

import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
UpperCAmelCase				   =  '''src/transformers'''

# Matches is_xxx_available()
UpperCAmelCase				   =  re.compile(r'''is\_([a-z_]*)_available()''')
# Matches from xxx import bla
UpperCAmelCase				   =  re.compile(r'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
UpperCAmelCase				   =  re.compile(r'''^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)''')


UpperCAmelCase				   =  '''
{0} = None
'''

UpperCAmelCase				   =  '''
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
'''


UpperCAmelCase				   =  '''
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
'''

def 				__UpperCamelCase							(	lowercase__						:  int ):






     '''simple docstring'''





     if _re_test_backend.search(lowercase__ ) is None:
          return None
     __lowercase					=[b[0] for b in _re_backend.findall(lowercase__ )]
     backends.sort()
     return "_and_".join(lowercase__ )

def 				__UpperCamelCase							(	):






     '''simple docstring'''





     with open(os.path.join(lowercase__,					'__init__.py' ),					'r',					encoding='utf-8',					newline='\n' ) as f:
          __lowercase					=f.readlines()

     # Get to the point we do the actual imports for type checking
     __lowercase					=0
     while not lines[line_index].startswith('if TYPE_CHECKING' ):
          line_index += 1

     __lowercase					={}
     # Go through the end of the file
     while line_index < len(lowercase__ ):
          # If the line is an if is_backend_available, we grab all objects associated.
          __lowercase					=find_backend(lines[line_index] )
          if backend is not None:
               while not lines[line_index].startswith('    else:' ):
                    line_index += 1
               line_index += 1

               __lowercase					=[]
               # Until we unindent, add backend objects to the list
               while len(lines[line_index] ) <= 1 or lines[line_index].startswith(' ' * 8 ):
                    __lowercase					=lines[line_index]
                    __lowercase					=_re_single_line_import.search(lowercase__ )
                    if single_line_import_search is not None:
                         objects.extend(single_line_import_search.groups()[0].split(', ' ) )
                    elif line.startswith(' ' * 12 ):
                         objects.append(line[12:-2] )
                    line_index += 1

               __lowercase					=objects
          else:
               line_index += 1

     return backend_specific_objects

def 				__UpperCamelCase							(	lowercase__						:  Tuple,					lowercase__						:  str ):






     '''simple docstring'''





     if name.isupper():
          return DUMMY_CONSTANT.format(lowercase__ )
     elif name.islower():
          return DUMMY_FUNCTION.format(lowercase__,					lowercase__ )
     else:
          return DUMMY_CLASS.format(lowercase__,					lowercase__ )

def 				__UpperCamelCase							(	lowercase__						:  int=None ):






     '''simple docstring'''





     if backend_specific_objects is None:
          __lowercase					=read_init()
     # For special correspondence backend to module name as used in the function requires_modulename
     __lowercase					={}

     for backend, objects in backend_specific_objects.items():
          __lowercase					='[' + ', '.join(F'''"{b}"''' for b in backend.split('_and_' ) ) + ']'
          __lowercase					='# This file is autogenerated by the command `make fix-copies`, do not edit.\n'
          dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
          dummy_file += "\n".join([create_dummy_object(lowercase__,					lowercase__ ) for o in objects] )
          __lowercase					=dummy_file

     return dummy_files

def 				__UpperCamelCase							(	lowercase__						:  Dict=False ):






     '''simple docstring'''





     __lowercase					=create_dummy_files()
     # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
     __lowercase					={'torch': 'pt'}

     # Locate actual dummy modules and read their content.
     __lowercase					=os.path.join(lowercase__,					'utils' )
     __lowercase					={
         backend: os.path.join(lowercase__,					F'''dummy_{short_names.get(lowercase__,					lowercase__ )}_objects.py''' )
         for backend in dummy_files.keys()
     }

     __lowercase					={}
     for backend, file_path in dummy_file_paths.items():
          if os.path.isfile(lowercase__ ):
               with open(lowercase__,					'r',					encoding='utf-8',					newline='\n' ) as f:
                    __lowercase					=f.read()
          else:
               __lowercase					=''

     for backend in dummy_files.keys():
          if dummy_files[backend] != actual_dummies[backend]:
               if overwrite:
                    print(
                        F'''Updating transformers.utils.dummy_{short_names.get(lowercase__,					lowercase__ )}_objects.py as the main '''
                        '__init__ has new objects.' )
                    with open(dummy_file_paths[backend],					'w',					encoding='utf-8',					newline='\n' ) as f:
                         f.write(dummy_files[backend] )
               else:
                    raise ValueError(
                        'The main __init__ has objects that are not present in '
                        F'''transformers.utils.dummy_{short_names.get(lowercase__,					lowercase__ )}_objects.py. Run `make fix-copies` '''
                        'to fix this.' )


if __name__ == "__main__":
       UpperCAmelCase				   =  argparse.ArgumentParser()
       parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
       UpperCAmelCase				   =  parser.parse_args()

       check_dummies(args.fix_and_overwrite)





'''simple docstring'''

import collections
import inspect
import unittest

from transformers import SwinConfig
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_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
       from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import AutoImageProcessor


class   lowerCAmelCase     :







       def __init__(							self			: List[str]						,      __lowercase			: Optional[int]						,      __lowercase			: Optional[int]=13						,      __lowercase			: Dict=32						,      __lowercase			: int=2						,      __lowercase			: Tuple=3						,      __lowercase			: Optional[int]=16						,      __lowercase			: Dict=[1, 2, 1]						,      __lowercase			: Dict=[2, 2, 4]						,      __lowercase			: int=2						,      __lowercase			: Optional[Any]=2.0						,      __lowercase			: int=True						,      __lowercase			: List[Any]=0.0						,      __lowercase			: int=0.0						,      __lowercase			: Optional[Any]=0.1						,      __lowercase			: List[str]="gelu"						,      __lowercase			: Any=False						,      __lowercase			: Dict=True						,      __lowercase			: Optional[Any]=0.0_2						,      __lowercase			: int=1E-5						,      __lowercase			: Dict=True						,      __lowercase			: Optional[int]=None						,      __lowercase			: List[Any]=True						,      __lowercase			: List[Any]=10						,      __lowercase			: Any=8						,      __lowercase			: Optional[int]=["stage1", "stage2"]						,      __lowercase			: str=[1, 2]						,      ):







            """simple docstring"""







            __lowercase					=parent
            __lowercase					=batch_size
            __lowercase					=image_size
            __lowercase					=patch_size
            __lowercase					=num_channels
            __lowercase					=embed_dim
            __lowercase					=depths
            __lowercase					=num_heads
            __lowercase					=window_size
            __lowercase					=mlp_ratio
            __lowercase					=qkv_bias
            __lowercase					=hidden_dropout_prob
            __lowercase					=attention_probs_dropout_prob
            __lowercase					=drop_path_rate
            __lowercase					=hidden_act
            __lowercase					=use_absolute_embeddings
            __lowercase					=patch_norm
            __lowercase					=layer_norm_eps
            __lowercase					=initializer_range
            __lowercase					=is_training
            __lowercase					=scope
            __lowercase					=use_labels
            __lowercase					=type_sequence_label_size
            __lowercase					=encoder_stride
            __lowercase					=out_features
            __lowercase					=out_indices







       def 			snake_case   (							self			: Tuple      ):







            """simple docstring"""







            __lowercase					=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

            __lowercase					=None
            if self.use_labels:
                 __lowercase					=ids_tensor([self.batch_size]						,      self.type_sequence_label_size      )

            __lowercase					=self.get_config()

            return config, pixel_values, labels







       def 			snake_case   (							self			: Dict      ):







            """simple docstring"""







            return SwinConfig(
                image_size=self.image_size						,      patch_size=self.patch_size						,      num_channels=self.num_channels						,      embed_dim=self.embed_dim						,      depths=self.depths						,      num_heads=self.num_heads						,      window_size=self.window_size						,      mlp_ratio=self.mlp_ratio						,      qkv_bias=self.qkv_bias						,      hidden_dropout_prob=self.hidden_dropout_prob						,      attention_probs_dropout_prob=self.attention_probs_dropout_prob						,      drop_path_rate=self.drop_path_rate						,      hidden_act=self.hidden_act						,      use_absolute_embeddings=self.use_absolute_embeddings						,      path_norm=self.patch_norm						,      layer_norm_eps=self.layer_norm_eps						,      initializer_range=self.initializer_range						,      encoder_stride=self.encoder_stride						,      out_features=self.out_features						,      out_indices=self.out_indices						,      )







       def 			snake_case   (							self			: Optional[Any]						,      __lowercase			: str						,      __lowercase			: Tuple						,      __lowercase			: int      ):







            """simple docstring"""







            __lowercase					=SwinModel(config=__lowercase      )
            model.to(__lowercase      )
            model.eval()
            __lowercase					=model(__lowercase      )

            __lowercase					=((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths      ) - 1))
            __lowercase					=int(config.embed_dim * 2 ** (len(config.depths      ) - 1)      )

            self.parent.assertEqual(result.last_hidden_state.shape						,      (self.batch_size, expected_seq_len, expected_dim)      )







       def 			snake_case   (							self			: str						,      __lowercase			: str						,      __lowercase			: Dict						,      __lowercase			: List[Any]      ):







            """simple docstring"""







            __lowercase					=SwinBackbone(config=__lowercase      )
            model.to(__lowercase      )
            model.eval()
            __lowercase					=model(__lowercase      )

            # verify hidden states
            self.parent.assertEqual(len(result.feature_maps      )						,      len(config.out_features      )      )
            self.parent.assertListEqual(list(result.feature_maps[0].shape      )						,      [self.batch_size, model.channels[0], 16, 16]      )

            # verify channels
            self.parent.assertEqual(len(model.channels      )						,      len(config.out_features      )      )

            # verify backbone works with out_features=None
            __lowercase					=None
            __lowercase					=SwinBackbone(config=__lowercase      )
            model.to(__lowercase      )
            model.eval()
            __lowercase					=model(__lowercase      )

            # verify feature maps
            self.parent.assertEqual(len(result.feature_maps      )						,      1      )
            self.parent.assertListEqual(list(result.feature_maps[0].shape      )						,      [self.batch_size, model.channels[-1], 4, 4]      )

            # verify channels
            self.parent.assertEqual(len(model.channels      )						,      1      )







       def 			snake_case   (							self			: int						,      __lowercase			: int						,      __lowercase			: List[str]						,      __lowercase			: Dict      ):







            """simple docstring"""







            __lowercase					=SwinForMaskedImageModeling(config=__lowercase      )
            model.to(__lowercase      )
            model.eval()
            __lowercase					=model(__lowercase      )
            self.parent.assertEqual(
                result.logits.shape						,      (self.batch_size, self.num_channels, self.image_size, self.image_size)      )

            # test greyscale images
            __lowercase					=1
            __lowercase					=SwinForMaskedImageModeling(__lowercase      )
            model.to(__lowercase      )
            model.eval()

            __lowercase					=floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
            __lowercase					=model(__lowercase      )
            self.parent.assertEqual(result.logits.shape						,      (self.batch_size, 1, self.image_size, self.image_size)      )







       def 			snake_case   (							self			: Any						,      __lowercase			: Optional[Any]						,      __lowercase			: List[str]						,      __lowercase			: Dict      ):







            """simple docstring"""







            __lowercase					=self.type_sequence_label_size
            __lowercase					=SwinForImageClassification(__lowercase      )
            model.to(__lowercase      )
            model.eval()
            __lowercase					=model(__lowercase						,      labels=__lowercase      )
            self.parent.assertEqual(result.logits.shape						,      (self.batch_size, self.type_sequence_label_size)      )

            # test greyscale images
            __lowercase					=1
            __lowercase					=SwinForImageClassification(__lowercase      )
            model.to(__lowercase      )
            model.eval()

            __lowercase					=floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
            __lowercase					=model(__lowercase      )
            self.parent.assertEqual(result.logits.shape						,      (self.batch_size, self.type_sequence_label_size)      )







       def 			snake_case   (							self			: Optional[Any]      ):







            """simple docstring"""







            __lowercase					=self.prepare_config_and_inputs()
            (
                (
                __lowercase
            )		,     (
                __lowercase
            )		,     (
                __lowercase
            )		,     
            )					=config_and_inputs
            __lowercase					={'pixel_values': pixel_values}
            return config, inputs_dict






@require_torch
class   lowerCAmelCase     (      A   ,			A   ,			unittest.TestCase   ):
       lowerCAmelCase_		= (
           (
               SwinModel,
               SwinBackbone,
               SwinForImageClassification,
               SwinForMaskedImageModeling,
           )
           if is_torch_available()
           else ()
       )
       lowerCAmelCase_		= (
           {"feature-extraction": SwinModel, "image-classification": SwinForImageClassification}
           if is_torch_available()
           else {}
       )
       lowerCAmelCase_		= True

       lowerCAmelCase_		= False
       lowerCAmelCase_		= False
       lowerCAmelCase_		= False







       def 			snake_case   (							self			: List[str]      ):







            """simple docstring"""







            __lowercase					=SwinModelTester(self      )
            __lowercase					=ConfigTester(self						,      config_class=__lowercase						,      embed_dim=37      )







       def 			snake_case   (							self			: Union[str, Any]      ):







            """simple docstring"""







            self.create_and_test_config_common_properties()
            self.config_tester.create_and_test_config_to_json_string()
            self.config_tester.create_and_test_config_to_json_file()
            self.config_tester.create_and_test_config_from_and_save_pretrained()
            self.config_tester.create_and_test_config_with_num_labels()
            self.config_tester.check_config_can_be_init_without_params()
            self.config_tester.check_config_arguments_init()







       def 			snake_case   (							self			: Optional[Any]      ):







            """simple docstring"""







            return







       def 			snake_case   (							self			: List[Any]      ):







            """simple docstring"""







            __lowercase					=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__lowercase      )







       @unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.'      )
       def 			snake_case   (							self			: Optional[Any]      ):







            """simple docstring"""







            pass







       def 			snake_case   (							self			: Optional[int]      ):







            """simple docstring"""







            super().test_training_gradient_checkpointing()







       def 			snake_case   (							self			: Union[str, Any]      ):







            """simple docstring"""







            __lowercase					=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_backbone(*__lowercase      )







       def 			snake_case   (							self			: List[str]      ):







            """simple docstring"""







            __lowercase					=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_image_modeling(*__lowercase      )







       def 			snake_case   (							self			: Dict      ):







            """simple docstring"""







            __lowercase					=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*__lowercase      )







       @unittest.skip(reason='Swin does not use inputs_embeds'      )
       def 			snake_case   (							self			: List[Any]      ):







            """simple docstring"""







            pass







       @unittest.skip(reason='Swin Transformer does not use feedforward chunking'      )
       def 			snake_case   (							self			: int      ):







            """simple docstring"""







            pass







       def 			snake_case   (							self			: Union[str, Any]      ):







            """simple docstring"""







            __lowercase		,     __lowercase					=self.model_tester.prepare_config_and_inputs_for_common()

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







       def 			snake_case   (							self			: Tuple      ):







            """simple docstring"""







            __lowercase		,     __lowercase					=self.model_tester.prepare_config_and_inputs_for_common()

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

                 __lowercase					=['pixel_values']
                 self.assertListEqual(arg_names[:1]						,      __lowercase      )







       def 			snake_case   (							self			: Optional[Any]      ):







            """simple docstring"""







            __lowercase		,     __lowercase					=self.model_tester.prepare_config_and_inputs_for_common()
            __lowercase					=True

            for model_class in self.all_model_classes:
                 __lowercase					=True
                 __lowercase					=False
                 __lowercase					=True
                 __lowercase					=model_class(__lowercase      )
                 model.to(__lowercase      )
                 model.eval()
                 with torch.no_grad():
                      __lowercase					=model(**self._prepare_for_class(__lowercase						,      __lowercase      )      )
                 __lowercase					=outputs.attentions
                 __lowercase					=len(self.model_tester.depths      )
                 self.assertEqual(len(__lowercase      )						,      __lowercase      )

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

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

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

                 # also another +1 for reshaped_hidden_states
                 __lowercase					=1 if model_class.__name__ == 'SwinBackbone' else 2
                 self.assertEqual(out_len + added_hidden_states						,      len(__lowercase      )      )

                 __lowercase					=outputs.attentions

                 self.assertEqual(len(__lowercase      )						,      __lowercase      )

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







       def 			snake_case   (							self			: List[str]						,      __lowercase			: Dict						,      __lowercase			: List[str]						,      __lowercase			: List[str]						,      __lowercase			: Any      ):







            """simple docstring"""







            __lowercase					=model_class(__lowercase      )
            model.to(__lowercase      )
            model.eval()

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

            __lowercase					=outputs.hidden_states

            __lowercase					=getattr(
                self.model_tester						,      'expected_num_hidden_layers'						,      len(self.model_tester.depths      ) + 1      )
            self.assertEqual(len(__lowercase      )						,      __lowercase      )

            # Swin has a different seq_length
            __lowercase					=(
                config.patch_size
                if isinstance(config.patch_size						,      collections.abc.Iterable      )
                else (config.patch_size, config.patch_size)
            )

            __lowercase					=(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

            self.assertListEqual(
                list(hidden_states[0].shape[-2:]      )						,      [num_patches, self.model_tester.embed_dim]						,      )

            if not model_class.__name__ == "SwinBackbone":
                 __lowercase					=outputs.reshaped_hidden_states
                 self.assertEqual(len(__lowercase      )						,      __lowercase      )

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







       def 			snake_case   (							self			: Union[str, Any]      ):







            """simple docstring"""







            __lowercase		,     __lowercase					=self.model_tester.prepare_config_and_inputs_for_common()

            __lowercase					=(
                self.model_tester.image_size
                if isinstance(self.model_tester.image_size						,      collections.abc.Iterable      )
                else (self.model_tester.image_size, self.model_tester.image_size)
            )

            for model_class in self.all_model_classes:
                 __lowercase					=True
                 self.check_hidden_states_output(__lowercase						,      __lowercase						,      __lowercase						,      __lowercase      )

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

                 self.check_hidden_states_output(__lowercase						,      __lowercase						,      __lowercase						,      __lowercase      )







       def 			snake_case   (							self			: Optional[Any]      ):







            """simple docstring"""







            __lowercase		,     __lowercase					=self.model_tester.prepare_config_and_inputs_for_common()
            __lowercase					=3

            __lowercase					=(
                self.model_tester.image_size
                if isinstance(self.model_tester.image_size						,      collections.abc.Iterable      )
                else (self.model_tester.image_size, self.model_tester.image_size)
            )
            __lowercase					=(
                config.patch_size
                if isinstance(config.patch_size						,      collections.abc.Iterable      )
                else (config.patch_size, config.patch_size)
            )

            __lowercase					=image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
            __lowercase					=image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

            for model_class in self.all_model_classes:
                 __lowercase					=True
                 self.check_hidden_states_output(__lowercase						,      __lowercase						,      __lowercase						,      (padded_height, padded_width)      )

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 __lowercase					=True
                 self.check_hidden_states_output(__lowercase						,      __lowercase						,      __lowercase						,      (padded_height, padded_width)      )







       @slow
       def 			snake_case   (							self			: Optional[Any]      ):







            """simple docstring"""







            for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 __lowercase					=SwinModel.from_pretrained(__lowercase      )
                 self.assertIsNotNone(__lowercase      )







       def 			snake_case   (							self			: List[Any]      ):







            """simple docstring"""







            __lowercase		,     __lowercase					=self.model_tester.prepare_config_and_inputs_for_common()

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






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







       @cached_property
       def 			snake_case   (							self			: str      ):







            """simple docstring"""







            return (
                AutoImageProcessor.from_pretrained('microsoft/swin-tiny-patch4-window7-224'      )
                if is_vision_available()
                else None
            )







       @slow
       def 			snake_case   (							self			: Dict      ):







            """simple docstring"""







            __lowercase					=SwinForImageClassification.from_pretrained('microsoft/swin-tiny-patch4-window7-224'      ).to(__lowercase      )
            __lowercase					=self.default_image_processor

            __lowercase					=Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
            __lowercase					=image_processor(images=__lowercase						,      return_tensors='pt'      ).to(__lowercase      )

            # forward pass
            with torch.no_grad():
                 __lowercase					=model(**__lowercase      )

            # verify the logits
            __lowercase					=torch.Size((1, 1000)      )
            self.assertEqual(outputs.logits.shape						,      __lowercase      )
            __lowercase					=torch.tensor([-0.0_9_4_8, -0.6_4_5_4, -0.0_9_2_1]      ).to(__lowercase      )
            self.assertTrue(torch.allclose(outputs.logits[0, :3]						,      __lowercase						,      atol=1E-4      )      )






@require_torch
class   lowerCAmelCase     (      unittest.TestCase   ,			A   ):
       lowerCAmelCase_		= (SwinBackbone,) if is_torch_available() else ()
       lowerCAmelCase_		= SwinConfig







       def 			snake_case   (							self			: List[Any]      ):







            """simple docstring"""







            __lowercase					=SwinModelTester(self      )


