


"""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 __snake_case		:







   """simple docstring"""




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




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

          _a							 =					None
          if self.use_labels:
                 _a							 =					ids_tensor([self.batch_size]					,	self.num_labels							)

          _a							 =					self.get_config()

          return config, pixel_values, labels




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




   def SCREAMING_SNAKE_CASE_   (		self				:str					,	UpperCamelCase__				:Dict					,	UpperCamelCase__				:Tuple					,	UpperCamelCase__				:Optional[int]							):
          _a							 =					SwiftFormerModel(config=lowerCamelCase__							)
          model.to(lowerCamelCase__							)
          model.eval()
          _a							 =					model(lowerCamelCase__							)
          self.parent.assertEqual(result.last_hidden_state.shape					,	(self.batch_size, self.embed_dims[-1], 7, 7)							)




   def SCREAMING_SNAKE_CASE_   (		self				:int					,	UpperCamelCase__				:int					,	UpperCamelCase__				:Dict					,	UpperCamelCase__				:Optional[Any]							):
          _a							 =					self.num_labels
          _a							 =					SwiftFormerForImageClassification(lowerCamelCase__							)
          model.to(lowerCamelCase__							)
          model.eval()
          _a							 =					model(lowerCamelCase__					,	labels=lowerCamelCase__							)
          self.parent.assertEqual(result.logits.shape					,	(self.batch_size, self.num_labels)							)

          _a							 =					SwiftFormerForImageClassification(lowerCamelCase__							)
          model.to(lowerCamelCase__							)
          model.eval()

          _a							 =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)
          _a							 =					model(lowerCamelCase__							)
          self.parent.assertEqual(result.logits.shape					,	(self.batch_size, self.num_labels)							)




   def SCREAMING_SNAKE_CASE_   (		self				:Dict							):
          (_a)							 =					self.prepare_config_and_inputs()
          _a							 =					{"pixel_values": pixel_values}
          return config, inputs_dict






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







   """simple docstring"""




   lowerCAmelCase_					: Any  						=      (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
   lowerCAmelCase_					: Optional[int]  						=      (
       {'feature-extraction': SwiftFormerModel, 'image-classification': SwiftFormerForImageClassification}
       if is_torch_available()
       else {}
   )

   lowerCAmelCase_					: List[str]  						=      False
   lowerCAmelCase_					: Any  						=      False
   lowerCAmelCase_					: Optional[int]  						=      False
   lowerCAmelCase_					: Union[str, Any]  						=      False
   lowerCAmelCase_					: Optional[Any]  						=      False




   def SCREAMING_SNAKE_CASE_   (		self				:List[Any]							):
          _a							 =					SwiftFormerModelTester(self							)
          _a							 =					ConfigTester(
              self					,	config_class=lowerCamelCase__					,	has_text_modality=lowerCamelCase__					,	hidden_size=37					,	num_attention_heads=12					,	num_hidden_layers=12					,	)




   def SCREAMING_SNAKE_CASE_   (		self				:List[Any]							):
          self.config_tester.run_common_tests()




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




   def SCREAMING_SNAKE_CASE_   (		self				:Optional[int]							):
          _a							 =					self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 _a							 =					model_class(lowerCamelCase__							)
                 _a							 =					model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(lowerCamelCase__					,	nn.Linear							)							)




   def SCREAMING_SNAKE_CASE_   (		self				:Tuple							):
          _a							 =					self.model_tester.prepare_config_and_inputs_for_common()

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

                 _a							 =					["pixel_values"]
                 self.assertListEqual(arg_names[:1]					,	lowerCamelCase__							)




   def SCREAMING_SNAKE_CASE_   (		self				:Tuple							):
          _a							 =					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowerCamelCase__							)




   def SCREAMING_SNAKE_CASE_   (		self				:Dict							):
          _a							 =					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__							)




   @slow
   def SCREAMING_SNAKE_CASE_   (		self				:str							):
          for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 _a							 =					SwiftFormerModel.from_pretrained(lowerCamelCase__							)
                 self.assertIsNotNone(lowerCamelCase__							)




   @unittest.skip(reason="SwiftFormer does not output attentions"							)
   def SCREAMING_SNAKE_CASE_   (		self				:List[str]							):
          pass




   def SCREAMING_SNAKE_CASE_   (		self				:int							):
          def check_hidden_states_output(UpperCamelCase__				:List[str]					,	UpperCamelCase__				:int					,	UpperCamelCase__				:Any							):
                 _a							 =					model_class(lowerCamelCase__							)
                 model.to(lowerCamelCase__							)
                 model.eval()

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

                 _a							 =					outputs.hidden_states

                 _a							 =					8
                 self.assertEqual(len(lowerCamelCase__							)					,	lowerCamelCase__							)  # 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(lowerCamelCase__							)							):
                        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),
                                ]							)					,	)

          _a							 =					self.model_tester.prepare_config_and_inputs_for_common()

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

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

                 check_hidden_states_output(lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__							)




   def SCREAMING_SNAKE_CASE_   (		self				:Any							):
          def _config_zero_init(UpperCamelCase__				:Dict							):
                 _a							 =					copy.deepcopy(lowerCamelCase__							)
                 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(lowerCamelCase__					,	lowerCamelCase__					,	1E-10							)
                        if isinstance(getattr(lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__							)					,	lowerCamelCase__							):
                               _a							 =					_config_zero_init(getattr(lowerCamelCase__					,	lowerCamelCase__							)							)
                               setattr(lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__							)
                 return configs_no_init

          _a							 =					self.model_tester.prepare_config_and_inputs_for_common()

          _a							 =					_config_zero_init(lowerCamelCase__							)
          for model_class in self.all_model_classes:
                 _a							 =					model_class(config=lowerCamelCase__							)
                 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				:List[str]							):
          pass






def     __a   (	):





       """simple docstring"""







       _a							 =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
       return image





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







   """simple docstring"""




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




   @slow
   def SCREAMING_SNAKE_CASE_   (		self				:List[Any]							):
          _a							 =					SwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs"							).to(lowerCamelCase__							)

          _a							 =					self.default_image_processor
          _a							 =					prepare_img()
          _a							 =					image_processor(images=lowerCamelCase__					,	return_tensors="pt"							).to(lowerCamelCase__							)

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

          # verify the logits
          _a							 =					torch.Size((1, 1_000)							)
          self.assertEqual(outputs.logits.shape					,	lowerCamelCase__							)

          _a							 =					torch.tensor([[-2.1703E00, 2.1107E00, -2.0811E00]]							).to(lowerCamelCase__							)
          self.assertTrue(torch.allclose(outputs.logits[0, :3]					,	lowerCamelCase__					,	atol=1E-4							)							)



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase  :      Union[str, Any]			 ={
    """configuration_clap""": [
        """CLAP_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ClapAudioConfig""",
        """ClapConfig""",
        """ClapTextConfig""",
    ],
    """processing_clap""": ["""ClapProcessor"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowerCAmelCase  :      Union[str, Any]			 =[
					    """CLAP_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """ClapModel""",
					    """ClapPreTrainedModel""",
					    """ClapTextModel""",
					    """ClapTextModelWithProjection""",
					    """ClapAudioModel""",
					    """ClapAudioModelWithProjection""",
					]
					_lowerCAmelCase  :      List[str]			 =["""ClapFeatureExtractor"""]

if TYPE_CHECKING:
					from .configuration_clap import (
					    CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
					    ClapAudioConfig,
					    ClapConfig,
					    ClapTextConfig,
					)
					from .processing_clap import ClapProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_clap import ClapFeatureExtractor
										from .modeling_clap import (
										    CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ClapAudioModel,
										    ClapAudioModelWithProjection,
										    ClapModel,
										    ClapPreTrainedModel,
										    ClapTextModel,
										    ClapTextModelWithProjection,
										)


else:
					import sys

					_lowerCAmelCase  :      Optional[int]			 =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)