

'''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   _SCREAMING_SNAKE_CASE   :



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



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

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

      __magic_name__						       =    self.get_config()

      return config, pixel_values, labels



 def 			snake_case__  (   self		:   List[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=a__			,				layer_scale_init_value=1E-5			,				)



 def 			snake_case__  (   self		:   Tuple			,				a__		:   Optional[Any]			,				a__		:   List[Any]			,				a__		:   int      ):
      __magic_name__						       =    SwiftFormerModel(config=a__      )
      model.to(a__      )
      model.eval()
      __magic_name__						       =    model(a__      )
      self.parent.assertEqual(result.last_hidden_state.shape			,				(self.batch_size, self.embed_dims[-1], 7, 7)      )



 def 			snake_case__  (   self		:   Any			,				a__		:   Tuple			,				a__		:   str			,				a__		:   Dict      ):
      __magic_name__						       =    self.num_labels
      __magic_name__						       =    SwiftFormerForImageClassification(a__      )
      model.to(a__      )
      model.eval()
      __magic_name__						       =    model(a__			,				labels=a__      )
      self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.num_labels)      )

      __magic_name__						       =    SwiftFormerForImageClassification(a__      )
      model.to(a__      )
      model.eval()

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



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      ((__magic_name__)       ,  (__magic_name__)       ,  (__magic_name__))						       =    self.prepare_config_and_inputs()
      __magic_name__						       =    {'''pixel_values''': pixel_values}
      return config, inputs_dict






@require_torch
class   _SCREAMING_SNAKE_CASE   (   __a		,__a		,unittest.TestCase     ):
 __SCREAMING_SNAKE_CASE					:Union[str, Any]      	=					(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
 __SCREAMING_SNAKE_CASE					:Union[str, Any]      	=					(
     {"""feature-extraction""": SwiftFormerModel, """image-classification""": SwiftFormerForImageClassification}
     if is_torch_available()
     else {}
 )

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



 def 			snake_case__  (   self		:   Optional[Any]      ):
      __magic_name__						       =    SwiftFormerModelTester(self      )
      __magic_name__						       =    ConfigTester(
          self			,				config_class=a__			,				has_text_modality=a__			,				hidden_size=37			,				num_attention_heads=12			,				num_hidden_layers=12			,				)



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



 @unittest.skip(reason='''SwiftFormer does not use inputs_embeds'''      )
 def 			snake_case__  (   self		:   Tuple      ):
      pass



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

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



 def 			snake_case__  (   self		:   str      ):
      __magic_name__       ,  __magic_name__						       =    self.model_tester.prepare_config_and_inputs_for_common()

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

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



 def 			snake_case__  (   self		:   Optional[Any]      ):
      __magic_name__						       =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*a__      )



 def 			snake_case__  (   self		:   Any      ):
      __magic_name__						       =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*a__      )



 @slow
 def 			snake_case__  (   self		:   List[str]      ):
      for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __magic_name__						       =    SwiftFormerModel.from_pretrained(a__      )
           self.assertIsNotNone(a__      )



 @unittest.skip(reason='''SwiftFormer does not output attentions'''      )
 def 			snake_case__  (   self		:   Optional[int]      ):
      pass



 def 			snake_case__  (   self		:   Dict      ):
      def check_hidden_states_output(a__		:   List[Any]			,				a__		:   int			,				a__		:   List[Any]      ):
           __magic_name__						       =    model_class(a__      )
           model.to(a__      )
           model.eval()

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

           __magic_name__						       =    outputs.hidden_states

           __magic_name__						       =    8
           self.assertEqual(len(a__      )			,				a__      )  # 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(a__      )      ):
                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),
                        ]      )			,				)

      __magic_name__       ,  __magic_name__						       =    self.model_tester.prepare_config_and_inputs_for_common()

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

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

           check_hidden_states_output(a__			,				a__			,				a__      )



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      def _config_zero_init(a__		:   Tuple      ):
           __magic_name__						       =    copy.deepcopy(a__      )
           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(a__			,				a__			,				1E-10      )
                if isinstance(getattr(a__			,				a__			,				a__      )			,				a__      ):
                     __magic_name__						       =    _config_zero_init(getattr(a__			,				a__      )      )
                     setattr(a__			,				a__			,				a__      )
           return configs_no_init

      __magic_name__       ,  __magic_name__						       =    self.model_tester.prepare_config_and_inputs_for_common()

      __magic_name__						       =    _config_zero_init(a__      )
      for model_class in self.all_model_classes:
           __magic_name__						       =    model_class(config=a__      )
           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 			snake_case__  (   self		:   int      ):
      pass




def 						UpperCamelCase	(			)				->     str:






     '''simple docstring'''
     __magic_name__						       =    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
     return image






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



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



 @slow
 def 			snake_case__  (   self		:   Dict      ):
      __magic_name__						       =    SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs'''      ).to(a__      )

      __magic_name__						       =    self.default_image_processor
      __magic_name__						       =    prepare_img()
      __magic_name__						       =    image_processor(images=a__			,				return_tensors='''pt'''      ).to(a__      )

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

      # verify the logits
      __magic_name__						       =    torch.Size((1, 1000)      )
      self.assertEqual(outputs.logits.shape			,				a__      )

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









'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase         =						{
    "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         =						[
          "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
          "ClapModel",
          "ClapPreTrainedModel",
          "ClapTextModel",
          "ClapTextModelWithProjection",
          "ClapAudioModel",
          "ClapAudioModelWithProjection",
      ]
      _lowerCAmelCase         =						["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         =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






