



"""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    lowercase__     :







       '''simple docstring'''


       def __init__(							self    :						List[Any]	,       _UpperCAmelCase    :						int	,       _UpperCAmelCase    :						List[str]=13	,       _UpperCAmelCase    :						List[Any]=3	,       _UpperCAmelCase    :						str=True	,       _UpperCAmelCase    :						List[Any]=True	,       _UpperCAmelCase    :						Dict=0.1	,       _UpperCAmelCase    :						int=0.1	,       _UpperCAmelCase    :						Dict=224	,       _UpperCAmelCase    :						List[Any]=1000	,       _UpperCAmelCase    :						Union[str, Any]=[3, 3, 6, 4]	,       _UpperCAmelCase    :						Optional[Any]=[48, 56, 112, 220]	,       )				->		List[str]:


             '''simple docstring'''




             UpperCAmelCase_    				=							parent
             UpperCAmelCase_    				=							batch_size
             UpperCAmelCase_    				=							num_channels
             UpperCAmelCase_    				=							is_training
             UpperCAmelCase_    				=							use_labels
             UpperCAmelCase_    				=							hidden_dropout_prob
             UpperCAmelCase_    				=							attention_probs_dropout_prob
             UpperCAmelCase_    				=							num_labels
             UpperCAmelCase_    				=							image_size
             UpperCAmelCase_    				=							layer_depths
             UpperCAmelCase_    				=							embed_dims



       def   lowercase__       (							self    :						int					)				->		Any:


             '''simple docstring'''




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

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

             UpperCAmelCase_    				=							self.get_config()

             return config, pixel_values, labels



       def   lowercase__       (							self    :						Tuple					)				->		int:


             '''simple docstring'''




             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=_UpperCAmelCase	,       layer_scale_init_value=1e-5	,       )



       def   lowercase__       (							self    :						Tuple	,       _UpperCAmelCase    :						Optional[Any]	,       _UpperCAmelCase    :						List[str]	,       _UpperCAmelCase    :						int					)				->		Union[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_    				=							SwiftFormerModel(config=_UpperCAmelCase					)
             model.to(_UpperCAmelCase					)
             model.eval()
             UpperCAmelCase_    				=							model(_UpperCAmelCase					)
             self.parent.assertEqual(result.last_hidden_state.shape	,       (self.batch_size, self.embed_dims[-1], 7, 7)					)



       def   lowercase__       (							self    :						Optional[int]	,       _UpperCAmelCase    :						int	,       _UpperCAmelCase    :						Any	,       _UpperCAmelCase    :						int					)				->		Union[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_    				=							self.num_labels
             UpperCAmelCase_    				=							SwiftFormerForImageClassification(_UpperCAmelCase					)
             model.to(_UpperCAmelCase					)
             model.eval()
             UpperCAmelCase_    				=							model(_UpperCAmelCase	,       labels=_UpperCAmelCase					)
             self.parent.assertEqual(result.logits.shape	,       (self.batch_size, self.num_labels)					)

             UpperCAmelCase_    				=							SwiftFormerForImageClassification(_UpperCAmelCase					)
             model.to(_UpperCAmelCase					)
             model.eval()

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







       def   lowercase__       (							self    :						Any					)				->		Optional[int]:


             '''simple docstring'''




             ((UpperCAmelCase_)				,					(UpperCAmelCase_)				,					(UpperCAmelCase_))    				=							self.prepare_config_and_inputs()
             UpperCAmelCase_    				=							{"pixel_values": pixel_values}
             return config, inputs_dict



@require_torch
class    lowercase__     (					SCREAMING_SNAKE_CASE       ,      SCREAMING_SNAKE_CASE       ,      unittest.TestCase   ):







       '''simple docstring'''


       UpperCamelCase		=  (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
       UpperCamelCase		=  (
           {'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
           if is_torch_available()
           else {}
       )

       UpperCamelCase		=  False
       UpperCamelCase		=  False
       UpperCamelCase		=  False
       UpperCamelCase		=  False
       UpperCamelCase		=  False



       def   lowercase__       (							self    :						Optional[int]					)				->		int:


             '''simple docstring'''




             UpperCAmelCase_    				=							SwiftFormerModelTester(self					)
             UpperCAmelCase_    				=							ConfigTester(
                 self	,       config_class=_UpperCAmelCase	,       has_text_modality=_UpperCAmelCase	,       hidden_size=37	,       num_attention_heads=12	,       num_hidden_layers=12	,       )



       def   lowercase__       (							self    :						Tuple					)				->		int:


             '''simple docstring'''




             self.config_tester.run_common_tests()



       @unittest.skip(reason="SwiftFormer does not use inputs_embeds"					)
       def   lowercase__       (							self    :						Union[str, Any]					)				->		Any:


             '''simple docstring'''




             pass



       def   lowercase__       (							self    :						str					)				->		Union[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_				,					UpperCAmelCase_    				=							self.model_tester.prepare_config_and_inputs_for_common()

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



       def   lowercase__       (							self    :						Optional[int]					)				->		Tuple:


             '''simple docstring'''




             UpperCAmelCase_				,					UpperCAmelCase_    				=							self.model_tester.prepare_config_and_inputs_for_common()

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

                   UpperCAmelCase_    				=							["pixel_values"]
                   self.assertListEqual(arg_names[:1]	,       _UpperCAmelCase					)



       def   lowercase__       (							self    :						Dict					)				->		int:


             '''simple docstring'''




             UpperCAmelCase_    				=							self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*_UpperCAmelCase					)



       def   lowercase__       (							self    :						Tuple					)				->		Union[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_    				=							self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase					)



       @slow
       def   lowercase__       (							self    :						List[Any]					)				->		Tuple:


             '''simple docstring'''




             for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   UpperCAmelCase_    				=							SwiftFormerModel.from_pretrained(_UpperCAmelCase					)
                   self.assertIsNotNone(_UpperCAmelCase					)



       @unittest.skip(reason="SwiftFormer does not output attentions"					)
       def   lowercase__       (							self    :						str					)				->		str:


             '''simple docstring'''




             pass



       def   lowercase__       (							self    :						List[Any]					)				->		Tuple:


             '''simple docstring'''




             def check_hidden_states_output(_UpperCAmelCase    :						Dict	,       _UpperCAmelCase    :						List[str]	,       _UpperCAmelCase    :						Tuple					):
                   UpperCAmelCase_    				=							model_class(_UpperCAmelCase					)
                   model.to(_UpperCAmelCase					)
                   model.eval()

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

                   UpperCAmelCase_    				=							outputs.hidden_states

                   UpperCAmelCase_    				=							8
                   self.assertEqual(len(_UpperCAmelCase					)	,       _UpperCAmelCase					)  # 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(_UpperCAmelCase					)					):
                         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),
                                 ]					)	,       )

             UpperCAmelCase_				,					UpperCAmelCase_    				=							self.model_tester.prepare_config_and_inputs_for_common()

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

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

                   check_hidden_states_output(_UpperCAmelCase	,       _UpperCAmelCase	,       _UpperCAmelCase					)



       def   lowercase__       (							self    :						Optional[Any]					)				->		Optional[Any]:


             '''simple docstring'''




             def _config_zero_init(_UpperCAmelCase    :						List[Any]					):
                   UpperCAmelCase_    				=							copy.deepcopy(_UpperCAmelCase					)
                   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(_UpperCAmelCase	,       _UpperCAmelCase	,       1e-10					)
                         if isinstance(getattr(_UpperCAmelCase	,       _UpperCAmelCase	,       _UpperCAmelCase					)	,       _UpperCAmelCase					):
                               UpperCAmelCase_    				=							_config_zero_init(getattr(_UpperCAmelCase	,       _UpperCAmelCase					)					)
                               setattr(_UpperCAmelCase	,       _UpperCAmelCase	,       _UpperCAmelCase					)
                   return configs_no_init

             UpperCAmelCase_				,					UpperCAmelCase_    				=							self.model_tester.prepare_config_and_inputs_for_common()

             UpperCAmelCase_    				=							_config_zero_init(_UpperCAmelCase					)
             for model_class in self.all_model_classes:
                   UpperCAmelCase_    				=							model_class(config=_UpperCAmelCase					)
                   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   lowercase__       (							self    :						int					)				->		Tuple:


             '''simple docstring'''




             pass



def      a__			(   ):
      UpperCAmelCase_    				=							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
      return image


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







       '''simple docstring'''


       @cached_property
       def   lowercase__       (							self    :						Optional[Any]					)				->		Optional[int]:


             '''simple docstring'''




             return ViTImageProcessor.from_pretrained("MBZUAI/swiftformer-xs"					) if is_vision_available() else None







       @slow
       def   lowercase__       (							self    :						List[Any]					)				->		str:


             '''simple docstring'''




             UpperCAmelCase_    				=							SwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs"					).to(_UpperCAmelCase					)

             UpperCAmelCase_    				=							self.default_image_processor
             UpperCAmelCase_    				=							prepare_img()
             UpperCAmelCase_    				=							image_processor(images=_UpperCAmelCase	,       return_tensors="pt"					).to(_UpperCAmelCase					)

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

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

             UpperCAmelCase_    				=							torch.tensor([[-2.1703e00, 2.1107e00, -2.0811e00]]					).to(_UpperCAmelCase					)
             self.assertTrue(torch.allclose(outputs.logits[0, :3]	,       _UpperCAmelCase	,       atol=1e-4					)					)









"""simple docstring"""





import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowerCamelCase      =		{
    """text_branch""": """text_model""",
    """audio_branch""": """audio_model.audio_encoder""",
    """attn""": """attention.self""",
    """self.proj""": """output.dense""",
    """attention.self_mask""": """attn_mask""",
    """mlp.fc1""": """intermediate.dense""",
    """mlp.fc2""": """output.dense""",
    """norm1""": """layernorm_before""",
    """norm2""": """layernorm_after""",
    """bn0""": """batch_norm""",
}

lowerCamelCase      =		AutoFeatureExtractor.from_pretrained("""laion/clap-htsat-unfused""", truncation="""rand_trunc""")
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__=False   ):
      UpperCAmelCase_				,					UpperCAmelCase_    				=							create_model(
          "HTSAT-tiny"			,    "roberta"			,    lowerCAmelCase__			,    precision="fp32"			,    device="cuda:0" if torch.cuda.is_available() else "cpu"			,    enable_fusion=lowerCAmelCase__			,    fusion_type="aff_2d" if enable_fusion else None			,    )
      return model, model_cfg
def      a__			(   lowerCAmelCase__   ):
      UpperCAmelCase_    				=							{}

      UpperCAmelCase_    				=							r".*sequential.(\d+).*"
      UpperCAmelCase_    				=							r".*_projection.(\d+).*"

      for key, value in state_dict.items():
            # check if any key needs to be modified
            for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
                  if key_to_modify in key:
                        UpperCAmelCase_    				=							key.replace(lowerCAmelCase__			,    lowerCAmelCase__   )

            if re.match(lowerCAmelCase__			,    lowerCAmelCase__   ):
                  # replace sequential layers with list
                  UpperCAmelCase_    				=							re.match(lowerCAmelCase__			,    lowerCAmelCase__   ).group(1   )

                  UpperCAmelCase_    				=							key.replace(f"""sequential.{sequential_layer}."""			,    f"""layers.{int(lowerCAmelCase__   )//3}.linear."""   )
            elif re.match(lowerCAmelCase__			,    lowerCAmelCase__   ):
                  UpperCAmelCase_    				=							int(re.match(lowerCAmelCase__			,    lowerCAmelCase__   ).group(1   )   )

                  # Because in CLAP they use `nn.Sequential`...
                  UpperCAmelCase_    				=							1 if projecton_layer == 0 else 2

                  UpperCAmelCase_    				=							key.replace(f"""_projection.{projecton_layer}."""			,    f"""_projection.linear{transformers_projection_layer}."""   )

            if "audio" and "qkv" in key:
                  # split qkv into query key and value
                  UpperCAmelCase_    				=							value
                  UpperCAmelCase_    				=							mixed_qkv.size(0   ) // 3

                  UpperCAmelCase_    				=							mixed_qkv[:qkv_dim]
                  UpperCAmelCase_    				=							mixed_qkv[qkv_dim : qkv_dim * 2]
                  UpperCAmelCase_    				=							mixed_qkv[qkv_dim * 2 :]

                  UpperCAmelCase_    				=							query_layer
                  UpperCAmelCase_    				=							key_layer
                  UpperCAmelCase_    				=							value_layer
            else:
                  UpperCAmelCase_    				=							value

      return model_state_dict
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__=False   ):
      UpperCAmelCase_				,					UpperCAmelCase_    				=							init_clap(lowerCAmelCase__			,    enable_fusion=lowerCAmelCase__   )

      clap_model.eval()
      UpperCAmelCase_    				=							clap_model.state_dict()
      UpperCAmelCase_    				=							rename_state_dict(lowerCAmelCase__   )

      UpperCAmelCase_    				=							ClapConfig()
      UpperCAmelCase_    				=							enable_fusion
      UpperCAmelCase_    				=							ClapModel(lowerCAmelCase__   )

      # ignore the spectrogram embedding layer
      model.load_state_dict(lowerCAmelCase__			,    strict=lowerCAmelCase__   )

      model.save_pretrained(lowerCAmelCase__   )
      transformers_config.save_pretrained(lowerCAmelCase__   )


if __name__ == "__main__":
  lowerCamelCase      =		argparse.ArgumentParser()
  parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
  parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
  parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
  parser.add_argument("""--enable_fusion""", action="""store_true""", help="""Whether to enable fusion or not""")
  lowerCamelCase      =		parser.parse_args()

  convert_clap_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.enable_fusion)




