase:				Any     =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

        __lowerCamelCase:				Any     =	None
        if self.use_labels:
           __lowerCamelCase:				Tuple     =	ids_tensor([self.batch_size]    ,	self.num_labels							)

        __lowerCamelCase:				Dict     =	self.get_config()

        return config, pixel_values, labels





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        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=SCREAMING_SNAKE_CASE_    ,	layer_scale_init_value=1E-5    ,	)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Tuple							):
        __lowerCamelCase:				Optional[Any]     =	SwiftFormerModel(config=SCREAMING_SNAKE_CASE_							)
        model.to(SCREAMING_SNAKE_CASE_							)
        model.eval()
        __lowerCamelCase:				Dict     =	model(SCREAMING_SNAKE_CASE_							)
        self.parent.assertEqual(result.last_hidden_state.shape    ,	(self.batch_size, self.embed_dims[-1], 7, 7)							)





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

        __lowerCamelCase:				Any     =	SwiftFormerForImageClassification(SCREAMING_SNAKE_CASE_							)
        model.to(SCREAMING_SNAKE_CASE_							)
        model.eval()

        __lowerCamelCase:				Tuple     =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)
        __lowerCamelCase:				Optional[Any]     =	model(SCREAMING_SNAKE_CASE_							)
        self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.num_labels)							)







     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        ((__lowerCamelCase)   ,     (__lowerCamelCase)   ,     (__lowerCamelCase)):				Union[str, Any]     =	self.prepare_config_and_inputs()
        __lowerCamelCase:				Optional[int]     =	{"""pixel_values""": pixel_values}
        return config, inputs_dict



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

     UpperCAmelCase__ :  Optional[Any]						      =	False
     UpperCAmelCase__ :  List[str]						      =	False
     UpperCAmelCase__ :  int						      =	False
     UpperCAmelCase__ :  Optional[Any]						      =	False
     UpperCAmelCase__ :  Dict						      =	False





     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        __lowerCamelCase:				Optional[Any]     =	SwiftFormerModelTester(self							)
        __lowerCamelCase:				Optional[Any]     =	ConfigTester(
            self    ,	config_class=SCREAMING_SNAKE_CASE_    ,	has_text_modality=SCREAMING_SNAKE_CASE_    ,	hidden_size=37    ,	num_attention_heads=12    ,	num_hidden_layers=12    ,	)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        self.config_tester.run_common_tests()





     @unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""							)
     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        pass





     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        __lowerCamelCase   ,     __lowerCamelCase:				Optional[int]     =	self.model_tester.prepare_config_and_inputs_for_common()

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





     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict							):
        __lowerCamelCase   ,     __lowerCamelCase:				Optional[int]     =	self.model_tester.prepare_config_and_inputs_for_common()

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

           __lowerCamelCase:				List[str]     =	["""pixel_values"""]
           self.assertListEqual(arg_names[:1]    ,	SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        __lowerCamelCase:				Tuple     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        __lowerCamelCase:				int     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_							)





     @slow
     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __lowerCamelCase:				Optional[Any]     =	SwiftFormerModel.from_pretrained(SCREAMING_SNAKE_CASE_							)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE_							)





     @unittest.skip(reason="""SwiftFormer does not output attentions"""							)
     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        pass





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
        def check_hidden_states_output(SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				List[str]							):
           __lowerCamelCase:				List[str]     =	model_class(SCREAMING_SNAKE_CASE_							)
           model.to(SCREAMING_SNAKE_CASE_							)
           model.eval()

           with torch.no_grad():
              __lowerCamelCase:				Any     =	model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)							)

           __lowerCamelCase:				Dict     =	outputs.hidden_states

           __lowerCamelCase:				Tuple     =	8
           self.assertEqual(len(SCREAMING_SNAKE_CASE_							)    ,	SCREAMING_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(SCREAMING_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),
                      ]							)    ,	)

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

        for model_class in self.all_model_classes:
           __lowerCamelCase:				Dict     =	True
           check_hidden_states_output(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           __lowerCamelCase:				Optional[int]     =	True

           check_hidden_states_output(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        def _config_zero_init(SCREAMING_SNAKE_CASE_					:				Any							):
           __lowerCamelCase:				Dict     =	copy.deepcopy(SCREAMING_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(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	1E-1_0							)
              if isinstance(getattr(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)    ,	SCREAMING_SNAKE_CASE_							):
                 __lowerCamelCase:				Tuple     =	_config_zero_init(getattr(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)							)
                 setattr(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
           return configs_no_init

        __lowerCamelCase   ,     __lowerCamelCase:				Optional[int]     =	self.model_tester.prepare_config_and_inputs_for_common()

        __lowerCamelCase:				str     =	_config_zero_init(SCREAMING_SNAKE_CASE_							)
        for model_class in self.all_model_classes:
           __lowerCamelCase:				Dict     =	model_class(config=SCREAMING_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  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        pass



def     __lowerCAmelCase						(							)       ->							List[Any]:
   __lowerCamelCase:				Optional[Any]     =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
   return image







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





     @cached_property
     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict							):
        return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs"""							) if is_vision_available() else None







     @slow
     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        __lowerCamelCase:				Any     =	SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""							).to(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Any     =	self.default_image_processor
        __lowerCamelCase:				Tuple     =	prepare_img()
        __lowerCamelCase:				int     =	image_processor(images=SCREAMING_SNAKE_CASE_    ,	return_tensors="""pt"""							).to(SCREAMING_SNAKE_CASE_							)

        # forward pass
        with torch.no_grad():
           __lowerCamelCase:				List[Any]     =	model(**SCREAMING_SNAKE_CASE_							)

        # verify the logits
        __lowerCamelCase:				List[Any]     =	torch.Size((1, 1000)							)
        self.assertEqual(outputs.logits.shape    ,	SCREAMING_SNAKE_CASE_							)

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


