
'''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 		_a     :







   """simple docstring"""




   def __init__(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE=13    ,__SCREAMING_SNAKE_CASE=32    ,__SCREAMING_SNAKE_CASE=2    ,__SCREAMING_SNAKE_CASE=3    ,__SCREAMING_SNAKE_CASE=16    ,__SCREAMING_SNAKE_CASE=[1, 2, 1]    ,__SCREAMING_SNAKE_CASE=[2, 2, 4]    ,__SCREAMING_SNAKE_CASE=2    ,__SCREAMING_SNAKE_CASE=2.0    ,__SCREAMING_SNAKE_CASE=True    ,__SCREAMING_SNAKE_CASE=0.0    ,__SCREAMING_SNAKE_CASE=0.0    ,__SCREAMING_SNAKE_CASE=0.1    ,__SCREAMING_SNAKE_CASE="gelu"    ,__SCREAMING_SNAKE_CASE=False    ,__SCREAMING_SNAKE_CASE=True    ,__SCREAMING_SNAKE_CASE=0.02    ,__SCREAMING_SNAKE_CASE=1e-5    ,__SCREAMING_SNAKE_CASE=True    ,__SCREAMING_SNAKE_CASE=None    ,__SCREAMING_SNAKE_CASE=True    ,__SCREAMING_SNAKE_CASE=10    ,__SCREAMING_SNAKE_CASE=8    ,__SCREAMING_SNAKE_CASE=["stage1", "stage2"]    ,__SCREAMING_SNAKE_CASE=[1, 2]    ,):
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		parent
    SCREAMING_SNAKE_CASE  :  Dict								=		batch_size
    SCREAMING_SNAKE_CASE  :  int								=		image_size
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		patch_size
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		num_channels
    SCREAMING_SNAKE_CASE  :  int								=		embed_dim
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		depths
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		num_heads
    SCREAMING_SNAKE_CASE  :  Dict								=		window_size
    SCREAMING_SNAKE_CASE  :  List[Any]								=		mlp_ratio
    SCREAMING_SNAKE_CASE  :  str								=		qkv_bias
    SCREAMING_SNAKE_CASE  :  Tuple								=		hidden_dropout_prob
    SCREAMING_SNAKE_CASE  :  str								=		attention_probs_dropout_prob
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		drop_path_rate
    SCREAMING_SNAKE_CASE  :  Any								=		hidden_act
    SCREAMING_SNAKE_CASE  :  List[Any]								=		use_absolute_embeddings
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		patch_norm
    SCREAMING_SNAKE_CASE  :  Dict								=		layer_norm_eps
    SCREAMING_SNAKE_CASE  :  Tuple								=		initializer_range
    SCREAMING_SNAKE_CASE  :  List[Any]								=		is_training
    SCREAMING_SNAKE_CASE  :  List[str]								=		scope
    SCREAMING_SNAKE_CASE  :  Any								=		use_labels
    SCREAMING_SNAKE_CASE  :  str								=		type_sequence_label_size
    SCREAMING_SNAKE_CASE  :  Any								=		encoder_stride
    SCREAMING_SNAKE_CASE  :  List[str]								=		out_features
    SCREAMING_SNAKE_CASE  :  List[Any]								=		out_indices






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

    SCREAMING_SNAKE_CASE  :  List[str]								=		None
    if self.use_labels:
     SCREAMING_SNAKE_CASE  :  int								=		ids_tensor([self.batch_size]    ,self.type_sequence_label_size      )

    SCREAMING_SNAKE_CASE  :  str								=		self.get_config()

    return config, pixel_values, labels






   def 					__a			(       self      ):
    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 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  Any								=		SwinModel(config=lowerCAmelCase_      )
    model.to(lowerCAmelCase_      )
    model.eval()
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		model(lowerCAmelCase_      )

    SCREAMING_SNAKE_CASE  :  Any								=		((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths      ) - 1))
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		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 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  Any								=		SwinBackbone(config=lowerCAmelCase_      )
    model.to(lowerCAmelCase_      )
    model.eval()
    SCREAMING_SNAKE_CASE  :  int								=		model(lowerCAmelCase_      )

    # 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
    SCREAMING_SNAKE_CASE  :  Dict								=		None
    SCREAMING_SNAKE_CASE  :  Any								=		SwinBackbone(config=lowerCAmelCase_      )
    model.to(lowerCAmelCase_      )
    model.eval()
    SCREAMING_SNAKE_CASE  :  Dict								=		model(lowerCAmelCase_      )

    # 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 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  str								=		SwinForMaskedImageModeling(config=lowerCAmelCase_      )
    model.to(lowerCAmelCase_      )
    model.eval()
    SCREAMING_SNAKE_CASE  :  List[Any]								=		model(lowerCAmelCase_      )
    self.parent.assertEqual(
        result.logits.shape    ,(self.batch_size, self.num_channels, self.image_size, self.image_size)      )

    # test greyscale images
    SCREAMING_SNAKE_CASE  :  List[Any]								=		1
    SCREAMING_SNAKE_CASE  :  List[Any]								=		SwinForMaskedImageModeling(lowerCAmelCase_      )
    model.to(lowerCAmelCase_      )
    model.eval()

    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
    SCREAMING_SNAKE_CASE  :  int								=		model(lowerCAmelCase_      )
    self.parent.assertEqual(result.logits.shape    ,(self.batch_size, 1, self.image_size, self.image_size)      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  Any								=		self.type_sequence_label_size
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		SwinForImageClassification(lowerCAmelCase_      )
    model.to(lowerCAmelCase_      )
    model.eval()
    SCREAMING_SNAKE_CASE  :  Tuple								=		model(lowerCAmelCase_    ,labels=lowerCAmelCase_      )
    self.parent.assertEqual(result.logits.shape    ,(self.batch_size, self.type_sequence_label_size)      )

    # test greyscale images
    SCREAMING_SNAKE_CASE  :  Tuple								=		1
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		SwinForImageClassification(lowerCAmelCase_      )
    model.to(lowerCAmelCase_      )
    model.eval()

    SCREAMING_SNAKE_CASE  :  int								=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
    SCREAMING_SNAKE_CASE  :  List[Any]								=		model(lowerCAmelCase_      )
    self.parent.assertEqual(result.logits.shape    ,(self.batch_size, self.type_sequence_label_size)      )






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  List[Any]								=		self.prepare_config_and_inputs()
    (
        SCREAMING_SNAKE_CASE    
    )  :  Union[str, Any]								=		config_and_inputs
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		{'''pixel_values''': pixel_values}
    return config, inputs_dict



@require_torch
class 		_a     (							SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    unittest.TestCase  ):







   """simple docstring"""




   A		= (
       (
           SwinModel,
           SwinBackbone,
           SwinForImageClassification,
           SwinForMaskedImageModeling,
       )
       if is_torch_available()
       else ()
   )
   A		= (
       {'''feature-extraction''': SwinModel, '''image-classification''': SwinForImageClassification}
       if is_torch_available()
       else {}
   )
   A		= True

   A		= False
   A		= False
   A		= False






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Dict								=		SwinModelTester(self      )
    SCREAMING_SNAKE_CASE  :  str								=		ConfigTester(self    ,config_class=lowerCAmelCase_    ,embed_dim=37      )






   def 					__a			(       self      ):
    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 					__a			(       self      ):
    return






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Any								=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*lowerCAmelCase_      )






   @unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.'      )
   def 					__a			(       self      ):
    pass






   def 					__a			(       self      ):
    super().test_training_gradient_checkpointing()






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  int								=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_backbone(*lowerCAmelCase_      )






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Any								=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase_      )






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Any								=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_      )






   @unittest.skip(reason='Swin does not use inputs_embeds'      )
   def 					__a			(       self      ):
    pass






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






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  int								=		self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     SCREAMING_SNAKE_CASE  :  Optional[int]								=		model_class(lowerCAmelCase_      )
     self.assertIsInstance(model.get_input_embeddings()    ,(nn.Module)      )
     SCREAMING_SNAKE_CASE  :  int								=		model.get_output_embeddings()
     self.assertTrue(x is None or isinstance(lowerCAmelCase_    ,nn.Linear      )      )






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Dict								=		self.model_tester.prepare_config_and_inputs_for_common()

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

     SCREAMING_SNAKE_CASE  :  List[Any]								=		['''pixel_values''']
     self.assertListEqual(arg_names[:1]    ,lowerCAmelCase_      )






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  str								=		self.model_tester.prepare_config_and_inputs_for_common()
    SCREAMING_SNAKE_CASE  :  List[str]								=		True

    for model_class in self.all_model_classes:
     SCREAMING_SNAKE_CASE  :  List[str]								=		True
     SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		False
     SCREAMING_SNAKE_CASE  :  Tuple								=		True
     SCREAMING_SNAKE_CASE  :  Tuple								=		model_class(lowerCAmelCase_      )
     model.to(lowerCAmelCase_      )
     model.eval()
     with torch.no_grad():
      SCREAMING_SNAKE_CASE  :  Any								=		model(**self._prepare_for_class(lowerCAmelCase_    ,lowerCAmelCase_      )      )
     SCREAMING_SNAKE_CASE  :  Any								=		outputs.attentions
     SCREAMING_SNAKE_CASE  :  str								=		len(self.model_tester.depths      )
     self.assertEqual(len(lowerCAmelCase_      )    ,lowerCAmelCase_      )

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

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

     # Check attention is always last and order is fine
     SCREAMING_SNAKE_CASE  :  List[Any]								=		True
     SCREAMING_SNAKE_CASE  :  List[str]								=		True
     SCREAMING_SNAKE_CASE  :  Optional[int]								=		model_class(lowerCAmelCase_      )
     model.to(lowerCAmelCase_      )
     model.eval()
     with torch.no_grad():
      SCREAMING_SNAKE_CASE  :  int								=		model(**self._prepare_for_class(lowerCAmelCase_    ,lowerCAmelCase_      )      )

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

     SCREAMING_SNAKE_CASE  :  Dict								=		outputs.attentions

     self.assertEqual(len(lowerCAmelCase_      )    ,lowerCAmelCase_      )

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






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  Tuple								=		model_class(lowerCAmelCase_      )
    model.to(lowerCAmelCase_      )
    model.eval()

    with torch.no_grad():
     SCREAMING_SNAKE_CASE  :  Tuple								=		model(**self._prepare_for_class(lowerCAmelCase_    ,lowerCAmelCase_      )      )

    SCREAMING_SNAKE_CASE  :  Any								=		outputs.hidden_states

    SCREAMING_SNAKE_CASE  :  str								=		getattr(
        self.model_tester    ,'expected_num_hidden_layers'    ,len(self.model_tester.depths      ) + 1      )
    self.assertEqual(len(lowerCAmelCase_      )    ,lowerCAmelCase_      )

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

    SCREAMING_SNAKE_CASE  :  List[Any]								=		(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":
     SCREAMING_SNAKE_CASE  :  Any								=		outputs.reshaped_hidden_states
     self.assertEqual(len(lowerCAmelCase_      )    ,lowerCAmelCase_      )

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






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		self.model_tester.prepare_config_and_inputs_for_common()

    SCREAMING_SNAKE_CASE  :  Any								=		(
        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:
     SCREAMING_SNAKE_CASE  :  Optional[int]								=		True
     self.check_hidden_states_output(lowerCAmelCase_    ,lowerCAmelCase_    ,lowerCAmelCase_    ,lowerCAmelCase_      )

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

     self.check_hidden_states_output(lowerCAmelCase_    ,lowerCAmelCase_    ,lowerCAmelCase_    ,lowerCAmelCase_      )






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  int								=		self.model_tester.prepare_config_and_inputs_for_common()
    SCREAMING_SNAKE_CASE  :  Tuple								=		3

    SCREAMING_SNAKE_CASE  :  Dict								=		(
        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)
    )
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		(
        config.patch_size
        if isinstance(config.patch_size    ,collections.abc.Iterable      )
        else (config.patch_size, config.patch_size)
    )

    SCREAMING_SNAKE_CASE  :  Optional[int]								=		image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
    SCREAMING_SNAKE_CASE  :  Tuple								=		image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

    for model_class in self.all_model_classes:
     SCREAMING_SNAKE_CASE  :  Optional[int]								=		True
     self.check_hidden_states_output(lowerCAmelCase_    ,lowerCAmelCase_    ,lowerCAmelCase_    ,(padded_height, padded_width)      )

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






   @slow
   def 					__a			(       self      ):
    for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     SCREAMING_SNAKE_CASE  :  List[str]								=		SwinModel.from_pretrained(lowerCAmelCase_      )
     self.assertIsNotNone(lowerCAmelCase_      )






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  List[str]								=		self.model_tester.prepare_config_and_inputs_for_common()

    SCREAMING_SNAKE_CASE  :  Tuple								=		_config_zero_init(lowerCAmelCase_      )
    for model_class in self.all_model_classes:
     SCREAMING_SNAKE_CASE  :  Tuple								=		model_class(config=lowerCAmelCase_      )
     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 		_a     (							unittest.TestCase  ):







   """simple docstring"""




   @cached_property
   def 					__a			(       self      ):
    return (
        AutoImageProcessor.from_pretrained('microsoft/swin-tiny-patch4-window7-224'      )
        if is_vision_available()
        else None
    )






   @slow
   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		SwinForImageClassification.from_pretrained('microsoft/swin-tiny-patch4-window7-224'      ).to(lowerCAmelCase_      )
    SCREAMING_SNAKE_CASE  :  Any								=		self.default_image_processor

    SCREAMING_SNAKE_CASE  :  Dict								=		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		image_processor(images=lowerCAmelCase_    ,return_tensors='pt'      ).to(lowerCAmelCase_      )

    # forward pass
    with torch.no_grad():
     SCREAMING_SNAKE_CASE  :  Optional[Any]								=		model(**lowerCAmelCase_      )

    # verify the logits
    SCREAMING_SNAKE_CASE  :  Tuple								=		torch.Size((1, 1000)      )
    self.assertEqual(outputs.logits.shape    ,lowerCAmelCase_      )
    SCREAMING_SNAKE_CASE  :  str								=		torch.tensor([-0.0948, -0.6454, -0.0921]      ).to(lowerCAmelCase_      )
    self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,lowerCAmelCase_    ,atol=1e-4      )      )



@require_torch
class 		_a     (							unittest.TestCase			,    SCREAMING_SNAKE_CASE__  ):







   """simple docstring"""




   A		= (SwinBackbone,) if is_torch_available() else ()
   A		= SwinConfig






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Any								=		SwinModelTester(self      )




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase__						:	Any	     = {
    'configuration_decision_transformer': [
        'DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'DecisionTransformerConfig',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__						:	Optional[Any]	     = [
       'DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'DecisionTransformerGPT2Model',
       'DecisionTransformerGPT2PreTrainedModel',
       'DecisionTransformerModel',
       'DecisionTransformerPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_decision_transformer import (
       DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       DecisionTransformerConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_decision_transformer import (
          DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          DecisionTransformerGPTaModel,
          DecisionTransformerGPTaPreTrainedModel,
          DecisionTransformerModel,
          DecisionTransformerPreTrainedModel,
      )


else:
   import sys

   UpperCAmelCase__						:	Optional[Any]	     = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



