
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   __UpperCamelCase						(				_lowerCAmelCase	,						_lowerCAmelCase=False ):
					"""simple docstring"""





					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   __UpperCamelCase						(				_lowerCAmelCase ):
					"""simple docstring"""





					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   __UpperCamelCase						(				_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase=False ):
					"""simple docstring"""





					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)









import os
import tempfile
import unittest
import uuid
from pathlib import Path

from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available


if is_torch_available():
    import torch

if is_soundfile_availble():
    import soundfile as sf

if is_vision_available():
    from PIL import Image






def 				_UpperCAmelCase			(    A=""     ):




   '''simple docstring'''

   UpperCAmelCase__						=tempfile.mkdtemp()
   return os.path.join(A				,							str(uuid.uuida()     ) + suffix     )

@require_soundfile
@require_torch
class     snake_case_		(   unittest.TestCase							):






 '''simple docstring'''





 def  __UpperCAmelCase						(  self		)	->       str:
    UpperCAmelCase__						=torch.rand(12,		dtype=torch.floataa		) - 0.5
    UpperCAmelCase__						=AgentAudio(A_		)
    UpperCAmelCase__						=str(agent_type.to_string()		)

    # Ensure that the tensor and the agent_type's tensor are the same
    self.assertTrue(torch.allclose(A_,		agent_type.to_raw(),		atol=1E-4		)		)

    del agent_type

    # Ensure the path remains even after the object deletion
    self.assertTrue(os.path.exists(A_		)		)

    # Ensure that the file contains the same value as the original tensor
    UpperCAmelCase__			,							UpperCAmelCase__						=sf.read(A_		)
    self.assertTrue(torch.allclose(A_,		torch.tensor(A_		),		atol=1E-4		)		)






 def  __UpperCAmelCase						(  self		)	->       Optional[Any]:
    UpperCAmelCase__						=torch.rand(12,		dtype=torch.floataa		) - 0.5
    UpperCAmelCase__						=get_new_path(suffix=".wav"		)
    sf.write(A_,		A_,		1_6000		)

    UpperCAmelCase__						=AgentAudio(A_		)

    self.assertTrue(torch.allclose(A_,		agent_type.to_raw(),		atol=1E-4		)		)
    self.assertEqual(agent_type.to_string(),		A_		)

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






 '''simple docstring'''





 def  __UpperCAmelCase						(  self		)	->       int:
    UpperCAmelCase__						=torch.randint(0,		256,		(64, 64, 3)		)
    UpperCAmelCase__						=AgentImage(A_		)
    UpperCAmelCase__						=str(agent_type.to_string()		)

    # Ensure that the tensor and the agent_type's tensor are the same
    self.assertTrue(torch.allclose(A_,		agent_type._tensor,		atol=1E-4		)		)

    self.assertIsInstance(agent_type.to_raw(),		Image.Image		)

    # Ensure the path remains even after the object deletion
    del agent_type
    self.assertTrue(os.path.exists(A_		)		)

 def  __UpperCAmelCase						(  self		)	->       List[str]:
    UpperCAmelCase__						=Path(get_tests_dir("fixtures/tests_samples/COCO"		)		) / "000000039769.png"
    UpperCAmelCase__						=Image.open(A_		)
    UpperCAmelCase__						=AgentImage(A_		)

    self.assertTrue(path.samefile(agent_type.to_string()		)		)
    self.assertTrue(image == agent_type.to_raw()		)

    # Ensure the path remains even after the object deletion
    del agent_type
    self.assertTrue(os.path.exists(A_		)		)






 def  __UpperCAmelCase						(  self		)	->       Tuple:
    UpperCAmelCase__						=Path(get_tests_dir("fixtures/tests_samples/COCO"		)		) / "000000039769.png"
    UpperCAmelCase__						=Image.open(A_		)
    UpperCAmelCase__						=AgentImage(A_		)

    self.assertFalse(path.samefile(agent_type.to_string()		)		)
    self.assertTrue(image == agent_type.to_raw()		)

    # Ensure the path remains even after the object deletion
    del agent_type
    self.assertTrue(os.path.exists(A_		)		)

class     snake_case_		(   unittest.TestCase							):






 '''simple docstring'''





 def  __UpperCAmelCase						(  self		)	->       List[Any]:
    UpperCAmelCase__						="Hey!"
    UpperCAmelCase__						=AgentText(A_		)

    self.assertEqual(A_,		agent_type.to_string()		)
    self.assertEqual(A_,		agent_type.to_raw()		)
    self.assertEqual(A_,		A_		)



