



"""simple docstring"""







import inspect
import unittest

from transformers import YolosConfig
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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch
     from torch import nn

     from transformers import YolosForObjectDetection, YolosModel
     from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor







class       UpperCamelCase__   :




      """simple docstring"""




      def __init__(      self				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]=1_3    ,			SCREAMING_SNAKE_CASE_				:      Any=[3_0, 3_0]    ,			SCREAMING_SNAKE_CASE_				:      List[str]=2    ,			SCREAMING_SNAKE_CASE_				:      List[Any]=3    ,			SCREAMING_SNAKE_CASE_				:      str=True    ,			SCREAMING_SNAKE_CASE_				:      int=True    ,			SCREAMING_SNAKE_CASE_				:      Tuple=3_2    ,			SCREAMING_SNAKE_CASE_				:      int=5    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]=4    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=3_7    ,			SCREAMING_SNAKE_CASE_				:      Tuple="gelu"    ,			SCREAMING_SNAKE_CASE_				:      Dict=0.1    ,			SCREAMING_SNAKE_CASE_				:      int=0.1    ,			SCREAMING_SNAKE_CASE_				:      int=1_0    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=0.02    ,			SCREAMING_SNAKE_CASE_				:      Dict=3    ,			SCREAMING_SNAKE_CASE_				:      List[Any]=None    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=8    ,			SCREAMING_SNAKE_CASE_				:      Any=1_0    ,			):
        lowerCAmelCase_   :				int								= parent
        lowerCAmelCase_   :				Optional[int]								= batch_size
        lowerCAmelCase_   :				Optional[int]								= image_size
        lowerCAmelCase_   :				Dict								= patch_size
        lowerCAmelCase_   :				Optional[Any]								= num_channels
        lowerCAmelCase_   :				List[Any]								= is_training
        lowerCAmelCase_   :				Tuple								= use_labels
        lowerCAmelCase_   :				Dict								= hidden_size
        lowerCAmelCase_   :				Optional[int]								= num_hidden_layers
        lowerCAmelCase_   :				Any								= num_attention_heads
        lowerCAmelCase_   :				Optional[Any]								= intermediate_size
        lowerCAmelCase_   :				Any								= hidden_act
        lowerCAmelCase_   :				Any								= hidden_dropout_prob
        lowerCAmelCase_   :				str								= attention_probs_dropout_prob
        lowerCAmelCase_   :				List[str]								= type_sequence_label_size
        lowerCAmelCase_   :				Optional[int]								= initializer_range
        lowerCAmelCase_   :				str								= num_labels
        lowerCAmelCase_   :				str								= scope
        lowerCAmelCase_   :				Dict								= n_targets
        lowerCAmelCase_   :				List[Any]								= num_detection_tokens
        # we set the expected sequence length (which is used in several tests)
        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
        lowerCAmelCase_   :				Any								= (image_size[1] // patch_size) * (image_size[0] // patch_size)
        lowerCAmelCase_   :				Optional[int]								= num_patches + 1 + self.num_detection_tokens



      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):
        lowerCAmelCase_   :				Optional[int]								= floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]       )

        lowerCAmelCase_   :				Any								= None
        if self.use_labels:
          # labels is a list of Dict (each Dict being the labels for a given example in the batch)
          lowerCAmelCase_   :				Union[str, Any]								= []
          for i in range(self.batch_size       ):
            lowerCAmelCase_   :				Any								= {}
            lowerCAmelCase_   :				Dict								= torch.randint(
                high=self.num_labels    ,			size=(self.n_targets,)    ,			device=lowerCamelCase__       )
            lowerCAmelCase_   :				Any								= torch.rand(self.n_targets    ,			4    ,			device=lowerCamelCase__       )
            labels.append(lowerCamelCase__       )

        lowerCAmelCase_   :				Optional[int]								= self.get_config()

        return config, pixel_values, labels



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        return YolosConfig(
            image_size=self.image_size    ,			patch_size=self.patch_size    ,			num_channels=self.num_channels    ,			hidden_size=self.hidden_size    ,			num_hidden_layers=self.num_hidden_layers    ,			num_attention_heads=self.num_attention_heads    ,			intermediate_size=self.intermediate_size    ,			hidden_act=self.hidden_act    ,			hidden_dropout_prob=self.hidden_dropout_prob    ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,			is_decoder=lowerCamelCase__    ,			initializer_range=self.initializer_range    ,			num_detection_tokens=self.num_detection_tokens    ,			num_labels=self.num_labels    ,			)



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      int    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]       ):
        lowerCAmelCase_   :				str								= YolosModel(config=lowerCamelCase__       )
        model.to(lowerCamelCase__       )
        model.eval()
        lowerCAmelCase_   :				List[str]								= model(lowerCamelCase__       )
        self.parent.assertEqual(
            result.last_hidden_state.shape    ,			(self.batch_size, self.expected_seq_len, self.hidden_size)       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      int    ,			SCREAMING_SNAKE_CASE_				:      Dict    ,			SCREAMING_SNAKE_CASE_				:      str    ,			SCREAMING_SNAKE_CASE_				:      List[Any]       ):
        lowerCAmelCase_   :				Dict								= YolosForObjectDetection(lowerCamelCase__       )
        model.to(lowerCamelCase__       )
        model.eval()

        lowerCAmelCase_   :				Optional[Any]								= model(pixel_values=lowerCamelCase__       )
        lowerCAmelCase_   :				int								= model(lowerCamelCase__       )

        self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.num_detection_tokens, self.num_labels + 1)       )
        self.parent.assertEqual(result.pred_boxes.shape    ,			(self.batch_size, self.num_detection_tokens, 4)       )

        lowerCAmelCase_   :				Any								= model(pixel_values=lowerCamelCase__    ,			labels=lowerCamelCase__       )

        self.parent.assertEqual(result.loss.shape    ,			()       )
        self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.num_detection_tokens, self.num_labels + 1)       )
        self.parent.assertEqual(result.pred_boxes.shape    ,			(self.batch_size, self.num_detection_tokens, 4)       )





      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]       ):
        lowerCAmelCase_   :				int								= self.prepare_config_and_inputs()
        lowerCAmelCase_   :				Optional[Any]								= config_and_inputs
        lowerCAmelCase_   :				Dict								= {"pixel_values": pixel_values}
        return config, inputs_dict







@require_torch
class       UpperCamelCase__   (	UpperCAmelCase__,					UpperCAmelCase__,					unittest.TestCase  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
      _SCREAMING_SNAKE_CASE  				=     (
          {"feature-extraction": YolosModel, "object-detection": YolosForObjectDetection} if is_torch_available() else {}
      )

      _SCREAMING_SNAKE_CASE  				=     False
      _SCREAMING_SNAKE_CASE  				=     False
      _SCREAMING_SNAKE_CASE  				=     False
      _SCREAMING_SNAKE_CASE  				=     False



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]    ,			SCREAMING_SNAKE_CASE_				:      int    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]=False       ):
        lowerCAmelCase_   :				Union[str, Any]								= super()._prepare_for_class(lowerCamelCase__    ,			lowerCamelCase__    ,			return_labels=lowerCamelCase__       )

        if return_labels:
          if model_class.__name__ == "YolosForObjectDetection":
            lowerCAmelCase_   :				Tuple								= []
            for i in range(self.model_tester.batch_size       ):
              lowerCAmelCase_   :				Any								= {}
              lowerCAmelCase_   :				Optional[Any]								= torch.ones(
                  size=(self.model_tester.n_targets,)    ,			device=lowerCamelCase__    ,			dtype=torch.long       )
              lowerCAmelCase_   :				Optional[int]								= torch.ones(
                  self.model_tester.n_targets    ,			4    ,			device=lowerCamelCase__    ,			dtype=torch.float       )
              labels.append(lowerCamelCase__       )
            lowerCAmelCase_   :				Dict								= labels

        return inputs_dict



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]       ):
        lowerCAmelCase_   :				int								= YolosModelTester(self       )
        lowerCAmelCase_   :				Any								= ConfigTester(self    ,			config_class=lowerCamelCase__    ,			has_text_modality=lowerCamelCase__    ,			hidden_size=3_7       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      int       ):
        self.config_tester.run_common_tests()



      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]       ):
        pass



      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):
        lowerCAmelCase_   :				Dict								= self.model_tester.prepare_config_and_inputs_for_common()

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



      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]       ):
        lowerCAmelCase_   :				Dict								= self.model_tester.prepare_config_and_inputs_for_common()

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

          lowerCAmelCase_   :				List[str]								= ["pixel_values"]
          self.assertListEqual(arg_names[:1]    ,			lowerCamelCase__       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]       ):
        lowerCAmelCase_   :				List[str]								= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCamelCase__       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]       ):
        lowerCAmelCase_   :				int								= self.model_tester.prepare_config_and_inputs_for_common()
        lowerCAmelCase_   :				Dict								= True

        # in YOLOS, the seq_len is different
        lowerCAmelCase_   :				int								= self.model_tester.expected_seq_len
        for model_class in self.all_model_classes:
          lowerCAmelCase_   :				str								= True
          lowerCAmelCase_   :				Union[str, Any]								= False
          lowerCAmelCase_   :				List[Any]								= True
          lowerCAmelCase_   :				List[str]								= model_class(lowerCamelCase__       )
          model.to(lowerCamelCase__       )
          model.eval()
          with torch.no_grad():
            lowerCAmelCase_   :				Optional[int]								= model(**self._prepare_for_class(lowerCamelCase__    ,			lowerCamelCase__       )       )
          lowerCAmelCase_   :				List[Any]								= outputs.attentions
          self.assertEqual(len(lowerCamelCase__       )    ,			self.model_tester.num_hidden_layers       )

          # check that output_attentions also work using config
          del inputs_dict["output_attentions"]
          lowerCAmelCase_   :				Dict								= True
          lowerCAmelCase_   :				Any								= model_class(lowerCamelCase__       )
          model.to(lowerCamelCase__       )
          model.eval()
          with torch.no_grad():
            lowerCAmelCase_   :				Union[str, Any]								= model(**self._prepare_for_class(lowerCamelCase__    ,			lowerCamelCase__       )       )
          lowerCAmelCase_   :				Union[str, Any]								= outputs.attentions
          self.assertEqual(len(lowerCamelCase__       )    ,			self.model_tester.num_hidden_layers       )

          self.assertListEqual(
              list(attentions[0].shape[-3:]       )    ,			[self.model_tester.num_attention_heads, seq_len, seq_len]    ,			)
          lowerCAmelCase_   :				Optional[int]								= len(lowerCamelCase__       )

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

          lowerCAmelCase_   :				str								= 1
          self.assertEqual(out_len + added_hidden_states    ,			len(lowerCamelCase__       )       )

          lowerCAmelCase_   :				Union[str, Any]								= outputs.attentions

          self.assertEqual(len(lowerCamelCase__       )    ,			self.model_tester.num_hidden_layers       )
          self.assertListEqual(
              list(self_attentions[0].shape[-3:]       )    ,			[self.model_tester.num_attention_heads, seq_len, seq_len]    ,			)



      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):







        def check_hidden_states_output(SCREAMING_SNAKE_CASE_				:      int    ,			SCREAMING_SNAKE_CASE_				:      Dict    ,			SCREAMING_SNAKE_CASE_				:      int       ):
          lowerCAmelCase_   :				str								= model_class(lowerCamelCase__       )
          model.to(lowerCamelCase__       )
          model.eval()

          with torch.no_grad():
            lowerCAmelCase_   :				List[Any]								= model(**self._prepare_for_class(lowerCamelCase__    ,			lowerCamelCase__       )       )

          lowerCAmelCase_   :				Union[str, Any]								= outputs.hidden_states

          lowerCAmelCase_   :				int								= getattr(
              self.model_tester    ,			'expected_num_hidden_layers'    ,			self.model_tester.num_hidden_layers + 1       )
          self.assertEqual(len(lowerCamelCase__       )    ,			lowerCamelCase__       )

          # YOLOS has a different seq_length
          lowerCAmelCase_   :				Union[str, Any]								= self.model_tester.expected_seq_len

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

        lowerCAmelCase_   :				str								= self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          lowerCAmelCase_   :				Optional[Any]								= True
          check_hidden_states_output(lowerCamelCase__    ,			lowerCamelCase__    ,			lowerCamelCase__       )

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          lowerCAmelCase_   :				Optional[int]								= True

          check_hidden_states_output(lowerCamelCase__    ,			lowerCamelCase__    ,			lowerCamelCase__       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        lowerCAmelCase_   :				List[Any]								= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_object_detection(*lowerCamelCase__       )





      @slow
      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]       ):







        for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowerCAmelCase_   :				List[str]								= YolosModel.from_pretrained(lowerCamelCase__       )
          self.assertIsNotNone(lowerCamelCase__       )




def 				UpperCamelCase_    (    )						->   Dict:

  """simple docstring"""




  lowerCAmelCase_   :				int								= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
  return image







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




      """simple docstring"""




      @cached_property
      def      SCREAMING_SNAKE_CASE__ (      self				:      Tuple       ):
        return AutoImageProcessor.from_pretrained('hustvl/yolos-small'       ) if is_vision_available() else None





      @slow
      def      SCREAMING_SNAKE_CASE__ (      self				:      Tuple       ):
        lowerCAmelCase_   :				Any								= YolosForObjectDetection.from_pretrained('hustvl/yolos-small'       ).to(lowerCamelCase__       )

        lowerCAmelCase_   :				Dict								= self.default_image_processor
        lowerCAmelCase_   :				Union[str, Any]								= prepare_img()
        lowerCAmelCase_   :				str								= image_processor(images=lowerCamelCase__    ,			return_tensors='pt'       ).to(lowerCamelCase__       )

        # forward pass
        with torch.no_grad():
          lowerCAmelCase_   :				Dict								= model(inputs.pixel_values       )

        # verify outputs
        lowerCAmelCase_   :				List[str]								= torch.Size((1, 1_0_0, 9_2)       )
        self.assertEqual(outputs.logits.shape    ,			lowerCamelCase__       )

        lowerCAmelCase_   :				List[Any]								= torch.tensor(
            [[-2_4.0_2_4_8, -1_0.3_0_2_4, -1_4.8_2_9_0], [-4_2.0_3_9_2, -1_6.8_2_0_0, -2_7.4_3_3_4], [-2_7.2_7_4_3, -1_1.8_1_5_4, -1_8.7_1_4_8]]    ,			device=lowerCamelCase__    ,			)
        lowerCAmelCase_   :				str								= torch.tensor(
            [[0.25_59, 0.54_55, 0.47_06], [0.29_89, 0.72_79, 0.18_75], [0.77_32, 0.40_17, 0.44_62]]    ,			device=lowerCamelCase__       )
        self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]    ,			lowerCamelCase__    ,			atol=1E-4       )       )
        self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]    ,			lowerCamelCase__    ,			atol=1E-4       )       )

        # verify postprocessing
        lowerCAmelCase_   :				Any								= image_processor.post_process_object_detection(
            lowerCamelCase__    ,			threshold=0.3    ,			target_sizes=[image.size[::-1]]       )[0]
        lowerCAmelCase_   :				Tuple								= torch.tensor([0.99_94, 0.97_90, 0.99_64, 0.99_72, 0.98_61]       ).to(lowerCamelCase__       )
        lowerCAmelCase_   :				Any								= [7_5, 7_5, 1_7, 6_3, 1_7]
        lowerCAmelCase_   :				Any								= torch.tensor([3_3_5.0_6_0_9, 7_9.3_8_4_8, 3_7_5.4_2_1_6, 1_8_7.2_4_9_5]       ).to(lowerCamelCase__       )

        self.assertEqual(len(results['scores']       )    ,			5       )
        self.assertTrue(torch.allclose(results['scores']    ,			lowerCamelCase__    ,			atol=1E-4       )       )
        self.assertSequenceEqual(results['labels'].tolist()    ,			lowerCamelCase__       )
        self.assertTrue(torch.allclose(results['boxes'][0, :]    ,			lowerCamelCase__       )       )









'''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        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase=False      ):
		_UpperCAmelCase    ,			_UpperCAmelCase						:				Optional[Any]	    =					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        __lowerCAmelCase       (__lowerCAmelCase      ):
		_UpperCAmelCase						:				Dict	    =					{}

		_UpperCAmelCase						:				str	    =					R".*sequential.(\d+).*"
		_UpperCAmelCase						:				Any	    =					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						:				Union[str, Any]	    =					key.replace(__lowerCAmelCase					,       __lowerCAmelCase      )

				if re.match(__lowerCAmelCase					,       __lowerCAmelCase      ):
						# replace sequential layers with list
						_UpperCAmelCase						:				List[Any]	    =					re.match(__lowerCAmelCase					,       __lowerCAmelCase      ).group(1      )

						_UpperCAmelCase						:				Optional[int]	    =					key.replace(F"""sequential.{sequential_layer}."""					,       F"""layers.{int(__lowerCAmelCase      )//3}.linear."""      )
				elif re.match(__lowerCAmelCase					,       __lowerCAmelCase      ):
						_UpperCAmelCase						:				List[str]	    =					int(re.match(__lowerCAmelCase					,       __lowerCAmelCase      ).group(1      )      )

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

						_UpperCAmelCase						:				Tuple	    =					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						:				List[str]	    =					value
						_UpperCAmelCase						:				Tuple	    =					mixed_qkv.size(0      ) // 3

						_UpperCAmelCase						:				Union[str, Any]	    =					mixed_qkv[:qkv_dim]
						_UpperCAmelCase						:				int	    =					mixed_qkv[qkv_dim : qkv_dim * 2]
						_UpperCAmelCase						:				Optional[int]	    =					mixed_qkv[qkv_dim * 2 :]

						_UpperCAmelCase						:				List[Any]	    =					query_layer
						_UpperCAmelCase						:				int	    =					key_layer
						_UpperCAmelCase						:				Any	    =					value_layer
				else:
						_UpperCAmelCase						:				Dict	    =					value

		return model_state_dict






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase=False      ):
		_UpperCAmelCase    ,			_UpperCAmelCase						:				List[str]	    =					init_clap(__lowerCAmelCase					,       enable_fusion=__lowerCAmelCase      )

		clap_model.eval()
		_UpperCAmelCase						:				List[str]	    =					clap_model.state_dict()
		_UpperCAmelCase						:				str	    =					rename_state_dict(__lowerCAmelCase      )

		_UpperCAmelCase						:				List[Any]	    =					ClapConfig()
		_UpperCAmelCase						:				str	    =					enable_fusion
		_UpperCAmelCase						:				Union[str, Any]	    =					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)



